2025-08-14T21:01:39.2899393Z Current runner version: '2.328.0' 2025-08-14T21:01:39.2907718Z Runner name: 'i-0c96d079ce61848eb' 2025-08-14T21:01:39.2908879Z Runner group name: 'Default' 2025-08-14T21:01:39.2910067Z Machine name: 'ip-10-0-65-99' 2025-08-14T21:01:39.2914130Z ##[group]GITHUB_TOKEN Permissions 2025-08-14T21:01:39.2916910Z Contents: read 2025-08-14T21:01:39.2917779Z Metadata: read 2025-08-14T21:01:39.2918531Z ##[endgroup] 2025-08-14T21:01:39.2921653Z Secret source: Actions 2025-08-14T21:01:39.2923011Z Prepare workflow directory 2025-08-14T21:01:39.3518681Z Prepare all required actions 2025-08-14T21:01:39.3565752Z Getting action download info 2025-08-14T21:01:39.6479300Z Download action repository 'pytorch/test-infra@main' (SHA:83f58f391e939c10dcb8cb6d745e4cefa3b98a84) 2025-08-14T21:01:41.5405150Z Download action repository 'pytorch/pytorch@main' (SHA:eac2d9d695a32dd456050f45cac35134ec3809f4) 2025-08-14T21:01:55.0108068Z Download action repository 'actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065' (SHA:a26af69be951a213d495a4c3e4e4022e16d87065) 2025-08-14T21:01:55.3416185Z Download action repository 'aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722' (SHA:ececac1a45f3b08a01d2dd070d28d111c5fe6722) 2025-08-14T21:01:55.5678101Z Download action repository 'aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076' (SHA:062b18b96a7aff071d4dc91bc00c4c1a7945b076) 2025-08-14T21:01:55.7716914Z Download action repository 'seemethere/upload-artifact-s3@baba72d0712b404f646cebe0730933554ebce96a' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-08-14T21:01:56.0540620Z Getting action download info 2025-08-14T21:01:56.1583206Z Download action repository 'actions/checkout@v4' (SHA:08eba0b27e820071cde6df949e0beb9ba4906955) 2025-08-14T21:01:56.5028281Z Getting action download info 2025-08-14T21:01:56.6121963Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-08-14T21:01:56.8428457Z Getting action download info 2025-08-14T21:01:56.9582095Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2025-08-14T21:01:57.1612173Z Getting action download info 2025-08-14T21:01:57.3009304Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/heads/main (1fc683cf17c8c673044538d10266c00f92987be2) 2025-08-14T21:01:57.3013599Z ##[group] Inputs 2025-08-14T21:01:57.3013968Z build-environment: linux-jammy-py3.13-clang12 2025-08-14T21:01:57.3017040Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "lf.linux.2xlarge"}]} 2025-08-14T21:01:57.3020473Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:01:57.3021274Z sync-tag: 2025-08-14T21:01:57.3022069Z timeout-minutes: 240 2025-08-14T21:01:57.3022318Z use-gha: 2025-08-14T21:01:57.3022537Z dashboard-tag: 2025-08-14T21:01:57.3022785Z s3-bucket: gha-artifacts 2025-08-14T21:01:57.3023040Z aws-role-to-assume: 2025-08-14T21:01:57.3023707Z disable-monitor: false 2025-08-14T21:01:57.3024268Z monitor-log-interval: 5 2025-08-14T21:01:57.3024680Z monitor-data-collect-interval: 1 2025-08-14T21:01:57.3025004Z ##[endgroup] 2025-08-14T21:01:57.3025458Z Complete job name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge) 2025-08-14T21:01:57.3474180Z A job started hook has been configured by the self-hosted runner administrator 2025-08-14T21:01:57.3574935Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2025-08-14T21:01:57.3583793Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:01:57.3584466Z ##[endgroup] 2025-08-14T21:01:58.5376414Z Runner Type: lf.linux.2xlarge 2025-08-14T21:01:58.5376832Z Instance Type: c5.2xlarge 2025-08-14T21:01:58.5377202Z AMI Name: unknown 2025-08-14T21:01:58.5402898Z AMI ID: ami-05ffe3c48a9991133 2025-08-14T21:02:03.8921863Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2025-08-14T21:02:03.8922312Z with: 2025-08-14T21:02:03.8922901Z github-secret: *** 2025-08-14T21:02:03.8923639Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-08-14T21:02:03.8924446Z activate-with-label: false 2025-08-14T21:02:03.8924715Z label: with-ssh 2025-08-14T21:02:03.8924964Z remove-existing-keys: true 2025-08-14T21:02:03.8925246Z fail-silently: true 2025-08-14T21:02:03.8925476Z env: 2025-08-14T21:02:03.8925690Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:02:03.8925969Z ##[endgroup] 2025-08-14T21:02:04.0055946Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2025-08-14T21:02:04.0057403Z Not on pull request and ciflow reference could not be extracted, skipping adding ssh keys 2025-08-14T21:02:04.0193597Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2025-08-14T21:02:04.0194050Z with: 2025-08-14T21:02:04.0194255Z no-sudo: true 2025-08-14T21:02:04.0194500Z submodules: recursive 2025-08-14T21:02:04.0194775Z fetch-depth: 0 2025-08-14T21:02:04.0194994Z env: 2025-08-14T21:02:04.0195231Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:02:04.0195503Z ##[endgroup] 2025-08-14T21:02:04.0275339Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-08-14T21:02:04.0276312Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-08-14T21:02:04.0285244Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:02:04.0285623Z env: 2025-08-14T21:02:04.0285875Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:02:04.0286178Z ##[endgroup] 2025-08-14T21:02:04.0373452Z ##[group]Run # Use all available CPUs for fetching 2025-08-14T21:02:04.0373898Z # Use all available CPUs for fetching 2025-08-14T21:02:04.0374239Z cd "${GITHUB_WORKSPACE}" 2025-08-14T21:02:04.0374588Z git config --global fetch.parallel 0 2025-08-14T21:02:04.0374989Z git config --global submodule.fetchJobs 0 2025-08-14T21:02:04.0375348Z  2025-08-14T21:02:04.0375707Z # Clean workspace. The default checkout action should also do this, but 2025-08-14T21:02:04.0376191Z # do it here as well just in case 2025-08-14T21:02:04.0376517Z if [[ -d .git ]]; then 2025-08-14T21:02:04.0376804Z  if [ -z "${NO_SUDO}" ]; then 2025-08-14T21:02:04.0377119Z  sudo git clean -ffdx 2025-08-14T21:02:04.0377409Z  else 2025-08-14T21:02:04.0377637Z  git clean -ffdx 2025-08-14T21:02:04.0377908Z  fi 2025-08-14T21:02:04.0378126Z fi 2025-08-14T21:02:04.0383503Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:02:04.0383897Z env: 2025-08-14T21:02:04.0384198Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:02:04.0384490Z NO_SUDO: true 2025-08-14T21:02:04.0384706Z ##[endgroup] 2025-08-14T21:02:04.0497196Z ##[group]Run actions/checkout@v4 2025-08-14T21:02:04.0497488Z with: 2025-08-14T21:02:04.0497745Z ref: 1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:02:04.0522433Z fetch-depth: 0 2025-08-14T21:02:04.0522770Z submodules: recursive 2025-08-14T21:02:04.0523053Z show-progress: false 2025-08-14T21:02:04.0523316Z repository: pytorch/pytorch 2025-08-14T21:02:04.0523762Z token: *** 2025-08-14T21:02:04.0523991Z ssh-strict: true 2025-08-14T21:02:04.0524229Z ssh-user: git 2025-08-14T21:02:04.0524464Z persist-credentials: true 2025-08-14T21:02:04.0524740Z clean: true 2025-08-14T21:02:04.0525000Z sparse-checkout-cone-mode: true 2025-08-14T21:02:04.0525290Z fetch-tags: false 2025-08-14T21:02:04.0525529Z lfs: false 2025-08-14T21:02:04.0525765Z set-safe-directory: true 2025-08-14T21:02:04.0526030Z env: 2025-08-14T21:02:04.0526250Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:02:04.0526510Z ##[endgroup] 2025-08-14T21:02:04.1633131Z Syncing repository: pytorch/pytorch 2025-08-14T21:02:04.1635305Z ##[group]Getting Git version info 2025-08-14T21:02:04.1636149Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-08-14T21:02:04.1637401Z [command]/usr/bin/git version 2025-08-14T21:02:04.1637929Z git version 2.47.1 2025-08-14T21:02:04.1644630Z ##[endgroup] 2025-08-14T21:02:04.1656618Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/7855ef3f-1a5b-464d-95a8-64d3dda97312/.gitconfig' 2025-08-14T21:02:04.1674844Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/7855ef3f-1a5b-464d-95a8-64d3dda97312' before making global git config changes 2025-08-14T21:02:04.1676761Z Adding repository directory to the temporary git global config as a safe directory 2025-08-14T21:02:04.1681356Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-08-14T21:02:04.1715344Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-08-14T21:02:04.1718858Z ##[group]Initializing the repository 2025-08-14T21:02:04.1723538Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-08-14T21:02:04.1752329Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-08-14T21:02:04.1753414Z hint: is subject to change. To configure the initial branch name to use in all 2025-08-14T21:02:04.1754425Z hint: of your new repositories, which will suppress this warning, call: 2025-08-14T21:02:04.1755160Z hint: 2025-08-14T21:02:04.1755685Z hint: git config --global init.defaultBranch 2025-08-14T21:02:04.1756308Z hint: 2025-08-14T21:02:04.1756896Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-08-14T21:02:04.1757943Z hint: 'development'. The just-created branch can be renamed via this command: 2025-08-14T21:02:04.1758733Z hint: 2025-08-14T21:02:04.1759124Z hint: git branch -m 2025-08-14T21:02:04.1760017Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2025-08-14T21:02:04.1764935Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2025-08-14T21:02:04.1788787Z ##[endgroup] 2025-08-14T21:02:04.1793528Z ##[group]Disabling automatic garbage collection 2025-08-14T21:02:04.1794282Z [command]/usr/bin/git config --local gc.auto 0 2025-08-14T21:02:04.1816926Z ##[endgroup] 2025-08-14T21:02:04.1817639Z ##[group]Setting up auth 2025-08-14T21:02:04.1823857Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-08-14T21:02:04.1849314Z [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' || :" 2025-08-14T21:02:04.2112663Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-08-14T21:02:04.2136772Z [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' || :" 2025-08-14T21:02:04.2380380Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-08-14T21:02:04.2419173Z ##[endgroup] 2025-08-14T21:02:04.2419658Z ##[group]Fetching the repository 2025-08-14T21:02:04.2426394Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2025-08-14T21:02:57.3309922Z From https://github.com/pytorch/pytorch 2025-08-14T21:02:57.3310679Z * [new branch] 2.6.0.dev20241004+ -> origin/2.6.0.dev20241004+ 2025-08-14T21:02:57.3311491Z * [new branch] 5addvllmbuild -> origin/5addvllmbuild 2025-08-14T21:02:57.3312148Z * [new branch] AaronWang04_addmmfusion_perftest -> origin/AaronWang04_addmmfusion_perftest 2025-08-14T21:02:57.3312853Z * [new branch] HDCharles-2.6.0-release-notes -> origin/HDCharles-2.6.0-release-notes 2025-08-14T21:02:57.3313595Z * [new branch] JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops 2025-08-14T21:02:57.3314339Z * [new branch] PR-AOTInductorNoneBug -> origin/PR-AOTInductorNoneBug 2025-08-14T21:02:57.3316165Z * [new branch] PR-AOTInductorNoneBugFix -> origin/PR-AOTInductorNoneBugFix 2025-08-14T21:02:57.3317082Z * [new branch] PR-FixConfigsIssue -> origin/PR-FixConfigsIssue 2025-08-14T21:02:57.3318178Z * [new branch] PR-NoneBugFix-viable -> origin/PR-NoneBugFix-viable 2025-08-14T21:02:57.3319434Z * [new branch] PR-ResetToZero -> origin/PR-ResetToZero 2025-08-14T21:02:57.3321056Z * [new branch] Update-Flash-Packaging -> origin/Update-Flash-Packaging 2025-08-14T21:02:57.3321969Z * [new branch] add-missing-args-normalization -> origin/add-missing-args-normalization 2025-08-14T21:02:57.3323114Z * [new branch] add-user-guide-structure -> origin/add-user-guide-structure 2025-08-14T21:02:57.3324227Z * [new branch] addVllmPin -> origin/addVllmPin 2025-08-14T21:02:57.3325536Z * [new branch] add_windows_testing_back -> origin/add_windows_testing_back 2025-08-14T21:02:57.3326977Z * [new branch] addbuildvllm -> origin/addbuildvllm 2025-08-14T21:02:57.3328663Z * [new branch] addmm-heuristic -> origin/addmm-heuristic 2025-08-14T21:02:57.3329666Z * [new branch] addsimde -> origin/addsimde 2025-08-14T21:02:57.3330983Z * [new branch] addvllpinnedfile -> origin/addvllpinnedfile 2025-08-14T21:02:57.3332584Z * [new branch] adi/acl_upgrade -> origin/adi/acl_upgrade 2025-08-14T21:02:57.3333647Z * [new branch] adi/skip_slow_tests -> origin/adi/skip_slow_tests 2025-08-14T21:02:57.3334909Z * [new branch] adi/test -> origin/adi/test 2025-08-14T21:02:57.3336071Z * [new branch] adi/test_bgemm -> origin/adi/test_bgemm 2025-08-14T21:02:57.3337151Z * [new branch] adi/test_fusions -> origin/adi/test_fusions 2025-08-14T21:02:57.3338385Z * [new branch] adi/test_onednn_v3.9 -> origin/adi/test_onednn_v3.9 2025-08-14T21:02:57.3339459Z * [new branch] adi/test_presve_change -> origin/adi/test_presve_change 2025-08-14T21:02:57.3340529Z * [new branch] adi/test_timm -> origin/adi/test_timm 2025-08-14T21:02:57.3343547Z * [new branch] adi/testpresve_change -> origin/adi/testpresve_change 2025-08-14T21:02:57.3344976Z * [new branch] aditew01/test/vec_bf16 -> origin/aditew01/test/vec_bf16 2025-08-14T21:02:57.3345698Z * [new branch] ah-globalfeedback-hook -> origin/ah-globalfeedback-hook 2025-08-14T21:02:57.3347000Z * [new branch] albanD-patch-1 -> origin/albanD-patch-1 2025-08-14T21:02:57.3348073Z * [new branch] alt-disable -> origin/alt-disable 2025-08-14T21:02:57.3349869Z * [new branch] angelayi/aoti_additional_files -> origin/angelayi/aoti_additional_files 2025-08-14T21:02:57.3350886Z * [new branch] angelayi/aoti_inductor_fx -> origin/angelayi/aoti_inductor_fx 2025-08-14T21:02:57.3352168Z * [new branch] angelayi/assert_tensor_metadata_device -> origin/angelayi/assert_tensor_metadata_device 2025-08-14T21:02:57.3353114Z * [new branch] angelayi/benchmark -> origin/angelayi/benchmark 2025-08-14T21:02:57.3354238Z * [new branch] angelayi/benchmark2 -> origin/angelayi/benchmark2 2025-08-14T21:02:57.3355966Z * [new branch] angelayi/change_pytree_serialization -> origin/angelayi/change_pytree_serialization 2025-08-14T21:02:57.3357139Z * [new branch] angelayi/cpp_loader -> origin/angelayi/cpp_loader 2025-08-14T21:02:57.3358865Z * [new branch] angelayi/custom_op_subgraph -> origin/angelayi/custom_op_subgraph 2025-08-14T21:02:57.3359859Z * [new branch] angelayi/customop -> origin/angelayi/customop 2025-08-14T21:02:57.3361158Z * [new branch] angelayi/del_lib -> origin/angelayi/del_lib 2025-08-14T21:02:57.3362192Z 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ciflow/binaries/158104 -> ciflow/binaries/158104 2025-08-14T21:02:57.9287601Z * [new tag] ciflow/binaries/158623 -> ciflow/binaries/158623 2025-08-14T21:02:57.9288264Z * [new tag] ciflow/binaries/159827 -> ciflow/binaries/159827 2025-08-14T21:02:57.9289378Z * [new tag] ciflow/binaries/159869 -> ciflow/binaries/159869 2025-08-14T21:02:57.9290414Z * [new tag] ciflow/binaries/160593 -> ciflow/binaries/160593 2025-08-14T21:02:57.9291210Z * [new tag] ciflow/binaries_libtorch/143959 -> ciflow/binaries_libtorch/143959 2025-08-14T21:02:57.9291896Z * [new tag] ciflow/binaries_libtorch/156049 -> ciflow/binaries_libtorch/156049 2025-08-14T21:02:57.9292460Z * [new tag] ciflow/binaries_libtorch/157432 -> ciflow/binaries_libtorch/157432 2025-08-14T21:02:57.9293292Z * [new tag] ciflow/binaries_wheel/143959 -> ciflow/binaries_wheel/143959 2025-08-14T21:02:57.9294292Z * [new tag] ciflow/binaries_wheel/156049 -> ciflow/binaries_wheel/156049 2025-08-14T21:02:57.9295095Z * [new tag] ciflow/binaries_wheel/157432 -> ciflow/binaries_wheel/157432 2025-08-14T21:02:57.9295960Z * [new tag] ciflow/binaries_wheel/158733 -> ciflow/binaries_wheel/158733 2025-08-14T21:02:57.9296618Z * [new tag] ciflow/binaries_wheel/160301 -> ciflow/binaries_wheel/160301 2025-08-14T21:02:57.9297397Z * [new tag] ciflow/binaries_wheel/160496 -> ciflow/binaries_wheel/160496 2025-08-14T21:02:57.9298397Z * [new tag] ciflow/h100-distributed/156703 -> ciflow/h100-distributed/156703 2025-08-14T21:02:57.9299148Z * [new tag] ciflow/h100-symm-mem/151845 -> ciflow/h100-symm-mem/151845 2025-08-14T21:02:57.9299725Z * [new tag] ciflow/h100-symm-mem/155923 -> ciflow/h100-symm-mem/155923 2025-08-14T21:02:57.9300368Z * [new tag] ciflow/h100-symm-mem/157635 -> ciflow/h100-symm-mem/157635 2025-08-14T21:02:57.9301132Z * [new tag] ciflow/h100-symm-mem/159118 -> ciflow/h100-symm-mem/159118 2025-08-14T21:02:57.9302379Z * [new tag] ciflow/h100-symm-mem/159562 -> ciflow/h100-symm-mem/159562 2025-08-14T21:02:57.9302932Z * [new tag] ciflow/h100-symm-mem/159889 -> ciflow/h100-symm-mem/159889 2025-08-14T21:02:57.9303754Z * [new tag] ciflow/h100/159158 -> ciflow/h100/159158 2025-08-14T21:02:57.9304748Z * [new tag] ciflow/h100/160450 -> ciflow/h100/160450 2025-08-14T21:02:57.9305541Z * [new tag] ciflow/h100/160480 -> ciflow/h100/160480 2025-08-14T21:02:57.9306500Z * [new tag] ciflow/h100/160614 -> ciflow/h100/160614 2025-08-14T21:02:57.9307568Z * [new tag] ciflow/inductor-perf-test-nightly-rocm/151845 -> ciflow/inductor-perf-test-nightly-rocm/151845 2025-08-14T21:02:57.9308194Z * [new tag] ciflow/inductor-perf-test-nightly-rocm/160538 -> ciflow/inductor-perf-test-nightly-rocm/160538 2025-08-14T21:02:57.9309438Z * [new tag] ciflow/inductor-perf-test-nightly-x86-zen/156599 -> ciflow/inductor-perf-test-nightly-x86-zen/156599 2025-08-14T21:02:57.9310104Z * [new tag] ciflow/inductor-periodic/160406 -> ciflow/inductor-periodic/160406 2025-08-14T21:02:57.9310724Z * [new tag] ciflow/inductor-periodic/160538 -> ciflow/inductor-periodic/160538 2025-08-14T21:02:57.9311477Z * [new tag] ciflow/inductor-rocm/151845 -> ciflow/inductor-rocm/151845 2025-08-14T21:02:57.9312075Z * [new tag] ciflow/inductor-rocm/159158 -> ciflow/inductor-rocm/159158 2025-08-14T21:02:57.9312862Z * [new tag] ciflow/inductor-rocm/160073 -> ciflow/inductor-rocm/160073 2025-08-14T21:02:57.9313446Z * [new tag] ciflow/inductor-rocm/160538 -> ciflow/inductor-rocm/160538 2025-08-14T21:02:57.9314349Z * [new tag] ciflow/inductor/134881 -> ciflow/inductor/134881 2025-08-14T21:02:57.9314975Z * [new tag] ciflow/inductor/137400 -> ciflow/inductor/137400 2025-08-14T21:02:57.9315584Z * [new tag] ciflow/inductor/144516 -> ciflow/inductor/144516 2025-08-14T21:02:57.9316232Z * [new tag] ciflow/inductor/146506 -> ciflow/inductor/146506 2025-08-14T21:02:57.9316848Z * [new tag] ciflow/inductor/147360 -> ciflow/inductor/147360 2025-08-14T21:02:57.9317523Z * [new tag] ciflow/inductor/147990 -> ciflow/inductor/147990 2025-08-14T21:02:57.9318156Z * [new tag] ciflow/inductor/148180 -> ciflow/inductor/148180 2025-08-14T21:02:57.9318811Z * [new tag] ciflow/inductor/148328 -> ciflow/inductor/148328 2025-08-14T21:02:57.9319459Z * [new tag] ciflow/inductor/148484 -> ciflow/inductor/148484 2025-08-14T21:02:57.9320054Z * [new tag] ciflow/inductor/148492 -> ciflow/inductor/148492 2025-08-14T21:02:57.9320721Z * [new tag] ciflow/inductor/150302 -> ciflow/inductor/150302 2025-08-14T21:02:57.9321680Z * [new tag] ciflow/inductor/151845 -> ciflow/inductor/151845 2025-08-14T21:02:57.9322513Z * [new tag] ciflow/inductor/152198 -> ciflow/inductor/152198 2025-08-14T21:02:57.9323337Z * [new tag] ciflow/inductor/152624 -> ciflow/inductor/152624 2025-08-14T21:02:57.9323997Z * [new tag] ciflow/inductor/153966 -> ciflow/inductor/153966 2025-08-14T21:02:57.9324718Z * [new tag] ciflow/inductor/154193 -> ciflow/inductor/154193 2025-08-14T21:02:57.9325549Z * [new tag] ciflow/inductor/154650 -> ciflow/inductor/154650 2025-08-14T21:02:57.9326214Z * [new tag] ciflow/inductor/154694 -> ciflow/inductor/154694 2025-08-14T21:02:57.9326868Z * [new tag] ciflow/inductor/155072 -> ciflow/inductor/155072 2025-08-14T21:02:57.9327596Z * [new tag] ciflow/inductor/155152 -> ciflow/inductor/155152 2025-08-14T21:02:57.9328200Z * [new tag] ciflow/inductor/155153 -> ciflow/inductor/155153 2025-08-14T21:02:57.9328895Z * [new tag] ciflow/inductor/155154 -> ciflow/inductor/155154 2025-08-14T21:02:57.9329552Z * [new tag] ciflow/inductor/155501 -> ciflow/inductor/155501 2025-08-14T21:02:57.9330353Z * [new tag] ciflow/inductor/155502 -> ciflow/inductor/155502 2025-08-14T21:02:57.9330958Z * [new tag] ciflow/inductor/155503 -> ciflow/inductor/155503 2025-08-14T21:02:57.9331991Z * [new tag] ciflow/inductor/155504 -> ciflow/inductor/155504 2025-08-14T21:02:57.9332659Z * [new tag] ciflow/inductor/155557 -> ciflow/inductor/155557 2025-08-14T21:02:57.9333263Z * [new tag] ciflow/inductor/155608 -> ciflow/inductor/155608 2025-08-14T21:02:57.9333839Z * [new tag] ciflow/inductor/155923 -> ciflow/inductor/155923 2025-08-14T21:02:57.9334534Z * [new tag] ciflow/inductor/155928 -> ciflow/inductor/155928 2025-08-14T21:02:57.9335340Z * [new tag] ciflow/inductor/155958 -> ciflow/inductor/155958 2025-08-14T21:02:57.9335970Z * [new tag] ciflow/inductor/156049 -> ciflow/inductor/156049 2025-08-14T21:02:57.9336637Z * [new tag] ciflow/inductor/156851 -> ciflow/inductor/156851 2025-08-14T21:02:57.9337320Z * [new tag] ciflow/inductor/156967 -> ciflow/inductor/156967 2025-08-14T21:02:57.9337961Z * [new tag] ciflow/inductor/157148 -> ciflow/inductor/157148 2025-08-14T21:02:57.9339100Z * [new tag] ciflow/inductor/157149 -> ciflow/inductor/157149 2025-08-14T21:02:57.9339738Z * [new tag] ciflow/inductor/157152 -> ciflow/inductor/157152 2025-08-14T21:02:57.9340397Z * [new tag] ciflow/inductor/157542 -> ciflow/inductor/157542 2025-08-14T21:02:57.9341104Z * [new tag] ciflow/inductor/157572 -> ciflow/inductor/157572 2025-08-14T21:02:57.9341713Z * [new tag] ciflow/inductor/157635 -> ciflow/inductor/157635 2025-08-14T21:02:57.9342361Z * [new tag] ciflow/inductor/157685 -> ciflow/inductor/157685 2025-08-14T21:02:57.9343041Z * [new tag] ciflow/inductor/157686 -> ciflow/inductor/157686 2025-08-14T21:02:57.9343766Z * [new tag] ciflow/inductor/157689 -> ciflow/inductor/157689 2025-08-14T21:02:57.9344481Z * [new tag] ciflow/inductor/157699 -> ciflow/inductor/157699 2025-08-14T21:02:57.9345245Z * [new tag] ciflow/inductor/157743 -> ciflow/inductor/157743 2025-08-14T21:02:57.9345909Z * [new tag] ciflow/inductor/157944 -> ciflow/inductor/157944 2025-08-14T21:02:57.9346616Z * [new tag] ciflow/inductor/157971 -> ciflow/inductor/157971 2025-08-14T21:02:57.9347372Z * [new tag] ciflow/inductor/157994 -> ciflow/inductor/157994 2025-08-14T21:02:57.9348130Z * [new tag] ciflow/inductor/158061 -> ciflow/inductor/158061 2025-08-14T21:02:57.9348728Z * [new tag] ciflow/inductor/158091 -> ciflow/inductor/158091 2025-08-14T21:02:57.9349431Z * [new tag] ciflow/inductor/158097 -> ciflow/inductor/158097 2025-08-14T21:02:57.9350081Z * [new tag] ciflow/inductor/158098 -> ciflow/inductor/158098 2025-08-14T21:02:57.9350758Z * [new tag] ciflow/inductor/158104 -> ciflow/inductor/158104 2025-08-14T21:02:57.9351431Z * [new tag] ciflow/inductor/158168 -> ciflow/inductor/158168 2025-08-14T21:02:57.9352208Z * [new tag] ciflow/inductor/158250 -> ciflow/inductor/158250 2025-08-14T21:02:57.9352975Z * [new tag] ciflow/inductor/158321 -> ciflow/inductor/158321 2025-08-14T21:02:57.9353618Z * [new tag] ciflow/inductor/158609 -> ciflow/inductor/158609 2025-08-14T21:02:57.9354276Z * [new tag] ciflow/inductor/158647 -> ciflow/inductor/158647 2025-08-14T21:02:57.9354987Z * [new tag] ciflow/inductor/158914 -> ciflow/inductor/158914 2025-08-14T21:02:57.9355794Z * [new tag] ciflow/inductor/158932 -> ciflow/inductor/158932 2025-08-14T21:02:57.9356530Z * [new tag] ciflow/inductor/158987 -> ciflow/inductor/158987 2025-08-14T21:02:57.9357178Z * [new tag] ciflow/inductor/159009 -> ciflow/inductor/159009 2025-08-14T21:02:57.9357935Z * [new tag] ciflow/inductor/159010 -> ciflow/inductor/159010 2025-08-14T21:02:57.9358535Z * [new tag] ciflow/inductor/159093 -> ciflow/inductor/159093 2025-08-14T21:02:57.9359137Z * [new tag] ciflow/inductor/159158 -> ciflow/inductor/159158 2025-08-14T21:02:57.9359827Z * [new tag] ciflow/inductor/159197 -> ciflow/inductor/159197 2025-08-14T21:02:57.9360654Z * [new tag] ciflow/inductor/159274 -> ciflow/inductor/159274 2025-08-14T21:02:57.9361372Z * [new tag] ciflow/inductor/159281 -> ciflow/inductor/159281 2025-08-14T21:02:57.9362062Z * [new tag] ciflow/inductor/159329 -> ciflow/inductor/159329 2025-08-14T21:02:57.9362738Z * [new tag] ciflow/inductor/159361 -> ciflow/inductor/159361 2025-08-14T21:02:57.9363366Z * [new tag] ciflow/inductor/159365 -> ciflow/inductor/159365 2025-08-14T21:02:57.9364048Z * [new tag] ciflow/inductor/159366 -> ciflow/inductor/159366 2025-08-14T21:02:57.9364732Z * [new tag] ciflow/inductor/159367 -> ciflow/inductor/159367 2025-08-14T21:02:57.9365365Z * [new tag] ciflow/inductor/159368 -> ciflow/inductor/159368 2025-08-14T21:02:57.9366409Z * [new tag] ciflow/inductor/159473 -> ciflow/inductor/159473 2025-08-14T21:02:57.9366886Z * [new tag] ciflow/inductor/159483 -> ciflow/inductor/159483 2025-08-14T21:02:57.9367580Z * [new tag] ciflow/inductor/159508 -> ciflow/inductor/159508 2025-08-14T21:02:57.9368200Z * [new tag] ciflow/inductor/159523 -> ciflow/inductor/159523 2025-08-14T21:02:57.9368938Z * [new tag] ciflow/inductor/159530 -> ciflow/inductor/159530 2025-08-14T21:02:57.9369605Z * [new tag] ciflow/inductor/159678 -> ciflow/inductor/159678 2025-08-14T21:02:57.9370243Z * [new tag] ciflow/inductor/159691 -> ciflow/inductor/159691 2025-08-14T21:02:57.9371061Z * [new tag] ciflow/inductor/159778 -> ciflow/inductor/159778 2025-08-14T21:02:57.9371774Z * [new tag] ciflow/inductor/159786 -> ciflow/inductor/159786 2025-08-14T21:02:57.9372423Z * [new tag] ciflow/inductor/159817 -> ciflow/inductor/159817 2025-08-14T21:02:57.9373082Z * [new tag] ciflow/inductor/159842 -> ciflow/inductor/159842 2025-08-14T21:02:57.9373780Z * [new tag] ciflow/inductor/159864 -> ciflow/inductor/159864 2025-08-14T21:02:57.9374469Z * [new tag] ciflow/inductor/159865 -> ciflow/inductor/159865 2025-08-14T21:02:57.9375223Z * [new tag] ciflow/inductor/159869 -> ciflow/inductor/159869 2025-08-14T21:02:57.9375879Z * [new tag] ciflow/inductor/159875 -> ciflow/inductor/159875 2025-08-14T21:02:57.9376564Z * [new tag] ciflow/inductor/159889 -> ciflow/inductor/159889 2025-08-14T21:02:57.9377289Z * [new tag] ciflow/inductor/159902 -> ciflow/inductor/159902 2025-08-14T21:02:57.9378103Z * [new tag] ciflow/inductor/159923 -> ciflow/inductor/159923 2025-08-14T21:02:57.9378868Z * [new tag] ciflow/inductor/159944 -> ciflow/inductor/159944 2025-08-14T21:02:57.9379701Z * [new tag] ciflow/inductor/160000 -> ciflow/inductor/160000 2025-08-14T21:02:57.9380749Z * [new tag] ciflow/inductor/160004 -> ciflow/inductor/160004 2025-08-14T21:02:57.9381278Z * [new tag] ciflow/inductor/160080 -> ciflow/inductor/160080 2025-08-14T21:02:57.9381951Z * [new tag] ciflow/inductor/160108 -> ciflow/inductor/160108 2025-08-14T21:02:57.9382630Z * [new tag] ciflow/inductor/160109 -> ciflow/inductor/160109 2025-08-14T21:02:57.9383536Z * [new tag] ciflow/inductor/160111 -> ciflow/inductor/160111 2025-08-14T21:02:57.9384858Z * [new tag] ciflow/inductor/160113 -> ciflow/inductor/160113 2025-08-14T21:02:57.9385421Z * [new tag] ciflow/inductor/160127 -> ciflow/inductor/160127 2025-08-14T21:02:57.9386069Z * [new tag] ciflow/inductor/160131 -> ciflow/inductor/160131 2025-08-14T21:02:57.9386734Z * [new tag] ciflow/inductor/160132 -> ciflow/inductor/160132 2025-08-14T21:02:57.9387393Z * [new tag] ciflow/inductor/160136 -> ciflow/inductor/160136 2025-08-14T21:02:57.9388067Z * [new tag] ciflow/inductor/160138 -> ciflow/inductor/160138 2025-08-14T21:02:57.9388729Z * [new tag] ciflow/inductor/160151 -> ciflow/inductor/160151 2025-08-14T21:02:57.9389416Z * [new tag] ciflow/inductor/160152 -> ciflow/inductor/160152 2025-08-14T21:02:57.9390067Z * [new tag] ciflow/inductor/160154 -> ciflow/inductor/160154 2025-08-14T21:02:57.9390776Z * [new tag] ciflow/inductor/160156 -> ciflow/inductor/160156 2025-08-14T21:02:57.9391570Z * [new tag] ciflow/inductor/160161 -> ciflow/inductor/160161 2025-08-14T21:02:57.9392370Z * [new tag] ciflow/inductor/160166 -> ciflow/inductor/160166 2025-08-14T21:02:57.9393145Z * [new tag] ciflow/inductor/160168 -> ciflow/inductor/160168 2025-08-14T21:02:57.9393935Z * [new tag] ciflow/inductor/160174 -> ciflow/inductor/160174 2025-08-14T21:02:57.9394618Z * [new tag] ciflow/inductor/160181 -> ciflow/inductor/160181 2025-08-14T21:02:57.9395382Z * [new tag] ciflow/inductor/160183 -> ciflow/inductor/160183 2025-08-14T21:02:57.9396340Z * [new tag] ciflow/inductor/160190 -> ciflow/inductor/160190 2025-08-14T21:02:57.9397296Z * [new tag] ciflow/inductor/160198 -> ciflow/inductor/160198 2025-08-14T21:02:57.9397939Z * [new tag] ciflow/inductor/160201 -> ciflow/inductor/160201 2025-08-14T21:02:57.9398621Z * [new tag] ciflow/inductor/160209 -> ciflow/inductor/160209 2025-08-14T21:02:57.9399702Z * [new tag] ciflow/inductor/160218 -> ciflow/inductor/160218 2025-08-14T21:02:57.9400316Z * [new tag] ciflow/inductor/160239 -> ciflow/inductor/160239 2025-08-14T21:02:57.9401082Z * [new tag] ciflow/inductor/160250 -> ciflow/inductor/160250 2025-08-14T21:02:57.9401716Z * [new tag] ciflow/inductor/160253 -> ciflow/inductor/160253 2025-08-14T21:02:57.9402405Z * [new tag] ciflow/inductor/160266 -> ciflow/inductor/160266 2025-08-14T21:02:57.9403014Z * [new tag] ciflow/inductor/160282 -> ciflow/inductor/160282 2025-08-14T21:02:57.9403777Z * [new tag] ciflow/inductor/160298 -> ciflow/inductor/160298 2025-08-14T21:02:57.9404432Z * [new tag] ciflow/inductor/160301 -> ciflow/inductor/160301 2025-08-14T21:02:57.9405171Z * [new tag] ciflow/inductor/160310 -> ciflow/inductor/160310 2025-08-14T21:02:57.9406154Z * [new tag] ciflow/inductor/160323 -> ciflow/inductor/160323 2025-08-14T21:02:57.9407166Z * [new tag] ciflow/inductor/160324 -> ciflow/inductor/160324 2025-08-14T21:02:57.9407865Z * [new tag] ciflow/inductor/160325 -> ciflow/inductor/160325 2025-08-14T21:02:57.9408896Z * [new tag] ciflow/inductor/160326 -> ciflow/inductor/160326 2025-08-14T21:02:57.9409577Z * [new tag] ciflow/inductor/160327 -> ciflow/inductor/160327 2025-08-14T21:02:57.9410308Z * [new tag] ciflow/inductor/160328 -> ciflow/inductor/160328 2025-08-14T21:02:57.9411104Z * [new tag] ciflow/inductor/160329 -> ciflow/inductor/160329 2025-08-14T21:02:57.9411854Z * [new tag] ciflow/inductor/160351 -> ciflow/inductor/160351 2025-08-14T21:02:57.9412714Z * [new tag] ciflow/inductor/160353 -> ciflow/inductor/160353 2025-08-14T21:02:57.9413286Z * [new tag] ciflow/inductor/160362 -> ciflow/inductor/160362 2025-08-14T21:02:57.9413863Z * [new tag] ciflow/inductor/160363 -> ciflow/inductor/160363 2025-08-14T21:02:57.9414558Z * [new tag] ciflow/inductor/160364 -> ciflow/inductor/160364 2025-08-14T21:02:57.9415262Z * [new tag] ciflow/inductor/160365 -> ciflow/inductor/160365 2025-08-14T21:02:57.9415930Z * [new tag] ciflow/inductor/160366 -> ciflow/inductor/160366 2025-08-14T21:02:57.9416603Z * [new tag] ciflow/inductor/160367 -> ciflow/inductor/160367 2025-08-14T21:02:57.9417273Z * [new tag] ciflow/inductor/160368 -> ciflow/inductor/160368 2025-08-14T21:02:57.9417961Z * [new tag] ciflow/inductor/160369 -> ciflow/inductor/160369 2025-08-14T21:02:57.9418623Z * [new tag] ciflow/inductor/160371 -> ciflow/inductor/160371 2025-08-14T21:02:57.9419296Z * [new tag] ciflow/inductor/160374 -> ciflow/inductor/160374 2025-08-14T21:02:57.9420006Z * [new tag] ciflow/inductor/160375 -> ciflow/inductor/160375 2025-08-14T21:02:57.9420682Z * [new tag] ciflow/inductor/160377 -> ciflow/inductor/160377 2025-08-14T21:02:57.9421349Z * [new tag] ciflow/inductor/160380 -> ciflow/inductor/160380 2025-08-14T21:02:57.9422206Z * [new tag] ciflow/inductor/160381 -> ciflow/inductor/160381 2025-08-14T21:02:57.9423297Z * [new tag] ciflow/inductor/160383 -> ciflow/inductor/160383 2025-08-14T21:02:57.9424200Z * [new tag] ciflow/inductor/160394 -> ciflow/inductor/160394 2025-08-14T21:02:57.9424846Z * [new tag] ciflow/inductor/160401 -> ciflow/inductor/160401 2025-08-14T21:02:57.9425534Z * [new tag] ciflow/inductor/160402 -> ciflow/inductor/160402 2025-08-14T21:02:57.9426225Z * [new tag] ciflow/inductor/160403 -> ciflow/inductor/160403 2025-08-14T21:02:57.9426908Z * [new tag] ciflow/inductor/160424 -> ciflow/inductor/160424 2025-08-14T21:02:57.9427607Z * [new tag] ciflow/inductor/160426 -> ciflow/inductor/160426 2025-08-14T21:02:57.9428436Z * [new tag] ciflow/inductor/160431 -> ciflow/inductor/160431 2025-08-14T21:02:57.9429093Z * [new tag] ciflow/inductor/160448 -> ciflow/inductor/160448 2025-08-14T21:02:57.9429773Z * [new tag] ciflow/inductor/160450 -> ciflow/inductor/160450 2025-08-14T21:02:57.9430466Z * [new tag] ciflow/inductor/160455 -> ciflow/inductor/160455 2025-08-14T21:02:57.9431448Z * [new tag] ciflow/inductor/160456 -> ciflow/inductor/160456 2025-08-14T21:02:57.9432575Z * [new tag] ciflow/inductor/160461 -> ciflow/inductor/160461 2025-08-14T21:02:57.9433211Z * [new tag] ciflow/inductor/160462 -> ciflow/inductor/160462 2025-08-14T21:02:57.9433878Z * [new tag] ciflow/inductor/160467 -> ciflow/inductor/160467 2025-08-14T21:02:57.9434535Z * [new tag] ciflow/inductor/160470 -> ciflow/inductor/160470 2025-08-14T21:02:57.9435242Z * [new tag] ciflow/inductor/160473 -> ciflow/inductor/160473 2025-08-14T21:02:57.9435933Z * [new tag] ciflow/inductor/160476 -> ciflow/inductor/160476 2025-08-14T21:02:57.9436600Z * [new tag] ciflow/inductor/160480 -> ciflow/inductor/160480 2025-08-14T21:02:57.9437382Z * [new tag] ciflow/inductor/160481 -> ciflow/inductor/160481 2025-08-14T21:02:57.9438087Z * [new tag] ciflow/inductor/160482 -> ciflow/inductor/160482 2025-08-14T21:02:57.9438819Z * [new tag] ciflow/inductor/160483 -> ciflow/inductor/160483 2025-08-14T21:02:57.9439453Z * [new tag] ciflow/inductor/160485 -> ciflow/inductor/160485 2025-08-14T21:02:57.9440305Z * [new tag] ciflow/inductor/160486 -> ciflow/inductor/160486 2025-08-14T21:02:57.9440986Z * [new tag] ciflow/inductor/160503 -> ciflow/inductor/160503 2025-08-14T21:02:57.9441757Z * [new tag] ciflow/inductor/160510 -> ciflow/inductor/160510 2025-08-14T21:02:57.9442388Z * [new tag] ciflow/inductor/160527 -> ciflow/inductor/160527 2025-08-14T21:02:57.9443096Z * [new tag] ciflow/inductor/160530 -> ciflow/inductor/160530 2025-08-14T21:02:57.9443740Z * [new tag] ciflow/inductor/160531 -> ciflow/inductor/160531 2025-08-14T21:02:57.9444485Z * [new tag] ciflow/inductor/160538 -> ciflow/inductor/160538 2025-08-14T21:02:57.9445822Z * [new tag] ciflow/inductor/160539 -> ciflow/inductor/160539 2025-08-14T21:02:57.9446775Z * [new tag] ciflow/inductor/160540 -> ciflow/inductor/160540 2025-08-14T21:02:57.9447509Z * [new tag] ciflow/inductor/160548 -> ciflow/inductor/160548 2025-08-14T21:02:57.9448184Z * [new tag] ciflow/inductor/160561 -> ciflow/inductor/160561 2025-08-14T21:02:57.9448978Z * [new tag] ciflow/inductor/160576 -> ciflow/inductor/160576 2025-08-14T21:02:57.9449675Z * [new tag] ciflow/inductor/160578 -> ciflow/inductor/160578 2025-08-14T21:02:57.9450366Z * [new tag] ciflow/inductor/160580 -> ciflow/inductor/160580 2025-08-14T21:02:57.9451093Z * [new tag] ciflow/inductor/160583 -> ciflow/inductor/160583 2025-08-14T21:02:57.9451853Z * [new tag] ciflow/inductor/160589 -> ciflow/inductor/160589 2025-08-14T21:02:57.9452693Z * [new tag] ciflow/inductor/160590 -> ciflow/inductor/160590 2025-08-14T21:02:57.9453521Z * [new tag] ciflow/inductor/160592 -> ciflow/inductor/160592 2025-08-14T21:02:57.9454228Z * [new tag] ciflow/inductor/160596 -> ciflow/inductor/160596 2025-08-14T21:02:57.9454864Z * [new tag] ciflow/inductor/160601 -> ciflow/inductor/160601 2025-08-14T21:02:57.9455689Z * [new tag] ciflow/inductor/160607 -> ciflow/inductor/160607 2025-08-14T21:02:57.9456513Z * [new tag] ciflow/inductor/160608 -> ciflow/inductor/160608 2025-08-14T21:02:57.9457228Z * [new tag] ciflow/inductor/160611 -> ciflow/inductor/160611 2025-08-14T21:02:57.9457920Z * [new tag] ciflow/inductor/160614 -> ciflow/inductor/160614 2025-08-14T21:02:57.9458609Z * [new tag] ciflow/inductor/160616 -> ciflow/inductor/160616 2025-08-14T21:02:57.9459298Z * [new tag] ciflow/inductor/160619 -> ciflow/inductor/160619 2025-08-14T21:02:57.9459961Z * [new tag] ciflow/inductor/160625 -> ciflow/inductor/160625 2025-08-14T21:02:57.9460560Z * [new tag] ciflow/inductor/160635 -> ciflow/inductor/160635 2025-08-14T21:02:57.9461598Z * [new tag] ciflow/inductor/160649 -> ciflow/inductor/160649 2025-08-14T21:02:57.9462134Z * [new tag] ciflow/inductor/160658 -> ciflow/inductor/160658 2025-08-14T21:02:57.9462811Z * [new tag] ciflow/inductor/160662 -> ciflow/inductor/160662 2025-08-14T21:02:57.9463506Z * [new tag] ciflow/inductor/160668 -> ciflow/inductor/160668 2025-08-14T21:02:57.9464252Z * [new tag] ciflow/inductor/160669 -> ciflow/inductor/160669 2025-08-14T21:02:57.9464968Z * [new tag] ciflow/inductor/160670 -> ciflow/inductor/160670 2025-08-14T21:02:57.9465905Z * [new tag] ciflow/inductor/160671 -> ciflow/inductor/160671 2025-08-14T21:02:57.9466720Z * [new tag] ciflow/inductor/160677 -> ciflow/inductor/160677 2025-08-14T21:02:57.9467498Z * [new tag] ciflow/inductor/3b9a386 -> ciflow/inductor/3b9a386 2025-08-14T21:02:57.9468455Z * [new tag] ciflow/inductor/3d4b92b -> ciflow/inductor/3d4b92b 2025-08-14T21:02:57.9469230Z * [new tag] ciflow/inductor/d224ac7 -> ciflow/inductor/d224ac7 2025-08-14T21:02:57.9470008Z * [new tag] ciflow/linux-aarch64/147855 -> ciflow/linux-aarch64/147855 2025-08-14T21:02:57.9470757Z * [new tag] ciflow/linux-aarch64/157994 -> ciflow/linux-aarch64/157994 2025-08-14T21:02:57.9471429Z * [new tag] ciflow/linux-aarch64/159737 -> ciflow/linux-aarch64/159737 2025-08-14T21:02:57.9472011Z * [new tag] ciflow/linux-aarch64/160078 -> ciflow/linux-aarch64/160078 2025-08-14T21:02:57.9472632Z * [new tag] ciflow/linux-aarch64/160299 -> ciflow/linux-aarch64/160299 2025-08-14T21:02:57.9473270Z * [new tag] ciflow/linux-aarch64/160301 -> ciflow/linux-aarch64/160301 2025-08-14T21:02:57.9474025Z * [new tag] ciflow/mps/155923 -> ciflow/mps/155923 2025-08-14T21:02:57.9474593Z * [new tag] ciflow/mps/157553 -> ciflow/mps/157553 2025-08-14T21:02:57.9475252Z * [new tag] ciflow/mps/157635 -> ciflow/mps/157635 2025-08-14T21:02:57.9475871Z * [new tag] ciflow/mps/160541 -> ciflow/mps/160541 2025-08-14T21:02:57.9476659Z * [new tag] ciflow/nightly/156049 -> ciflow/nightly/156049 2025-08-14T21:02:57.9477345Z * [new tag] ciflow/nightly/158104 -> ciflow/nightly/158104 2025-08-14T21:02:57.9478084Z * [new tag] ciflow/op-benchmark/157994 -> ciflow/op-benchmark/157994 2025-08-14T21:02:57.9478957Z * [new tag] ciflow/periodic-rocm-mi300/139971 -> ciflow/periodic-rocm-mi300/139971 2025-08-14T21:02:57.9479543Z * [new tag] ciflow/periodic-rocm-mi300/160073 -> ciflow/periodic-rocm-mi300/160073 2025-08-14T21:02:57.9480226Z * [new tag] ciflow/periodic-rocm-mi300/160538 -> ciflow/periodic-rocm-mi300/160538 2025-08-14T21:02:57.9481213Z * [new tag] ciflow/periodic/054a2fd -> ciflow/periodic/054a2fd 2025-08-14T21:02:57.9481866Z * [new tag] ciflow/periodic/131296 -> ciflow/periodic/131296 2025-08-14T21:02:57.9482471Z * [new tag] ciflow/periodic/139971 -> ciflow/periodic/139971 2025-08-14T21:02:57.9483092Z * [new tag] ciflow/periodic/143959 -> ciflow/periodic/143959 2025-08-14T21:02:57.9483762Z * [new tag] ciflow/periodic/154595 -> ciflow/periodic/154595 2025-08-14T21:02:57.9484371Z * [new tag] ciflow/periodic/156703 -> ciflow/periodic/156703 2025-08-14T21:02:57.9485037Z * [new tag] ciflow/periodic/160201 -> ciflow/periodic/160201 2025-08-14T21:02:57.9485717Z * [new tag] ciflow/periodic/160424 -> ciflow/periodic/160424 2025-08-14T21:02:57.9486300Z * [new tag] ciflow/periodic/160538 -> ciflow/periodic/160538 2025-08-14T21:02:57.9487973Z * [new tag] ciflow/periodic/1febab2a89302464f6c7d69cfbef7a24c421ea65 -> ciflow/periodic/1febab2a89302464f6c7d69cfbef7a24c421ea65 2025-08-14T21:02:57.9488716Z * [new tag] ciflow/periodic/2a6d37d -> ciflow/periodic/2a6d37d 2025-08-14T21:02:57.9489825Z * [new tag] ciflow/periodic/2ee22e435131369a7e4f8cc4732579acc29a941b -> ciflow/periodic/2ee22e435131369a7e4f8cc4732579acc29a941b 2025-08-14T21:02:57.9490435Z * [new tag] ciflow/periodic/317eeb8 -> ciflow/periodic/317eeb8 2025-08-14T21:02:57.9491236Z * [new tag] ciflow/periodic/3c32 -> ciflow/periodic/3c32 2025-08-14T21:02:57.9492285Z * [new tag] ciflow/periodic/3e98831 -> ciflow/periodic/3e98831 2025-08-14T21:02:57.9493553Z * [new tag] ciflow/periodic/4a773e1e867f28a8ff0b15203e5cd9548f74fcee -> ciflow/periodic/4a773e1e867f28a8ff0b15203e5cd9548f74fcee 2025-08-14T21:02:57.9494466Z * [new tag] ciflow/periodic/5f5f508aa836a46dfe88857fb223049616b94e93 -> ciflow/periodic/5f5f508aa836a46dfe88857fb223049616b94e93 2025-08-14T21:02:57.9495099Z * [new tag] ciflow/periodic/94512-point -> ciflow/periodic/94512-point 2025-08-14T21:02:57.9496250Z * [new tag] ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519 2025-08-14T21:02:57.9496963Z * [new tag] ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275 2025-08-14T21:02:57.9497723Z * [new tag] ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761 2025-08-14T21:02:57.9499146Z * [new tag] ciflow/periodic/d7114f05b10de8e6de81ffc567d63944c3117d51 -> ciflow/periodic/d7114f05b10de8e6de81ffc567d63944c3117d51 2025-08-14T21:02:57.9499612Z * [new tag] ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12 2025-08-14T21:02:57.9500644Z * [new tag] ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0 2025-08-14T21:02:57.9501513Z * [new tag] ciflow/periodic/sha-ec5b83 -> ciflow/periodic/sha-ec5b83 2025-08-14T21:02:57.9502401Z * [new tag] ciflow/rocm-mi300/151360 -> ciflow/rocm-mi300/151360 2025-08-14T21:02:57.9503051Z * [new tag] ciflow/rocm-mi300/159158 -> ciflow/rocm-mi300/159158 2025-08-14T21:02:57.9503717Z * [new tag] ciflow/rocm-mi300/160073 -> ciflow/rocm-mi300/160073 2025-08-14T21:02:57.9504479Z * [new tag] ciflow/rocm-mi300/160468 -> ciflow/rocm-mi300/160468 2025-08-14T21:02:57.9505147Z * [new tag] ciflow/rocm-mi300/160538 -> ciflow/rocm-mi300/160538 2025-08-14T21:02:57.9506127Z * [new tag] ciflow/rocm-mi355/160215 -> ciflow/rocm-mi355/160215 2025-08-14T21:02:57.9506845Z * [new tag] ciflow/rocm/148492 -> ciflow/rocm/148492 2025-08-14T21:02:57.9507503Z * [new tag] ciflow/rocm/151360 -> ciflow/rocm/151360 2025-08-14T21:02:57.9508087Z * [new tag] ciflow/rocm/151845 -> ciflow/rocm/151845 2025-08-14T21:02:57.9508883Z * [new tag] ciflow/rocm/154864 -> ciflow/rocm/154864 2025-08-14T21:02:57.9509614Z * [new tag] ciflow/rocm/156491 -> ciflow/rocm/156491 2025-08-14T21:02:57.9510189Z * [new tag] ciflow/rocm/158219 -> ciflow/rocm/158219 2025-08-14T21:02:57.9510844Z * [new tag] ciflow/rocm/158220 -> ciflow/rocm/158220 2025-08-14T21:02:57.9511521Z * [new tag] ciflow/rocm/158224 -> ciflow/rocm/158224 2025-08-14T21:02:57.9512113Z * [new tag] ciflow/rocm/159158 -> ciflow/rocm/159158 2025-08-14T21:02:57.9512840Z * [new tag] ciflow/rocm/160215 -> ciflow/rocm/160215 2025-08-14T21:02:57.9513486Z * [new tag] ciflow/rocm/160468 -> ciflow/rocm/160468 2025-08-14T21:02:57.9514449Z * [new tag] ciflow/rocm/160538 -> ciflow/rocm/160538 2025-08-14T21:02:57.9515230Z * [new tag] ciflow/s390/143959 -> ciflow/s390/143959 2025-08-14T21:02:57.9516229Z * [new tag] ciflow/slow/01c7106 -> ciflow/slow/01c7106 2025-08-14T21:02:57.9516916Z * [new tag] ciflow/slow/0577043 -> ciflow/slow/0577043 2025-08-14T21:02:57.9518226Z * [new tag] ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym 2025-08-14T21:02:57.9518630Z * [new tag] ciflow/slow/0e81104 -> ciflow/slow/0e81104 2025-08-14T21:02:57.9519264Z * [new tag] ciflow/slow/154595 -> ciflow/slow/154595 2025-08-14T21:02:57.9520122Z * [new tag] ciflow/slow/1732077 -> ciflow/slow/1732077 2025-08-14T21:02:57.9520871Z * [new tag] ciflow/slow/187eb7c -> ciflow/slow/187eb7c 2025-08-14T21:02:57.9521635Z * [new tag] ciflow/slow/1faef89 -> ciflow/slow/1faef89 2025-08-14T21:02:57.9522714Z * [new tag] ciflow/slow/3920ec1 -> ciflow/slow/3920ec1 2025-08-14T21:02:57.9523761Z * [new tag] ciflow/slow/3b7c6b2 -> ciflow/slow/3b7c6b2 2025-08-14T21:02:57.9524505Z * [new tag] ciflow/slow/59a3759 -> ciflow/slow/59a3759 2025-08-14T21:02:57.9525283Z * [new tag] ciflow/slow/70ef0bb -> ciflow/slow/70ef0bb 2025-08-14T21:02:57.9526319Z * [new tag] ciflow/slow/788ff06 -> ciflow/slow/788ff06 2025-08-14T21:02:57.9527444Z * [new tag] ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym 2025-08-14T21:02:57.9527846Z * [new tag] ciflow/slow/9d85864 -> ciflow/slow/9d85864 2025-08-14T21:02:57.9528620Z * [new tag] ciflow/slow/9ffad5b -> ciflow/slow/9ffad5b 2025-08-14T21:02:57.9529577Z * [new tag] ciflow/slow/a206e8b -> ciflow/slow/a206e8b 2025-08-14T21:02:57.9530333Z * [new tag] ciflow/slow/a837609 -> ciflow/slow/a837609 2025-08-14T21:02:57.9531110Z * [new tag] ciflow/slow/af841f3 -> ciflow/slow/af841f3 2025-08-14T21:02:57.9532532Z * [new tag] ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym 2025-08-14T21:02:57.9532827Z * [new tag] ciflow/trunk/131296 -> ciflow/trunk/131296 2025-08-14T21:02:57.9533493Z * [new tag] ciflow/trunk/137400 -> ciflow/trunk/137400 2025-08-14T21:02:57.9534066Z * [new tag] ciflow/trunk/138996 -> ciflow/trunk/138996 2025-08-14T21:02:57.9534714Z * [new tag] ciflow/trunk/139971 -> ciflow/trunk/139971 2025-08-14T21:02:57.9535302Z * [new tag] ciflow/trunk/147360 -> ciflow/trunk/147360 2025-08-14T21:02:57.9536090Z * [new tag] ciflow/trunk/147855 -> ciflow/trunk/147855 2025-08-14T21:02:57.9536623Z * [new tag] ciflow/trunk/148180 -> ciflow/trunk/148180 2025-08-14T21:02:57.9537261Z * [new tag] ciflow/trunk/148328 -> ciflow/trunk/148328 2025-08-14T21:02:57.9537932Z * [new tag] ciflow/trunk/148492 -> ciflow/trunk/148492 2025-08-14T21:02:57.9538710Z * [new tag] ciflow/trunk/150282 -> ciflow/trunk/150282 2025-08-14T21:02:57.9539371Z * [new tag] ciflow/trunk/150302 -> ciflow/trunk/150302 2025-08-14T21:02:57.9540191Z * [new tag] ciflow/trunk/151845 -> ciflow/trunk/151845 2025-08-14T21:02:57.9541209Z * [new tag] ciflow/trunk/152624 -> ciflow/trunk/152624 2025-08-14T21:02:57.9541943Z * [new tag] ciflow/trunk/154193 -> ciflow/trunk/154193 2025-08-14T21:02:57.9542535Z * [new tag] ciflow/trunk/154595 -> ciflow/trunk/154595 2025-08-14T21:02:57.9543231Z * [new tag] ciflow/trunk/154650 -> ciflow/trunk/154650 2025-08-14T21:02:57.9544081Z * [new tag] ciflow/trunk/154694 -> ciflow/trunk/154694 2025-08-14T21:02:57.9544797Z * [new tag] ciflow/trunk/155958 -> ciflow/trunk/155958 2025-08-14T21:02:57.9545357Z * [new tag] ciflow/trunk/156049 -> ciflow/trunk/156049 2025-08-14T21:02:57.9546060Z * [new tag] ciflow/trunk/156703 -> ciflow/trunk/156703 2025-08-14T21:02:57.9546707Z * [new tag] ciflow/trunk/156851 -> ciflow/trunk/156851 2025-08-14T21:02:57.9547526Z * [new tag] ciflow/trunk/157148 -> ciflow/trunk/157148 2025-08-14T21:02:57.9548105Z * [new tag] ciflow/trunk/157432 -> ciflow/trunk/157432 2025-08-14T21:02:57.9548700Z * [new tag] ciflow/trunk/157685 -> ciflow/trunk/157685 2025-08-14T21:02:57.9549413Z * [new tag] ciflow/trunk/157689 -> ciflow/trunk/157689 2025-08-14T21:02:57.9550042Z * [new tag] ciflow/trunk/157699 -> ciflow/trunk/157699 2025-08-14T21:02:57.9550672Z * [new tag] ciflow/trunk/157813 -> ciflow/trunk/157813 2025-08-14T21:02:57.9551315Z * [new tag] ciflow/trunk/157994 -> ciflow/trunk/157994 2025-08-14T21:02:57.9552004Z * [new tag] ciflow/trunk/158091 -> ciflow/trunk/158091 2025-08-14T21:02:57.9552647Z * [new tag] ciflow/trunk/158104 -> ciflow/trunk/158104 2025-08-14T21:02:57.9553293Z * [new tag] ciflow/trunk/158219 -> ciflow/trunk/158219 2025-08-14T21:02:57.9553914Z * [new tag] ciflow/trunk/158220 -> ciflow/trunk/158220 2025-08-14T21:02:57.9554599Z * [new tag] ciflow/trunk/158224 -> ciflow/trunk/158224 2025-08-14T21:02:57.9555583Z * [new tag] ciflow/trunk/158529 -> ciflow/trunk/158529 2025-08-14T21:02:57.9556012Z * [new tag] ciflow/trunk/158647 -> ciflow/trunk/158647 2025-08-14T21:02:57.9556707Z * [new tag] ciflow/trunk/158810 -> ciflow/trunk/158810 2025-08-14T21:02:57.9557384Z * [new tag] ciflow/trunk/158812 -> ciflow/trunk/158812 2025-08-14T21:02:57.9558180Z * [new tag] ciflow/trunk/158863 -> ciflow/trunk/158863 2025-08-14T21:02:57.9558827Z * [new tag] ciflow/trunk/158864 -> ciflow/trunk/158864 2025-08-14T21:02:57.9559447Z * [new tag] ciflow/trunk/158883 -> ciflow/trunk/158883 2025-08-14T21:02:57.9560121Z * [new tag] ciflow/trunk/158914 -> ciflow/trunk/158914 2025-08-14T21:02:57.9560773Z * [new tag] ciflow/trunk/158965 -> ciflow/trunk/158965 2025-08-14T21:02:57.9561900Z * [new tag] ciflow/trunk/158987 -> ciflow/trunk/158987 2025-08-14T21:02:57.9562666Z * [new tag] ciflow/trunk/159033 -> ciflow/trunk/159033 2025-08-14T21:02:57.9563453Z * [new tag] ciflow/trunk/159140 -> ciflow/trunk/159140 2025-08-14T21:02:57.9564058Z * [new tag] ciflow/trunk/159158 -> ciflow/trunk/159158 2025-08-14T21:02:57.9564752Z * [new tag] ciflow/trunk/159530 -> ciflow/trunk/159530 2025-08-14T21:02:57.9565390Z * [new tag] ciflow/trunk/159553 -> ciflow/trunk/159553 2025-08-14T21:02:57.9566069Z * [new tag] ciflow/trunk/159562 -> ciflow/trunk/159562 2025-08-14T21:02:57.9566858Z * [new tag] ciflow/trunk/159682 -> ciflow/trunk/159682 2025-08-14T21:02:57.9567533Z * [new tag] ciflow/trunk/159691 -> ciflow/trunk/159691 2025-08-14T21:02:57.9568191Z * [new tag] ciflow/trunk/159842 -> ciflow/trunk/159842 2025-08-14T21:02:57.9568909Z * [new tag] ciflow/trunk/159889 -> ciflow/trunk/159889 2025-08-14T21:02:57.9569567Z * [new tag] ciflow/trunk/159923 -> ciflow/trunk/159923 2025-08-14T21:02:57.9570213Z * [new tag] ciflow/trunk/160000 -> ciflow/trunk/160000 2025-08-14T21:02:57.9570926Z * [new tag] ciflow/trunk/160004 -> ciflow/trunk/160004 2025-08-14T21:02:57.9571554Z * [new tag] ciflow/trunk/160113 -> ciflow/trunk/160113 2025-08-14T21:02:57.9572218Z * [new tag] ciflow/trunk/160161 -> ciflow/trunk/160161 2025-08-14T21:02:57.9572843Z * [new tag] ciflow/trunk/160168 -> ciflow/trunk/160168 2025-08-14T21:02:57.9573600Z * [new tag] ciflow/trunk/160181 -> ciflow/trunk/160181 2025-08-14T21:02:57.9574247Z * [new tag] ciflow/trunk/160183 -> ciflow/trunk/160183 2025-08-14T21:02:57.9574814Z * [new tag] ciflow/trunk/160190 -> ciflow/trunk/160190 2025-08-14T21:02:57.9575525Z * [new tag] ciflow/trunk/160198 -> ciflow/trunk/160198 2025-08-14T21:02:57.9576171Z * [new tag] ciflow/trunk/160205 -> ciflow/trunk/160205 2025-08-14T21:02:57.9576994Z * [new tag] ciflow/trunk/160219 -> ciflow/trunk/160219 2025-08-14T21:02:57.9577674Z * [new tag] ciflow/trunk/160224 -> ciflow/trunk/160224 2025-08-14T21:02:57.9578336Z * [new tag] ciflow/trunk/160250 -> ciflow/trunk/160250 2025-08-14T21:02:57.9578997Z * [new tag] ciflow/trunk/160253 -> ciflow/trunk/160253 2025-08-14T21:02:57.9579783Z * [new tag] ciflow/trunk/160335 -> ciflow/trunk/160335 2025-08-14T21:02:57.9580739Z * [new tag] ciflow/trunk/160338 -> ciflow/trunk/160338 2025-08-14T21:02:57.9581343Z * [new tag] ciflow/trunk/160383 -> ciflow/trunk/160383 2025-08-14T21:02:57.9581975Z * [new tag] ciflow/trunk/160401 -> ciflow/trunk/160401 2025-08-14T21:02:57.9582648Z * [new tag] ciflow/trunk/160403 -> ciflow/trunk/160403 2025-08-14T21:02:57.9583372Z * [new tag] ciflow/trunk/160430 -> ciflow/trunk/160430 2025-08-14T21:02:57.9584072Z * [new tag] ciflow/trunk/160431 -> ciflow/trunk/160431 2025-08-14T21:02:57.9585006Z * [new tag] ciflow/trunk/160439 -> ciflow/trunk/160439 2025-08-14T21:02:57.9585578Z * [new tag] ciflow/trunk/160449 -> ciflow/trunk/160449 2025-08-14T21:02:57.9586535Z * [new tag] ciflow/trunk/160454 -> ciflow/trunk/160454 2025-08-14T21:02:57.9587098Z * [new tag] ciflow/trunk/160468 -> ciflow/trunk/160468 2025-08-14T21:02:57.9587784Z * [new tag] ciflow/trunk/160481 -> ciflow/trunk/160481 2025-08-14T21:02:57.9588505Z * [new tag] ciflow/trunk/160485 -> ciflow/trunk/160485 2025-08-14T21:02:57.9589251Z * [new tag] ciflow/trunk/160519 -> ciflow/trunk/160519 2025-08-14T21:02:57.9589949Z * [new tag] ciflow/trunk/160527 -> ciflow/trunk/160527 2025-08-14T21:02:57.9590784Z * [new tag] ciflow/trunk/160560 -> ciflow/trunk/160560 2025-08-14T21:02:57.9591485Z * [new tag] ciflow/trunk/160578 -> ciflow/trunk/160578 2025-08-14T21:02:57.9592179Z * [new tag] ciflow/trunk/160589 -> ciflow/trunk/160589 2025-08-14T21:02:57.9592805Z * [new tag] ciflow/trunk/160592 -> ciflow/trunk/160592 2025-08-14T21:02:57.9594710Z * [new tag] ciflow/trunk/160649 -> ciflow/trunk/160649 2025-08-14T21:02:57.9595709Z * [new tag] ciflow/trunk/160656 -> ciflow/trunk/160656 2025-08-14T21:02:57.9596925Z * [new tag] ciflow/unstable/123 -> ciflow/unstable/123 2025-08-14T21:02:57.9597695Z * [new tag] ciflow/vllm/160116 -> ciflow/vllm/160116 2025-08-14T21:02:57.9598383Z * [new tag] ciflow/vllm/160583 -> ciflow/vllm/160583 2025-08-14T21:02:57.9598996Z * [new tag] ciflow/vllm/160619 -> ciflow/vllm/160619 2025-08-14T21:02:57.9599650Z * [new tag] ciflow/vllm/160625 -> ciflow/vllm/160625 2025-08-14T21:02:57.9600278Z * [new tag] ciflow/vllm/160627 -> ciflow/vllm/160627 2025-08-14T21:02:57.9601072Z * [new tag] ciflow/win-arm64/156049 -> ciflow/win-arm64/156049 2025-08-14T21:02:57.9601916Z * [new tag] ciflow/win-arm64/158104 -> ciflow/win-arm64/158104 2025-08-14T21:02:57.9602358Z * [new tag] ciflow/win-arm64/159553 -> ciflow/win-arm64/159553 2025-08-14T21:02:57.9603037Z * [new tag] ciflow/win-arm64/159562 -> ciflow/win-arm64/159562 2025-08-14T21:02:57.9603853Z * [new tag] ciflow/win-arm64/159777 -> ciflow/win-arm64/159777 2025-08-14T21:02:57.9604525Z * [new tag] ciflow/win-arm64/159780 -> ciflow/win-arm64/159780 2025-08-14T21:02:57.9605135Z * [new tag] ciflow/win-arm64/159842 -> ciflow/win-arm64/159842 2025-08-14T21:02:57.9605754Z * [new tag] ciflow/win-arm64/160250 -> ciflow/win-arm64/160250 2025-08-14T21:02:57.9606338Z * [new tag] ciflow/win-arm64/160253 -> ciflow/win-arm64/160253 2025-08-14T21:02:57.9606999Z * [new tag] ciflow/win-arm64/160454 -> ciflow/win-arm64/160454 2025-08-14T21:02:57.9607649Z * [new tag] ciflow/win-arm64/160560 -> ciflow/win-arm64/160560 2025-08-14T21:02:57.9608389Z * [new tag] ciflow/xpu/138996 -> ciflow/xpu/138996 2025-08-14T21:02:57.9609023Z * [new tag] ciflow/xpu/139971 -> ciflow/xpu/139971 2025-08-14T21:02:57.9609935Z * [new tag] ciflow/xpu/140972 -> ciflow/xpu/140972 2025-08-14T21:02:57.9610528Z * [new tag] ciflow/xpu/143553 -> ciflow/xpu/143553 2025-08-14T21:02:57.9611278Z * [new tag] ciflow/xpu/156272 -> ciflow/xpu/156272 2025-08-14T21:02:57.9611950Z * [new tag] ciflow/xpu/156812 -> ciflow/xpu/156812 2025-08-14T21:02:57.9612585Z * [new tag] ciflow/xpu/157699 -> ciflow/xpu/157699 2025-08-14T21:02:57.9613206Z * [new tag] ciflow/xpu/157994 -> ciflow/xpu/157994 2025-08-14T21:02:57.9613814Z * [new tag] ciflow/xpu/158336 -> ciflow/xpu/158336 2025-08-14T21:02:57.9614503Z * [new tag] ciflow/xpu/158733 -> ciflow/xpu/158733 2025-08-14T21:02:57.9615061Z * [new tag] ciflow/xpu/159033 -> ciflow/xpu/159033 2025-08-14T21:02:57.9616113Z * [new tag] ciflow/xpu/159118 -> ciflow/xpu/159118 2025-08-14T21:02:57.9616925Z * [new tag] ciflow/xpu/159140 -> ciflow/xpu/159140 2025-08-14T21:02:57.9617985Z * [new tag] ciflow/xpu/159241 -> ciflow/xpu/159241 2025-08-14T21:02:57.9618537Z * [new tag] ciflow/xpu/159473 -> ciflow/xpu/159473 2025-08-14T21:02:57.9619486Z * [new tag] ciflow/xpu/159474 -> ciflow/xpu/159474 2025-08-14T21:02:57.9620056Z * [new tag] ciflow/xpu/159553 -> ciflow/xpu/159553 2025-08-14T21:02:57.9620741Z * [new tag] ciflow/xpu/159944 -> ciflow/xpu/159944 2025-08-14T21:02:57.9621684Z * [new tag] ciflow/xpu/160062 -> ciflow/xpu/160062 2025-08-14T21:02:57.9622289Z * [new tag] ciflow/xpu/160067 -> ciflow/xpu/160067 2025-08-14T21:02:57.9623035Z * [new tag] ciflow/xpu/160158 -> ciflow/xpu/160158 2025-08-14T21:02:57.9623786Z * [new tag] ciflow/xpu/160173 -> ciflow/xpu/160173 2025-08-14T21:02:57.9624479Z * [new tag] ciflow/xpu/160183 -> ciflow/xpu/160183 2025-08-14T21:02:57.9625267Z * [new tag] ciflow/xpu/160301 -> ciflow/xpu/160301 2025-08-14T21:02:57.9625869Z * [new tag] ciflow/xpu/160403 -> ciflow/xpu/160403 2025-08-14T21:02:57.9626694Z * [new tag] ciflow/xpu/160606 -> ciflow/xpu/160606 2025-08-14T21:02:57.9627466Z * [new tag] cslpull75 -> cslpull75 2025-08-14T21:02:57.9628173Z * [new tag] cslpull76 -> cslpull76 2025-08-14T21:02:57.9628998Z * [new tag] cslpull77 -> cslpull77 2025-08-14T21:02:57.9629714Z * [new tag] cslpull78 -> cslpull78 2025-08-14T21:02:57.9630804Z * [new tag] cslpull79 -> cslpull79 2025-08-14T21:02:57.9634006Z * [new tag] cslpull80 -> cslpull80 2025-08-14T21:02:57.9634185Z * [new tag] cslpull81 -> cslpull81 2025-08-14T21:02:57.9634376Z * [new tag] cslpull82 -> cslpull82 2025-08-14T21:02:57.9634610Z * [new tag] cslpull83 -> cslpull83 2025-08-14T21:02:57.9635076Z * [new tag] cslpull84 -> cslpull84 2025-08-14T21:02:57.9636141Z * [new tag] cslpull85 -> cslpull85 2025-08-14T21:02:57.9636766Z * [new tag] cslpull86 -> cslpull86 2025-08-14T21:02:57.9637382Z * [new tag] cslpull87 -> cslpull87 2025-08-14T21:02:57.9638289Z * [new tag] cslpull88 -> cslpull88 2025-08-14T21:02:57.9638987Z * [new tag] cslpull89 -> cslpull89 2025-08-14T21:02:57.9639658Z * [new tag] cslpull90 -> cslpull90 2025-08-14T21:02:57.9640898Z * [new tag] cslpull91 -> cslpull91 2025-08-14T21:02:57.9641621Z * [new tag] cslpull92 -> cslpull92 2025-08-14T21:02:57.9642392Z * [new tag] flight_5 -> flight_5 2025-08-14T21:02:57.9643147Z * [new tag] flight_5.1 -> flight_5.1 2025-08-14T21:02:57.9644082Z * [new tag] flight_5.2 -> flight_5.2 2025-08-14T21:02:57.9644719Z * [new tag] flight_5.3 -> flight_5.3 2025-08-14T21:02:57.9645472Z * [new tag] forpull1 -> forpull1 2025-08-14T21:02:57.9646502Z * [new tag] malfet/tag-2ef5611 -> malfet/tag-2ef5611 2025-08-14T21:02:57.9647231Z * [new tag] malfet/tag-317b1a0 -> malfet/tag-317b1a0 2025-08-14T21:02:57.9648008Z * [new tag] malfet/tag-ec6f767 -> malfet/tag-ec6f767 2025-08-14T21:02:57.9649030Z * [new tag] nightly-binary -> nightly-binary 2025-08-14T21:02:57.9649518Z * [new tag] sqzhang_flight4_plus -> sqzhang_flight4_plus 2025-08-14T21:02:57.9650491Z * [new tag] sqzhang_flight_3 -> sqzhang_flight_3 2025-08-14T21:02:57.9651659Z * [new tag] trunk/01584d2a7d029c9749eb73678cf1dc313cc35df6 -> trunk/01584d2a7d029c9749eb73678cf1dc313cc35df6 2025-08-14T21:02:57.9652382Z * [new tag] trunk/017259f9c65b6fad55fb9597d7077e2543eaae46 -> trunk/017259f9c65b6fad55fb9597d7077e2543eaae46 2025-08-14T21:02:57.9653912Z * [new tag] trunk/01bcf9a40dea937637d2cdd530bed2652510943d -> trunk/01bcf9a40dea937637d2cdd530bed2652510943d 2025-08-14T21:02:57.9654744Z * [new tag] trunk/01f66d08d93365015f4af005a252f439c4d4013a -> trunk/01f66d08d93365015f4af005a252f439c4d4013a 2025-08-14T21:02:57.9655523Z * [new tag] trunk/03b254e49f2d4c092e6ca712e5702cf2895aa47e -> trunk/03b254e49f2d4c092e6ca712e5702cf2895aa47e 2025-08-14T21:02:57.9656216Z * [new tag] trunk/05029ad1c30865d3f7e7fd13384db9d826e563eb -> trunk/05029ad1c30865d3f7e7fd13384db9d826e563eb 2025-08-14T21:02:57.9657026Z * [new tag] trunk/05c19d1acecc01b0d2512364183058a6885b9869 -> trunk/05c19d1acecc01b0d2512364183058a6885b9869 2025-08-14T21:02:57.9657882Z * [new tag] trunk/05c417715f791875fbf28cfc3fc86142de1a3206 -> trunk/05c417715f791875fbf28cfc3fc86142de1a3206 2025-08-14T21:02:57.9658695Z * [new tag] trunk/06824f3c7268bb807a422b663047cd0900ddd126 -> trunk/06824f3c7268bb807a422b663047cd0900ddd126 2025-08-14T21:02:57.9659458Z * [new tag] trunk/077cb389746a7d61cfc018aad2ba29a8aa195610 -> trunk/077cb389746a7d61cfc018aad2ba29a8aa195610 2025-08-14T21:02:57.9660227Z * [new tag] trunk/089c4a1ba007ed4abb3e5e0eafd97b7584566057 -> trunk/089c4a1ba007ed4abb3e5e0eafd97b7584566057 2025-08-14T21:02:57.9661097Z * [new tag] trunk/09381f5dacda7bbbfa361f5df76bde5cd309adc1 -> trunk/09381f5dacda7bbbfa361f5df76bde5cd309adc1 2025-08-14T21:02:57.9661871Z * [new tag] trunk/0bd3af4fb87445f4de3a1f9b823e399c8b3cefde -> trunk/0bd3af4fb87445f4de3a1f9b823e399c8b3cefde 2025-08-14T21:02:57.9662711Z * [new tag] trunk/0d3461bac0fb5177e35152d980b301ea3a0aa2c4 -> trunk/0d3461bac0fb5177e35152d980b301ea3a0aa2c4 2025-08-14T21:02:57.9663549Z * [new tag] trunk/0d40ff3b496e68193bc16d5391fa2e3623709f81 -> trunk/0d40ff3b496e68193bc16d5391fa2e3623709f81 2025-08-14T21:02:57.9664441Z * [new tag] trunk/0d71ca2c46753bb268bfdcf815c14415c122a289 -> trunk/0d71ca2c46753bb268bfdcf815c14415c122a289 2025-08-14T21:02:57.9665268Z * [new tag] trunk/0d88593dd826544c9e7bd4aa615ef86847a78d2b -> trunk/0d88593dd826544c9e7bd4aa615ef86847a78d2b 2025-08-14T21:02:57.9666025Z * [new tag] trunk/0e3e377bd5126cfcc69d70c4d77b352d3404cc11 -> trunk/0e3e377bd5126cfcc69d70c4d77b352d3404cc11 2025-08-14T21:02:57.9666834Z * [new tag] trunk/0f3b10b8eebe68e3c75d473d499b87dfe14a2eca -> trunk/0f3b10b8eebe68e3c75d473d499b87dfe14a2eca 2025-08-14T21:02:57.9667746Z * [new tag] trunk/101276f81b4d2a8c31bfd6796b986d4c1bfdf483 -> trunk/101276f81b4d2a8c31bfd6796b986d4c1bfdf483 2025-08-14T21:02:57.9668565Z * [new tag] trunk/1028c5e2d50e121865bf98307e7c035f549a24b2 -> trunk/1028c5e2d50e121865bf98307e7c035f549a24b2 2025-08-14T21:02:57.9669396Z * [new tag] trunk/10bc36fe840cb3510fab84d2ea22663b76702f1e -> trunk/10bc36fe840cb3510fab84d2ea22663b76702f1e 2025-08-14T21:02:57.9670208Z * [new tag] trunk/10e3514c962b58cbbee994257872a626ff76d51b -> trunk/10e3514c962b58cbbee994257872a626ff76d51b 2025-08-14T21:02:57.9671004Z * [new tag] trunk/1128f4c2a822cbe34a9d966306af15097179ffe1 -> trunk/1128f4c2a822cbe34a9d966306af15097179ffe1 2025-08-14T21:02:57.9671820Z * [new tag] trunk/114a6c40434bfb9cfa5abc30e9e34d81300d743e -> trunk/114a6c40434bfb9cfa5abc30e9e34d81300d743e 2025-08-14T21:02:57.9672661Z * [new tag] trunk/118bc97b14c24ac88a4b0c0750a9e7bf93154c76 -> trunk/118bc97b14c24ac88a4b0c0750a9e7bf93154c76 2025-08-14T21:02:57.9673294Z * [new tag] trunk/1196bb1c2e4d5a7edc09f2260e3034132f0c6c91 -> trunk/1196bb1c2e4d5a7edc09f2260e3034132f0c6c91 2025-08-14T21:02:57.9674141Z * [new tag] trunk/11a3565f1872bbad9c253a127e8d4ce7a1b40ec8 -> trunk/11a3565f1872bbad9c253a127e8d4ce7a1b40ec8 2025-08-14T21:02:57.9674986Z * [new tag] trunk/15e49f61643e4c0eef420f0981609709ef55b848 -> trunk/15e49f61643e4c0eef420f0981609709ef55b848 2025-08-14T21:02:57.9675843Z * [new tag] trunk/16d15445f8bd8740095b23de4af89d757af793ca -> trunk/16d15445f8bd8740095b23de4af89d757af793ca 2025-08-14T21:02:57.9676679Z * [new tag] trunk/178515d0ff6833c8e9221482b2a650ab31e00019 -> trunk/178515d0ff6833c8e9221482b2a650ab31e00019 2025-08-14T21:02:57.9677530Z * [new tag] trunk/182efe31dbe43376e7eef7338356aaf94d5bcabe -> trunk/182efe31dbe43376e7eef7338356aaf94d5bcabe 2025-08-14T21:02:57.9678331Z * [new tag] trunk/194fcfcfbdad0add1a1b695321e31a576058f4cf -> trunk/194fcfcfbdad0add1a1b695321e31a576058f4cf 2025-08-14T21:02:57.9679124Z * [new tag] trunk/195b5c2e27eb8f21cbc8ad1e90f42db5a8cfccca -> trunk/195b5c2e27eb8f21cbc8ad1e90f42db5a8cfccca 2025-08-14T21:02:57.9679982Z * [new tag] trunk/198b5fd2d47fa3d5110ceba6827a3b18e0064014 -> trunk/198b5fd2d47fa3d5110ceba6827a3b18e0064014 2025-08-14T21:02:57.9680848Z * [new tag] trunk/199e9abb6a366bbd27c39d1da7c3123b4eea9b5a -> trunk/199e9abb6a366bbd27c39d1da7c3123b4eea9b5a 2025-08-14T21:02:57.9681569Z * [new tag] trunk/19b4283884b2d9b3a0eb364da10b1540d14ab7a7 -> trunk/19b4283884b2d9b3a0eb364da10b1540d14ab7a7 2025-08-14T21:02:57.9682615Z * [new tag] trunk/1c2587119152cec3905647a47c65d3d26619c5a8 -> trunk/1c2587119152cec3905647a47c65d3d26619c5a8 2025-08-14T21:02:57.9683377Z * [new tag] trunk/1c26c53851c212a7c90a325549a72f0571613a8c -> trunk/1c26c53851c212a7c90a325549a72f0571613a8c 2025-08-14T21:02:57.9684163Z * [new tag] trunk/1c2cba17eab2b09d87142883da2bdbdbcf018613 -> trunk/1c2cba17eab2b09d87142883da2bdbdbcf018613 2025-08-14T21:02:57.9684974Z * [new tag] trunk/1d80d697a269234b47ec7ede192faf3bb9b159e3 -> trunk/1d80d697a269234b47ec7ede192faf3bb9b159e3 2025-08-14T21:02:57.9685765Z * [new tag] trunk/1ea688f9a2602fbcde32c0302b822526ca4219dc -> trunk/1ea688f9a2602fbcde32c0302b822526ca4219dc 2025-08-14T21:02:57.9686691Z * [new tag] trunk/1f4057c11ac941fb324386ca594d0a6882185aad -> trunk/1f4057c11ac941fb324386ca594d0a6882185aad 2025-08-14T21:02:57.9687535Z * [new tag] trunk/1fc683cf17c8c673044538d10266c00f92987be2 -> trunk/1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:02:57.9688342Z * [new tag] trunk/1febab2a89302464f6c7d69cfbef7a24c421ea65 -> trunk/1febab2a89302464f6c7d69cfbef7a24c421ea65 2025-08-14T21:02:57.9689083Z * [new tag] trunk/206c1eef6571f906c2792d899a09136b3fce9673 -> trunk/206c1eef6571f906c2792d899a09136b3fce9673 2025-08-14T21:02:57.9689961Z * [new tag] trunk/20bdabbb3c5d6b118a94b2e045c777662563d5bb -> trunk/20bdabbb3c5d6b118a94b2e045c777662563d5bb 2025-08-14T21:02:57.9690724Z * [new tag] trunk/21392c0e06ac2b2621950455975ca6332f0bf641 -> trunk/21392c0e06ac2b2621950455975ca6332f0bf641 2025-08-14T21:02:57.9691362Z * [new tag] trunk/2247aa6d1d43e256255f5c74a781c3190a4387b6 -> trunk/2247aa6d1d43e256255f5c74a781c3190a4387b6 2025-08-14T21:02:57.9692030Z * [new tag] trunk/2259dbed4e0d3f2a8174b5847fd0741aed42451d -> trunk/2259dbed4e0d3f2a8174b5847fd0741aed42451d 2025-08-14T21:02:57.9693028Z * [new tag] trunk/231c72240d80091f099c95e326d3600cba866eee -> trunk/231c72240d80091f099c95e326d3600cba866eee 2025-08-14T21:02:57.9693624Z * [new tag] trunk/24257f5bfaa37795f74d9f64c1b43584128d4b8c -> trunk/24257f5bfaa37795f74d9f64c1b43584128d4b8c 2025-08-14T21:02:57.9695121Z * [new tag] trunk/24f43d0da7ad9c6e95a09a2fee610387728cc1cd -> trunk/24f43d0da7ad9c6e95a09a2fee610387728cc1cd 2025-08-14T21:02:57.9695850Z * [new tag] trunk/2898d3f965e5cd9d02fc2ecdab7c580fd457fea9 -> trunk/2898d3f965e5cd9d02fc2ecdab7c580fd457fea9 2025-08-14T21:02:57.9696680Z * [new tag] trunk/28ccc9e7247798980fe00a11bcd64a8016b5f227 -> trunk/28ccc9e7247798980fe00a11bcd64a8016b5f227 2025-08-14T21:02:57.9697445Z * [new tag] trunk/29712314dd5cf500a8ea3d1c69483a3cb768ca72 -> trunk/29712314dd5cf500a8ea3d1c69483a3cb768ca72 2025-08-14T21:02:57.9698232Z * [new tag] trunk/29d20d49f0b7f4e362e1cefdcdc4b5659969312c -> trunk/29d20d49f0b7f4e362e1cefdcdc4b5659969312c 2025-08-14T21:02:57.9699113Z * [new tag] trunk/2c5e10a5fceb208b11c3d569ae02e348b5893b31 -> trunk/2c5e10a5fceb208b11c3d569ae02e348b5893b31 2025-08-14T21:02:57.9699767Z * [new tag] trunk/2d0cdee394bccadcd0abe19dd4623ed978a331ad -> trunk/2d0cdee394bccadcd0abe19dd4623ed978a331ad 2025-08-14T21:02:57.9700605Z * [new tag] trunk/2e4e5ab4be9e0aeffd9c49b5b2f9f820bd0895b1 -> trunk/2e4e5ab4be9e0aeffd9c49b5b2f9f820bd0895b1 2025-08-14T21:02:57.9701454Z * [new tag] trunk/2ea40fba841b3af8103f332ba62e54f350ba9a51 -> trunk/2ea40fba841b3af8103f332ba62e54f350ba9a51 2025-08-14T21:02:57.9702100Z * [new tag] trunk/2ee22e435131369a7e4f8cc4732579acc29a941b -> trunk/2ee22e435131369a7e4f8cc4732579acc29a941b 2025-08-14T21:02:57.9703160Z * [new tag] trunk/2f4c2226175512af787725c4d5ad7313c60d4db1 -> trunk/2f4c2226175512af787725c4d5ad7313c60d4db1 2025-08-14T21:02:57.9703889Z * [new tag] trunk/3008d985a8fc155eb89374afff50cb33a6bd10d5 -> trunk/3008d985a8fc155eb89374afff50cb33a6bd10d5 2025-08-14T21:02:57.9704693Z * [new tag] trunk/3028fa6ce9d9c96671722ab8213a1a30670d7cf2 -> trunk/3028fa6ce9d9c96671722ab8213a1a30670d7cf2 2025-08-14T21:02:57.9705503Z * [new tag] trunk/303c614f3df95ae2b659c5f6c1838b14e4776ce6 -> trunk/303c614f3df95ae2b659c5f6c1838b14e4776ce6 2025-08-14T21:02:57.9707081Z * [new tag] trunk/305fa2239365ad17ac9c534a68bba8a149c42d67 -> trunk/305fa2239365ad17ac9c534a68bba8a149c42d67 2025-08-14T21:02:57.9707990Z * [new tag] trunk/31c9ac4319c0cc2ed8c6be701c6ccf73f6cb4706 -> trunk/31c9ac4319c0cc2ed8c6be701c6ccf73f6cb4706 2025-08-14T21:02:57.9708758Z * [new tag] trunk/32099961d588fc19ead8afe805d6b5108de75669 -> trunk/32099961d588fc19ead8afe805d6b5108de75669 2025-08-14T21:02:57.9709728Z * [new tag] trunk/32e5e2f596d55bb9441d5d53f3c58bcb55828047 -> trunk/32e5e2f596d55bb9441d5d53f3c58bcb55828047 2025-08-14T21:02:57.9710266Z * [new tag] trunk/334b38ccc4427b1d14981c48a3a0b92180d58225 -> trunk/334b38ccc4427b1d14981c48a3a0b92180d58225 2025-08-14T21:02:57.9711159Z * [new tag] trunk/334ecbd4ffe11858cae7d23d1190ddb4777c2513 -> trunk/334ecbd4ffe11858cae7d23d1190ddb4777c2513 2025-08-14T21:02:57.9711934Z * [new tag] trunk/33d94018668951611b318b7515ae96f04e48eac0 -> trunk/33d94018668951611b318b7515ae96f04e48eac0 2025-08-14T21:02:57.9712831Z * [new tag] trunk/34358f335d95213d96b6cca6a83e7bf3af6a9fcb -> trunk/34358f335d95213d96b6cca6a83e7bf3af6a9fcb 2025-08-14T21:02:57.9713607Z * [new tag] trunk/34ec5ed275f8aa875c80daa97b3e82af0b06f673 -> trunk/34ec5ed275f8aa875c80daa97b3e82af0b06f673 2025-08-14T21:02:57.9714396Z * [new tag] trunk/355462e1278d818deb9ef4a184073d5b66074816 -> trunk/355462e1278d818deb9ef4a184073d5b66074816 2025-08-14T21:02:57.9719187Z * [new tag] trunk/3626ba711b34397d1fbf0a9b1979f85cbf68b919 -> trunk/3626ba711b34397d1fbf0a9b1979f85cbf68b919 2025-08-14T21:02:57.9719910Z * [new tag] trunk/36f46d082a4954921cb8493223f000f2aab79ed7 -> trunk/36f46d082a4954921cb8493223f000f2aab79ed7 2025-08-14T21:02:57.9720806Z * [new tag] trunk/39aa3d1471549b7829c207d634dfdc1d26e346a2 -> trunk/39aa3d1471549b7829c207d634dfdc1d26e346a2 2025-08-14T21:02:57.9721730Z * [new tag] trunk/3a562374401113187ce2566b87e3f1d87d7c53aa -> trunk/3a562374401113187ce2566b87e3f1d87d7c53aa 2025-08-14T21:02:57.9722446Z * [new tag] trunk/3ac86e728dfaa7383ff7f865e9e7d33486188dae -> trunk/3ac86e728dfaa7383ff7f865e9e7d33486188dae 2025-08-14T21:02:57.9723243Z * [new tag] trunk/3cec82a7e9aea040a34dd7a2587ae6d3bd65dba0 -> trunk/3cec82a7e9aea040a34dd7a2587ae6d3bd65dba0 2025-08-14T21:02:57.9724077Z * [new tag] trunk/3cf7b4024ef83e44e9ae223dbff7c7ab68240cb2 -> trunk/3cf7b4024ef83e44e9ae223dbff7c7ab68240cb2 2025-08-14T21:02:57.9724887Z * [new tag] trunk/3ef2e1ef769582a82c6ddf150e9d11bf4bf1c44f -> trunk/3ef2e1ef769582a82c6ddf150e9d11bf4bf1c44f 2025-08-14T21:02:57.9725696Z * [new tag] trunk/3f1636ebef9b45e8a3cb0eb20d327ee6acb74be0 -> trunk/3f1636ebef9b45e8a3cb0eb20d327ee6acb74be0 2025-08-14T21:02:57.9726496Z * [new tag] trunk/3faee0a6318afcbbbb48687009a459214910d820 -> trunk/3faee0a6318afcbbbb48687009a459214910d820 2025-08-14T21:02:57.9727276Z * [new tag] trunk/3fcd79e023da7156ac584992ebab29205d3b7881 -> trunk/3fcd79e023da7156ac584992ebab29205d3b7881 2025-08-14T21:02:57.9728184Z * [new tag] trunk/3fe19a7a0af3f4d692af30476c320be18c7e8ae6 -> trunk/3fe19a7a0af3f4d692af30476c320be18c7e8ae6 2025-08-14T21:02:57.9729151Z * [new tag] trunk/41673110cd7c5960824cc74a6fcaeda1a8bc7a23 -> trunk/41673110cd7c5960824cc74a6fcaeda1a8bc7a23 2025-08-14T21:02:57.9729855Z * [new tag] trunk/4183d4ff3dcc1d87400326a9a7998c3f9e966f60 -> trunk/4183d4ff3dcc1d87400326a9a7998c3f9e966f60 2025-08-14T21:02:57.9730688Z * [new tag] trunk/422bd6808bb98cbbac31d157d9c82ad11ba9732d -> trunk/422bd6808bb98cbbac31d157d9c82ad11ba9732d 2025-08-14T21:02:57.9731472Z * [new tag] trunk/42e51cd4b3973a053fcfa80878a3f346fd158e9f -> trunk/42e51cd4b3973a053fcfa80878a3f346fd158e9f 2025-08-14T21:02:57.9732330Z * [new tag] trunk/4416433c7c625127b7f975c92f8ec98ea4c67fd3 -> trunk/4416433c7c625127b7f975c92f8ec98ea4c67fd3 2025-08-14T21:02:57.9733102Z * [new tag] trunk/45ba7ecda876685b083cbbe932450560c566826b -> trunk/45ba7ecda876685b083cbbe932450560c566826b 2025-08-14T21:02:57.9733881Z * [new tag] trunk/4a773e1e867f28a8ff0b15203e5cd9548f74fcee -> trunk/4a773e1e867f28a8ff0b15203e5cd9548f74fcee 2025-08-14T21:02:57.9734748Z * [new tag] trunk/4a90dc0c1f68d1f98832b169f792ed1bb195a0f3 -> trunk/4a90dc0c1f68d1f98832b169f792ed1bb195a0f3 2025-08-14T21:02:57.9735553Z * [new tag] trunk/4cde0acc0e4e795e1a12cbdd9b93c8c04c1fa05d -> trunk/4cde0acc0e4e795e1a12cbdd9b93c8c04c1fa05d 2025-08-14T21:02:57.9736376Z * [new tag] trunk/4d419a74610c32b1372f8802dcc61893740a23cf -> trunk/4d419a74610c32b1372f8802dcc61893740a23cf 2025-08-14T21:02:57.9737241Z * [new tag] trunk/4d5b3f2d5af7c8e4f41da4ffca53fafe8bb86235 -> trunk/4d5b3f2d5af7c8e4f41da4ffca53fafe8bb86235 2025-08-14T21:02:57.9738135Z * [new tag] trunk/4e2ddb5db67617f9f5309c8bba0c17adc84cadbc -> trunk/4e2ddb5db67617f9f5309c8bba0c17adc84cadbc 2025-08-14T21:02:57.9738999Z * [new tag] trunk/50a8c118754a6c5a46968f5c8e215ccba6831d42 -> trunk/50a8c118754a6c5a46968f5c8e215ccba6831d42 2025-08-14T21:02:57.9739809Z * [new tag] trunk/50f23ff6f883db5021dd6bab4c146434f98dd15d -> trunk/50f23ff6f883db5021dd6bab4c146434f98dd15d 2025-08-14T21:02:57.9740652Z * [new tag] trunk/515cb70367e84fcbad23fcc5b39eb1d7706df2aa -> trunk/515cb70367e84fcbad23fcc5b39eb1d7706df2aa 2025-08-14T21:02:57.9741520Z * [new tag] trunk/53e39494958b7e2278cc8176f63636e812e8945f -> trunk/53e39494958b7e2278cc8176f63636e812e8945f 2025-08-14T21:02:57.9742184Z * [new tag] trunk/556e2a73f4f0643f7c2aeb5c7dddda43388a40ce -> trunk/556e2a73f4f0643f7c2aeb5c7dddda43388a40ce 2025-08-14T21:02:57.9743077Z * [new tag] trunk/5665dc9ab76b84d7c90d845ffb0f6349b3621919 -> trunk/5665dc9ab76b84d7c90d845ffb0f6349b3621919 2025-08-14T21:02:57.9743920Z * [new tag] trunk/566c6d52ef1411c8262d7b9cf85e2044fdfbe1a3 -> trunk/566c6d52ef1411c8262d7b9cf85e2044fdfbe1a3 2025-08-14T21:02:57.9744843Z * [new tag] trunk/56c828bef93eada0e18d2cc013207831ca80cc99 -> trunk/56c828bef93eada0e18d2cc013207831ca80cc99 2025-08-14T21:02:57.9745639Z * [new tag] trunk/5737372862253a0ac0292407a5844796f02380ad -> trunk/5737372862253a0ac0292407a5844796f02380ad 2025-08-14T21:02:57.9746450Z * [new tag] trunk/57f738b6357cc8fcdde479a0948e723809a1a44d -> trunk/57f738b6357cc8fcdde479a0948e723809a1a44d 2025-08-14T21:02:57.9747216Z * [new tag] trunk/5a40c5784482255b9baf14086cc4b9349fc6d512 -> trunk/5a40c5784482255b9baf14086cc4b9349fc6d512 2025-08-14T21:02:57.9748123Z * [new tag] trunk/5a9c4cfce42b9eb87da0de40c5633f083115c307 -> trunk/5a9c4cfce42b9eb87da0de40c5633f083115c307 2025-08-14T21:02:57.9748893Z * [new tag] trunk/5ace061254af71aa83d1baae81aa1864c9746add -> trunk/5ace061254af71aa83d1baae81aa1864c9746add 2025-08-14T21:02:57.9749692Z * [new tag] trunk/5dddcd5b07c6644efca8d613f4eca1dc95daa87f -> trunk/5dddcd5b07c6644efca8d613f4eca1dc95daa87f 2025-08-14T21:02:57.9750527Z * [new tag] trunk/5ed4f9177907fe403ec4c4499d0d0e9be6b68fcf -> trunk/5ed4f9177907fe403ec4c4499d0d0e9be6b68fcf 2025-08-14T21:02:57.9751600Z * [new tag] trunk/5f1010fbb3850d99c8fdf9a9de2f79260cdc586a -> trunk/5f1010fbb3850d99c8fdf9a9de2f79260cdc586a 2025-08-14T21:02:57.9752153Z * [new tag] trunk/5f5f508aa836a46dfe88857fb223049616b94e93 -> trunk/5f5f508aa836a46dfe88857fb223049616b94e93 2025-08-14T21:02:57.9753049Z * [new tag] trunk/62bac0798100e0e06a86b7a4cee1788413e3d0ca -> trunk/62bac0798100e0e06a86b7a4cee1788413e3d0ca 2025-08-14T21:02:57.9753865Z * [new tag] trunk/63654ba4c5178fd12220cfc9d1c878af2fdd07cc -> trunk/63654ba4c5178fd12220cfc9d1c878af2fdd07cc 2025-08-14T21:02:57.9754642Z * [new tag] trunk/639778b3ee3b80e0894367fdc4442b58ae1b3a62 -> trunk/639778b3ee3b80e0894367fdc4442b58ae1b3a62 2025-08-14T21:02:57.9755449Z * [new tag] trunk/641ee7478150f26969968f49d8b358e199679a8a -> trunk/641ee7478150f26969968f49d8b358e199679a8a 2025-08-14T21:02:57.9756341Z * [new tag] trunk/65053c03a3d209060cb239d20a229dac37cf9dd1 -> trunk/65053c03a3d209060cb239d20a229dac37cf9dd1 2025-08-14T21:02:57.9757160Z * [new tag] trunk/652a6f5954d039d61dc6e6575ccf89d385d74537 -> trunk/652a6f5954d039d61dc6e6575ccf89d385d74537 2025-08-14T21:02:57.9757997Z * [new tag] trunk/685f15dbea66e8ffa8564752f81ad2f6cb447a14 -> trunk/685f15dbea66e8ffa8564752f81ad2f6cb447a14 2025-08-14T21:02:57.9758625Z * [new tag] trunk/68a4b4b2e336cfd4451ce6546d900568e5ddf96c -> trunk/68a4b4b2e336cfd4451ce6546d900568e5ddf96c 2025-08-14T21:02:57.9759532Z * [new tag] trunk/69a0a9aa7f5e320a02e97fa789d2f72baff1554f -> trunk/69a0a9aa7f5e320a02e97fa789d2f72baff1554f 2025-08-14T21:02:57.9760519Z * [new tag] trunk/6be6d06295c870c77a6eb69f96b3170d983520d5 -> trunk/6be6d06295c870c77a6eb69f96b3170d983520d5 2025-08-14T21:02:57.9761412Z * [new tag] trunk/6c05ea6475beaf3acc05e1bda0f3f8fe3bdc1d49 -> trunk/6c05ea6475beaf3acc05e1bda0f3f8fe3bdc1d49 2025-08-14T21:02:57.9762315Z * [new tag] trunk/6da11d9aafc0d84dc7f66030c181608ff2614f66 -> trunk/6da11d9aafc0d84dc7f66030c181608ff2614f66 2025-08-14T21:02:57.9763193Z * [new tag] trunk/6e8865fbc161270e2ffc52817e6c667df417a3f7 -> trunk/6e8865fbc161270e2ffc52817e6c667df417a3f7 2025-08-14T21:02:57.9764502Z * [new tag] trunk/6ea8376f84232048d6be0f7b2edf82aec1b61d58 -> trunk/6ea8376f84232048d6be0f7b2edf82aec1b61d58 2025-08-14T21:02:57.9765304Z * [new tag] trunk/6ee175195ac7853734d64704171993cc6265eb38 -> trunk/6ee175195ac7853734d64704171993cc6265eb38 2025-08-14T21:02:57.9766142Z * [new tag] trunk/6f0f4e0c3eacd479864319127915f869f64e1935 -> trunk/6f0f4e0c3eacd479864319127915f869f64e1935 2025-08-14T21:02:57.9766866Z * [new tag] trunk/70ccdec44b89e355a2cb03ba14a634284f7750f8 -> trunk/70ccdec44b89e355a2cb03ba14a634284f7750f8 2025-08-14T21:02:57.9767710Z * [new tag] trunk/72009ec6bebca7714f99c18449183787f202af4d -> trunk/72009ec6bebca7714f99c18449183787f202af4d 2025-08-14T21:02:57.9768530Z * [new tag] trunk/731ee31f7b6ba19307daab323f6196172b71aaf8 -> trunk/731ee31f7b6ba19307daab323f6196172b71aaf8 2025-08-14T21:02:57.9769474Z * [new tag] trunk/76a0609b6bddb2bc40f1eb4ade12885023653d59 -> trunk/76a0609b6bddb2bc40f1eb4ade12885023653d59 2025-08-14T21:02:57.9770262Z * [new tag] trunk/781e9a7724c47496e3d38a81e6dd6194cf098c41 -> trunk/781e9a7724c47496e3d38a81e6dd6194cf098c41 2025-08-14T21:02:57.9771167Z * [new tag] trunk/78a2fe1d42edeaa2ef7020b0fa0ac82ee4a640e4 -> trunk/78a2fe1d42edeaa2ef7020b0fa0ac82ee4a640e4 2025-08-14T21:02:57.9772070Z * [new tag] trunk/7a974a88f2c529a614baeabe4debd00fc8a3b299 -> trunk/7a974a88f2c529a614baeabe4debd00fc8a3b299 2025-08-14T21:02:57.9772928Z * [new tag] trunk/7ae0629d64b404e0ef5d9c931433ad25e65d6114 -> trunk/7ae0629d64b404e0ef5d9c931433ad25e65d6114 2025-08-14T21:02:57.9774056Z * [new tag] trunk/7d2ec704e47f4b740cdecda5534b305e8e1875ef -> trunk/7d2ec704e47f4b740cdecda5534b305e8e1875ef 2025-08-14T21:02:57.9774998Z * [new tag] trunk/7d87e358ac8440f666fabbfd99058bb5342be6ac -> trunk/7d87e358ac8440f666fabbfd99058bb5342be6ac 2025-08-14T21:02:57.9775671Z * [new tag] trunk/7e27347fd353928c99620495c8c531a5eba7d56b -> trunk/7e27347fd353928c99620495c8c531a5eba7d56b 2025-08-14T21:02:57.9776683Z * [new tag] trunk/7e91394955721c77645fcdb75a5d47a255d65020 -> trunk/7e91394955721c77645fcdb75a5d47a255d65020 2025-08-14T21:02:57.9777548Z * [new tag] trunk/7f4cb4a3e018a621add2a37a3a2f67b982d51001 -> trunk/7f4cb4a3e018a621add2a37a3a2f67b982d51001 2025-08-14T21:02:57.9778296Z * [new tag] trunk/7fbc22855c17741ae016992803b2e147a13aa22d -> trunk/7fbc22855c17741ae016992803b2e147a13aa22d 2025-08-14T21:02:57.9779192Z * [new tag] trunk/8047421fbb607d70ede13b9cd5a60b7b8bdfe348 -> trunk/8047421fbb607d70ede13b9cd5a60b7b8bdfe348 2025-08-14T21:02:57.9780046Z * [new tag] trunk/8088cfa592504a2897b4c78f8a46fe658ab5c2c2 -> trunk/8088cfa592504a2897b4c78f8a46fe658ab5c2c2 2025-08-14T21:02:57.9780828Z * [new tag] trunk/80cca8307943ba64168208b54028f55b2c71daff -> trunk/80cca8307943ba64168208b54028f55b2c71daff 2025-08-14T21:02:57.9781662Z * [new tag] trunk/8147370733bbdcd034cad54e9212e51885a11892 -> trunk/8147370733bbdcd034cad54e9212e51885a11892 2025-08-14T21:02:57.9782541Z * [new tag] trunk/83875cdb5594ccb3c9206b8eb5745fe1d011cf26 -> trunk/83875cdb5594ccb3c9206b8eb5745fe1d011cf26 2025-08-14T21:02:57.9783389Z * [new tag] trunk/8399cf88ce8399d2be93355f29d4cb69f51c0654 -> trunk/8399cf88ce8399d2be93355f29d4cb69f51c0654 2025-08-14T21:02:57.9784352Z * [new tag] trunk/842cc77ab9aafd518593c2fce077d6abb42a5b7f -> trunk/842cc77ab9aafd518593c2fce077d6abb42a5b7f 2025-08-14T21:02:57.9785194Z * [new tag] trunk/85db508af533649d0b3447ff3f0d5fe083150c84 -> trunk/85db508af533649d0b3447ff3f0d5fe083150c84 2025-08-14T21:02:57.9785929Z * [new tag] trunk/86eb65f7f06016bcd5d7951dc9d74bc3993a827a -> trunk/86eb65f7f06016bcd5d7951dc9d74bc3993a827a 2025-08-14T21:02:57.9786780Z * [new tag] trunk/87e6c4079d8ec7d04aff00ed82096b39836a8367 -> trunk/87e6c4079d8ec7d04aff00ed82096b39836a8367 2025-08-14T21:02:57.9787640Z * [new tag] trunk/89654db1abccf7e5f261989a150db4d1619ea2aa -> trunk/89654db1abccf7e5f261989a150db4d1619ea2aa 2025-08-14T21:02:57.9788404Z * [new tag] trunk/8a37f0c90392a2c38b7c5955471fa49edcaf5cb1 -> trunk/8a37f0c90392a2c38b7c5955471fa49edcaf5cb1 2025-08-14T21:02:57.9789244Z * [new tag] trunk/8ab5868a2199fe485c2d66533b9244ccb97e487d -> trunk/8ab5868a2199fe485c2d66533b9244ccb97e487d 2025-08-14T21:02:57.9790108Z * [new tag] trunk/8ae4d2652f64b8444b3d5314b9232bd2119bcde6 -> trunk/8ae4d2652f64b8444b3d5314b9232bd2119bcde6 2025-08-14T21:02:57.9790860Z * [new tag] trunk/8c41cb800ae0411f02ea5da34bd5ccc3790633b0 -> trunk/8c41cb800ae0411f02ea5da34bd5ccc3790633b0 2025-08-14T21:02:57.9791714Z * [new tag] trunk/8cb91e20bc205b1416648d0ffd98d1ba1f3a6fc4 -> trunk/8cb91e20bc205b1416648d0ffd98d1ba1f3a6fc4 2025-08-14T21:02:57.9792604Z * [new tag] trunk/8cfaf51d4e29c9bd9f49ecc98d955ed53df1a13d -> trunk/8cfaf51d4e29c9bd9f49ecc98d955ed53df1a13d 2025-08-14T21:02:57.9793540Z * [new tag] trunk/8d1cf529229dce7cd5ea04abb0faac83b87ca6d1 -> trunk/8d1cf529229dce7cd5ea04abb0faac83b87ca6d1 2025-08-14T21:02:57.9794320Z * [new tag] trunk/8d3d1c844303cb1d46123a1caa76d4cf83973347 -> trunk/8d3d1c844303cb1d46123a1caa76d4cf83973347 2025-08-14T21:02:57.9795160Z * [new tag] trunk/8d6d3246316e1767a57d5e855acd6208da753b75 -> trunk/8d6d3246316e1767a57d5e855acd6208da753b75 2025-08-14T21:02:57.9796126Z * [new tag] trunk/8e6a3138581152ab827a0997f34c470271399f5e -> trunk/8e6a3138581152ab827a0997f34c470271399f5e 2025-08-14T21:02:57.9796857Z * [new tag] trunk/8eee08d2279b98af2522debb6512d37e837e89e3 -> trunk/8eee08d2279b98af2522debb6512d37e837e89e3 2025-08-14T21:02:57.9797648Z * [new tag] trunk/90b78ee50f73b5c963996076a3d54b74b1b965be -> trunk/90b78ee50f73b5c963996076a3d54b74b1b965be 2025-08-14T21:02:57.9798381Z * [new tag] trunk/94b91a876327820a4bb6f5d39d156f13f2553ab6 -> trunk/94b91a876327820a4bb6f5d39d156f13f2553ab6 2025-08-14T21:02:57.9799668Z * [new tag] trunk/95210cc409dd578988c7116b47725c304dea54c7 -> trunk/95210cc409dd578988c7116b47725c304dea54c7 2025-08-14T21:02:57.9800322Z * [new tag] trunk/96bd33b2de79598566df395f32e27c4d33673f05 -> trunk/96bd33b2de79598566df395f32e27c4d33673f05 2025-08-14T21:02:57.9801125Z * [new tag] trunk/9708fcf92db88b80b9010c68662d634434da3106 -> trunk/9708fcf92db88b80b9010c68662d634434da3106 2025-08-14T21:02:57.9801971Z * [new tag] trunk/97c8c98f8dcb9c5c188b691d156e0043dba6c7f8 -> trunk/97c8c98f8dcb9c5c188b691d156e0043dba6c7f8 2025-08-14T21:02:57.9802823Z * [new tag] trunk/9903ca4f70bdc1653016256f5b4fd74fdfc609f8 -> trunk/9903ca4f70bdc1653016256f5b4fd74fdfc609f8 2025-08-14T21:02:57.9803679Z * [new tag] trunk/99bc2f94c1955657e950ebdad5f77e518785ccbd -> trunk/99bc2f94c1955657e950ebdad5f77e518785ccbd 2025-08-14T21:02:57.9804431Z * [new tag] trunk/9a06e6d0310da9d8a59ae05e8ec9c0201b55cacd -> trunk/9a06e6d0310da9d8a59ae05e8ec9c0201b55cacd 2025-08-14T21:02:57.9805234Z * [new tag] trunk/9a0f7a3bb01b235ea04581ee540970a098071b72 -> trunk/9a0f7a3bb01b235ea04581ee540970a098071b72 2025-08-14T21:02:57.9806148Z * [new tag] trunk/9b803cdbe298009f08340c1aaccb25aafbca95d8 -> trunk/9b803cdbe298009f08340c1aaccb25aafbca95d8 2025-08-14T21:02:57.9807268Z * [new tag] trunk/9ccd0f5e31ea54fcf42101dfbaacc103494e34df -> trunk/9ccd0f5e31ea54fcf42101dfbaacc103494e34df 2025-08-14T21:02:57.9808043Z * [new tag] trunk/9d37c960a4fc44d5ac334ca8bf775f85b95d76fc -> trunk/9d37c960a4fc44d5ac334ca8bf775f85b95d76fc 2025-08-14T21:02:57.9808904Z * [new tag] trunk/9e07673deb212c87b1c6fea23799a97474c476ed -> trunk/9e07673deb212c87b1c6fea23799a97474c476ed 2025-08-14T21:02:57.9809665Z * [new tag] trunk/9eedd2a20b64302d0d116ea2802b50948d2ebb09 -> trunk/9eedd2a20b64302d0d116ea2802b50948d2ebb09 2025-08-14T21:02:57.9810534Z * [new tag] trunk/9fa8ce26cf638504469852cbc3e7d04579fc8674 -> trunk/9fa8ce26cf638504469852cbc3e7d04579fc8674 2025-08-14T21:02:57.9811411Z * [new tag] trunk/a06ec54d40013c97fbffc174ea8f524ea5a95715 -> trunk/a06ec54d40013c97fbffc174ea8f524ea5a95715 2025-08-14T21:02:57.9812258Z * [new tag] trunk/a288b15ea9f87ddd665f249d492e0fb0861f5a69 -> trunk/a288b15ea9f87ddd665f249d492e0fb0861f5a69 2025-08-14T21:02:57.9813102Z * [new tag] trunk/a2fd106d670bb4990cebfd00f25ecbae4145e76c -> trunk/a2fd106d670bb4990cebfd00f25ecbae4145e76c 2025-08-14T21:02:57.9814198Z * [new tag] trunk/a354fa91e26b376d96385a2206c5ff5b42aa4600 -> trunk/a354fa91e26b376d96385a2206c5ff5b42aa4600 2025-08-14T21:02:57.9814992Z * [new tag] trunk/a4f69a5da08eace1c1e6469dec6a18aa842da73b -> trunk/a4f69a5da08eace1c1e6469dec6a18aa842da73b 2025-08-14T21:02:57.9815872Z * [new tag] trunk/a53d14d5f846ac44f6c205abb1c5bc4d2f3126ae -> trunk/a53d14d5f846ac44f6c205abb1c5bc4d2f3126ae 2025-08-14T21:02:57.9816784Z * [new tag] trunk/a5652407e4f3d772fc44486ac2abf756decf0861 -> trunk/a5652407e4f3d772fc44486ac2abf756decf0861 2025-08-14T21:02:57.9817905Z * [new tag] trunk/a7abf57aabec0ce686092e2d66e53ba185dbc56b -> trunk/a7abf57aabec0ce686092e2d66e53ba185dbc56b 2025-08-14T21:02:57.9818684Z * [new tag] trunk/a84b60c0c4016785fd93b7b8a0c04f2d0770d332 -> trunk/a84b60c0c4016785fd93b7b8a0c04f2d0770d332 2025-08-14T21:02:57.9820117Z * [new tag] trunk/aa75e917bdb0f95bb6dee81853c2d3c4ab3e1883 -> trunk/aa75e917bdb0f95bb6dee81853c2d3c4ab3e1883 2025-08-14T21:02:57.9820909Z * [new tag] trunk/adcca7d9a1c053495e99012de801b2ea237faad0 -> trunk/adcca7d9a1c053495e99012de801b2ea237faad0 2025-08-14T21:02:57.9821726Z * [new tag] trunk/af10f1f86cc4effc93142a447693d8be55966615 -> trunk/af10f1f86cc4effc93142a447693d8be55966615 2025-08-14T21:02:57.9822643Z * [new tag] trunk/af3cabc55d5699f4da528e1ca39d83338f84ae8c -> trunk/af3cabc55d5699f4da528e1ca39d83338f84ae8c 2025-08-14T21:02:57.9823592Z * [new tag] trunk/b0df7715e8c590c0001d1f9cdb97057be80c9107 -> trunk/b0df7715e8c590c0001d1f9cdb97057be80c9107 2025-08-14T21:02:57.9824493Z * [new tag] trunk/b149c7204c218e7c4d6594a89dd74f72bd480ec5 -> trunk/b149c7204c218e7c4d6594a89dd74f72bd480ec5 2025-08-14T21:02:57.9825356Z * [new tag] trunk/b1a602762e6a6674b406a3137e7e7a678885a97b -> trunk/b1a602762e6a6674b406a3137e7e7a678885a97b 2025-08-14T21:02:57.9826216Z * [new tag] trunk/b1f43548cad8fc0e30bda250f6e196310fa7a4bc -> trunk/b1f43548cad8fc0e30bda250f6e196310fa7a4bc 2025-08-14T21:02:57.9827167Z * [new tag] trunk/b219ca2a00a305753c4f1ea4c9c5d23243d54753 -> trunk/b219ca2a00a305753c4f1ea4c9c5d23243d54753 2025-08-14T21:02:57.9827976Z * [new tag] trunk/b4596895b9d85a686c2cb978938b0a7797b3690a -> trunk/b4596895b9d85a686c2cb978938b0a7797b3690a 2025-08-14T21:02:57.9828868Z * [new tag] trunk/b5fd7223b1bf44720dc9183bda7dfcf7aeccff02 -> trunk/b5fd7223b1bf44720dc9183bda7dfcf7aeccff02 2025-08-14T21:02:57.9829722Z * [new tag] trunk/b602ea9cab7d43a7ee7b4051227090f23fbd3dbf -> trunk/b602ea9cab7d43a7ee7b4051227090f23fbd3dbf 2025-08-14T21:02:57.9830848Z * [new tag] trunk/b6b74aed604bd2e96389ff99aaaf39abc64fdc64 -> trunk/b6b74aed604bd2e96389ff99aaaf39abc64fdc64 2025-08-14T21:02:57.9831631Z * [new tag] trunk/b7db86600a2614adc71c92ca42d359a7ac534d78 -> trunk/b7db86600a2614adc71c92ca42d359a7ac534d78 2025-08-14T21:02:57.9832427Z * [new tag] trunk/b9003ed3d87699e81e436719625a21996a6654e5 -> trunk/b9003ed3d87699e81e436719625a21996a6654e5 2025-08-14T21:02:57.9833281Z * [new tag] trunk/b90feeac86bda00afc2789321bcd706015ff44e3 -> trunk/b90feeac86bda00afc2789321bcd706015ff44e3 2025-08-14T21:02:57.9834800Z * [new tag] trunk/b9d7de3a094598c3dc0dd52e57bce30eb684c9d8 -> trunk/b9d7de3a094598c3dc0dd52e57bce30eb684c9d8 2025-08-14T21:02:57.9835596Z * [new tag] trunk/ba47821f524eee50a214ed39fa2e7765d54aabf4 -> trunk/ba47821f524eee50a214ed39fa2e7765d54aabf4 2025-08-14T21:02:57.9836429Z * [new tag] trunk/ba4ccf5d67e3d237f435eacc2bce3c6025f08491 -> trunk/ba4ccf5d67e3d237f435eacc2bce3c6025f08491 2025-08-14T21:02:57.9837307Z * [new tag] trunk/bcf23ecc476df2bd7479f142567213e2623308ee -> trunk/bcf23ecc476df2bd7479f142567213e2623308ee 2025-08-14T21:02:57.9838155Z * [new tag] trunk/be53f609aaf6f01e2863f490975ea9eaac3ee9ff -> trunk/be53f609aaf6f01e2863f490975ea9eaac3ee9ff 2025-08-14T21:02:57.9838996Z * [new tag] trunk/beb4d7816dedc67a5de1f82e5a45b5910f407941 -> trunk/beb4d7816dedc67a5de1f82e5a45b5910f407941 2025-08-14T21:02:57.9840056Z * [new tag] trunk/bfc873d02ec413344717493e4175a902921359fd -> trunk/bfc873d02ec413344717493e4175a902921359fd 2025-08-14T21:02:57.9840860Z * [new tag] trunk/c184cb3852f0ff2d16a489d61abc3739c309e6ca -> trunk/c184cb3852f0ff2d16a489d61abc3739c309e6ca 2025-08-14T21:02:57.9841756Z * [new tag] trunk/c24ca7f4bf79f62fd623d76346ca27e53f731431 -> trunk/c24ca7f4bf79f62fd623d76346ca27e53f731431 2025-08-14T21:02:57.9842588Z * [new tag] trunk/c3dc8dc4122977893004c49d10e4676cd0a97da4 -> trunk/c3dc8dc4122977893004c49d10e4676cd0a97da4 2025-08-14T21:02:57.9843542Z * [new tag] trunk/c5ec5458a547f7a774468ea0eb2258d3de596492 -> trunk/c5ec5458a547f7a774468ea0eb2258d3de596492 2025-08-14T21:02:57.9844373Z * [new tag] trunk/c5efc5c8a66eca84865015058b3221013ebfe685 -> trunk/c5efc5c8a66eca84865015058b3221013ebfe685 2025-08-14T21:02:57.9845399Z * [new tag] trunk/c6563341208003f64c131854a9cf029555f786d2 -> trunk/c6563341208003f64c131854a9cf029555f786d2 2025-08-14T21:02:57.9846236Z * [new tag] trunk/c6d78d4dbda53837d298d23a5fbc09af90a42d9e -> trunk/c6d78d4dbda53837d298d23a5fbc09af90a42d9e 2025-08-14T21:02:57.9846975Z * [new tag] trunk/c8205cb35435f39d2c26f6c94b45e4adeb6dcb23 -> trunk/c8205cb35435f39d2c26f6c94b45e4adeb6dcb23 2025-08-14T21:02:57.9848015Z * [new tag] trunk/c859ba7114b1fcb49527e090745fa17091d1f8d5 -> trunk/c859ba7114b1fcb49527e090745fa17091d1f8d5 2025-08-14T21:02:57.9848849Z * [new tag] trunk/c86040a8e68f754b90a84099187d3624954c7f36 -> trunk/c86040a8e68f754b90a84099187d3624954c7f36 2025-08-14T21:02:57.9849731Z * [new tag] trunk/c9671dc865aa0fc1cb86df754e355b44d8e02bb4 -> trunk/c9671dc865aa0fc1cb86df754e355b44d8e02bb4 2025-08-14T21:02:57.9850813Z * [new tag] trunk/ca7315c17162ea21b1ca5ba23f4bf6168766c7b9 -> trunk/ca7315c17162ea21b1ca5ba23f4bf6168766c7b9 2025-08-14T21:02:57.9851601Z * [new tag] trunk/cae2b5e3d223829bdc553fc8601df4b1c1554cff -> trunk/cae2b5e3d223829bdc553fc8601df4b1c1554cff 2025-08-14T21:02:57.9852422Z * [new tag] trunk/cbffde774557752cf20447d42d99ec6102673c31 -> trunk/cbffde774557752cf20447d42d99ec6102673c31 2025-08-14T21:02:57.9853300Z * [new tag] trunk/cd8d8c18f5bafdc1c73d5ac0129e7b4d76ab45bc -> trunk/cd8d8c18f5bafdc1c73d5ac0129e7b4d76ab45bc 2025-08-14T21:02:57.9854162Z * [new tag] trunk/cf0a0dcb0afa5e84b95461cc542f862b51ca96bf -> trunk/cf0a0dcb0afa5e84b95461cc542f862b51ca96bf 2025-08-14T21:02:57.9855056Z * [new tag] trunk/cf4964be68fa9f4ffc334f01cce42d7424b1cc81 -> trunk/cf4964be68fa9f4ffc334f01cce42d7424b1cc81 2025-08-14T21:02:57.9856288Z * [new tag] trunk/d0e2240f680ea2a553f7ee8188f52482e130bfd0 -> trunk/d0e2240f680ea2a553f7ee8188f52482e130bfd0 2025-08-14T21:02:57.9857119Z * [new tag] trunk/d1950d4bb5cba8fb6b23e4d283eea5b9801737e2 -> trunk/d1950d4bb5cba8fb6b23e4d283eea5b9801737e2 2025-08-14T21:02:57.9857982Z * [new tag] trunk/d20c4c20e61adecf00335c4d8c22eb1ace472cd3 -> trunk/d20c4c20e61adecf00335c4d8c22eb1ace472cd3 2025-08-14T21:02:57.9858806Z * [new tag] trunk/d25c4f954d599ea512e2f70cd6df101c21479d4c -> trunk/d25c4f954d599ea512e2f70cd6df101c21479d4c 2025-08-14T21:02:57.9859685Z * [new tag] trunk/d3d359dbafa89173a371e2637f22b47398e94a24 -> trunk/d3d359dbafa89173a371e2637f22b47398e94a24 2025-08-14T21:02:57.9860766Z * [new tag] trunk/d46768db04499d07a5b0db984112a6d1b7d3b0c1 -> trunk/d46768db04499d07a5b0db984112a6d1b7d3b0c1 2025-08-14T21:02:57.9861625Z * [new tag] trunk/d4c1a08c89f37d249a0146ff511c82ecc5c53b8f -> trunk/d4c1a08c89f37d249a0146ff511c82ecc5c53b8f 2025-08-14T21:02:57.9862506Z * [new tag] trunk/d556586448f3caab85673c7da0978fe31c7748f7 -> trunk/d556586448f3caab85673c7da0978fe31c7748f7 2025-08-14T21:02:57.9863642Z * [new tag] trunk/d670304001429a1a833255a918ed788d7ec4989a -> trunk/d670304001429a1a833255a918ed788d7ec4989a 2025-08-14T21:02:57.9864471Z * [new tag] trunk/d6786741a77aba200c78002646cc069b7a1799b0 -> trunk/d6786741a77aba200c78002646cc069b7a1799b0 2025-08-14T21:02:57.9865653Z * [new tag] trunk/d68c323692dedcbb74e670801e3502944fd790ff -> trunk/d68c323692dedcbb74e670801e3502944fd790ff 2025-08-14T21:02:57.9866477Z * [new tag] trunk/d8cb3db5339b45e4b745b2b883ef3ecde9843e2c -> trunk/d8cb3db5339b45e4b745b2b883ef3ecde9843e2c 2025-08-14T21:02:57.9867205Z * [new tag] trunk/da1f608ca33f3062535d0a4866d95db19e72fcbd -> trunk/da1f608ca33f3062535d0a4866d95db19e72fcbd 2025-08-14T21:02:57.9868036Z * [new tag] trunk/db0b7f1cc9bb3fe71aaf8b964a644147ae8e1c35 -> trunk/db0b7f1cc9bb3fe71aaf8b964a644147ae8e1c35 2025-08-14T21:02:57.9868792Z * [new tag] trunk/db32b60662b2f2bdcad980127d5dc4b66b02a7e4 -> trunk/db32b60662b2f2bdcad980127d5dc4b66b02a7e4 2025-08-14T21:02:57.9869701Z * [new tag] trunk/db763b17175553ba09637362eb9773a91997a7ad -> trunk/db763b17175553ba09637362eb9773a91997a7ad 2025-08-14T21:02:57.9870560Z * [new tag] trunk/db78943a1ca13a32a3d6045eb15e2b719ee13a2f -> trunk/db78943a1ca13a32a3d6045eb15e2b719ee13a2f 2025-08-14T21:02:57.9871454Z * [new tag] trunk/dc0d18e023d9b7e314ebba0f234b6cb1579dbcfd -> trunk/dc0d18e023d9b7e314ebba0f234b6cb1579dbcfd 2025-08-14T21:02:57.9872277Z * [new tag] trunk/dd21c8a578038ab2841a7ba809a06921093ac9d8 -> trunk/dd21c8a578038ab2841a7ba809a06921093ac9d8 2025-08-14T21:02:57.9873126Z * [new tag] trunk/deea71a90e05eb320c04bebfead5317746637f0d -> trunk/deea71a90e05eb320c04bebfead5317746637f0d 2025-08-14T21:02:57.9874054Z * [new tag] trunk/df55ec7d4b35f6d21691e9dd41c82f27de762948 -> trunk/df55ec7d4b35f6d21691e9dd41c82f27de762948 2025-08-14T21:02:57.9874911Z * [new tag] trunk/e1cf0d496ea85d1807c8c740f296e77bf7bdc1df -> trunk/e1cf0d496ea85d1807c8c740f296e77bf7bdc1df 2025-08-14T21:02:57.9875983Z * [new tag] trunk/e248719ac03c103767ab72034f6b9fd56855bf98 -> trunk/e248719ac03c103767ab72034f6b9fd56855bf98 2025-08-14T21:02:57.9876787Z * [new tag] trunk/e49762026070f66be41bfa6537fbcf9bfc24e558 -> trunk/e49762026070f66be41bfa6537fbcf9bfc24e558 2025-08-14T21:02:57.9878447Z * [new tag] trunk/e4de93f6a3e342bab34d3757cf90ec0ccc87e168 -> trunk/e4de93f6a3e342bab34d3757cf90ec0ccc87e168 2025-08-14T21:02:57.9879124Z * [new tag] trunk/e619c6bb90b9dedaccd3cbeed86a288993a4e33f -> trunk/e619c6bb90b9dedaccd3cbeed86a288993a4e33f 2025-08-14T21:02:57.9880004Z * [new tag] trunk/e63c2b21c186a7d2ab8a8953b8aa1535f2e96e58 -> trunk/e63c2b21c186a7d2ab8a8953b8aa1535f2e96e58 2025-08-14T21:02:57.9880875Z * [new tag] trunk/e7152ff8a6a929a0db7f3f4a72a5b6d471769cd3 -> trunk/e7152ff8a6a929a0db7f3f4a72a5b6d471769cd3 2025-08-14T21:02:57.9881765Z * [new tag] trunk/e96c7c4bb0f6aeae2ab3b6f040f7d67edbec199a -> trunk/e96c7c4bb0f6aeae2ab3b6f040f7d67edbec199a 2025-08-14T21:02:57.9882641Z * [new tag] trunk/e9eb2096a59a79e7a94c3e28a0715e040369f34c -> trunk/e9eb2096a59a79e7a94c3e28a0715e040369f34c 2025-08-14T21:02:57.9883380Z * [new tag] trunk/eac2d9d695a32dd456050f45cac35134ec3809f4 -> trunk/eac2d9d695a32dd456050f45cac35134ec3809f4 2025-08-14T21:02:57.9884231Z * [new tag] trunk/ecde76c764752540edf9ef62a97936c86d984b17 -> trunk/ecde76c764752540edf9ef62a97936c86d984b17 2025-08-14T21:02:57.9884988Z * [new tag] trunk/ecea81117b2fdc52907c97b3c32d779e07b5d55b -> trunk/ecea81117b2fdc52907c97b3c32d779e07b5d55b 2025-08-14T21:02:57.9885888Z * [new tag] trunk/edaa151d0d5a4e75fbec9843f49cc78770eb61fb -> trunk/edaa151d0d5a4e75fbec9843f49cc78770eb61fb 2025-08-14T21:02:57.9886627Z * [new tag] trunk/ee1b0412b919dfb358d5a697b3be49621497fbc2 -> trunk/ee1b0412b919dfb358d5a697b3be49621497fbc2 2025-08-14T21:02:57.9887472Z * [new tag] trunk/ee1fb43450c2e985657f95a91b68328d6f20f24e -> trunk/ee1fb43450c2e985657f95a91b68328d6f20f24e 2025-08-14T21:02:57.9888609Z * [new tag] trunk/ee89cc7a0acd69de25f98fe4ef828546db7b444c -> trunk/ee89cc7a0acd69de25f98fe4ef828546db7b444c 2025-08-14T21:02:57.9889437Z * [new tag] trunk/ee9f8ba11d664b871a9e0c7933fdc8571635b78c -> trunk/ee9f8ba11d664b871a9e0c7933fdc8571635b78c 2025-08-14T21:02:57.9890497Z * [new tag] trunk/eed9dbf70f43ee529fec78ac00ed9a4fd74c6e76 -> trunk/eed9dbf70f43ee529fec78ac00ed9a4fd74c6e76 2025-08-14T21:02:57.9891336Z * [new tag] trunk/f077c2402e4eb5b0ed562b4ee5b7a0503f26ef94 -> trunk/f077c2402e4eb5b0ed562b4ee5b7a0503f26ef94 2025-08-14T21:02:57.9892134Z * [new tag] trunk/f0980fc0bbd656d6c02d23ad97e945353b314f35 -> trunk/f0980fc0bbd656d6c02d23ad97e945353b314f35 2025-08-14T21:02:57.9893079Z * [new tag] trunk/f15ada5c6fad97a7dcbfa4673f067b6942dda640 -> trunk/f15ada5c6fad97a7dcbfa4673f067b6942dda640 2025-08-14T21:02:57.9894141Z * [new tag] trunk/f27232a2134150cb5e55d26a74d8c36c6a961ca5 -> trunk/f27232a2134150cb5e55d26a74d8c36c6a961ca5 2025-08-14T21:02:57.9894909Z * [new tag] trunk/f33ce40bc062a281e1a1f57e8c1926d0a7d155cc -> trunk/f33ce40bc062a281e1a1f57e8c1926d0a7d155cc 2025-08-14T21:02:57.9895743Z * [new tag] trunk/f341077ce4710172da20cfad916ee37159bfe9fe -> trunk/f341077ce4710172da20cfad916ee37159bfe9fe 2025-08-14T21:02:57.9896584Z * [new tag] trunk/f3a4d742ece08de4cb0e59dcc62e0093a7d0b0c7 -> trunk/f3a4d742ece08de4cb0e59dcc62e0093a7d0b0c7 2025-08-14T21:02:57.9897441Z * [new tag] trunk/f3f159ff8c4bad2edec99c68a941c628e983d04c -> trunk/f3f159ff8c4bad2edec99c68a941c628e983d04c 2025-08-14T21:02:57.9898309Z * [new tag] trunk/f60454cce8b93e5bbf67f2f3c88c8ac01ed65457 -> trunk/f60454cce8b93e5bbf67f2f3c88c8ac01ed65457 2025-08-14T21:02:57.9899230Z * [new tag] trunk/f7b2f3314cf7aede67d5fa5c75e4243208484344 -> trunk/f7b2f3314cf7aede67d5fa5c75e4243208484344 2025-08-14T21:02:57.9900279Z * [new tag] trunk/f8f0414a5983ff481a2188e0c18594150430c8c5 -> trunk/f8f0414a5983ff481a2188e0c18594150430c8c5 2025-08-14T21:02:57.9901079Z * [new tag] trunk/f95b58c2844b3444cd8446fed8570729dc4216eb -> trunk/f95b58c2844b3444cd8446fed8570729dc4216eb 2025-08-14T21:02:57.9901992Z * [new tag] trunk/f990490a23815ea6ee27e487c70ba2cf513ba43d -> trunk/f990490a23815ea6ee27e487c70ba2cf513ba43d 2025-08-14T21:02:57.9902929Z * [new tag] trunk/fb887c3bb588cfe782615e67f6c26db636b8539b -> trunk/fb887c3bb588cfe782615e67f6c26db636b8539b 2025-08-14T21:02:57.9904768Z * [new tag] trunk/fc25c68f20f772290927a7031b998b92615259cf -> trunk/fc25c68f20f772290927a7031b998b92615259cf 2025-08-14T21:02:57.9905596Z * [new tag] trunk/fc80f6859e0ccf66513a40f04b9e735e759d4ddb -> trunk/fc80f6859e0ccf66513a40f04b9e735e759d4ddb 2025-08-14T21:02:57.9906505Z * [new tag] trunk/fdfd69bb05488d76123db9cc1cdd90ac4137bbfb -> trunk/fdfd69bb05488d76123db9cc1cdd90ac4137bbfb 2025-08-14T21:02:57.9907374Z * [new tag] trunk/fe3f5fe4ea2ff6f56406dc5d954636ebb08d0a08 -> trunk/fe3f5fe4ea2ff6f56406dc5d954636ebb08d0a08 2025-08-14T21:02:57.9908234Z * [new tag] trunk/fea7e9dd37c02c334b130f6624af6163fde6b2ab -> trunk/fea7e9dd37c02c334b130f6624af6163fde6b2ab 2025-08-14T21:02:57.9908989Z * [new tag] trunk/ff0d56d03592aa03f3ced8359241d21df1783393 -> trunk/ff0d56d03592aa03f3ced8359241d21df1783393 2025-08-14T21:02:57.9909566Z * [new tag] v0.1.1 -> v0.1.1 2025-08-14T21:02:57.9910453Z * [new tag] v0.1.10 -> v0.1.10 2025-08-14T21:02:57.9911251Z * [new tag] v0.1.11 -> v0.1.11 2025-08-14T21:02:57.9912050Z * [new tag] v0.1.12 -> v0.1.12 2025-08-14T21:02:57.9912704Z * [new tag] v0.1.2 -> v0.1.2 2025-08-14T21:02:57.9913405Z * [new tag] v0.1.3 -> v0.1.3 2025-08-14T21:02:57.9914165Z * [new tag] v0.1.4 -> v0.1.4 2025-08-14T21:02:57.9914920Z * [new tag] v0.1.5 -> v0.1.5 2025-08-14T21:02:57.9915699Z * [new tag] v0.1.6 -> v0.1.6 2025-08-14T21:02:57.9916871Z * [new tag] v0.1.7 -> v0.1.7 2025-08-14T21:02:57.9917595Z * [new tag] v0.1.8 -> v0.1.8 2025-08-14T21:02:57.9918445Z * [new tag] v0.1.9 -> v0.1.9 2025-08-14T21:02:57.9919103Z * [new tag] v0.2.0 -> v0.2.0 2025-08-14T21:02:57.9919868Z * [new tag] v0.3.0 -> v0.3.0 2025-08-14T21:02:57.9920798Z * [new tag] v0.3.1 -> v0.3.1 2025-08-14T21:02:57.9921504Z * [new tag] v0.4.0 -> v0.4.0 2025-08-14T21:02:57.9922227Z * [new tag] v0.4.1 -> v0.4.1 2025-08-14T21:02:57.9922967Z * [new tag] v1.0.0 -> v1.0.0 2025-08-14T21:02:57.9923771Z * [new tag] v1.0.0a0 -> v1.0.0a0 2025-08-14T21:02:57.9924543Z * [new tag] v1.0.1 -> v1.0.1 2025-08-14T21:02:57.9925359Z * [new tag] v1.0rc0 -> v1.0rc0 2025-08-14T21:02:57.9925957Z * [new tag] v1.0rc1 -> v1.0rc1 2025-08-14T21:02:57.9926741Z * [new tag] v1.1.0 -> v1.1.0 2025-08-14T21:02:57.9927568Z * [new tag] v1.1.0a0 -> v1.1.0a0 2025-08-14T21:02:57.9928631Z * [new tag] v1.10.0 -> v1.10.0 2025-08-14T21:02:57.9929631Z * [new tag] v1.10.0-rc1 -> v1.10.0-rc1 2025-08-14T21:02:57.9930340Z * [new tag] v1.10.0-rc2 -> v1.10.0-rc2 2025-08-14T21:02:57.9930972Z * [new tag] v1.10.0-rc3 -> v1.10.0-rc3 2025-08-14T21:02:57.9931773Z * [new tag] v1.10.1 -> v1.10.1 2025-08-14T21:02:57.9932373Z * [new tag] v1.10.1-rc1 -> v1.10.1-rc1 2025-08-14T21:02:57.9933025Z * [new tag] v1.10.2 -> v1.10.2 2025-08-14T21:02:57.9933643Z * [new tag] v1.10.2-rc1 -> v1.10.2-rc1 2025-08-14T21:02:57.9934579Z * [new tag] v1.11.0 -> v1.11.0 2025-08-14T21:02:57.9935311Z * [new tag] v1.11.0-rc1 -> v1.11.0-rc1 2025-08-14T21:02:57.9936254Z * [new tag] v1.11.0-rc2 -> v1.11.0-rc2 2025-08-14T21:02:57.9937188Z * [new tag] v1.11.0-rc3 -> v1.11.0-rc3 2025-08-14T21:02:57.9937945Z * [new tag] v1.11.0-rc4 -> v1.11.0-rc4 2025-08-14T21:02:57.9938836Z * [new tag] v1.11.0-rc5 -> v1.11.0-rc5 2025-08-14T21:02:57.9939410Z * [new tag] v1.11.0-rc6 -> v1.11.0-rc6 2025-08-14T21:02:57.9939972Z * [new tag] v1.11.0-rc7 -> v1.11.0-rc7 2025-08-14T21:02:57.9940850Z * [new tag] v1.12.0 -> v1.12.0 2025-08-14T21:02:57.9941709Z * [new tag] v1.12.0-rc1 -> v1.12.0-rc1 2025-08-14T21:02:57.9942544Z * [new tag] v1.12.0-rc2 -> v1.12.0-rc2 2025-08-14T21:02:57.9943440Z * [new tag] v1.12.0-rc3 -> v1.12.0-rc3 2025-08-14T21:02:57.9944344Z * [new tag] v1.12.0-rc4 -> v1.12.0-rc4 2025-08-14T21:02:57.9945193Z * [new tag] v1.12.0-rc5 -> v1.12.0-rc5 2025-08-14T21:02:57.9946064Z * [new tag] v1.12.0-rc6 -> v1.12.0-rc6 2025-08-14T21:02:57.9946592Z * [new tag] v1.12.0-rc7 -> v1.12.0-rc7 2025-08-14T21:02:57.9947194Z * [new tag] v1.12.0-rc8 -> v1.12.0-rc8 2025-08-14T21:02:57.9947830Z * [new tag] v1.12.1 -> v1.12.1 2025-08-14T21:02:57.9948812Z * [new tag] v1.12.1-rc1 -> v1.12.1-rc1 2025-08-14T21:02:57.9949662Z * [new tag] v1.12.1-rc2 -> v1.12.1-rc2 2025-08-14T21:02:57.9950574Z * [new tag] v1.12.1-rc3 -> v1.12.1-rc3 2025-08-14T21:02:57.9951290Z * [new tag] v1.12.1-rc4 -> v1.12.1-rc4 2025-08-14T21:02:57.9952007Z * [new tag] v1.12.1-rc5 -> v1.12.1-rc5 2025-08-14T21:02:57.9952856Z * [new tag] v1.13.0 -> v1.13.0 2025-08-14T21:02:57.9953600Z * [new tag] v1.13.0-rc1 -> v1.13.0-rc1 2025-08-14T21:02:57.9954572Z * [new tag] v1.13.0-rc2 -> v1.13.0-rc2 2025-08-14T21:02:57.9955337Z * [new tag] v1.13.0-rc3 -> v1.13.0-rc3 2025-08-14T21:02:57.9956339Z * [new tag] v1.13.0-rc4 -> v1.13.0-rc4 2025-08-14T21:02:57.9956852Z * [new tag] v1.13.0-rc5 -> v1.13.0-rc5 2025-08-14T21:02:57.9957398Z * [new tag] v1.13.0-rc6 -> v1.13.0-rc6 2025-08-14T21:02:57.9958316Z * [new tag] v1.13.1 -> v1.13.1 2025-08-14T21:02:57.9958837Z * [new tag] v1.13.1-rc1 -> v1.13.1-rc1 2025-08-14T21:02:57.9959711Z * [new tag] v1.2.0 -> v1.2.0 2025-08-14T21:02:57.9960593Z * [new tag] v1.2.0a0 -> v1.2.0a0 2025-08-14T21:02:57.9961387Z * [new tag] v1.3.0 -> v1.3.0 2025-08-14T21:02:57.9962165Z * [new tag] v1.3.0a0 -> v1.3.0a0 2025-08-14T21:02:57.9962686Z * [new tag] v1.3.1 -> v1.3.1 2025-08-14T21:02:57.9963543Z * [new tag] v1.4.0 -> v1.4.0 2025-08-14T21:02:57.9964382Z * [new tag] v1.4.0a0 -> v1.4.0a0 2025-08-14T21:02:57.9964902Z * [new tag] v1.4.1 -> v1.4.1 2025-08-14T21:02:57.9965866Z * [new tag] v1.5.0 -> v1.5.0 2025-08-14T21:02:57.9967166Z * [new tag] v1.5.0-rc1 -> v1.5.0-rc1 2025-08-14T21:02:57.9968027Z * [new tag] v1.5.0-rc2 -> v1.5.0-rc2 2025-08-14T21:02:57.9969342Z * [new tag] v1.5.0-rc3 -> v1.5.0-rc3 2025-08-14T21:02:57.9969661Z * [new tag] v1.5.0-rc4 -> v1.5.0-rc4 2025-08-14T21:02:57.9970193Z * [new tag] v1.5.0-rc5 -> v1.5.0-rc5 2025-08-14T21:02:57.9971141Z * [new tag] v1.5.1 -> v1.5.1 2025-08-14T21:02:57.9971796Z * [new tag] v1.5.1-rc1 -> v1.5.1-rc1 2025-08-14T21:02:57.9972357Z * [new tag] v1.6.0 -> v1.6.0 2025-08-14T21:02:57.9973159Z * [new tag] v1.6.0-rc1 -> v1.6.0-rc1 2025-08-14T21:02:57.9974135Z * [new tag] v1.6.0-rc2 -> v1.6.0-rc2 2025-08-14T21:02:57.9974950Z * [new tag] v1.6.0-rc3 -> v1.6.0-rc3 2025-08-14T21:02:57.9975741Z * [new tag] v1.6.0-rc4 -> v1.6.0-rc4 2025-08-14T21:02:57.9976541Z * [new tag] v1.6.0-rc5 -> v1.6.0-rc5 2025-08-14T21:02:57.9977523Z * [new tag] v1.6.0-rc6 -> v1.6.0-rc6 2025-08-14T21:02:57.9977959Z * [new tag] v1.6.0-rc7 -> v1.6.0-rc7 2025-08-14T21:02:57.9978812Z * [new tag] v1.7.0 -> v1.7.0 2025-08-14T21:02:57.9979760Z * [new tag] v1.7.0-rc1 -> v1.7.0-rc1 2025-08-14T21:02:57.9980655Z * [new tag] v1.7.0-rc2 -> v1.7.0-rc2 2025-08-14T21:02:57.9981368Z * [new tag] v1.7.0-rc3 -> v1.7.0-rc3 2025-08-14T21:02:57.9982063Z * [new tag] v1.7.0-rc4 -> v1.7.0-rc4 2025-08-14T21:02:57.9983022Z * [new tag] v1.7.1 -> v1.7.1 2025-08-14T21:02:57.9983993Z * [new tag] v1.7.1-rc1 -> v1.7.1-rc1 2025-08-14T21:02:57.9984814Z * [new tag] v1.7.1-rc2 -> v1.7.1-rc2 2025-08-14T21:02:57.9985408Z * [new tag] v1.7.1-rc3 -> v1.7.1-rc3 2025-08-14T21:02:57.9986332Z * [new tag] v1.8.0 -> v1.8.0 2025-08-14T21:02:57.9986853Z * [new tag] v1.8.0-rc1 -> v1.8.0-rc1 2025-08-14T21:02:57.9987745Z * [new tag] v1.8.0-rc2 -> v1.8.0-rc2 2025-08-14T21:02:57.9988601Z * [new tag] v1.8.0-rc3 -> v1.8.0-rc3 2025-08-14T21:02:57.9989291Z * [new tag] v1.8.0-rc4 -> v1.8.0-rc4 2025-08-14T21:02:57.9989902Z * [new tag] v1.8.0-rc5 -> v1.8.0-rc5 2025-08-14T21:02:57.9990518Z * [new tag] v1.8.1 -> v1.8.1 2025-08-14T21:02:57.9991493Z * [new tag] v1.8.1-rc1 -> v1.8.1-rc1 2025-08-14T21:02:57.9992006Z * [new tag] v1.8.1-rc2 -> v1.8.1-rc2 2025-08-14T21:02:57.9992648Z * [new tag] v1.8.1-rc3 -> v1.8.1-rc3 2025-08-14T21:02:57.9996745Z * [new tag] v1.8.2 -> v1.8.2 2025-08-14T21:02:57.9997447Z * [new tag] v1.8.2-rc1 -> v1.8.2-rc1 2025-08-14T21:02:57.9998240Z * [new tag] v1.9.0 -> v1.9.0 2025-08-14T21:02:57.9999157Z * [new tag] v1.9.0-rc1 -> v1.9.0-rc1 2025-08-14T21:02:57.9999966Z * [new tag] v1.9.0-rc2 -> v1.9.0-rc2 2025-08-14T21:02:58.0000755Z * [new tag] v1.9.0-rc3 -> v1.9.0-rc3 2025-08-14T21:02:58.0001389Z * [new tag] v1.9.0-rc4 -> v1.9.0-rc4 2025-08-14T21:02:58.0002341Z * [new tag] v1.9.1 -> v1.9.1 2025-08-14T21:02:58.0003353Z * [new tag] v1.9.1-rc1 -> v1.9.1-rc1 2025-08-14T21:02:58.0003853Z * [new tag] v1.9.1-rc2 -> v1.9.1-rc2 2025-08-14T21:02:58.0004734Z * [new tag] v2.0.0 -> v2.0.0 2025-08-14T21:02:58.0005464Z * [new tag] v2.0.0-rc1 -> v2.0.0-rc1 2025-08-14T21:02:58.0006374Z * [new tag] v2.0.0-rc2 -> v2.0.0-rc2 2025-08-14T21:02:58.0007279Z * [new tag] v2.0.0-rc3 -> v2.0.0-rc3 2025-08-14T21:02:58.0008204Z * [new tag] v2.0.0-rc4 -> v2.0.0-rc4 2025-08-14T21:02:58.0009070Z * [new tag] v2.0.0-rc5 -> v2.0.0-rc5 2025-08-14T21:02:58.0009696Z * [new tag] v2.0.0-rc6 -> v2.0.0-rc6 2025-08-14T21:02:58.0010503Z * [new tag] v2.0.1 -> v2.0.1 2025-08-14T21:02:58.0011430Z * [new tag] v2.0.1-rc1 -> v2.0.1-rc1 2025-08-14T21:02:58.0011924Z * [new tag] v2.0.1-rc2 -> v2.0.1-rc2 2025-08-14T21:02:58.0012876Z * [new tag] v2.0.1-rc3 -> v2.0.1-rc3 2025-08-14T21:02:58.0013345Z * [new tag] v2.0.1-rc4 -> v2.0.1-rc4 2025-08-14T21:02:58.0014654Z * [new tag] v2.1.0 -> v2.1.0 2025-08-14T21:02:58.0015442Z * [new tag] v2.1.0-rc1 -> v2.1.0-rc1 2025-08-14T21:02:58.0016324Z * [new tag] v2.1.0-rc2 -> v2.1.0-rc2 2025-08-14T21:02:58.0017265Z * [new tag] v2.1.0-rc3 -> v2.1.0-rc3 2025-08-14T21:02:58.0018053Z * [new tag] v2.1.0-rc4 -> v2.1.0-rc4 2025-08-14T21:02:58.0018982Z * [new tag] v2.1.0-rc5 -> v2.1.0-rc5 2025-08-14T21:02:58.0019769Z * [new tag] v2.1.0-rc6 -> v2.1.0-rc6 2025-08-14T21:02:58.0020454Z * [new tag] v2.1.1 -> v2.1.1 2025-08-14T21:02:58.0021654Z * [new tag] v2.1.1-rc1 -> v2.1.1-rc1 2025-08-14T21:02:58.0022384Z * [new tag] v2.1.1-rc2 -> v2.1.1-rc2 2025-08-14T21:02:58.0023492Z * [new tag] v2.1.1-rc3 -> v2.1.1-rc3 2025-08-14T21:02:58.0024300Z * [new tag] v2.1.1-rc4 -> v2.1.1-rc4 2025-08-14T21:02:58.0025074Z * [new tag] v2.1.1-rc5 -> v2.1.1-rc5 2025-08-14T21:02:58.0025714Z * [new tag] v2.1.1-rc6 -> v2.1.1-rc6 2025-08-14T21:02:58.0026519Z * [new tag] v2.1.2 -> v2.1.2 2025-08-14T21:02:58.0027515Z * [new tag] v2.1.2-rc1 -> v2.1.2-rc1 2025-08-14T21:02:58.0028270Z * [new tag] v2.1.2-rc2 -> v2.1.2-rc2 2025-08-14T21:02:58.0028953Z * [new tag] v2.1.2-rc3 -> v2.1.2-rc3 2025-08-14T21:02:58.0029923Z * [new tag] v2.2.0 -> v2.2.0 2025-08-14T21:02:58.0030691Z * [new tag] v2.2.0-rc1 -> v2.2.0-rc1 2025-08-14T21:02:58.0031453Z * [new tag] v2.2.0-rc2 -> v2.2.0-rc2 2025-08-14T21:02:58.0032233Z * [new tag] v2.2.0-rc3 -> v2.2.0-rc3 2025-08-14T21:02:58.0033024Z * [new tag] v2.2.0-rc4 -> v2.2.0-rc4 2025-08-14T21:02:58.0033777Z * [new tag] v2.2.0-rc5 -> v2.2.0-rc5 2025-08-14T21:02:58.0034780Z * [new tag] v2.2.0-rc6 -> v2.2.0-rc6 2025-08-14T21:02:58.0035312Z * [new tag] v2.2.0-rc7 -> v2.2.0-rc7 2025-08-14T21:02:58.0036007Z * [new tag] v2.2.0-rc8 -> v2.2.0-rc8 2025-08-14T21:02:58.0036787Z * [new tag] v2.2.1 -> v2.2.1 2025-08-14T21:02:58.0037737Z * [new tag] v2.2.1-rc1 -> v2.2.1-rc1 2025-08-14T21:02:58.0038246Z * [new tag] v2.2.1-rc2 -> v2.2.1-rc2 2025-08-14T21:02:58.0038877Z * [new tag] v2.2.1-rc3 -> v2.2.1-rc3 2025-08-14T21:02:58.0039505Z * [new tag] v2.2.2 -> v2.2.2 2025-08-14T21:02:58.0040512Z * [new tag] v2.2.2-rc1 -> v2.2.2-rc1 2025-08-14T21:02:58.0041099Z * [new tag] v2.2.2-rc2 -> v2.2.2-rc2 2025-08-14T21:02:58.0041671Z * [new tag] v2.2.2-rc3 -> v2.2.2-rc3 2025-08-14T21:02:58.0042504Z * [new tag] v2.3.0 -> v2.3.0 2025-08-14T21:02:58.0043201Z * [new tag] v2.3.0-rc1 -> v2.3.0-rc1 2025-08-14T21:02:58.0044204Z * [new tag] v2.3.0-rc10 -> v2.3.0-rc10 2025-08-14T21:02:58.0045071Z * [new tag] v2.3.0-rc11 -> v2.3.0-rc11 2025-08-14T21:02:58.0045611Z * [new tag] v2.3.0-rc12 -> v2.3.0-rc12 2025-08-14T21:02:58.0046547Z * [new tag] v2.3.0-rc2 -> v2.3.0-rc2 2025-08-14T21:02:58.0047258Z * [new tag] v2.3.0-rc3 -> v2.3.0-rc3 2025-08-14T21:02:58.0048180Z * [new tag] v2.3.0-rc4 -> v2.3.0-rc4 2025-08-14T21:02:58.0048965Z * [new tag] v2.3.0-rc5 -> v2.3.0-rc5 2025-08-14T21:02:58.0049558Z * [new tag] v2.3.0-rc6 -> v2.3.0-rc6 2025-08-14T21:02:58.0050430Z * [new tag] v2.3.0-rc7 -> v2.3.0-rc7 2025-08-14T21:02:58.0051258Z * [new tag] v2.3.0-rc8 -> v2.3.0-rc8 2025-08-14T21:02:58.0051820Z * [new tag] v2.3.0-rc9 -> v2.3.0-rc9 2025-08-14T21:02:58.0052396Z * [new tag] v2.3.1 -> v2.3.1 2025-08-14T21:02:58.0053320Z * [new tag] v2.3.1-rc1 -> v2.3.1-rc1 2025-08-14T21:02:58.0054186Z * [new tag] v2.3.1-rc2 -> v2.3.1-rc2 2025-08-14T21:02:58.0054908Z * [new tag] v2.3.1-rc3 -> v2.3.1-rc3 2025-08-14T21:02:58.0055866Z * [new tag] v2.4.0 -> v2.4.0 2025-08-14T21:02:58.0056708Z * [new tag] v2.4.0-rc1 -> v2.4.0-rc1 2025-08-14T21:02:58.0057507Z * [new tag] v2.4.0-rc2 -> v2.4.0-rc2 2025-08-14T21:02:58.0058307Z * [new tag] v2.4.0-rc3 -> v2.4.0-rc3 2025-08-14T21:02:58.0059248Z * [new tag] v2.4.0-rc4 -> v2.4.0-rc4 2025-08-14T21:02:58.0060122Z * [new tag] v2.4.0-rc5 -> v2.4.0-rc5 2025-08-14T21:02:58.0060990Z * [new tag] v2.4.0-rc6 -> v2.4.0-rc6 2025-08-14T21:02:58.0061789Z * [new tag] v2.4.0-rc7 -> v2.4.0-rc7 2025-08-14T21:02:58.0062613Z * [new tag] v2.4.0-rc8 -> v2.4.0-rc8 2025-08-14T21:02:58.0063595Z * [new tag] v2.4.0-rc9 -> v2.4.0-rc9 2025-08-14T21:02:58.0064214Z * [new tag] v2.4.1 -> v2.4.1 2025-08-14T21:02:58.0065181Z * [new tag] v2.4.1-rc1 -> v2.4.1-rc1 2025-08-14T21:02:58.0066039Z * [new tag] v2.4.1-rc2 -> v2.4.1-rc2 2025-08-14T21:02:58.0066950Z * [new tag] v2.4.1-rc3 -> v2.4.1-rc3 2025-08-14T21:02:58.0067836Z * [new tag] v2.5.0 -> v2.5.0 2025-08-14T21:02:58.0068532Z * [new tag] v2.5.0-rc1 -> v2.5.0-rc1 2025-08-14T21:02:58.0069205Z * [new tag] v2.5.0-rc10 -> v2.5.0-rc10 2025-08-14T21:02:58.0070111Z * [new tag] v2.5.0-rc2 -> v2.5.0-rc2 2025-08-14T21:02:58.0070918Z * [new tag] v2.5.0-rc3 -> v2.5.0-rc3 2025-08-14T21:02:58.0071764Z * [new tag] v2.5.0-rc4 -> v2.5.0-rc4 2025-08-14T21:02:58.0072973Z * [new tag] v2.5.0-rc5 -> v2.5.0-rc5 2025-08-14T21:02:58.0074002Z * [new tag] v2.5.0-rc6 -> v2.5.0-rc6 2025-08-14T21:02:58.0074844Z * [new tag] v2.5.0-rc7 -> v2.5.0-rc7 2025-08-14T21:02:58.0075694Z * [new tag] v2.5.0-rc8 -> v2.5.0-rc8 2025-08-14T21:02:58.0076590Z * [new tag] v2.5.0-rc9 -> v2.5.0-rc9 2025-08-14T21:02:58.0077132Z * [new tag] v2.5.1 -> v2.5.1 2025-08-14T21:02:58.0077822Z * [new tag] v2.5.1-rc1 -> v2.5.1-rc1 2025-08-14T21:02:58.0078457Z * [new tag] v2.6.0 -> v2.6.0 2025-08-14T21:02:58.0079386Z * [new tag] v2.6.0-rc1 -> v2.6.0-rc1 2025-08-14T21:02:58.0080308Z * [new tag] v2.6.0-rc2 -> v2.6.0-rc2 2025-08-14T21:02:58.0081198Z * [new tag] v2.6.0-rc3 -> v2.6.0-rc3 2025-08-14T21:02:58.0081864Z * [new tag] v2.6.0-rc4 -> v2.6.0-rc4 2025-08-14T21:02:58.0083007Z * [new tag] v2.6.0-rc5 -> v2.6.0-rc5 2025-08-14T21:02:58.0083972Z * [new tag] v2.6.0-rc6 -> v2.6.0-rc6 2025-08-14T21:02:58.0084868Z * [new tag] v2.6.0-rc7 -> v2.6.0-rc7 2025-08-14T21:02:58.0085775Z * [new tag] v2.6.0-rc8 -> v2.6.0-rc8 2025-08-14T21:02:58.0086792Z * [new tag] v2.6.0-rc9 -> v2.6.0-rc9 2025-08-14T21:02:58.0087764Z * [new tag] v2.7.0 -> v2.7.0 2025-08-14T21:02:58.0088623Z * [new tag] v2.7.0-rc1 -> v2.7.0-rc1 2025-08-14T21:02:58.0089169Z * [new tag] v2.7.0-rc10 -> v2.7.0-rc10 2025-08-14T21:02:58.0090224Z * [new tag] v2.7.0-rc2 -> v2.7.0-rc2 2025-08-14T21:02:58.0091083Z * [new tag] v2.7.0-rc3 -> v2.7.0-rc3 2025-08-14T21:02:58.0091987Z * [new tag] v2.7.0-rc4 -> v2.7.0-rc4 2025-08-14T21:02:58.0092669Z * [new tag] v2.7.0-rc5 -> v2.7.0-rc5 2025-08-14T21:02:58.0093749Z * [new tag] v2.7.0-rc6 -> v2.7.0-rc6 2025-08-14T21:02:58.0094549Z * [new tag] v2.7.0-rc7 -> v2.7.0-rc7 2025-08-14T21:02:58.0095503Z * [new tag] v2.7.0-rc8 -> v2.7.0-rc8 2025-08-14T21:02:58.0096380Z * [new tag] v2.7.0-rc9 -> v2.7.0-rc9 2025-08-14T21:02:58.0096896Z * [new tag] v2.7.1 -> v2.7.1 2025-08-14T21:02:58.0097912Z * [new tag] v2.7.1-rc1 -> v2.7.1-rc1 2025-08-14T21:02:58.0098822Z * [new tag] v2.7.1-rc2 -> v2.7.1-rc2 2025-08-14T21:02:58.0099733Z * [new tag] v2.7.1-rc3 -> v2.7.1-rc3 2025-08-14T21:02:58.0100627Z * [new tag] v2.7.1-rc4 -> v2.7.1-rc4 2025-08-14T21:02:58.0101411Z * [new tag] v2.7.1-rc5 -> v2.7.1-rc5 2025-08-14T21:02:58.0102069Z * [new tag] v2.8.0 -> v2.8.0 2025-08-14T21:02:58.0102958Z * [new tag] v2.8.0-rc1 -> v2.8.0-rc1 2025-08-14T21:02:58.0103900Z * [new tag] v2.8.0-rc2 -> v2.8.0-rc2 2025-08-14T21:02:58.0104937Z * [new tag] v2.8.0-rc3 -> v2.8.0-rc3 2025-08-14T21:02:58.0105843Z * [new tag] v2.8.0-rc4 -> v2.8.0-rc4 2025-08-14T21:02:58.0106730Z * [new tag] v2.8.0-rc5 -> v2.8.0-rc5 2025-08-14T21:02:58.0107578Z * [new tag] v2.8.0-rc6 -> v2.8.0-rc6 2025-08-14T21:02:58.0108486Z * [new tag] v2.8.0-rc7 -> v2.8.0-rc7 2025-08-14T21:02:58.0109339Z * [new tag] v2.8.0-rc8 -> v2.8.0-rc8 2025-08-14T21:02:58.0110029Z * [new tag] whc_flight_1 -> whc_flight_1 2025-08-14T21:02:58.0110685Z * [new tag] whc_flight_2 -> whc_flight_2 2025-08-14T21:02:58.0111478Z * [new tag] whc_flight_4 -> whc_flight_4 2025-08-14T21:02:58.0692819Z [command]/usr/bin/git rev-parse --verify --quiet 1fc683cf17c8c673044538d10266c00f92987be2^{object} 2025-08-14T21:02:58.0713978Z 1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:02:58.0717415Z ##[endgroup] 2025-08-14T21:02:58.0717822Z ##[group]Determining the checkout info 2025-08-14T21:02:58.0718718Z ##[endgroup] 2025-08-14T21:02:58.0722295Z [command]/usr/bin/git sparse-checkout disable 2025-08-14T21:02:58.0751384Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-08-14T21:02:58.0773846Z ##[group]Checking out the ref 2025-08-14T21:02:58.0776782Z [command]/usr/bin/git checkout --progress --force 1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:02:59.1075990Z Updating files: 89% (17375/19474) 2025-08-14T21:02:59.1231831Z Updating files: 90% (17527/19474) 2025-08-14T21:02:59.1351137Z Updating files: 91% (17722/19474) 2025-08-14T21:02:59.1470725Z Updating files: 92% (17917/19474) 2025-08-14T21:02:59.1646386Z Updating files: 93% (18111/19474) 2025-08-14T21:02:59.1821786Z Updating files: 94% (18306/19474) 2025-08-14T21:02:59.1996202Z Updating files: 95% (18501/19474) 2025-08-14T21:02:59.2144514Z Updating files: 96% (18696/19474) 2025-08-14T21:02:59.2302713Z Updating files: 97% (18890/19474) 2025-08-14T21:02:59.2577010Z Updating files: 98% (19085/19474) 2025-08-14T21:02:59.2719838Z Updating files: 99% (19280/19474) 2025-08-14T21:02:59.2720178Z Updating files: 100% (19474/19474) 2025-08-14T21:02:59.2720497Z Updating files: 100% (19474/19474), done. 2025-08-14T21:02:59.2979724Z Note: switching to '1fc683cf17c8c673044538d10266c00f92987be2'. 2025-08-14T21:02:59.2980056Z 2025-08-14T21:02:59.2980274Z You are in 'detached HEAD' state. You can look around, make experimental 2025-08-14T21:02:59.2980835Z changes and commit them, and you can discard any commits you make in this 2025-08-14T21:02:59.2981393Z state without impacting any branches by switching back to a branch. 2025-08-14T21:02:59.2981715Z 2025-08-14T21:02:59.2981927Z If you want to create a new branch to retain commits you create, you may 2025-08-14T21:02:59.2982439Z do so (now or later) by using -c with the switch command. Example: 2025-08-14T21:02:59.2982738Z 2025-08-14T21:02:59.2990639Z git switch -c 2025-08-14T21:02:59.2990985Z 2025-08-14T21:02:59.2991111Z Or undo this operation with: 2025-08-14T21:02:59.2991308Z 2025-08-14T21:02:59.2991392Z git switch - 2025-08-14T21:02:59.2991518Z 2025-08-14T21:02:59.2991774Z Turn off this advice by setting config variable advice.detachedHead to false 2025-08-14T21:02:59.2992144Z 2025-08-14T21:02:59.2992553Z HEAD is now at 1fc683cf17c [Inductor] Allow indexing a flexible layout for extract_input_node_reduction_ranges (#160645) 2025-08-14T21:02:59.3045942Z ##[endgroup] 2025-08-14T21:02:59.3046392Z ##[group]Setting up auth for fetching submodules 2025-08-14T21:02:59.3052165Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-08-14T21:02:59.3094823Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-08-14T21:02:59.3118635Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-08-14T21:02:59.3140963Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-08-14T21:02:59.3161739Z ##[endgroup] 2025-08-14T21:02:59.3162179Z ##[group]Fetching submodules 2025-08-14T21:02:59.3166129Z [command]/usr/bin/git submodule sync --recursive 2025-08-14T21:02:59.3448976Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2025-08-14T21:02:59.3720568Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2025-08-14T21:02:59.3722558Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2025-08-14T21:02:59.3725305Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2025-08-14T21:02:59.3728176Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2025-08-14T21:02:59.3731485Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2025-08-14T21:02:59.3734938Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2025-08-14T21:02:59.4115398Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2025-08-14T21:02:59.4118002Z Submodule 'third_party/aiter' (https://github.com/ROCm/aiter.git) registered for path 'third_party/aiter' 2025-08-14T21:02:59.4121407Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2025-08-14T21:02:59.4125049Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2025-08-14T21:02:59.4128635Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2025-08-14T21:02:59.4132027Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2025-08-14T21:02:59.4136073Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2025-08-14T21:02:59.4139852Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2025-08-14T21:02:59.4143821Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2025-08-14T21:02:59.4148639Z Submodule 'third_party/flash-attention' (https://github.com/Dao-AILab/flash-attention.git) registered for path 'third_party/flash-attention' 2025-08-14T21:02:59.4164187Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2025-08-14T21:02:59.4168252Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2025-08-14T21:02:59.4172666Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2025-08-14T21:02:59.4176946Z Submodule 'third_party/gloo' (https://github.com/pytorch/gloo) registered for path 'third_party/gloo' 2025-08-14T21:02:59.4181578Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2025-08-14T21:02:59.4186275Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2025-08-14T21:02:59.4192415Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2025-08-14T21:02:59.4197546Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2025-08-14T21:02:59.4202451Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai' 2025-08-14T21:02:59.4207353Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2025-08-14T21:02:59.4212309Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2025-08-14T21:02:59.4229193Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2025-08-14T21:02:59.4234635Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2025-08-14T21:02:59.4239750Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2025-08-14T21:02:59.4245199Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2025-08-14T21:02:59.4250645Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2025-08-14T21:02:59.4256263Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2025-08-14T21:02:59.4261770Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2025-08-14T21:02:59.4267763Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2025-08-14T21:02:59.4273505Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2025-08-14T21:02:59.4279534Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2025-08-14T21:02:59.4307923Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2025-08-14T21:02:59.6940948Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2025-08-14T21:02:59.6942631Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2025-08-14T21:02:59.6974050Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2025-08-14T21:03:00.1187526Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2025-08-14T21:03:00.1188784Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'... 2025-08-14T21:03:00.1218184Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'... 2025-08-14T21:03:08.5333381Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2025-08-14T21:03:08.5334875Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2025-08-14T21:03:08.5336421Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention'... 2025-08-14T21:03:08.5337934Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2025-08-14T21:03:08.5339454Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2025-08-14T21:03:08.5341064Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2025-08-14T21:03:08.5342483Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2025-08-14T21:03:08.5343952Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2025-08-14T21:03:08.5345310Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2025-08-14T21:03:08.5346642Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2025-08-14T21:03:08.5347959Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2025-08-14T21:03:08.5349291Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2025-08-14T21:03:08.5350639Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kleidiai'... 2025-08-14T21:03:08.5351984Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2025-08-14T21:03:08.5544527Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2025-08-14T21:03:16.4261676Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2025-08-14T21:03:16.4263181Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2025-08-14T21:03:16.4264635Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/aiter'... 2025-08-14T21:03:16.4265987Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2025-08-14T21:03:16.4267389Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2025-08-14T21:03:16.4268770Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2025-08-14T21:03:16.4270406Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2025-08-14T21:03:16.4272229Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2025-08-14T21:03:16.4273963Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2025-08-14T21:03:16.4419001Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2025-08-14T21:03:16.4420448Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2025-08-14T21:03:16.5262601Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2025-08-14T21:03:37.8279686Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2025-08-14T21:03:37.8281257Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2025-08-14T21:03:37.9280884Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2025-08-14T21:03:39.4507286Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2025-08-14T21:03:39.4618795Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2025-08-14T21:03:39.4706048Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2025-08-14T21:03:39.4956453Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2025-08-14T21:03:39.5703508Z Submodule path 'third_party/NVTX': checked out '2942f167cc30c5e3a44a2aecd5b0d9c07ff61a07' 2025-08-14T21:03:39.6198657Z Submodule path 'third_party/VulkanMemoryAllocator': checked out '1d8f600fd424278486eade7ed3e877c99f0846b1' 2025-08-14T21:03:40.3307510Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883' 2025-08-14T21:03:40.4763626Z Submodule path 'third_party/aiter': checked out '01aae101b9e5e94d6c16a9514c9fb8df99c93150' 2025-08-14T21:03:40.4782196Z Submodule '3rdparty/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/aiter/3rdparty/composable_kernel' 2025-08-14T21:03:40.4806178Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/aiter/3rdparty/composable_kernel'... 2025-08-14T21:03:43.9341371Z Submodule path 'third_party/aiter/3rdparty/composable_kernel': checked out 'cffe8fa2a442ac8e80dd236a1a5d24fe3d7e0cbf' 2025-08-14T21:03:43.9565220Z Submodule path 'third_party/benchmark': checked out '299e5928955cc62af9968370293b916f5130916f' 2025-08-14T21:03:44.2584241Z Submodule path 'third_party/composable_kernel': checked out '7fe50dc3da2069d6645d9deb8c017a876472a977' 2025-08-14T21:03:44.3110216Z Submodule path 'third_party/cpp-httplib': checked out '3af7f2c16147f3fbc6e4d717032daf505dc1652c' 2025-08-14T21:03:44.4056352Z Submodule path 'third_party/cpuinfo': checked out '5e3d2445e6a84d9599bee2bf78edbb4d80865e1d' 2025-08-14T21:03:44.4469264Z Submodule path 'third_party/cudnn_frontend': checked out 'f937055efc6d414d11f4c6577e3977fe74f35fb6' 2025-08-14T21:03:45.0509664Z Submodule path 'third_party/cutlass': checked out 'e51efbfe18fe4f4cbb66ab814c55bf4aa0185491' 2025-08-14T21:03:45.1851385Z Submodule path 'third_party/fbgemm': checked out '21c7d30c526c0f1ad873ecc632dca6cfa8a69067' 2025-08-14T21:03:45.1871342Z Submodule 'external/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/external/asmjit' 2025-08-14T21:03:45.1874127Z Submodule 'external/composable_kernel' (https://github.com/jwfromm/composable_kernel.git) registered for path 'third_party/fbgemm/external/composable_kernel' 2025-08-14T21:03:45.1876309Z Submodule 'external/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/external/cpuinfo' 2025-08-14T21:03:45.1878790Z Submodule 'external/cutlass' (https://github.com/jwfromm/cutlass) registered for path 'third_party/fbgemm/external/cutlass' 2025-08-14T21:03:45.1881394Z Submodule 'external/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/external/googletest' 2025-08-14T21:03:45.1884162Z Submodule 'external/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/external/hipify_torch' 2025-08-14T21:03:45.1886710Z Submodule 'external/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/fbgemm/external/json' 2025-08-14T21:03:45.1913631Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/asmjit'... 2025-08-14T21:03:47.1013146Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/hipify_torch'... 2025-08-14T21:03:47.1015552Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/cpuinfo'... 2025-08-14T21:03:47.2013940Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/composable_kernel'... 2025-08-14T21:03:47.4292065Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/googletest'... 2025-08-14T21:03:47.5293100Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/cutlass'... 2025-08-14T21:03:48.3498046Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/json'... 2025-08-14T21:03:53.9840695Z Submodule path 'third_party/fbgemm/external/asmjit': checked out 'a3199e8857792cd10b7589ff5d58343d2c9008ea' 2025-08-14T21:03:54.2343262Z Submodule path 'third_party/fbgemm/external/composable_kernel': checked out 'b1281b8b08d973a7064f864f47eeb30f3e2596e9' 2025-08-14T21:03:54.3318650Z Submodule path 'third_party/fbgemm/external/cpuinfo': checked out '6543fec09b2f04ac4a666882998b534afc9c1349' 2025-08-14T21:03:54.9259042Z Submodule path 'third_party/fbgemm/external/cutlass': checked out 'b40777404c174b9694a870bff5c13ce6b7f656ad' 2025-08-14T21:03:54.9710601Z Submodule path 'third_party/fbgemm/external/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723' 2025-08-14T21:03:54.9821622Z Submodule path 'third_party/fbgemm/external/hipify_torch': checked out 'a4337c69fe0e2552a7b7b0669178926beeed828c' 2025-08-14T21:03:55.0804532Z Submodule path 'third_party/fbgemm/external/json': checked out '9cca280a4d0ccf0c08f47a99aa71d1b0e52f8d03' 2025-08-14T21:03:55.1467512Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5' 2025-08-14T21:03:55.1485187Z Submodule 'csrc/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/flash-attention/csrc/composable_kernel' 2025-08-14T21:03:55.1486920Z Submodule 'csrc/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/flash-attention/csrc/cutlass' 2025-08-14T21:03:55.1511605Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/composable_kernel'... 2025-08-14T21:03:58.5698202Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/cutlass'... 2025-08-14T21:03:58.8000974Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33' 2025-08-14T21:03:59.3461624Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420' 2025-08-14T21:03:59.4791702Z Submodule path 'third_party/flatbuffers': checked out 'a2cd1ea3b6d3fee220106b5fed3f7ce8da9eb757' 2025-08-14T21:03:59.5115108Z Submodule path 'third_party/fmt': checked out '40626af88bd7df9a5fb80be7b25ac85b122d6c21' 2025-08-14T21:03:59.5501396Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2025-08-14T21:03:59.5734430Z Submodule path 'third_party/gloo': checked out 'c7b7b022c124d9643957d9bd55f57ac59fce8fa2' 2025-08-14T21:03:59.6164681Z Submodule path 'third_party/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723' 2025-08-14T21:03:59.6286112Z Submodule path 'third_party/ideep': checked out '719d8e6cd7f7a0e01b155657526d693acf97c2b3' 2025-08-14T21:03:59.6301117Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2025-08-14T21:03:59.6323632Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2025-08-14T21:04:12.3403574Z Submodule path 'third_party/ideep/mkl-dnn': checked out '8d263e693366ef8db40acc569cc7d8edf644556d' 2025-08-14T21:04:12.3597814Z Submodule path 'third_party/ittapi': checked out 'dec1d23ca65ab069d225dfe40dea14f455170959' 2025-08-14T21:04:12.4462539Z Submodule path 'third_party/kineto': checked out '5e7501833f1021ce6f618572d3baf657b6319658' 2025-08-14T21:04:12.4478603Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2025-08-14T21:04:12.4480677Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2025-08-14T21:04:12.4483206Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2025-08-14T21:04:12.4509524Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2025-08-14T21:04:13.3131957Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2025-08-14T21:04:14.3700995Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2025-08-14T21:04:14.4507563Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2025-08-14T21:04:14.4523313Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-08-14T21:04:14.4525877Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-08-14T21:04:14.4528696Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-08-14T21:04:14.4531682Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-08-14T21:04:14.4534691Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-08-14T21:04:14.4537909Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-08-14T21:04:14.4541148Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-08-14T21:04:14.4544539Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-08-14T21:04:14.4571687Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2025-08-14T21:04:16.3595160Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2025-08-14T21:04:16.3597814Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2025-08-14T21:04:16.3600358Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2025-08-14T21:04:16.3602832Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2025-08-14T21:04:16.4596601Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2025-08-14T21:04:16.7937914Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2025-08-14T21:04:16.8938581Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2025-08-14T21:04:23.4610889Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2025-08-14T21:04:23.4784595Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2025-08-14T21:04:23.5137185Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2025-08-14T21:04:23.5268349Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2025-08-14T21:04:23.5283044Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-08-14T21:04:23.5308799Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2025-08-14T21:04:23.8365193Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2025-08-14T21:04:23.8546903Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2025-08-14T21:04:23.8942876Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2025-08-14T21:04:23.9890415Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2025-08-14T21:04:24.0047900Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2025-08-14T21:04:24.0372411Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2025-08-14T21:04:24.0947801Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2025-08-14T21:04:24.1360291Z Submodule path 'third_party/kleidiai': checked out 'cca02c2f69dd18e1f12647c1c0bdc8cf90e680c7' 2025-08-14T21:04:24.1729081Z Submodule path 'third_party/mimalloc': checked out 'fbd8b99c2b828428947d70fdc046bb55609be93e' 2025-08-14T21:04:24.2788298Z Submodule path 'third_party/nlohmann': checked out '55f93686c01528224f448c19128836e7df245f72' 2025-08-14T21:04:24.6630174Z Submodule path 'third_party/onnx': checked out 'e709452ef2bbc1d113faf678c24e6d3467696e83' 2025-08-14T21:04:24.6665761Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2025-08-14T21:04:24.6691075Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2025-08-14T21:04:25.9121401Z Submodule path 'third_party/onnx/third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4' 2025-08-14T21:04:25.9788203Z Submodule path 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2025-08-14T21:04:25.9819116Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-08-14T21:04:25.9821509Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-08-14T21:04:25.9824204Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-08-14T21:04:25.9849677Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2025-08-14T21:04:26.4913029Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'... 2025-08-14T21:04:26.4915290Z Cloning into 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2025-08-14T21:04:37.3219690Z Submodule 'civetweb' (https://github.com/civetweb/civetweb.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-08-14T21:04:37.3222353Z Submodule 'googletest' (https://github.com/google/googletest.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-08-14T21:04:37.3245565Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'... 2025-08-14T21:04:39.6914433Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'... 2025-08-14T21:04:39.9341982Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4' 2025-08-14T21:04:39.9795405Z Submodule path 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'third_party/kineto/libkineto/third_party/dynolog' 2025-08-14T21:04:45.6926906Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-08-14T21:04:45.6966149Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-08-14T21:04:45.7004004Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-08-14T21:04:45.7041445Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-08-14T21:04:45.7077699Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-08-14T21:04:45.7116944Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-08-14T21:04:45.7155088Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-08-14T21:04:45.7192637Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-08-14T21:04:45.7230528Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-08-14T21:04:45.7268411Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-08-14T21:04:45.7304595Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-08-14T21:04:45.7342758Z Entering 'third_party/kleidiai' 2025-08-14T21:04:45.7381114Z Entering 'third_party/mimalloc' 2025-08-14T21:04:45.7420769Z Entering 'third_party/nlohmann' 2025-08-14T21:04:45.7459803Z Entering 'third_party/onnx' 2025-08-14T21:04:45.7516098Z Entering 'third_party/onnx/third_party/pybind11' 2025-08-14T21:04:45.7556558Z Entering 'third_party/opentelemetry-cpp' 2025-08-14T21:04:45.7599082Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-08-14T21:04:45.7635666Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-08-14T21:04:45.7672953Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-08-14T21:04:45.7710136Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-08-14T21:04:45.7748295Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-08-14T21:04:45.7784667Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-08-14T21:04:45.7822067Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-08-14T21:04:45.7857672Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-08-14T21:04:45.7895380Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-08-14T21:04:45.7933811Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-08-14T21:04:45.7989639Z Entering 'third_party/pocketfft' 2025-08-14T21:04:45.8027523Z Entering 'third_party/protobuf' 2025-08-14T21:04:45.8068096Z Entering 'third_party/protobuf/third_party/benchmark' 2025-08-14T21:04:45.8105159Z Entering 'third_party/protobuf/third_party/googletest' 2025-08-14T21:04:45.8143449Z Entering 'third_party/psimd' 2025-08-14T21:04:45.8181621Z Entering 'third_party/pthreadpool' 2025-08-14T21:04:45.8220436Z Entering 'third_party/pybind11' 2025-08-14T21:04:45.8258062Z Entering 'third_party/python-peachpy' 2025-08-14T21:04:45.8296001Z Entering 'third_party/sleef' 2025-08-14T21:04:45.8333818Z Entering 'third_party/tensorpipe' 2025-08-14T21:04:45.8372235Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-08-14T21:04:45.8408879Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-08-14T21:04:45.8445236Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-08-14T21:04:45.8481779Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-08-14T21:04:45.8518127Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-08-14T21:04:45.8569933Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-08-14T21:04:45.8843733Z Entering 'android/libs/fbjni' 2025-08-14T21:04:45.8882512Z Entering 'third_party/FP16' 2025-08-14T21:04:45.8921205Z Entering 'third_party/FXdiv' 2025-08-14T21:04:45.8958827Z Entering 'third_party/NNPACK' 2025-08-14T21:04:45.8996850Z Entering 'third_party/NVTX' 2025-08-14T21:04:45.9035382Z Entering 'third_party/VulkanMemoryAllocator' 2025-08-14T21:04:45.9073216Z Entering 'third_party/XNNPACK' 2025-08-14T21:04:45.9127910Z Entering 'third_party/aiter' 2025-08-14T21:04:45.9166966Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-08-14T21:04:45.9213060Z Entering 'third_party/benchmark' 2025-08-14T21:04:45.9252567Z Entering 'third_party/composable_kernel' 2025-08-14T21:04:45.9298985Z Entering 'third_party/cpp-httplib' 2025-08-14T21:04:45.9337515Z Entering 'third_party/cpuinfo' 2025-08-14T21:04:45.9376347Z Entering 'third_party/cudnn_frontend' 2025-08-14T21:04:45.9415004Z Entering 'third_party/cutlass' 2025-08-14T21:04:45.9461374Z Entering 'third_party/fbgemm' 2025-08-14T21:04:45.9502176Z Entering 'third_party/fbgemm/external/asmjit' 2025-08-14T21:04:45.9539408Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-08-14T21:04:45.9583648Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-08-14T21:04:45.9621369Z Entering 'third_party/fbgemm/external/cutlass' 2025-08-14T21:04:45.9666125Z Entering 'third_party/fbgemm/external/googletest' 2025-08-14T21:04:45.9704046Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-08-14T21:04:45.9740352Z Entering 'third_party/fbgemm/external/json' 2025-08-14T21:04:45.9780645Z Entering 'third_party/flash-attention' 2025-08-14T21:04:45.9820444Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-08-14T21:04:45.9863167Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-08-14T21:04:45.9909188Z Entering 'third_party/flatbuffers' 2025-08-14T21:04:45.9949445Z Entering 'third_party/fmt' 2025-08-14T21:04:45.9987048Z Entering 'third_party/gemmlowp/gemmlowp' 2025-08-14T21:04:46.0025183Z Entering 'third_party/gloo' 2025-08-14T21:04:46.0062904Z Entering 'third_party/googletest' 2025-08-14T21:04:46.0101985Z Entering 'third_party/ideep' 2025-08-14T21:04:46.0139195Z Entering 'third_party/ideep/mkl-dnn' 2025-08-14T21:04:46.0184737Z Entering 'third_party/ittapi' 2025-08-14T21:04:46.0223520Z Entering 'third_party/kineto' 2025-08-14T21:04:46.0262540Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-08-14T21:04:46.0299651Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-08-14T21:04:46.0338635Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-08-14T21:04:46.0375873Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-08-14T21:04:46.0413948Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-08-14T21:04:46.0450043Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-08-14T21:04:46.0489069Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-08-14T21:04:46.0526406Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-08-14T21:04:46.0563552Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-08-14T21:04:46.0602366Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-08-14T21:04:46.0640601Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-08-14T21:04:46.0677175Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-08-14T21:04:46.0715373Z Entering 'third_party/kleidiai' 2025-08-14T21:04:46.0753732Z Entering 'third_party/mimalloc' 2025-08-14T21:04:46.0792361Z Entering 'third_party/nlohmann' 2025-08-14T21:04:46.0831596Z Entering 'third_party/onnx' 2025-08-14T21:04:46.0888429Z Entering 'third_party/onnx/third_party/pybind11' 2025-08-14T21:04:46.0928828Z Entering 'third_party/opentelemetry-cpp' 2025-08-14T21:04:46.0970217Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-08-14T21:04:46.1007115Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-08-14T21:04:46.1043328Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-08-14T21:04:46.1079403Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-08-14T21:04:46.1117333Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-08-14T21:04:46.1153384Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-08-14T21:04:46.1189520Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-08-14T21:04:46.1225872Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-08-14T21:04:46.1264555Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-08-14T21:04:46.1303917Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-08-14T21:04:46.1359739Z Entering 'third_party/pocketfft' 2025-08-14T21:04:46.1397885Z Entering 'third_party/protobuf' 2025-08-14T21:04:46.1464805Z Entering 'third_party/protobuf/third_party/benchmark' 2025-08-14T21:04:46.1501783Z Entering 'third_party/protobuf/third_party/googletest' 2025-08-14T21:04:46.1539824Z Entering 'third_party/psimd' 2025-08-14T21:04:46.1578030Z Entering 'third_party/pthreadpool' 2025-08-14T21:04:46.1618240Z Entering 'third_party/pybind11' 2025-08-14T21:04:46.1656158Z Entering 'third_party/python-peachpy' 2025-08-14T21:04:46.1694868Z Entering 'third_party/sleef' 2025-08-14T21:04:46.1732904Z Entering 'third_party/tensorpipe' 2025-08-14T21:04:46.1771156Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-08-14T21:04:46.1809483Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-08-14T21:04:46.1846206Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-08-14T21:04:46.1882962Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-08-14T21:04:46.1919111Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-08-14T21:04:46.1967302Z ##[endgroup] 2025-08-14T21:04:46.1999698Z [command]/usr/bin/git log -1 --format=%H 2025-08-14T21:04:46.2019505Z 1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:04:46.2202455Z Prepare all required actions 2025-08-14T21:04:46.2202993Z Getting action download info 2025-08-14T21:04:46.3599694Z ##[group]Run ./.github/actions/setup-linux 2025-08-14T21:04:46.3600030Z env: 2025-08-14T21:04:46.3600252Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:46.3600502Z ##[endgroup] 2025-08-14T21:04:46.3648302Z ##[group]Run set -euo pipefail 2025-08-14T21:04:46.3648642Z set -euo pipefail 2025-08-14T21:04:46.3648938Z function get_ec2_metadata() { 2025-08-14T21:04:46.3649308Z  # Pulled from instance metadata endpoint for EC2 2025-08-14T21:04:46.3649951Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2025-08-14T21:04:46.3650515Z  category=$1 2025-08-14T21:04:46.3650865Z  # If it is GCP runner (runner name contains gcp), do not run this 2025-08-14T21:04:46.3651306Z  runner_name_str=i-0c96d079ce61848eb 2025-08-14T21:04:46.3651712Z  if [[ -f /.inarc ]]; then 2025-08-14T21:04:46.3652048Z  echo "ARC Runner, no info on ec2 metadata" 2025-08-14T21:04:46.3652440Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2025-08-14T21:04:46.3652907Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2025-08-14T21:04:46.3653325Z  else 2025-08-14T21:04:46.3654194Z  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}" 2025-08-14T21:04:46.3655101Z  fi 2025-08-14T21:04:46.3655314Z } 2025-08-14T21:04:46.3655562Z echo "ami-id: $(get_ec2_metadata ami-id)" 2025-08-14T21:04:46.3655988Z echo "instance-id: $(get_ec2_metadata instance-id)" 2025-08-14T21:04:46.3656468Z echo "instance-type: $(get_ec2_metadata instance-type)" 2025-08-14T21:04:46.3656892Z echo "system info $(uname -a)" 2025-08-14T21:04:46.3663431Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:04:46.3663816Z env: 2025-08-14T21:04:46.3664033Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:46.3664285Z ##[endgroup] 2025-08-14T21:04:46.3810372Z ami-id: ami-05ffe3c48a9991133 2025-08-14T21:04:46.3914274Z instance-id: i-0c96d079ce61848eb 2025-08-14T21:04:46.4015923Z instance-type: c5.2xlarge 2025-08-14T21:04:46.4026826Z system info Linux ip-10-0-65-99.ec2.internal 6.1.141-155.222.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 17 10:29:47 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux 2025-08-14T21:04:46.4058657Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-08-14T21:04:46.4059611Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-08-14T21:04:46.4065951Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:04:46.4066480Z env: 2025-08-14T21:04:46.4066708Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:46.4066973Z ##[endgroup] 2025-08-14T21:04:46.4122346Z ##[group]Run if systemctl is-active --quiet docker; then 2025-08-14T21:04:46.4122802Z if systemctl is-active --quiet docker; then 2025-08-14T21:04:46.4123193Z  echo "Docker daemon is running..."; 2025-08-14T21:04:46.4123507Z else 2025-08-14T21:04:46.4123866Z  echo "Starting docker daemon..." && sudo systemctl start docker; 2025-08-14T21:04:46.4124295Z fi 2025-08-14T21:04:46.4129408Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:04:46.4129779Z env: 2025-08-14T21:04:46.4129998Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:46.4130258Z ##[endgroup] 2025-08-14T21:04:46.4203900Z Docker daemon is running... 2025-08-14T21:04:46.4252933Z ##[group]Run nick-fields/retry@v3.0.0 2025-08-14T21:04:46.4253225Z with: 2025-08-14T21:04:46.4253452Z shell: bash 2025-08-14T21:04:46.4253821Z timeout_minutes: 5 2025-08-14T21:04:46.4254055Z max_attempts: 3 2025-08-14T21:04:46.4254292Z retry_wait_seconds: 30 2025-08-14T21:04:46.4257115Z command: AWS_ACCOUNT_ID=$(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 2025-08-14T21:04:46.4260243Z polling_interval_seconds: 1 2025-08-14T21:04:46.4260531Z warning_on_retry: true 2025-08-14T21:04:46.4260798Z continue_on_error: false 2025-08-14T21:04:46.4261044Z env: 2025-08-14T21:04:46.4261269Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:46.4261537Z AWS_RETRY_MODE: standard 2025-08-14T21:04:46.4261785Z AWS_MAX_ATTEMPTS: 5 2025-08-14T21:04:46.4262041Z AWS_DEFAULT_REGION: us-east-1 2025-08-14T21:04:46.4262922Z ##[endgroup] 2025-08-14T21:04:47.5856931Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-08-14T21:04:47.5857577Z Configure a credential helper to remove this warning. See 2025-08-14T21:04:47.5858144Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-08-14T21:04:47.5858544Z 2025-08-14T21:04:47.5858637Z Login Succeeded 2025-08-14T21:04:48.0988751Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-08-14T21:04:48.0989602Z Configure a credential helper to remove this warning. See 2025-08-14T21:04:48.0990238Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-08-14T21:04:48.0990644Z 2025-08-14T21:04:48.0990777Z Login Succeeded 2025-08-14T21:04:48.5595297Z Command completed after 1 attempt(s). 2025-08-14T21:04:48.5652018Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-08-14T21:04:48.5652554Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-08-14T21:04:48.5653019Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-08-14T21:04:48.5660255Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:04:48.5660645Z env: 2025-08-14T21:04:48.5660870Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:48.5661125Z ##[endgroup] 2025-08-14T21:04:48.5749436Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-08-14T21:04:48.5750037Z # ignore expansion of "docker ps -q" since it could be empty 2025-08-14T21:04:48.5750474Z # shellcheck disable=SC2046 2025-08-14T21:04:48.5750805Z docker stop $(docker ps -q) || true 2025-08-14T21:04:48.5751158Z # Prune all of the docker images 2025-08-14T21:04:48.5751619Z docker system prune -af 2025-08-14T21:04:48.5757340Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:04:48.5757720Z env: 2025-08-14T21:04:48.5757940Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:48.5758190Z ##[endgroup] 2025-08-14T21:04:48.5974453Z "docker stop" requires at least 1 argument. 2025-08-14T21:04:48.5974839Z See 'docker stop --help'. 2025-08-14T21:04:48.5975028Z 2025-08-14T21:04:48.5975190Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-08-14T21:04:48.5975461Z 2025-08-14T21:04:48.5975583Z Stop one or more running containers 2025-08-14T21:04:48.6247989Z Total reclaimed space: 0B 2025-08-14T21:04:48.6286230Z ##[group]Run set +e 2025-08-14T21:04:48.6286511Z set +e 2025-08-14T21:04:48.6286751Z set -x 2025-08-14T21:04:48.6286978Z  2025-08-14T21:04:48.6287209Z PT_DOMAIN=download.pytorch.org 2025-08-14T21:04:48.6287800Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2025-08-14T21:04:48.6288734Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2025-08-14T21:04:48.6289276Z # one is returned at random 2025-08-14T21:04:48.6289660Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2025-08-14T21:04:48.6290037Z  2025-08-14T21:04:48.6290272Z if [ -z "${RESOLVED_IP}" ]; then 2025-08-14T21:04:48.6290695Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2025-08-14T21:04:48.6291227Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2025-08-14T21:04:48.6291622Z  2025-08-14T21:04:48.6291857Z  if [ -z "${RESOLVED_IP}" ]; then 2025-08-14T21:04:48.6292231Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2025-08-14T21:04:48.6305465Z  exit 1 2025-08-14T21:04:48.6305917Z  fi 2025-08-14T21:04:48.6306232Z fi 2025-08-14T21:04:48.6306549Z  2025-08-14T21:04:48.6306837Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2025-08-14T21:04:48.6307212Z  # Clean up any old records first 2025-08-14T21:04:48.6307582Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2025-08-14T21:04:48.6307911Z fi 2025-08-14T21:04:48.6308110Z  2025-08-14T21:04:48.6308422Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2025-08-14T21:04:48.6308825Z cat /etc/hosts 2025-08-14T21:04:48.6314426Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:04:48.6314808Z env: 2025-08-14T21:04:48.6315027Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:48.6315289Z ##[endgroup] 2025-08-14T21:04:48.6336862Z + PT_DOMAIN=download.pytorch.org 2025-08-14T21:04:48.6341859Z ++ dig -4 +short download.pytorch.org 2025-08-14T21:04:48.6342531Z ++ tail -n1 2025-08-14T21:04:48.6721190Z + RESOLVED_IP=18.160.10.76 2025-08-14T21:04:48.6721603Z + '[' -z 18.160.10.76 ']' 2025-08-14T21:04:48.6721933Z + grep -r download.pytorch.org /etc/hosts 2025-08-14T21:04:48.6735279Z + echo '18.160.10.76 download.pytorch.org' 2025-08-14T21:04:48.6736094Z + sudo tee -a /etc/hosts 2025-08-14T21:04:48.9795923Z 18.160.10.76 download.pytorch.org 2025-08-14T21:04:48.9811178Z + cat /etc/hosts 2025-08-14T21:04:48.9819073Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2025-08-14T21:04:48.9825507Z ::1 localhost6 localhost6.localdomain6 2025-08-14T21:04:48.9825916Z 18.160.10.76 download.pytorch.org 2025-08-14T21:04:49.0000209Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2025-08-14T21:04:49.0000694Z with: 2025-08-14T21:04:49.0001423Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:49.0002267Z use-custom-docker-registry: true 2025-08-14T21:04:49.0002591Z docker-build-dir: .ci/docker 2025-08-14T21:04:49.0002888Z docker-build-script: ./build.sh 2025-08-14T21:04:49.0003314Z working-directory: . 2025-08-14T21:04:49.0003672Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-14T21:04:49.0004077Z force-push: false 2025-08-14T21:04:49.0004296Z env: 2025-08-14T21:04:49.0004512Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:49.0004773Z ##[endgroup] 2025-08-14T21:04:49.0032564Z ##[group]Run set -ex 2025-08-14T21:04:49.0032881Z set -ex 2025-08-14T21:04:49.0033115Z  2025-08-14T21:04:49.0033557Z # If the docker build directory or the build script doesn't exist, the action will 2025-08-14T21:04:49.0034234Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-08-14T21:04:49.0034794Z # job could then download the pre-built image as usual 2025-08-14T21:04:49.0035493Z if [[ -d "${DOCKER_BUILD_DIR}" ]] && [[ -f "${DOCKER_BUILD_DIR}/${DOCKER_BUILD_SCRIPT}" ]] && [[ "${USE_CUSTOM_DOCKER_REGISTRY}" == "true" ]]; then 2025-08-14T21:04:49.0036157Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-08-14T21:04:49.0036478Z else 2025-08-14T21:04:49.0036739Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-08-14T21:04:49.0037186Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-08-14T21:04:49.0037598Z  2025-08-14T21:04:49.0038146Z  echo "Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ${REPO_NAME} repo..." 2025-08-14T21:04:49.0038790Z  exit 0 2025-08-14T21:04:49.0039015Z fi 2025-08-14T21:04:49.0039210Z  2025-08-14T21:04:49.0039548Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-08-14T21:04:49.0040154Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-08-14T21:04:49.0040691Z  # use it as it is, but first let's extract the tag 2025-08-14T21:04:49.0041164Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-08-14T21:04:49.0041679Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-08-14T21:04:49.0042170Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-08-14T21:04:49.0042577Z else 2025-08-14T21:04:49.0042829Z  if [[ "${DOCKER_IMAGE_NAME}" == *:* ]]; then 2025-08-14T21:04:49.0043214Z  CUSTOM_TAG_PREFIX=${DOCKER_IMAGE_NAME#*:} 2025-08-14T21:04:49.0043616Z  DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME%%:*} 2025-08-14T21:04:49.0043942Z  fi 2025-08-14T21:04:49.0044396Z  DOCKER_TAG=${CUSTOM_TAG_PREFIX:+${CUSTOM_TAG_PREFIX}-}$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-08-14T21:04:49.0045011Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-08-14T21:04:49.0045656Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-08-14T21:04:49.0046359Z  echo "custom-tag-prefix=${CUSTOM_TAG_PREFIX}" >> "${GITHUB_OUTPUT}" 2025-08-14T21:04:49.0046791Z fi 2025-08-14T21:04:49.0054821Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:04:49.0055194Z env: 2025-08-14T21:04:49.0055414Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:49.0055689Z REPO_NAME: pytorch 2025-08-14T21:04:49.0056574Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:49.0057381Z DOCKER_BUILD_DIR: .ci/docker 2025-08-14T21:04:49.0057670Z DOCKER_BUILD_SCRIPT: ./build.sh 2025-08-14T21:04:49.0058056Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-14T21:04:49.0058451Z USE_CUSTOM_DOCKER_REGISTRY: true 2025-08-14T21:04:49.0058744Z CUSTOM_TAG_PREFIX: 2025-08-14T21:04:49.0058986Z ##[endgroup] 2025-08-14T21:04:49.0084077Z + [[ -d .ci/docker ]] 2025-08-14T21:04:49.0084589Z + [[ -f .ci/docker/./build.sh ]] 2025-08-14T21:04:49.0085218Z + [[ true == \t\r\u\e ]] 2025-08-14T21:04:49.0085666Z + echo skip=false 2025-08-14T21:04:49.0087316Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe == *\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* ]] 2025-08-14T21:04:49.0091120Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:49.0092017Z ++ awk -F '[:,]' '{print $2}' 2025-08-14T21:04:49.0112462Z + DOCKER_TAG=pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:49.0113295Z + echo docker-tag=pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:49.0114382Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:49.0145901Z ##[group]Run set +e 2025-08-14T21:04:49.0146205Z set +e 2025-08-14T21:04:49.0146439Z set -x 2025-08-14T21:04:49.0146651Z  2025-08-14T21:04:49.0146869Z login() { 2025-08-14T21:04:49.0147356Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-08-14T21:04:49.0147885Z } 2025-08-14T21:04:49.0148082Z  2025-08-14T21:04:49.0148305Z retry () { 2025-08-14T21:04:49.0148584Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-08-14T21:04:49.0148889Z } 2025-08-14T21:04:49.0149099Z  2025-08-14T21:04:49.0149332Z retry login "${DOCKER_REGISTRY}" 2025-08-14T21:04:49.0149626Z  2025-08-14T21:04:49.0149844Z START_TIME=$(date +%s) 2025-08-14T21:04:49.0150136Z # Wait up to 120 minutes 2025-08-14T21:04:49.0150504Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do 2025-08-14T21:04:49.0150989Z  # Check if image already exists, if it does then skip building it 2025-08-14T21:04:49.0151481Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2025-08-14T21:04:49.0151845Z  exit 0 2025-08-14T21:04:49.0152065Z  fi 2025-08-14T21:04:49.0152276Z  2025-08-14T21:04:49.0152656Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2025-08-14T21:04:49.0153327Z  # use this to differentiate between the Docker build and regular build jobs. For the 2025-08-14T21:04:49.0153980Z  # latter, it will wait for the Docker images to become available before continuing 2025-08-14T21:04:49.0154502Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2025-08-14T21:04:49.0154903Z  # It's a Docker build job, let's build the image 2025-08-14T21:04:49.0155241Z  break 2025-08-14T21:04:49.0155469Z  else 2025-08-14T21:04:49.0155816Z  # It's a regular build job, wait for the image to become available 2025-08-14T21:04:49.0156235Z  sleep 300 2025-08-14T21:04:49.0156469Z  fi 2025-08-14T21:04:49.0156685Z done 2025-08-14T21:04:49.0156901Z  2025-08-14T21:04:49.0157239Z # NB: This part requires a full checkout. Otherwise, the merge base will 2025-08-14T21:04:49.0157814Z # be empty. The default action would be to continue rebuild the image 2025-08-14T21:04:49.0158455Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2025-08-14T21:04:49.0158911Z  # if we're on the base branch then use the parent commit 2025-08-14T21:04:49.0159316Z  MERGE_BASE=$(git rev-parse HEAD~) 2025-08-14T21:04:49.0159631Z else 2025-08-14T21:04:49.0159959Z  # otherwise we're on a PR, so use the most recent base commit 2025-08-14T21:04:49.0160429Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2025-08-14T21:04:49.0160872Z fi 2025-08-14T21:04:49.0161082Z  2025-08-14T21:04:49.0161306Z if [[ -z "${MERGE_BASE}" ]]; then 2025-08-14T21:04:49.0161667Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-08-14T21:04:49.0162007Z  2025-08-14T21:04:49.0162484Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2025-08-14T21:04:49.0163029Z  exit 0 2025-08-14T21:04:49.0163255Z fi 2025-08-14T21:04:49.0163467Z  2025-08-14T21:04:49.0163763Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2025-08-14T21:04:49.0164446Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2025-08-14T21:04:49.0165033Z  exit 1 2025-08-14T21:04:49.0165242Z fi 2025-08-14T21:04:49.0165449Z  2025-08-14T21:04:49.0165810Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2025-08-14T21:04:49.0166472Z # If no image exists but the hash is the same as the previous hash then we should error out here 2025-08-14T21:04:49.0167054Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2025-08-14T21:04:49.0167731Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2025-08-14T21:04:49.0168506Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2025-08-14T21:04:49.0168964Z fi 2025-08-14T21:04:49.0169162Z  2025-08-14T21:04:49.0169420Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-08-14T21:04:49.0174768Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:04:49.0175131Z env: 2025-08-14T21:04:49.0175350Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:49.0175625Z DOCKER_BUILD_DIR: .ci/docker 2025-08-14T21:04:49.0175952Z BASE_REVISION: 1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:04:49.0176810Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:49.0177818Z DOCKER_TAG: pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:49.0178436Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-14T21:04:49.0178816Z DOCKER_PUSH: 2025-08-14T21:04:49.0179042Z ##[endgroup] 2025-08-14T21:04:49.0200811Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-14T21:04:49.0201260Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-14T21:04:49.0203639Z + aws ecr get-login-password --region us-east-1 2025-08-14T21:04:49.0204560Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-14T21:04:49.5241366Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-08-14T21:04:49.5242430Z Configure a credential helper to remove this warning. See 2025-08-14T21:04:49.5243304Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-08-14T21:04:49.5243944Z 2025-08-14T21:04:49.5247744Z Login Succeeded 2025-08-14T21:04:49.5260530Z ++ date +%s 2025-08-14T21:04:49.5270087Z + START_TIME=1755205489 2025-08-14T21:04:49.5273600Z ++ date +%s 2025-08-14T21:04:49.5281346Z + [[ 1755198289 -lt 1755205489 ]] 2025-08-14T21:04:49.5282487Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:49.7810028Z { 2025-08-14T21:04:49.7810403Z "schemaVersion": 2, 2025-08-14T21:04:49.7811024Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2025-08-14T21:04:49.7811533Z "config": { 2025-08-14T21:04:49.7812108Z "mediaType": "application/vnd.docker.container.image.v1+json", 2025-08-14T21:04:49.7812763Z "size": 30028, 2025-08-14T21:04:49.7813629Z "digest": "sha256:d23aa400d15c13cf4cd8d85388ce0304ef46d5163b945cf305903ce37fa5c8ba" 2025-08-14T21:04:49.7814356Z }, 2025-08-14T21:04:49.7814631Z "layers": [ 2025-08-14T21:04:49.7814935Z { 2025-08-14T21:04:49.7815343Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-14T21:04:49.7815752Z "size": 30448173, 2025-08-14T21:04:49.7816177Z "digest": "sha256:660ffc76f83b006444a5731b215acc2e35138d8be5cac8ed1ffd40f947117495" 2025-08-14T21:04:49.7816652Z }, 2025-08-14T21:04:49.7816831Z { 2025-08-14T21:04:49.7817168Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-14T21:04:49.7817586Z "size": 1554, 2025-08-14T21:04:49.7817982Z "digest": "sha256:a7ac6822069caec96042d8957a65289c4aaae3ce024ea77fc19125134222c906" 2025-08-14T21:04:49.7818451Z }, 2025-08-14T21:04:49.7818643Z { 2025-08-14T21:04:49.7818965Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-14T21:04:49.7819370Z "size": 344010930, 2025-08-14T21:04:49.7819786Z "digest": 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"sha256:e7bcb72e6b9e10c8e2f101145bd6d1b6ca9bebfdb042a7199218aa2ee2e3a725" 2025-08-14T21:04:49.7964549Z }, 2025-08-14T21:04:49.7964740Z { 2025-08-14T21:04:49.7965051Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-14T21:04:49.7965467Z "size": 32, 2025-08-14T21:04:49.7965873Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-08-14T21:04:49.7966328Z } 2025-08-14T21:04:49.7966520Z ] 2025-08-14T21:04:49.7966709Z } 2025-08-14T21:04:49.8001723Z ##[group]Run set -eux 2025-08-14T21:04:49.8002006Z set -eux 2025-08-14T21:04:49.8002887Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin 2025-08-14T21:04:49.8010344Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:04:49.8010710Z env: 2025-08-14T21:04:49.8010932Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:49.8011204Z ##[endgroup] 2025-08-14T21:04:49.8037046Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-08-14T21:04:49.8038429Z + jq --raw-output .SecretString 2025-08-14T21:04:49.8040031Z + jq -r .docker_hub_readonly_token 2025-08-14T21:04:49.8040712Z + docker login --username pytorchbot --password-stdin 2025-08-14T21:04:50.3614557Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-08-14T21:04:50.3615212Z Configure a credential helper to remove this warning. See 2025-08-14T21:04:50.3615873Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-08-14T21:04:50.3616569Z 2025-08-14T21:04:50.3616697Z Login Succeeded 2025-08-14T21:04:50.3707241Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*:} 2025-08-14T21:04:50.3707625Z tag=${ECR_DOCKER_IMAGE##*:} 2025-08-14T21:04:50.3708020Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2025-08-14T21:04:50.3713744Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:04:50.3714151Z env: 2025-08-14T21:04:50.3714356Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:50.3715133Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:50.3715936Z ##[endgroup] 2025-08-14T21:04:50.3740622Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:50.3793600Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2025-08-14T21:04:50.3794042Z with: 2025-08-14T21:04:50.3794751Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:50.3795644Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-14T21:04:50.3796033Z env: 2025-08-14T21:04:50.3796233Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:50.3796512Z ##[endgroup] 2025-08-14T21:04:50.3819721Z ##[group]Run set -x 2025-08-14T21:04:50.3819990Z set -x 2025-08-14T21:04:50.3820224Z set +e 2025-08-14T21:04:50.3820446Z  2025-08-14T21:04:50.3820644Z login() { 2025-08-14T21:04:50.3821125Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-08-14T21:04:50.3821644Z } 2025-08-14T21:04:50.3821842Z  2025-08-14T21:04:50.3822097Z retry () { 2025-08-14T21:04:50.3822359Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-08-14T21:04:50.3822674Z } 2025-08-14T21:04:50.3822894Z  2025-08-14T21:04:50.3823126Z retry login "${DOCKER_REGISTRY}" 2025-08-14T21:04:50.3823537Z  2025-08-14T21:04:50.3824031Z IMAGE_SIZE=$(docker manifest inspect "${DOCKER_IMAGE}" | jq '[.layers[].size, .config.size] | add / 1024 / 1024') 2025-08-14T21:04:50.3824695Z echo "Compressed size of image in MB: ${IMAGE_SIZE}" 2025-08-14T21:04:50.3825192Z  2025-08-14T21:04:50.3825401Z set -e 2025-08-14T21:04:50.3825751Z # ignore output since only exit code is used for conditional 2025-08-14T21:04:50.3826244Z # only pull docker image if it's not available locally 2025-08-14T21:04:50.3826776Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-08-14T21:04:50.3827283Z  retry docker pull "${DOCKER_IMAGE}" 2025-08-14T21:04:50.3827604Z fi 2025-08-14T21:04:50.3832602Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:04:50.3832982Z env: 2025-08-14T21:04:50.3833200Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:04:50.3833953Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:50.3834842Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-14T21:04:50.3835246Z ##[endgroup] 2025-08-14T21:04:50.3855960Z + set +e 2025-08-14T21:04:50.3856819Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-14T21:04:50.3857562Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-14T21:04:50.3859830Z + aws ecr get-login-password --region us-east-1 2025-08-14T21:04:50.3861235Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-14T21:04:50.8980762Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-08-14T21:04:50.8981392Z Configure a credential helper to remove this warning. See 2025-08-14T21:04:50.8981958Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-08-14T21:04:50.8982398Z 2025-08-14T21:04:50.8982575Z Login Succeeded 2025-08-14T21:04:50.8999769Z ++ docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:50.9000752Z ++ jq '[.layers[].size, .config.size] | add / 1024 / 1024' 2025-08-14T21:04:51.1318377Z + IMAGE_SIZE=4410.942840576172 2025-08-14T21:04:51.1318800Z + echo 'Compressed size of image in MB: 4410.942840576172' 2025-08-14T21:04:51.1319165Z + set -e 2025-08-14T21:04:51.1319936Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:51.1321131Z Compressed size of image in MB: 4410.942840576172 2025-08-14T21:04:51.1432801Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:51.1434135Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:04:51.3769488Z pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe: Pulling from pytorch/ci-image 2025-08-14T21:04:51.3772116Z 660ffc76f83b: Pulling fs layer 2025-08-14T21:04:51.3777669Z a7ac6822069c: Pulling fs layer 2025-08-14T21:04:51.3778578Z 31f42505d41a: Pulling fs layer 2025-08-14T21:04:51.3779080Z fed7b1c3e821: Pulling fs layer 2025-08-14T21:04:51.3779508Z f6ff1c4ec221: Pulling fs layer 2025-08-14T21:04:51.3779876Z 2b3c95d5bdd3: Pulling fs layer 2025-08-14T21:04:51.3780410Z 64e5c40daf9a: Pulling fs layer 2025-08-14T21:04:51.3780826Z 0113773a5bba: Pulling fs layer 2025-08-14T21:04:51.3781278Z 12513ab032b1: Pulling fs layer 2025-08-14T21:04:51.3781591Z 72da40fd831c: Pulling fs layer 2025-08-14T21:04:51.3781904Z 4ea4b6ca480f: Pulling fs layer 2025-08-14T21:04:51.3782397Z fed7b1c3e821: Waiting 2025-08-14T21:04:51.3782628Z f6ff1c4ec221: Waiting 2025-08-14T21:04:51.3782871Z 2b3c95d5bdd3: Waiting 2025-08-14T21:04:51.3783122Z dea7b5130129: Pulling fs layer 2025-08-14T21:04:51.3783470Z 7a90440a78c7: Pulling fs layer 2025-08-14T21:04:51.3783748Z 258d086c6347: Pulling fs layer 2025-08-14T21:04:51.3784214Z 4f4fb700ef54: Pulling fs layer 2025-08-14T21:04:51.3784472Z 64e5c40daf9a: Waiting 2025-08-14T21:04:51.3784711Z 0113773a5bba: Waiting 2025-08-14T21:04:51.3784952Z 72da40fd831c: Waiting 2025-08-14T21:04:51.3785191Z 4ea4b6ca480f: Waiting 2025-08-14T21:04:51.3785430Z ab642c9771e0: Pulling fs layer 2025-08-14T21:04:51.3785699Z 258d086c6347: Waiting 2025-08-14T21:04:51.3785952Z d947308ec6c0: Pulling fs layer 2025-08-14T21:04:51.3786308Z 82f3234546b3: Pulling fs layer 2025-08-14T21:04:51.3786666Z 12513ab032b1: Waiting 2025-08-14T21:04:51.3787040Z dea7b5130129: Waiting 2025-08-14T21:04:51.3787438Z 960d25169df1: Pulling fs layer 2025-08-14T21:04:51.3787934Z ab642c9771e0: Waiting 2025-08-14T21:04:51.3788344Z d947308ec6c0: Waiting 2025-08-14T21:04:51.3788722Z 4f4fb700ef54: Waiting 2025-08-14T21:04:51.3788959Z 7a90440a78c7: Waiting 2025-08-14T21:04:51.3789207Z dbc40137a811: Pulling fs layer 2025-08-14T21:04:51.3789475Z 28d6214975c0: Pulling fs layer 2025-08-14T21:04:51.3789861Z e75d4694ae82: Pulling fs layer 2025-08-14T21:04:51.3790211Z c8fd1f66c221: Pulling fs layer 2025-08-14T21:04:51.3790502Z 73a036f4a5d4: Pulling fs layer 2025-08-14T21:04:51.3790843Z 92c311949fb1: Pulling fs layer 2025-08-14T21:04:51.3791137Z 28d6214975c0: Waiting 2025-08-14T21:04:51.3791374Z 29f6cfbe4c15: Pulling fs layer 2025-08-14T21:04:51.3791642Z 73a036f4a5d4: Waiting 2025-08-14T21:04:51.3791877Z e75d4694ae82: Waiting 2025-08-14T21:04:51.3792126Z fb6c7ee8061b: Pulling fs layer 2025-08-14T21:04:51.3792382Z 92c311949fb1: Waiting 2025-08-14T21:04:51.3792640Z 0302c90f502f: Pulling fs layer 2025-08-14T21:04:51.3793114Z c8fd1f66c221: Waiting 2025-08-14T21:04:51.3793348Z dbc40137a811: Waiting 2025-08-14T21:04:51.3793598Z 3ca1010f3846: Pulling fs layer 2025-08-14T21:04:51.3793873Z fb6c7ee8061b: Waiting 2025-08-14T21:04:51.3794108Z 072215d97c72: Pulling fs layer 2025-08-14T21:04:51.3794377Z 960d25169df1: Waiting 2025-08-14T21:04:51.3794660Z 0302c90f502f: Waiting 2025-08-14T21:04:51.3794882Z 3ca1010f3846: Waiting 2025-08-14T21:04:51.3795119Z 29f6cfbe4c15: Waiting 2025-08-14T21:04:51.3795409Z 51a8de8f5ab2: Pulling fs layer 2025-08-14T21:04:51.3795680Z 8a649a6063cf: Pulling fs layer 2025-08-14T21:04:51.3795957Z 6f5614ef0259: Pulling fs layer 2025-08-14T21:04:51.3796253Z 072215d97c72: Waiting 2025-08-14T21:04:51.3796484Z 5c0a31477213: Pulling fs layer 2025-08-14T21:04:51.3796750Z 51a8de8f5ab2: Waiting 2025-08-14T21:04:51.3796999Z b9bc257d5af6: Pulling fs layer 2025-08-14T21:04:51.3797253Z 5c0a31477213: Waiting 2025-08-14T21:04:51.3797672Z 4a977d8955e6: Pulling fs layer 2025-08-14T21:04:51.3797940Z 6f5614ef0259: Waiting 2025-08-14T21:04:51.3798188Z 32a65e5a5d0d: Pulling fs layer 2025-08-14T21:04:51.3798518Z 88b9c7fbb7f5: Pulling fs layer 2025-08-14T21:04:51.3798784Z 4a977d8955e6: Waiting 2025-08-14T21:04:51.3799026Z 32a65e5a5d0d: Waiting 2025-08-14T21:04:51.3799305Z b9bc257d5af6: Waiting 2025-08-14T21:04:51.3799577Z 88b9c7fbb7f5: Waiting 2025-08-14T21:04:51.3799830Z ad0aa3946f36: Pulling fs layer 2025-08-14T21:04:51.3800103Z f78553d91b8d: Pulling fs layer 2025-08-14T21:04:51.3800387Z 0b7ea6453230: Pulling fs layer 2025-08-14T21:04:51.3800671Z 8330e7ea1b20: Pulling fs layer 2025-08-14T21:04:51.3800930Z ad0aa3946f36: Waiting 2025-08-14T21:04:51.3801183Z e7aff2fc4378: Pulling fs layer 2025-08-14T21:04:51.3801472Z 8330e7ea1b20: Waiting 2025-08-14T21:04:51.3801706Z 667e68628282: Pulling fs layer 2025-08-14T21:04:51.3801975Z f78553d91b8d: Waiting 2025-08-14T21:04:51.3802217Z 0b7ea6453230: Waiting 2025-08-14T21:04:51.3802455Z 8b05e09cad0d: Pulling fs layer 2025-08-14T21:04:51.3802742Z a20271f3e135: Pulling fs layer 2025-08-14T21:04:51.3803016Z e7aff2fc4378: Waiting 2025-08-14T21:04:51.3803240Z 667e68628282: Waiting 2025-08-14T21:04:51.3803480Z 8b05e09cad0d: Waiting 2025-08-14T21:04:51.3803771Z 6de635554f5b: Pulling fs layer 2025-08-14T21:04:51.3804135Z 2327901bebfd: Pulling fs layer 2025-08-14T21:04:51.3804405Z a20271f3e135: Waiting 2025-08-14T21:04:51.3804645Z 6de635554f5b: Waiting 2025-08-14T21:04:51.3804886Z 2327901bebfd: Waiting 2025-08-14T21:04:51.3805125Z 0e1acd221de3: Pulling fs layer 2025-08-14T21:04:51.3805561Z 883fad14abaf: Pulling fs layer 2025-08-14T21:04:51.3805838Z 0e1acd221de3: Waiting 2025-08-14T21:04:51.3806065Z 883fad14abaf: Waiting 2025-08-14T21:04:51.3806315Z 3cdf0c0557b9: Pulling fs layer 2025-08-14T21:04:51.3806601Z 49f35c818257: Pulling fs layer 2025-08-14T21:04:51.3806879Z d7a2ef6024dc: Pulling fs layer 2025-08-14T21:04:51.3807176Z 49f35c818257: Waiting 2025-08-14T21:04:51.3807421Z e069f49d0a35: Pulling fs layer 2025-08-14T21:04:51.3807693Z c5150ca5ac67: Pulling fs layer 2025-08-14T21:04:51.3808016Z d7a2ef6024dc: Waiting 2025-08-14T21:04:51.3808313Z bebe10ca43f6: Pulling fs layer 2025-08-14T21:04:51.3808724Z adc4a16c409d: Pulling fs layer 2025-08-14T21:04:51.3809077Z 3cdf0c0557b9: Waiting 2025-08-14T21:04:51.3809327Z b7028bbd1f0e: Pulling fs layer 2025-08-14T21:04:51.3809609Z 53acff155ed9: Pulling fs layer 2025-08-14T21:04:51.3809864Z c5150ca5ac67: Waiting 2025-08-14T21:04:51.3810105Z bebe10ca43f6: Waiting 2025-08-14T21:04:51.3810345Z adc4a16c409d: Waiting 2025-08-14T21:04:51.3810573Z b7028bbd1f0e: Waiting 2025-08-14T21:04:51.3810942Z dee80f9872f3: Pulling fs layer 2025-08-14T21:04:51.3811392Z 53acff155ed9: Waiting 2025-08-14T21:04:51.3811791Z a254ee8e6915: Pulling fs layer 2025-08-14T21:04:51.3812242Z dee80f9872f3: Waiting 2025-08-14T21:04:51.3812663Z e7bcb72e6b9e: Pulling fs layer 2025-08-14T21:04:51.3813086Z a254ee8e6915: Waiting 2025-08-14T21:04:51.3813460Z e069f49d0a35: Waiting 2025-08-14T21:04:51.3813776Z e7bcb72e6b9e: Waiting 2025-08-14T21:04:51.4916732Z a7ac6822069c: Download complete 2025-08-14T21:04:51.5676046Z fed7b1c3e821: Verifying Checksum 2025-08-14T21:04:51.5676428Z fed7b1c3e821: Download complete 2025-08-14T21:04:51.7369145Z 660ffc76f83b: Verifying Checksum 2025-08-14T21:04:51.7369608Z 660ffc76f83b: Download complete 2025-08-14T21:04:51.8059053Z 2b3c95d5bdd3: Verifying Checksum 2025-08-14T21:04:51.8059720Z 2b3c95d5bdd3: Download complete 2025-08-14T21:04:51.9360816Z 64e5c40daf9a: Download complete 2025-08-14T21:04:52.0019511Z 0113773a5bba: Verifying Checksum 2025-08-14T21:04:52.0020317Z 0113773a5bba: Download complete 2025-08-14T21:04:52.0889047Z 12513ab032b1: Verifying Checksum 2025-08-14T21:04:52.0889608Z 12513ab032b1: Download complete 2025-08-14T21:04:52.1821133Z 72da40fd831c: Download complete 2025-08-14T21:04:52.2584491Z f6ff1c4ec221: Verifying Checksum 2025-08-14T21:04:52.2585058Z f6ff1c4ec221: Download complete 2025-08-14T21:04:52.2642191Z 4ea4b6ca480f: Download complete 2025-08-14T21:04:52.3251631Z dea7b5130129: Verifying Checksum 2025-08-14T21:04:52.3252252Z dea7b5130129: Download complete 2025-08-14T21:04:52.3489912Z 7a90440a78c7: Download complete 2025-08-14T21:04:52.3577688Z 4f4fb700ef54: Verifying Checksum 2025-08-14T21:04:52.3578224Z 4f4fb700ef54: Download complete 2025-08-14T21:04:52.4547142Z ab642c9771e0: Verifying Checksum 2025-08-14T21:04:52.4547542Z ab642c9771e0: Download complete 2025-08-14T21:04:52.5534582Z d947308ec6c0: Verifying Checksum 2025-08-14T21:04:52.5535026Z d947308ec6c0: Download complete 2025-08-14T21:04:52.6538058Z 660ffc76f83b: Pull complete 2025-08-14T21:04:52.6747737Z a7ac6822069c: Pull complete 2025-08-14T21:04:52.6941702Z 82f3234546b3: Verifying Checksum 2025-08-14T21:04:52.6942241Z 82f3234546b3: Download complete 2025-08-14T21:04:52.9067713Z 960d25169df1: Verifying Checksum 2025-08-14T21:04:53.0002754Z dbc40137a811: Verifying 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2025-08-14T21:06:42.7473137Z c5150ca5ac67: Pull complete 2025-08-14T21:06:42.7672762Z bebe10ca43f6: Pull complete 2025-08-14T21:06:42.7879902Z adc4a16c409d: Pull complete 2025-08-14T21:06:42.8099141Z b7028bbd1f0e: Pull complete 2025-08-14T21:06:42.8317067Z 53acff155ed9: Pull complete 2025-08-14T21:06:42.8527203Z dee80f9872f3: Pull complete 2025-08-14T21:06:42.8943089Z a254ee8e6915: Pull complete 2025-08-14T21:06:44.8345810Z e7bcb72e6b9e: Pull complete 2025-08-14T21:06:44.9701457Z Digest: sha256:e5a191418e9f11a59a218b040bad54b9c3113ac41271ba5a6874e1c6d47db7c7 2025-08-14T21:06:44.9969545Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:06:45.0111666Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:06:45.0151620Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-08-14T21:06:45.0152740Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-08-14T21:06:45.0159716Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:06:45.0160106Z env: 2025-08-14T21:06:45.0160326Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:06:45.0160583Z ##[endgroup] 2025-08-14T21:06:45.0257821Z Prepare all required actions 2025-08-14T21:06:45.0346605Z ##[group]Run ./.github/actions/get-workflow-job-id 2025-08-14T21:06:45.0346947Z with: 2025-08-14T21:06:45.0347514Z github-token: *** 2025-08-14T21:06:45.0347757Z env: 2025-08-14T21:06:45.0347978Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:06:45.0348232Z ##[endgroup] 2025-08-14T21:06:45.0423549Z ##[group]Run set -eux 2025-08-14T21:06:45.0423832Z set -eux 2025-08-14T21:06:45.0424286Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-08-14T21:06:45.0430333Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:06:45.0430718Z env: 2025-08-14T21:06:45.0430936Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:06:45.0431419Z GITHUB_TOKEN: *** 2025-08-14T21:06:45.0431646Z ##[endgroup] 2025-08-14T21:06:45.0454264Z + python3 .github/scripts/get_workflow_job_id.py 16976255017 i-0c96d079ce61848eb 2025-08-14T21:06:46.5067457Z Setting output job-id=48127032091 2025-08-14T21:06:46.5068165Z Setting output job-name=linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge) 2025-08-14T21:06:46.5181004Z ##[group]Run python3 -m pip install psutil==5.9.8 dataclasses_json==0.6.7 nvidia-ml-py==11.525.84 2025-08-14T21:06:46.5181760Z python3 -m pip install psutil==5.9.8 dataclasses_json==0.6.7 nvidia-ml-py==11.525.84 2025-08-14T21:06:46.5182721Z python3 -m tools.stats.monitor --log-interval "$MONITOR_LOG_INTERVAL" --data-collect-interval "$MONITOR_DATA_COLLECT_INTERVAL" > usage_log.txt 2>&1 & 2025-08-14T21:06:46.5183720Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2025-08-14T21:06:46.5190075Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:06:46.5190463Z env: 2025-08-14T21:06:46.5190688Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:06:46.5190945Z JOB_ID: 48127032091 2025-08-14T21:06:46.5191371Z JOB_NAME: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge) 2025-08-14T21:06:46.5191862Z WORKFLOW_NAME: pull 2025-08-14T21:06:46.5192122Z WORKFLOW_RUN_ID: 16976255017 2025-08-14T21:06:46.5192445Z MONITOR_LOG_INTERVAL: 5 2025-08-14T21:06:46.5192713Z MONITOR_DATA_COLLECT_INTERVAL: 1 2025-08-14T21:06:46.5193494Z ##[endgroup] 2025-08-14T21:06:46.9046416Z Defaulting to user installation because normal site-packages is not writeable 2025-08-14T21:06:47.2512720Z Collecting psutil==5.9.8 2025-08-14T21:06:47.2673856Z 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) 2025-08-14T21:06:47.3383220Z Collecting dataclasses_json==0.6.7 2025-08-14T21:06:47.3423520Z Downloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB) 2025-08-14T21:06:47.3685613Z Collecting nvidia-ml-py==11.525.84 2025-08-14T21:06:47.3726756Z Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB) 2025-08-14T21:06:47.4894068Z Collecting marshmallow<4.0.0,>=3.18.0 2025-08-14T21:06:47.4930602Z Downloading marshmallow-3.26.1-py3-none-any.whl (50 kB) 2025-08-14T21:06:47.5154859Z Collecting typing-inspect<1,>=0.4.0 2025-08-14T21:06:47.5190511Z Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB) 2025-08-14T21:06:47.5721657Z Collecting packaging>=17.0 2025-08-14T21:06:47.5759416Z Downloading packaging-25.0-py3-none-any.whl (66 kB) 2025-08-14T21:06:47.6249245Z Collecting typing-extensions>=3.7.4 2025-08-14T21:06:47.6281941Z Downloading typing_extensions-4.14.1-py3-none-any.whl (43 kB) 2025-08-14T21:06:47.6479381Z Collecting mypy-extensions>=0.3.0 2025-08-14T21:06:47.6511843Z Downloading mypy_extensions-1.1.0-py3-none-any.whl (5.0 kB) 2025-08-14T21:06:47.7377788Z Installing collected packages: typing-extensions, packaging, mypy-extensions, typing-inspect, marshmallow, psutil, nvidia-ml-py, dataclasses-json 2025-08-14T21:06:48.0179678Z 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-4.14.1 typing-inspect-0.9.0 2025-08-14T21:06:48.1884755Z Prepare all required actions 2025-08-14T21:06:48.1885143Z Getting action download info 2025-08-14T21:06:48.3280078Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2025-08-14T21:06:48.5745641Z Download action repository 'actions/download-artifact@v4' (SHA:d3f86a106a0bac45b974a628896c90dbdf5c8093) 2025-08-14T21:06:48.8010471Z ##[group]Run ./.github/actions/download-build-artifacts 2025-08-14T21:06:48.8010845Z with: 2025-08-14T21:06:48.8011105Z name: linux-jammy-py3.13-clang12 2025-08-14T21:06:48.8011405Z s3-bucket: gha-artifacts 2025-08-14T21:06:48.8011673Z env: 2025-08-14T21:06:48.8011896Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:06:48.8012148Z ##[endgroup] 2025-08-14T21:06:48.8051819Z ##[group]Run seemethere/download-artifact-s3@v4 2025-08-14T21:06:48.8052167Z with: 2025-08-14T21:06:48.8052403Z name: linux-jammy-py3.13-clang12 2025-08-14T21:06:48.8052713Z s3-bucket: gha-artifacts 2025-08-14T21:06:48.8053026Z region: us-east-1 2025-08-14T21:06:48.8053242Z env: 2025-08-14T21:06:48.8053456Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:06:48.8053719Z ##[endgroup] 2025-08-14T21:06:49.2835496Z (node:41592) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-08-14T21:06:49.2836012Z 2025-08-14T21:06:49.2836216Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-08-14T21:06:49.2836751Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-08-14T21:06:49.2837350Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-08-14T21:06:49.5023437Z Found 1 objects with prefix pytorch/pytorch/16976255017/linux-jammy-py3.13-clang12/ 2025-08-14T21:06:49.5024669Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-08-14T21:07:00.2298686Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-08-14T21:07:00.2305687Z Artifact download has finished successfully 2025-08-14T21:07:00.2484839Z ##[group]Run unzip -o artifacts.zip 2025-08-14T21:07:00.2485194Z unzip -o artifacts.zip 2025-08-14T21:07:00.2491460Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:07:00.2491856Z env: 2025-08-14T21:07:00.2492101Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:07:00.2492475Z ##[endgroup] 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2025-08-14T21:07:07.8557884Z ##[group]Run seemethere/download-artifact-s3@v4 2025-08-14T21:07:07.8558379Z with: 2025-08-14T21:07:07.8558593Z name: td_results 2025-08-14T21:07:07.8558837Z s3-bucket: gha-artifacts 2025-08-14T21:07:07.8559095Z region: us-east-1 2025-08-14T21:07:07.8559324Z env: 2025-08-14T21:07:07.8559540Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:07:07.8559805Z ##[endgroup] 2025-08-14T21:07:08.3131707Z (node:41612) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-08-14T21:07:08.3132384Z 2025-08-14T21:07:08.3132669Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-08-14T21:07:08.3133209Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-08-14T21:07:08.3133780Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-08-14T21:07:08.4315274Z Found 1 objects with prefix pytorch/pytorch/16976255017/td_results/ 2025-08-14T21:07:08.4315949Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2025-08-14T21:07:08.5246276Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2025-08-14T21:07:08.5252168Z Artifact download has finished successfully 2025-08-14T21:07:08.5425840Z ##[group]Run mkdir -p .additional_ci_files 2025-08-14T21:07:08.5426225Z mkdir -p .additional_ci_files 2025-08-14T21:07:08.5426667Z mv td_results.json .additional_ci_files/td_results.json || true 2025-08-14T21:07:08.5433108Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:07:08.5433494Z env: 2025-08-14T21:07:08.5433743Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:07:08.5434000Z ##[endgroup] 2025-08-14T21:07:08.5538398Z ##[group]Run .github/scripts/parse_ref.py 2025-08-14T21:07:08.5538964Z .github/scripts/parse_ref.py 2025-08-14T21:07:08.5547122Z shell: /usr/bin/bash -e {0} 2025-08-14T21:07:08.5547591Z env: 2025-08-14T21:07:08.5547966Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:07:08.5548438Z ##[endgroup] 2025-08-14T21:07:08.5763346Z Setting output branch=main 2025-08-14T21:07:08.5896905Z Prepare all required actions 2025-08-14T21:07:08.5897311Z Getting action download info 2025-08-14T21:07:08.7198542Z ##[group]Run ./.github/actions/filter-test-configs 2025-08-14T21:07:08.7198991Z with: 2025-08-14T21:07:08.7199407Z github-token: *** 2025-08-14T21:07:08.7202364Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "lf.linux.2xlarge"}]} 2025-08-14T21:07:08.7205507Z job-name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge) 2025-08-14T21:07:08.7205979Z env: 2025-08-14T21:07:08.7206192Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:07:08.7206455Z ##[endgroup] 2025-08-14T21:07:08.7252412Z ##[group]Run nick-fields/retry@v3.0.0 2025-08-14T21:07:08.7252712Z with: 2025-08-14T21:07:08.7252922Z shell: bash 2025-08-14T21:07:08.7253149Z timeout_minutes: 10 2025-08-14T21:07:08.7253387Z max_attempts: 5 2025-08-14T21:07:08.7253633Z retry_wait_seconds: 30 2025-08-14T21:07:08.7254440Z 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 2025-08-14T21:07:08.7255443Z polling_interval_seconds: 1 2025-08-14T21:07:08.7255717Z warning_on_retry: true 2025-08-14T21:07:08.7255985Z continue_on_error: false 2025-08-14T21:07:08.7256246Z env: 2025-08-14T21:07:08.7256448Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:07:08.7256873Z GITHUB_TOKEN: *** 2025-08-14T21:07:08.7257118Z ##[endgroup] 2025-08-14T21:07:08.8170809Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.2 2025-08-14T21:07:09.0465463Z Defaulting to user installation because normal site-packages is not writeable 2025-08-14T21:07:09.1564669Z Collecting requests==2.27.1 2025-08-14T21:07:09.1726669Z Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB) 2025-08-14T21:07:09.3285378Z Collecting pyyaml==6.0.2 2025-08-14T21:07:09.3324005Z Downloading PyYAML-6.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (737 kB) 2025-08-14T21:07:09.4079951Z Collecting certifi>=2017.4.17 2025-08-14T21:07:09.4117184Z Downloading certifi-2025.8.3-py3-none-any.whl (161 kB) 2025-08-14T21:07:09.4187009Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2025-08-14T21:07:09.4195802Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2025-08-14T21:07:09.7662297Z Collecting charset-normalizer~=2.0.0 2025-08-14T21:07:09.7699907Z Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB) 2025-08-14T21:07:09.8571915Z Installing collected packages: charset-normalizer, certifi, requests, pyyaml 2025-08-14T21:07:09.9784054Z Successfully installed certifi-2025.8.3 charset-normalizer-2.0.12 pyyaml-6.0.2 requests-2.27.1 2025-08-14T21:07:10.8005118Z Command completed after 1 attempt(s). 2025-08-14T21:07:10.8061817Z ##[group]Run set -x 2025-08-14T21:07:10.8062085Z set -x 2025-08-14T21:07:10.8062321Z  2025-08-14T21:07:10.8062700Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-08-14T21:07:10.8063470Z # in runner workspace 2025-08-14T21:07:10.8063893Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2025-08-14T21:07:10.8070290Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:07:10.8070689Z env: 2025-08-14T21:07:10.8070914Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:07:10.8071185Z ##[endgroup] 2025-08-14T21:07:10.8095493Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2025-08-14T21:07:10.8269165Z Setting output branch=main 2025-08-14T21:07:10.8328032Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2025-08-14T21:07:10.8328462Z echo "Workflow: ${GITHUB_WORKFLOW}" 2025-08-14T21:07:10.8328818Z echo "Job name: ${JOB_NAME}" 2025-08-14T21:07:10.8329126Z  2025-08-14T21:07:10.8329507Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-08-14T21:07:10.8329993Z # in runner workspace 2025-08-14T21:07:10.8330438Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2025-08-14T21:07:10.8330924Z  --workflow "${GITHUB_WORKFLOW}" \ 2025-08-14T21:07:10.8331260Z  --job-name "${JOB_NAME}" \ 2025-08-14T21:07:10.8334212Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "lf.linux.2xlarge"}]}" \ 2025-08-14T21:07:10.8337336Z  --selected-test-configs "" \ 2025-08-14T21:07:10.8337681Z  --pr-number "${PR_NUMBER}" \ 2025-08-14T21:07:10.8337984Z  --tag "${TAG}" \ 2025-08-14T21:07:10.8338279Z  --event-name "${EVENT_NAME}" \ 2025-08-14T21:07:10.8338607Z  --schedule "${SCHEDULE}" \ 2025-08-14T21:07:10.8338909Z  --branch "${HEAD_BRANCH}" 2025-08-14T21:07:10.8344415Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:07:10.8344791Z env: 2025-08-14T21:07:10.8345009Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:07:10.8345556Z GITHUB_TOKEN: *** 2025-08-14T21:07:10.8345977Z JOB_NAME: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge) 2025-08-14T21:07:10.8346458Z PR_NUMBER: 2025-08-14T21:07:10.8346682Z TAG: 2025-08-14T21:07:10.8346890Z EVENT_NAME: push 2025-08-14T21:07:10.8347123Z SCHEDULE: 2025-08-14T21:07:10.8347331Z HEAD_BRANCH: main 2025-08-14T21:07:10.8347564Z ##[endgroup] 2025-08-14T21:07:10.8369160Z Workflow: pull 2025-08-14T21:07:10.8369875Z Job name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge) 2025-08-14T21:07:11.0161924Z Setting output keep-going=True 2025-08-14T21:07:11.0162433Z Setting output ci-verbose-test-logs=False 2025-08-14T21:07:11.0162786Z Setting output ci-test-showlocals=False 2025-08-14T21:07:11.0163128Z Setting output ci-no-test-timeout=False 2025-08-14T21:07:11.0163461Z Setting output ci-no-td=False 2025-08-14T21:07:11.0163802Z Setting output ci-td-distributed=False 2025-08-14T21:07:11.0164133Z Setting output is-unstable=False 2025-08-14T21:07:11.0164445Z Setting output reenabled-issues= 2025-08-14T21:07:11.0167709Z Setting output test-matrix={"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "lf.linux.2xlarge"}]} 2025-08-14T21:07:11.0170790Z Setting output is-test-matrix-empty=False 2025-08-14T21:07:11.0304843Z ##[group]Run echo "Filtered matrix:" 2025-08-14T21:07:11.0305222Z echo "Filtered matrix:" 2025-08-14T21:07:11.0308175Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "lf.linux.2xlarge"}]}" 2025-08-14T21:07:11.0311250Z  2025-08-14T21:07:11.0311462Z echo 2025-08-14T21:07:11.0311726Z echo "Is the current job unstable? False" 2025-08-14T21:07:11.0312063Z  2025-08-14T21:07:11.0312271Z echo 2025-08-14T21:07:11.0312516Z echo "Is keep-going label set? True" 2025-08-14T21:07:11.0312836Z  2025-08-14T21:07:11.0313041Z echo 2025-08-14T21:07:11.0313268Z echo "Reenabled issues? " 2025-08-14T21:07:11.0318896Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:07:11.0319275Z env: 2025-08-14T21:07:11.0319494Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:07:11.0319745Z ##[endgroup] 2025-08-14T21:07:11.0341730Z Filtered matrix: 2025-08-14T21:07:11.0346247Z {include: [{config: default, shard: 1, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 2, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 3, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 4, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 5, num_shards: 5, runner: lf.linux.4xlarge}, {config: crossref, shard: 1, num_shards: 2, runner: lf.linux.2xlarge}, {config: crossref, shard: 2, num_shards: 2, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 1, num_shards: 3, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 2, num_shards: 3, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 3, num_shards: 3, runner: lf.linux.2xlarge}, {config: einops, shard: 1, num_shards: 1, runner: lf.linux.2xlarge}]} 2025-08-14T21:07:11.0349016Z 2025-08-14T21:07:11.0349138Z Is the current job unstable? False 2025-08-14T21:07:11.0349344Z 2025-08-14T21:07:11.0349459Z Is keep-going label set? True 2025-08-14T21:07:11.0349643Z 2025-08-14T21:07:11.0349737Z Reenabled issues? 2025-08-14T21:07:11.0394521Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-08-14T21:07:11.0395059Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-08-14T21:07:11.0400387Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:07:11.0400789Z env: 2025-08-14T21:07:11.0401008Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:07:11.0401263Z JOB_TIMEOUT: 240 2025-08-14T21:07:11.0401496Z ##[endgroup] 2025-08-14T21:07:11.0453141Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-08-14T21:07:11.0453668Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-08-14T21:07:11.0454122Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-08-14T21:07:11.0459224Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T21:07:11.0459604Z env: 2025-08-14T21:07:11.0459823Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:07:11.0460071Z ##[endgroup] 2025-08-14T21:07:11.0555230Z ##[group]Run set -x 2025-08-14T21:07:11.0555590Z set -x 2025-08-14T21:07:11.0555807Z  2025-08-14T21:07:11.0556066Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2025-08-14T21:07:11.0556473Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2025-08-14T21:07:11.0556879Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2025-08-14T21:07:11.0557246Z  TEST_COMMAND=.ci/onnx/test.sh 2025-08-14T21:07:11.0557555Z else 2025-08-14T21:07:11.0557810Z  TEST_COMMAND=.ci/pytorch/test.sh 2025-08-14T21:07:11.0558110Z fi 2025-08-14T21:07:11.0558319Z  2025-08-14T21:07:11.0558586Z # Leaving 1GB for the runner and other things 2025-08-14T21:07:11.0559162Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo) 2025-08-14T21:07:11.0560062Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap 2025-08-14T21:07:11.0560779Z # comes from https://github.com/pytorch/test-infra/pull/6058 2025-08-14T21:07:11.0561309Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3)) 2025-08-14T21:07:11.0561729Z  2025-08-14T21:07:11.0562126Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-08-14T21:07:11.0562465Z  SHM_OPTS= 2025-08-14T21:07:11.0562717Z  JENKINS_USER= 2025-08-14T21:07:11.0563080Z  # ensure that docker container cleanly exits in 12 hours 2025-08-14T21:07:11.0563553Z  # if for some reason cleanup action doesn't stop container 2025-08-14T21:07:11.0563961Z  # when job is cancelled 2025-08-14T21:07:11.0564278Z  DOCKER_SHELL_CMD="sleep 12h" 2025-08-14T21:07:11.0564584Z else 2025-08-14T21:07:11.0564827Z  SHM_OPTS="--shm-size=${SHM_SIZE}" 2025-08-14T21:07:11.0565171Z  JENKINS_USER="--user jenkins" 2025-08-14T21:07:11.0565495Z  DOCKER_SHELL_CMD= 2025-08-14T21:07:11.0565747Z fi 2025-08-14T21:07:11.0565953Z  2025-08-14T21:07:11.0566291Z # detached container should get cleaned up by teardown_ec2_linux 2025-08-14T21:07:11.0577295Z # TODO: Stop building test binaries as part of the build phase 2025-08-14T21:07:11.0577959Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice 2025-08-14T21:07:11.0578511Z # shellcheck disable=SC2086,SC2090 2025-08-14T21:07:11.0578848Z container_name=$(docker run \ 2025-08-14T21:07:11.0579169Z  ${GPU_FLAG:-} \ 2025-08-14T21:07:11.0579474Z  ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \ 2025-08-14T21:07:11.0579818Z  -e BUILD_ENVIRONMENT \ 2025-08-14T21:07:11.0580124Z  -e PR_NUMBER \ 2025-08-14T21:07:11.0580402Z  -e GITHUB_ACTIONS \ 2025-08-14T21:07:11.0580682Z  -e GITHUB_REPOSITORY \ 2025-08-14T21:07:11.0580994Z  -e GITHUB_WORKFLOW \ 2025-08-14T21:07:11.0581284Z  -e GITHUB_JOB \ 2025-08-14T21:07:11.0581560Z  -e GITHUB_RUN_ID \ 2025-08-14T21:07:11.0581836Z  -e GITHUB_RUN_NUMBER \ 2025-08-14T21:07:11.0582138Z  -e GITHUB_RUN_ATTEMPT \ 2025-08-14T21:07:11.0582437Z  -e JOB_ID \ 2025-08-14T21:07:11.0582682Z  -e JOB_NAME \ 2025-08-14T21:07:11.0582943Z  -e BASE_SHA \ 2025-08-14T21:07:11.0583200Z  -e BRANCH \ 2025-08-14T21:07:11.0583517Z  -e SHA1 \ 2025-08-14T21:07:11.0583771Z  -e AWS_DEFAULT_REGION \ 2025-08-14T21:07:11.0584071Z  -e IN_WHEEL_TEST \ 2025-08-14T21:07:11.0584339Z  -e SHARD_NUMBER \ 2025-08-14T21:07:11.0584616Z  -e TEST_CONFIG \ 2025-08-14T21:07:11.0584895Z  -e NUM_TEST_SHARDS \ 2025-08-14T21:07:11.0585192Z  -e REENABLED_ISSUES \ 2025-08-14T21:07:11.0585486Z  -e CONTINUE_THROUGH_ERROR \ 2025-08-14T21:07:11.0585948Z  -e VERBOSE_TEST_LOGS \ 2025-08-14T21:07:11.0586253Z  -e TEST_SHOWLOCALS \ 2025-08-14T21:07:11.0586537Z  -e NO_TEST_TIMEOUT \ 2025-08-14T21:07:11.0586819Z  -e NO_TD \ 2025-08-14T21:07:11.0587078Z  -e TD_DISTRIBUTED \ 2025-08-14T21:07:11.0587354Z  -e PR_LABELS \ 2025-08-14T21:07:11.0587653Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2025-08-14T21:07:11.0587991Z  -e SCCACHE_BUCKET \ 2025-08-14T21:07:11.0588278Z  -e SCCACHE_REGION \ 2025-08-14T21:07:11.0588545Z  -e XLA_CUDA \ 2025-08-14T21:07:11.0588839Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2025-08-14T21:07:11.0589207Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2025-08-14T21:07:11.0589567Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2025-08-14T21:07:11.0589940Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2025-08-14T21:07:11.0590283Z  -e HUGGING_FACE_HUB_TOKEN \ 2025-08-14T21:07:11.0590609Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2025-08-14T21:07:11.0590943Z  -e DASHBOARD_TAG \ 2025-08-14T21:07:11.0591233Z  -e ARTIFACTS_FILE_SUFFIX \ 2025-08-14T21:07:11.0591600Z  --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \ 2025-08-14T21:07:11.0592087Z  --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \ 2025-08-14T21:07:11.0592506Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2025-08-14T21:07:11.0593130Z  --security-opt seccomp=unconfined \ 2025-08-14T21:07:11.0593475Z  --cap-add=SYS_PTRACE \ 2025-08-14T21:07:11.0593777Z  --ipc=host \ 2025-08-14T21:07:11.0594035Z  ${SHM_OPTS} \ 2025-08-14T21:07:11.0594290Z  --tty \ 2025-08-14T21:07:11.0594516Z  --detach \ 2025-08-14T21:07:11.0594782Z  --name="${container_name}" \ 2025-08-14T21:07:11.0595102Z  ${JENKINS_USER} \ 2025-08-14T21:07:11.0595439Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2025-08-14T21:07:11.0595848Z  -w /var/lib/jenkins/workspace \ 2025-08-14T21:07:11.0596172Z  "${DOCKER_IMAGE}" \ 2025-08-14T21:07:11.0596442Z  ${DOCKER_SHELL_CMD} 2025-08-14T21:07:11.0596707Z ) 2025-08-14T21:07:11.0597003Z # Propagate download.pytorch.org IP to container 2025-08-14T21:07:11.0597685Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2025-08-14T21:07:11.0598393Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2025-08-14T21:07:11.0598804Z  2025-08-14T21:07:11.0599072Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-08-14T21:07:11.0599639Z  docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt" 2025-08-14T21:07:11.0600165Z fi 2025-08-14T21:07:11.0600375Z  2025-08-14T21:07:11.0600871Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2025-08-14T21:07:11.0606175Z shell: /usr/bin/bash -e {0} 2025-08-14T21:07:11.0606453Z env: 2025-08-14T21:07:11.0606677Z GIT_DEFAULT_BRANCH: main 2025-08-14T21:07:11.0606986Z BUILD_ENVIRONMENT: linux-jammy-py3.13-clang12 2025-08-14T21:07:11.0607340Z PR_NUMBER: 2025-08-14T21:07:11.0607593Z GITHUB_REPOSITORY: pytorch/pytorch 2025-08-14T21:07:11.0607896Z GITHUB_WORKFLOW: pull 2025-08-14T21:07:11.0608149Z GITHUB_JOB: test 2025-08-14T21:07:11.0608388Z GITHUB_RUN_ID: 16976255017 2025-08-14T21:07:11.0608649Z GITHUB_RUN_NUMBER: 346100 2025-08-14T21:07:11.0608917Z GITHUB_RUN_ATTEMPT: 1 2025-08-14T21:07:11.0609164Z JOB_ID: 48127032091 2025-08-14T21:07:11.0609583Z JOB_NAME: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge) 2025-08-14T21:07:11.0610055Z BRANCH: main 2025-08-14T21:07:11.0610314Z SHA1: 1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:07:11.0610817Z BASE_SHA: 1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:07:11.0611162Z TEST_CONFIG: dynamo_wrapped 2025-08-14T21:07:11.0611437Z SHARD_NUMBER: 2 2025-08-14T21:07:11.0611677Z NUM_TEST_SHARDS: 3 2025-08-14T21:07:11.0611909Z REENABLED_ISSUES: 2025-08-14T21:07:11.0612165Z CONTINUE_THROUGH_ERROR: True 2025-08-14T21:07:11.0612455Z VERBOSE_TEST_LOGS: False 2025-08-14T21:07:11.0612713Z TEST_SHOWLOCALS: False 2025-08-14T21:07:11.0612976Z NO_TEST_TIMEOUT: False 2025-08-14T21:07:11.0613230Z NO_TD: False 2025-08-14T21:07:11.0613464Z TD_DISTRIBUTED: False 2025-08-14T21:07:11.0613766Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2025-08-14T21:07:11.0614137Z SCCACHE_REGION: us-east-1 2025-08-14T21:07:11.0614403Z SHM_SIZE: 1g 2025-08-14T21:07:11.0615109Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:07:11.0615901Z XLA_CUDA: 2025-08-14T21:07:11.0616266Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2025-08-14T21:07:11.0616728Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2025-08-14T21:07:11.0617047Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2025-08-14T21:07:11.0617354Z DASHBOARD_TAG: 2025-08-14T21:07:11.0617779Z HUGGING_FACE_HUB_TOKEN: *** 2025-08-14T21:07:11.0618315Z SCRIBE_GRAPHQL_ACCESS_TOKEN: *** 2025-08-14T21:07:11.0618748Z ARTIFACTS_FILE_SUFFIX: test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091 2025-08-14T21:07:11.0619203Z ##[endgroup] 2025-08-14T21:07:11.0640351Z + [[ dynamo_wrapped == \m\u\l\t\i\g\p\u ]] 2025-08-14T21:07:11.0640991Z + [[ linux-jammy-py3.13-clang12 == *onnx* ]] 2025-08-14T21:07:11.0641336Z + TEST_COMMAND=.ci/pytorch/test.sh 2025-08-14T21:07:11.0643514Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo 2025-08-14T21:07:11.0674254Z + TOTAL_AVAILABLE_MEMORY_IN_GB='14.244 ' 2025-08-14T21:07:11.0674806Z + TOTAL_MEMORY_WITH_SWAP=17 2025-08-14T21:07:11.0675401Z + [[ linux-jammy-py3.13-clang12 == *\s\3\9\0\x* ]] 2025-08-14T21:07:11.0675769Z + SHM_OPTS=--shm-size=1g 2025-08-14T21:07:11.0676043Z + JENKINS_USER='--user jenkins' 2025-08-14T21:07:11.0676331Z + DOCKER_SHELL_CMD= 2025-08-14T21:07:11.0682373Z +++ nproc --ignore=2 2025-08-14T21:07:11.0705307Z ++ 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 SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG -e ARTIFACTS_FILE_SUFFIX --memory=14g --memory-swap=17g --env-file=/tmp/github_env_16976255017 --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-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T21:07:18.1442070Z + container_name=3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T21:07:18.1446796Z + grep download.pytorch.org /etc/hosts 2025-08-14T21:07:18.1448042Z + docker exec -i 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 sudo bash -c '/bin/cat >> /etc/hosts' 2025-08-14T21:07:18.2730534Z + echo DOCKER_CONTAINER_ID=3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T21:07:18.2731637Z + [[ linux-jammy-py3.13-clang12 == *\s\3\9\0\x* ]] 2025-08-14T21:07:18.2735389Z ++ echo dist/torch-2.9.0a0+git1fc683c-cp313-cp313-linux_x86_64.whl 2025-08-14T21:07:18.2738683Z + docker exec -t 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 sh -c 'python3 -m pip install dist/torch-2.9.0a0+git1fc683c-cp313-cp313-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2025-08-14T21:07:18.8543072Z Processing ./dist/torch-2.9.0a0+git1fc683c-cp313-cp313-linux_x86_64.whl (from torch==2.9.0a0+git1fc683c) 2025-08-14T21:07:19.3493527Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git1fc683c->torch==2.9.0a0+git1fc683c) (3.18.0) 2025-08-14T21:07:19.3497405Z 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+git1fc683c->torch==2.9.0a0+git1fc683c) (4.14.1) 2025-08-14T21:07:19.3502313Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git1fc683c->torch==2.9.0a0+git1fc683c) (80.9.0) 2025-08-14T21:07:19.3506122Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git1fc683c->torch==2.9.0a0+git1fc683c) (1.13.3) 2025-08-14T21:07:19.3509825Z Requirement already satisfied: networkx>=2.5.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git1fc683c->torch==2.9.0a0+git1fc683c) (2.8.8) 2025-08-14T21:07:19.3513011Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git1fc683c->torch==2.9.0a0+git1fc683c) (3.1.6) 2025-08-14T21:07:19.3516612Z Requirement already satisfied: fsspec>=0.8.5 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git1fc683c->torch==2.9.0a0+git1fc683c) (2025.5.1) 2025-08-14T21:07:19.3529656Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git1fc683c->torch==2.9.0a0+git1fc683c) (3.3.0) 2025-08-14T21:07:19.3634517Z 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+git1fc683c->torch==2.9.0a0+git1fc683c) (2.1.2) 2025-08-14T21:07:19.3679758Z 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+git1fc683c->torch==2.9.0a0+git1fc683c) (1.3.0) 2025-08-14T21:07:19.3725123Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch==2.9.0a0+git1fc683c->torch==2.9.0a0+git1fc683c) (3.0.2) 2025-08-14T21:07:19.5811677Z Installing collected packages: torch 2025-08-14T21:07:30.6748206Z Successfully installed torch-2.9.0a0+git1fc683c 2025-08-14T21:07:30.7446268Z + export TERM=vt100 2025-08-14T21:07:30.7446608Z + TERM=vt100 2025-08-14T21:07:30.7448384Z ++ dirname .ci/pytorch/test.sh 2025-08-14T21:07:30.7464340Z + source .ci/pytorch/common.sh 2025-08-14T21:07:30.7471599Z +++ dirname .ci/pytorch/common.sh 2025-08-14T21:07:30.7477882Z ++ source .ci/pytorch/common_utils.sh 2025-08-14T21:07:30.7483721Z +++ declare -f -t trap_add 2025-08-14T21:07:30.7488565Z ++ set -ex -o pipefail 2025-08-14T21:07:30.7488943Z ++ [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-08-14T21:07:30.7489273Z ++ BUILD_TEST_LIBTORCH=0 2025-08-14T21:07:30.7491852Z ++ dirname .ci/pytorch/test.sh 2025-08-14T21:07:30.7498256Z + source .ci/pytorch/common-build.sh 2025-08-14T21:07:30.7502530Z ++ [[ linux-jammy-py3.13-clang12 != *win-* ]] 2025-08-14T21:07:30.7508294Z ++++ dirname .ci/pytorch/common-build.sh 2025-08-14T21:07:30.7514599Z +++ cd .ci/pytorch 2025-08-14T21:07:30.7514853Z +++ pwd -P 2025-08-14T21:07:30.7516919Z ++ script_dir=/var/lib/jenkins/workspace/.ci/pytorch 2025-08-14T21:07:30.7517384Z ++ [[ linux-jammy-py3.13-clang12 == *-pch* ]] 2025-08-14T21:07:30.7517709Z ++ which sccache 2025-08-14T21:07:30.7534577Z ++ [[ -z ossci-compiler-cache-circleci-v2 ]] 2025-08-14T21:07:30.7534956Z ++ sccache --stop-server 2025-08-14T21:07:30.7585660Z ++ true 2025-08-14T21:07:30.7586238Z ++ rm -f /var/lib/jenkins/sccache_error.log 2025-08-14T21:07:30.7604102Z ++ trap_add sccache_epilogue EXIT 2025-08-14T21:07:30.7604447Z ++ trap_add_cmd=sccache_epilogue 2025-08-14T21:07:30.7604730Z ++ shift 2025-08-14T21:07:30.7604944Z ++ for trap_add_name in "$@" 2025-08-14T21:07:30.7609743Z ++++ trap -p EXIT 2025-08-14T21:07:30.7612902Z +++ eval 'extract_trap_cmd ' 2025-08-14T21:07:30.7613425Z ++++ extract_trap_cmd 2025-08-14T21:07:30.7613721Z ++++ printf '%s\n' '' 2025-08-14T21:07:30.7613990Z +++ printf '%s\n' sccache_epilogue 2025-08-14T21:07:30.7615205Z ++ trap -- ' 2025-08-14T21:07:30.7615573Z sccache_epilogue' EXIT 2025-08-14T21:07:30.7616205Z ++ [[ -n 1 ]] 2025-08-14T21:07:30.7616708Z ++ echo 'Skipping sccache server initialization, setting environment variables' 2025-08-14T21:07:30.7617316Z Skipping sccache server initialization, setting environment variables 2025-08-14T21:07:30.7617771Z ++ export SCCACHE_IDLE_TIMEOUT=0 2025-08-14T21:07:30.7618068Z ++ SCCACHE_IDLE_TIMEOUT=0 2025-08-14T21:07:30.7618424Z ++ export SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-08-14T21:07:30.7618878Z ++ SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-08-14T21:07:30.7619289Z ++ export RUST_LOG=sccache::server=error 2025-08-14T21:07:30.7619618Z ++ RUST_LOG=sccache::server=error 2025-08-14T21:07:30.7620099Z ++ sccache --zero-stats 2025-08-14T21:07:30.8642815Z Statistics zeroed. 2025-08-14T21:07:30.8647150Z ++ which ccache 2025-08-14T21:07:30.8657889Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-08-14T21:07:30.8658291Z + [[ linux-jammy-py3.13-clang12 != *s390x* ]] 2025-08-14T21:07:30.8658643Z + [[ -d /var/lib/jenkins/workspace ]] 2025-08-14T21:07:30.8661066Z ++ stat -c %u /var/lib/jenkins/workspace 2025-08-14T21:07:30.8708699Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2025-08-14T21:07:30.8709349Z + trap_add cleanup_workspace EXIT 2025-08-14T21:07:30.8709778Z + trap_add_cmd=cleanup_workspace 2025-08-14T21:07:30.8710048Z + shift 2025-08-14T21:07:30.8710273Z + for trap_add_name in "$@" 2025-08-14T21:07:30.8715913Z +++ trap -p EXIT 2025-08-14T21:07:30.8718510Z ++ eval 'extract_trap_cmd trap -- '\'' 2025-08-14T21:07:30.8718943Z sccache_epilogue'\'' EXIT' 2025-08-14T21:07:30.8719229Z +++ extract_trap_cmd trap -- ' 2025-08-14T21:07:30.8719575Z sccache_epilogue' EXIT 2025-08-14T21:07:30.8719823Z +++ printf '%s\n' ' 2025-08-14T21:07:30.8720073Z sccache_epilogue' 2025-08-14T21:07:30.8720366Z ++ printf '%s\n' cleanup_workspace 2025-08-14T21:07:30.8721230Z + trap -- ' 2025-08-14T21:07:30.8721503Z sccache_epilogue 2025-08-14T21:07:30.8721798Z cleanup_workspace' EXIT 2025-08-14T21:07:30.8722100Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2025-08-14T21:07:31.4746911Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2025-08-14T21:07:31.4942996Z + echo 'Environment variables:' 2025-08-14T21:07:31.4943568Z Environment variables: 2025-08-14T21:07:31.4944026Z + env 2025-08-14T21:07:31.4962745Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-08-14T21:07:31.4963494Z CONTINUE_THROUGH_ERROR=True 2025-08-14T21:07:31.4963985Z BUILD_ENVIRONMENT=linux-jammy-py3.13-clang12 2025-08-14T21:07:31.4964456Z HOSTNAME=3c4d0c0501a3 2025-08-14T21:07:31.4965407Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_ed66981e-eeb8-4a37-a770-97e6ca8ab784 2025-08-14T21:07:31.4966182Z GITHUB_ACTION=__run_2 2025-08-14T21:07:31.4966489Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-08-14T21:07:31.4967016Z GITHUB_RUN_NUMBER=346100 2025-08-14T21:07:31.4967515Z TEST_CONFIG=dynamo_wrapped 2025-08-14T21:07:31.4967932Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-08-14T21:07:31.4968359Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-08-14T21:07:31.4968700Z SCCACHE_IDLE_TIMEOUT=0 2025-08-14T21:07:31.4969348Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-08-14T21:07:31.4969742Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-08-14T21:07:31.4970128Z GITHUB_REF_TYPE=branch 2025-08-14T21:07:31.4970467Z TORCH_CUDA_ARCH_LIST=Maxwell 2025-08-14T21:07:31.4970768Z BASE_SHA=1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:07:31.4971421Z XLA_CUDA= 2025-08-14T21:07:31.4971719Z NCCL_LIB_DIR=/usr/local/cuda/lib64/ 2025-08-14T21:07:31.4972131Z HUGGING_FACE_HUB_TOKEN=*** 2025-08-14T21:07:31.4973702Z *** 2025-08-14T21:07:31.4973926Z GITHUB_REPOSITORY_ID=65600975 2025-08-14T21:07:31.4974208Z GITHUB_ACTIONS=true 2025-08-14T21:07:31.4974594Z SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-08-14T21:07:31.4975008Z SHA1=1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:07:31.4975379Z GITHUB_SHA=1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:07:31.4975876Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/main 2025-08-14T21:07:31.4976349Z UCC_HOME=/usr 2025-08-14T21:07:31.4976582Z VERBOSE_TEST_LOGS=False 2025-08-14T21:07:31.4976830Z GITHUB_REF=refs/heads/main 2025-08-14T21:07:31.4977091Z SHARD_NUMBER=2 2025-08-14T21:07:31.4977324Z GITHUB_REF_PROTECTED=true 2025-08-14T21:07:31.4977574Z HOME=/var/lib/jenkins 2025-08-14T21:07:31.4977890Z GITHUB_API_URL=https://api.github.com 2025-08-14T21:07:31.4978231Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-08-14T21:07:31.4978529Z UCX_COMMIT= 2025-08-14T21:07:31.4978735Z USE_SYSTEM_NCCL=1 2025-08-14T21:07:31.4979010Z NUM_TEST_SHARDS=3 2025-08-14T21:07:31.4979308Z UCX_HOME=/usr 2025-08-14T21:07:31.4979924Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_ed66981e-eeb8-4a37-a770-97e6ca8ab784 2025-08-14T21:07:31.4980986Z JOB_NAME=linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge) 2025-08-14T21:07:31.4981885Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_ed66981e-eeb8-4a37-a770-97e6ca8ab784 2025-08-14T21:07:31.4982938Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-08-14T21:07:31.4983654Z GITHUB_EVENT_NAME=push 2025-08-14T21:07:31.4983986Z DASHBOARD_TAG= 2025-08-14T21:07:31.4984226Z GITHUB_RUN_ID=16976255017 2025-08-14T21:07:31.4984478Z INSTALLED_OPENBLAS= 2025-08-14T21:07:31.4985232Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_ed66981e-eeb8-4a37-a770-97e6ca8ab784 2025-08-14T21:07:31.4985927Z GITHUB_ACTOR=pytorchmergebot 2025-08-14T21:07:31.4986295Z PR_NUMBER= 2025-08-14T21:07:31.4986530Z DESIRED_CUDA= 2025-08-14T21:07:31.4986761Z GITHUB_RUN_ATTEMPT=1 2025-08-14T21:07:31.4987024Z ANACONDA_PYTHON_VERSION=3.13 2025-08-14T21:07:31.4987351Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-08-14T21:07:31.4987705Z TERM=vt100 2025-08-14T21:07:31.4987922Z INSTALLED_VISION=yes 2025-08-14T21:07:31.4988149Z BRANCH=main 2025-08-14T21:07:31.4988371Z SCCACHE_REGION=us-east-1 2025-08-14T21:07:31.4988646Z OPENSSL_ROOT_DIR=/opt/openssl 2025-08-14T21:07:31.4988916Z CUDA_PATH=/usr/local/cuda 2025-08-14T21:07:31.4989579Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-08-14T21:07:31.4990378Z GITHUB_SERVER_URL=https://github.com 2025-08-14T21:07:31.4990667Z UCC_COMMIT= 2025-08-14T21:07:31.4990880Z REENABLED_ISSUES= 2025-08-14T21:07:31.4991104Z DOCS= 2025-08-14T21:07:31.4991295Z SHLVL=1 2025-08-14T21:07:31.4991494Z MAX_JOBS=6 2025-08-14T21:07:31.4991710Z GITHUB_ACTOR_ID=97764156 2025-08-14T21:07:31.4992040Z GITHUB_WORKFLOW_SHA=1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:07:31.4992421Z GITHUB_REF_NAME=main 2025-08-14T21:07:31.4992808Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-08-14T21:07:31.4993430Z GITHUB_JOB=test 2025-08-14T21:07:31.4993652Z NO_TEST_TIMEOUT=False 2025-08-14T21:07:31.4993906Z TD_DISTRIBUTED=False 2025-08-14T21:07:31.4994179Z GITHUB_REPOSITORY=pytorch/pytorch 2025-08-14T21:07:31.4994473Z GITHUB_RETENTION_DAYS=90 2025-08-14T21:07:31.4994740Z OPENSSL_DIR=/opt/openssl 2025-08-14T21:07:31.4995007Z GITHUB_ACTION_REPOSITORY= 2025-08-14T21:07:31.4995771Z 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 2025-08-14T21:07:31.4996580Z GITHUB_BASE_REF= 2025-08-14T21:07:31.4996813Z INSTALLED_ACL= 2025-08-14T21:07:31.4997331Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091 2025-08-14T21:07:31.4997784Z CI=true 2025-08-14T21:07:31.4998010Z GITHUB_REPOSITORY_OWNER=pytorch 2025-08-14T21:07:31.4998340Z RUST_LOG=sccache::server=error 2025-08-14T21:07:31.4998609Z JOB_ID=48127032091 2025-08-14T21:07:31.4998855Z GITHUB_HEAD_REF= 2025-08-14T21:07:31.4999092Z GITHUB_ACTION_REF= 2025-08-14T21:07:31.4999372Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-08-14T21:07:31.4999730Z TEST_SHOWLOCALS=False 2025-08-14T21:07:31.4999984Z GITHUB_WORKFLOW=pull 2025-08-14T21:07:31.5000238Z DEBIAN_FRONTEND=noninteractive 2025-08-14T21:07:31.5000872Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_ed66981e-eeb8-4a37-a770-97e6ca8ab784 2025-08-14T21:07:31.5001519Z NO_TD=False 2025-08-14T21:07:31.5001742Z SKIP_SCCACHE_INITIALIZATION=1 2025-08-14T21:07:31.5002056Z NCCL_INCLUDE_DIR=/usr/local/cuda/include/ 2025-08-14T21:07:31.5002373Z _=/usr/bin/env 2025-08-14T21:07:31.5002699Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2025-08-14T21:07:31.5144537Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch 2025-08-14T21:07:31.5145481Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-08-14T21:07:31.5146737Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib 2025-08-14T21:07:31.5147328Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/test 2025-08-14T21:07:31.5147782Z + BUILD_DIR=build 2025-08-14T21:07:31.5148034Z + BUILD_RENAMED_DIR=build_renamed 2025-08-14T21:07:31.5148334Z + BUILD_BIN_DIR=build/bin 2025-08-14T21:07:31.5148579Z + SHARD_NUMBER=2 2025-08-14T21:07:31.5148813Z + NUM_TEST_SHARDS=3 2025-08-14T21:07:31.5149085Z + export TORCH_SERIALIZATION_DEBUG=1 2025-08-14T21:07:31.5149390Z + TORCH_SERIALIZATION_DEBUG=1 2025-08-14T21:07:31.5149766Z + export VALGRIND=ON 2025-08-14T21:07:31.5150139Z + VALGRIND=ON 2025-08-14T21:07:31.5150464Z + [[ linux-jammy-py3.13-clang12 == *clang9* ]] 2025-08-14T21:07:31.5151095Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-08-14T21:07:31.5151600Z + [[ linux-jammy-py3.13-clang12 == *s390x* ]] 2025-08-14T21:07:31.5151899Z + [[ 0 == \1 ]] 2025-08-14T21:07:31.5152118Z + [[ True == \1 ]] 2025-08-14T21:07:31.5152388Z + [[ linux-jammy-py3.13-clang12 != *bazel* ]] 2025-08-14T21:07:31.5152735Z ++ realpath build/custom_test_artifacts 2025-08-14T21:07:31.5170634Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2025-08-14T21:07:31.5171228Z + [[ -n '' ]] 2025-08-14T21:07:31.5171473Z + echo 'Environment variables' 2025-08-14T21:07:31.5171754Z Environment variables 2025-08-14T21:07:31.5172000Z + env 2025-08-14T21:07:31.5179142Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-08-14T21:07:31.5179917Z CONTINUE_THROUGH_ERROR=True 2025-08-14T21:07:31.5180462Z BUILD_ENVIRONMENT=linux-jammy-py3.13-clang12 2025-08-14T21:07:31.5180913Z HOSTNAME=3c4d0c0501a3 2025-08-14T21:07:31.5181625Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_ed66981e-eeb8-4a37-a770-97e6ca8ab784 2025-08-14T21:07:31.5182535Z GITHUB_ACTION=__run_2 2025-08-14T21:07:31.5183034Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-08-14T21:07:31.5183612Z GITHUB_RUN_NUMBER=346100 2025-08-14T21:07:31.5184025Z TEST_CONFIG=dynamo_wrapped 2025-08-14T21:07:31.5184521Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-08-14T21:07:31.5185049Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-08-14T21:07:31.5185371Z SCCACHE_IDLE_TIMEOUT=0 2025-08-14T21:07:31.5185808Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-08-14T21:07:31.5186126Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-08-14T21:07:31.5186451Z GITHUB_REF_TYPE=branch 2025-08-14T21:07:31.5186720Z TORCH_CUDA_ARCH_LIST=Maxwell 2025-08-14T21:07:31.5187024Z BASE_SHA=1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:07:31.5187425Z XLA_CUDA= 2025-08-14T21:07:31.5187795Z NCCL_LIB_DIR=/usr/local/cuda/lib64/ 2025-08-14T21:07:31.5188497Z HUGGING_FACE_HUB_TOKEN=*** 2025-08-14T21:07:31.5188833Z *** 2025-08-14T21:07:31.5189054Z GITHUB_REPOSITORY_ID=65600975 2025-08-14T21:07:31.5189334Z GITHUB_ACTIONS=true 2025-08-14T21:07:31.5189619Z SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-08-14T21:07:31.5190015Z SHA1=1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:07:31.5190396Z GITHUB_SHA=1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:07:31.5190889Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/main 2025-08-14T21:07:31.5191358Z UCC_HOME=/usr 2025-08-14T21:07:31.5191600Z TORCH_SERIALIZATION_DEBUG=1 2025-08-14T21:07:31.5192042Z VERBOSE_TEST_LOGS=False 2025-08-14T21:07:31.5192309Z GITHUB_REF=refs/heads/main 2025-08-14T21:07:31.5192568Z SHARD_NUMBER=2 2025-08-14T21:07:31.5192789Z GITHUB_REF_PROTECTED=true 2025-08-14T21:07:31.5193263Z HOME=/var/lib/jenkins 2025-08-14T21:07:31.5193548Z GITHUB_API_URL=https://api.github.com 2025-08-14T21:07:31.5193868Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-08-14T21:07:31.5194165Z UCX_COMMIT= 2025-08-14T21:07:31.5194384Z USE_SYSTEM_NCCL=1 2025-08-14T21:07:31.5194602Z NUM_TEST_SHARDS=3 2025-08-14T21:07:31.5194830Z UCX_HOME=/usr 2025-08-14T21:07:31.5195398Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_ed66981e-eeb8-4a37-a770-97e6ca8ab784 2025-08-14T21:07:31.5196359Z JOB_NAME=linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge) 2025-08-14T21:07:31.5197177Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_ed66981e-eeb8-4a37-a770-97e6ca8ab784 2025-08-14T21:07:31.5198170Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-08-14T21:07:31.5198674Z GITHUB_EVENT_NAME=push 2025-08-14T21:07:31.5198916Z DASHBOARD_TAG= 2025-08-14T21:07:31.5199154Z GITHUB_RUN_ID=16976255017 2025-08-14T21:07:31.5199419Z INSTALLED_OPENBLAS= 2025-08-14T21:07:31.5200048Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_ed66981e-eeb8-4a37-a770-97e6ca8ab784 2025-08-14T21:07:31.5200743Z GITHUB_ACTOR=pytorchmergebot 2025-08-14T21:07:31.5201180Z PR_NUMBER= 2025-08-14T21:07:31.5201404Z DESIRED_CUDA= 2025-08-14T21:07:31.5201619Z GITHUB_RUN_ATTEMPT=1 2025-08-14T21:07:31.5201863Z VALGRIND=ON 2025-08-14T21:07:31.5202094Z ANACONDA_PYTHON_VERSION=3.13 2025-08-14T21:07:31.5202430Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-08-14T21:07:31.5202779Z TERM=vt100 2025-08-14T21:07:31.5202994Z INSTALLED_VISION=yes 2025-08-14T21:07:31.5203224Z BRANCH=main 2025-08-14T21:07:31.5203448Z SCCACHE_REGION=us-east-1 2025-08-14T21:07:31.5203725Z OPENSSL_ROOT_DIR=/opt/openssl 2025-08-14T21:07:31.5204000Z CUDA_PATH=/usr/local/cuda 2025-08-14T21:07:31.5204692Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-08-14T21:07:31.5205289Z GITHUB_SERVER_URL=https://github.com 2025-08-14T21:07:31.5205598Z UCC_COMMIT= 2025-08-14T21:07:31.5205806Z REENABLED_ISSUES= 2025-08-14T21:07:31.5206039Z DOCS= 2025-08-14T21:07:31.5206250Z SHLVL=1 2025-08-14T21:07:31.5206442Z MAX_JOBS=6 2025-08-14T21:07:31.5206668Z GITHUB_ACTOR_ID=97764156 2025-08-14T21:07:31.5207017Z GITHUB_WORKFLOW_SHA=1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T21:07:31.5207392Z GITHUB_REF_NAME=main 2025-08-14T21:07:31.5207785Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-08-14T21:07:31.5208217Z GITHUB_JOB=test 2025-08-14T21:07:31.5208434Z NO_TEST_TIMEOUT=False 2025-08-14T21:07:31.5208801Z TD_DISTRIBUTED=False 2025-08-14T21:07:31.5209209Z GITHUB_REPOSITORY=pytorch/pytorch 2025-08-14T21:07:31.5209503Z GITHUB_RETENTION_DAYS=90 2025-08-14T21:07:31.5209767Z OPENSSL_DIR=/opt/openssl 2025-08-14T21:07:31.5210034Z GITHUB_ACTION_REPOSITORY= 2025-08-14T21:07:31.5210801Z 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 2025-08-14T21:07:31.5211631Z GITHUB_BASE_REF= 2025-08-14T21:07:31.5211968Z INSTALLED_ACL= 2025-08-14T21:07:31.5212854Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091 2025-08-14T21:07:31.5213461Z CI=true 2025-08-14T21:07:31.5213691Z GITHUB_REPOSITORY_OWNER=pytorch 2025-08-14T21:07:31.5214011Z RUST_LOG=sccache::server=error 2025-08-14T21:07:31.5214276Z JOB_ID=48127032091 2025-08-14T21:07:31.5214523Z GITHUB_HEAD_REF= 2025-08-14T21:07:31.5214763Z GITHUB_ACTION_REF= 2025-08-14T21:07:31.5215047Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-08-14T21:07:31.5215437Z TEST_SHOWLOCALS=False 2025-08-14T21:07:31.5215711Z GITHUB_WORKFLOW=pull 2025-08-14T21:07:31.5215988Z DEBIAN_FRONTEND=noninteractive 2025-08-14T21:07:31.5216801Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_ed66981e-eeb8-4a37-a770-97e6ca8ab784 2025-08-14T21:07:31.5217449Z NO_TD=False 2025-08-14T21:07:31.5217680Z SKIP_SCCACHE_INITIALIZATION=1 2025-08-14T21:07:31.5217975Z NCCL_INCLUDE_DIR=/usr/local/cuda/include/ 2025-08-14T21:07:31.5218290Z _=/usr/bin/env 2025-08-14T21:07:31.5218539Z + echo 'Testing pytorch' 2025-08-14T21:07:31.5218802Z Testing pytorch 2025-08-14T21:07:31.5219035Z + export LANG=C.UTF-8 2025-08-14T21:07:31.5219279Z + LANG=C.UTF-8 2025-08-14T21:07:31.5229648Z + PR_NUMBER= 2025-08-14T21:07:31.5230523Z + [[ dynamo_wrapped == \d\e\f\a\u\l\t ]] 2025-08-14T21:07:31.5231384Z + [[ dynamo_wrapped == \d\i\s\t\r\i\b\u\t\e\d ]] 2025-08-14T21:07:31.5231858Z + [[ dynamo_wrapped == \s\l\o\w ]] 2025-08-14T21:07:31.5232345Z + [[ linux-jammy-py3.13-clang12 == *slow-gradcheck* ]] 2025-08-14T21:07:31.5232824Z + [[ linux-jammy-py3.13-clang12 == *cuda* ]] 2025-08-14T21:07:31.5233171Z + [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-08-14T21:07:31.5233500Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-08-14T21:07:31.5233834Z + [[ dynamo_wrapped == *crossref* ]] 2025-08-14T21:07:31.5234174Z + [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-08-14T21:07:31.5234591Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-08-14T21:07:31.5234928Z + [[ linux-jammy-py3.13-clang12 != *-bazel-* ]] 2025-08-14T21:07:31.5235277Z + pip_install ninja==1.10.2 2025-08-14T21:07:31.5235655Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-08-14T21:07:31.5236193Z + python3 -m pip install --progress-bar off ninja==1.10.2 2025-08-14T21:07:31.9303306Z Collecting ninja==1.10.2 2025-08-14T21:07:31.9456833Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2025-08-14T21:07:31.9565115Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2025-08-14T21:07:32.1395926Z Installing collected packages: ninja 2025-08-14T21:07:32.1396367Z Attempting uninstall: ninja 2025-08-14T21:07:32.1432369Z Found existing installation: ninja 1.11.1.3 2025-08-14T21:07:32.1453312Z Uninstalling ninja-1.11.1.3: 2025-08-14T21:07:32.1504685Z Successfully uninstalled ninja-1.11.1.3 2025-08-14T21:07:32.1737178Z Successfully installed ninja-1.10.2 2025-08-14T21:07:32.2326415Z + 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 2025-08-14T21:07:32.2328196Z + 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 2025-08-14T21:07:32.2329408Z + [[ linux-jammy-py3.13-clang12 == *aarch64* ]] 2025-08-14T21:07:32.2329890Z + [[ linux-jammy-py3.13-clang12 == *asan* ]] 2025-08-14T21:07:32.2330341Z + [[ linux-jammy-py3.13-clang12 == *-debug* ]] 2025-08-14T21:07:32.2330712Z + [[ linux-jammy-py3.13-clang12 != *-bazel-* ]] 2025-08-14T21:07:32.2331241Z + echo 'We are not in debug mode: linux-jammy-py3.13-clang12. Expect the assertion to pass' 2025-08-14T21:07:32.2331880Z We are not in debug mode: linux-jammy-py3.13-clang12. Expect the assertion to pass 2025-08-14T21:07:32.2332337Z + cd test 2025-08-14T21:07:32.2332966Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2025-08-14T21:07:33.5587490Z + [[ dynamo_wrapped == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2025-08-14T21:07:33.5588268Z + [[ dynamo_wrapped == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2025-08-14T21:07:33.5589057Z + [[ dynamo_wrapped == \l\e\g\a\c\y\_\n\v\i\d\i\a\_\d\r\i\v\e\r ]] 2025-08-14T21:07:33.5593245Z + DYNAMO_BENCHMARK_FLAGS=() 2025-08-14T21:07:33.5594906Z + [[ dynamo_wrapped == *pr_time_benchmarks* ]] 2025-08-14T21:07:33.5595571Z + [[ dynamo_wrapped == *dynamo_eager* ]] 2025-08-14T21:07:33.5596185Z + [[ dynamo_wrapped == *aot_eager* ]] 2025-08-14T21:07:33.5596787Z + [[ dynamo_wrapped == *aot_inductor* ]] 2025-08-14T21:07:33.5597427Z + [[ dynamo_wrapped == *max_autotune_inductor* ]] 2025-08-14T21:07:33.5598061Z + [[ dynamo_wrapped == *inductor* ]] 2025-08-14T21:07:33.5598659Z + [[ dynamo_wrapped == *dynamic* ]] 2025-08-14T21:07:33.5599234Z + [[ dynamo_wrapped == *cpu* ]] 2025-08-14T21:07:33.5599807Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2025-08-14T21:07:33.5621062Z + [[ linux-jammy-py3.13-clang12 == *libtorch* ]] 2025-08-14T21:07:33.5621490Z + [[ linux-jammy-py3.13-clang12 == *-bazel-* ]] 2025-08-14T21:07:33.5624320Z + cd test 2025-08-14T21:07:33.5624748Z + python -c 'import torch; print(torch.__config__.show())' 2025-08-14T21:07:34.6451948Z PyTorch built with: 2025-08-14T21:07:34.6452569Z - GCC 4.2 2025-08-14T21:07:34.6452799Z - C++ Version: 201703 2025-08-14T21:07:34.6453060Z - clang 12.0.1 2025-08-14T21:07:34.6453593Z - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-08-14T21:07:34.6454327Z - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-08-14T21:07:34.6454754Z - OpenMP 201811 2025-08-14T21:07:34.6455033Z - LAPACK is enabled (usually provided by MKL) 2025-08-14T21:07:34.6455355Z - NNPACK is enabled 2025-08-14T21:07:34.6455624Z - CPU capability usage: AVX512 2025-08-14T21:07:34.6460911Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=1fc683cf17c8c673044538d10266c00f92987be2, 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, 2025-08-14T21:07:34.6466472Z 2025-08-14T21:07:34.8922018Z + cd test 2025-08-14T21:07:34.8922434Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2025-08-14T21:07:35.9489436Z ATen/Parallel: 2025-08-14T21:07:35.9489921Z at::get_num_threads() : 4 2025-08-14T21:07:35.9490220Z at::get_num_interop_threads() : 4 2025-08-14T21:07:35.9490525Z OpenMP 201811 2025-08-14T21:07:35.9490774Z omp_get_max_threads() : 4 2025-08-14T21:07:35.9491327Z Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-08-14T21:07:35.9491929Z mkl_get_max_threads() : 4 2025-08-14T21:07:35.9492314Z Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-08-14T21:07:35.9492770Z std::thread::hardware_concurrency() : 8 2025-08-14T21:07:35.9493219Z Environment variables: 2025-08-14T21:07:35.9493819Z OMP_NUM_THREADS : [not set] 2025-08-14T21:07:35.9494103Z MKL_NUM_THREADS : [not set] 2025-08-14T21:07:35.9494373Z ATen parallel backend: OpenMP 2025-08-14T21:07:35.9494570Z 2025-08-14T21:07:36.1815778Z + [[ dynamo_wrapped == *numpy_2* ]] 2025-08-14T21:07:36.1816410Z + [[ linux-jammy-py3.13-clang12 == *aarch64* ]] 2025-08-14T21:07:36.1816955Z + [[ dynamo_wrapped == *backward* ]] 2025-08-14T21:07:36.1817418Z + [[ dynamo_wrapped == *xla* ]] 2025-08-14T21:07:36.1817887Z + [[ dynamo_wrapped == *executorch* ]] 2025-08-14T21:07:36.1818353Z + [[ dynamo_wrapped == \j\i\t\_\l\e\g\a\c\y ]] 2025-08-14T21:07:36.1818711Z + [[ linux-jammy-py3.13-clang12 == *libtorch* ]] 2025-08-14T21:07:36.1819052Z + [[ dynamo_wrapped == distributed ]] 2025-08-14T21:07:36.1819391Z + [[ dynamo_wrapped == *operator_benchmark* ]] 2025-08-14T21:07:36.1819751Z + [[ dynamo_wrapped == *inductor_distributed* ]] 2025-08-14T21:07:36.1820094Z + [[ dynamo_wrapped == *inductor-halide* ]] 2025-08-14T21:07:36.1820471Z + [[ dynamo_wrapped == *inductor-triton-cpu* ]] 2025-08-14T21:07:36.1821064Z + [[ dynamo_wrapped == *inductor-micro-benchmark* ]] 2025-08-14T21:07:36.1821564Z + [[ dynamo_wrapped == *huggingface* ]] 2025-08-14T21:07:36.1822169Z + [[ dynamo_wrapped == *timm* ]] 2025-08-14T21:07:36.1822480Z + [[ dynamo_wrapped == cachebench ]] 2025-08-14T21:07:36.1823060Z + [[ dynamo_wrapped == verify_cachebench ]] 2025-08-14T21:07:36.1823463Z + [[ dynamo_wrapped == *torchbench* ]] 2025-08-14T21:07:36.1823835Z + [[ dynamo_wrapped == *inductor_cpp_wrapper* ]] 2025-08-14T21:07:36.1824182Z + [[ dynamo_wrapped == *inductor* ]] 2025-08-14T21:07:36.1824479Z + [[ dynamo_wrapped == *einops* ]] 2025-08-14T21:07:36.1824793Z + [[ dynamo_wrapped == *dynamo_wrapped* ]] 2025-08-14T21:07:36.1825114Z + install_torchvision 2025-08-14T21:07:36.1825353Z + local orig_preload 2025-08-14T21:07:36.1825600Z + local commit 2025-08-14T21:07:36.1825832Z ++ get_pinned_commit vision 2025-08-14T21:07:36.1826109Z ++ cat .github/ci_commit_pins/vision.txt 2025-08-14T21:07:36.1843793Z + commit=966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-14T21:07:36.1844393Z + orig_preload= 2025-08-14T21:07:36.1844709Z + '[' -n '' ']' 2025-08-14T21:07:36.1845000Z + [[ linux-jammy-py3.13-clang12 == *cuda* ]] 2025-08-14T21:07:36.1845684Z + pip_build_and_install git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 dist/vision 2025-08-14T21:07:36.1846608Z + local build_target=git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-14T21:07:36.1847186Z + local wheel_dir=dist/vision 2025-08-14T21:07:36.1847454Z + local found_whl=0 2025-08-14T21:07:36.1847711Z + for file in "${wheel_dir}"/*.whl 2025-08-14T21:07:36.1848017Z + [[ -f dist/vision/*.whl ]] 2025-08-14T21:07:36.1848267Z + '[' 0 == 0 ']' 2025-08-14T21:07:36.1849012Z + python3 -m pip wheel --no-build-isolation --no-deps --no-use-pep517 -w dist/vision git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-14T21:07:36.4859514Z Collecting git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-14T21:07:36.4863687Z Cloning https://github.com/pytorch/vision.git (to revision 966da7e46f65d6d49df3e31214470a4fe5cc8e66) to /tmp/pip-req-build-6o97yysl 2025-08-14T21:07:36.4881830Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-6o97yysl 2025-08-14T21:07:38.1498855Z Running command git rev-parse -q --verify 'sha^966da7e46f65d6d49df3e31214470a4fe5cc8e66' 2025-08-14T21:07:38.1516842Z Running command git fetch -q https://github.com/pytorch/vision.git 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-14T21:07:38.2717041Z Running command git checkout -q 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-14T21:07:38.5759876Z Resolved https://github.com/pytorch/vision.git to commit 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-14T21:07:40.4552722Z Preparing metadata (setup.py) ... [?25l- \ | done 2025-08-14T21:07:40.4593085Z [?25hBuilding wheels for collected packages: torchvision 2025-08-14T21:07:40.4638505Z  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 2025-08-14T21:09:14.7599289Z  Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done 2025-08-14T21:09:14.7600799Z [?25h Created wheel for torchvision: filename=torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl size=1201347 sha256=ebe138bc6137c6aae62f067d981c40ca5bce792d98b876ed2a06e84569ed5f8e 2025-08-14T21:09:14.7602062Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/89/53/07/16d05bff0990e585bfa232438657da6e5e4f6d0cea71bd3e09 2025-08-14T21:09:14.7602725Z Successfully built torchvision 2025-08-14T21:09:14.8407664Z + for file in "${wheel_dir}"/*.whl 2025-08-14T21:09:14.8408955Z + pip_install_whl dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-08-14T21:09:14.8410243Z + args=('dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl') 2025-08-14T21:09:14.8410805Z + local args 2025-08-14T21:09:14.8411218Z + [[ dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl == *\ * ]] 2025-08-14T21:09:14.8411716Z + for path in "${args[@]}" 2025-08-14T21:09:14.8412194Z + echo 'Installing dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl' 2025-08-14T21:09:14.8412880Z Installing dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-08-14T21:09:14.8413695Z + python3 -mpip install --no-index --no-deps dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-08-14T21:09:15.1776773Z Processing ./dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-08-14T21:09:15.1874700Z Installing collected packages: torchvision 2025-08-14T21:09:15.6424641Z Successfully installed torchvision-0.22.0a0+966da7e 2025-08-14T21:09:15.6858839Z + '[' -n '' ']' 2025-08-14T21:09:15.6859199Z + test_dynamo_wrapped_shard 2 2025-08-14T21:09:15.6859484Z + [[ -z 3 ]] 2025-08-14T21:09:15.6859737Z + python tools/dynamo/verify_dynamo.py 2025-08-14T21:09:16.7631228Z Python version: 3.13.5 2025-08-14T21:09:16.7631577Z `torch` version: 2.9.0a0+git1fc683c 2025-08-14T21:09:16.7631879Z CUDA version: None 2025-08-14T21:09:16.7632107Z ROCM version: None 2025-08-14T21:09:16.7632258Z 2025-08-14T21:09:16.7632794Z /var/lib/jenkins/workspace/tools/dynamo/verify_dynamo.py:220: UserWarning: Dynamo not yet supported in Python 3.13. Skipping check. 2025-08-14T21:09:16.7633681Z warnings.warn("Dynamo not yet supported in Python 3.13. Skipping check.") 2025-08-14T21:09:16.7634137Z All required checks passed 2025-08-14T21:09:16.9998553Z + python test/run_test.py --dynamo --exclude-inductor-tests --exclude-jit-executor --exclude-distributed-tests --exclude-torch-export-tests --exclude-aot-dispatch-tests --shard 2 3 --verbose --upload-artifacts-while-running 2025-08-14T21:09:19.8681839Z /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. 2025-08-14T21:09:19.8684458Z import pkg_resources 2025-08-14T21:09:21.4732514Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-08-14T21:09:21.5291567Z Ignoring disabled issues: [''] 2025-08-14T21:09:21.5418838Z Found test times from artifacts 2025-08-14T21:09:21.5920401Z Found test times from artifacts 2025-08-14T21:09:21.5935944Z Running all tests 2025-08-14T21:09:21.6112758Z Running parallel tests on 3 processes 2025-08-14T21:09:21.6120752Z Name: tests to run (est. time: 112.68min) 2025-08-14T21:09:21.6121199Z Serial tests (6): 2025-08-14T21:09:21.6121465Z test_tensor_creation_ops 1/1 2025-08-14T21:09:21.6121749Z test_nn 1/4 2025-08-14T21:09:21.6121973Z test_nn 3/4 2025-08-14T21:09:21.6122189Z test_overrides 1/1 2025-08-14T21:09:21.6122445Z doctests 1/1 2025-08-14T21:09:21.6122687Z test_cpp_api_parity 1/1 2025-08-14T21:09:21.6122967Z Parallel tests (139): 2025-08-14T21:09:21.6123215Z dynamo/test_repros 1/1 2025-08-14T21:09:21.6123483Z dynamo/test_misc 1/1 2025-08-14T21:09:21.6123757Z dynamo/test_error_messages 1/1 2025-08-14T21:09:21.6124090Z dynamo/test_precompile_context 1/1 2025-08-14T21:09:21.6124592Z dynamo/test_unspec 1/1 2025-08-14T21:09:21.6125012Z dynamo/test_after_aot 1/1 2025-08-14T21:09:21.6125452Z dynamo/test_aot_autograd 1/1 2025-08-14T21:09:21.6126049Z dynamo/test_deque_reconstruct 1/1 2025-08-14T21:09:21.6126394Z dynamo/test_interop 1/1 2025-08-14T21:09:21.6126675Z dynamo/test_base_output 1/1 2025-08-14T21:09:21.6127226Z dynamo/test_nops 1/1 2025-08-14T21:09:21.6127503Z dynamo/test_pre_dispatch 1/1 2025-08-14T21:09:21.6127811Z dynamo/test_metrics_context 1/1 2025-08-14T21:09:21.6128143Z dynamo/test_subgraphs 1/1 2025-08-14T21:09:21.6128482Z dynamo/test_install_free_tensors 1/1 2025-08-14T21:09:21.6128804Z dynamo/test_config 1/1 2025-08-14T21:09:21.6129078Z dynamo/test_export_mutations 1/1 2025-08-14T21:09:21.6129397Z dynamo/test_sources 1/1 2025-08-14T21:09:21.6129690Z dynamo/test_guard_serialization 1/1 2025-08-14T21:09:21.6129998Z dynamo/test_einops 1/1 2025-08-14T21:09:21.6130273Z dynamo/test_export 1/1 2025-08-14T21:09:21.6130550Z dynamo/test_base_hop 1/1 2025-08-14T21:09:21.6130835Z dynamo/test_optimizers 1/1 2025-08-14T21:09:21.6131136Z dynamo/test_compiler_bisector 1/1 2025-08-14T21:09:21.6131458Z dynamo/test_bytecode_utils 1/1 2025-08-14T21:09:21.6131789Z dynamo/test_cudagraphs_expandable_segments 1/1 2025-08-14T21:09:21.6132145Z dynamo/test_minifier 1/1 2025-08-14T21:09:21.6132433Z dynamo/test_reorder_logs 1/1 2025-08-14T21:09:21.6132733Z dynamo/test_python_autograd 1/1 2025-08-14T21:09:21.6133022Z test_quantization 2/9 2025-08-14T21:09:21.6133292Z test_quantization 6/9 2025-08-14T21:09:21.6133557Z test_quantization 7/9 2025-08-14T21:09:21.6133808Z test_quantization 8/9 2025-08-14T21:09:21.6134069Z test_transformers 2/7 2025-08-14T21:09:21.6134331Z test_transformers 3/7 2025-08-14T21:09:21.6134581Z test_transformers 4/7 2025-08-14T21:09:21.6134870Z test_ao_sparsity 1/1 2025-08-14T21:09:21.6135136Z test_license 1/1 2025-08-14T21:09:21.6135447Z torch_np/test_binary_ufuncs 1/1 2025-08-14T21:09:21.6135745Z xpu/test_fusion 1/1 2025-08-14T21:09:21.6136021Z torch_np/test_unary_ufuncs 1/1 2025-08-14T21:09:21.6136297Z test_foreach 1/1 2025-08-14T21:09:21.6136555Z test_expanded_weights 1/1 2025-08-14T21:09:21.6136852Z typing/test_python_operators 1/1 2025-08-14T21:09:21.6137154Z test_fx_experimental 1/1 2025-08-14T21:09:21.6137433Z test_file_check 1/1 2025-08-14T21:09:21.6137687Z torch_np/test_dtype 1/1 2025-08-14T21:09:21.6137982Z higher_order_ops/test_with_effects 1/1 2025-08-14T21:09:21.6138295Z test_native_functions 1/1 2025-08-14T21:09:21.6138580Z functorch/test_minifier 1/1 2025-08-14T21:09:21.6138948Z test_flop_counter 1/1 2025-08-14T21:09:21.6139312Z torch_np/test_nep50_examples 1/1 2025-08-14T21:09:21.6139637Z higher_order_ops/test_invoke_quant 1/1 2025-08-14T21:09:21.6139961Z test_per_overload_api 1/1 2025-08-14T21:09:21.6140234Z test_tensorexpr_pybind 1/1 2025-08-14T21:09:21.6140513Z test_pytree 1/1 2025-08-14T21:09:21.6140750Z test_fx_passes 1/1 2025-08-14T21:09:21.6141138Z test_module_tracker 1/1 2025-08-14T21:09:21.6141459Z test_typing 1/1 2025-08-14T21:09:21.6141723Z test_openmp 1/1 2025-08-14T21:09:21.6142005Z torch_np/numpy_tests/core/test_scalarinherit 1/1 2025-08-14T21:09:21.6142379Z functorch/test_ac_knapsack 1/1 2025-08-14T21:09:21.6142694Z backends/xeon/test_launch 1/1 2025-08-14T21:09:21.6142985Z test_nestedtensor 1/1 2025-08-14T21:09:21.6143325Z test_namedtensor 1/1 2025-08-14T21:09:21.6143602Z torch_np/test_ufuncs_basic 1/1 2025-08-14T21:09:21.6143897Z test_meta 1/1 2025-08-14T21:09:21.6144128Z test_utils_filelock 1/1 2025-08-14T21:09:21.6144405Z torch_np/test_random 1/1 2025-08-14T21:09:21.6144689Z profiler/test_kineto 1/1 2025-08-14T21:09:21.6144963Z test_compile_benchmark_util 1/1 2025-08-14T21:09:21.6145258Z test_optim 1/3 2025-08-14T21:09:21.6145495Z test_legacy_vmap 2/2 2025-08-14T21:09:21.6145737Z test_modules 1/1 2025-08-14T21:09:21.6145978Z test_matmul_cuda 1/1 2025-08-14T21:09:21.6146237Z test_weak 1/1 2025-08-14T21:09:21.6146469Z functorch/test_logging 1/1 2025-08-14T21:09:21.6146745Z test_logging 1/1 2025-08-14T21:09:21.6146993Z test_out_dtype_op 1/1 2025-08-14T21:09:21.6147319Z test_complex 1/1 2025-08-14T21:09:21.6147799Z torch_np/numpy_tests/core/test_einsum 1/1 2025-08-14T21:09:21.6148220Z test_ops_jit 1/1 2025-08-14T21:09:21.6148573Z lazy/test_step_closures 1/1 2025-08-14T21:09:21.6149020Z test_ops_gradients 1/1 2025-08-14T21:09:21.6149554Z test_fx_reinplace_pass 1/1 2025-08-14T21:09:21.6149990Z nn/test_multihead_attention 1/1 2025-08-14T21:09:21.6150343Z functorch/test_aot_joint_with_descriptors 1/1 2025-08-14T21:09:21.6150867Z test_pruning_op 1/1 2025-08-14T21:09:21.6151264Z lazy/test_functionalization 1/1 2025-08-14T21:09:21.6151807Z functorch/test_ac 1/1 2025-08-14T21:09:21.6152283Z test_ops_fwd_gradients 1/1 2025-08-14T21:09:21.6152569Z test_hub 1/1 2025-08-14T21:09:21.6152836Z test_set_default_mobile_cpu_allocator 1/1 2025-08-14T21:09:21.6153167Z test_stateless 1/1 2025-08-14T21:09:21.6153441Z distributions/test_constraints 1/1 2025-08-14T21:09:21.6153757Z profiler/test_record_function 1/1 2025-08-14T21:09:21.6163304Z nn/test_lazy_modules 1/1 2025-08-14T21:09:21.6163711Z test_jit_fuser_te 1/2 2025-08-14T21:09:21.6163987Z optim/test_lrscheduler 1/1 2025-08-14T21:09:21.6164278Z test_jit 2/2 2025-08-14T21:09:21.6164527Z nn/test_packed_sequence 1/1 2025-08-14T21:09:21.6164964Z cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic 1/1 2025-08-14T21:09:21.6165441Z functorch/test_ops 1/1 2025-08-14T21:09:21.6165713Z test_schema_check 1/1 2025-08-14T21:09:21.6165966Z test_datapipe 1/1 2025-08-14T21:09:21.6166227Z test_bundled_inputs 1/1 2025-08-14T21:09:21.6166529Z profiler/test_execution_trace 1/1 2025-08-14T21:09:21.6166828Z lazy/test_generator 1/1 2025-08-14T21:09:21.6167139Z torch_np/numpy_tests/core/test_indexing 1/1 2025-08-14T21:09:21.6167488Z test_maskedtensor 1/1 2025-08-14T21:09:21.6167744Z test_tensorboard 1/1 2025-08-14T21:09:21.6168045Z torch_np/numpy_tests/lib/test_type_check 1/1 2025-08-14T21:09:21.6168385Z lazy/test_ts_opinfo 1/1 2025-08-14T21:09:21.6168657Z test_mkldnn_fusion 1/1 2025-08-14T21:09:21.6168950Z benchmark_utils/test_benchmark_utils 1/1 2025-08-14T21:09:21.6169271Z test_linalg 1/6 2025-08-14T21:09:21.6169506Z test_linalg 5/6 2025-08-14T21:09:21.6169723Z test_linalg 6/6 2025-08-14T21:09:21.6169958Z test_futures 1/1 2025-08-14T21:09:21.6170241Z torch_np/numpy_tests/lib/test_histograms 1/1 2025-08-14T21:09:21.6170654Z nn/test_embedding 1/1 2025-08-14T21:09:21.6170990Z functorch/test_vmap 4/6 2025-08-14T21:09:21.6171266Z functorch/test_vmap 5/6 2025-08-14T21:09:21.6171527Z functorch/test_vmap 6/6 2025-08-14T21:09:21.6171796Z test_sparse_csr 2/2 2025-08-14T21:09:21.6172049Z test_view_ops 1/1 2025-08-14T21:09:21.6172420Z test_accelerator 1/1 2025-08-14T21:09:21.6172713Z profiler/test_memory_profiler 1/1 2025-08-14T21:09:21.6173060Z torch_np/numpy_tests/fft/test_pocketfft 1/1 2025-08-14T21:09:21.6173387Z test_function_schema 1/1 2025-08-14T21:09:21.6173712Z torch_np/numpy_tests/lib/test_index_tricks 1/1 2025-08-14T21:09:21.6174070Z test_scatter_gather_ops 1/1 2025-08-14T21:09:21.6174364Z test_unary_ufuncs 1/3 2025-08-14T21:09:21.6174667Z torch_np/numpy_tests/core/test_scalar_methods 1/1 2025-08-14T21:09:21.6175017Z nn/test_init 1/1 2025-08-14T21:09:21.6175312Z torch_np/numpy_tests/core/test_numerictypes 1/1 2025-08-14T21:09:21.6175686Z torch_np/numpy_tests/lib/test_arraypad 1/1 2025-08-14T21:09:21.6176059Z torch_np/numpy_tests/lib/test_arraysetops 1/1 2025-08-14T21:09:21.6176396Z test_sparse 1/2 2025-08-14T21:09:21.6176638Z Name: excluded (est. time: 0.0min) 2025-08-14T21:09:21.6176934Z Serial tests (0): 2025-08-14T21:09:21.6177175Z Parallel tests (0): 2025-08-14T21:09:21.6177547Z Running test_tensor_creation_ops 1/1 ... [2025-08-14 21:09:21.614682] 2025-08-14T21:09:21.6177982Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:09:21.6179049Z 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'] ... [2025-08-14 21:09:21.615001] 2025-08-14T21:17:21.0156967Z 2025-08-14T21:17:21.0158383Z 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_041545048ab6343f_.log 2025-08-14T21:17:21.0395577Z Running 641 items in this shard: test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_strided_neg_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_scipy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cartesian_prod_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_big_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_in_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_memory_format_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_preserve_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_stack_cross_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_combinations_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_complex_type_conversions_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_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_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 2025-08-14T21:17:21.0625287Z 2025-08-14T21:17:21.5477724Z Uploading artifacts took 0.53 seconds 2025-08-14T21:17:21.5480889Z Running test_nn 1/4 ... [2025-08-14 21:17:21.547922] 2025-08-14T21:17:21.5481270Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:17:21.5485084Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nn.py', '--shard-id=1', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:17:21.548290] 2025-08-14T21:25:59.6407612Z 2025-08-14T21:25:59.6408357Z test_nn 1/4 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.4_fac8eee882efe94a_.log 2025-08-14T21:25:59.6697604Z Running 534 items in this shard: test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_CELU_no_batch_dim, test/test_nn.py::TestNN::test_CTCLoss_zero_lengths, test/test_nn.py::TestNN::test_Conv1d_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_stride, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated, test/test_nn.py::TestNN::test_Conv2d_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_groups, test/test_nn.py::TestNN::test_Conv2d_groups_thnn, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_no_bias, test/test_nn.py::TestNN::test_Conv2d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_dilated_strided_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_pad_same, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_stride, test/test_nn.py::TestNN::test_Conv3d_stride_padding, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_zero_batch_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_CrossMapLRN2d, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_sparse, test/test_nn.py::TestNN::test_EmbeddingBag_sum, test/test_nn.py::TestNN::test_Embedding_discontiguous, test/test_nn.py::TestNN::test_Embedding_discontiguous_cuda, test/test_nn.py::TestNN::test_Embedding_sparse, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input_cuda, test/test_nn.py::TestNN::test_GELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_L1Loss_no_reduce, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_LSTM_cell, test/test_nn.py::TestNN::test_LSTM_cell_forward_input_size, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_cuda, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MSELoss_no_reduce, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MaxUnpool1d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ModuleList, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index, test/test_nn.py::TestNN::test_PReLU_no_batch_dim, test/test_nn.py::TestNN::test_PReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance, test/test_nn.py::TestNN::test_ParameterDict, test/test_nn.py::TestNN::test_ParameterList_meta, test/test_nn.py::TestNN::test_PixelUnshuffle, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce, test/test_nn.py::TestNN::test_RNN_cell_forward_zero_hidden_size, test/test_nn.py::TestNN::test_RReLU_no_batch_dim, test/test_nn.py::TestNN::test_RReLU_with_up_down_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar, test/test_nn.py::TestNN::test_ReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_complex_cuda, test/test_nn.py::TestNN::test_Sequential_rmul, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta_cuda, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim, test/test_nn.py::TestNN::test_Softsign_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation, test/test_nn.py::TestNN::test_Transformer_multilayer_coder, test/test_nn.py::TestNN::test_Transformer_multilayer_coder_cuda, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_Unfold, test/test_nn.py::TestNN::test_batch_norm_update_stats, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_cudnn_nhwc, test/test_nn.py::TestNN::test_batchnorm_load_state_dict, test/test_nn.py::TestNN::test_batchnorm_nhwc_cpu, test/test_nn.py::TestNN::test_batchnorm_nonaffine_cuda_half_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_bias_is_not_same_size_as_input, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_weights, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_forward_grad, test/test_nn.py::TestNN::test_buffer_bad_module_subclass, test/test_nn.py::TestNN::test_buffer_not_persistent_load, test/test_nn.py::TestNN::test_buffers_and_named_buffers, test/test_nn.py::TestNN::test_cosine_embedding_loss_margin_no_reduce, test/test_nn.py::TestNN::test_cudnn_rnn_dropout_states_device, test/test_nn.py::TestNN::test_fb_fc_packed, test/test_nn.py::TestNN::test_grid_sample_error_checking, test/test_nn.py::TestNN::test_hardtanh_backward, test/test_nn.py::TestNN::test_hardtanh_inplace_gradgrad, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_nn.py::TestNN::test_l1_loss_correct, test/test_nn.py::TestNN::test_layer_norm_grads_with_create_graph_flag, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightStrided, test/test_nn.py::TestNN::test_log_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_margin_ranking_loss_no_reduce, test/test_nn.py::TestNN::test_module_backcompat, test/test_nn.py::TestNN::test_named_parameters_remove_duplicate, test/test_nn.py::TestNN::test_nested_tensor_from_mask, test/test_nn.py::TestNN::test_parameterlistdict_setting_attributes, test/test_nn.py::TestNN::test_pdist_empty_col, test/test_nn.py::TestNN::test_pixel_shuffle_unshuffle, test/test_nn.py::TestNN::test_projections_errors_on_gru_and_rnn, test/test_nn.py::TestNN::test_projections_lstm_args_check, test/test_nn.py::TestNN::test_register_buffer_allows_tensor_like_object, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_relu_inplace_on_view, test/test_nn.py::TestNN::test_rnn_check_device, test/test_nn.py::TestNN::test_rnn_weight_norm, test/test_nn.py::TestNN::test_softmax_functional_dim0, test/test_nn.py::TestNN::test_softmax_functional_dim3, test/test_nn.py::TestNN::test_softmax_lastdim, test/test_nn.py::TestNN::test_softmax_lastdim_dtype, test/test_nn.py::TestNN::test_softmax_spatial_dtype_cuda, test/test_nn.py::TestNN::test_spectral_norm_load_state_dict, test/test_nn.py::TestNN::test_spectral_norm_pickle, test/test_nn.py::TestNN::test_swap_module_params_poisons_acc_grad, test/test_nn.py::TestNN::test_transformer_args_check, test/test_nn.py::TestNN::test_transformerdecoderlayer_gelu, test/test_nn.py::TestNN::test_triplet_margin_loss_no_reduce, test/test_nn.py::TestNN::test_triplet_margin_loss_swap, test/test_nn.py::TestNN::test_unflatten, test/test_nn.py::TestNN::test_upsamplingBilinear2d_spatial_invariance, test/test_nn.py::TestNN::test_upsampling_not_recompute_scale_factor, test/test_nn.py::TestNN::test_weighted_huber_loss, test/test_nn.py::TestNN::test_weighted_l1_loss_with_weights, test/test_nn.py::TestFusionEval::test_fuse_module_eval_numerics, test/test_nn.py::TestConstantPadNd::test_constant_pad_nd, test/test_nn.py::TestAddRelu::test_add_relu, test/test_nn.py::TestFunctionalPickle::test_pickle_softsign, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_memory_format_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm2d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_race_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_3d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_errors_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_weight_ignore_indices_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_with_neg_alpha_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_glu_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_less_than_one_value_per_channel_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_neg_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_zero_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_byte_target_matches_long_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_out_of_bounds_ignore_index_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_total_weight_is_zero_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nonlinearity_propagate_nan_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_overwrite_module_params_on_conversion_cpu_device_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_prelu_backward_32bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_epsilon_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_numeric_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_skip_init_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_double_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_results_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_threshold_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_fast_path_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_default_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_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_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_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_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_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_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_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_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_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_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_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_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_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_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_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_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_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_fail_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_rocm_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsampling_64bit_indexing_channels_last_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingnearest2d_backward_64bit_indexing_cpu_float16 2025-08-14T21:25:59.6974455Z 2025-08-14T21:25:59.6974635Z Running test_nn 3/4 ... [2025-08-14 21:25:59.642321] 2025-08-14T21:25:59.6974994Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:25:59.6975976Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nn.py', '--shard-id=3', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:25:59.642661] 2025-08-14T21:36:15.5899361Z 2025-08-14T21:36:15.5900110Z test_nn 3/4 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_3.4_5d30033a80fc77d2_.log 2025-08-14T21:36:15.6183956Z Running 542 items in this shard: test/test_nn.py::TestNN::test_AdaptiveLogSoftmax, test/test_nn.py::TestNN::test_AdaptiveLogSoftmax_cuda, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_CELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Conv1d, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_dilated, test/test_nn.py::TestNN::test_Conv1d_groups_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1, test/test_nn.py::TestNN::test_Conv1d_pad_same2, test/test_nn.py::TestNN::test_Conv1d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv1d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv1d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise, test/test_nn.py::TestNN::test_Conv2d_depthwise_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_pad_same, test/test_nn.py::TestNN::test_Conv2d_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_valid, test/test_nn.py::TestNN::test_Conv3d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zero_batch, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_groups, test/test_nn.py::TestNN::test_ConvTranspose2d_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_ELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sparse_cuda, test/test_nn.py::TestNN::test_Flatten_cuda, test/test_nn.py::TestNN::test_Fold_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex_cuda, test/test_nn.py::TestNN::test_Linear_no_bias_cuda, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool3d_net, test/test_nn.py::TestNN::test_MaxUnpool3d_net_cuda, test/test_nn.py::TestNN::test_Mish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_reduce, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args_cuda, test/test_nn.py::TestNN::test_ParameterList, test/test_nn.py::TestNN::test_PixelShuffle_cuda, test/test_nn.py::TestNN::test_PixelUnshuffle_cuda, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_RNN_cell_no_broadcasting, test/test_nn.py::TestNN::test_RNN_dropout_state, test/test_nn.py::TestNN::test_RReLU_cuda, test/test_nn.py::TestNN::test_RReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sequential_extend, test/test_nn.py::TestNN::test_Sequential_getitem, test/test_nn.py::TestNN::test_Sequential_iadd, test/test_nn.py::TestNN::test_SiLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_beta, test/test_nn.py::TestNN::test_SmoothL1Loss_beta_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_Softplus_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_Transformer_cell, test/test_nn.py::TestNN::test_Unfold_cuda, test/test_nn.py::TestNN::test_add_module_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_affine_grid_3d, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_2, test/test_nn.py::TestNN::test_affine_grid_error_checking, test/test_nn.py::TestNN::test_assignment, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_buffer_update_when_stats_are_not_tracked, test/test_nn.py::TestNN::test_batchnorm_cudnn_half, test/test_nn.py::TestNN::test_batchnorm_nhwc_cuda, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_BatchNorm2d, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_less_than_one_value_per_channel, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss_large_tensors_with_grad, test/test_nn.py::TestNN::test_bce_with_logits_ones_in_pos_weights_are_the_same_as_none, test/test_nn.py::TestNN::test_bce_with_logits_with_pos_weight_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bilinear, test/test_nn.py::TestNN::test_broadcast_double_backwards_gpu, test/test_nn.py::TestNN::test_broadcast_no_grad, test/test_nn.py::TestNN::test_cosine_embedding_loss_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_with_diff_type, test/test_nn.py::TestNN::test_cross_entropy_loss_precision, test/test_nn.py::TestNN::test_cudnn_weight_tying, test/test_nn.py::TestNN::test_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_set_extra_state, test/test_nn.py::TestNN::test_flatten, test/test_nn.py::TestNN::test_fractional_max_pool2d_invalid_output_ratio, test/test_nn.py::TestNN::test_gaussian_nll_loss_args, test/test_nn.py::TestNN::test_gaussian_nll_loss_broadcasting, test/test_nn.py::TestNN::test_gaussian_nll_loss_scalar_var, test/test_nn.py::TestNN::test_get_buffer, test/test_nn.py::TestNN::test_grid_sample, test/test_nn.py::TestNN::test_grid_sample_3d, test/test_nn.py::TestNN::test_huber_loss_invalid_delta, test/test_nn.py::TestNN::test_inplace_thnn, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs, test/test_nn.py::TestNN::test_interpolate_nearest_3d, test/test_nn.py::TestNN::test_interpolate_nearest_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d, test/test_nn.py::TestNN::test_interpolate_trilinear_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_undefined_behavior_casting, test/test_nn.py::TestNN::test_layer_norm_large_tensor, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCOO, test/test_nn.py::TestNN::test_log_softmax_scalar, test/test_nn.py::TestNN::test_loss_equal_input_target_shape, test/test_nn.py::TestNN::test_module_super_init, test/test_nn.py::TestNN::test_modules, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce, test/test_nn.py::TestNN::test_named_modules, test/test_nn.py::TestNN::test_overwrite_module_params_on_conversion, test/test_nn.py::TestNN::test_pack_sequence_batch_sizes_throw, test/test_nn.py::TestNN::test_padding_list, test/test_nn.py::TestNN::test_pickle_module_no_weights_only_warning, test/test_nn.py::TestNN::test_pixel_shuffle_nhwc_cpu, test/test_nn.py::TestNN::test_pointwise_loss_broadcast, test/test_nn.py::TestNN::test_projections_lstm_initial_hidden_state, test/test_nn.py::TestNN::test_register_buffer_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_rnn_initial_hidden_state, test/test_nn.py::TestNN::test_set_submodule, test/test_nn.py::TestNN::test_smoothl1loss_intergral_target, test/test_nn.py::TestNN::test_softmax_functional_dim0_cuda, test/test_nn.py::TestNN::test_softmax_spatial, test/test_nn.py::TestNN::test_softmax_spatial_special, test/test_nn.py::TestNN::test_softmin, test/test_nn.py::TestNN::test_spectral_norm, test/test_nn.py::TestNN::test_spectral_norm_dim, test/test_nn.py::TestNN::test_spectral_norm_forward, test/test_nn.py::TestNN::test_state_dict, test/test_nn.py::TestNN::test_to, test/test_nn.py::TestNN::test_type, test/test_nn.py::TestNN::test_unfold_invalid_arg, test/test_nn.py::TestNN::test_upsamplingLinear1d_spatial_invariance, test/test_nn.py::TestNN::test_upsampling_bfloat16, test/test_nn.py::TestNN::test_upsampling_small_scale, test/test_nn.py::TestNN::test_weighted_mse_loss, test/test_nn.py::TestFusionUtils::test_fuse_linear_bn_requires_grad, test/test_nn.py::TestUtils::test_consume_prefix_in_state_dict_if_present, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm1d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm3d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_race_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_warnings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_Unfold_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_adaptiveavg_pool1d_shmem_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_one_hot_target_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_tensor_cpu_length_cuda_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_for_single_spatial_element_during_training_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_invalid_reduction_strings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_linear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_big_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_forward_with_nans_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_transformer_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_one_hot_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_vs_huber_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_unaligned_grad_output_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_complex64, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_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_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_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_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_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_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_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_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_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_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_correctness_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_variable_sequence_cpu_float32 2025-08-14T21:36:15.6450164Z 2025-08-14T21:36:15.6450367Z Running test_overrides 1/1 ... [2025-08-14 21:36:15.591947] 2025-08-14T21:36:15.6450953Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:36:15.6451979Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_overrides.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:36:15.592353] 2025-08-14T21:38:34.8409540Z 2025-08-14T21:38:34.8410514Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_0726ecebecdb626f_.log 2025-08-14T21:38:34.8893044Z Running 1470 items in this shard: test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_H___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_T___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__backward_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__base___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__cdata___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__post_accumulate_grad_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__version___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_data___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_device___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_dtype___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_imag___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_cpu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_cuda___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_ipu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_leaf___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_maia___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_meta___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mkldnn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mps___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mtia___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_nested___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_quantized___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_sparse___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_sparse_csr___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_vulkan___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_xla___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_xpu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_itemsize___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_layout___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_mH___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_mT___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_name___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_names___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_nbytes___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_ndim___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_output_nr___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_real___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_requires_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_retains_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_shape___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_volatile___get__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___add__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___and__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___array__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___array_wrap__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___bool__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___complex__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___contains__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___cuda_array_interface_____get__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___deepcopy__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___div__, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_istft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_item, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kron, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_long, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_max, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_median, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_min, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mode, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_module_load, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_msort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mtia, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mul_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multiply_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mv, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mvlgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ndimension, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nelement, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_norm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numpy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_outer, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_permute, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pin_memory, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_positive, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qscheme, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_random_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_record_stream, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_refine_names, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_post_accumulate_grad_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_renorm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_renorm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_repeat, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_repeat_interleave, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_requires_grad_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reshape, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reshape_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as_sparse_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resolve_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resolve_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_retain_grad, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_roll, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rot90, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_round, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_round_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_row_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rsqrt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rsqrt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_select_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_set_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sgn, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sgn_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_share_memory_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_short, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sigmoid_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sign, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sign_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_signbit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_smm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_mask, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_and_clear_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sspaddmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_std, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_stft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_offset, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_type, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum_to_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_svd, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take_along_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tan_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tanh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_dense, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_mkldnn, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_sparse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tolist, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_topk, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trace, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_transpose_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triangular_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tril, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tril_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triu_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_true_divide, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_true_divide_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trunc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trunc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_type, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_type_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unbind, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unfold, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unique, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unique_consecutive, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsafe_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsafe_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsafe_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsqueeze, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsqueeze_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_untyped_storage, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_values, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_var, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_vdot, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_view, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_view_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_vsplit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_where, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xlogy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xpu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_zero_, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifftshift, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfft2, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_psi, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_round, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_scaled_modified_bessel_k0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_scaled_modified_bessel_k1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_t, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_u, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_v, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_w, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_spherical_bessel_j0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_xlog1py, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_zeta, test/test_overrides.py::TestTorchFunctionOverride::test_torch__assert_async, test/test_overrides.py::TestTorchFunctionOverride::test_torch__conj_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__functional_assert_async, test/test_overrides.py::TestTorchFunctionOverride::test_torch__fused_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch__fw_primal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lobpcg_lobpcg, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lowrank_pca_lowrank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lowrank_svd_lowrank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__make_dual_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__native_batch_norm_legit, test/test_overrides.py::TestTorchFunctionOverride::test_torch__neg_view_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__reshape_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__rowwise_prune, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sparse_broadcast_to_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__values_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_linear_prepack, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_quantized_linear_prepacked, test/test_overrides.py::TestTorchFunctionOverride::test_torch_abs, test/test_overrides.py::TestTorchFunctionOverride::test_torch_absolute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcdiv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adjoint, test/test_overrides.py::TestTorchFunctionOverride::test_torch_affine_grid_generator, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_all, test/test_overrides.py::TestTorchFunctionOverride::test_torch_allclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_aminmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_angle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_any, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argsort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argwhere, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_baddbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats_with_counts, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_update_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bernoulli, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bilinear, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bincount, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_left_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_right_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_broadcast_to, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bucketize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ccol_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ceil, test/test_overrides.py::TestTorchFunctionOverride::test_torch_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_choose_qparams_optimized, test/test_overrides.py::TestTorchFunctionOverride::test_torch_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clone, test/test_overrides.py::TestTorchFunctionOverride::test_torch_col_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_column_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_combinations, test/test_overrides.py::TestTorchFunctionOverride::test_torch_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concatenate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj_physical, test/test_overrides.py::TestTorchFunctionOverride::test_torch_constant_pad_nd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_tbc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_copysign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_corrcoef, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_similarity, test/test_overrides.py::TestTorchFunctionOverride::test_torch_count_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cov, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cross, test/test_overrides.py::TestTorchFunctionOverride::test_torch_crow_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumprod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumulative_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_deg2rad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dequantize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_det, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag_embed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagflat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diff, test/test_overrides.py::TestTorchFunctionOverride::test_torch_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_empty_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_eq, test/test_overrides.py::TestTorchFunctionOverride::test_torch_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expand_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expm1, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_channel_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_tensor_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_quantize_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_gemm_matrix_fp16, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_quantized_matrix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fliplr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flipud, test/test_overrides.py::TestTorchFunctionOverride::test_torch_float_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frobenius_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_full_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_block_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_broadcast_tensors, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cartesian_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_chain_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_einsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_lu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_meshgrid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_stft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_tensordot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique_consecutive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unravel_index, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fused_moving_avg_obs_fake_quant, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gather, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gcd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ge, test/test_overrides.py::TestTorchFunctionOverride::test_torch_geqrf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ger, test/test_overrides.py::TestTorchFunctionOverride::test_torch_get_device, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gradient, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hardshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hash_tensor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_heaviside, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogram, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogramdd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_i0, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_imag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inner, test/test_overrides.py::TestTorchFunctionOverride::test_torch_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_istft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kron, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_torch_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_le, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log10, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_unpack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_median, test/test_overrides.py::TestTorchFunctionOverride::test_torch_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_add_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_depthwise_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_rnn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mode, test/test_overrides.py::TestTorchFunctionOverride::test_torch_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_msort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ne, test/test_overrides.py::TestTorchFunctionOverride::test_torch_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_negative, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional__threshold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_affine_grid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_elu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gaussian_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_glu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_grid_sample, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gumbel_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hardtanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_huber_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_interpolate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_leaky_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_local_response_norm, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_where, test/test_overrides.py::TestTorchFunctionOverride::test_torch_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_zeros_like, test/test_overrides.py::TestTorchFunctionOverride::test_user_implementation_raises, test/test_overrides.py::TestEinsumOverride::test_wrapper, test/test_overrides.py::TestGradCheckOverride::test_gradcheck, test/test_overrides.py::TestNamedTuple::test_max, test/test_overrides.py::TestGradNewOnesOverride::test_newones, test/test_overrides.py::TestPickle::test_pickle, test/test_overrides.py::TestBroadcastAllOverride::test_broadcast_all, test/test_overrides.py::TestWrapTorchFunction::test_wrap_torch_function, test/test_overrides.py::TestIndexing::test_getitem, test/test_overrides.py::TestIndexing::test_getitem_subclass, test/test_overrides.py::TestIndexing::test_setitem, test/test_overrides.py::TestIndexing::test_setitem_subclass, test/test_overrides.py::TestIndexing::test_setitem_val, test/test_overrides.py::TestIterator::test_iterator, test/test_overrides.py::TestRNN::test_rnn, test/test_overrides.py::TestDisabledTorchFunction::test_parameter_does_not_prevent_dispatch, test/test_overrides.py::TestResolveName::test_resolve_name, test/test_overrides.py::TestTorchFunctionWarning::test_warn_on_invalid_torch_function_standalone_class, test/test_overrides.py::TestTorchFunctionWarning::test_warn_on_invalid_torch_function_tensor_subclass, test/test_overrides.py::TestDisabledUserWarnings::test_no_implicit_user_warning_for_deprecated_functions, test/test_overrides.py::TestTorchFunctionMode::test_all_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_basic, test/test_overrides.py::TestTorchFunctionMode::test_custom_device_type, test/test_overrides.py::TestTorchFunctionMode::test_device_context_semantics, test/test_overrides.py::TestTorchFunctionMode::test_disable_enable_subclass, test/test_overrides.py::TestTorchFunctionMode::test_disable_enable_torch_function_ctx, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_mode, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_not_mode, test/test_overrides.py::TestTorchFunctionMode::test_distributions_bernoulli, test/test_overrides.py::TestTorchFunctionMode::test_error_using_class_method_on_mode, test/test_overrides.py::TestTorchFunctionMode::test_factory_override, test/test_overrides.py::TestTorchFunctionMode::test_get_cur_mode, test/test_overrides.py::TestTorchFunctionMode::test_get_mode_stack, test/test_overrides.py::TestTorchFunctionMode::test_getitem_call, test/test_overrides.py::TestTorchFunctionMode::test_mode_notimplemented_loop, test/test_overrides.py::TestTorchFunctionMode::test_modes_handle_first, test/test_overrides.py::TestTorchFunctionMode::test_modes_return_notimplemented, test/test_overrides.py::TestTorchFunctionMode::test_nested_modes_with_python_has_torch_function, test/test_overrides.py::TestTorchFunctionMode::test_nested_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_nn_parse_to, test/test_overrides.py::TestTorchFunctionMode::test_reentrant_mode_idiom, test/test_overrides.py::TestTorchFunctionMode::test_restacking_with_ancestor, test/test_overrides.py::TestTorchFunctionMode::test_subclass_hash, test/test_overrides.py::TestTorchFunctionMode::test_torch_function_all_disabled_api, test/test_overrides.py::TestTorchFunctionMode::test_with_mode, test/test_overrides.py::TestTorchFunctionMode::test_with_mode_created_separately, test/test_overrides.py::TestTorchFunctionMode::test_with_nested_modes 2025-08-14T21:38:34.9381493Z 2025-08-14T21:38:35.3280653Z Uploading artifacts took 0.49 seconds 2025-08-14T21:38:35.3284051Z Running doctests 1/1 ... [2025-08-14 21:38:35.328185] 2025-08-14T21:38:35.4167469Z Start doctest_module('/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch') 2025-08-14T21:38:35.4167983Z Listing tests 2025-08-14T21:38:35.6241800Z 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. 2025-08-14T21:38:35.6242725Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.6243300Z 2025-08-14T21:38:35.6243498Z dim_order(ambiguity_check=False) -> tuple 2025-08-14T21:38:35.6243884Z 2025-08-14T21:38:35.6244346Z Returns the uniquely determined tuple of int describing the dim order or 2025-08-14T21:38:35.6244959Z physical layout of :attr:`self`. 2025-08-14T21:38:35.6245153Z 2025-08-14T21:38:35.6245418Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-08-14T21:38:35.6245945Z starting from the outermost to the innermost dimension. 2025-08-14T21:38:35.6246243Z 2025-08-14T21:38:35.6246423Z Note that the dim order may not always be uniquely determined. 2025-08-14T21:38:35.6247097Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-08-14T21:38:35.6247988Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-08-14T21:38:35.6248726Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-08-14T21:38:35.6249416Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-08-14T21:38:35.6249967Z Otherwise, it will raise TypeError. 2025-08-14T21:38:35.6250174Z 2025-08-14T21:38:35.6250474Z Args: 2025-08-14T21:38:35.6250915Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-08-14T21:38:35.6251355Z 2025-08-14T21:38:35.6251472Z Examples:: 2025-08-14T21:38:35.6251595Z 2025-08-14T21:38:35.6251727Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-08-14T21:38:35.6252021Z (0, 1, 2, 3) 2025-08-14T21:38:35.6252319Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-08-14T21:38:35.6252673Z (0, 2, 1, 3) 2025-08-14T21:38:35.6253000Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-08-14T21:38:35.6253403Z (0, 2, 3, 1) 2025-08-14T21:38:35.6253656Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-08-14T21:38:35.6253983Z (0, 1, 2, 3) 2025-08-14T21:38:35.6254192Z >>> try: 2025-08-14T21:38:35.6254492Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-08-14T21:38:35.6254891Z ... except RuntimeError as e: 2025-08-14T21:38:35.6255174Z ... print(e) 2025-08-14T21:38:35.6255633Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-08-14T21:38:35.6256167Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-08-14T21:38:35.6256581Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-08-14T21:38:35.6257137Z ... ) # It can be mapped to contiguous format 2025-08-14T21:38:35.6257462Z (0, 1, 2, 3) 2025-08-14T21:38:35.6257672Z >>> try: 2025-08-14T21:38:35.6257985Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-08-14T21:38:35.6258394Z ... except TypeError as e: 2025-08-14T21:38:35.6258673Z ... print(e) 2025-08-14T21:38:35.6259038Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-08-14T21:38:35.6259390Z 2025-08-14T21:38:35.6259478Z .. warning:: 2025-08-14T21:38:35.6259800Z The dim_order tensor API is experimental and subject to change. 2025-08-14T21:38:35.6260099Z 2025-08-14T21:38:35.6260349Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.6260716Z 2025-08-14T21:38:35.6782873Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=446. 2025-08-14T21:38:35.6784652Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.6785839Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-08-14T21:38:35.6786527Z 2025-08-14T21:38:35.6786865Z This is helpful when you want to visualize data over some 2025-08-14T21:38:35.6787677Z range of inputs. See below for a plotting example. 2025-08-14T21:38:35.6788179Z 2025-08-14T21:38:35.6788474Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-08-14T21:38:35.6789333Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-08-14T21:38:35.6790260Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-08-14T21:38:35.6791108Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-08-14T21:38:35.6791975Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-08-14T21:38:35.6792716Z to the result shape. 2025-08-14T21:38:35.6793285Z 2025-08-14T21:38:35.6793473Z .. note:: 2025-08-14T21:38:35.6794022Z 0D inputs are treated equivalently to 1D inputs of a 2025-08-14T21:38:35.6794722Z single element. 2025-08-14T21:38:35.6795039Z 2025-08-14T21:38:35.6795208Z .. warning:: 2025-08-14T21:38:35.6795802Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-08-14T21:38:35.6796660Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-08-14T21:38:35.6797175Z 2025-08-14T21:38:35.6797461Z In the future `torch.meshgrid` will transition to 2025-08-14T21:38:35.6798146Z `indexing='xy'` as the default. 2025-08-14T21:38:35.6798569Z 2025-08-14T21:38:35.6799164Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-08-14T21:38:35.6800075Z this issue with the goal of migrating to NumPy's behavior. 2025-08-14T21:38:35.6800638Z 2025-08-14T21:38:35.6800811Z .. seealso:: 2025-08-14T21:38:35.6801079Z 2025-08-14T21:38:35.6801401Z :func:`torch.cartesian_prod` has the same effect but it 2025-08-14T21:38:35.6802168Z collects the data in a tensor of vectors. 2025-08-14T21:38:35.6802605Z 2025-08-14T21:38:35.6802807Z Args: 2025-08-14T21:38:35.6803534Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-08-14T21:38:35.6804541Z treated as tensors of size :math:`(1,)` automatically 2025-08-14T21:38:35.6805077Z 2025-08-14T21:38:35.6805403Z indexing: (str, optional): the indexing mode, either "xy" 2025-08-14T21:38:35.6806270Z or "ij", defaults to "ij". See warning for future changes. 2025-08-14T21:38:35.6806806Z 2025-08-14T21:38:35.6807081Z If "xy" is selected, the first dimension corresponds 2025-08-14T21:38:35.6807801Z to the cardinality of the second input and the second 2025-08-14T21:38:35.6808646Z dimension corresponds to the cardinality of the first 2025-08-14T21:38:35.6809535Z input. 2025-08-14T21:38:35.6809775Z 2025-08-14T21:38:35.6810032Z If "ij" is selected, the dimensions are in the same 2025-08-14T21:38:35.6810756Z order as the cardinality of the inputs. 2025-08-14T21:38:35.6811218Z 2025-08-14T21:38:35.6811367Z Returns: 2025-08-14T21:38:35.6811920Z seq (sequence of Tensors): If the input has :math:`N` 2025-08-14T21:38:35.6812690Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-08-14T21:38:35.6813459Z output will also have :math:`N` tensors, where each tensor 2025-08-14T21:38:35.6814153Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-08-14T21:38:35.6814522Z 2025-08-14T21:38:35.6814716Z Example:: 2025-08-14T21:38:35.6814939Z 2025-08-14T21:38:35.6815111Z >>> x = torch.tensor([1, 2, 3]) 2025-08-14T21:38:35.6815709Z >>> y = torch.tensor([4, 5, 6]) 2025-08-14T21:38:35.6816108Z 2025-08-14T21:38:35.6816478Z Observe the element-wise pairings across the grid, (1, 4), 2025-08-14T21:38:35.6817298Z (1, 5), ..., (3, 6). This is the same thing as the 2025-08-14T21:38:35.6817914Z cartesian product. 2025-08-14T21:38:35.6818540Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-08-14T21:38:35.6819198Z >>> grid_x 2025-08-14T21:38:35.6819630Z tensor([[1, 1, 1], 2025-08-14T21:38:35.6820083Z [2, 2, 2], 2025-08-14T21:38:35.6820534Z [3, 3, 3]]) 2025-08-14T21:38:35.6820969Z >>> grid_y 2025-08-14T21:38:35.6821392Z tensor([[4, 5, 6], 2025-08-14T21:38:35.6821863Z [4, 5, 6], 2025-08-14T21:38:35.6822354Z [4, 5, 6]]) 2025-08-14T21:38:35.6822671Z 2025-08-14T21:38:35.6822966Z This correspondence can be seen when these grids are 2025-08-14T21:38:35.6823757Z stacked properly. 2025-08-14T21:38:35.6824421Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-08-14T21:38:35.6825222Z ... torch.cartesian_prod(x, y)) 2025-08-14T21:38:35.6825807Z True 2025-08-14T21:38:35.6826037Z 2025-08-14T21:38:35.6826359Z `torch.meshgrid` is commonly used to produce a grid for 2025-08-14T21:38:35.6827036Z plotting. 2025-08-14T21:38:35.6827532Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-08-14T21:38:35.6828184Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-08-14T21:38:35.6828844Z >>> import matplotlib.pyplot as plt 2025-08-14T21:38:35.6829476Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-08-14T21:38:35.6830277Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-08-14T21:38:35.6830966Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-08-14T21:38:35.6831634Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-08-14T21:38:35.6832293Z >>> ax = plt.axes(projection='3d') 2025-08-14T21:38:35.6832993Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-08-14T21:38:35.6833655Z >>> plt.show() 2025-08-14T21:38:35.6833953Z 2025-08-14T21:38:35.6834180Z .. image:: ../_static/img/meshgrid.png 2025-08-14T21:38:35.6834774Z :width: 512 2025-08-14T21:38:35.6835026Z 2025-08-14T21:38:35.6835174Z 2025-08-14T21:38:35.6835830Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.6836500Z 2025-08-14T21:38:35.6837489Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=842. 2025-08-14T21:38:35.6839136Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.6840522Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-08-14T21:38:35.6841458Z 2025-08-14T21:38:35.6841732Z Returns the unique elements of the input tensor. 2025-08-14T21:38:35.6842358Z 2025-08-14T21:38:35.6842919Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-08-14T21:38:35.6844063Z this function also eliminates non-consecutive duplicate values. 2025-08-14T21:38:35.6844652Z 2025-08-14T21:38:35.6845069Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-08-14T21:38:35.6846201Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-08-14T21:38:35.6847479Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-08-14T21:38:35.6848567Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-08-14T21:38:35.6849113Z 2025-08-14T21:38:35.6849275Z Args: 2025-08-14T21:38:35.6849692Z input (Tensor): the input tensor 2025-08-14T21:38:35.6850475Z sorted (bool): Whether to sort the unique elements in ascending order 2025-08-14T21:38:35.6851282Z before returning as output. 2025-08-14T21:38:35.6852057Z return_inverse (bool): Whether to also return the indices for where 2025-08-14T21:38:35.6853037Z elements in the original input ended up in the returned unique list. 2025-08-14T21:38:35.6854033Z return_counts (bool): Whether to also return the counts for each unique 2025-08-14T21:38:35.6854822Z element. 2025-08-14T21:38:35.6855469Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-08-14T21:38:35.6856429Z unique of the flattened input is returned. Otherwise, each of the 2025-08-14T21:38:35.6857400Z tensors indexed by the given dimension is treated as one of the 2025-08-14T21:38:35.6858373Z elements to apply the unique operation upon. See examples for more 2025-08-14T21:38:35.6859179Z details. Default: ``None`` 2025-08-14T21:38:35.6859565Z 2025-08-14T21:38:35.6859727Z Returns: 2025-08-14T21:38:35.6860471Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-08-14T21:38:35.6861252Z 2025-08-14T21:38:35.6861605Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-08-14T21:38:35.6862435Z - **inverse_indices** (*Tensor*): (optional) if 2025-08-14T21:38:35.6863227Z :attr:`return_inverse` is True, there will be an additional 2025-08-14T21:38:35.6864198Z returned tensor (same shape as input) representing the indices 2025-08-14T21:38:35.6865146Z for where elements in the original input map to in the output; 2025-08-14T21:38:35.6866081Z otherwise, this function will only return a single tensor. 2025-08-14T21:38:35.6867010Z - **counts** (*Tensor*): (optional) if 2025-08-14T21:38:35.6867763Z :attr:`return_counts` is True, there will be an additional 2025-08-14T21:38:35.6868624Z returned tensor (same shape as output or output.size(dim), 2025-08-14T21:38:35.6869540Z if dim was specified) representing the number of occurrences 2025-08-14T21:38:35.6870286Z for each unique value or tensor. 2025-08-14T21:38:35.6870722Z 2025-08-14T21:38:35.6870897Z Example:: 2025-08-14T21:38:35.6871147Z 2025-08-14T21:38:35.6871519Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-08-14T21:38:35.6872298Z >>> output 2025-08-14T21:38:35.6872704Z tensor([1, 2, 3]) 2025-08-14T21:38:35.6873021Z 2025-08-14T21:38:35.6873231Z >>> output, inverse_indices = torch.unique( 2025-08-14T21:38:35.6874118Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-08-14T21:38:35.6874929Z >>> output 2025-08-14T21:38:35.6875354Z tensor([1, 2, 3]) 2025-08-14T21:38:35.6875829Z >>> inverse_indices 2025-08-14T21:38:35.6876300Z tensor([0, 2, 1, 2]) 2025-08-14T21:38:35.6876618Z 2025-08-14T21:38:35.6876838Z >>> output, inverse_indices = torch.unique( 2025-08-14T21:38:35.6877862Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-08-14T21:38:35.6878635Z >>> output 2025-08-14T21:38:35.6879008Z tensor([1, 2, 3]) 2025-08-14T21:38:35.6879466Z >>> inverse_indices 2025-08-14T21:38:35.6879929Z tensor([[0, 2], 2025-08-14T21:38:35.6880362Z [1, 2]]) 2025-08-14T21:38:35.6880646Z 2025-08-14T21:38:35.6880816Z >>> a = torch.tensor([ 2025-08-14T21:38:35.6881292Z ... [ 2025-08-14T21:38:35.6881674Z ... [1, 1, 0, 0], 2025-08-14T21:38:35.6882141Z ... [1, 1, 0, 0], 2025-08-14T21:38:35.6882652Z ... [0, 0, 1, 1], 2025-08-14T21:38:35.6883115Z ... ], 2025-08-14T21:38:35.6883496Z ... [ 2025-08-14T21:38:35.6883885Z ... [0, 0, 1, 1], 2025-08-14T21:38:35.6884359Z ... [0, 0, 1, 1], 2025-08-14T21:38:35.6884832Z ... [1, 1, 1, 1], 2025-08-14T21:38:35.6885308Z ... ], 2025-08-14T21:38:35.6885693Z ... [ 2025-08-14T21:38:35.6886071Z ... [1, 1, 0, 0], 2025-08-14T21:38:35.6886541Z ... [1, 1, 0, 0], 2025-08-14T21:38:35.6887034Z ... [0, 0, 1, 1], 2025-08-14T21:38:35.6887505Z ... ], 2025-08-14T21:38:35.6887884Z ... ]) 2025-08-14T21:38:35.6888127Z 2025-08-14T21:38:35.6888513Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-08-14T21:38:35.6889443Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-08-14T21:38:35.6890250Z >>> # each other, so one of them will be removed. 2025-08-14T21:38:35.6890910Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-08-14T21:38:35.6891446Z tensor(True) 2025-08-14T21:38:35.6891928Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-08-14T21:38:35.6892501Z >>> a_unique_dim0 2025-08-14T21:38:35.6893129Z tensor([[[0, 0, 1, 1], 2025-08-14T21:38:35.6893597Z [0, 0, 1, 1], 2025-08-14T21:38:35.6894044Z [1, 1, 1, 1]], 2025-08-14T21:38:35.6894513Z [[1, 1, 0, 0], 2025-08-14T21:38:35.6894973Z [1, 1, 0, 0], 2025-08-14T21:38:35.6895423Z [0, 0, 1, 1]]]) 2025-08-14T21:38:35.6895761Z 2025-08-14T21:38:35.6896143Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-08-14T21:38:35.6896923Z >>> # `a_unique_dim0`: 2025-08-14T21:38:35.6897460Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-08-14T21:38:35.6898032Z tensor(True) 2025-08-14T21:38:35.6898728Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-08-14T21:38:35.6899322Z tensor(True) 2025-08-14T21:38:35.6899591Z 2025-08-14T21:38:35.6899954Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-08-14T21:38:35.6900865Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-08-14T21:38:35.6901595Z >>> # them will be removed. 2025-08-14T21:38:35.6902102Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-08-14T21:38:35.6902625Z tensor(True) 2025-08-14T21:38:35.6903086Z >>> torch.unique(a, dim=1) 2025-08-14T21:38:35.6903713Z tensor([[[0, 0, 1, 1], 2025-08-14T21:38:35.6904188Z [1, 1, 0, 0]], 2025-08-14T21:38:35.6904660Z [[1, 1, 1, 1], 2025-08-14T21:38:35.6905128Z [0, 0, 1, 1]], 2025-08-14T21:38:35.6905553Z [[0, 0, 1, 1], 2025-08-14T21:38:35.6906022Z [1, 1, 0, 0]]]) 2025-08-14T21:38:35.6906342Z 2025-08-14T21:38:35.6906727Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-08-14T21:38:35.6907592Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-08-14T21:38:35.6908403Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-08-14T21:38:35.6909394Z >>> # sub-tensors will be removed. 2025-08-14T21:38:35.6909974Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-08-14T21:38:35.6910498Z tensor(True) 2025-08-14T21:38:35.6910940Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-08-14T21:38:35.6911474Z tensor(True) 2025-08-14T21:38:35.6911902Z >>> torch.unique(a, dim=2) 2025-08-14T21:38:35.6912414Z tensor([[[0, 1], 2025-08-14T21:38:35.6912861Z [0, 1], 2025-08-14T21:38:35.6913304Z [1, 0]], 2025-08-14T21:38:35.6913723Z [[1, 0], 2025-08-14T21:38:35.6914159Z [1, 0], 2025-08-14T21:38:35.6914600Z [1, 1]], 2025-08-14T21:38:35.6915015Z [[0, 1], 2025-08-14T21:38:35.6915428Z [0, 1], 2025-08-14T21:38:35.6915842Z [1, 0]]]) 2025-08-14T21:38:35.6916249Z 2025-08-14T21:38:35.6916918Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.6917641Z 2025-08-14T21:38:35.7024871Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=565. 2025-08-14T21:38:35.7026338Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.7027058Z 2025-08-14T21:38:35.7027355Z Load a model from a github repo or a local directory. 2025-08-14T21:38:35.7027832Z 2025-08-14T21:38:35.7028195Z Note: Loading a model is the typical use case, but this can also be used to 2025-08-14T21:38:35.7029206Z for loading other objects such as tokenizers, loss functions, etc. 2025-08-14T21:38:35.7029804Z 2025-08-14T21:38:35.7030113Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-08-14T21:38:35.7030918Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-08-14T21:38:35.7031596Z ref (a tag or a branch). 2025-08-14T21:38:35.7031909Z 2025-08-14T21:38:35.7032188Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-08-14T21:38:35.7032894Z path to a local directory. 2025-08-14T21:38:35.7033212Z 2025-08-14T21:38:35.7033349Z Args: 2025-08-14T21:38:35.7033740Z repo_or_dir (str): If ``source`` is 'github', 2025-08-14T21:38:35.7034698Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-08-14T21:38:35.7036041Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-08-14T21:38:35.7037298Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-08-14T21:38:35.7038342Z If ``source`` is 'local' then it should be a path to a local directory. 2025-08-14T21:38:35.7039461Z model (str): the name of a callable (entrypoint) defined in the 2025-08-14T21:38:35.7040222Z repo/dir's ``hubconf.py``. 2025-08-14T21:38:35.7040899Z *args (optional): the corresponding args for callable ``model``. 2025-08-14T21:38:35.7041758Z source (str, optional): 'github' or 'local'. Specifies how 2025-08-14T21:38:35.7042555Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-08-14T21:38:35.7055267Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-08-14T21:38:35.7056392Z This parameter was introduced in v1.12 and helps ensuring that users 2025-08-14T21:38:35.7057243Z only run code from repos that they trust. 2025-08-14T21:38:35.7057685Z 2025-08-14T21:38:35.7058048Z - If ``False``, a prompt will ask the user whether the repo should 2025-08-14T21:38:35.7058763Z be trusted. 2025-08-14T21:38:35.7059372Z - If ``True``, the repo will be added to the trusted list and loaded 2025-08-14T21:38:35.7060176Z without requiring explicit confirmation. 2025-08-14T21:38:35.7060889Z - If ``"check"``, the repo will be checked against the list of 2025-08-14T21:38:35.7061763Z trusted repos in the cache. If it is not present in that list, the 2025-08-14T21:38:35.7062714Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-08-14T21:38:35.7063932Z - If ``None``: this will raise a warning, inviting the user to set 2025-08-14T21:38:35.7064815Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-08-14T21:38:35.7065760Z is only present for backward compatibility and will be removed in 2025-08-14T21:38:35.7066511Z v2.0. 2025-08-14T21:38:35.7066743Z 2025-08-14T21:38:35.7067106Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-08-14T21:38:35.7068089Z force_reload (bool, optional): whether to force a fresh download of 2025-08-14T21:38:35.7069039Z the github repo unconditionally. Does not have any effect if 2025-08-14T21:38:35.7069831Z ``source = 'local'``. Default is ``False``. 2025-08-14T21:38:35.7070592Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-08-14T21:38:35.7071550Z local caches. Note that the message about first download cannot be 2025-08-14T21:38:35.7072459Z muted. Does not have any effect if ``source = 'local'``. 2025-08-14T21:38:35.7073123Z Default is ``True``. 2025-08-14T21:38:35.7073980Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-08-14T21:38:35.7075275Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-08-14T21:38:35.7076529Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-08-14T21:38:35.7077569Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-08-14T21:38:35.7078454Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-08-14T21:38:35.7079064Z 2025-08-14T21:38:35.7079226Z Returns: 2025-08-14T21:38:35.7079760Z The output of the ``model`` callable when called with the given 2025-08-14T21:38:35.7080487Z ``*args`` and ``**kwargs``. 2025-08-14T21:38:35.7080834Z 2025-08-14T21:38:35.7080989Z Example: 2025-08-14T21:38:35.7081433Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-08-14T21:38:35.7082040Z >>> # from a github repo 2025-08-14T21:38:35.7082528Z >>> repo = "pytorch/vision" 2025-08-14T21:38:35.7083038Z >>> model = torch.hub.load( 2025-08-14T21:38:35.7083668Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-08-14T21:38:35.7084355Z ... ) 2025-08-14T21:38:35.7084746Z >>> # from a local directory 2025-08-14T21:38:35.7085287Z >>> path = "/some/local/path/pytorch/vision" 2025-08-14T21:38:35.7085879Z >>> # xdoctest: +SKIP 2025-08-14T21:38:35.7086609Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-08-14T21:38:35.7087271Z 2025-08-14T21:38:35.7087910Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.7088605Z 2025-08-14T21:38:35.7089445Z msg = Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=657. 2025-08-14T21:38:35.7091005Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.7091721Z 2025-08-14T21:38:35.7092053Z Load a model from a local directory with a ``hubconf.py``. 2025-08-14T21:38:35.7092576Z 2025-08-14T21:38:35.7092732Z Args: 2025-08-14T21:38:35.7093458Z hubconf_dir (str): path to a local directory that contains a 2025-08-14T21:38:35.7094191Z ``hubconf.py``. 2025-08-14T21:38:35.7094800Z model (str): name of an entrypoint defined in the directory's 2025-08-14T21:38:35.7095499Z ``hubconf.py``. 2025-08-14T21:38:35.7096127Z *args (optional): the corresponding args for callable ``model``. 2025-08-14T21:38:35.7097101Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-08-14T21:38:35.7097719Z 2025-08-14T21:38:35.7097863Z Returns: 2025-08-14T21:38:35.7098359Z a single model with corresponding pretrained weights. 2025-08-14T21:38:35.7098875Z 2025-08-14T21:38:35.7099021Z Example: 2025-08-14T21:38:35.7099620Z >>> # xdoctest: +SKIP("stub local path") 2025-08-14T21:38:35.7100237Z >>> path = "/some/local/path/pytorch/vision" 2025-08-14T21:38:35.7100835Z >>> model = _load_local( 2025-08-14T21:38:35.7101297Z ... path, 2025-08-14T21:38:35.7101683Z ... "resnet50", 2025-08-14T21:38:35.7102197Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-08-14T21:38:35.7102799Z ... ) 2025-08-14T21:38:35.7103007Z 2025-08-14T21:38:35.7103551Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.7104256Z 2025-08-14T21:38:35.7105175Z 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. 2025-08-14T21:38:35.7106794Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.7107763Z Download object at the given URL to a local path. 2025-08-14T21:38:35.7108234Z 2025-08-14T21:38:35.7108380Z Args: 2025-08-14T21:38:35.7108804Z url (str): URL of the object to download 2025-08-14T21:38:35.7109655Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-08-14T21:38:35.7110884Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-08-14T21:38:35.7111953Z Default: None 2025-08-14T21:38:35.7112643Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-08-14T21:38:35.7113448Z Default: True 2025-08-14T21:38:35.7113734Z 2025-08-14T21:38:35.7113884Z Example: 2025-08-14T21:38:35.7114328Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-08-14T21:38:35.7114944Z >>> # xdoctest: +REQUIRES(POSIX) 2025-08-14T21:38:35.7115456Z >>> torch.hub.download_url_to_file( 2025-08-14T21:38:35.7116159Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-08-14T21:38:35.7116907Z ... "/tmp/temporary_file", 2025-08-14T21:38:35.7117405Z ... ) 2025-08-14T21:38:35.7117627Z 2025-08-14T21:38:35.7117756Z 2025-08-14T21:38:35.7118385Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.7119041Z 2025-08-14T21:38:35.7119911Z 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. 2025-08-14T21:38:35.7121253Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.7122221Z Loads the Torch serialized object at the given URL. 2025-08-14T21:38:35.7122703Z 2025-08-14T21:38:35.7123025Z If downloaded file is a zip file, it will be automatically 2025-08-14T21:38:35.7123927Z decompressed. 2025-08-14T21:38:35.7124169Z 2025-08-14T21:38:35.7124553Z If the object is already present in `model_dir`, it's deserialized and 2025-08-14T21:38:35.7125312Z returned. 2025-08-14T21:38:35.7125908Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-08-14T21:38:35.7126821Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-08-14T21:38:35.7127395Z 2025-08-14T21:38:35.7127534Z Args: 2025-08-14T21:38:35.7127954Z url (str): URL of the object to download 2025-08-14T21:38:35.7128706Z model_dir (str, optional): directory in which to save the object 2025-08-14T21:38:35.7129892Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-08-14T21:38:35.7131201Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-08-14T21:38:35.7132047Z Default: True 2025-08-14T21:38:35.7132923Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-08-14T21:38:35.7134139Z ``filename-.ext`` where ```` is the first eight or more 2025-08-14T21:38:35.7135165Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-08-14T21:38:35.7136261Z ensure unique names and to verify the contents of the file. 2025-08-14T21:38:35.7136975Z Default: False 2025-08-14T21:38:35.7137896Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-08-14T21:38:35.7139337Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-08-14T21:38:35.7140621Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-08-14T21:38:35.7141319Z 2025-08-14T21:38:35.7141467Z Example: 2025-08-14T21:38:35.7141894Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-08-14T21:38:35.7142544Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-08-14T21:38:35.7143404Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-08-14T21:38:35.7144244Z ... ) 2025-08-14T21:38:35.7144475Z 2025-08-14T21:38:35.7144620Z 2025-08-14T21:38:35.7145264Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.7145968Z 2025-08-14T21:38:35.7167064Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=375. 2025-08-14T21:38:35.7168716Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:35.7169768Z Registers the function implementation as the fallback for the given key. 2025-08-14T21:38:35.7170399Z 2025-08-14T21:38:35.7170798Z This function only works for a library with global namespace ("_"). 2025-08-14T21:38:35.7171417Z 2025-08-14T21:38:35.7171573Z Args: 2025-08-14T21:38:35.7172297Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-08-14T21:38:35.7173226Z to register a fallthrough. 2025-08-14T21:38:35.7174203Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-08-14T21:38:35.7175281Z the dispatch key that the library was created with. 2025-08-14T21:38:35.7176460Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-08-14T21:38:35.7177962Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-08-14T21:38:35.7178752Z 2025-08-14T21:38:35.7178920Z Example:: 2025-08-14T21:38:35.7179179Z 2025-08-14T21:38:35.7179377Z >>> my_lib = Library("_", "IMPL") 2025-08-14T21:38:35.7180152Z >>> def fallback_kernel(op, *args, **kwargs): 2025-08-14T21:38:35.7180821Z >>> # Handle all autocast ops generically 2025-08-14T21:38:35.7181420Z >>> # ... 2025-08-14T21:38:35.7181949Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-08-14T21:38:35.7182540Z 2025-08-14T21:38:35.7184050Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-08-14T21:38:35.7185395Z 2025-08-14T21:38:35.7185624Z my_lib.fallback(fallback_kernel, "Autocast") 2025-08-14T21:38:35.7186191Z ^ 2025-08-14T21:38:35.7255189Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=948. 2025-08-14T21:38:35.7256828Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:35.7257923Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-08-14T21:38:35.7258573Z 2025-08-14T21:38:35.7258902Z Also sometimes known as a "meta kernel", "abstract impl". 2025-08-14T21:38:35.7259437Z 2025-08-14T21:38:35.7259897Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-08-14T21:38:35.7260908Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-08-14T21:38:35.7262124Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-08-14T21:38:35.7263028Z what the properties of the output Tensors are. 2025-08-14T21:38:35.7263597Z 2025-08-14T21:38:35.7264027Z The FakeTensor implementation has the same signature as the operator. 2025-08-14T21:38:35.7265030Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-08-14T21:38:35.7265984Z implementation, assume that all Tensor inputs to the operator are 2025-08-14T21:38:35.7266980Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-08-14T21:38:35.7267927Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-08-14T21:38:35.7268946Z The FakeTensor implementation must consist of only PyTorch operations 2025-08-14T21:38:35.7269921Z (and may not directly access the storage or data of any input or 2025-08-14T21:38:35.7270655Z intermediate Tensors). 2025-08-14T21:38:35.7270954Z 2025-08-14T21:38:35.7271173Z This API may be used as a decorator (see examples). 2025-08-14T21:38:35.7271616Z 2025-08-14T21:38:35.7271825Z For a detailed guide on custom ops, please see 2025-08-14T21:38:35.7272616Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-08-14T21:38:35.7273269Z 2025-08-14T21:38:35.7273408Z Args: 2025-08-14T21:38:35.7274024Z op_name: Operator name (along with the overload) or OpOverload object. 2025-08-14T21:38:35.7274849Z func: Fake tensor implementation. 2025-08-14T21:38:35.7275606Z lib (Optional[Library]): Library to register the fake tensor to. 2025-08-14T21:38:35.7276485Z allow_override: Flag controlling if we want to override an 2025-08-14T21:38:35.7277309Z existing registered fake impl. This is by default off, 2025-08-14T21:38:35.7278148Z and will error you're trying to register a fake impl to 2025-08-14T21:38:35.7278990Z an operator that already has a fake impl. This also only 2025-08-14T21:38:35.7279824Z applies if the custom operator was not created via 2025-08-14T21:38:35.7280676Z torch.library.custom_op, as overriding and existing fake 2025-08-14T21:38:35.7281431Z impl is already allowed. 2025-08-14T21:38:35.7281832Z 2025-08-14T21:38:35.7281979Z Examples: 2025-08-14T21:38:35.7282366Z >>> import torch 2025-08-14T21:38:35.7282828Z >>> import numpy as np 2025-08-14T21:38:35.7283320Z >>> from torch import Tensor 2025-08-14T21:38:35.7283831Z >>> 2025-08-14T21:38:35.7284557Z >>> # Example 1: an operator without data-dependent output shape 2025-08-14T21:38:35.7285452Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-08-14T21:38:35.7286448Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-08-14T21:38:35.7287375Z >>> raise NotImplementedError("Implementation goes here") 2025-08-14T21:38:35.7288053Z >>> 2025-08-14T21:38:35.7288562Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-08-14T21:38:35.7289242Z >>> def _(x, weight, bias): 2025-08-14T21:38:35.7289763Z >>> assert x.dim() == 2 2025-08-14T21:38:35.7290279Z >>> assert weight.dim() == 2 2025-08-14T21:38:35.7290839Z >>> assert bias.dim() == 1 2025-08-14T21:38:35.7291433Z >>> assert x.shape[1] == weight.shape[1] 2025-08-14T21:38:35.7292075Z >>> assert weight.shape[0] == bias.shape[0] 2025-08-14T21:38:35.7292724Z >>> assert x.device == weight.device 2025-08-14T21:38:35.7293529Z >>> 2025-08-14T21:38:35.7293941Z >>> return (x @ weight.t()) + bias 2025-08-14T21:38:35.7294478Z >>> 2025-08-14T21:38:35.7294986Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-08-14T21:38:35.7295716Z >>> x = torch.randn(2, 3) 2025-08-14T21:38:35.7296458Z >>> w = torch.randn(3, 3) 2025-08-14T21:38:35.7296996Z >>> b = torch.randn(3) 2025-08-14T21:38:35.7297536Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-08-14T21:38:35.7298140Z >>> 2025-08-14T21:38:35.7298497Z >>> assert y.shape == (2, 3) 2025-08-14T21:38:35.7299008Z >>> 2025-08-14T21:38:35.7299535Z >>> # Example 2: an operator with data-dependent output shape 2025-08-14T21:38:35.7300468Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-08-14T21:38:35.7301326Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-08-14T21:38:35.7301973Z >>> x_np = x.numpy(force=True) 2025-08-14T21:38:35.7302612Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-08-14T21:38:35.7303280Z >>> return torch.tensor(res, device=x.device) 2025-08-14T21:38:35.7303973Z >>> 2025-08-14T21:38:35.7304508Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-08-14T21:38:35.7305198Z >>> def _(x): 2025-08-14T21:38:35.7305739Z >>> # Number of nonzero-elements is data-dependent. 2025-08-14T21:38:35.7306486Z >>> # Since we cannot peek at the data in an fake impl, 2025-08-14T21:38:35.7307236Z >>> # we use the ctx object to construct a new symint that 2025-08-14T21:38:35.7307963Z >>> # represents the data-dependent size. 2025-08-14T21:38:35.7308612Z >>> ctx = torch.library.get_ctx() 2025-08-14T21:38:35.7309231Z >>> nnz = ctx.new_dynamic_size() 2025-08-14T21:38:35.7309813Z >>> shape = [nnz, x.dim()] 2025-08-14T21:38:35.7310450Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-08-14T21:38:35.7311104Z >>> return result 2025-08-14T21:38:35.7311560Z >>> 2025-08-14T21:38:35.7312092Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-08-14T21:38:35.7312776Z >>> 2025-08-14T21:38:35.7313169Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-08-14T21:38:35.7313965Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-08-14T21:38:35.7314809Z >>> trace.print_readable() 2025-08-14T21:38:35.7315314Z >>> 2025-08-14T21:38:35.7315895Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-08-14T21:38:35.7316540Z 2025-08-14T21:38:35.7316677Z 2025-08-14T21:38:35.7317895Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-08-14T21:38:35.7319083Z 2025-08-14T21:38:35.7319232Z _._ = None 2025-08-14T21:38:35.7319599Z ^ 2025-08-14T21:38:35.7320995Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1083. 2025-08-14T21:38:35.7322680Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.7323632Z Register a backward formula for this custom op. 2025-08-14T21:38:35.7324103Z 2025-08-14T21:38:35.7324474Z In order for an operator to work with autograd, you need to register 2025-08-14T21:38:35.7325242Z a backward formula: 2025-08-14T21:38:35.7325908Z 1. You must tell us how to compute gradients during the backward pass 2025-08-14T21:38:35.7326703Z by providing us a "backward" function. 2025-08-14T21:38:35.7327512Z 2. If you need any values from the forward to compute gradients, you can 2025-08-14T21:38:35.7328387Z use `setup_context` to save values for backward. 2025-08-14T21:38:35.7328846Z 2025-08-14T21:38:35.7329242Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-08-14T21:38:35.7330194Z - ``grads`` is one or more gradients. The number of gradients matches 2025-08-14T21:38:35.7331002Z the number of outputs of the operator. 2025-08-14T21:38:35.7331821Z The ``ctx`` object is `the same ctx object `_ used by 2025-08-14T21:38:35.7333043Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-08-14T21:38:35.7333990Z same as :meth:`torch.autograd.Function.backward`. 2025-08-14T21:38:35.7334501Z 2025-08-14T21:38:35.7334889Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-08-14T21:38:35.7335942Z Please save quantities needed for backward onto the ``ctx`` object via 2025-08-14T21:38:35.7336959Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-08-14T21:38:35.7337953Z or assigning them as attributes of ``ctx``. If your custom op has 2025-08-14T21:38:35.7338929Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-08-14T21:38:35.7339878Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-08-14T21:38:35.7340462Z 2025-08-14T21:38:35.7340854Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-08-14T21:38:35.7341905Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-08-14T21:38:35.7342972Z not depend on or mutate global state. If you need a non-traceable backward, 2025-08-14T21:38:35.7344116Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-08-14T21:38:35.7344773Z 2025-08-14T21:38:35.7345166Z If you need different autograd behavior on different devices, then we 2025-08-14T21:38:35.7346212Z recommend creating two different custom operators, one for each device 2025-08-14T21:38:35.7347237Z that needs different behavior, and switching between them at runtime. 2025-08-14T21:38:35.7347845Z 2025-08-14T21:38:35.7347996Z Examples: 2025-08-14T21:38:35.7348411Z >>> import torch 2025-08-14T21:38:35.7348904Z >>> import numpy as np 2025-08-14T21:38:35.7349415Z >>> from torch import Tensor 2025-08-14T21:38:35.7349885Z >>> 2025-08-14T21:38:35.7350454Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-08-14T21:38:35.7351245Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-08-14T21:38:35.7351844Z >>> x_np = x.cpu().numpy() 2025-08-14T21:38:35.7352391Z >>> y_np = np.sin(x_np) 2025-08-14T21:38:35.7353024Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-08-14T21:38:35.7353656Z >>> 2025-08-14T21:38:35.7354145Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-08-14T21:38:35.7354800Z >>> x, = inputs 2025-08-14T21:38:35.7355276Z >>> ctx.save_for_backward(x) 2025-08-14T21:38:35.7355812Z >>> 2025-08-14T21:38:35.7356210Z >>> def backward(ctx, grad): 2025-08-14T21:38:35.7356743Z >>> x, = ctx.saved_tensors 2025-08-14T21:38:35.7357434Z >>> return grad * x.cos() 2025-08-14T21:38:35.7357956Z >>> 2025-08-14T21:38:35.7358376Z >>> torch.library.register_autograd( 2025-08-14T21:38:35.7359131Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-08-14T21:38:35.7359823Z ... ) 2025-08-14T21:38:35.7360203Z >>> 2025-08-14T21:38:35.7360620Z >>> x = torch.randn(3, requires_grad=True) 2025-08-14T21:38:35.7361205Z >>> y = numpy_sin(x) 2025-08-14T21:38:35.7361815Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-08-14T21:38:35.7362522Z >>> assert torch.allclose(grad_x, x.cos()) 2025-08-14T21:38:35.7363112Z >>> 2025-08-14T21:38:35.7363536Z >>> # Example with a keyword-only arg 2025-08-14T21:38:35.7364275Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-08-14T21:38:35.7365045Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-08-14T21:38:35.7365629Z >>> x_np = x.cpu().numpy() 2025-08-14T21:38:35.7366170Z >>> y_np = x_np * val 2025-08-14T21:38:35.7366810Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-08-14T21:38:35.7367476Z >>> 2025-08-14T21:38:35.7368107Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-08-14T21:38:35.7369106Z >>> ctx.val = keyword_only_inputs["val"] 2025-08-14T21:38:35.7369706Z >>> 2025-08-14T21:38:35.7370120Z >>> def backward(ctx, grad): 2025-08-14T21:38:35.7370664Z >>> return grad * ctx.val 2025-08-14T21:38:35.7371193Z >>> 2025-08-14T21:38:35.7371631Z >>> torch.library.register_autograd( 2025-08-14T21:38:35.7372513Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-08-14T21:38:35.7373220Z ... ) 2025-08-14T21:38:35.7373594Z >>> 2025-08-14T21:38:35.7374022Z >>> x = torch.randn(3, requires_grad=True) 2025-08-14T21:38:35.7374657Z >>> y = numpy_mul(x, val=3.14) 2025-08-14T21:38:35.7375308Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-08-14T21:38:35.7376121Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-08-14T21:38:35.7376628Z 2025-08-14T21:38:35.7376800Z 2025-08-14T21:38:35.7377470Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.7378160Z 2025-08-14T21:38:35.7379083Z msg = Cannot scrape callname=get_kernel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1482. 2025-08-14T21:38:35.7380595Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:35.7381661Z Returns the computed kernel for a given operator and dispatch key. 2025-08-14T21:38:35.7382292Z 2025-08-14T21:38:35.7382707Z This function retrieves the kernel that would be executed for a given 2025-08-14T21:38:35.7383864Z operator and dispatch key combination. The returned SafeKernelFunction 2025-08-14T21:38:35.7384890Z can be used to call the kernel in a boxed fashion. The intended use 2025-08-14T21:38:35.7385840Z case for this function is to retrieve the original kernel for a given 2025-08-14T21:38:35.7386848Z dispatch key and then register another kernel to the same dispatch key 2025-08-14T21:38:35.7387701Z that calls into the original kernel for certain cases. 2025-08-14T21:38:35.7388146Z 2025-08-14T21:38:35.7388289Z Args: 2025-08-14T21:38:35.7388801Z op: Operator name (along with the overload) or OpOverload object 2025-08-14T21:38:35.7389758Z Can be a string (e.g., "aten::add.Tensor"), an OpOverload, or a CustomOpDef. 2025-08-14T21:38:35.7390849Z dispatch_key (str | torch.DispatchKey): The dispatch key to get the kernel for. 2025-08-14T21:38:35.7391874Z Can be a string (e.g., "CPU", "CUDA") or a DispatchKey enum value. 2025-08-14T21:38:35.7392456Z 2025-08-14T21:38:35.7392609Z Returns: 2025-08-14T21:38:35.7393596Z torch._C._SafeKernelFunction: A safe kernel function that can be used to 2025-08-14T21:38:35.7394428Z call the kernel. 2025-08-14T21:38:35.7394719Z 2025-08-14T21:38:35.7394857Z Raises: 2025-08-14T21:38:35.7395306Z RuntimeError: If the operator does not exist. 2025-08-14T21:38:35.7395757Z 2025-08-14T21:38:35.7395927Z Example: 2025-08-14T21:38:35.7396332Z >>> # Get the CPU kernel for torch.add 2025-08-14T21:38:35.7397080Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", "CPU") 2025-08-14T21:38:35.7397773Z >>> 2025-08-14T21:38:35.7398173Z >>> # You can also use DispatchKey enum 2025-08-14T21:38:35.7399028Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", torch.DispatchKey.CPU) 2025-08-14T21:38:35.7399852Z >>> 2025-08-14T21:38:35.7400256Z >>> # Or use an OpOverload directly 2025-08-14T21:38:35.7401027Z >>> kernel = torch.library.get_kernel(torch.ops.aten.add.Tensor, "CPU") 2025-08-14T21:38:35.7401766Z >>> 2025-08-14T21:38:35.7402366Z >>> # Example: Using get_kernel in a custom op with conditional dispatch 2025-08-14T21:38:35.7403159Z >>> # Get the original kernel for torch.sin 2025-08-14T21:38:35.7403966Z >>> original_sin_kernel = torch.library.get_kernel("aten::sin", "CPU") 2025-08-14T21:38:35.7404896Z >>> 2025-08-14T21:38:35.7405503Z >>> # If input has negative values, use original sin, otherwise return zeros 2025-08-14T21:38:35.7406395Z >>> def conditional_sin_impl(dispatch_keys, x): 2025-08-14T21:38:35.7407028Z >>> if (x < 0).any(): 2025-08-14T21:38:35.7407683Z >>> return original_sin_kernel.call_boxed(dispatch_keys, x) 2025-08-14T21:38:35.7408383Z >>> else: 2025-08-14T21:38:35.7408855Z >>> return torch.zeros_like(x) 2025-08-14T21:38:35.7409419Z >>> 2025-08-14T21:38:35.7409820Z >>> lib = torch.library.Library("aten", "IMPL") 2025-08-14T21:38:35.7410753Z >>> # with_keyset=True so the first argument to the impl is the current DispatchKeySet 2025-08-14T21:38:35.7411794Z >>> which needs to be the first argument to ``kernel.call_boxed`` 2025-08-14T21:38:35.7412692Z >>> lib.impl("sin", conditional_sin_impl, "CPU", with_keyset=True) 2025-08-14T21:38:35.7413396Z >>> 2025-08-14T21:38:35.7413823Z >>> # Test the conditional behavior 2025-08-14T21:38:35.7414433Z >>> x_positive = torch.tensor([1.0, 2.0]) 2025-08-14T21:38:35.7415043Z >>> x_mixed = torch.tensor([-1.0, 2.0]) 2025-08-14T21:38:35.7415646Z >>> torch.sin(x_positive) 2025-08-14T21:38:35.7416154Z tensor([0., 0.]) 2025-08-14T21:38:35.7416608Z >>> torch.sin(x_mixed) 2025-08-14T21:38:35.7417098Z tensor([-0.8415, 0.9093]) 2025-08-14T21:38:35.7417585Z 2025-08-14T21:38:35.7418670Z Original Error: SyntaxError('invalid syntax', ('', 23, 7, 'which needs to be the first argument to ``kernel.call_boxed``\n', 23, 12)) 2025-08-14T21:38:35.7419805Z 2025-08-14T21:38:35.7420146Z which needs to be the first argument to ``kernel.call_boxed`` 2025-08-14T21:38:35.7420835Z ^ 2025-08-14T21:38:35.7421956Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1571. 2025-08-14T21:38:35.7423625Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.7424724Z Given an operator and some sample arguments, tests if the operator is 2025-08-14T21:38:35.7425462Z registered correctly. 2025-08-14T21:38:35.7425769Z 2025-08-14T21:38:35.7426150Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-08-14T21:38:35.7427197Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-08-14T21:38:35.7428262Z and these APIs require that the functions you pass them satisfy certain 2025-08-14T21:38:35.7429310Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-08-14T21:38:35.7430338Z ``opcheck`` tests these metadata and properties. 2025-08-14T21:38:35.7430812Z 2025-08-14T21:38:35.7431012Z Concretely, we test the following: 2025-08-14T21:38:35.7431424Z 2025-08-14T21:38:35.7431744Z - test_schema: If the schema matches the implementation of 2025-08-14T21:38:35.7432688Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-08-14T21:38:35.7433712Z then we check the implementation mutates the Tensor. If the schema 2025-08-14T21:38:35.7434632Z specifies that we return a new Tensor, then we check that the 2025-08-14T21:38:35.7435612Z implementation returns a new Tensor (instead of an existing one or 2025-08-14T21:38:35.7436411Z a view of an existing one). 2025-08-14T21:38:35.7437126Z - test_autograd_registration: If the operator supports training 2025-08-14T21:38:35.7438064Z (autograd): we check that its autograd formula is registered via 2025-08-14T21:38:35.7439023Z torch.library.register_autograd or a manual registration to one 2025-08-14T21:38:35.7440019Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-08-14T21:38:35.7440822Z registrations may lead to undefined behavior. 2025-08-14T21:38:35.7441592Z - test_faketensor: If the operator has a FakeTensor kernel 2025-08-14T21:38:35.7442416Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-08-14T21:38:35.7443442Z but not sufficient) for the operator to work with PyTorch compilation 2025-08-14T21:38:35.7444466Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-08-14T21:38:35.7445416Z (also sometimes known as a meta kernel) was registered for the 2025-08-14T21:38:35.7446349Z operator and that it is correct. This test takes the result of 2025-08-14T21:38:35.7447251Z running the operator on real tensors and the result of running 2025-08-14T21:38:35.7448184Z the operator on FakeTensors and checks that they have the same 2025-08-14T21:38:35.7449025Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-08-14T21:38:35.7449868Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-08-14T21:38:35.7450798Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-08-14T21:38:35.7451725Z This checks that the outputs (and gradients, if applicable) are the 2025-08-14T21:38:35.7452598Z same under eager-mode PyTorch and torch.compile. 2025-08-14T21:38:35.7453409Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-08-14T21:38:35.7454263Z other things it tests are that the operator supports 2025-08-14T21:38:35.7455145Z functionalization and that the backward pass (if it exists) also 2025-08-14T21:38:35.7455946Z supports FakeTensor and functionalization. 2025-08-14T21:38:35.7456364Z 2025-08-14T21:38:35.7456704Z For best results, please call ``opcheck`` multiple times with a 2025-08-14T21:38:35.7457597Z representative set of inputs. If your operator supports 2025-08-14T21:38:35.7458598Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-08-14T21:38:35.7459622Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-08-14T21:38:35.7460533Z use ``opcheck`` with inputs on all supported devices. 2025-08-14T21:38:35.7461023Z 2025-08-14T21:38:35.7461186Z Args: 2025-08-14T21:38:35.7461690Z op: The operator. Must either be a function decorated with 2025-08-14T21:38:35.7462612Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-08-14T21:38:35.7463672Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-08-14T21:38:35.7464468Z args: The args to the operator 2025-08-14T21:38:35.7465041Z kwargs: The kwargs to the operator 2025-08-14T21:38:35.7465763Z test_utils: Tests that we should run. Default: all of them. 2025-08-14T21:38:35.7466562Z Example: ("test_schema", "test_faketensor") 2025-08-14T21:38:35.7467468Z raise_exception: If we should raise an exception on the first 2025-08-14T21:38:35.7468322Z error. If False, we will return a dict with information 2025-08-14T21:38:35.7469045Z on if each test passed or not. 2025-08-14T21:38:35.7469852Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-08-14T21:38:35.7470763Z If specified ``atol`` must also be specified. 2025-08-14T21:38:35.7471545Z If omitted, default values based on the ``dtype`` are selected 2025-08-14T21:38:35.7472411Z (see the table in :func:`torch.testing.assert_close`). 2025-08-14T21:38:35.7473346Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-08-14T21:38:35.7474248Z If specified ``rtol`` must also be specified. 2025-08-14T21:38:35.7475049Z If omitted, default values based on the ``dtype`` are selected 2025-08-14T21:38:35.7475902Z (see the table in :func:`torch.testing.assert_close`). 2025-08-14T21:38:35.7476434Z 2025-08-14T21:38:35.7476598Z .. warning:: 2025-08-14T21:38:35.7476855Z 2025-08-14T21:38:35.7477234Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-08-14T21:38:35.7478212Z opcheck tests if your usage of torch.library APIs is correct while 2025-08-14T21:38:35.7479315Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-08-14T21:38:35.7480309Z mathematically correct. Use both to test custom ops that support 2025-08-14T21:38:35.7481110Z gradient computation. 2025-08-14T21:38:35.7481441Z 2025-08-14T21:38:35.7481607Z Example: 2025-08-14T21:38:35.7481823Z 2025-08-14T21:38:35.7482065Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-14T21:38:35.7482886Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-08-14T21:38:35.7483687Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-08-14T21:38:35.7484420Z >>> x_np = x.numpy(force=True) 2025-08-14T21:38:35.7484989Z >>> z_np = x_np * y 2025-08-14T21:38:35.7485559Z >>> return torch.from_numpy(z_np).to(x.device) 2025-08-14T21:38:35.7486155Z >>> 2025-08-14T21:38:35.7486561Z >>> @numpy_mul.register_fake 2025-08-14T21:38:35.7487096Z >>> def _(x, y): 2025-08-14T21:38:35.7487591Z >>> return torch.empty_like(x) 2025-08-14T21:38:35.7488124Z >>> 2025-08-14T21:38:35.7488556Z >>> def setup_context(ctx, inputs, output): 2025-08-14T21:38:35.7489157Z >>> y, = inputs 2025-08-14T21:38:35.7489608Z >>> ctx.y = y 2025-08-14T21:38:35.7490045Z >>> 2025-08-14T21:38:35.7490441Z >>> def backward(ctx, grad): 2025-08-14T21:38:35.7490971Z >>> return grad * ctx.y, None 2025-08-14T21:38:35.7491496Z >>> 2025-08-14T21:38:35.7492109Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-08-14T21:38:35.7493029Z >>> 2025-08-14T21:38:35.7493412Z >>> sample_inputs = [ 2025-08-14T21:38:35.7493923Z >>> (torch.randn(3), 3.14), 2025-08-14T21:38:35.7494507Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-08-14T21:38:35.7495174Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-08-14T21:38:35.7496106Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-08-14T21:38:35.7496822Z >>> ] 2025-08-14T21:38:35.7497174Z >>> 2025-08-14T21:38:35.7497572Z >>> for args in sample_inputs: 2025-08-14T21:38:35.7498182Z >>> torch.library.opcheck(numpy_mul, args) 2025-08-14T21:38:35.7498632Z 2025-08-14T21:38:35.7498773Z 2025-08-14T21:38:35.7499443Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.7500135Z 2025-08-14T21:38:35.7851851Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1285. 2025-08-14T21:38:35.7853347Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.7854049Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-08-14T21:38:35.7854498Z 2025-08-14T21:38:35.7854673Z Loads an object saved with :func:`torch.save` from a file. 2025-08-14T21:38:35.7854979Z 2025-08-14T21:38:35.7855209Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-08-14T21:38:35.7855779Z which underlie tensors, specially. They are first deserialized on the 2025-08-14T21:38:35.7856316Z CPU and are then moved to the device they were saved from. If this fails 2025-08-14T21:38:35.7856869Z (e.g. because the run time system doesn't have certain devices), an exception 2025-08-14T21:38:35.7857445Z is raised. However, storages can be dynamically remapped to an alternative 2025-08-14T21:38:35.7857962Z set of devices using the :attr:`map_location` argument. 2025-08-14T21:38:35.7858233Z 2025-08-14T21:38:35.7858473Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-08-14T21:38:35.7859052Z storage with two arguments: storage and location. The storage argument 2025-08-14T21:38:35.7859616Z will be the initial deserialization of the storage, residing on the CPU. 2025-08-14T21:38:35.7860245Z Each serialized storage has a location tag associated with it which 2025-08-14T21:38:35.7860783Z identifies the device it was saved from, and this tag is the second 2025-08-14T21:38:35.7861357Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-08-14T21:38:35.7861944Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-08-14T21:38:35.7862468Z :attr:`map_location` should return either ``None`` or a storage. If 2025-08-14T21:38:35.7863034Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-08-14T21:38:35.7863712Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-08-14T21:38:35.7864353Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-08-14T21:38:35.7864707Z 2025-08-14T21:38:35.7864947Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-08-14T21:38:35.7865516Z a device tag, it indicates the location where all tensors should be loaded. 2025-08-14T21:38:35.7865871Z 2025-08-14T21:38:35.7866117Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-08-14T21:38:35.7866697Z appearing in the file (keys), to ones that specify where to put the 2025-08-14T21:38:35.7867098Z storages (values). 2025-08-14T21:38:35.7867265Z 2025-08-14T21:38:35.7867477Z User extensions can register their own location tags and tagging and 2025-08-14T21:38:35.7868074Z deserialization methods using :func:`torch.serialization.register_package`. 2025-08-14T21:38:35.7868445Z 2025-08-14T21:38:35.7868704Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-08-14T21:38:35.7869055Z 2025-08-14T21:38:35.7869140Z Args: 2025-08-14T21:38:35.7869573Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-08-14T21:38:35.7870179Z or a string or os.PathLike object containing a file name 2025-08-14T21:38:35.7870778Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-08-14T21:38:35.7871287Z locations 2025-08-14T21:38:35.7871666Z pickle_module: module used for unpickling metadata and objects (has to 2025-08-14T21:38:35.7872177Z match the :attr:`pickle_module` used to serialize file) 2025-08-14T21:38:35.7872653Z weights_only: Indicates whether unpickler should be restricted to 2025-08-14T21:38:35.7873141Z loading only tensors, primitive types, dictionaries 2025-08-14T21:38:35.7873625Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-08-14T21:38:35.7874155Z See :ref:`weights-only` for more details. 2025-08-14T21:38:35.7874713Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-08-14T21:38:35.7875489Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-08-14T21:38:35.7876275Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-08-14T21:38:35.7877045Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-08-14T21:38:35.7877793Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-08-14T21:38:35.7878384Z will be lazily loaded when their data is accessed. 2025-08-14T21:38:35.7878884Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-08-14T21:38:35.7879449Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-08-14T21:38:35.7879883Z :attr:`errors=...`. 2025-08-14T21:38:35.7880073Z 2025-08-14T21:38:35.7880177Z .. warning:: 2025-08-14T21:38:35.7880525Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-08-14T21:38:35.7881158Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-08-14T21:38:35.7881741Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-08-14T21:38:35.7882366Z during unpickling. Never load data that could have come from an untrusted 2025-08-14T21:38:35.7882994Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-08-14T21:38:35.7883408Z 2025-08-14T21:38:35.7883495Z .. note:: 2025-08-14T21:38:35.7883884Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-08-14T21:38:35.7884517Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-08-14T21:38:35.7885139Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-08-14T21:38:35.7885528Z 2025-08-14T21:38:35.7885616Z .. note:: 2025-08-14T21:38:35.7885987Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-08-14T21:38:35.7886558Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-08-14T21:38:35.7887076Z when loading files saved by Python 2 in Python 3. If this default 2025-08-14T21:38:35.7887646Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-08-14T21:38:35.7888248Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-08-14T21:38:35.7888819Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-08-14T21:38:35.7889394Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-08-14T21:38:35.7889743Z 2025-08-14T21:38:35.7889828Z Example: 2025-08-14T21:38:35.7890086Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-08-14T21:38:35.7890452Z >>> torch.load("tensors.pt", weights_only=True) 2025-08-14T21:38:35.7890807Z # Load all tensors onto the CPU 2025-08-14T21:38:35.7891110Z >>> torch.load( 2025-08-14T21:38:35.7891352Z ... "tensors.pt", 2025-08-14T21:38:35.7891652Z ... map_location=torch.device("cpu"), 2025-08-14T21:38:35.7891987Z ... weights_only=True, 2025-08-14T21:38:35.7892254Z ... ) 2025-08-14T21:38:35.7892521Z # Load all tensors onto the CPU, using a function 2025-08-14T21:38:35.7893088Z >>> torch.load( 2025-08-14T21:38:35.7893342Z ... "tensors.pt", 2025-08-14T21:38:35.7893645Z ... map_location=lambda storage, loc: storage, 2025-08-14T21:38:35.7893994Z ... weights_only=True, 2025-08-14T21:38:35.7894390Z ... ) 2025-08-14T21:38:35.7894614Z # Load all tensors onto GPU 1 2025-08-14T21:38:35.7894912Z >>> torch.load( 2025-08-14T21:38:35.7895166Z ... "tensors.pt", 2025-08-14T21:38:35.7895489Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-08-14T21:38:35.7895866Z ... weights_only=True, 2025-08-14T21:38:35.7896172Z ... ) # type: ignore[attr-defined] 2025-08-14T21:38:35.7896490Z # Map tensors from GPU 1 to GPU 0 2025-08-14T21:38:35.7896798Z >>> torch.load( 2025-08-14T21:38:35.7897056Z ... "tensors.pt", 2025-08-14T21:38:35.7897342Z ... map_location={"cuda:1": "cuda:0"}, 2025-08-14T21:38:35.7897674Z ... weights_only=True, 2025-08-14T21:38:35.7897959Z ... ) 2025-08-14T21:38:35.7898199Z # Load tensor from io.BytesIO object 2025-08-14T21:38:35.7898653Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-08-14T21:38:35.7899139Z >>> with open("tensor.pt", "rb") as f: 2025-08-14T21:38:35.7899476Z ... buffer = io.BytesIO(f.read()) 2025-08-14T21:38:35.7899805Z >>> torch.load(buffer, weights_only=False) 2025-08-14T21:38:35.7900190Z # Load a module with 'ascii' encoding for unpickling 2025-08-14T21:38:35.7900801Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-08-14T21:38:35.7901359Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-08-14T21:38:35.7901755Z 2025-08-14T21:38:35.7902118Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.7902475Z 2025-08-14T21:38:35.8373925Z 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=1851. 2025-08-14T21:38:35.8374926Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.8375515Z Computes the minimum storage size to hold the given tensor geometry. 2025-08-14T21:38:35.8375852Z 2025-08-14T21:38:35.8375976Z Example 2025-08-14T21:38:35.8376187Z ======= 2025-08-14T21:38:35.8376303Z 2025-08-14T21:38:35.8376528Z This is the size of a newly allocated tensor's storage, in units of elements 2025-08-14T21:38:35.8376884Z 2025-08-14T21:38:35.8376986Z >>> t = torch.empty((10, 20)) 2025-08-14T21:38:35.8377421Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-08-14T21:38:35.8377858Z 200 2025-08-14T21:38:35.8377970Z 2025-08-14T21:38:35.8378072Z >>> # xdoctest: +SKIP(failing) 2025-08-14T21:38:35.8378403Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-08-14T21:38:35.8378766Z >>> size = compute_required_storage_length( 2025-08-14T21:38:35.8379121Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-08-14T21:38:35.8379446Z ... ) 2025-08-14T21:38:35.8379668Z >>> size == t.storage().size() 2025-08-14T21:38:35.8379948Z True 2025-08-14T21:38:35.8380058Z 2025-08-14T21:38:35.8380251Z A valid tensor may have a larger storage size, but never smaller 2025-08-14T21:38:35.8380564Z 2025-08-14T21:38:35.8380669Z >>> slice = torch.empty(100)[20:40] 2025-08-14T21:38:35.8380986Z >>> slice.storage().size() 2025-08-14T21:38:35.8381238Z 100 2025-08-14T21:38:35.8381364Z 2025-08-14T21:38:35.8381470Z >>> compute_required_storage_length( 2025-08-14T21:38:35.8381848Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-08-14T21:38:35.8382194Z ... ) 2025-08-14T21:38:35.8382391Z 40 2025-08-14T21:38:35.8382497Z 2025-08-14T21:38:35.8382584Z 2025-08-14T21:38:35.8382939Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.8383293Z 2025-08-14T21:38:35.8934787Z 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. 2025-08-14T21:38:35.8936765Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:35.8937857Z Check if the current accelerator is available at runtime: it was build, all the 2025-08-14T21:38:35.8938904Z required drivers are available and at least one device is visible. 2025-08-14T21:38:35.8939752Z See :ref:`accelerator` for details. 2025-08-14T21:38:35.8940251Z 2025-08-14T21:38:35.8940396Z Returns: 2025-08-14T21:38:35.8941139Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-08-14T21:38:35.8941869Z 2025-08-14T21:38:35.8942365Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-08-14T21:38:35.8943600Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-08-14T21:38:35.8944782Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-08-14T21:38:35.8945730Z :ref:`multiprocessing-poison-fork-note`. 2025-08-14T21:38:35.8946209Z 2025-08-14T21:38:35.8946374Z Example:: 2025-08-14T21:38:35.8946620Z 2025-08-14T21:38:35.8947114Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-14T21:38:35.8947949Z 2025-08-14T21:38:35.8949386Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-08-14T21:38:35.8950657Z 2025-08-14T21:38:35.8951137Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-14T21:38:35.8952026Z ^ 2025-08-14T21:38:35.8958856Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=212. 2025-08-14T21:38:35.8960504Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:35.8961556Z Wait for all kernels in all streams on the given device to complete. 2025-08-14T21:38:35.8962178Z 2025-08-14T21:38:35.8962349Z Args: 2025-08-14T21:38:35.8963121Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-08-14T21:38:35.8964267Z the current :ref:`accelerator` device type. If not given, 2025-08-14T21:38:35.8965247Z use :func:`torch.accelerator.current_device_index` by default. 2025-08-14T21:38:35.8965833Z 2025-08-14T21:38:35.8966371Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-08-14T21:38:35.8966838Z 2025-08-14T21:38:35.8966932Z Example:: 2025-08-14T21:38:35.8967062Z 2025-08-14T21:38:35.8967211Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-14T21:38:35.8967709Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-14T21:38:35.8968235Z >>> start_event = torch.Event(enable_timing=True) 2025-08-14T21:38:35.8968627Z >>> end_event = torch.Event(enable_timing=True) 2025-08-14T21:38:35.8968962Z >>> start_event.record() 2025-08-14T21:38:35.8969392Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-08-14T21:38:35.8969848Z >>> sum = torch.sum(tensor) 2025-08-14T21:38:35.8970156Z >>> end_event.record() 2025-08-14T21:38:35.8970450Z >>> torch.accelerator.synchronize() 2025-08-14T21:38:35.8970845Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-08-14T21:38:35.8971207Z 2025-08-14T21:38:35.8971859Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-08-14T21:38:35.8972740Z 2025-08-14T21:38:35.8973162Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-14T21:38:35.8974000Z ^ 2025-08-14T21:38:35.9230573Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=434. 2025-08-14T21:38:35.9232222Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:35.9233112Z Retrieves the CUDA runtime API module. 2025-08-14T21:38:35.9233544Z 2025-08-14T21:38:35.9233552Z 2025-08-14T21:38:35.9234012Z This function initializes the CUDA runtime environment if it is not already 2025-08-14T21:38:35.9235138Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-08-14T21:38:35.9236198Z runtime API module provides access to various CUDA runtime functions. 2025-08-14T21:38:35.9236865Z 2025-08-14T21:38:35.9237009Z Args: 2025-08-14T21:38:35.9237367Z ``None`` 2025-08-14T21:38:35.9237605Z 2025-08-14T21:38:35.9237766Z Returns: 2025-08-14T21:38:35.9238227Z module: The CUDA runtime API module (_cudart). 2025-08-14T21:38:35.9238699Z 2025-08-14T21:38:35.9238847Z Raises: 2025-08-14T21:38:35.9239496Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-08-14T21:38:35.9240817Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-08-14T21:38:35.9241735Z 2025-08-14T21:38:35.9241969Z Example of CUDA operations with profiling: 2025-08-14T21:38:35.9242747Z >>> import torch 2025-08-14T21:38:35.9243283Z >>> from torch.cuda import cudart, check_error 2025-08-14T21:38:35.9243878Z >>> import os 2025-08-14T21:38:35.9244296Z >>> 2025-08-14T21:38:35.9244710Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-08-14T21:38:35.9245247Z >>> 2025-08-14T21:38:35.9245703Z >>> def perform_cuda_operations_with_streams(): 2025-08-14T21:38:35.9246363Z >>> stream = torch.cuda.Stream() 2025-08-14T21:38:35.9246972Z >>> with torch.cuda.stream(stream): 2025-08-14T21:38:35.9247618Z >>> x = torch.randn(100, 100, device='cuda') 2025-08-14T21:38:35.9248279Z >>> y = torch.randn(100, 100, device='cuda') 2025-08-14T21:38:35.9248898Z >>> z = torch.mul(x, y) 2025-08-14T21:38:35.9249419Z >>> return z 2025-08-14T21:38:35.9249850Z >>> 2025-08-14T21:38:35.9250261Z >>> torch.cuda.synchronize() 2025-08-14T21:38:35.9250875Z >>> print("====== Start nsys profiling ======") 2025-08-14T21:38:35.9251569Z >>> check_error(cudart().cudaProfilerStart()) 2025-08-14T21:38:35.9252279Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-08-14T21:38:35.9253001Z >>> result = perform_cuda_operations_with_streams() 2025-08-14T21:38:35.9253723Z >>> print("CUDA operations completed.") 2025-08-14T21:38:35.9254458Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-08-14T21:38:35.9255199Z >>> print("====== End nsys profiling ======") 2025-08-14T21:38:35.9255660Z 2025-08-14T21:38:35.9256018Z To run this example and save the profiling information, execute: 2025-08-14T21:38:35.9257298Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-08-14T21:38:35.9258192Z 2025-08-14T21:38:35.9258659Z This command profiles the CUDA operations in the provided script and saves 2025-08-14T21:38:35.9259690Z the profiling information to a file named `trace_name.prof`. 2025-08-14T21:38:35.9260703Z The `--profile-from-start off` option ensures that profiling starts only 2025-08-14T21:38:35.9261626Z after the `cudaProfilerStart` call in the script. 2025-08-14T21:38:35.9262526Z The `--csv` and `--print-summary` options format the profiling output as a 2025-08-14T21:38:35.9263387Z CSV file and print a summary, respectively. 2025-08-14T21:38:35.9264385Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-08-14T21:38:35.9265346Z overwrite of the output file if it already exists. 2025-08-14T21:38:35.9265983Z 2025-08-14T21:38:35.9267566Z 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)) 2025-08-14T21:38:35.9269036Z 2025-08-14T21:38:35.9269700Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-08-14T21:38:35.9270726Z ^ 2025-08-14T21:38:35.9416080Z 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. 2025-08-14T21:38:35.9417732Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.9418463Z 2025-08-14T21:38:35.9418875Z Append the given callback function to this ``Future``, which will be run 2025-08-14T21:38:35.9419859Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-08-14T21:38:35.9420777Z the same ``Future``, but the order in which they will be executed cannot 2025-08-14T21:38:35.9421690Z be guaranteed (to enforce a certain order consider chaining: 2025-08-14T21:38:35.9422598Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-08-14T21:38:35.9423654Z is the reference to this ``Future``. The callback function can use the 2025-08-14T21:38:35.9424628Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-08-14T21:38:35.9425855Z already completed, the given callback will be run immediately inline. 2025-08-14T21:38:35.9426475Z 2025-08-14T21:38:35.9426814Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-08-14T21:38:35.9427766Z callback might be invoked while the async kernels that are populating 2025-08-14T21:38:35.9428761Z those tensors haven't yet finished executing on the device. However, the 2025-08-14T21:38:35.9429787Z callback will be invoked with some dedicated streams set as current 2025-08-14T21:38:35.9430762Z (fetched from a global pool) which will be synchronized with those 2025-08-14T21:38:35.9431709Z kernels. Hence any operation performed by the callback on these tensors 2025-08-14T21:38:35.9432688Z will be scheduled on the device after the kernels complete. In other 2025-08-14T21:38:35.9433639Z words, as long as the callback doesn't switch streams, it can safely 2025-08-14T21:38:35.9434614Z manipulate the result without any additional synchronization. This is 2025-08-14T21:38:35.9435539Z similar to the non-blocking behavior of :meth:`wait`. 2025-08-14T21:38:35.9436043Z 2025-08-14T21:38:35.9436398Z Similarly, if the callback returns a value that contains tensors that 2025-08-14T21:38:35.9437356Z reside on a GPU, it can do so even if the kernels that are producing 2025-08-14T21:38:35.9438292Z these tensors are still running on the device, as long as the callback 2025-08-14T21:38:35.9439280Z didn't change streams during its execution. If one wants to change 2025-08-14T21:38:35.9440269Z streams, one must be careful to re-synchronize them with the original 2025-08-14T21:38:35.9441238Z streams, that is, those that were current when the callback was invoked. 2025-08-14T21:38:35.9441852Z 2025-08-14T21:38:35.9441998Z Args: 2025-08-14T21:38:35.9442578Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-08-14T21:38:35.9443370Z the only argument. 2025-08-14T21:38:35.9443770Z 2025-08-14T21:38:35.9443946Z Returns: 2025-08-14T21:38:35.9444439Z A new ``Future`` object that holds the return value of the 2025-08-14T21:38:35.9445243Z ``callback`` and will be marked as completed when the given 2025-08-14T21:38:35.9445942Z ``callback`` finishes. 2025-08-14T21:38:35.9446236Z 2025-08-14T21:38:35.9446566Z .. note:: Note that if the callback function throws, either 2025-08-14T21:38:35.9447461Z through the original future being completed with an exception and 2025-08-14T21:38:35.9448413Z calling ``fut.wait()``, or through other code in the callback, the 2025-08-14T21:38:35.9449363Z future returned by ``then`` will be marked appropriately with the 2025-08-14T21:38:35.9450527Z encountered error. However, if this callback later completes 2025-08-14T21:38:35.9451517Z additional futures, those futures are not marked as completed with 2025-08-14T21:38:35.9452508Z an error and the user is responsible for handling completion/waiting 2025-08-14T21:38:35.9453241Z on those futures independently. 2025-08-14T21:38:35.9453618Z 2025-08-14T21:38:35.9453768Z Example:: 2025-08-14T21:38:35.9453923Z 2025-08-14T21:38:35.9454087Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-08-14T21:38:35.9454431Z >>> def callback(fut): 2025-08-14T21:38:35.9454739Z ... print(f"RPC return value is {fut.wait()}.") 2025-08-14T21:38:35.9455092Z >>> fut = torch.futures.Future() 2025-08-14T21:38:35.9455464Z >>> # The inserted callback will print the return value when 2025-08-14T21:38:35.9455853Z >>> # receiving the response from "worker1" 2025-08-14T21:38:35.9456185Z >>> cb_fut = fut.then(callback) 2025-08-14T21:38:35.9456486Z >>> chain_cb_fut = cb_fut.then( 2025-08-14T21:38:35.9456827Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-08-14T21:38:35.9457173Z ... ) 2025-08-14T21:38:35.9457388Z >>> fut.set_result(5) 2025-08-14T21:38:35.9457642Z RPC return value is 5. 2025-08-14T21:38:35.9457911Z Chained cb done. None 2025-08-14T21:38:35.9458172Z 2025-08-14T21:38:35.9458438Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.9458798Z 2025-08-14T21:38:35.9459361Z 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. 2025-08-14T21:38:35.9460867Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.9461549Z 2025-08-14T21:38:35.9461903Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-08-14T21:38:35.9462865Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-08-14T21:38:35.9463769Z cannot be marked completed twice. 2025-08-14T21:38:35.9464150Z 2025-08-14T21:38:35.9464561Z If the result contains tensors that reside on GPUs, this method can be 2025-08-14T21:38:35.9465579Z called even if the asynchronous kernels that are populating those 2025-08-14T21:38:35.9466572Z tensors haven't yet completed running on the device, provided that the 2025-08-14T21:38:35.9467621Z streams on which those kernels were enqueued are set as the current ones 2025-08-14T21:38:35.9468635Z when this method is called. Put simply, it's safe to call this method 2025-08-14T21:38:35.9469658Z immediately after launching those kernels, without any additional 2025-08-14T21:38:35.9470657Z synchronization, as long as one doesn't change streams in between. This 2025-08-14T21:38:35.9471457Z method will record events on all the relevant current streams and will 2025-08-14T21:38:35.9471996Z use them to ensure proper scheduling for all the consumers of this 2025-08-14T21:38:35.9472400Z ``Future``. 2025-08-14T21:38:35.9472524Z 2025-08-14T21:38:35.9472619Z Args: 2025-08-14T21:38:35.9472894Z result (object): the result object of this ``Future``. 2025-08-14T21:38:35.9473174Z 2025-08-14T21:38:35.9473274Z Example:: 2025-08-14T21:38:35.9473394Z 2025-08-14T21:38:35.9473553Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-08-14T21:38:35.9473896Z >>> import threading 2025-08-14T21:38:35.9474153Z >>> import time 2025-08-14T21:38:35.9474408Z >>> def slow_set_future(fut, value): 2025-08-14T21:38:35.9474712Z ... time.sleep(0.5) 2025-08-14T21:38:35.9474991Z ... fut.set_result(value) 2025-08-14T21:38:35.9475297Z >>> fut = torch.futures.Future() 2025-08-14T21:38:35.9475614Z >>> t = threading.Thread( 2025-08-14T21:38:35.9475886Z ... target=slow_set_future, 2025-08-14T21:38:35.9476189Z ... args=(fut, torch.ones(2) * 3) 2025-08-14T21:38:35.9476484Z ... ) 2025-08-14T21:38:35.9476675Z >>> t.start() 2025-08-14T21:38:35.9476907Z >>> print(fut.wait()) 2025-08-14T21:38:35.9477365Z tensor([3., 3.]) 2025-08-14T21:38:35.9477891Z >>> t.join() 2025-08-14T21:38:35.9477900Z 2025-08-14T21:38:35.9478340Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.9478348Z 2025-08-14T21:38:35.9553206Z 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. 2025-08-14T21:38:35.9553730Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.9554120Z Compiles compute shader from source and allows one to invoke kernels 2025-08-14T21:38:35.9554382Z defined there from the comfort of Python runtime 2025-08-14T21:38:35.9554562Z Example:: 2025-08-14T21:38:35.9554571Z 2025-08-14T21:38:35.9554805Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-08-14T21:38:35.9555003Z >>> lib = torch.mps.compile_shader( 2025-08-14T21:38:35.9555744Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-08-14T21:38:35.9555889Z ... ) 2025-08-14T21:38:35.9556096Z >>> x = torch.zeros(16, device="mps") 2025-08-14T21:38:35.9556264Z >>> lib.full(x, 3.14) 2025-08-14T21:38:35.9556402Z 2025-08-14T21:38:35.9556871Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.9557077Z 2025-08-14T21:38:35.9729977Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202. 2025-08-14T21:38:35.9730512Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.9730803Z Return the sum of each row of the given sparse tensor. 2025-08-14T21:38:35.9730815Z 2025-08-14T21:38:35.9731231Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-08-14T21:38:35.9731585Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-08-14T21:38:35.9731977Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-08-14T21:38:35.9732257Z returns a dense tensor instead of a sparse tensor. 2025-08-14T21:38:35.9732266Z 2025-08-14T21:38:35.9732699Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-08-14T21:38:35.9733035Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-08-14T21:38:35.9733045Z 2025-08-14T21:38:35.9733460Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-08-14T21:38:35.9733893Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-08-14T21:38:35.9733902Z 2025-08-14T21:38:35.9734054Z Args: 2025-08-14T21:38:35.9734253Z input (Tensor): the input sparse tensor 2025-08-14T21:38:35.9734708Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-08-14T21:38:35.9734889Z over all dims. 2025-08-14T21:38:35.9735338Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-08-14T21:38:35.9735543Z Default: dtype of :attr:`input`. 2025-08-14T21:38:35.9735566Z 2025-08-14T21:38:35.9735728Z Example:: 2025-08-14T21:38:35.9735736Z 2025-08-14T21:38:35.9735875Z >>> nnz = 3 2025-08-14T21:38:35.9736051Z >>> dims = [5, 5, 2, 3] 2025-08-14T21:38:35.9736329Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-08-14T21:38:35.9736653Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-08-14T21:38:35.9736881Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-08-14T21:38:35.9737062Z >>> size = torch.Size(dims) 2025-08-14T21:38:35.9737312Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-14T21:38:35.9737543Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-08-14T21:38:35.9737688Z >>> S 2025-08-14T21:38:35.9737894Z tensor(indices=tensor([[2, 0, 3], 2025-08-14T21:38:35.9738056Z [2, 4, 1]]), 2025-08-14T21:38:35.9738491Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-08-14T21:38:35.9738701Z [ 0.3411, 0.0918, -0.2312]], 2025-08-14T21:38:35.9738710Z 2025-08-14T21:38:35.9738896Z [[ 0.5348, 0.0634, -2.0494], 2025-08-14T21:38:35.9739090Z [-0.7125, -1.0646, 2.1844]], 2025-08-14T21:38:35.9739113Z 2025-08-14T21:38:35.9739291Z [[ 0.1276, 0.1874, -0.6334], 2025-08-14T21:38:35.9739477Z [-1.9682, -0.5340, 0.7483]]]), 2025-08-14T21:38:35.9739742Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-08-14T21:38:35.9739751Z 2025-08-14T21:38:35.9740100Z # when sum over only part of sparse_dims, return a sparse tensor 2025-08-14T21:38:35.9740298Z >>> torch.sparse.sum(S, [1, 3]) 2025-08-14T21:38:35.9740511Z tensor(indices=tensor([[0, 2, 3]]), 2025-08-14T21:38:35.9740709Z values=tensor([[-1.4512, 0.4073], 2025-08-14T21:38:35.9740882Z [-0.8901, 0.2017], 2025-08-14T21:38:35.9741071Z [-0.3183, -1.7539]]), 2025-08-14T21:38:35.9741307Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-08-14T21:38:35.9741445Z 2025-08-14T21:38:35.9741731Z # when sum over all sparse dim, return a dense tensor 2025-08-14T21:38:35.9741911Z # with summed dims squeezed 2025-08-14T21:38:35.9742099Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-08-14T21:38:35.9742260Z tensor([-2.6596, -1.1450]) 2025-08-14T21:38:35.9742387Z 2025-08-14T21:38:35.9742801Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.9742826Z 2025-08-14T21:38:35.9745036Z 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. 2025-08-14T21:38:35.9745533Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:35.9745872Z Decorate function, to extend gradcheck for sparse tensors. 2025-08-14T21:38:35.9745881Z 2025-08-14T21:38:35.9746235Z Decorator for torch.autograd.gradcheck or its functools.partial 2025-08-14T21:38:35.9746636Z variants that extends the gradcheck function with support to input 2025-08-14T21:38:35.9746953Z functions that operate on or/and return sparse tensors. 2025-08-14T21:38:35.9746962Z 2025-08-14T21:38:35.9747338Z The specified gradcheck function itself is guaranteed to operate 2025-08-14T21:38:35.9747532Z on strided tensors only. 2025-08-14T21:38:35.9747540Z 2025-08-14T21:38:35.9747693Z For example: 2025-08-14T21:38:35.9747701Z 2025-08-14T21:38:35.9748127Z >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck) 2025-08-14T21:38:35.9748286Z >>> x = ( 2025-08-14T21:38:35.9748568Z ... torch.tensor([[0, 1], [2, 3]], dtype=torch.float64) 2025-08-14T21:38:35.9748752Z ... .to_sparse_coo() 2025-08-14T21:38:35.9748943Z ... .requires_grad_(True) 2025-08-14T21:38:35.9749087Z ... ) 2025-08-14T21:38:35.9749330Z >>> gradcheck(lambda x: x.to_sparse_csr(), x) 2025-08-14T21:38:35.9749476Z True 2025-08-14T21:38:35.9749615Z 2025-08-14T21:38:35.9750098Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:35.9750107Z 2025-08-14T21:38:36.7868430Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=39. 2025-08-14T21:38:36.7869486Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:36.7869922Z 2025-08-14T21:38:36.7870225Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-08-14T21:38:36.7870899Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-08-14T21:38:36.7871753Z pushes the map into PyTorch operations called by ``func``, effectively 2025-08-14T21:38:36.7872596Z vectorizing those operations. 2025-08-14T21:38:36.7872866Z 2025-08-14T21:38:36.7873076Z vmap is useful for handling batch dimensions: one can write a function 2025-08-14T21:38:36.7873602Z ``func`` that runs on examples and then lift it to a function that can 2025-08-14T21:38:36.7874140Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-08-14T21:38:36.7874631Z compute batched gradients when composed with autograd. 2025-08-14T21:38:36.7874906Z 2025-08-14T21:38:36.7875019Z .. note:: 2025-08-14T21:38:36.7875326Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-08-14T21:38:36.7875751Z convenience. Use whichever one you'd like. 2025-08-14T21:38:36.7875981Z 2025-08-14T21:38:36.7876061Z Args: 2025-08-14T21:38:36.7876387Z func (function): A Python function that takes one or more arguments. 2025-08-14T21:38:36.7876822Z Must return one or more Tensors. 2025-08-14T21:38:36.7877237Z in_dims (int or nested structure): Specifies which dimension of the 2025-08-14T21:38:36.7877714Z inputs should be mapped over. ``in_dims`` should have a 2025-08-14T21:38:36.7878184Z structure like the inputs. If the ``in_dim`` for a particular 2025-08-14T21:38:36.7878673Z input is None, then that indicates there is no map dimension. 2025-08-14T21:38:36.7879150Z Default: 0. 2025-08-14T21:38:36.7879495Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-08-14T21:38:36.7880000Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-08-14T21:38:36.7880458Z it should have one element per output. Default: 0. 2025-08-14T21:38:36.7880892Z randomness (str): Specifies whether the randomness in this 2025-08-14T21:38:36.7881392Z vmap should be the same or different across batches. If 'different', 2025-08-14T21:38:36.7881914Z the randomness for each batch will be different. If 'same', the 2025-08-14T21:38:36.7882423Z randomness will be the same across batches. If 'error', any calls to 2025-08-14T21:38:36.7882969Z random functions will error. Default: 'error'. WARNING: this flag 2025-08-14T21:38:36.7883492Z only applies to random PyTorch operations and does not apply to 2025-08-14T21:38:36.7883946Z Python's random module or numpy randomness. 2025-08-14T21:38:36.7884406Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-08-14T21:38:36.7884963Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-08-14T21:38:36.7885545Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-08-14T21:38:36.7886166Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-08-14T21:38:36.7886555Z 2025-08-14T21:38:36.7886638Z Returns: 2025-08-14T21:38:36.7886955Z Returns a new "batched" function. It takes the same inputs as 2025-08-14T21:38:36.7887436Z ``func``, except each input has an extra dimension at the index 2025-08-14T21:38:36.7887907Z specified by ``in_dims``. It takes returns the same outputs as 2025-08-14T21:38:36.7888390Z ``func``, except each output has an extra dimension at the index 2025-08-14T21:38:36.7888793Z specified by ``out_dims``. 2025-08-14T21:38:36.7888976Z 2025-08-14T21:38:36.7889065Z .. warning: 2025-08-14T21:38:36.7889385Z :func:`vmap` works best with functional-style code. Please do not 2025-08-14T21:38:36.7889875Z perform any side-effects in ``func``, with the exception of 2025-08-14T21:38:36.7890389Z in-place PyTorch operations. Examples of side-effects include mutating 2025-08-14T21:38:36.7890959Z Python data structures and assigning values to variables not captured 2025-08-14T21:38:36.7891383Z in ``func``. 2025-08-14T21:38:36.7891516Z 2025-08-14T21:38:36.7891759Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-08-14T21:38:36.7892302Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-08-14T21:38:36.7893192Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-08-14T21:38:36.7893533Z 2025-08-14T21:38:36.7893667Z >>> torch.dot # [D], [D] -> [] 2025-08-14T21:38:36.7894073Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-08-14T21:38:36.7894508Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-08-14T21:38:36.7894844Z >>> batched_dot(x, y) 2025-08-14T21:38:36.7895009Z 2025-08-14T21:38:36.7895247Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-08-14T21:38:36.7895678Z model authoring experience. 2025-08-14T21:38:36.7895868Z 2025-08-14T21:38:36.7895975Z >>> batch_size, feature_size = 3, 5 2025-08-14T21:38:36.7896358Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-08-14T21:38:36.7896711Z >>> 2025-08-14T21:38:36.7896929Z >>> def model(feature_vec): 2025-08-14T21:38:36.7897243Z >>> # Very simple linear model with activation 2025-08-14T21:38:36.7897613Z >>> return feature_vec.dot(weights).relu() 2025-08-14T21:38:36.7897922Z >>> 2025-08-14T21:38:36.7898188Z >>> examples = torch.randn(batch_size, feature_size) 2025-08-14T21:38:36.7898563Z >>> result = torch.vmap(model)(examples) 2025-08-14T21:38:36.7898779Z 2025-08-14T21:38:36.7899022Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-08-14T21:38:36.7899698Z or impossible to batch. One example is higher-order gradient computation. 2025-08-14T21:38:36.7900270Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-08-14T21:38:36.7900839Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-08-14T21:38:36.7901414Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-08-14T21:38:36.7902006Z we can vectorize the whole computation, computing the Jacobian in a single 2025-08-14T21:38:36.7902462Z call to ``autograd.grad``. 2025-08-14T21:38:36.7902635Z 2025-08-14T21:38:36.7902717Z >>> # Setup 2025-08-14T21:38:36.7902944Z >>> N = 5 2025-08-14T21:38:36.7903169Z >>> f = lambda x: x**2 2025-08-14T21:38:36.7903520Z >>> x = torch.randn(N, requires_grad=True) 2025-08-14T21:38:36.7903841Z >>> y = f(x) 2025-08-14T21:38:36.7904081Z >>> I_N = torch.eye(N) 2025-08-14T21:38:36.7904327Z >>> 2025-08-14T21:38:36.7904549Z >>> # Sequential approach 2025-08-14T21:38:36.7904948Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-08-14T21:38:36.7905389Z >>> for v in I_N.unbind()] 2025-08-14T21:38:36.7905717Z >>> jacobian = torch.stack(jacobian_rows) 2025-08-14T21:38:36.7906031Z >>> 2025-08-14T21:38:36.7906268Z >>> # vectorized gradient computation 2025-08-14T21:38:36.7906572Z >>> def get_vjp(v): 2025-08-14T21:38:36.7906853Z >>> return torch.autograd.grad(y, x, v) 2025-08-14T21:38:36.7907204Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-08-14T21:38:36.7907418Z 2025-08-14T21:38:36.7907683Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-08-14T21:38:36.7908070Z 2025-08-14T21:38:36.7908170Z >>> torch.dot # [D], [D] -> [] 2025-08-14T21:38:36.7908473Z >>> batched_dot = torch.vmap( 2025-08-14T21:38:36.7908769Z ... torch.vmap(torch.dot) 2025-08-14T21:38:36.7909064Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-08-14T21:38:36.7909420Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-08-14T21:38:36.7909793Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-08-14T21:38:36.7910021Z 2025-08-14T21:38:36.7910252Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-08-14T21:38:36.7910752Z the dimension that each inputs are batched along as 2025-08-14T21:38:36.7911017Z 2025-08-14T21:38:36.7911117Z >>> torch.dot # [N], [N] -> [] 2025-08-14T21:38:36.7911531Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-08-14T21:38:36.7911973Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-08-14T21:38:36.7912370Z >>> batched_dot( 2025-08-14T21:38:36.7912611Z ... x, y 2025-08-14T21:38:36.7912924Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-08-14T21:38:36.7913235Z 2025-08-14T21:38:36.7913484Z If there are multiple inputs each of which is batched along different dimensions, 2025-08-14T21:38:36.7914058Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-08-14T21:38:36.7914360Z 2025-08-14T21:38:36.7914478Z >>> torch.dot # [D], [D] -> [] 2025-08-14T21:38:36.7914893Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-08-14T21:38:36.7915366Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-08-14T21:38:36.7915687Z >>> batched_dot( 2025-08-14T21:38:36.7915913Z ... x, y 2025-08-14T21:38:36.7916247Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-08-14T21:38:36.7916548Z 2025-08-14T21:38:36.7916794Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-08-14T21:38:36.7917251Z matching the shape of the input: 2025-08-14T21:38:36.7917443Z 2025-08-14T21:38:36.7917578Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-08-14T21:38:36.7917944Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-08-14T21:38:36.7918333Z >>> input = {"x": x, "y": y} 2025-08-14T21:38:36.7918679Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-08-14T21:38:36.7919062Z >>> batched_dot(input) 2025-08-14T21:38:36.7919225Z 2025-08-14T21:38:36.7919513Z By default, the output is batched along the first dimension. However, it can be batched 2025-08-14T21:38:36.7920003Z along any dimension by using ``out_dims`` 2025-08-14T21:38:36.7920234Z 2025-08-14T21:38:36.7920326Z >>> f = lambda x: x**2 2025-08-14T21:38:36.7920591Z >>> x = torch.randn(2, 5) 2025-08-14T21:38:36.7920894Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-08-14T21:38:36.7921212Z >>> batched_pow(x) # [5, 2] 2025-08-14T21:38:36.7921409Z 2025-08-14T21:38:36.7921687Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-08-14T21:38:36.7922174Z accept kwargs 2025-08-14T21:38:36.7922303Z 2025-08-14T21:38:36.7922399Z >>> x = torch.randn([2, 5]) 2025-08-14T21:38:36.7922683Z >>> def fn(x, scale=4.): 2025-08-14T21:38:36.7922951Z >>> return x * scale 2025-08-14T21:38:36.7923184Z >>> 2025-08-14T21:38:36.7923403Z >>> batched_pow = torch.vmap(fn) 2025-08-14T21:38:36.7923744Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-08-14T21:38:36.7924210Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-08-14T21:38:36.7924542Z 2025-08-14T21:38:36.7924633Z .. note:: 2025-08-14T21:38:36.7924967Z vmap does not provide general autobatching or handle variable-length 2025-08-14T21:38:36.7925397Z sequences out of the box. 2025-08-14T21:38:36.7925576Z 2025-08-14T21:38:36.7925823Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:36.7926191Z 2025-08-14T21:38:36.7926652Z msg = Cannot scrape callname=grad in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=306. 2025-08-14T21:38:36.7927482Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:36.7928090Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-08-14T21:38:36.7928618Z input(s) specified by ``argnums``. This operator can be nested to 2025-08-14T21:38:36.7929037Z compute higher-order gradients. 2025-08-14T21:38:36.7929242Z 2025-08-14T21:38:36.7929343Z Args: 2025-08-14T21:38:36.7929661Z func (Callable): A Python function that takes one or more arguments. 2025-08-14T21:38:36.7930244Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-08-14T21:38:36.7930877Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-08-14T21:38:36.7931425Z ``(output, aux)``. 2025-08-14T21:38:36.7931862Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-08-14T21:38:36.7932446Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-08-14T21:38:36.7932983Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-08-14T21:38:36.7933460Z auxiliary objects: ``(output, aux)``. Default: False. 2025-08-14T21:38:36.7933744Z 2025-08-14T21:38:36.7933831Z Returns: 2025-08-14T21:38:36.7934239Z Function to compute gradients with respect to its inputs. By default, the output of 2025-08-14T21:38:36.7934869Z the function is the gradient tensor(s) with respect to the first argument. 2025-08-14T21:38:36.7935469Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-08-14T21:38:36.7936099Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-08-14T21:38:36.7936599Z respect to each ``argnums`` value is returned. 2025-08-14T21:38:36.7936842Z 2025-08-14T21:38:36.7936956Z Example of using ``grad``: 2025-08-14T21:38:36.7937136Z 2025-08-14T21:38:36.7937229Z >>> # xdoctest: +SKIP 2025-08-14T21:38:36.7937580Z >>> from torch.func import grad 2025-08-14T21:38:36.7937887Z >>> x = torch.randn([]) 2025-08-14T21:38:36.7938176Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-08-14T21:38:36.7938530Z >>> assert torch.allclose(cos_x, x.cos()) 2025-08-14T21:38:36.7938848Z >>> 2025-08-14T21:38:36.7939063Z >>> # Second-order gradients 2025-08-14T21:38:36.7939408Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-08-14T21:38:36.7939794Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-08-14T21:38:36.7940031Z 2025-08-14T21:38:36.7940285Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-08-14T21:38:36.7940639Z 2025-08-14T21:38:36.7940740Z >>> # xdoctest: +SKIP 2025-08-14T21:38:36.7941034Z >>> from torch.func import grad, vmap 2025-08-14T21:38:36.7941368Z >>> batch_size, feature_size = 3, 5 2025-08-14T21:38:36.7941659Z >>> 2025-08-14T21:38:36.7941895Z >>> def model(weights, feature_vec): 2025-08-14T21:38:36.7942249Z >>> # Very simple linear model with activation 2025-08-14T21:38:36.7942597Z >>> assert feature_vec.dim() == 1 2025-08-14T21:38:36.7942925Z >>> return feature_vec.dot(weights).relu() 2025-08-14T21:38:36.7943241Z >>> 2025-08-14T21:38:36.7943575Z >>> def compute_loss(weights, example, target): 2025-08-14T21:38:36.7943915Z >>> y = model(weights, example) 2025-08-14T21:38:36.7944268Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-08-14T21:38:36.7944596Z >>> 2025-08-14T21:38:36.7944899Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-08-14T21:38:36.7945329Z >>> examples = torch.randn(batch_size, feature_size) 2025-08-14T21:38:36.7945705Z >>> targets = torch.randn(batch_size) 2025-08-14T21:38:36.7946050Z >>> inputs = (weights, examples, targets) 2025-08-14T21:38:36.7946503Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-08-14T21:38:36.7946947Z ... *inputs 2025-08-14T21:38:36.7947187Z ... ) 2025-08-14T21:38:36.7947311Z 2025-08-14T21:38:36.7947496Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-08-14T21:38:36.7947777Z 2025-08-14T21:38:36.7947872Z >>> # xdoctest: +SKIP 2025-08-14T21:38:36.7948164Z >>> from torch.func import grad 2025-08-14T21:38:36.7948488Z >>> def my_loss_func(y, y_pred): 2025-08-14T21:38:36.7948818Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-08-14T21:38:36.7949174Z >>> loss = loss_per_sample.mean() 2025-08-14T21:38:36.7949525Z >>> return loss, (y_pred, loss_per_sample) 2025-08-14T21:38:36.7949921Z >>> 2025-08-14T21:38:36.7950180Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-08-14T21:38:36.7950544Z >>> y_true = torch.rand(4) 2025-08-14T21:38:36.7950873Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-08-14T21:38:36.7951211Z >>> out = fn(y_true, y_preds) 2025-08-14T21:38:36.7951653Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-08-14T21:38:36.7952001Z 2025-08-14T21:38:36.7952103Z .. note:: 2025-08-14T21:38:36.7952385Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-08-14T21:38:36.7952670Z 2025-08-14T21:38:36.7952812Z Case 1: Using ``torch.no_grad`` inside a function: 2025-08-14T21:38:36.7953071Z 2025-08-14T21:38:36.7953167Z >>> # xdoctest: +SKIP 2025-08-14T21:38:36.7953443Z >>> def f(x): 2025-08-14T21:38:36.7953694Z >>> with torch.no_grad(): 2025-08-14T21:38:36.7954000Z >>> c = x ** 2 2025-08-14T21:38:36.7954286Z >>> return x - c 2025-08-14T21:38:36.7954463Z 2025-08-14T21:38:36.7954652Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-08-14T21:38:36.7954965Z 2025-08-14T21:38:36.7955145Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-08-14T21:38:36.7955505Z 2025-08-14T21:38:36.7955600Z >>> # xdoctest: +SKIP 2025-08-14T21:38:36.7955893Z >>> with torch.no_grad(): 2025-08-14T21:38:36.7956178Z >>> grad(f)(x) 2025-08-14T21:38:36.7956360Z 2025-08-14T21:38:36.7956573Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-08-14T21:38:36.7957129Z outer one. This is because ``grad`` is a "function transform": its result 2025-08-14T21:38:36.7957662Z should not depend on the result of a context manager outside of ``f``. 2025-08-14T21:38:36.7957999Z 2025-08-14T21:38:36.7958082Z 2025-08-14T21:38:36.7958445Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:36.7958802Z 2025-08-14T21:38:38.2560743Z 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. 2025-08-14T21:38:38.2561745Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:38.2562276Z Register a FakeTensor implementation for this custom op. 2025-08-14T21:38:38.2562576Z 2025-08-14T21:38:38.2562818Z This is necessary to get the operator to work efficiently with torch.compile. 2025-08-14T21:38:38.2563189Z 2025-08-14T21:38:38.2563424Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-08-14T21:38:38.2563980Z specifies the behavior of this operator on Tensors that carry no data. 2025-08-14T21:38:38.2564469Z Given some input Tensors with certain properties 2025-08-14T21:38:38.2564977Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-08-14T21:38:38.2565448Z the output Tensors are. 2025-08-14T21:38:38.2565644Z 2025-08-14T21:38:38.2565843Z Please see :func:`torch.library.register_fake` for more details. 2025-08-14T21:38:38.2566153Z 2025-08-14T21:38:38.2566254Z Args: 2025-08-14T21:38:38.2566543Z fn (Callable): The function to register as the FakeTensor 2025-08-14T21:38:38.2566926Z implementation. 2025-08-14T21:38:38.2567105Z 2025-08-14T21:38:38.2567203Z Examples: 2025-08-14T21:38:38.2567427Z >>> import torch 2025-08-14T21:38:38.2567703Z >>> import numpy as np 2025-08-14T21:38:38.2568008Z >>> from torch import Tensor 2025-08-14T21:38:38.2568290Z >>> 2025-08-14T21:38:38.2568609Z >>> # Example 1: an operator without data-dependent output shape 2025-08-14T21:38:38.2569125Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-08-14T21:38:38.2569866Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-08-14T21:38:38.2570279Z >>> return (x @ weight.t()) + bias 2025-08-14T21:38:38.2570589Z >>> 2025-08-14T21:38:38.2570831Z >>> @linear.register_fake 2025-08-14T21:38:38.2571138Z >>> def _(x, weight, bias): 2025-08-14T21:38:38.2571447Z >>> assert x.dim() == 2 2025-08-14T21:38:38.2571764Z >>> assert weight.dim() == 2 2025-08-14T21:38:38.2572076Z >>> assert bias.dim() == 1 2025-08-14T21:38:38.2572412Z >>> assert x.shape[1] == weight.shape[1] 2025-08-14T21:38:38.2572777Z >>> assert weight.shape[0] == bias.shape[0] 2025-08-14T21:38:38.2573140Z >>> assert x.device == weight.device 2025-08-14T21:38:38.2573506Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-08-14T21:38:38.2573851Z >>> 2025-08-14T21:38:38.2574090Z >>> x = torch.randn(2, 2) 2025-08-14T21:38:38.2574395Z >>> weight = torch.randn(2, 2) 2025-08-14T21:38:38.2574713Z >>> bias = torch.randn(2) 2025-08-14T21:38:38.2575053Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-14T21:38:38.2575479Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-08-14T21:38:38.2576028Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-14T21:38:38.2576514Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-08-14T21:38:38.2576961Z >>> 2025-08-14T21:38:38.2577259Z >>> # Example 2: an operator with data-dependent output shape 2025-08-14T21:38:38.2577747Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-08-14T21:38:38.2578169Z >>> def nonzero(x: Tensor) -> Tensor: 2025-08-14T21:38:38.2578490Z >>> x_np = x.cpu().numpy() 2025-08-14T21:38:38.2578837Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-08-14T21:38:38.2579218Z >>> return torch.tensor(res, device=x.device) 2025-08-14T21:38:38.2579553Z >>> 2025-08-14T21:38:38.2579786Z >>> @nonzero.register_fake 2025-08-14T21:38:38.2580091Z >>> def _(x): 2025-08-14T21:38:38.2580407Z >>> # Number of nonzero-elements is data-dependent. 2025-08-14T21:38:38.2580823Z >>> # Since we cannot peek at the data in an abstract impl, 2025-08-14T21:38:38.2581264Z >>> # we use the ctx object to construct a new symint that 2025-08-14T21:38:38.2581668Z >>> # represents the data-dependent size. 2025-08-14T21:38:38.2582010Z >>> ctx = torch.library.get_ctx() 2025-08-14T21:38:38.2582355Z >>> nnz = ctx.new_dynamic_size() 2025-08-14T21:38:38.2582684Z >>> shape = [nnz, x.dim()] 2025-08-14T21:38:38.2583043Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-08-14T21:38:38.2583393Z >>> return result 2025-08-14T21:38:38.2583789Z >>> 2025-08-14T21:38:38.2584042Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-08-14T21:38:38.2584396Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-14T21:38:38.2584792Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-08-14T21:38:38.2585197Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-14T21:38:38.2585561Z >>> assert torch.allclose(out, x.nonzero()) 2025-08-14T21:38:38.2585808Z 2025-08-14T21:38:38.2585889Z 2025-08-14T21:38:38.2586527Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-08-14T21:38:38.2587148Z 2025-08-14T21:38:38.2587248Z _._ = None 2025-08-14T21:38:38.2587438Z ^ 2025-08-14T21:38:38.2682981Z 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. 2025-08-14T21:38:38.2683962Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.2684334Z 2025-08-14T21:38:38.2684568Z Registers a fake kernel based on the given operator profiles. This fake 2025-08-14T21:38:38.2685148Z kernel registration will override any existing fake kernel registrations. 2025-08-14T21:38:38.2685511Z 2025-08-14T21:38:38.2685718Z The input is a dictionary mapping operator names to a set of operator 2025-08-14T21:38:38.2686267Z profiles, which we will use to generate fake kernels. The operator profiles 2025-08-14T21:38:38.2686806Z are a record of the input and output tensor metadata. Based on this 2025-08-14T21:38:38.2687334Z information we will match a given input to the recorded profile, and return 2025-08-14T21:38:38.2687903Z an output with the same metadata as in the recorded profile. If a profile 2025-08-14T21:38:38.2688382Z doesn't exist then an exception will be thrown. 2025-08-14T21:38:38.2688624Z 2025-08-14T21:38:38.2688852Z The fake kernel generation is considered unsafe because it relies on the 2025-08-14T21:38:38.2689428Z rigid, pre-defined operator profiles that do not account for potential 2025-08-14T21:38:38.2690013Z variations in output behavior. Specifically, the generated kernels assume a 2025-08-14T21:38:38.2690717Z fixed relationship between input and output ranks. However, in reality, it's 2025-08-14T21:38:38.2691305Z possible that data-dependent operations may produce outputs of different 2025-08-14T21:38:38.2691874Z ranks even when given inputs of the same rank. The generated fake kernels 2025-08-14T21:38:38.2692421Z are inflexible and unable to accommodate these nuances, making them 2025-08-14T21:38:38.2693019Z potentially unsafe. 2025-08-14T21:38:38.2693173Z 2025-08-14T21:38:38.2693254Z Args: 2025-08-14T21:38:38.2693589Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-08-14T21:38:38.2694120Z name to a set of operator profiles from which we will generate fake 2025-08-14T21:38:38.2694514Z kernels. 2025-08-14T21:38:38.2694657Z 2025-08-14T21:38:38.2694742Z Examples: 2025-08-14T21:38:38.2694860Z 2025-08-14T21:38:38.2695038Z >>> # Example: Registering an op-profile from draft-export 2025-08-14T21:38:38.2695410Z >>> import torch 2025-08-14T21:38:38.2695715Z >>> from torch.export._draft_export import draft_export 2025-08-14T21:38:38.2696069Z >>> 2025-08-14T21:38:38.2696374Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-08-14T21:38:38.2696773Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-08-14T21:38:38.2697101Z >>> return x + y 2025-08-14T21:38:38.2697344Z >>> 2025-08-14T21:38:38.2697552Z >>> class M(torch.nn.Module): 2025-08-14T21:38:38.2697852Z >>> def forward(self, a, b): 2025-08-14T21:38:38.2698201Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-08-14T21:38:38.2698541Z >>> return res 2025-08-14T21:38:38.2698786Z >>> 2025-08-14T21:38:38.2699072Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-08-14T21:38:38.2699421Z >>> 2025-08-14T21:38:38.2699830Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-08-14T21:38:38.2700357Z >>> decomp = ep.run_decompositions() 2025-08-14T21:38:38.2700578Z 2025-08-14T21:38:38.2700582Z 2025-08-14T21:38:38.2700836Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.2701192Z 2025-08-14T21:38:38.2742579Z 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. 2025-08-14T21:38:38.2743561Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.2744174Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-08-14T21:38:38.2744549Z 2025-08-14T21:38:38.2744750Z This is a more structured way of using triton kernels with PyTorch. 2025-08-14T21:38:38.2745482Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-08-14T21:38:38.2746086Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-08-14T21:38:38.2746546Z that is simpler; 2025-08-14T21:38:38.2746954Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-08-14T21:38:38.2747561Z want to create an operator that behaves like PyTorch built-in operators. 2025-08-14T21:38:38.2748094Z For example, you may use a ``torch.library`` wrapper API to define the 2025-08-14T21:38:38.2748629Z behavior of the triton kernel when passed a tensor subclass or under 2025-08-14T21:38:38.2749070Z a TorchDispatchMode. 2025-08-14T21:38:38.2749338Z 2025-08-14T21:38:38.2749640Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-08-14T21:38:38.2750106Z when the implementation 2025-08-14T21:38:38.2750666Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-08-14T21:38:38.2751551Z custom operators as opaque (:func:`torch.compile` and 2025-08-14T21:38:38.2752047Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-08-14T21:38:38.2752613Z makes the implementation visible to these subsystems, allowing them 2025-08-14T21:38:38.2753169Z to optimize the triton kernel(s). 2025-08-14T21:38:38.2753377Z 2025-08-14T21:38:38.2753559Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-08-14T21:38:38.2754081Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-08-14T21:38:38.2754607Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-08-14T21:38:38.2754903Z 2025-08-14T21:38:38.2755000Z Args: 2025-08-14T21:38:38.2755341Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-08-14T21:38:38.2755887Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-08-14T21:38:38.2756381Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-08-14T21:38:38.2756892Z To avoid name collisions, please use your project name as the namespace; 2025-08-14T21:38:38.2757438Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-08-14T21:38:38.2758038Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-08-14T21:38:38.2758654Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-08-14T21:38:38.2759268Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-08-14T21:38:38.2759828Z schema (None | str): A schema string for the operator. If None 2025-08-14T21:38:38.2760335Z (recommended) we'll infer a schema for the operator from its type 2025-08-14T21:38:38.2760858Z annotations. We recommend letting us infer a schema unless you 2025-08-14T21:38:38.2761278Z have a specific reason not to. 2025-08-14T21:38:38.2761658Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-08-14T21:38:38.2761912Z 2025-08-14T21:38:38.2762013Z Example:: 2025-08-14T21:38:38.2762142Z 2025-08-14T21:38:38.2762294Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-14T21:38:38.2762628Z >>> import torch 2025-08-14T21:38:38.2762948Z >>> from torch.library import triton_op, wrap_triton 2025-08-14T21:38:38.2763300Z >>> 2025-08-14T21:38:38.2763508Z >>> import triton 2025-08-14T21:38:38.2763798Z >>> from triton import language as tl 2025-08-14T21:38:38.2764113Z >>> 2025-08-14T21:38:38.2764317Z >>> @triton.jit 2025-08-14T21:38:38.2764564Z >>> def add_kernel( 2025-08-14T21:38:38.2764822Z >>> in_ptr0, 2025-08-14T21:38:38.2765060Z >>> in_ptr1, 2025-08-14T21:38:38.2765304Z >>> out_ptr, 2025-08-14T21:38:38.2765556Z >>> n_elements, 2025-08-14T21:38:38.2765930Z >>> BLOCK_SIZE: "tl.constexpr", 2025-08-14T21:38:38.2766237Z >>> ): 2025-08-14T21:38:38.2766482Z >>> pid = tl.program_id(axis=0) 2025-08-14T21:38:38.2766799Z >>> block_start = pid * BLOCK_SIZE 2025-08-14T21:38:38.2767165Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-08-14T21:38:38.2767536Z >>> mask = offsets < n_elements 2025-08-14T21:38:38.2767870Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-08-14T21:38:38.2768219Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-08-14T21:38:38.2768551Z >>> output = x + y 2025-08-14T21:38:38.2768871Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-08-14T21:38:38.2769196Z >>> 2025-08-14T21:38:38.2769447Z >>> @triton_op("mylib::add", mutates_args={}) 2025-08-14T21:38:38.2769866Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-08-14T21:38:38.2770260Z >>> output = torch.empty_like(x) 2025-08-14T21:38:38.2770600Z >>> n_elements = output.numel() 2025-08-14T21:38:38.2770904Z >>> 2025-08-14T21:38:38.2771128Z >>> def grid(meta): 2025-08-14T21:38:38.2771466Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-08-14T21:38:38.2771900Z >>> 2025-08-14T21:38:38.2772198Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-08-14T21:38:38.2772663Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-08-14T21:38:38.2773056Z >>> return output 2025-08-14T21:38:38.2773317Z >>> 2025-08-14T21:38:38.2773522Z >>> @torch.compile 2025-08-14T21:38:38.2773779Z >>> def f(x, y): 2025-08-14T21:38:38.2774037Z >>> return add(x, y) 2025-08-14T21:38:38.2774291Z >>> 2025-08-14T21:38:38.2774529Z >>> x = torch.randn(3, device="cuda") 2025-08-14T21:38:38.2774862Z >>> y = torch.randn(3, device="cuda") 2025-08-14T21:38:38.2775162Z >>> 2025-08-14T21:38:38.2775363Z >>> z = f(x, y) 2025-08-14T21:38:38.2775630Z >>> assert torch.allclose(z, x + y) 2025-08-14T21:38:38.2775843Z 2025-08-14T21:38:38.2775934Z 2025-08-14T21:38:38.2776281Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.2776654Z 2025-08-14T21:38:38.2777172Z 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. 2025-08-14T21:38:38.2778030Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.2778591Z Allows capture of a triton kernel into a graph via make_fx or 2025-08-14T21:38:38.2779000Z non-strict ``torch.export``. 2025-08-14T21:38:38.2779192Z 2025-08-14T21:38:38.2779371Z These technologies perform Dispatcher-based tracing (via 2025-08-14T21:38:38.2779860Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-08-14T21:38:38.2780366Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-08-14T21:38:38.2780774Z can actually be traced into a graph. 2025-08-14T21:38:38.2780997Z 2025-08-14T21:38:38.2781199Z Please use this API together with :func:`torch.library.triton_op`. 2025-08-14T21:38:38.2781524Z 2025-08-14T21:38:38.2781612Z Examples: 2025-08-14T21:38:38.2781740Z 2025-08-14T21:38:38.2781849Z >>> # xdoctest: +SKIP 2025-08-14T21:38:38.2782107Z >>> import torch 2025-08-14T21:38:38.2782362Z >>> import triton 2025-08-14T21:38:38.2782640Z >>> from triton import language as tl 2025-08-14T21:38:38.2783029Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-08-14T21:38:38.2783548Z >>> from torch.library import wrap_triton 2025-08-14T21:38:38.2783876Z >>> 2025-08-14T21:38:38.2784074Z >>> @triton.jit 2025-08-14T21:38:38.2784321Z >>> def add_kernel( 2025-08-14T21:38:38.2784579Z >>> in_ptr0, 2025-08-14T21:38:38.2784811Z >>> in_ptr1, 2025-08-14T21:38:38.2785126Z >>> out_ptr, 2025-08-14T21:38:38.2785376Z >>> n_elements, 2025-08-14T21:38:38.2785643Z >>> BLOCK_SIZE: "tl.constexpr", 2025-08-14T21:38:38.2785945Z >>> ): 2025-08-14T21:38:38.2786188Z >>> pid = tl.program_id(axis=0) 2025-08-14T21:38:38.2786530Z >>> block_start = pid * BLOCK_SIZE 2025-08-14T21:38:38.2786886Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-08-14T21:38:38.2787260Z >>> mask = offsets < n_elements 2025-08-14T21:38:38.2787608Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-08-14T21:38:38.2787962Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-08-14T21:38:38.2788299Z >>> output = x + y 2025-08-14T21:38:38.2788620Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-08-14T21:38:38.2788943Z >>> 2025-08-14T21:38:38.2789159Z >>> def add(x, y): 2025-08-14T21:38:38.2789440Z >>> output = torch.empty_like(x) 2025-08-14T21:38:38.2789775Z >>> n_elements = output.numel() 2025-08-14T21:38:38.2790062Z >>> 2025-08-14T21:38:38.2790279Z >>> def grid_fn(meta): 2025-08-14T21:38:38.2790638Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-08-14T21:38:38.2791051Z >>> 2025-08-14T21:38:38.2791364Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-08-14T21:38:38.2791771Z >>> return output 2025-08-14T21:38:38.2792023Z >>> 2025-08-14T21:38:38.2792259Z >>> x = torch.randn(3, device="cuda") 2025-08-14T21:38:38.2792595Z >>> y = torch.randn(3, device="cuda") 2025-08-14T21:38:38.2793143Z >>> gm = make_fx(add)(x, y) 2025-08-14T21:38:38.2793471Z >>> print(gm.code) 2025-08-14T21:38:38.2793756Z >>> # def forward(self, x_1, y_1): 2025-08-14T21:38:38.2794207Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-08-14T21:38:38.2794795Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-08-14T21:38:38.2795287Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-08-14T21:38:38.2795645Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-08-14T21:38:38.2796013Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-08-14T21:38:38.2796409Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-08-14T21:38:38.2796732Z >>> # }) 2025-08-14T21:38:38.2796975Z >>> # return empty_like 2025-08-14T21:38:38.2797182Z 2025-08-14T21:38:38.2797260Z 2025-08-14T21:38:38.2797621Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.2797975Z 2025-08-14T21:38:38.3629089Z 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. 2025-08-14T21:38:38.3630084Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.3630458Z 2025-08-14T21:38:38.3630688Z Test if two objects are equal, and print an error message if test fails. 2025-08-14T21:38:38.3631262Z 2025-08-14T21:38:38.3631452Z The test is performed with ``actual == desired``. 2025-08-14T21:38:38.3631723Z 2025-08-14T21:38:38.3631813Z Parameters 2025-08-14T21:38:38.3632050Z ---------- 2025-08-14T21:38:38.3632411Z test_string : str 2025-08-14T21:38:38.3632907Z The message supplied to AssertionError. 2025-08-14T21:38:38.3633413Z actual : object 2025-08-14T21:38:38.3633686Z The object to test for equality against `desired`. 2025-08-14T21:38:38.3634037Z desired : object 2025-08-14T21:38:38.3634274Z The expected result. 2025-08-14T21:38:38.3634435Z 2025-08-14T21:38:38.3634517Z Examples 2025-08-14T21:38:38.3634722Z -------- 2025-08-14T21:38:38.3634957Z >>> np.testing.print_assert_equal( 2025-08-14T21:38:38.3635422Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-08-14T21:38:38.3636093Z ... ) # doctest: +SKIP 2025-08-14T21:38:38.3636524Z >>> np.testing.print_assert_equal( 2025-08-14T21:38:38.3637111Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-08-14T21:38:38.3637565Z ... ) # doctest: +SKIP 2025-08-14T21:38:38.3637838Z Traceback (most recent call last): 2025-08-14T21:38:38.3638127Z ... 2025-08-14T21:38:38.3638347Z AssertionError: Test XYZ of func xyz failed 2025-08-14T21:38:38.3638665Z ACTUAL: 2025-08-14T21:38:38.3638860Z [0, 1] 2025-08-14T21:38:38.3639044Z DESIRED: 2025-08-14T21:38:38.3639242Z [0, 2] 2025-08-14T21:38:38.3639349Z 2025-08-14T21:38:38.3639353Z 2025-08-14T21:38:38.3639611Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.3639971Z 2025-08-14T21:38:38.3640824Z 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. 2025-08-14T21:38:38.3642201Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.3642631Z 2025-08-14T21:38:38.3642913Z Raises an AssertionError if two items are not equal up to desired 2025-08-14T21:38:38.3643305Z precision. 2025-08-14T21:38:38.3643439Z 2025-08-14T21:38:38.3643771Z .. note:: It is recommended to use one of `assert_allclose`, 2025-08-14T21:38:38.3644455Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-08-14T21:38:38.3644928Z instead of this function for more consistent floating point 2025-08-14T21:38:38.3645397Z comparisons. 2025-08-14T21:38:38.3645582Z 2025-08-14T21:38:38.3645949Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-08-14T21:38:38.3646313Z 2025-08-14T21:38:38.3646581Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-08-14T21:38:38.3646988Z 2025-08-14T21:38:38.3647210Z That is a looser test than originally documented, but agrees with what the 2025-08-14T21:38:38.3647779Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-08-14T21:38:38.3648367Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-08-14T21:38:38.3648821Z delegates to assert_array_almost_equal 2025-08-14T21:38:38.3649043Z 2025-08-14T21:38:38.3649129Z Parameters 2025-08-14T21:38:38.3649341Z ---------- 2025-08-14T21:38:38.3649567Z actual : array_like 2025-08-14T21:38:38.3649797Z The object to check. 2025-08-14T21:38:38.3650057Z desired : array_like 2025-08-14T21:38:38.3650305Z The expected object. 2025-08-14T21:38:38.3650552Z decimal : int, optional 2025-08-14T21:38:38.3650822Z Desired precision, default is 7. 2025-08-14T21:38:38.3651123Z err_msg : str, optional 2025-08-14T21:38:38.3651416Z The error message to be printed in case of failure. 2025-08-14T21:38:38.3651772Z verbose : bool, optional 2025-08-14T21:38:38.3652134Z If True, the conflicting values are appended to the error message. 2025-08-14T21:38:38.3652442Z 2025-08-14T21:38:38.3652519Z Raises 2025-08-14T21:38:38.3652715Z ------ 2025-08-14T21:38:38.3652928Z AssertionError 2025-08-14T21:38:38.3653244Z If actual and desired are not equal up to specified precision. 2025-08-14T21:38:38.3653561Z 2025-08-14T21:38:38.3653644Z See Also 2025-08-14T21:38:38.3653846Z -------- 2025-08-14T21:38:38.3654200Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-14T21:38:38.3654667Z relative and/or absolute precision. 2025-08-14T21:38:38.3655111Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-14T21:38:38.3655535Z 2025-08-14T21:38:38.3655633Z Examples 2025-08-14T21:38:38.3655828Z -------- 2025-08-14T21:38:38.3656111Z >>> from torch._numpy.testing import assert_almost_equal 2025-08-14T21:38:38.3656506Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-08-14T21:38:38.3656907Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-08-14T21:38:38.3657298Z Traceback (most recent call last): 2025-08-14T21:38:38.3657582Z ... 2025-08-14T21:38:38.3657790Z AssertionError: 2025-08-14T21:38:38.3658144Z Arrays are not almost equal to 10 decimals 2025-08-14T21:38:38.3658468Z ACTUAL: 2.3333333333333 2025-08-14T21:38:38.3658739Z DESIRED: 2.33333334 2025-08-14T21:38:38.3658888Z 2025-08-14T21:38:38.3658984Z >>> assert_almost_equal( 2025-08-14T21:38:38.3659360Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-08-14T21:38:38.3659763Z ... ) 2025-08-14T21:38:38.3659976Z Traceback (most recent call last): 2025-08-14T21:38:38.3660264Z ... 2025-08-14T21:38:38.3660476Z AssertionError: 2025-08-14T21:38:38.3660724Z Arrays are not almost equal to 9 decimals 2025-08-14T21:38:38.3661040Z 2025-08-14T21:38:38.3661270Z Mismatched elements: 1 / 2 (50%) 2025-08-14T21:38:38.3661581Z Max absolute difference: 6.666699636781459e-09 2025-08-14T21:38:38.3661942Z Max relative difference: 2.8571569790287484e-09 2025-08-14T21:38:38.3662319Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-08-14T21:38:38.3662703Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-08-14T21:38:38.3662940Z 2025-08-14T21:38:38.3662943Z 2025-08-14T21:38:38.3663188Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.3663639Z 2025-08-14T21:38:38.3664190Z 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. 2025-08-14T21:38:38.3665211Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.3665578Z 2025-08-14T21:38:38.3665810Z Raises an AssertionError if two items are not equal up to significant 2025-08-14T21:38:38.3666213Z digits. 2025-08-14T21:38:38.3666340Z 2025-08-14T21:38:38.3666514Z .. note:: It is recommended to use one of `assert_allclose`, 2025-08-14T21:38:38.3666976Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-08-14T21:38:38.3667438Z instead of this function for more consistent floating point 2025-08-14T21:38:38.3667828Z comparisons. 2025-08-14T21:38:38.3667999Z 2025-08-14T21:38:38.3668174Z Given two numbers, check that they are approximately equal. 2025-08-14T21:38:38.3668677Z Approximately equal is defined as the number of significant digits 2025-08-14T21:38:38.3669074Z that agree. 2025-08-14T21:38:38.3669208Z 2025-08-14T21:38:38.3669293Z Parameters 2025-08-14T21:38:38.3669502Z ---------- 2025-08-14T21:38:38.3669700Z actual : scalar 2025-08-14T21:38:38.3669931Z The object to check. 2025-08-14T21:38:38.3670188Z desired : scalar 2025-08-14T21:38:38.3670414Z The expected object. 2025-08-14T21:38:38.3670682Z significant : int, optional 2025-08-14T21:38:38.3670979Z Desired precision, default is 7. 2025-08-14T21:38:38.3671275Z err_msg : str, optional 2025-08-14T21:38:38.3671586Z The error message to be printed in case of failure. 2025-08-14T21:38:38.3671945Z verbose : bool, optional 2025-08-14T21:38:38.3672315Z If True, the conflicting values are appended to the error message. 2025-08-14T21:38:38.3672625Z 2025-08-14T21:38:38.3672708Z Raises 2025-08-14T21:38:38.3672906Z ------ 2025-08-14T21:38:38.3673112Z AssertionError 2025-08-14T21:38:38.3673425Z If actual and desired are not equal up to specified precision. 2025-08-14T21:38:38.3673732Z 2025-08-14T21:38:38.3673813Z See Also 2025-08-14T21:38:38.3674018Z -------- 2025-08-14T21:38:38.3674354Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-14T21:38:38.3674827Z relative and/or absolute precision. 2025-08-14T21:38:38.3675268Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-14T21:38:38.3675579Z 2025-08-14T21:38:38.3675673Z Examples 2025-08-14T21:38:38.3675865Z -------- 2025-08-14T21:38:38.3676095Z >>> np.testing.assert_approx_equal( 2025-08-14T21:38:38.3676408Z ... 0.12345677777777e-20, 0.1234567e-20 2025-08-14T21:38:38.3676697Z ... ) # doctest: +SKIP 2025-08-14T21:38:38.3676967Z >>> np.testing.assert_approx_equal( 2025-08-14T21:38:38.3677261Z ... 0.12345670e-20, 2025-08-14T21:38:38.3677571Z ... 0.12345671e-20, # doctest: +SKIP 2025-08-14T21:38:38.3677883Z ... significant=8, 2025-08-14T21:38:38.3678125Z ... ) 2025-08-14T21:38:38.3678340Z >>> np.testing.assert_approx_equal( 2025-08-14T21:38:38.3678637Z ... 0.12345670e-20, 2025-08-14T21:38:38.3678905Z ... 0.12345672e-20, # doctest: +SKIP 2025-08-14T21:38:38.3679197Z ... significant=8, 2025-08-14T21:38:38.3679438Z ... ) 2025-08-14T21:38:38.3679663Z Traceback (most recent call last): 2025-08-14T21:38:38.3679935Z ... 2025-08-14T21:38:38.3680140Z AssertionError: 2025-08-14T21:38:38.3680408Z Items are not equal to 8 significant digits: 2025-08-14T21:38:38.3680718Z ACTUAL: 1.234567e-21 2025-08-14T21:38:38.3680961Z DESIRED: 1.2345672e-21 2025-08-14T21:38:38.3681126Z 2025-08-14T21:38:38.3681280Z the evaluated condition that raises the exception is 2025-08-14T21:38:38.3681541Z 2025-08-14T21:38:38.3681738Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-08-14T21:38:38.3682109Z True 2025-08-14T21:38:38.3682230Z 2025-08-14T21:38:38.3682234Z 2025-08-14T21:38:38.3682477Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.3682843Z 2025-08-14T21:38:38.3683436Z 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. 2025-08-14T21:38:38.3684406Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.3684783Z 2025-08-14T21:38:38.3684984Z Raises an AssertionError if two array_like objects are not equal. 2025-08-14T21:38:38.3685293Z 2025-08-14T21:38:38.3685501Z Given two array_like objects, check that the shape is equal and all 2025-08-14T21:38:38.3686016Z elements of these objects are equal (but see the Notes for the special 2025-08-14T21:38:38.3686539Z handling of a scalar). An exception is raised at shape mismatch or 2025-08-14T21:38:38.3687065Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-08-14T21:38:38.3687616Z are compared like numbers, no assertion is raised if both objects have 2025-08-14T21:38:38.3688039Z NaNs in the same positions. 2025-08-14T21:38:38.3688231Z 2025-08-14T21:38:38.3688449Z The usual caution for verifying equality with floating point numbers is 2025-08-14T21:38:38.3688882Z advised. 2025-08-14T21:38:38.3688997Z 2025-08-14T21:38:38.3689083Z Parameters 2025-08-14T21:38:38.3689295Z ---------- 2025-08-14T21:38:38.3689506Z x : array_like 2025-08-14T21:38:38.3689728Z The actual object to check. 2025-08-14T21:38:38.3690007Z y : array_like 2025-08-14T21:38:38.3690245Z The desired, expected object. 2025-08-14T21:38:38.3690523Z err_msg : str, optional 2025-08-14T21:38:38.3690832Z The error message to be printed in case of failure. 2025-08-14T21:38:38.3691189Z verbose : bool, optional 2025-08-14T21:38:38.3691559Z If True, the conflicting values are appended to the error message. 2025-08-14T21:38:38.3691951Z strict : bool, optional 2025-08-14T21:38:38.3692312Z If True, raise an AssertionError when either the shape or the data 2025-08-14T21:38:38.3692797Z type of the array_like objects does not match. The special 2025-08-14T21:38:38.3693578Z handling for scalars mentioned in the Notes section is disabled. 2025-08-14T21:38:38.3693905Z 2025-08-14T21:38:38.3693986Z Raises 2025-08-14T21:38:38.3694191Z ------ 2025-08-14T21:38:38.3694389Z AssertionError 2025-08-14T21:38:38.3694659Z If actual and desired objects are not equal. 2025-08-14T21:38:38.3694909Z 2025-08-14T21:38:38.3694993Z See Also 2025-08-14T21:38:38.3695200Z -------- 2025-08-14T21:38:38.3695545Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-14T21:38:38.3696027Z relative and/or absolute precision. 2025-08-14T21:38:38.3696478Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-14T21:38:38.3696792Z 2025-08-14T21:38:38.3696872Z Notes 2025-08-14T21:38:38.3697077Z ----- 2025-08-14T21:38:38.3698047Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-08-14T21:38:38.3698679Z function checks that each element of the array_like object is equal to 2025-08-14T21:38:38.3699449Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-08-14T21:38:38.3699796Z 2025-08-14T21:38:38.3699887Z Examples 2025-08-14T21:38:38.3700093Z -------- 2025-08-14T21:38:38.3700330Z The first assert does not raise an exception: 2025-08-14T21:38:38.3700580Z 2025-08-14T21:38:38.3700689Z >>> np.testing.assert_array_equal( 2025-08-14T21:38:38.3701037Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-08-14T21:38:38.3701364Z ... ) 2025-08-14T21:38:38.3701487Z 2025-08-14T21:38:38.3701712Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-08-14T21:38:38.3702155Z functions for these cases instead: 2025-08-14T21:38:38.3702352Z 2025-08-14T21:38:38.3702468Z >>> np.testing.assert_allclose( 2025-08-14T21:38:38.3702849Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-08-14T21:38:38.3703239Z ... ) 2025-08-14T21:38:38.3703346Z 2025-08-14T21:38:38.3703629Z As mentioned in the Notes section, `assert_array_equal` has special 2025-08-14T21:38:38.3704149Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-08-14T21:38:38.3704596Z 2025-08-14T21:38:38.3704700Z >>> x = np.full((2, 5), fill_value=3) 2025-08-14T21:38:38.3705024Z >>> np.testing.assert_array_equal(x, 3) 2025-08-14T21:38:38.3705239Z 2025-08-14T21:38:38.3705461Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-08-14T21:38:38.3705860Z array: 2025-08-14T21:38:38.3705988Z 2025-08-14T21:38:38.3706136Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-08-14T21:38:38.3706513Z Traceback (most recent call last): 2025-08-14T21:38:38.3706790Z ... 2025-08-14T21:38:38.3706996Z AssertionError: 2025-08-14T21:38:38.3707232Z Arrays are not equal 2025-08-14T21:38:38.3707461Z 2025-08-14T21:38:38.3707680Z (shapes (2, 5), () mismatch) 2025-08-14T21:38:38.3707966Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-08-14T21:38:38.3708242Z [3, 3, 3, 3, 3]]) 2025-08-14T21:38:38.3708495Z y: torch.ndarray(3) 2025-08-14T21:38:38.3708643Z 2025-08-14T21:38:38.3708859Z The `strict` parameter also ensures that the array data types match: 2025-08-14T21:38:38.3709183Z 2025-08-14T21:38:38.3709290Z >>> x = np.array([2, 2, 2]) 2025-08-14T21:38:38.3709576Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-08-14T21:38:38.3709963Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-08-14T21:38:38.3710329Z Traceback (most recent call last): 2025-08-14T21:38:38.3710601Z ... 2025-08-14T21:38:38.3710810Z AssertionError: 2025-08-14T21:38:38.3711044Z Arrays are not equal 2025-08-14T21:38:38.3711271Z 2025-08-14T21:38:38.3711533Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-08-14T21:38:38.3711887Z x: torch.ndarray([2, 2, 2]) 2025-08-14T21:38:38.3712154Z y: torch.ndarray([2., 2., 2.]) 2025-08-14T21:38:38.3712344Z 2025-08-14T21:38:38.3712595Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.3712967Z 2025-08-14T21:38:38.3713535Z 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. 2025-08-14T21:38:38.3714472Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.3714837Z 2025-08-14T21:38:38.3715043Z Raises an AssertionError if two objects are not equal up to desired 2025-08-14T21:38:38.3715452Z precision. 2025-08-14T21:38:38.3715584Z 2025-08-14T21:38:38.3715759Z .. note:: It is recommended to use one of `assert_allclose`, 2025-08-14T21:38:38.3716210Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-08-14T21:38:38.3716673Z instead of this function for more consistent floating point 2025-08-14T21:38:38.3717063Z comparisons. 2025-08-14T21:38:38.3717215Z 2025-08-14T21:38:38.3717579Z The test verifies identical shapes and that the elements of ``actual`` and 2025-08-14T21:38:38.3718012Z ``desired`` satisfy. 2025-08-14T21:38:38.3718173Z 2025-08-14T21:38:38.3718298Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-08-14T21:38:38.3718545Z 2025-08-14T21:38:38.3718770Z That is a looser test than originally documented, but agrees with what the 2025-08-14T21:38:38.3719348Z actual implementation did up to rounding vagaries. An exception is raised 2025-08-14T21:38:38.3719915Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-08-14T21:38:38.3720471Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-08-14T21:38:38.3720923Z objects have NaNs in the same positions. 2025-08-14T21:38:38.3721139Z 2025-08-14T21:38:38.3721237Z Parameters 2025-08-14T21:38:38.3721431Z ---------- 2025-08-14T21:38:38.3721638Z x : array_like 2025-08-14T21:38:38.3721875Z The actual object to check. 2025-08-14T21:38:38.3722137Z y : array_like 2025-08-14T21:38:38.3722377Z The desired, expected object. 2025-08-14T21:38:38.3722673Z decimal : int, optional 2025-08-14T21:38:38.3722932Z Desired precision, default is 6. 2025-08-14T21:38:38.3723240Z err_msg : str, optional 2025-08-14T21:38:38.3723556Z The error message to be printed in case of failure. 2025-08-14T21:38:38.3723971Z verbose : bool, optional 2025-08-14T21:38:38.3724342Z If True, the conflicting values are appended to the error message. 2025-08-14T21:38:38.3724655Z 2025-08-14T21:38:38.3724754Z Raises 2025-08-14T21:38:38.3724941Z ------ 2025-08-14T21:38:38.3725145Z AssertionError 2025-08-14T21:38:38.3725476Z If actual and desired are not equal up to specified precision. 2025-08-14T21:38:38.3725775Z 2025-08-14T21:38:38.3725867Z See Also 2025-08-14T21:38:38.3726056Z -------- 2025-08-14T21:38:38.3726405Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-14T21:38:38.3726878Z relative and/or absolute precision. 2025-08-14T21:38:38.3727311Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-14T21:38:38.3727646Z 2025-08-14T21:38:38.3727728Z Examples 2025-08-14T21:38:38.3727931Z -------- 2025-08-14T21:38:38.3728167Z the first assert does not raise an exception 2025-08-14T21:38:38.3728408Z 2025-08-14T21:38:38.3728653Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-08-14T21:38:38.3729027Z 2025-08-14T21:38:38.3729142Z >>> np.testing.assert_array_almost_equal( 2025-08-14T21:38:38.3729518Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-08-14T21:38:38.3729852Z ... ) 2025-08-14T21:38:38.3730080Z Traceback (most recent call last): 2025-08-14T21:38:38.3730370Z ... 2025-08-14T21:38:38.3730567Z AssertionError: 2025-08-14T21:38:38.3730825Z Arrays are not almost equal to 5 decimals 2025-08-14T21:38:38.3731142Z 2025-08-14T21:38:38.3731363Z Mismatched elements: 1 / 3 (33.3%) 2025-08-14T21:38:38.3731693Z Max absolute difference: 5.999999999994898e-05 2025-08-14T21:38:38.3732065Z Max relative difference: 2.5713661239633743e-05 2025-08-14T21:38:38.3732453Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-08-14T21:38:38.3732883Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-08-14T21:38:38.3733157Z 2025-08-14T21:38:38.3733276Z >>> np.testing.assert_array_almost_equal( 2025-08-14T21:38:38.3733632Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-08-14T21:38:38.3733948Z ... ) 2025-08-14T21:38:38.3734174Z Traceback (most recent call last): 2025-08-14T21:38:38.3734462Z ... 2025-08-14T21:38:38.3734656Z AssertionError: 2025-08-14T21:38:38.3734914Z Arrays are not almost equal to 5 decimals 2025-08-14T21:38:38.3735222Z 2025-08-14T21:38:38.3735436Z x and y nan location mismatch: 2025-08-14T21:38:38.3735778Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-08-14T21:38:38.3736199Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-08-14T21:38:38.3736461Z 2025-08-14T21:38:38.3736465Z 2025-08-14T21:38:38.3736790Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.3737147Z 2025-08-14T21:38:38.3737743Z 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. 2025-08-14T21:38:38.3738676Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.3739253Z Context manager that resets warning registry for catching warnings 2025-08-14T21:38:38.3739566Z 2025-08-14T21:38:38.3739817Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-08-14T21:38:38.3740379Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-08-14T21:38:38.3740950Z it impossible to retrigger the warning in this module, whatever you put in 2025-08-14T21:38:38.3741533Z the warnings filters. This context manager accepts a sequence of `modules` 2025-08-14T21:38:38.3742033Z as a keyword argument to its constructor and: 2025-08-14T21:38:38.3742270Z 2025-08-14T21:38:38.3742489Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-08-14T21:38:38.3742916Z on entry; 2025-08-14T21:38:38.3743236Z * resets ``__warningregistry__`` to its previous state on exit. 2025-08-14T21:38:38.3743699Z 2025-08-14T21:38:38.3743930Z This makes it possible to trigger any warning afresh inside the context 2025-08-14T21:38:38.3744432Z manager without disturbing the state of warnings outside. 2025-08-14T21:38:38.3744730Z 2025-08-14T21:38:38.3744959Z For compatibility with Python 3.0, please consider all arguments to be 2025-08-14T21:38:38.3745396Z keyword-only. 2025-08-14T21:38:38.3745535Z 2025-08-14T21:38:38.3745622Z Parameters 2025-08-14T21:38:38.3745845Z ---------- 2025-08-14T21:38:38.3746076Z record : bool, optional 2025-08-14T21:38:38.3746429Z Specifies whether warnings should be captured by a custom 2025-08-14T21:38:38.3746964Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-08-14T21:38:38.3747515Z returned by the context manager. Otherwise None is returned by the 2025-08-14T21:38:38.3748059Z context manager. The objects appended to the list are arguments whose 2025-08-14T21:38:38.3748554Z attributes mirror the arguments to ``showwarning()``. 2025-08-14T21:38:38.3748941Z modules : sequence, optional 2025-08-14T21:38:38.3749351Z Sequence of modules for which to reset warnings registry on entry and 2025-08-14T21:38:38.3749865Z restore on exit. To work correctly, all 'ignore' filters should 2025-08-14T21:38:38.3750275Z filter by one of these modules. 2025-08-14T21:38:38.3750493Z 2025-08-14T21:38:38.3750574Z Examples 2025-08-14T21:38:38.3750788Z -------- 2025-08-14T21:38:38.3750996Z >>> import warnings 2025-08-14T21:38:38.3751333Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-08-14T21:38:38.3751746Z ... modules=[np.core.fromnumeric] 2025-08-14T21:38:38.3752036Z ... ): 2025-08-14T21:38:38.3752274Z ... warnings.simplefilter("always") 2025-08-14T21:38:38.3752717Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-08-14T21:38:38.3753213Z ... # do something that raises a warning but ignore those in 2025-08-14T21:38:38.3753599Z ... # np.core.fromnumeric 2025-08-14T21:38:38.3753872Z 2025-08-14T21:38:38.3754217Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.3754582Z 2025-08-14T21:38:38.7535323Z 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. 2025-08-14T21:38:38.7536272Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.7536848Z Applies a 1D convolution over a quantized input signal composed of 2025-08-14T21:38:38.7537283Z several quantized input planes. 2025-08-14T21:38:38.7537785Z 2025-08-14T21:38:38.7538006Z For details on input arguments, parameters, and implementation see 2025-08-14T21:38:38.7538438Z :class:`~torch.nn.Conv1d`. 2025-08-14T21:38:38.7538625Z 2025-08-14T21:38:38.7538727Z .. note:: 2025-08-14T21:38:38.7539087Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-08-14T21:38:38.7539394Z 2025-08-14T21:38:38.7539491Z .. note:: 2025-08-14T21:38:38.7539789Z Only `torch.quint8` is supported for the input data type. 2025-08-14T21:38:38.7540086Z 2025-08-14T21:38:38.7540090Z 2025-08-14T21:38:38.7540178Z Attributes: 2025-08-14T21:38:38.7540525Z weight (Tensor): packed tensor derived from the learnable weight 2025-08-14T21:38:38.7540948Z parameter. 2025-08-14T21:38:38.7541282Z scale (Tensor): scalar for the output scale 2025-08-14T21:38:38.7541690Z zero_point (Tensor): scalar for the output zero point 2025-08-14T21:38:38.7549909Z 2025-08-14T21:38:38.7550142Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-08-14T21:38:38.7550419Z 2025-08-14T21:38:38.7550535Z Examples:: 2025-08-14T21:38:38.7550674Z 2025-08-14T21:38:38.7550819Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-08-14T21:38:38.7551425Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-08-14T21:38:38.7551777Z >>> input = torch.randn(20, 16, 100) 2025-08-14T21:38:38.7552109Z >>> # quantize input to quint8 2025-08-14T21:38:38.7552407Z >>> # xdoctest: +SKIP 2025-08-14T21:38:38.7552799Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-08-14T21:38:38.7553261Z ... dtype=torch.quint8) 2025-08-14T21:38:38.7553594Z >>> output = m(q_input) 2025-08-14T21:38:38.7553791Z 2025-08-14T21:38:38.7553871Z 2025-08-14T21:38:38.7554237Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.7554603Z 2025-08-14T21:38:38.7760166Z 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. 2025-08-14T21:38:38.7761053Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.7761568Z A quantized long short-term memory (LSTM). 2025-08-14T21:38:38.7761796Z 2025-08-14T21:38:38.7762082Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-08-14T21:38:38.7762465Z 2025-08-14T21:38:38.7762569Z Attributes: 2025-08-14T21:38:38.7762815Z layers : instances of the `_LSTMLayer` 2025-08-14T21:38:38.7763053Z 2025-08-14T21:38:38.7763142Z .. note:: 2025-08-14T21:38:38.7763476Z To access the weights and biases, you need to access them per layer. 2025-08-14T21:38:38.7763958Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-08-14T21:38:38.7764248Z 2025-08-14T21:38:38.7764335Z Examples:: 2025-08-14T21:38:38.7764578Z >>> # xdoctest: +SKIP 2025-08-14T21:38:38.7764868Z >>> custom_module_config = { 2025-08-14T21:38:38.7765194Z ... 'float_to_observed_custom_module_class': { 2025-08-14T21:38:38.7765565Z ... nn.LSTM: nn.quantizable.LSTM, 2025-08-14T21:38:38.7765890Z ... }, 2025-08-14T21:38:38.7766162Z ... 'observed_to_quantized_custom_module_class': { 2025-08-14T21:38:38.7766566Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-08-14T21:38:38.7766907Z ... } 2025-08-14T21:38:38.7767115Z ... } 2025-08-14T21:38:38.7767462Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-08-14T21:38:38.7768001Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-08-14T21:38:38.7768396Z 2025-08-14T21:38:38.7768756Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.7769124Z 2025-08-14T21:38:38.8516950Z 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. 2025-08-14T21:38:38.8518145Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.8518523Z 2025-08-14T21:38:38.8518796Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-08-14T21:38:38.8519380Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-08-14T21:38:38.8519958Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-08-14T21:38:38.8520409Z input by the user. 2025-08-14T21:38:38.8520759Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-08-14T21:38:38.8521245Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-08-14T21:38:38.8521515Z 2025-08-14T21:38:38.8521609Z Note:: 2025-08-14T21:38:38.8522069Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-08-14T21:38:38.8522496Z 2025-08-14T21:38:38.8522589Z Args: 2025-08-14T21:38:38.8522787Z model (nn.Module): 2025-08-14T21:38:38.8523167Z The model whose layers will be sparsified. The layers that needs to be 2025-08-14T21:38:38.8523857Z sparsified should be added separately using the register_layer() function 2025-08-14T21:38:38.8524315Z aggregate_fn (Optional, Callable): 2025-08-14T21:38:38.8524787Z default aggregate_fn that is used if not specified while registering the layer. 2025-08-14T21:38:38.8525325Z specifies how inputs should be aggregated over time. 2025-08-14T21:38:38.8525883Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-08-14T21:38:38.8526352Z Example 2025-08-14T21:38:38.8526665Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-08-14T21:38:38.8527069Z reduce_fn (Optional, Callable): 2025-08-14T21:38:38.8527522Z default reduce_fn that is used if not specified while registering the layer. 2025-08-14T21:38:38.8528134Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-08-14T21:38:38.8528624Z calling agg_fn() on all inputs. 2025-08-14T21:38:38.8528928Z Example 2025-08-14T21:38:38.8529279Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-08-14T21:38:38.8529701Z mask_fn (Optional, Callable): 2025-08-14T21:38:38.8530223Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-08-14T21:38:38.8530879Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-08-14T21:38:38.8531344Z register_layer(). 2025-08-14T21:38:38.8531878Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-08-14T21:38:38.8532433Z arguments. 2025-08-14T21:38:38.8532698Z features (Optional, list): 2025-08-14T21:38:38.8533035Z default selected features to sparsify. 2025-08-14T21:38:38.8533530Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-08-14T21:38:38.8533994Z For example, 2025-08-14T21:38:38.8534419Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-08-14T21:38:38.8534895Z feature_dim (Optional, int): 2025-08-14T21:38:38.8535369Z default dimension of input features. Again, features along this dim will be chosen 2025-08-14T21:38:38.8535864Z for sparsification. 2025-08-14T21:38:38.8536166Z sparse_config (Dict): 2025-08-14T21:38:38.8536574Z Default configuration for the mask_fn. This config will be passed 2025-08-14T21:38:38.8536994Z with the mask_fn() 2025-08-14T21:38:38.8537264Z 2025-08-14T21:38:38.8537352Z Example: 2025-08-14T21:38:38.8537571Z >>> # xdoctest: +SKIP 2025-08-14T21:38:38.8537826Z >>> model = SomeModel() 2025-08-14T21:38:38.8538243Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-08-14T21:38:38.8538712Z >>> # Initialize aggregate_fn 2025-08-14T21:38:38.8538992Z >>> def agg_fn(x, y): 2025-08-14T21:38:38.8539252Z >>> return x + y 2025-08-14T21:38:38.8539498Z >>> 2025-08-14T21:38:38.8539702Z >>> # Initialize reduce_fn 2025-08-14T21:38:38.8539981Z >>> def reduce_fn(x): 2025-08-14T21:38:38.8540255Z >>> return torch.mean(x, dim=0) 2025-08-14T21:38:38.8540555Z >>> 2025-08-14T21:38:38.8540755Z >>> # Initialize mask_fn 2025-08-14T21:38:38.8541028Z >>> def mask_fn(data): 2025-08-14T21:38:38.8541340Z >>> return torch.eye(data.shape).to(data.device) 2025-08-14T21:38:38.8541668Z >>> 2025-08-14T21:38:38.8541867Z >>> 2025-08-14T21:38:38.8542101Z >>> act_sparsifier.register_layer( 2025-08-14T21:38:38.8542403Z ... model.some_layer, 2025-08-14T21:38:38.8542686Z ... aggregate_fn=agg_fn, 2025-08-14T21:38:38.8542975Z ... reduce_fn=reduce_fn, 2025-08-14T21:38:38.8543238Z ... mask_fn=mask_fn, 2025-08-14T21:38:38.8543737Z ... ) 2025-08-14T21:38:38.8544042Z >>> 2025-08-14T21:38:38.8544340Z >>> # start training process 2025-08-14T21:38:38.8544631Z >>> for _ in [...]: 2025-08-14T21:38:38.8544953Z >>> # epoch starts 2025-08-14T21:38:38.8545364Z >>> # model.forward(), compute_loss() and model.backwards() 2025-08-14T21:38:38.8545741Z >>> # epoch ends 2025-08-14T21:38:38.8545998Z >>> act_sparsifier.step() 2025-08-14T21:38:38.8546285Z >>> # end training process 2025-08-14T21:38:38.8546579Z >>> sparsifier.squash_mask() 2025-08-14T21:38:38.8546787Z 2025-08-14T21:38:38.8547033Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.8547390Z 2025-08-14T21:38:38.8548254Z 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. 2025-08-14T21:38:38.8549433Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.8549803Z 2025-08-14T21:38:38.8549999Z Abstract method that needs to be implemented by the child class. 2025-08-14T21:38:38.8550562Z The expected return type should is a dictionary of name to schedule_param value 2025-08-14T21:38:38.8551189Z The returned values will be updated in sparsifier when the scheduler step() function 2025-08-14T21:38:38.8551645Z is called. 2025-08-14T21:38:38.8551780Z 2025-08-14T21:38:38.8551863Z Example: 2025-08-14T21:38:38.8552098Z >>> def get_schedule_param(self): 2025-08-14T21:38:38.8552401Z ... new_param = {} 2025-08-14T21:38:38.8552710Z ... for name in self.sparsifier.data_groups.keys(): 2025-08-14T21:38:38.8553077Z ... new_param[name] = ( 2025-08-14T21:38:38.8553478Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-08-14T21:38:38.8553879Z ... ) 2025-08-14T21:38:38.8554097Z ... return new_param 2025-08-14T21:38:38.8554280Z 2025-08-14T21:38:38.8554610Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-08-14T21:38:38.8555147Z would be halved 2025-08-14T21:38:38.8555276Z 2025-08-14T21:38:38.8555516Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.8555882Z 2025-08-14T21:38:38.8877423Z 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. 2025-08-14T21:38:38.8878482Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.8879020Z Squashes the sparse masks into the appropriate tensors. 2025-08-14T21:38:38.8879480Z 2025-08-14T21:38:38.8879698Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-08-14T21:38:38.8880182Z the module will have a `sparse_params` dict attached to it. 2025-08-14T21:38:38.8880481Z 2025-08-14T21:38:38.8880573Z Args: 2025-08-14T21:38:38.8880885Z params_to_keep: List of keys to save in the module or a dict 2025-08-14T21:38:38.8881328Z representing the modules and keys that will have 2025-08-14T21:38:38.8881720Z sparsity parameters saved 2025-08-14T21:38:38.8882162Z params_to_keep_per_layer: Dict to specify the params that should be 2025-08-14T21:38:38.8882642Z saved for specific layers. The keys in the dict 2025-08-14T21:38:38.8883057Z should be the module fqn, while the values should 2025-08-14T21:38:38.8883484Z be a list of strings with the names of the variables 2025-08-14T21:38:38.8883888Z to save in the `sparse_params` 2025-08-14T21:38:38.8884118Z 2025-08-14T21:38:38.8884216Z Examples: 2025-08-14T21:38:38.8884475Z >>> # xdoctest: +SKIP("locals are undefined") 2025-08-14T21:38:38.8884925Z >>> # Don't save any sparse params 2025-08-14T21:38:38.8885260Z >>> sparsifier.squash_mask() 2025-08-14T21:38:38.8885605Z >>> hasattr(model.submodule1, "sparse_params") 2025-08-14T21:38:38.8885938Z False 2025-08-14T21:38:38.8886070Z 2025-08-14T21:38:38.8886199Z >>> # Keep sparse params per layer 2025-08-14T21:38:38.8886519Z >>> sparsifier.squash_mask( 2025-08-14T21:38:38.8886848Z ... params_to_keep_per_layer={ 2025-08-14T21:38:38.8887197Z ... "submodule1.linear1": ("foo", "bar"), 2025-08-14T21:38:38.8887565Z ... "submodule2.linear42": ("baz",), 2025-08-14T21:38:38.8887871Z ... } 2025-08-14T21:38:38.8888106Z ... ) 2025-08-14T21:38:38.8888398Z >>> print(model.submodule1.linear1.sparse_params) 2025-08-14T21:38:38.8888748Z {'foo': 42, 'bar': 24} 2025-08-14T21:38:38.8889094Z >>> print(model.submodule2.linear42.sparse_params) 2025-08-14T21:38:38.8889457Z {'baz': 0.1} 2025-08-14T21:38:38.8889612Z 2025-08-14T21:38:38.8889729Z >>> # Keep sparse params for all layers 2025-08-14T21:38:38.8890132Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-08-14T21:38:38.8890568Z >>> print(model.submodule1.linear1.sparse_params) 2025-08-14T21:38:38.8890928Z {'foo': 42, 'bar': 24} 2025-08-14T21:38:38.8891263Z >>> print(model.submodule2.linear42.sparse_params) 2025-08-14T21:38:38.8891624Z {'foo': 42, 'bar': 24} 2025-08-14T21:38:38.8891805Z 2025-08-14T21:38:38.8892020Z >>> # Keep some sparse params for all layers, and specific ones for 2025-08-14T21:38:38.8892417Z >>> # some other layers 2025-08-14T21:38:38.8892723Z >>> sparsifier.squash_mask( 2025-08-14T21:38:38.8893284Z ... params_to_keep=("foo", "bar"), 2025-08-14T21:38:38.8893701Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-08-14T21:38:38.8894081Z ... ) 2025-08-14T21:38:38.8894371Z >>> print(model.submodule1.linear1.sparse_params) 2025-08-14T21:38:38.8894730Z {'foo': 42, 'bar': 24} 2025-08-14T21:38:38.8895060Z >>> print(model.submodule2.linear42.sparse_params) 2025-08-14T21:38:38.8895430Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-08-14T21:38:38.8895725Z 2025-08-14T21:38:38.8896075Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.8896445Z 2025-08-14T21:38:38.9810514Z 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. 2025-08-14T21:38:38.9811567Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:38.9811937Z 2025-08-14T21:38:38.9812192Z Config object that specifies the supported data types passed as arguments to 2025-08-14T21:38:38.9812802Z quantize ops in the reference model spec, for input and output activations, 2025-08-14T21:38:38.9813235Z weights, and biases. 2025-08-14T21:38:38.9813400Z 2025-08-14T21:38:38.9813555Z For example, consider the following reference model: 2025-08-14T21:38:38.9813829Z 2025-08-14T21:38:38.9813986Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-08-14T21:38:38.9814283Z 2025-08-14T21:38:38.9814493Z The pattern in the square brackets refers to the reference pattern of 2025-08-14T21:38:38.9815055Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-08-14T21:38:38.9815616Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-08-14T21:38:38.9816184Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-08-14T21:38:38.9816745Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-08-14T21:38:38.9817178Z the second quantize op (quant2). 2025-08-14T21:38:38.9817384Z 2025-08-14T21:38:38.9817596Z Note that the dtype here does not refer to the interface dtypes of the 2025-08-14T21:38:38.9818218Z op. For example, the "input dtype" here is not the dtype of the input 2025-08-14T21:38:38.9818751Z tensor passed to the quantized linear op. Though it can still be the 2025-08-14T21:38:38.9819262Z same as the interface dtype, this is not always the case, e.g. the 2025-08-14T21:38:38.9819785Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-08-14T21:38:38.9820326Z specified in the DTypeConfig would still be quint8. The semantics of 2025-08-14T21:38:38.9820842Z dtypes here are the same as the semantics of the dtypes specified in 2025-08-14T21:38:38.9821255Z the observers. 2025-08-14T21:38:38.9821397Z 2025-08-14T21:38:38.9821599Z These dtypes are matched against the ones specified in the user's 2025-08-14T21:38:38.9822132Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-08-14T21:38:38.9822660Z specified in the DTypeConfig (if any), then we will quantize the given 2025-08-14T21:38:38.9823217Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-08-14T21:38:38.9823726Z the pattern will not be quantized. 2025-08-14T21:38:38.9823928Z 2025-08-14T21:38:38.9824036Z Example usage:: 2025-08-14T21:38:38.9824185Z 2025-08-14T21:38:38.9824287Z >>> # xdoctest: +SKIP(failing) 2025-08-14T21:38:38.9824587Z >>> dtype_config1 = DTypeConfig( 2025-08-14T21:38:38.9824898Z ... input_dtype=torch.quint8, 2025-08-14T21:38:38.9825200Z ... output_dtype=torch.quint8, 2025-08-14T21:38:38.9825509Z ... weight_dtype=torch.qint8, 2025-08-14T21:38:38.9825819Z ... bias_dtype=torch.float) 2025-08-14T21:38:38.9826013Z 2025-08-14T21:38:38.9826121Z >>> dtype_config2 = DTypeConfig( 2025-08-14T21:38:38.9826452Z ... input_dtype=DTypeWithConstraints( 2025-08-14T21:38:38.9826785Z ... dtype=torch.quint8, 2025-08-14T21:38:38.9827074Z ... quant_min_lower_bound=0, 2025-08-14T21:38:38.9827397Z ... quant_max_upper_bound=255, 2025-08-14T21:38:38.9827697Z ... ), 2025-08-14T21:38:38.9827954Z ... output_dtype=DTypeWithConstraints( 2025-08-14T21:38:38.9828275Z ... dtype=torch.quint8, 2025-08-14T21:38:38.9828573Z ... quant_min_lower_bound=0, 2025-08-14T21:38:38.9828887Z ... quant_max_upper_bound=255, 2025-08-14T21:38:38.9829171Z ... ), 2025-08-14T21:38:38.9829428Z ... weight_dtype=DTypeWithConstraints( 2025-08-14T21:38:38.9829763Z ... dtype=torch.qint8, 2025-08-14T21:38:38.9830053Z ... quant_min_lower_bound=-128, 2025-08-14T21:38:38.9830378Z ... quant_max_upper_bound=127, 2025-08-14T21:38:38.9830672Z ... ), 2025-08-14T21:38:38.9830961Z ... bias_dtype=torch.float) 2025-08-14T21:38:38.9831170Z 2025-08-14T21:38:38.9831274Z >>> dtype_config1.input_dtype 2025-08-14T21:38:38.9831559Z torch.quint8 2025-08-14T21:38:38.9831696Z 2025-08-14T21:38:38.9831813Z >>> dtype_config2.input_dtype 2025-08-14T21:38:38.9832092Z torch.quint8 2025-08-14T21:38:38.9832242Z 2025-08-14T21:38:38.9832373Z >>> dtype_config2.input_dtype_with_constraints 2025-08-14T21:38:38.9833151Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-08-14T21:38:38.9833782Z 2025-08-14T21:38:38.9834025Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:38.9834394Z 2025-08-14T21:38:39.0921491Z 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. 2025-08-14T21:38:39.0922829Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.0923214Z 2025-08-14T21:38:39.0923513Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-08-14T21:38:39.0924241Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-08-14T21:38:39.0925159Z though the requirements on the type of model more-so depends on the specific report the user 2025-08-14T21:38:39.0925864Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-08-14T21:38:39.0926529Z a set of Detector classes, each of which generate reports on quantization configuration 2025-08-14T21:38:39.0927018Z issues a use might have. 2025-08-14T21:38:39.0927187Z 2025-08-14T21:38:39.0927328Z Currently supports generating reports on: 2025-08-14T21:38:39.0927790Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-08-14T21:38:39.0928385Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-08-14T21:38:39.0929049Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-08-14T21:38:39.0929661Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-08-14T21:38:39.0929977Z 2025-08-14T21:38:39.0930370Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-08-14T21:38:39.0931240Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-08-14T21:38:39.0932084Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-08-14T21:38:39.0932774Z has the capability to remove all the observers it inserted as well. 2025-08-14T21:38:39.0933086Z 2025-08-14T21:38:39.0933356Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-08-14T21:38:39.0933756Z 2025-08-14T21:38:39.0934122Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-08-14T21:38:39.0934907Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-08-14T21:38:39.0935321Z 2025-08-14T21:38:39.0935617Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-08-14T21:38:39.0936223Z This set is generated by calling the get_detector_name() of each detector 2025-08-14T21:38:39.0936567Z 2025-08-14T21:38:39.0936889Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-08-14T21:38:39.0937638Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-08-14T21:38:39.0938175Z can be removed at the end if desired 2025-08-14T21:38:39.0938385Z 2025-08-14T21:38:39.0938693Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-08-14T21:38:39.0939446Z This is to ensure we only insert observers once with the ModelReport instance 2025-08-14T21:38:39.0939805Z 2025-08-14T21:38:39.0940051Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-08-14T21:38:39.0940704Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-08-14T21:38:39.0941410Z instance. This also allows the functionality where we can generate the report multiple times 2025-08-14T21:38:39.0941979Z as long as we haven't removed the observers yet. 2025-08-14T21:38:39.0942226Z 2025-08-14T21:38:39.0942323Z Note: 2025-08-14T21:38:39.0942733Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-08-14T21:38:39.0943510Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-08-14T21:38:39.0944227Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-08-14T21:38:39.0944747Z the QuantizationTracer class. 2025-08-14T21:38:39.0944944Z 2025-08-14T21:38:39.0945045Z General Flow for Fx workflow: 2025-08-14T21:38:39.0945614Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-08-14T21:38:39.0946317Z 2.) Prepare your model with prepare_fx 2025-08-14T21:38:39.0946771Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-08-14T21:38:39.0947219Z 4.) Calibrate your model with data 2025-08-14T21:38:39.0947768Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-08-14T21:38:39.0948325Z Optional 2025-08-14T21:38:39.0948699Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-08-14T21:38:39.0949323Z 7.) To help in parsing report information and debugging, view report info as a: 2025-08-14T21:38:39.0949769Z - Table 2025-08-14T21:38:39.0949984Z - Histogram 2025-08-14T21:38:39.0950224Z - Line plot 2025-08-14T21:38:39.0950679Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-08-14T21:38:39.0951100Z 2025-08-14T21:38:39.0951227Z Example (with QuantizationTracer): 2025-08-14T21:38:39.0951525Z >>> # xdoctest: +SKIP 2025-08-14T21:38:39.0951800Z >>> # get the necessary qconfig 2025-08-14T21:38:39.0952119Z >>> config = PrepareCustomConfig() 2025-08-14T21:38:39.0952476Z >>> skipped_module_names, skipped_module_classes = ( 2025-08-14T21:38:39.0952894Z ... get_skipped_module_name_and_classes(config, False) 2025-08-14T21:38:39.0953249Z ... ) 2025-08-14T21:38:39.0953364Z 2025-08-14T21:38:39.0953501Z >>> # initialize our model and get GraphModule 2025-08-14T21:38:39.0953824Z >>> model = SomeModel() 2025-08-14T21:38:39.0954246Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-08-14T21:38:39.0954782Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-08-14T21:38:39.0955053Z 2025-08-14T21:38:39.0955200Z >>> # get our set of detectors and ModelReport instance 2025-08-14T21:38:39.0955562Z >>> detector_set = set( 2025-08-14T21:38:39.0955820Z ... [ 2025-08-14T21:38:39.0956071Z ... DynamicStaticDetector(tolerance=0.5), 2025-08-14T21:38:39.0956510Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-08-14T21:38:39.0956896Z ... ] 2025-08-14T21:38:39.0957106Z ... ) 2025-08-14T21:38:39.0957415Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-08-14T21:38:39.0957743Z 2025-08-14T21:38:39.0957894Z >>> # now we insert the observers and calibrate the model 2025-08-14T21:38:39.0958405Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-08-14T21:38:39.0958891Z >>> for i in range(num_callibration_batches): 2025-08-14T21:38:39.0959261Z >>> example_input = get_callibration_input() 2025-08-14T21:38:39.0959704Z >>> tracer_model_with_observers(example_input) 2025-08-14T21:38:39.0959944Z 2025-08-14T21:38:39.0960207Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-08-14T21:38:39.0960719Z >>> reports = tracer_reporter.generate_model_report( 2025-08-14T21:38:39.0961102Z ... remove_inserted_observers=True 2025-08-14T21:38:39.0961408Z ... ) 2025-08-14T21:38:39.0961522Z 2025-08-14T21:38:39.0961740Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-08-14T21:38:39.0962231Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-08-14T21:38:39.0962493Z 2025-08-14T21:38:39.0962737Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-08-14T21:38:39.0963268Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-08-14T21:38:39.0963551Z 2025-08-14T21:38:39.0963822Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-08-14T21:38:39.0964424Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-08-14T21:38:39.0964739Z 2025-08-14T21:38:39.0964743Z 2025-08-14T21:38:39.0964996Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.0965410Z 2025-08-14T21:38:39.1004550Z 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. 2025-08-14T21:38:39.1005885Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.1006276Z 2025-08-14T21:38:39.1006674Z Takes in optional filter values and generates two tables with desired information. 2025-08-14T21:38:39.1007375Z 2025-08-14T21:38:39.1007578Z The generated tables are presented in both a list-of-lists format 2025-08-14T21:38:39.1007899Z 2025-08-14T21:38:39.1008109Z The reason for the two tables are that they handle different things: 2025-08-14T21:38:39.1008584Z 1.) the first table handles all tensor level information 2025-08-14T21:38:39.1009049Z 2.) the second table handles and displays all channel based information 2025-08-14T21:38:39.1009379Z 2025-08-14T21:38:39.1009687Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-08-14T21:38:39.1010505Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-08-14T21:38:39.1011714Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-08-14T21:38:39.1012170Z 2025-08-14T21:38:39.1012263Z Tensor table columns: 2025-08-14T21:38:39.1012603Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-08-14T21:38:39.1013054Z ---- --------- --------- --------- --------- --------- 2025-08-14T21:38:39.1013353Z 2025-08-14T21:38:39.1013457Z Per-Channel table columns: 2025-08-14T21:38:39.1014061Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-08-14T21:38:39.1014792Z ---- --------- ------- --------- --------- --------- --------- 2025-08-14T21:38:39.1015067Z 2025-08-14T21:38:39.1015160Z Args: 2025-08-14T21:38:39.1015529Z feature_filter (str, optional): Filters the features presented to only those that 2025-08-14T21:38:39.1016013Z contain this filter substring 2025-08-14T21:38:39.1016383Z Default = "", results in all the features being printed 2025-08-14T21:38:39.1016897Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-14T21:38:39.1017514Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-14T21:38:39.1017881Z 2025-08-14T21:38:39.1017990Z Returns a dictionary with two keys: 2025-08-14T21:38:39.1018369Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-08-14T21:38:39.1018910Z "tensor_level_info", "channel_level_info" 2025-08-14T21:38:39.1019255Z Each key maps to a tuple with: 2025-08-14T21:38:39.1019587Z A list of the headers of each table 2025-08-14T21:38:39.1019978Z A list of lists containing the table information row by row 2025-08-14T21:38:39.1020449Z The 0th index row will contain the headers of the columns 2025-08-14T21:38:39.1020853Z The rest of the rows will contain data 2025-08-14T21:38:39.1021080Z 2025-08-14T21:38:39.1021182Z Example Use: 2025-08-14T21:38:39.1021426Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:39.1021801Z >>> mod_report_visualizer.generate_filtered_tables( 2025-08-14T21:38:39.1022249Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-14T21:38:39.1022807Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-08-14T21:38:39.1023194Z 2025-08-14T21:38:39.1023504Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.1023877Z 2025-08-14T21:38:39.1024797Z 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. 2025-08-14T21:38:39.1026129Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.1026496Z 2025-08-14T21:38:39.1026771Z Takes in optional filter values and prints out formatted tables of the information. 2025-08-14T21:38:39.1027144Z 2025-08-14T21:38:39.1027505Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-08-14T21:38:39.1028120Z 1.) the first table handles all tensor level information 2025-08-14T21:38:39.1028588Z 2.) the second table handles and displays all channel based information 2025-08-14T21:38:39.1028923Z 2025-08-14T21:38:39.1029232Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-08-14T21:38:39.1029984Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-08-14T21:38:39.1030770Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-08-14T21:38:39.1031233Z 2025-08-14T21:38:39.1031329Z Tensor table columns: 2025-08-14T21:38:39.1031677Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-08-14T21:38:39.1032127Z ---- --------- --------- --------- --------- --------- 2025-08-14T21:38:39.1032382Z 2025-08-14T21:38:39.1032483Z Per-Channel table columns: 2025-08-14T21:38:39.1032668Z 2025-08-14T21:38:39.1032881Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-08-14T21:38:39.1033367Z ---- --------- ------- --------- --------- --------- --------- 2025-08-14T21:38:39.1033640Z 2025-08-14T21:38:39.1033735Z Args: 2025-08-14T21:38:39.1034106Z feature_filter (str, optional): Filters the features presented to only those that 2025-08-14T21:38:39.1034597Z contain this filter substring 2025-08-14T21:38:39.1034966Z Default = "", results in all the features being printed 2025-08-14T21:38:39.1035479Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-14T21:38:39.1036101Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-14T21:38:39.1036468Z 2025-08-14T21:38:39.1036554Z Example Use: 2025-08-14T21:38:39.1036810Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:39.1037193Z >>> mod_report_visualizer.generate_table_visualization( 2025-08-14T21:38:39.1037657Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-14T21:38:39.1038046Z ... ) 2025-08-14T21:38:39.1038337Z >>> # prints out neatly formatted table with per_channel_min info 2025-08-14T21:38:39.1038754Z >>> # for all modules in block 1 of the model 2025-08-14T21:38:39.1039039Z 2025-08-14T21:38:39.1039297Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.1039652Z 2025-08-14T21:38:39.1040546Z 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. 2025-08-14T21:38:39.1041779Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.1042156Z 2025-08-14T21:38:39.1042385Z Takes in a feature and optional module_filter and plots of the desired data. 2025-08-14T21:38:39.1042737Z 2025-08-14T21:38:39.1043000Z For per channel features, it averages the value across the channels and plots a point 2025-08-14T21:38:39.1043634Z per module. The reason for this is that for models with hundreds of channels, it can 2025-08-14T21:38:39.1044263Z be hard to differentiate one channel line from another, and so the point of generating 2025-08-14T21:38:39.1044915Z a single average point per module is to give a sense of general trends that encourage 2025-08-14T21:38:39.1045392Z further deep dives. 2025-08-14T21:38:39.1045538Z 2025-08-14T21:38:39.1045628Z Note: 2025-08-14T21:38:39.1045997Z Only features in the report that have tensor value data are plottable by this class 2025-08-14T21:38:39.1046588Z When the tensor information is plotted, it will plot: 2025-08-14T21:38:39.1046994Z idx as the x val, feature value as the y_val 2025-08-14T21:38:39.1047387Z When the channel information is plotted, it will plot: 2025-08-14T21:38:39.1047916Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-08-14T21:38:39.1048514Z The reason for this is that we want to be able to compare values across the 2025-08-14T21:38:39.1049086Z channels for same layer, and it will be hard if values are staggered by idx 2025-08-14T21:38:39.1049592Z This means each module is represented by only 1 x value 2025-08-14T21:38:39.1049949Z Args: 2025-08-14T21:38:39.1050284Z feature_filter (str): Filters the features presented to only those that 2025-08-14T21:38:39.1050716Z contain this filter substring 2025-08-14T21:38:39.1051180Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-14T21:38:39.1051804Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-14T21:38:39.1052155Z 2025-08-14T21:38:39.1052254Z Example Use: 2025-08-14T21:38:39.1052495Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:39.1052884Z >>> mod_report_visualizer.generate_plot_visualization( 2025-08-14T21:38:39.1053342Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-14T21:38:39.1053716Z ... ) 2025-08-14T21:38:39.1054012Z >>> # outputs line plot of per_channel_min information for all 2025-08-14T21:38:39.1054482Z >>> # modules in block1 of model each channel gets it's own line, 2025-08-14T21:38:39.1054933Z >>> # and it's plotted across the in-order modules on the x-axis 2025-08-14T21:38:39.1055223Z 2025-08-14T21:38:39.1055469Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.1055838Z 2025-08-14T21:38:39.1056743Z 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. 2025-08-14T21:38:39.1058005Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.1058368Z 2025-08-14T21:38:39.1058649Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-08-14T21:38:39.1059033Z 2025-08-14T21:38:39.1059111Z Note: 2025-08-14T21:38:39.1059484Z Only features in the report that have tensor value data can be viewed as a histogram 2025-08-14T21:38:39.1060197Z If you want to plot a histogram from all the channel values of a specific feature for 2025-08-14T21:38:39.1060804Z a specific model, make sure to specify both the model and the feature properly 2025-08-14T21:38:39.1061408Z in the filters and you should be able to see a distribution of the channel data 2025-08-14T21:38:39.1061778Z 2025-08-14T21:38:39.1061858Z Args: 2025-08-14T21:38:39.1062233Z feature_filter (str, optional): Filters the features presented to only those that 2025-08-14T21:38:39.1062704Z contain this filter substring 2025-08-14T21:38:39.1063076Z Default = "", results in all the features being printed 2025-08-14T21:38:39.1063699Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-14T21:38:39.1064303Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-14T21:38:39.1064885Z num_bins (int, optional): The number of bins to create the histogram with 2025-08-14T21:38:39.1065411Z Default = 10, the values will be split into 10 equal sized bins 2025-08-14T21:38:39.1065701Z 2025-08-14T21:38:39.1065803Z Example Use: 2025-08-14T21:38:39.1066020Z >>> # xdoctest: +SKIP 2025-08-14T21:38:39.1066484Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-08-14T21:38:39.1067148Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-14T21:38:39.1067523Z ... ) 2025-08-14T21:38:39.1067915Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-08-14T21:38:39.1068558Z information is gathered across all channels for all modules in block 1 for the 2025-08-14T21:38:39.1069146Z per_channel_min and is displayed in a histogram of equally sized bins 2025-08-14T21:38:39.1069471Z 2025-08-14T21:38:39.1069718Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.1070087Z 2025-08-14T21:38:39.2392409Z 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. 2025-08-14T21:38:39.2393465Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.2394222Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-08-14T21:38:39.2394889Z Label will only appear if CPU activity tracing is enabled. 2025-08-14T21:38:39.2395189Z 2025-08-14T21:38:39.2395318Z It is useful when tracing the code profile. 2025-08-14T21:38:39.2395562Z 2025-08-14T21:38:39.2395644Z Args: 2025-08-14T21:38:39.2395906Z name (str): Label assigned to the block of code. 2025-08-14T21:38:39.2396325Z node_id (int): ID of node, for distributed profiling. Unset in 2025-08-14T21:38:39.2396727Z non-distributed cases. 2025-08-14T21:38:39.2396921Z 2025-08-14T21:38:39.2397017Z Example: 2025-08-14T21:38:39.2397316Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-08-14T21:38:39.2397739Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-08-14T21:38:39.2398136Z >>> with torch.autograd.profiler.profile() as prof: 2025-08-14T21:38:39.2398501Z ... y = x**2 2025-08-14T21:38:39.2398810Z ... with torch.autograd.profiler.record_function( 2025-08-14T21:38:39.2399179Z ... "label-z" 2025-08-14T21:38:39.2399457Z ... ): # label the block 2025-08-14T21:38:39.2399736Z ... z = y**3 2025-08-14T21:38:39.2400007Z ... y.backward() 2025-08-14T21:38:39.2400286Z >>> # xdoctest: +IGNORE_WANT 2025-08-14T21:38:39.2400611Z >>> # NOTE: some columns were removed for brevity 2025-08-14T21:38:39.2401059Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-08-14T21:38:39.2401582Z ----------------------------------- --------------- --------------- --------------- 2025-08-14T21:38:39.2402272Z Name Self CPU total % CPU time avg Number of Calls 2025-08-14T21:38:39.2402752Z ----------------------------------- --------------- --------------- --------------- 2025-08-14T21:38:39.2403197Z pow 60.77% 47.470us 3 2025-08-14T21:38:39.2403586Z mul 21.73% 25.465us 2 2025-08-14T21:38:39.2403993Z PowBackward0 12.03% 121.891us 1 2025-08-14T21:38:39.2404513Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-08-14T21:38:39.2404984Z label-z 2.13% 12.421us 1 2025-08-14T21:38:39.2405443Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-08-14T21:38:39.2405933Z ----------------------------------- --------------- --------------- --------------- 2025-08-14T21:38:39.2406352Z Self CPU time total: 234.344us 2025-08-14T21:38:39.2406667Z CUDA time total: 0.000us 2025-08-14T21:38:39.2406850Z 2025-08-14T21:38:39.2406939Z 2025-08-14T21:38:39.2407282Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.2407647Z 2025-08-14T21:38:39.4065678Z 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=722. 2025-08-14T21:38:39.4066747Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:39.4067138Z 2025-08-14T21:38:39.4067416Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-08-14T21:38:39.4068121Z The submesh created consists of the dimensions and the communicators indicated by 2025-08-14T21:38:39.4068662Z ``mesh_dim_names`` 2025-08-14T21:38:39.4068804Z 2025-08-14T21:38:39.4068898Z Args: 2025-08-14T21:38:39.4069321Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-08-14T21:38:39.4069899Z mesh dimension of the DeviceMesh to create the submesh for. 2025-08-14T21:38:39.4070306Z Returns: 2025-08-14T21:38:39.4070530Z A :class:`DeviceMesh` object 2025-08-14T21:38:39.4070788Z 2025-08-14T21:38:39.4071065Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-08-14T21:38:39.4071633Z In the first example: 2025-08-14T21:38:39.4072044Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-08-14T21:38:39.4072700Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-08-14T21:38:39.4073350Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-08-14T21:38:39.4073972Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-08-14T21:38:39.4074551Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-08-14T21:38:39.4075140Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-08-14T21:38:39.4075549Z 2025-08-14T21:38:39.4075644Z In the second example: 2025-08-14T21:38:39.4076076Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-08-14T21:38:39.4076764Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-08-14T21:38:39.4077471Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-08-14T21:38:39.4078169Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-08-14T21:38:39.4078602Z 2025-08-14T21:38:39.4078722Z Example:: 2025-08-14T21:38:39.4078842Z 2025-08-14T21:38:39.4078944Z >>> # xdoctest: +SKIP("no rank") 2025-08-14T21:38:39.4079407Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-08-14T21:38:39.4079777Z >>> 2025-08-14T21:38:39.4080400Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-08-14T21:38:39.4080912Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-08-14T21:38:39.4081417Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-08-14T21:38:39.4081959Z >>> tp_mesh = mesh_2d["tp"] 2025-08-14T21:38:39.4082233Z >>> dp_mesh = mesh_2d["dp"] 2025-08-14T21:38:39.4082563Z >>> 2025-08-14T21:38:39.4082778Z >>> # Initialize a 3D mesh. 2025-08-14T21:38:39.4083294Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-08-14T21:38:39.4084019Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-08-14T21:38:39.4084552Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-08-14T21:38:39.4084942Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-08-14T21:38:39.4085152Z 2025-08-14T21:38:39.4085880Z 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)) 2025-08-14T21:38:39.4086725Z 2025-08-14T21:38:39.4086962Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-08-14T21:38:39.4087611Z ^ 2025-08-14T21:38:39.4401789Z 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=2706. 2025-08-14T21:38:39.4402762Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.4403133Z 2025-08-14T21:38:39.4403381Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-08-14T21:38:39.4403750Z 2025-08-14T21:38:39.4403989Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-08-14T21:38:39.4404553Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-08-14T21:38:39.4404849Z 2025-08-14T21:38:39.4404942Z Args: 2025-08-14T21:38:39.4405270Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-08-14T21:38:39.4405833Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-08-14T21:38:39.4406383Z matters and it needs to match with corresponding isend/irecv on the 2025-08-14T21:38:39.4406779Z remote end. 2025-08-14T21:38:39.4406930Z 2025-08-14T21:38:39.4407012Z Returns: 2025-08-14T21:38:39.4407375Z A list of distributed request objects returned by calling the corresponding 2025-08-14T21:38:39.4407961Z op in the op_list. 2025-08-14T21:38:39.4408122Z 2025-08-14T21:38:39.4408257Z Examples: 2025-08-14T21:38:39.4408489Z >>> # xdoctest: +SKIP("no rank") 2025-08-14T21:38:39.4409083Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-08-14T21:38:39.4409527Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-08-14T21:38:39.4410009Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-08-14T21:38:39.4410435Z >>> recv_op = dist.P2POp( 2025-08-14T21:38:39.4410797Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-08-14T21:38:39.4411187Z ... ) 2025-08-14T21:38:39.4411439Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-08-14T21:38:39.4411763Z >>> for req in reqs: 2025-08-14T21:38:39.4412018Z >>> req.wait() 2025-08-14T21:38:39.4412262Z >>> recv_tensor 2025-08-14T21:38:39.4412499Z tensor([2, 3]) # Rank 0 2025-08-14T21:38:39.4412758Z tensor([0, 1]) # Rank 1 2025-08-14T21:38:39.4412986Z 2025-08-14T21:38:39.4413245Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-08-14T21:38:39.4413818Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-08-14T21:38:39.4414266Z lead to unexpected hang issues. 2025-08-14T21:38:39.4414470Z 2025-08-14T21:38:39.4414870Z In addition, if this API is the first collective call in the ``group`` 2025-08-14T21:38:39.4415417Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-08-14T21:38:39.4415974Z this API call; otherwise, the behavior is undefined. If this API call is 2025-08-14T21:38:39.4416539Z not the first collective call in the ``group``, batched P2P operations 2025-08-14T21:38:39.4417053Z involving only a subset of ranks of the ``group`` are allowed. 2025-08-14T21:38:39.4417367Z 2025-08-14T21:38:39.4417611Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.4417965Z 2025-08-14T21:38:39.4418535Z 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=2838. 2025-08-14T21:38:39.4419464Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.4419833Z 2025-08-14T21:38:39.4420097Z Reduces the tensor data across all machines in a way that all get the final result. 2025-08-14T21:38:39.4420489Z 2025-08-14T21:38:39.4420714Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-08-14T21:38:39.4421066Z 2025-08-14T21:38:39.4421176Z Complex tensors are supported. 2025-08-14T21:38:39.4421457Z 2025-08-14T21:38:39.4421552Z Args: 2025-08-14T21:38:39.4421858Z tensor (Tensor): Input and output of the collective. The function 2025-08-14T21:38:39.4422281Z operates in-place. 2025-08-14T21:38:39.4422576Z op (optional): One of the values from 2025-08-14T21:38:39.4422900Z ``torch.distributed.ReduceOp`` 2025-08-14T21:38:39.4423316Z enum. Specifies an operation used for element-wise reductions. 2025-08-14T21:38:39.4423931Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-14T21:38:39.4424385Z the default process group will be used. 2025-08-14T21:38:39.4424816Z async_op (bool, optional): Whether this op should be an async op 2025-08-14T21:38:39.4425183Z 2025-08-14T21:38:39.4425292Z Returns: 2025-08-14T21:38:39.4425626Z Async work handle, if async_op is set to True. 2025-08-14T21:38:39.4426148Z None, if not async_op or if not part of the group 2025-08-14T21:38:39.4426573Z 2025-08-14T21:38:39.4426714Z Examples: 2025-08-14T21:38:39.4427046Z >>> # xdoctest: +SKIP("no rank") 2025-08-14T21:38:39.4427534Z >>> # All tensors below are of torch.int64 type. 2025-08-14T21:38:39.4427891Z >>> # We have 2 process groups, 2 ranks. 2025-08-14T21:38:39.4428229Z >>> device = torch.device(f"cuda:{rank}") 2025-08-14T21:38:39.4428670Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-08-14T21:38:39.4429091Z >>> tensor 2025-08-14T21:38:39.4429337Z tensor([1, 2], device='cuda:0') # Rank 0 2025-08-14T21:38:39.4429669Z tensor([3, 4], device='cuda:1') # Rank 1 2025-08-14T21:38:39.4430001Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-08-14T21:38:39.4430332Z >>> tensor 2025-08-14T21:38:39.4430577Z tensor([4, 6], device='cuda:0') # Rank 0 2025-08-14T21:38:39.4430889Z tensor([4, 6], device='cuda:1') # Rank 1 2025-08-14T21:38:39.4431110Z 2025-08-14T21:38:39.4431242Z >>> # All tensors below are of torch.cfloat type. 2025-08-14T21:38:39.4431602Z >>> # We have 2 process groups, 2 ranks. 2025-08-14T21:38:39.4431911Z >>> tensor = torch.tensor( 2025-08-14T21:38:39.4432242Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-08-14T21:38:39.4432593Z ... ) + 2 * rank * (1 + 1j) 2025-08-14T21:38:39.4432859Z >>> tensor 2025-08-14T21:38:39.4433115Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-08-14T21:38:39.4433496Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-08-14T21:38:39.4433868Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-08-14T21:38:39.4434180Z >>> tensor 2025-08-14T21:38:39.4434444Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-08-14T21:38:39.4434927Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-08-14T21:38:39.4435166Z 2025-08-14T21:38:39.4435170Z 2025-08-14T21:38:39.4435434Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.4435792Z 2025-08-14T21:38:39.4436414Z 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=3198. 2025-08-14T21:38:39.4437341Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.4437721Z 2025-08-14T21:38:39.4437939Z Gathers picklable objects from the whole group in a single process. 2025-08-14T21:38:39.4438280Z 2025-08-14T21:38:39.4438506Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-08-14T21:38:39.4438997Z object must be picklable in order to be gathered. 2025-08-14T21:38:39.4439243Z 2025-08-14T21:38:39.4439323Z Args: 2025-08-14T21:38:39.4439563Z obj (Any): Input object. Must be picklable. 2025-08-14T21:38:39.4440004Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-08-14T21:38:39.4440492Z should be correctly sized as the size of the group for this 2025-08-14T21:38:39.4441000Z collective and will contain the output. Must be ``None`` on non-dst 2025-08-14T21:38:39.4441492Z ranks. (default is ``None``) 2025-08-14T21:38:39.4442010Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-08-14T21:38:39.4442619Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-08-14T21:38:39.4443161Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-08-14T21:38:39.4443692Z the default process group will be used. Default is ``None``. 2025-08-14T21:38:39.4444313Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-08-14T21:38:39.4444772Z 2025-08-14T21:38:39.4444855Z Returns: 2025-08-14T21:38:39.4445158Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-08-14T21:38:39.4445553Z output of the collective. 2025-08-14T21:38:39.4445735Z 2025-08-14T21:38:39.4445950Z .. note:: Note that this API differs slightly from the gather collective 2025-08-14T21:38:39.4446487Z since it does not provide an async_op handle and thus will be a blocking 2025-08-14T21:38:39.4446900Z call. 2025-08-14T21:38:39.4447013Z 2025-08-14T21:38:39.4447238Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-08-14T21:38:39.4447785Z of objects must be moved to the GPU device before communication takes 2025-08-14T21:38:39.4448248Z place. In this case, the device used is given by 2025-08-14T21:38:39.4448713Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-08-14T21:38:39.4449231Z ensure that this is set so that each rank has an individual GPU, via 2025-08-14T21:38:39.4449657Z ``torch.cuda.set_device()``. 2025-08-14T21:38:39.4449850Z 2025-08-14T21:38:39.4449947Z .. warning:: 2025-08-14T21:38:39.4450301Z Object collectives have a number of serious performance and scalability 2025-08-14T21:38:39.4450825Z limitations. See :ref:`object_collectives` for details. 2025-08-14T21:38:39.4451122Z 2025-08-14T21:38:39.4451208Z .. warning:: 2025-08-14T21:38:39.4451537Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-08-14T21:38:39.4452046Z known to be insecure. It is possible to construct malicious pickle data 2025-08-14T21:38:39.4452587Z which will execute arbitrary code during unpickling. Only call this 2025-08-14T21:38:39.4453022Z function with data you trust. 2025-08-14T21:38:39.4453214Z 2025-08-14T21:38:39.4453300Z .. warning:: 2025-08-14T21:38:39.4453636Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-08-14T21:38:39.4454182Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-08-14T21:38:39.4454779Z pickled. Please consider using :func:`gather` instead. 2025-08-14T21:38:39.4455053Z 2025-08-14T21:38:39.4455142Z Example:: 2025-08-14T21:38:39.4455405Z >>> # xdoctest: +SKIP("need process group init") 2025-08-14T21:38:39.4455836Z >>> # Note: Process group initialization omitted on each rank. 2025-08-14T21:38:39.4456235Z >>> import torch.distributed as dist 2025-08-14T21:38:39.4456563Z >>> # Assumes world_size of 3. 2025-08-14T21:38:39.4456936Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-08-14T21:38:39.4457332Z >>> output = [None for _ in gather_objects] 2025-08-14T21:38:39.4457665Z >>> dist.gather_object( 2025-08-14T21:38:39.4457965Z ... gather_objects[dist.get_rank()], 2025-08-14T21:38:39.4458323Z ... output if dist.get_rank() == 0 else None, 2025-08-14T21:38:39.4458639Z ... dst=0 2025-08-14T21:38:39.4458865Z ... ) 2025-08-14T21:38:39.4459076Z >>> # On rank 0 2025-08-14T21:38:39.4459293Z >>> output 2025-08-14T21:38:39.4459523Z ['foo', 12, {1: 2}] 2025-08-14T21:38:39.4459673Z 2025-08-14T21:38:39.4459935Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.4460293Z 2025-08-14T21:38:39.4467830Z 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=3794. 2025-08-14T21:38:39.4468913Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.4469297Z 2025-08-14T21:38:39.4469433Z Gathers tensors from the whole group in a list. 2025-08-14T21:38:39.4469685Z 2025-08-14T21:38:39.4469826Z Complex and uneven sized tensors are supported. 2025-08-14T21:38:39.4470071Z 2025-08-14T21:38:39.4470167Z Args: 2025-08-14T21:38:39.4470445Z tensor_list (list[Tensor]): Output list. It should contain 2025-08-14T21:38:39.4470948Z correctly-sized tensors to be used for output of the collective. 2025-08-14T21:38:39.4471393Z Uneven sized tensors are supported. 2025-08-14T21:38:39.4471797Z tensor (Tensor): Tensor to be broadcast from current process. 2025-08-14T21:38:39.4472323Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-14T21:38:39.4472785Z the default process group will be used. 2025-08-14T21:38:39.4473211Z async_op (bool, optional): Whether this op should be an async op 2025-08-14T21:38:39.4473510Z 2025-08-14T21:38:39.4473593Z Returns: 2025-08-14T21:38:39.4473845Z Async work handle, if async_op is set to True. 2025-08-14T21:38:39.4474235Z None, if not async_op or if not part of the group 2025-08-14T21:38:39.4474482Z 2025-08-14T21:38:39.4474565Z Examples: 2025-08-14T21:38:39.4474820Z >>> # xdoctest: +SKIP("need process group init") 2025-08-14T21:38:39.4475197Z >>> # All tensors below are of torch.int64 dtype. 2025-08-14T21:38:39.4475541Z >>> # We have 2 process groups, 2 ranks. 2025-08-14T21:38:39.4475878Z >>> device = torch.device(f"cuda:{rank}") 2025-08-14T21:38:39.4476207Z >>> tensor_list = [ 2025-08-14T21:38:39.4476571Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-08-14T21:38:39.4476965Z ... ] 2025-08-14T21:38:39.4477171Z >>> tensor_list 2025-08-14T21:38:39.4477513Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-08-14T21:38:39.4478006Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-08-14T21:38:39.4478535Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-08-14T21:38:39.4478956Z >>> tensor 2025-08-14T21:38:39.4479186Z tensor([1, 2], device='cuda:0') # Rank 0 2025-08-14T21:38:39.4479515Z tensor([3, 4], device='cuda:1') # Rank 1 2025-08-14T21:38:39.4479844Z >>> dist.all_gather(tensor_list, tensor) 2025-08-14T21:38:39.4480150Z >>> tensor_list 2025-08-14T21:38:39.4480471Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-08-14T21:38:39.4481042Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-08-14T21:38:39.4481349Z 2025-08-14T21:38:39.4481504Z >>> # All tensors below are of torch.cfloat dtype. 2025-08-14T21:38:39.4481857Z >>> # We have 2 process groups, 2 ranks. 2025-08-14T21:38:39.4482177Z >>> tensor_list = [ 2025-08-14T21:38:39.4482553Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-08-14T21:38:39.4482971Z ... ] 2025-08-14T21:38:39.4483165Z >>> tensor_list 2025-08-14T21:38:39.4483551Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-08-14T21:38:39.4484143Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-08-14T21:38:39.4484586Z >>> tensor = torch.tensor( 2025-08-14T21:38:39.4484916Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-08-14T21:38:39.4485264Z ... ) + 2 * rank * (1 + 1j) 2025-08-14T21:38:39.4485514Z >>> tensor 2025-08-14T21:38:39.4485783Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-08-14T21:38:39.4486159Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-08-14T21:38:39.4486516Z >>> dist.all_gather(tensor_list, tensor) 2025-08-14T21:38:39.4486813Z >>> tensor_list 2025-08-14T21:38:39.4487251Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-08-14T21:38:39.4487837Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-08-14T21:38:39.4488185Z 2025-08-14T21:38:39.4488189Z 2025-08-14T21:38:39.4488433Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.4488801Z 2025-08-14T21:38:39.4525288Z 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=4500. 2025-08-14T21:38:39.4526342Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.4526715Z 2025-08-14T21:38:39.4526970Z Split input tensor and then scatter the split list to all processes in a group. 2025-08-14T21:38:39.4527329Z 2025-08-14T21:38:39.4527593Z Later the received tensors are concatenated from all the processes in the group 2025-08-14T21:38:39.4528074Z and returned as a single output tensor. 2025-08-14T21:38:39.4528299Z 2025-08-14T21:38:39.4528407Z Complex tensors are supported. 2025-08-14T21:38:39.4528591Z 2025-08-14T21:38:39.4528682Z Args: 2025-08-14T21:38:39.4528949Z output (Tensor): Gathered concatenated output tensor. 2025-08-14T21:38:39.4529343Z input (Tensor): Input tensor to scatter. 2025-08-14T21:38:39.4529784Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-08-14T21:38:39.4530316Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-08-14T21:38:39.4530722Z equally by ``world_size``. 2025-08-14T21:38:39.4531133Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-08-14T21:38:39.4531657Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-08-14T21:38:39.4532063Z equally by ``world_size``. 2025-08-14T21:38:39.4532488Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-14T21:38:39.4532961Z the default process group will be used. 2025-08-14T21:38:39.4533413Z async_op (bool, optional): Whether this op should be an async op. 2025-08-14T21:38:39.4533713Z 2025-08-14T21:38:39.4533810Z Returns: 2025-08-14T21:38:39.4534061Z Async work handle, if async_op is set to True. 2025-08-14T21:38:39.4534448Z None, if not async_op or if not part of the group. 2025-08-14T21:38:39.4534694Z 2025-08-14T21:38:39.4534801Z .. warning:: 2025-08-14T21:38:39.4535088Z `all_to_all_single` is experimental and subject to change. 2025-08-14T21:38:39.4535374Z 2025-08-14T21:38:39.4535460Z Examples: 2025-08-14T21:38:39.4535705Z >>> # xdoctest: +SKIP("Undefined rank") 2025-08-14T21:38:39.4536171Z >>> input = torch.arange(4) + rank * 4 2025-08-14T21:38:39.4536667Z >>> input 2025-08-14T21:38:39.4537012Z tensor([0, 1, 2, 3]) # Rank 0 2025-08-14T21:38:39.4537436Z tensor([4, 5, 6, 7]) # Rank 1 2025-08-14T21:38:39.4537817Z tensor([8, 9, 10, 11]) # Rank 2 2025-08-14T21:38:39.4538679Z tensor([12, 13, 14, 15]) # Rank 3 2025-08-14T21:38:39.4539163Z >>> output = torch.empty([4], dtype=torch.int64) 2025-08-14T21:38:39.4539526Z >>> dist.all_to_all_single(output, input) 2025-08-14T21:38:39.4539843Z >>> output 2025-08-14T21:38:39.4540078Z tensor([0, 4, 8, 12]) # Rank 0 2025-08-14T21:38:39.4540364Z tensor([1, 5, 9, 13]) # Rank 1 2025-08-14T21:38:39.4540659Z tensor([2, 6, 10, 14]) # Rank 2 2025-08-14T21:38:39.4540960Z tensor([3, 7, 11, 15]) # Rank 3 2025-08-14T21:38:39.4541152Z 2025-08-14T21:38:39.4541311Z >>> # Essentially, it is similar to following operation: 2025-08-14T21:38:39.4541722Z >>> scatter_list = list(input.chunk(world_size)) 2025-08-14T21:38:39.4542117Z >>> gather_list = list(output.chunk(world_size)) 2025-08-14T21:38:39.4542471Z >>> for i in range(world_size): 2025-08-14T21:38:39.4542879Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-08-14T21:38:39.4543841Z 2025-08-14T21:38:39.4543956Z >>> # Another example with uneven split 2025-08-14T21:38:39.4544268Z >>> input 2025-08-14T21:38:39.4544538Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-08-14T21:38:39.4544962Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-08-14T21:38:39.4545388Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-08-14T21:38:39.4545814Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-08-14T21:38:39.4546168Z >>> input_splits 2025-08-14T21:38:39.4546446Z [2, 2, 1, 1] # Rank 0 2025-08-14T21:38:39.4546809Z [3, 2, 2, 2] # Rank 1 2025-08-14T21:38:39.4547152Z [2, 1, 1, 1] # Rank 2 2025-08-14T21:38:39.4547506Z [2, 2, 2, 1] # Rank 3 2025-08-14T21:38:39.4547837Z >>> output_splits 2025-08-14T21:38:39.4548111Z [2, 3, 2, 2] # Rank 0 2025-08-14T21:38:39.4548455Z [2, 2, 1, 2] # Rank 1 2025-08-14T21:38:39.4548813Z [1, 2, 1, 2] # Rank 2 2025-08-14T21:38:39.4549171Z [1, 2, 1, 1] # Rank 3 2025-08-14T21:38:39.4549490Z >>> output = ... 2025-08-14T21:38:39.4549846Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-08-14T21:38:39.4550251Z >>> output 2025-08-14T21:38:39.4550533Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-08-14T21:38:39.4550961Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-08-14T21:38:39.4551392Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-08-14T21:38:39.4551817Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-08-14T21:38:39.4552081Z 2025-08-14T21:38:39.4552085Z 2025-08-14T21:38:39.4552243Z >>> # Another example with tensors of torch.cfloat type. 2025-08-14T21:38:39.4552617Z >>> input = torch.tensor( 2025-08-14T21:38:39.4552939Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-08-14T21:38:39.4553283Z ... ) + 4 * rank * (1 + 1j) 2025-08-14T21:38:39.4553535Z >>> input 2025-08-14T21:38:39.4553841Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-08-14T21:38:39.4554294Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-08-14T21:38:39.4554811Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-08-14T21:38:39.4555294Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-08-14T21:38:39.4555735Z >>> output = torch.empty([4], dtype=torch.int64) 2025-08-14T21:38:39.4556099Z >>> dist.all_to_all_single(output, input) 2025-08-14T21:38:39.4556412Z >>> output 2025-08-14T21:38:39.4556713Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-08-14T21:38:39.4557174Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-08-14T21:38:39.4557620Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-08-14T21:38:39.4558076Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-08-14T21:38:39.4558359Z 2025-08-14T21:38:39.4558613Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.4558967Z 2025-08-14T21:38:39.4559574Z 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=4642. 2025-08-14T21:38:39.4560490Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.4560857Z 2025-08-14T21:38:39.4561267Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-08-14T21:38:39.4561747Z 2025-08-14T21:38:39.4561855Z Complex tensors are supported. 2025-08-14T21:38:39.4562054Z 2025-08-14T21:38:39.4562133Z Args: 2025-08-14T21:38:39.4562464Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-08-14T21:38:39.4562870Z per rank. 2025-08-14T21:38:39.4563234Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-08-14T21:38:39.4563801Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-14T21:38:39.4564255Z the default process group will be used. 2025-08-14T21:38:39.4564685Z async_op (bool, optional): Whether this op should be an async op. 2025-08-14T21:38:39.4564987Z 2025-08-14T21:38:39.4565081Z Returns: 2025-08-14T21:38:39.4565324Z Async work handle, if async_op is set to True. 2025-08-14T21:38:39.4565712Z None, if not async_op or if not part of the group. 2025-08-14T21:38:39.4565977Z 2025-08-14T21:38:39.4566069Z .. warning:: 2025-08-14T21:38:39.4566340Z `all_to_all` is experimental and subject to change. 2025-08-14T21:38:39.4566590Z 2025-08-14T21:38:39.4566674Z Examples: 2025-08-14T21:38:39.4566909Z >>> # xdoctest: +SKIP("Undefined rank") 2025-08-14T21:38:39.4567243Z >>> input = torch.arange(4) + rank * 4 2025-08-14T21:38:39.4567551Z >>> input = list(input.chunk(4)) 2025-08-14T21:38:39.4567838Z >>> input 2025-08-14T21:38:39.4568140Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-08-14T21:38:39.4568571Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-08-14T21:38:39.4569019Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-08-14T21:38:39.4569458Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-08-14T21:38:39.4569916Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-08-14T21:38:39.4570311Z >>> dist.all_to_all(output, input) 2025-08-14T21:38:39.4570607Z >>> output 2025-08-14T21:38:39.4570901Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-08-14T21:38:39.4571325Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-08-14T21:38:39.4571760Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-08-14T21:38:39.4572199Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-08-14T21:38:39.4572470Z 2025-08-14T21:38:39.4572641Z >>> # Essentially, it is similar to following operation: 2025-08-14T21:38:39.4573000Z >>> scatter_list = input 2025-08-14T21:38:39.4582892Z >>> gather_list = output 2025-08-14T21:38:39.4583331Z >>> for i in range(world_size): 2025-08-14T21:38:39.4583856Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-08-14T21:38:39.4584194Z 2025-08-14T21:38:39.4584294Z >>> input 2025-08-14T21:38:39.4584570Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-08-14T21:38:39.4585008Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-08-14T21:38:39.4585441Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-08-14T21:38:39.4585854Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-08-14T21:38:39.4586219Z >>> input_splits 2025-08-14T21:38:39.4586495Z [2, 2, 1, 1] # Rank 0 2025-08-14T21:38:39.4586846Z [3, 2, 2, 2] # Rank 1 2025-08-14T21:38:39.4587202Z [2, 1, 1, 1] # Rank 2 2025-08-14T21:38:39.4587563Z [2, 2, 2, 1] # Rank 3 2025-08-14T21:38:39.4587898Z >>> output_splits 2025-08-14T21:38:39.4588162Z [2, 3, 2, 2] # Rank 0 2025-08-14T21:38:39.4588610Z [2, 2, 1, 2] # Rank 1 2025-08-14T21:38:39.4588970Z [1, 2, 1, 2] # Rank 2 2025-08-14T21:38:39.4589398Z [1, 2, 1, 1] # Rank 3 2025-08-14T21:38:39.4589903Z >>> input = list(input.split(input_splits)) 2025-08-14T21:38:39.4590412Z >>> input 2025-08-14T21:38:39.4590903Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-08-14T21:38:39.4591826Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-08-14T21:38:39.4592677Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-08-14T21:38:39.4593430Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-08-14T21:38:39.4593831Z >>> output = ... 2025-08-14T21:38:39.4594102Z >>> dist.all_to_all(output, input) 2025-08-14T21:38:39.4594418Z >>> output 2025-08-14T21:38:39.4594753Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-08-14T21:38:39.4595253Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-08-14T21:38:39.4595768Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-08-14T21:38:39.4596285Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-08-14T21:38:39.4596670Z 2025-08-14T21:38:39.4596825Z >>> # Another example with tensors of torch.cfloat type. 2025-08-14T21:38:39.4597205Z >>> input = torch.tensor( 2025-08-14T21:38:39.4597537Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-08-14T21:38:39.4597885Z ... ) + 4 * rank * (1 + 1j) 2025-08-14T21:38:39.4598166Z >>> input = list(input.chunk(4)) 2025-08-14T21:38:39.4598462Z >>> input 2025-08-14T21:38:39.4598809Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-08-14T21:38:39.4599337Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-08-14T21:38:39.4599879Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-08-14T21:38:39.4600428Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-08-14T21:38:39.4600951Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-08-14T21:38:39.4601349Z >>> dist.all_to_all(output, input) 2025-08-14T21:38:39.4601654Z >>> output 2025-08-14T21:38:39.4602003Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-08-14T21:38:39.4602669Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-08-14T21:38:39.4603211Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-08-14T21:38:39.4603742Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-08-14T21:38:39.4604068Z 2025-08-14T21:38:39.4604073Z 2025-08-14T21:38:39.4604334Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.4604694Z 2025-08-14T21:38:39.4605229Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2. 2025-08-14T21:38:39.4606071Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.4606438Z 2025-08-14T21:38:39.4606554Z Module ``torch.distributed.launch``. 2025-08-14T21:38:39.4606777Z 2025-08-14T21:38:39.4607022Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-08-14T21:38:39.4607532Z training processes on each of the training nodes. 2025-08-14T21:38:39.4607784Z 2025-08-14T21:38:39.4607892Z .. warning:: 2025-08-14T21:38:39.4608015Z 2025-08-14T21:38:39.4608265Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-08-14T21:38:39.4608725Z 2025-08-14T21:38:39.4608957Z The utility can be used for single-node distributed training, in which one or 2025-08-14T21:38:39.4609540Z more processes per node will be spawned. The utility can be used for either 2025-08-14T21:38:39.4610106Z CPU training or GPU training. If the utility is used for GPU training, 2025-08-14T21:38:39.4610662Z each distributed process will be operating on a single GPU. This can achieve 2025-08-14T21:38:39.4611251Z well-improved single-node training performance. It can also be used in 2025-08-14T21:38:39.4611861Z multi-node distributed training, by spawning up multiple processes on each node 2025-08-14T21:38:39.4612466Z for well-improved multi-node distributed training performance as well. 2025-08-14T21:38:39.4613040Z This will especially be beneficial for systems with multiple Infiniband 2025-08-14T21:38:39.4613632Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-08-14T21:38:39.4614121Z aggregated communication bandwidth. 2025-08-14T21:38:39.4614329Z 2025-08-14T21:38:39.4614559Z In both cases of single-node distributed training or multi-node distributed 2025-08-14T21:38:39.4615140Z training, this utility will launch the given number of processes per node 2025-08-14T21:38:39.4615711Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-08-14T21:38:39.4616253Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-08-14T21:38:39.4616783Z and each process will be operating on a single GPU from *GPU 0 to 2025-08-14T21:38:39.4617202Z GPU (nproc_per_node - 1)*. 2025-08-14T21:38:39.4617379Z 2025-08-14T21:38:39.4617490Z **How to use this module:** 2025-08-14T21:38:39.4617672Z 2025-08-14T21:38:39.4617820Z 1. Single-Node multi-process distributed training 2025-08-14T21:38:39.4618088Z 2025-08-14T21:38:39.4618172Z :: 2025-08-14T21:38:39.4618278Z 2025-08-14T21:38:39.4618526Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-08-14T21:38:39.4619070Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-08-14T21:38:39.4619486Z arguments of your training script) 2025-08-14T21:38:39.4619727Z 2025-08-14T21:38:39.4619936Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-08-14T21:38:39.4620250Z 2025-08-14T21:38:39.4620254Z 2025-08-14T21:38:39.4620406Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-08-14T21:38:39.4620650Z 2025-08-14T21:38:39.4620736Z :: 2025-08-14T21:38:39.4620857Z 2025-08-14T21:38:39.4621086Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-08-14T21:38:39.4621652Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-08-14T21:38:39.4622133Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-08-14T21:38:39.4622597Z and all other arguments of your training script) 2025-08-14T21:38:39.4622872Z 2025-08-14T21:38:39.4622960Z Node 2: 2025-08-14T21:38:39.4623073Z 2025-08-14T21:38:39.4623171Z :: 2025-08-14T21:38:39.4623276Z 2025-08-14T21:38:39.4623600Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-08-14T21:38:39.4624096Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-08-14T21:38:39.4624574Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-08-14T21:38:39.4625054Z and all other arguments of your training script) 2025-08-14T21:38:39.4625313Z 2025-08-14T21:38:39.4625473Z 3. To look up what optional arguments this module offers: 2025-08-14T21:38:39.4625752Z 2025-08-14T21:38:39.4625836Z :: 2025-08-14T21:38:39.4625963Z 2025-08-14T21:38:39.4626099Z python -m torch.distributed.launch --help 2025-08-14T21:38:39.4626337Z 2025-08-14T21:38:39.4626341Z 2025-08-14T21:38:39.4626453Z **Important Notices:** 2025-08-14T21:38:39.4626609Z 2025-08-14T21:38:39.4626790Z 1. This utility and multi-process distributed (single-node or 2025-08-14T21:38:39.4627396Z multi-node) GPU training currently only achieves the best performance using 2025-08-14T21:38:39.4628004Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-08-14T21:38:39.4628467Z use for GPU training. 2025-08-14T21:38:39.4628618Z 2025-08-14T21:38:39.4628829Z 2. In your training program, you must parse the command-line argument: 2025-08-14T21:38:39.4629387Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-08-14T21:38:39.4629960Z If your training program uses GPUs, you should ensure that your code only 2025-08-14T21:38:39.4630478Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-08-14T21:38:39.4630789Z 2025-08-14T21:38:39.4630900Z Parsing the local_rank argument 2025-08-14T21:38:39.4631104Z 2025-08-14T21:38:39.4631185Z :: 2025-08-14T21:38:39.4631292Z 2025-08-14T21:38:39.4631403Z >>> # xdoctest: +SKIP 2025-08-14T21:38:39.4631656Z >>> import argparse 2025-08-14T21:38:39.4631944Z >>> parser = argparse.ArgumentParser() 2025-08-14T21:38:39.4632377Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-08-14T21:38:39.4632778Z >>> args = parser.parse_args() 2025-08-14T21:38:39.4632991Z 2025-08-14T21:38:39.4633110Z Set your device to local rank using either 2025-08-14T21:38:39.4633332Z 2025-08-14T21:38:39.4633425Z :: 2025-08-14T21:38:39.4633530Z 2025-08-14T21:38:39.4633744Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-08-14T21:38:39.4634047Z 2025-08-14T21:38:39.4634132Z or 2025-08-14T21:38:39.4634235Z 2025-08-14T21:38:39.4634327Z :: 2025-08-14T21:38:39.4634429Z 2025-08-14T21:38:39.4634568Z >>> with torch.cuda.device(args.local_rank): 2025-08-14T21:38:39.4634893Z >>> # your code to run 2025-08-14T21:38:39.4635157Z >>> ... 2025-08-14T21:38:39.4635283Z 2025-08-14T21:38:39.4635395Z .. versionchanged:: 2.0.0 2025-08-14T21:38:39.4635561Z 2025-08-14T21:38:39.4635796Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-08-14T21:38:39.4636388Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-08-14T21:38:39.4636872Z previously used underscored ``--local_rank``. 2025-08-14T21:38:39.4637110Z 2025-08-14T21:38:39.4637356Z For backward compatibility, it may be necessary for users to handle both 2025-08-14T21:38:39.4637951Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-08-14T21:38:39.4638534Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-08-14T21:38:39.4639110Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-08-14T21:38:39.4639743Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-08-14T21:38:39.4640232Z including ``"--local-rank"`` should be sufficient. 2025-08-14T21:38:39.4640494Z 2025-08-14T21:38:39.4640721Z 3. In your training program, you are supposed to call the following function 2025-08-14T21:38:39.4641304Z at the beginning to start the distributed backend. It is strongly recommended 2025-08-14T21:38:39.4641869Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-08-14T21:38:39.4642382Z but ``env://`` is the one that is officially supported by this module. 2025-08-14T21:38:39.4642677Z 2025-08-14T21:38:39.4642771Z :: 2025-08-14T21:38:39.4642874Z 2025-08-14T21:38:39.4643088Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-08-14T21:38:39.4643534Z >>> init_method='env://') 2025-08-14T21:38:39.4643780Z 2025-08-14T21:38:39.4644014Z 4. In your training program, you can either use regular distributed functions 2025-08-14T21:38:39.4644604Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-08-14T21:38:39.4645144Z training program uses GPUs for training and you would like to use 2025-08-14T21:38:39.4645651Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-08-14T21:38:39.4646114Z here is how to configure it. 2025-08-14T21:38:39.4646291Z 2025-08-14T21:38:39.4646381Z :: 2025-08-14T21:38:39.4646486Z 2025-08-14T21:38:39.4646674Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-08-14T21:38:39.4647111Z >>> device_ids=[args.local_rank], 2025-08-14T21:38:39.4647507Z >>> output_device=args.local_rank) 2025-08-14T21:38:39.4647757Z 2025-08-14T21:38:39.4647994Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-08-14T21:38:39.4648576Z that your code will be operating on. This is generally the local rank of the 2025-08-14T21:38:39.4649159Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-08-14T21:38:39.4649718Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-08-14T21:38:39.4650116Z utility 2025-08-14T21:38:39.4650242Z 2025-08-14T21:38:39.4650478Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-08-14T21:38:39.4651047Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-08-14T21:38:39.4651590Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-08-14T21:38:39.4652103Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-08-14T21:38:39.4652574Z will not pass ``--local-rank`` when you specify this flag. 2025-08-14T21:38:39.4652847Z 2025-08-14T21:38:39.4652950Z .. warning:: 2025-08-14T21:38:39.4653073Z 2025-08-14T21:38:39.4653269Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-08-14T21:38:39.4653773Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-08-14T21:38:39.4654187Z write to a networked filesystem. See 2025-08-14T21:38:39.4654628Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-08-14T21:38:39.4655105Z how things can go wrong if you don't do this correctly. 2025-08-14T21:38:39.4655387Z 2025-08-14T21:38:39.4655391Z 2025-08-14T21:38:39.4655394Z 2025-08-14T21:38:39.4655398Z 2025-08-14T21:38:39.4655643Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.4656011Z 2025-08-14T21:38:39.5291449Z 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. 2025-08-14T21:38:39.5292496Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.5293025Z 2025-08-14T21:38:39.5293298Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-08-14T21:38:39.5293977Z Needs to be called on all ranks in an SPMD fashion. 2025-08-14T21:38:39.5294243Z 2025-08-14T21:38:39.5294326Z Args: 2025-08-14T21:38:39.5294715Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-08-14T21:38:39.5295259Z of shards that represent the local shards on this rank. 2025-08-14T21:38:39.5295769Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-08-14T21:38:39.5296227Z shape of the overall sharded tensor. 2025-08-14T21:38:39.5296443Z 2025-08-14T21:38:39.5296545Z Keyword args: 2025-08-14T21:38:39.5296924Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-08-14T21:38:39.5297426Z the default process group will be used. 2025-08-14T21:38:39.5297827Z init_rrefs (bool, optional): Whether or not to initialize 2025-08-14T21:38:39.5298300Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-08-14T21:38:39.5298814Z Need to initialize the RPC Framework if specified as ``True``. 2025-08-14T21:38:39.5299217Z Default: ``False``. 2025-08-14T21:38:39.5299392Z 2025-08-14T21:38:39.5299472Z Returns: 2025-08-14T21:38:39.5299730Z A :class:`ShardedTensor` object handle on this rank 2025-08-14T21:38:39.5300069Z 2025-08-14T21:38:39.5300073Z 2025-08-14T21:38:39.5300155Z Examples: 2025-08-14T21:38:39.5300519Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-08-14T21:38:39.5301063Z each shard have a (5, 5) local tensor, we can do it like below: 2025-08-14T21:38:39.5301351Z 2025-08-14T21:38:39.5301445Z on rank 0: 2025-08-14T21:38:39.5301684Z >>> # xdoctest: +SKIP("not distributed") 2025-08-14T21:38:39.5302023Z >>> local_shard_metadata = ShardMetadata( 2025-08-14T21:38:39.5302345Z >>> shard_offsets=[0, 0], 2025-08-14T21:38:39.5302627Z >>> shard_lengths=[5, 5], 2025-08-14T21:38:39.5302921Z >>> placement="rank:0/cuda:0" 2025-08-14T21:38:39.5303206Z >>> ) 2025-08-14T21:38:39.5303578Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-08-14T21:38:39.5304077Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-08-14T21:38:39.5304376Z 2025-08-14T21:38:39.5304468Z on rank 1: 2025-08-14T21:38:39.5304707Z >>> # xdoctest: +SKIP("not distributed") 2025-08-14T21:38:39.5305043Z >>> local_shard_metadata = ShardMetadata( 2025-08-14T21:38:39.5305366Z >>> shard_offsets=[5, 0], 2025-08-14T21:38:39.5305648Z >>> shard_lengths=[5, 5], 2025-08-14T21:38:39.5305925Z >>> placement="rank:1/cuda:1" 2025-08-14T21:38:39.5306207Z >>> ) 2025-08-14T21:38:39.5306513Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-08-14T21:38:39.5307087Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-08-14T21:38:39.5307401Z 2025-08-14T21:38:39.5307676Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.5308033Z 2025-08-14T21:38:39.5419024Z 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. 2025-08-14T21:38:39.5420117Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.5420511Z 2025-08-14T21:38:39.5420765Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-08-14T21:38:39.5421245Z size and sharding spec on each rank. 2025-08-14T21:38:39.5421451Z 2025-08-14T21:38:39.5421529Z Args: 2025-08-14T21:38:39.5421860Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-08-14T21:38:39.5422448Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-08-14T21:38:39.5422987Z The specification describing how to shard the Tensor. 2025-08-14T21:38:39.5423417Z global_size (Sequence[int]): Size of the sharded tensor. 2025-08-14T21:38:39.5424178Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-08-14T21:38:39.5424640Z Default: None 2025-08-14T21:38:39.5424948Z init_rrefs (bool, optional): Whether or not to initialize 2025-08-14T21:38:39.5425457Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-08-14T21:38:39.5426128Z Need to initialize the RPC Framework if specified as ``True``. 2025-08-14T21:38:39.5426707Z Default: ``False``. 2025-08-14T21:38:39.5427038Z 2025-08-14T21:38:39.5427197Z Returns: 2025-08-14T21:38:39.5427587Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-08-14T21:38:39.5428055Z tensor stored in the current rank. 2025-08-14T21:38:39.5428267Z 2025-08-14T21:38:39.5428351Z Examples: 2025-08-14T21:38:39.5428563Z >>> # xdoctest: +SKIP 2025-08-14T21:38:39.5428863Z >>> # All tensors below are of torch.int64 type. 2025-08-14T21:38:39.5429246Z >>> # We have 2 process groups, 2 ranks. 2025-08-14T21:38:39.5429631Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-08-14T21:38:39.5430112Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-08-14T21:38:39.5430515Z >>> local_tensor 2025-08-14T21:38:39.5430759Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-08-14T21:38:39.5431149Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-08-14T21:38:39.5431438Z >>> sharding_dim = 0 2025-08-14T21:38:39.5431721Z >>> sharding_spec = ChunkShardingSpec( 2025-08-14T21:38:39.5432035Z dim=sharding_dim, 2025-08-14T21:38:39.5432304Z placements=[ 2025-08-14T21:38:39.5432561Z "rank:0/cuda:0", 2025-08-14T21:38:39.5432821Z "rank:1/cuda:1", 2025-08-14T21:38:39.5433083Z ], 2025-08-14T21:38:39.5433291Z ) 2025-08-14T21:38:39.5433524Z >>> st = ShardedTensor._init_from_local_tensor( 2025-08-14T21:38:39.5433884Z ... local_tensor, sharding_spec, [2, 4] 2025-08-14T21:38:39.5434192Z ... ) 2025-08-14T21:38:39.5434378Z >>> st 2025-08-14T21:38:39.5434591Z ShardedTensor( 2025-08-14T21:38:39.5434838Z ShardedTensorMetadata( 2025-08-14T21:38:39.5435106Z shards_metadata=[ 2025-08-14T21:38:39.5435546Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-08-14T21:38:39.5436182Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-08-14T21:38:39.5436633Z ], 2025-08-14T21:38:39.5436852Z size=torch.Size([2, 4]) 2025-08-14T21:38:39.5437135Z ) 2025-08-14T21:38:39.5437338Z >>> st.local_tensor() 2025-08-14T21:38:39.5437584Z tensor([1, 2, 3, 4]) # Rank 0 2025-08-14T21:38:39.5437861Z tensor([3, 4, 5, 6]) # Rank 1 2025-08-14T21:38:39.5438038Z 2025-08-14T21:38:39.5438305Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-08-14T21:38:39.5438902Z rank validations, and we only validate the local shard on the current rank. 2025-08-14T21:38:39.5439463Z We fully rely on the user to ensure local tensor is sharded based on the 2025-08-14T21:38:39.5439877Z sharding spec. 2025-08-14T21:38:39.5440028Z 2025-08-14T21:38:39.5440281Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.5440638Z 2025-08-14T21:38:39.5441301Z 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. 2025-08-14T21:38:39.5442328Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.5442708Z 2025-08-14T21:38:39.5442949Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-08-14T21:38:39.5443402Z single local shard. 2025-08-14T21:38:39.5443541Z 2025-08-14T21:38:39.5443758Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-08-14T21:38:39.5444390Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-08-14T21:38:39.5444856Z we swap local shards directly. 2025-08-14T21:38:39.5445306Z For more generic cases, we merge different shards across different ranks and split 2025-08-14T21:38:39.5445909Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-08-14T21:38:39.5446284Z 2025-08-14T21:38:39.5446365Z Args: 2025-08-14T21:38:39.5446763Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-08-14T21:38:39.5447311Z specification describing how the tensor is sharded. 2025-08-14T21:38:39.5447588Z 2025-08-14T21:38:39.5447669Z Returns: 2025-08-14T21:38:39.5447993Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-08-14T21:38:39.5448301Z 2025-08-14T21:38:39.5448396Z Examples: 2025-08-14T21:38:39.5448601Z >>> # xdoctest: +SKIP 2025-08-14T21:38:39.5448884Z >>> # We have 2 process groups, 2 ranks. 2025-08-14T21:38:39.5449278Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-08-14T21:38:39.5449670Z >>> tensor = torch.stack([tensor, tensor]) 2025-08-14T21:38:39.5449981Z >>> tensor 2025-08-14T21:38:39.5450232Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-08-14T21:38:39.5450557Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-08-14T21:38:39.5450977Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-08-14T21:38:39.5451320Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-08-14T21:38:39.5451631Z >>> sharding_dim = 0 2025-08-14T21:38:39.5451910Z >>> spec = ChunkShardingSpec( 2025-08-14T21:38:39.5452209Z dim=sharding_dim, 2025-08-14T21:38:39.5452481Z placements=[ 2025-08-14T21:38:39.5452727Z "rank:0/cuda:0", 2025-08-14T21:38:39.5453008Z "rank:1/cuda:1", 2025-08-14T21:38:39.5453283Z "rank:2/cuda:2", 2025-08-14T21:38:39.5453543Z "rank:3/cuda:3", 2025-08-14T21:38:39.5453806Z ], 2025-08-14T21:38:39.5454019Z ) 2025-08-14T21:38:39.5454230Z >>> current_offsets = [0] * 2 2025-08-14T21:38:39.5454527Z >>> current_offsets[0] = rank * 2 2025-08-14T21:38:39.5454844Z >>> shard_metadata = ShardMetadata( 2025-08-14T21:38:39.5455194Z shard_offsets=copy.deepcopy(current_offsets), 2025-08-14T21:38:39.5455564Z shard_sizes=tensor.size(), 2025-08-14T21:38:39.5455893Z placement=spec.placements[rank], 2025-08-14T21:38:39.5456192Z ) 2025-08-14T21:38:39.5456405Z >>> local_shards = [ 2025-08-14T21:38:39.5456650Z Shard( 2025-08-14T21:38:39.5456883Z tensor=tensor, 2025-08-14T21:38:39.5457151Z metadata=shard_metadata, 2025-08-14T21:38:39.5457444Z ) 2025-08-14T21:38:39.5457651Z ] 2025-08-14T21:38:39.5457979Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-08-14T21:38:39.5458410Z >>> sharding_dim = 1 2025-08-14T21:38:39.5458694Z >>> resharding_spec = ChunkShardingSpec( 2025-08-14T21:38:39.5459011Z dim=sharding_dim, 2025-08-14T21:38:39.5459283Z placements=[ 2025-08-14T21:38:39.5459541Z "rank:0/cuda:0", 2025-08-14T21:38:39.5459804Z "rank:1/cuda:1", 2025-08-14T21:38:39.5460075Z "rank:2/cuda:2", 2025-08-14T21:38:39.5460346Z "rank:3/cuda:3", 2025-08-14T21:38:39.5460593Z ], 2025-08-14T21:38:39.5460803Z ) 2025-08-14T21:38:39.5461024Z >>> st.reshard(resharding_spec) 2025-08-14T21:38:39.5461325Z >>> tensor = st.local_shards()[0].tensor 2025-08-14T21:38:39.5461625Z >>> tensor 2025-08-14T21:38:39.5461895Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-08-14T21:38:39.5462276Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-08-14T21:38:39.5462641Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-08-14T21:38:39.5463024Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-08-14T21:38:39.5463269Z 2025-08-14T21:38:39.5463706Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.5464069Z 2025-08-14T21:38:39.5634797Z 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. 2025-08-14T21:38:39.5635779Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.5636159Z 2025-08-14T21:38:39.5636370Z Representation of a sharding plan, describes how to shard a module 2025-08-14T21:38:39.5636957Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-08-14T21:38:39.5637623Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-08-14T21:38:39.5638251Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-08-14T21:38:39.5638618Z 2025-08-14T21:38:39.5638699Z Args: 2025-08-14T21:38:39.5639083Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-08-14T21:38:39.5639621Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-08-14T21:38:39.5640163Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-08-14T21:38:39.5640929Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-08-14T21:38:39.5641419Z a parameter to a `ShardingSpec`. 2025-08-14T21:38:39.5641891Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-08-14T21:38:39.5642366Z to a `Sharder` object. 2025-08-14T21:38:39.5642898Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-08-14T21:38:39.5643582Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-08-14T21:38:39.5644186Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-08-14T21:38:39.5644632Z Default: `None` 2025-08-14T21:38:39.5645042Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-08-14T21:38:39.5645630Z a module's sharded output to be returned as a Tensor from its local shards to 2025-08-14T21:38:39.5646231Z ensure further processing in a data parallel fashion. ("" in list means the 2025-08-14T21:38:39.5646686Z root module). 2025-08-14T21:38:39.5646914Z Default: None 2025-08-14T21:38:39.5647149Z Example: 2025-08-14T21:38:39.5647560Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-08-14T21:38:39.5648227Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-08-14T21:38:39.5648616Z 2025-08-14T21:38:39.5648781Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-08-14T21:38:39.5649160Z >>> class MyModule(nn.Module): 2025-08-14T21:38:39.5649480Z >>> def __init__(self) -> None: 2025-08-14T21:38:39.5649787Z >>> super().__init__() 2025-08-14T21:38:39.5650062Z >>> self.fc1 = nn.Linear() 2025-08-14T21:38:39.5650364Z >>> self.gelu = nn.GELU() 2025-08-14T21:38:39.5650669Z >>> self.fc2 = nn.Linear() 2025-08-14T21:38:39.5650961Z >>> self.relu = nn.Linear() 2025-08-14T21:38:39.5651247Z >>> 2025-08-14T21:38:39.5651470Z >>> def forward(self, input): 2025-08-14T21:38:39.5651829Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-08-14T21:38:39.5652125Z 2025-08-14T21:38:39.5652129Z 2025-08-14T21:38:39.5652256Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-08-14T21:38:39.5652605Z >>> sharding_plan = ShardingPlan( 2025-08-14T21:38:39.5652905Z >>> plan={ 2025-08-14T21:38:39.5653135Z >>> "fc1.weight": spec1, 2025-08-14T21:38:39.5653436Z >>> "fc2.weight": spec2 2025-08-14T21:38:39.5653715Z >>> }, 2025-08-14T21:38:39.5653925Z >>> output_plan={ 2025-08-14T21:38:39.5654274Z >>> "fc2": output_spec 2025-08-14T21:38:39.5654550Z >>> }, 2025-08-14T21:38:39.5654771Z >>> return_local_tensor=["fc2"] 2025-08-14T21:38:39.5655069Z >>> ) 2025-08-14T21:38:39.5655180Z 2025-08-14T21:38:39.5655437Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.5655799Z 2025-08-14T21:38:39.6597724Z 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. 2025-08-14T21:38:39.6598843Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.6599215Z 2025-08-14T21:38:39.6599321Z Run post-localSGD algorithm. 2025-08-14T21:38:39.6599519Z 2025-08-14T21:38:39.6599747Z This DDP communication hook is used for running post-localSGD algorithm, 2025-08-14T21:38:39.6600251Z by combining with a model averaging component (e.g., 2025-08-14T21:38:39.6600843Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-08-14T21:38:39.6601395Z that runs after the optimizer step. 2025-08-14T21:38:39.6601610Z 2025-08-14T21:38:39.6601687Z Args: 2025-08-14T21:38:39.6602026Z state (PostLocalSGDState): State information to run post-localSGD. 2025-08-14T21:38:39.6602817Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-08-14T21:38:39.6603613Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-08-14T21:38:39.6604385Z Note that since DDP comm hook only supports single process single device mode, 2025-08-14T21:38:39.6604884Z only exactly one tensor is stored in this bucket. 2025-08-14T21:38:39.6605152Z 2025-08-14T21:38:39.6605235Z Returns: 2025-08-14T21:38:39.6605593Z Future handler of the communication, which updates the gradients in place. 2025-08-14T21:38:39.6605941Z 2025-08-14T21:38:39.6606056Z Example:: 2025-08-14T21:38:39.6606264Z >>> # xdoctest: +SKIP 2025-08-14T21:38:39.6606683Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-08-14T21:38:39.6607166Z start_localSGD_iter=10) 2025-08-14T21:38:39.6607555Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-08-14T21:38:39.6608166Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-08-14T21:38:39.6608949Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-08-14T21:38:39.6609401Z 2025-08-14T21:38:39.6609655Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.6610012Z 2025-08-14T21:38:39.6650190Z 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. 2025-08-14T21:38:39.6651250Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.6651632Z 2025-08-14T21:38:39.6651744Z Implement PowerSGD algorithm. 2025-08-14T21:38:39.6651931Z 2025-08-14T21:38:39.6652162Z This DDP communication hook implements PowerSGD gradient compression 2025-08-14T21:38:39.6652753Z algorithm described in the `paper `_. 2025-08-14T21:38:39.6653320Z Once gradient tensors are aggregated across all workers, this hook applies 2025-08-14T21:38:39.6653772Z compression as follows: 2025-08-14T21:38:39.6653933Z 2025-08-14T21:38:39.6654372Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-08-14T21:38:39.6654911Z 2025-08-14T21:38:39.6655319Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-08-14T21:38:39.6655848Z 2025-08-14T21:38:39.6656361Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-08-14T21:38:39.6656891Z 2025-08-14T21:38:39.6657001Z 2. Handles uncompressed tensors: 2025-08-14T21:38:39.6657196Z 2025-08-14T21:38:39.6657709Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-08-14T21:38:39.6658326Z 2025-08-14T21:38:39.6658673Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-08-14T21:38:39.6659121Z 2025-08-14T21:38:39.6659346Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-08-14T21:38:39.6659702Z 2025-08-14T21:38:39.6659934Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-08-14T21:38:39.6660600Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-08-14T21:38:39.6661009Z 2025-08-14T21:38:39.6661164Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-08-14T21:38:39.6661407Z 2025-08-14T21:38:39.6661511Z 3.3. Allreduces Ps as a batch; 2025-08-14T21:38:39.6661714Z 2025-08-14T21:38:39.6661847Z 3.4. Orthogonalizes each P in Ps; 2025-08-14T21:38:39.6662197Z 2025-08-14T21:38:39.6662386Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-08-14T21:38:39.6662687Z 2025-08-14T21:38:39.6662803Z 3.6. Allreduces Qs as a batch; 2025-08-14T21:38:39.6662996Z 2025-08-14T21:38:39.6663282Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-08-14T21:38:39.6663766Z 2025-08-14T21:38:39.6664165Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-08-14T21:38:39.6664956Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-08-14T21:38:39.6665773Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-08-14T21:38:39.6666302Z 2025-08-14T21:38:39.6666380Z Args: 2025-08-14T21:38:39.6666919Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-08-14T21:38:39.6667807Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-08-14T21:38:39.6668379Z and ``min_compression_rate``. 2025-08-14T21:38:39.6668985Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-08-14T21:38:39.6669749Z Note that since DDP comm hook only supports single process single device mode, 2025-08-14T21:38:39.6670259Z only exactly one tensor is stored in this bucket. 2025-08-14T21:38:39.6670508Z 2025-08-14T21:38:39.6670603Z Returns: 2025-08-14T21:38:39.6670946Z Future handler of the communication, which updates the gradients in place. 2025-08-14T21:38:39.6671308Z 2025-08-14T21:38:39.6671398Z Example:: 2025-08-14T21:38:39.6671615Z >>> # xdoctest: +SKIP 2025-08-14T21:38:39.6672041Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-08-14T21:38:39.6672588Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-08-14T21:38:39.6673024Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-08-14T21:38:39.6673287Z 2025-08-14T21:38:39.6673543Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.6673897Z 2025-08-14T21:38:39.6701458Z 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. 2025-08-14T21:38:39.6702577Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.6702958Z 2025-08-14T21:38:39.6703296Z Averages parameters periodically after the warm-up stage. 2025-08-14T21:38:39.6703653Z 2025-08-14T21:38:39.6703924Z This can be used for running `post-local SGD `_, 2025-08-14T21:38:39.6704483Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-08-14T21:38:39.6705009Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-08-14T21:38:39.6705360Z 2025-08-14T21:38:39.6705440Z Args: 2025-08-14T21:38:39.6705719Z period (int): The number of steps per model averaging. 2025-08-14T21:38:39.6706244Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-08-14T21:38:39.6706750Z Otherwise, only DDP needs to be used. 2025-08-14T21:38:39.6707189Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-08-14T21:38:39.6707625Z model averaging is skipped. 2025-08-14T21:38:39.6708028Z process_group: The process group to be used for all-reduce. 2025-08-14T21:38:39.6708471Z If ``None``, the default process group, which 2025-08-14T21:38:39.6708914Z is created by :func:`torch.distributed.init_process_group`, 2025-08-14T21:38:39.6709325Z will be used. (default: ``None``) 2025-08-14T21:38:39.6709650Z 2025-08-14T21:38:39.6709744Z Example:: 2025-08-14T21:38:39.6709862Z 2025-08-14T21:38:39.6709999Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:39.6710327Z >>> import torch 2025-08-14T21:38:39.6710581Z >>> import torch.distributed as dist 2025-08-14T21:38:39.6711115Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-08-14T21:38:39.6711810Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-08-14T21:38:39.6712277Z >>> import torch.nn as nn 2025-08-14T21:38:39.6712545Z >>> 2025-08-14T21:38:39.6712837Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-08-14T21:38:39.6713223Z >>> torch.cuda.set_device(rank) 2025-08-14T21:38:39.6713559Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-08-14T21:38:39.6713945Z >>> model = nn.parallel.DistributedDataParallel( 2025-08-14T21:38:39.6714330Z >>> module, device_ids=[rank], output_device=rank 2025-08-14T21:38:39.6714664Z >>> ) 2025-08-14T21:38:39.6714926Z >>> # Register a post-localSGD communication hook. 2025-08-14T21:38:39.6715463Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-08-14T21:38:39.6716015Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-08-14T21:38:39.6716365Z >>> 2025-08-14T21:38:39.6716739Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-08-14T21:38:39.6717256Z >>> # After 100 steps, run model averaging every 4 steps. 2025-08-14T21:38:39.6717828Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-08-14T21:38:39.6718496Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-08-14T21:38:39.6718958Z >>> for step in range(0, 200): 2025-08-14T21:38:39.6719245Z >>> optimizer.zero_grad() 2025-08-14T21:38:39.6719546Z >>> loss = loss_fn(output, labels) 2025-08-14T21:38:39.6719866Z >>> loss.backward() 2025-08-14T21:38:39.6720126Z >>> optimizer.step() 2025-08-14T21:38:39.6720494Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-08-14T21:38:39.6721004Z >>> # inter-node communication only occurs every 4 iterations after 2025-08-14T21:38:39.6721432Z >>> # the initial ``warmup_steps`` period. 2025-08-14T21:38:39.6721873Z >>> averager.average_parameters(model.parameters()) 2025-08-14T21:38:39.6722327Z 2025-08-14T21:38:39.6722712Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.6723264Z 2025-08-14T21:38:39.6724688Z 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. 2025-08-14T21:38:39.6725889Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.6726253Z 2025-08-14T21:38:39.6726604Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-08-14T21:38:39.6727045Z 2025-08-14T21:38:39.6727344Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-08-14T21:38:39.6727959Z by using different periods concurrently after the warm-up stage. 2025-08-14T21:38:39.6728665Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-08-14T21:38:39.6729508Z that supports `post-local SGD `_, which essentially only supports 2025-08-14T21:38:39.6730246Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-08-14T21:38:39.6731007Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-08-14T21:38:39.6731761Z Similarly, the process groups within this class do not have such an intra-machine process 2025-08-14T21:38:39.6732481Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-08-14T21:38:39.6732877Z 2025-08-14T21:38:39.6732958Z Args: 2025-08-14T21:38:39.6733331Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-08-14T21:38:39.6733910Z process group size, used for initializing process groups of 2025-08-14T21:38:39.6734433Z different sizes in a hierarchy to average parameters concurrently. 2025-08-14T21:38:39.6734977Z Particularly, at each iteration, there will be at most a single 2025-08-14T21:38:39.6735534Z process group that runs averaging -- the period of such group should 2025-08-14T21:38:39.6736084Z have the largest period which the current step can be divided by. 2025-08-14T21:38:39.6736563Z For example, if the dict has three keys: 2, 4, and 8, 2025-08-14T21:38:39.6737047Z then this means totally three process groups will be created to 2025-08-14T21:38:39.6737569Z average parameters every 2, 4, and 8 iterations, respectively. 2025-08-14T21:38:39.6738063Z At the 4th iteration, only the second process group will run 2025-08-14T21:38:39.6738536Z averaging, because the first process group should be a 2025-08-14T21:38:39.6739040Z subset of the second process group, and no need to execute the first 2025-08-14T21:38:39.6739497Z process group redundantly. 2025-08-14T21:38:39.6739911Z On the other hand, the third process group can only be triggered 2025-08-14T21:38:39.6740443Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-08-14T21:38:39.6741076Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-08-14T21:38:39.6741923Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-08-14T21:38:39.6742630Z If ``None``, the default process group, which is created 2025-08-14T21:38:39.6743118Z by :func:`torch.distributed.init_process_group`, will be used. 2025-08-14T21:38:39.6743637Z (default: ``None``) 2025-08-14T21:38:39.6743862Z 2025-08-14T21:38:39.6743952Z Example:: 2025-08-14T21:38:39.6744191Z >>> # xdoctest: +SKIP('undefined rank') 2025-08-14T21:38:39.6744532Z >>> from collections import OrderedDict 2025-08-14T21:38:39.6744908Z >>> import torch 2025-08-14T21:38:39.6745161Z >>> import torch.distributed as dist 2025-08-14T21:38:39.6745653Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-08-14T21:38:39.6746146Z >>> PostLocalSGDState, 2025-08-14T21:38:39.6746423Z >>> post_localSGD_hook, 2025-08-14T21:38:39.6746695Z >>> ) 2025-08-14T21:38:39.6747185Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-08-14T21:38:39.6747764Z >>> import torch.nn as nn 2025-08-14T21:38:39.6748031Z >>> 2025-08-14T21:38:39.6748321Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-08-14T21:38:39.6748704Z >>> torch.cuda.set_device(rank) 2025-08-14T21:38:39.6749042Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-08-14T21:38:39.6749436Z >>> model = nn.parallel.DistributedDataParallel( 2025-08-14T21:38:39.6749840Z >>> module, device_ids=[rank], output_device=rank 2025-08-14T21:38:39.6750159Z >>> ) 2025-08-14T21:38:39.6750421Z >>> # Register a post-localSGD communication hook. 2025-08-14T21:38:39.6750939Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-08-14T21:38:39.6751488Z >>> subgroup, _ = dist.new_subgroups() 2025-08-14T21:38:39.6752027Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-08-14T21:38:39.6752608Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-08-14T21:38:39.6752965Z >>> 2025-08-14T21:38:39.6753356Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-08-14T21:38:39.6753872Z >>> # the 16 processes every 16 iterations. 2025-08-14T21:38:39.6754287Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-08-14T21:38:39.6754796Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-08-14T21:38:39.6755467Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-08-14T21:38:39.6756156Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-08-14T21:38:39.6756685Z >>> # After 100 steps, run model averaging at two levels. 2025-08-14T21:38:39.6757041Z >>> for step in range(0, 200): 2025-08-14T21:38:39.6757343Z >>> optimizer.zero_grad() 2025-08-14T21:38:39.6757649Z >>> loss = loss_fn(output, labels) 2025-08-14T21:38:39.6757955Z >>> loss.backward() 2025-08-14T21:38:39.6758229Z >>> optimizer.step() 2025-08-14T21:38:39.6758552Z >>> # Average parameters after ``optimizer.step()``. 2025-08-14T21:38:39.6759084Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-08-14T21:38:39.6759658Z >>> averager.average_parameters(model.parameters()) 2025-08-14T21:38:39.6759933Z 2025-08-14T21:38:39.6760020Z .. warning :: 2025-08-14T21:38:39.6760399Z The last group size in the dict must be the size of the provided ``process_group``, 2025-08-14T21:38:39.6760982Z which indicates model averaging at the highest level of the hierarchy. 2025-08-14T21:38:39.6761623Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-08-14T21:38:39.6762036Z 2025-08-14T21:38:39.6762135Z .. warning :: 2025-08-14T21:38:39.6762493Z `HierarchicalModelAverager` is experimental and subject to change. 2025-08-14T21:38:39.6762828Z 2025-08-14T21:38:39.6763070Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.6763436Z 2025-08-14T21:38:39.7236619Z 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. 2025-08-14T21:38:39.7237708Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.7238075Z 2025-08-14T21:38:39.7238939Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-08-14T21:38:39.7239844Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-08-14T21:38:39.7240278Z 2025-08-14T21:38:39.7240447Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-08-14T21:38:39.7240724Z 2025-08-14T21:38:39.7240818Z .. warning:: 2025-08-14T21:38:39.7241117Z Current implementation only supports loading Tensors. 2025-08-14T21:38:39.7241392Z 2025-08-14T21:38:39.7241502Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:39.7241812Z >>> sd = {"mode": model} 2025-08-14T21:38:39.7242063Z >>> dcp.load( 2025-08-14T21:38:39.7242264Z >>> sd, 2025-08-14T21:38:39.7242535Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-08-14T21:38:39.7242915Z >>> planner=DynamicMetaLoadPlanner(), 2025-08-14T21:38:39.7243244Z >>> checkpoint_id="path_to_model.pt" 2025-08-14T21:38:39.7243543Z >>> ) 2025-08-14T21:38:39.7243662Z 2025-08-14T21:38:39.7243911Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.7244269Z 2025-08-14T21:38:39.7244965Z 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. 2025-08-14T21:38:39.7246111Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.7246478Z 2025-08-14T21:38:39.7246835Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-08-14T21:38:39.7247626Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-08-14T21:38:39.7248178Z metadata file, like Torch Save files. 2025-08-14T21:38:39.7248387Z 2025-08-14T21:38:39.7248577Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-08-14T21:38:39.7248861Z 2025-08-14T21:38:39.7248950Z .. warning:: 2025-08-14T21:38:39.7249257Z Current implementation only supports loading Tensors. 2025-08-14T21:38:39.7249527Z 2025-08-14T21:38:39.7249649Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:39.7249946Z >>> sd = {"mode": model} 2025-08-14T21:38:39.7250195Z >>> dcp.load( 2025-08-14T21:38:39.7250408Z >>> sd, 2025-08-14T21:38:39.7250667Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-08-14T21:38:39.7251044Z >>> planner=DynamicMetaLoadPlanner(), 2025-08-14T21:38:39.7251379Z >>> checkpoint_id="path_to_model.pt" 2025-08-14T21:38:39.7251662Z >>> ) 2025-08-14T21:38:39.7251781Z 2025-08-14T21:38:39.7252025Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.7252393Z 2025-08-14T21:38:39.7346893Z 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. 2025-08-14T21:38:39.7347945Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.7348320Z 2025-08-14T21:38:39.7348534Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-08-14T21:38:39.7348845Z 2025-08-14T21:38:39.7349002Z This is the current recommended way to checkpoint FSDP. 2025-08-14T21:38:39.7349363Z >>> # xdoctest: +SKIP 2025-08-14T21:38:39.7349671Z >>> import torch.distributed.checkpoint as dist_cp 2025-08-14T21:38:39.7350003Z >>> # Save 2025-08-14T21:38:39.7350227Z >>> model: torch.nn.Model 2025-08-14T21:38:39.7350513Z >>> optim_params = model.parameters() 2025-08-14T21:38:39.7350855Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-08-14T21:38:39.7351186Z >>> # Save 2025-08-14T21:38:39.7351518Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-08-14T21:38:39.7351938Z >>> state_dict = { 2025-08-14T21:38:39.7352233Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-08-14T21:38:39.7352601Z >>> "model": model.state_dict() 2025-08-14T21:38:39.7352891Z >>> } 2025-08-14T21:38:39.7353251Z >>> dist_cp.save_state_dict( 2025-08-14T21:38:39.7353551Z >>> state_dict=optim_state, 2025-08-14T21:38:39.7353930Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-08-14T21:38:39.7354334Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-08-14T21:38:39.7354662Z >>> ) 2025-08-14T21:38:39.7354864Z >>> 2025-08-14T21:38:39.7355046Z >>> # Load 2025-08-14T21:38:39.7355389Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-08-14T21:38:39.7355856Z >>> model_state_dict = model_tp.state_dict() 2025-08-14T21:38:39.7356188Z >>> checkpoint = { 2025-08-14T21:38:39.7356440Z >>> "model": model_state_dict 2025-08-14T21:38:39.7356729Z >>> } 2025-08-14T21:38:39.7356953Z >>> dist_cp.load_state_dict( 2025-08-14T21:38:39.7357234Z >>> state_dict=checkpoint, 2025-08-14T21:38:39.7357628Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-08-14T21:38:39.7358059Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-08-14T21:38:39.7358369Z >>> ) 2025-08-14T21:38:39.7358638Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-08-14T21:38:39.7358975Z >>> 2025-08-14T21:38:39.7359262Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-08-14T21:38:39.7359708Z >>> model_state_dict, 2025-08-14T21:38:39.7360000Z >>> optimizer_key="optimizer", 2025-08-14T21:38:39.7360389Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-08-14T21:38:39.7360751Z >>> ) 2025-08-14T21:38:39.7360954Z >>> 2025-08-14T21:38:39.7361215Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-08-14T21:38:39.7361580Z >>> model, optim, optim_state["optimizer"] 2025-08-14T21:38:39.7361904Z >>> ) 2025-08-14T21:38:39.7362114Z >>> 2025-08-14T21:38:39.7362336Z >>> optim.load_state_dict(flattened_osd) 2025-08-14T21:38:39.7362573Z 2025-08-14T21:38:39.7362817Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.7363192Z 2025-08-14T21:38:39.7379493Z 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. 2025-08-14T21:38:39.7381396Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.7382142Z 2025-08-14T21:38:39.7382690Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-08-14T21:38:39.7383547Z 2025-08-14T21:38:39.7384119Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-08-14T21:38:39.7384913Z 2025-08-14T21:38:39.7385430Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-08-14T21:38:39.7386404Z will be visible to the whole process. 2025-08-14T21:38:39.7386803Z 2025-08-14T21:38:39.7387338Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-08-14T21:38:39.7388096Z 2025-08-14T21:38:39.7388298Z 1) set_up_planner - called on all ranks. 2025-08-14T21:38:39.7388887Z Signals the start of a checkpoint save. 2025-08-14T21:38:39.7389273Z 2025-08-14T21:38:39.7389462Z 2) create_local_plan - called on all ranks. 2025-08-14T21:38:39.7390304Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-08-14T21:38:39.7391053Z 2025-08-14T21:38:39.7391377Z 3) create_global_plan - called on the coordinator rank only. 2025-08-14T21:38:39.7392275Z Takes the SavePlan from all ranks and make any global decision. 2025-08-14T21:38:39.7393066Z 2025-08-14T21:38:39.7393271Z 4) finish_plan - called on all ranks. 2025-08-14T21:38:39.7394052Z This gives each rank a chance to adjust to global planning decisions. 2025-08-14T21:38:39.7394674Z 2025-08-14T21:38:39.7394949Z 5) resolve_data - called multiple times on each rank 2025-08-14T21:38:39.7395800Z Lookups a value on the `state_dict` for the storage layer to write. 2025-08-14T21:38:39.7396391Z 2025-08-14T21:38:39.7397186Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-08-14T21:38:39.7398296Z most changes can be expressed by changes in a single method. 2025-08-14T21:38:39.7398824Z 2025-08-14T21:38:39.7399021Z There are 3 usual patterns of extension: 2025-08-14T21:38:39.7399355Z 2025-08-14T21:38:39.7399605Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-08-14T21:38:39.7400204Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-08-14T21:38:39.7400535Z 2025-08-14T21:38:39.7400647Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:39.7401001Z >>> class RenamePlanner(DefaultSavePlanner): 2025-08-14T21:38:39.7401338Z >>> def set_up_planner( 2025-08-14T21:38:39.7401597Z >>> self, 2025-08-14T21:38:39.7401832Z >>> state_dict: STATE_DICT_TYPE, 2025-08-14T21:38:39.7402165Z >>> storage_meta: Optional[StorageMeta], 2025-08-14T21:38:39.7402501Z >>> is_coordinator: bool, 2025-08-14T21:38:39.7402776Z >>> ) -> None: 2025-08-14T21:38:39.7403025Z >>> # prefix all keys with `foo_`` 2025-08-14T21:38:39.7403513Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-08-14T21:38:39.7403906Z 2025-08-14T21:38:39.7404356Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-08-14T21:38:39.7404809Z 2025-08-14T21:38:39.7404921Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:39.7405267Z >>> class FP16Planner(DefaultSavePlanner): 2025-08-14T21:38:39.7405607Z >>> def create_local_plan(self): 2025-08-14T21:38:39.7406219Z >>> plan = super().create_local_plan() 2025-08-14T21:38:39.7406805Z >>> for p in plan: 2025-08-14T21:38:39.7407284Z >>> if p.tensor_data is not None: 2025-08-14T21:38:39.7407914Z >>> p.tensor_data.properties.dtype = torch.float16 2025-08-14T21:38:39.7408574Z >>> return plan 2025-08-14T21:38:39.7408993Z >>> 2025-08-14T21:38:39.7409393Z >>> def resolve_data(self, write_item): 2025-08-14T21:38:39.7410013Z >>> item = super().resolve_data(write_item) 2025-08-14T21:38:39.7410927Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-08-14T21:38:39.7411654Z 2025-08-14T21:38:39.7412303Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-08-14T21:38:39.7413169Z 2025-08-14T21:38:39.7413364Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:39.7413970Z >>> from itertools import zip_longest 2025-08-14T21:38:39.7414540Z >>> from dataclasses import replace 2025-08-14T21:38:39.7415186Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-08-14T21:38:39.7416190Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-08-14T21:38:39.7417158Z >>> # This sample doesn't handle ShardedTensors 2025-08-14T21:38:39.7417825Z >>> def create_global_plan(self, all_plans): 2025-08-14T21:38:39.7418515Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-08-14T21:38:39.7419179Z >>> items_per_rank = [ 2025-08-14T21:38:39.7419747Z >>> [item for item in items if item is not None] 2025-08-14T21:38:39.7420458Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-08-14T21:38:39.7421109Z >>> ] 2025-08-14T21:38:39.7421496Z >>> all_plans = [ 2025-08-14T21:38:39.7421962Z >>> replace(plan, items=items) 2025-08-14T21:38:39.7422679Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-08-14T21:38:39.7423349Z >>> ] 2025-08-14T21:38:39.7423917Z >>> return super().create_global_plan(all_plans) 2025-08-14T21:38:39.7424382Z 2025-08-14T21:38:39.7424866Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-08-14T21:38:39.7426101Z accomplished by having each rank contribute their data items in the local plan and 2025-08-14T21:38:39.7427195Z the global planner aggregate them: 2025-08-14T21:38:39.7427577Z 2025-08-14T21:38:39.7427779Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:39.7428456Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-08-14T21:38:39.7429179Z >>> def create_local_plan(self) -> SavePlan: 2025-08-14T21:38:39.7429843Z >>> plan = super().create_local_plan() 2025-08-14T21:38:39.7430539Z >>> return replace(plan, planner_data="per-rank-data") 2025-08-14T21:38:39.7431195Z >>> 2025-08-14T21:38:39.7431951Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-08-14T21:38:39.7433081Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-08-14T21:38:39.7433959Z >>> merged_data = [p.planner_data for p in global_plan] 2025-08-14T21:38:39.7434772Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-08-14T21:38:39.7435479Z >>> return global_plan, metadata 2025-08-14T21:38:39.7435871Z 2025-08-14T21:38:39.7436337Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.7436996Z 2025-08-14T21:38:39.7438171Z 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. 2025-08-14T21:38:39.7440002Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.7440707Z 2025-08-14T21:38:39.7441260Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-08-14T21:38:39.7442042Z 2025-08-14T21:38:39.7442593Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-08-14T21:38:39.7443353Z 2025-08-14T21:38:39.7443853Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-08-14T21:38:39.7444777Z will be visible to the whole process. 2025-08-14T21:38:39.7445154Z 2025-08-14T21:38:39.7445673Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-08-14T21:38:39.7446415Z 2025-08-14T21:38:39.7446646Z 1) set_up_planner - called on all ranks. 2025-08-14T21:38:39.7447278Z Signals the start of loading a checkpoint. 2025-08-14T21:38:39.7447735Z 2025-08-14T21:38:39.7447962Z 2) create_local_plan - called on all ranks. 2025-08-14T21:38:39.7448925Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-08-14T21:38:39.7449693Z 2025-08-14T21:38:39.7450021Z 3) create_global_plan - called on the coordinator rank only. 2025-08-14T21:38:39.7450933Z Takes the LoadPlan from all ranks and make any global decision. 2025-08-14T21:38:39.7451518Z 2025-08-14T21:38:39.7451771Z 4) load_bytes - called multiple times on each rank 2025-08-14T21:38:39.7452559Z This is called once per non-tensor value in state_dict. 2025-08-14T21:38:39.7453074Z 2025-08-14T21:38:39.7453488Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-08-14T21:38:39.7454421Z They are called in pair for each Tensor value in state_dict. 2025-08-14T21:38:39.7454916Z 2025-08-14T21:38:39.7455319Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-08-14T21:38:39.7455922Z most changes can be expressed by changes in a single method. 2025-08-14T21:38:39.7456217Z 2025-08-14T21:38:39.7456344Z There are two usual patterns of extension: 2025-08-14T21:38:39.7456586Z 2025-08-14T21:38:39.7456832Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-08-14T21:38:39.7457450Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-08-14T21:38:39.7458045Z to keep a reference to the original state_dict as load happens in place so 2025-08-14T21:38:39.7458496Z we need to be able to perform it in place 2025-08-14T21:38:39.7458729Z 2025-08-14T21:38:39.7458841Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:39.7459187Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-08-14T21:38:39.7459612Z >>> def set_up_planner( 2025-08-14T21:38:39.7459878Z >>> self, 2025-08-14T21:38:39.7460126Z >>> state_dict: STATE_DICT_TYPE, 2025-08-14T21:38:39.7460428Z >>> metadata: Metadata, 2025-08-14T21:38:39.7460713Z >>> is_coordinator: bool, 2025-08-14T21:38:39.7461131Z >>> ) -> None: 2025-08-14T21:38:39.7461536Z >>> self.original_state_dict = state_dict 2025-08-14T21:38:39.7462232Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-08-14T21:38:39.7462902Z >>> 2025-08-14T21:38:39.7463289Z >>> if self.flatten_sharded_tensors: 2025-08-14T21:38:39.7464057Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-08-14T21:38:39.7464694Z >>> 2025-08-14T21:38:39.7465089Z >>> if self.flatten_state_dict: 2025-08-14T21:38:39.7465778Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-08-14T21:38:39.7466431Z >>> 2025-08-14T21:38:39.7466812Z >>> self.state_dict = state_dict 2025-08-14T21:38:39.7467353Z >>> self.metadata = metadata 2025-08-14T21:38:39.7467918Z >>> self.is_coordinator = is_coordinator 2025-08-14T21:38:39.7468485Z >>> 2025-08-14T21:38:39.7468881Z >>> def load_bytes(self, read_item, value): 2025-08-14T21:38:39.7469453Z >>> # Remove the "foo_" prefix 2025-08-14T21:38:39.7470452Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-08-14T21:38:39.7470897Z 2025-08-14T21:38:39.7470902Z 2025-08-14T21:38:39.7471171Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-08-14T21:38:39.7471537Z 2025-08-14T21:38:39.7471650Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:39.7472018Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-08-14T21:38:39.7472401Z >>> def resolve_tensor(self, read_item): 2025-08-14T21:38:39.7472743Z >>> tensor = super().resolve_tensor(read_item) 2025-08-14T21:38:39.7473135Z >>> return torch.empty_like(tensor, device="cpu") 2025-08-14T21:38:39.7473470Z >>> 2025-08-14T21:38:39.7473712Z >>> def commit_tensor(self, read_item, tensor): 2025-08-14T21:38:39.7474091Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-08-14T21:38:39.7474363Z 2025-08-14T21:38:39.7474607Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.7474965Z 2025-08-14T21:38:39.7619119Z 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. 2025-08-14T21:38:39.7621075Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.7621798Z 2025-08-14T21:38:39.7622107Z Return the model state_dict and optimizers state_dict. 2025-08-14T21:38:39.7622621Z 2025-08-14T21:38:39.7623027Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-08-14T21:38:39.7624234Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-08-14T21:38:39.7625421Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-08-14T21:38:39.7626523Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-08-14T21:38:39.7627526Z with a different number of trainers and/or different parallelisms. 2025-08-14T21:38:39.7628619Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-08-14T21:38:39.7629486Z these APIs. 2025-08-14T21:38:39.7629927Z 3.) sanity checking the result state_dict. 2025-08-14T21:38:39.7630363Z 2025-08-14T21:38:39.7630722Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-08-14T21:38:39.7631713Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-08-14T21:38:39.7632830Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-08-14T21:38:39.7633867Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-08-14T21:38:39.7635041Z ``module.named_buffers()`` when the module is not distributed by any 2025-08-14T21:38:39.7636120Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-08-14T21:38:39.7636924Z a parameter, there will be a conversion from the parameter IDs to the 2025-08-14T21:38:39.7637376Z canonical FQNs when calling this API. 2025-08-14T21:38:39.7637586Z 2025-08-14T21:38:39.7637814Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-08-14T21:38:39.7638368Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-08-14T21:38:39.7638832Z optimizer parameter IDs to the canonical FQNs. 2025-08-14T21:38:39.7639088Z 2025-08-14T21:38:39.7639172Z Example: 2025-08-14T21:38:39.7639389Z >>> # xdoctest: +SKIP 2025-08-14T21:38:39.7639635Z >>> import torch 2025-08-14T21:38:39.7640021Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-14T21:38:39.7640569Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-08-14T21:38:39.7641096Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-08-14T21:38:39.7641445Z 2025-08-14T21:38:39.7641570Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-08-14T21:38:39.7641991Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-08-14T21:38:39.7642529Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-08-14T21:38:39.7642918Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-08-14T21:38:39.7643453Z 2025-08-14T21:38:39.7643460Z 2025-08-14T21:38:39.7643876Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-08-14T21:38:39.7644778Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-08-14T21:38:39.7645458Z ... fsdp_model, fsdp_optim 2025-08-14T21:38:39.7645944Z ... ) 2025-08-14T21:38:39.7646166Z 2025-08-14T21:38:39.7646535Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-08-14T21:38:39.7647343Z >>> # the asserts will fail. 2025-08-14T21:38:39.7647897Z >>> assert ddp_state_dict == fsdp_state_dict 2025-08-14T21:38:39.7648586Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-08-14T21:38:39.7649053Z 2025-08-14T21:38:39.7649061Z 2025-08-14T21:38:39.7649225Z Args: 2025-08-14T21:38:39.7649664Z model (nn.Module): the nn.Module to the model. 2025-08-14T21:38:39.7667089Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-08-14T21:38:39.7667959Z The optimizers that are used to optimize ``model``. 2025-08-14T21:38:39.7668967Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-08-14T21:38:39.7669935Z that belong to the submodules. 2025-08-14T21:38:39.7670649Z options (StateDictOptions): the options to control how 2025-08-14T21:38:39.7671554Z model state_dict and optimizer state_dict should be returned. See 2025-08-14T21:38:39.7672357Z `StateDictOptions` for the details. 2025-08-14T21:38:39.7672797Z 2025-08-14T21:38:39.7672960Z Returns: 2025-08-14T21:38:39.7673531Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-08-14T21:38:39.7674071Z 2025-08-14T21:38:39.7674486Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-08-14T21:38:39.7675055Z 2025-08-14T21:38:39.7675487Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.7676127Z 2025-08-14T21:38:39.7677689Z 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. 2025-08-14T21:38:39.7679562Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.7680272Z 2025-08-14T21:38:39.7680642Z Load a checkpoint into a distributed state dict in SPMD style. 2025-08-14T21:38:39.7681183Z 2025-08-14T21:38:39.7681572Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-08-14T21:38:39.7682640Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-08-14T21:38:39.7683197Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-08-14T21:38:39.7683601Z costs). 2025-08-14T21:38:39.7683729Z 2025-08-14T21:38:39.7683904Z Each rank will try to read the least amount of data necessary 2025-08-14T21:38:39.7684429Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-08-14T21:38:39.7685022Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-08-14T21:38:39.7685378Z 2025-08-14T21:38:39.7685627Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-08-14T21:38:39.7686257Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-08-14T21:38:39.7686789Z ``load_state_dict`` once the deserialization is complete. 2025-08-14T21:38:39.7687317Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-08-14T21:38:39.7687829Z it in the ``state_dict`` with the deserialized object. 2025-08-14T21:38:39.7688096Z 2025-08-14T21:38:39.7688188Z .. warning:: 2025-08-14T21:38:39.7688488Z All tensors in ``state_dict`` must be allocated on their 2025-08-14T21:38:39.7688917Z destination device *prior to* calling this function. 2025-08-14T21:38:39.7689421Z 2025-08-14T21:38:39.7689798Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-08-14T21:38:39.7690568Z on state_dict. 2025-08-14T21:38:39.7690808Z 2025-08-14T21:38:39.7690973Z .. warning:: 2025-08-14T21:38:39.7691509Z Users must call `load_state_dict` on the root module to ensure load 2025-08-14T21:38:39.7692394Z pos-processing and non-tensor data properly propagates. 2025-08-14T21:38:39.7693105Z 2025-08-14T21:38:39.7693273Z .. note: 2025-08-14T21:38:39.7693871Z If no process group is initialized, this function will assume the intent 2025-08-14T21:38:39.7694899Z is to load a checkpoint into the local process. This can be useful in the 2025-08-14T21:38:39.7695986Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-08-14T21:38:39.7696830Z or ShardedTensor) 2025-08-14T21:38:39.7697118Z 2025-08-14T21:38:39.7697263Z .. note: 2025-08-14T21:38:39.7697702Z Rank 0 is assumed to be the coordinator rank. 2025-08-14T21:38:39.7698154Z 2025-08-14T21:38:39.7698303Z Args: 2025-08-14T21:38:39.7698873Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-08-14T21:38:39.7699705Z checkpoint_id (Union[str, os.PathLike, None]): 2025-08-14T21:38:39.7700554Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-08-14T21:38:39.7701504Z depends on the storage. It can be a path to a folder or to a file. 2025-08-14T21:38:39.7702426Z It can also be a key if the storage is a key-value store. 2025-08-14T21:38:39.7703109Z (Default: ``None``) 2025-08-14T21:38:39.7703734Z storage_reader (Optional[StorageReader]): 2025-08-14T21:38:39.7704537Z Instance of StorageWriter used to perform reads. If this is not 2025-08-14T21:38:39.7705505Z specified, DCP will automatically infer the reader based on the 2025-08-14T21:38:39.7706487Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-08-14T21:38:39.7707291Z be raised. (Default: ``None``) 2025-08-14T21:38:39.7707870Z planner (Optional[LoadPlanner]): 2025-08-14T21:38:39.7708636Z Instance of LoadPlanner. If this is not specified, the default 2025-08-14T21:38:39.7709466Z planner will be used. (Default: ``None``) 2025-08-14T21:38:39.7710116Z process_group (Optional[ProcessGroup]): 2025-08-14T21:38:39.7710882Z ProcessGroup to be used for cross-rank synchronization. 2025-08-14T21:38:39.7711570Z (Default: ``None``) 2025-08-14T21:38:39.7712251Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-08-14T21:38:39.7713269Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-08-14T21:38:39.7714297Z Returns: 2025-08-14T21:38:39.7714651Z None. 2025-08-14T21:38:39.7714856Z 2025-08-14T21:38:39.7715006Z Examples 2025-08-14T21:38:39.7715384Z >>> # xdoctest: +SKIP 2025-08-14T21:38:39.7715853Z >>> my_model = MyModule() 2025-08-14T21:38:39.7716415Z >>> optimizer = Adagrad(my_model.parameters()) 2025-08-14T21:38:39.7717094Z >>> model_state_dict = my_model.state_dict() 2025-08-14T21:38:39.7717953Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-08-14T21:38:39.7718754Z ... "/checkpoint/1" 2025-08-14T21:38:39.7719225Z ... ) 2025-08-14T21:38:39.7719450Z 2025-08-14T21:38:39.7719727Z >>> torch.distributed.checkpoint.load_state_dict( 2025-08-14T21:38:39.7720397Z >>> state_dict=model_state_dict, 2025-08-14T21:38:39.7720963Z >>> storage_reader=fs_storage_reader, 2025-08-14T21:38:39.7721477Z >>> ) 2025-08-14T21:38:39.7721668Z 2025-08-14T21:38:39.7722043Z >>> # module.load_state_dict() function might have customized steps 2025-08-14T21:38:39.7722846Z >>> # to flush the state_dict, must call it to 2025-08-14T21:38:39.7723482Z >>> # ensure correct behavior. 2025-08-14T21:38:39.7724082Z >>> my_model.load_state_dict(model_state_dict) 2025-08-14T21:38:39.7724526Z 2025-08-14T21:38:39.7724879Z .. note:: 2025-08-14T21:38:39.7725469Z load_state_dict uses collectives to coordinate reads across ranks. 2025-08-14T21:38:39.7726496Z For NCCL-based process groups, internal tensor representations of 2025-08-14T21:38:39.7727538Z objects must be moved to the GPU device before communication takes place. 2025-08-14T21:38:39.7728506Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-08-14T21:38:39.7729515Z and it is the user's responsibility to ensure that this is set so that each 2025-08-14T21:38:39.7730466Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-08-14T21:38:39.7731013Z 2025-08-14T21:38:39.7731510Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.7732211Z 2025-08-14T21:38:39.7736114Z 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. 2025-08-14T21:38:39.7738044Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.7738776Z 2025-08-14T21:38:39.7738992Z Save a distributed model in SPMD style. 2025-08-14T21:38:39.7739422Z 2025-08-14T21:38:39.7739764Z This function is different from ``torch.save()`` as it handles 2025-08-14T21:38:39.7740819Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-08-14T21:38:39.7741487Z 2025-08-14T21:38:39.7741955Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-08-14T21:38:39.7742891Z save will call ``state_dict`` before serialization. 2025-08-14T21:38:39.7743347Z 2025-08-14T21:38:39.7743601Z .. warning:: 2025-08-14T21:38:39.7744287Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-08-14T21:38:39.7745135Z for saved state_dicts. 2025-08-14T21:38:39.7745448Z 2025-08-14T21:38:39.7745600Z .. warning:: 2025-08-14T21:38:39.7746180Z If using the `process_group` argument, make sure that only its ranks 2025-08-14T21:38:39.7747039Z call `save_state_dict` and that all data in state_dict belong to it. 2025-08-14T21:38:39.7747603Z 2025-08-14T21:38:39.7747754Z .. note:: 2025-08-14T21:38:39.7748423Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-08-14T21:38:39.7749486Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-08-14T21:38:39.7749970Z group needs to be passed in. 2025-08-14T21:38:39.7750178Z 2025-08-14T21:38:39.7750269Z .. note:: 2025-08-14T21:38:39.7750654Z If no process group is available, this function assumes the intention is to save the 2025-08-14T21:38:39.7751130Z state_dict in the local process. 2025-08-14T21:38:39.7751465Z 2025-08-14T21:38:39.7751549Z .. note: 2025-08-14T21:38:39.7751802Z Rank 0 is assumed to be the coordinator rank. 2025-08-14T21:38:39.7752057Z 2025-08-14T21:38:39.7752061Z 2025-08-14T21:38:39.7752140Z Args: 2025-08-14T21:38:39.7752409Z state_dict (Dict[str, Any]): The state_dict to save. 2025-08-14T21:38:39.7752803Z checkpoint_id (Union[str, os.PathLike, None]): 2025-08-14T21:38:39.7753256Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-08-14T21:38:39.7753774Z depends on the storage. It can be a path to a folder or to a file. 2025-08-14T21:38:39.7754240Z It can also be a key if the storage is a key-value store. 2025-08-14T21:38:39.7754612Z (Default: ``None``) 2025-08-14T21:38:39.7754910Z storage_writer (Optional[StorageWriter]): 2025-08-14T21:38:39.7755527Z Instance of StorageWriter used to perform writes. If this is not 2025-08-14T21:38:39.7756426Z specified, DCP will automatically infer the writer based on the 2025-08-14T21:38:39.7757356Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-08-14T21:38:39.7758091Z be raised. (Default: ``None``) 2025-08-14T21:38:39.7758287Z planner (Optional[SavePlanner]): 2025-08-14T21:38:39.7758803Z Instance of SavePlanner. If this is not specified, the default 2025-08-14T21:38:39.7759032Z planner will be used. (Default: ``None``) 2025-08-14T21:38:39.7759248Z process_group (Optional[ProcessGroup]): 2025-08-14T21:38:39.7759573Z ProcessGroup to be used for cross-rank synchronization. 2025-08-14T21:38:39.7759729Z (Default: ``None``) 2025-08-14T21:38:39.7759873Z no_dist (bool): 2025-08-14T21:38:39.7760167Z If ``True``, this function will assume the intent is to load 2025-08-14T21:38:39.7760386Z a checkpoint on a single rank/process. 2025-08-14T21:38:39.7760546Z (Default: ``False``) 2025-08-14T21:38:39.7761034Z use_collectives (bool): If ``False``, this function will assume the intent is to save 2025-08-14T21:38:39.7761336Z a checkpoint without using cross-rank synchronization. 2025-08-14T21:38:39.7761499Z (Default: ``True``) 2025-08-14T21:38:39.7761923Z This configuration is experimental and should be used with caution. 2025-08-14T21:38:39.7762416Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-08-14T21:38:39.7762427Z 2025-08-14T21:38:39.7762584Z Returns: 2025-08-14T21:38:39.7762855Z Metadata: Metadata object for the saved checkpoint. 2025-08-14T21:38:39.7762865Z 2025-08-14T21:38:39.7763007Z Example: 2025-08-14T21:38:39.7763172Z >>> # xdoctest: +SKIP 2025-08-14T21:38:39.7763337Z >>> my_model = MyModule() 2025-08-14T21:38:39.7763345Z 2025-08-14T21:38:39.7763532Z >>> state_dict = {"model": my_model} 2025-08-14T21:38:39.7763554Z 2025-08-14T21:38:39.7763956Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-08-14T21:38:39.7764118Z ... "/checkpoint/1" 2025-08-14T21:38:39.7764264Z ... ) 2025-08-14T21:38:39.7764456Z >>> torch.distributed.checkpoint.save( 2025-08-14T21:38:39.7764566Z >>> state_dict=state_dict, 2025-08-14T21:38:39.7764701Z >>> storage_writer=fs_storage_writer, 2025-08-14T21:38:39.7764789Z >>> ) 2025-08-14T21:38:39.7764794Z 2025-08-14T21:38:39.7764884Z .. note:: 2025-08-14T21:38:39.7765104Z save_state_dict uses collectives to coordinate writes across ranks. 2025-08-14T21:38:39.7765312Z For NCCL-based process groups, internal tensor representations of 2025-08-14T21:38:39.7765549Z objects must be moved to the GPU device before communication takes place. 2025-08-14T21:38:39.7765763Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-08-14T21:38:39.7765963Z and it is the user's responsibility to ensure that this is set so that 2025-08-14T21:38:39.7766167Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-08-14T21:38:39.7766172Z 2025-08-14T21:38:39.7766516Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.7766521Z 2025-08-14T21:38:39.7767180Z 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. 2025-08-14T21:38:39.7767437Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.7767706Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-08-14T21:38:39.7767986Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-08-14T21:38:39.7767991Z 2025-08-14T21:38:39.7768079Z .. warning:: 2025-08-14T21:38:39.7768253Z This feature is experimental and subject to change. 2025-08-14T21:38:39.7768393Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-08-14T21:38:39.7768398Z 2025-08-14T21:38:39.7768492Z Args: 2025-08-14T21:38:39.7768646Z state_dict (Dict[str, Any]): The state_dict to save. 2025-08-14T21:38:39.7768784Z checkpoint_id (Union[str, os.PathLike, None]): 2025-08-14T21:38:39.7769002Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-08-14T21:38:39.7769254Z depends on the storage. It can be a path to a folder or to a file. 2025-08-14T21:38:39.7769416Z It can also be a key if the storage is a key-value store. 2025-08-14T21:38:39.7769524Z (Default: ``None``) 2025-08-14T21:38:39.7769656Z storage_writer (Optional[StorageWriter]): 2025-08-14T21:38:39.7769865Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-08-14T21:38:39.7770114Z this is not specified, DCP will automatically infer the writer based on the 2025-08-14T21:38:39.7770543Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-08-14T21:38:39.7770750Z be raised. (Default: ``None``) 2025-08-14T21:38:39.7770948Z planner (Optional[SavePlanner]): 2025-08-14T21:38:39.7771278Z Instance of SavePlanner. If this is not specified, the default 2025-08-14T21:38:39.7771503Z planner will be used. (Default: ``None``) 2025-08-14T21:38:39.7771720Z process_group (Optional[ProcessGroup]): 2025-08-14T21:38:39.7772040Z ProcessGroup to be used for cross-rank synchronization. 2025-08-14T21:38:39.7772218Z (Default: ``None``) 2025-08-14T21:38:39.7772495Z async_checkpointer_type (AsyncCheckpointerType): 2025-08-14T21:38:39.7772806Z whether to do checkpoint in separate thread or process 2025-08-14T21:38:39.7773052Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-08-14T21:38:39.7773239Z async_stager (AsyncStager): 2025-08-14T21:38:39.7773709Z provides staging implementation. If storage_writer implements AsyncStager 2025-08-14T21:38:39.7774092Z and async_stager is provided, async_stager will be used for staging 2025-08-14T21:38:39.7774260Z no_dist (bool): 2025-08-14T21:38:39.7774574Z If ``True``, this function will assume the intent is to save 2025-08-14T21:38:39.7774793Z a checkpoint on a single rank/process. 2025-08-14T21:38:39.7774971Z (Default: ``False``) 2025-08-14T21:38:39.7775531Z use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``) 2025-08-14T21:38:39.7775943Z This configuration is experimental and should be used with caution. 2025-08-14T21:38:39.7776446Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-08-14T21:38:39.7776456Z 2025-08-14T21:38:39.7776604Z Returns: 2025-08-14T21:38:39.7776989Z Future: A future holding the resultant Metadata object from `save`. 2025-08-14T21:38:39.7777010Z 2025-08-14T21:38:39.7777163Z Example: 2025-08-14T21:38:39.7777340Z >>> # xdoctest: +SKIP 2025-08-14T21:38:39.7777659Z >>> my_model = MyModule() 2025-08-14T21:38:39.7777669Z 2025-08-14T21:38:39.7777871Z >>> state_dict = {"model": my_model} 2025-08-14T21:38:39.7777878Z 2025-08-14T21:38:39.7778277Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-08-14T21:38:39.7778469Z ... "/checkpoint/1" 2025-08-14T21:38:39.7778608Z ... ) 2025-08-14T21:38:39.7778971Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-08-14T21:38:39.7779165Z >>> state_dict=state_dict, 2025-08-14T21:38:39.7779383Z >>> storage_writer=fs_storage_writer, 2025-08-14T21:38:39.7779519Z >>> ) 2025-08-14T21:38:39.7779642Z >>> 2025-08-14T21:38:39.7779804Z >>> # ... do some work ... 2025-08-14T21:38:39.7779945Z >>> 2025-08-14T21:38:39.7780126Z >>> checkpoint_future.result() 2025-08-14T21:38:39.7780135Z 2025-08-14T21:38:39.7780267Z 2025-08-14T21:38:39.7780725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.7780733Z 2025-08-14T21:38:39.8095237Z 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. 2025-08-14T21:38:39.8095543Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:39.8095699Z 2025-08-14T21:38:39.8095898Z Initialize rendezvous event object and record its operations. 2025-08-14T21:38:39.8095903Z 2025-08-14T21:38:39.8095997Z Args: 2025-08-14T21:38:39.8096124Z run_id (str): The run id of the rendezvous. 2025-08-14T21:38:39.8096266Z message (str): The message describing the event. 2025-08-14T21:38:39.8096527Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-08-14T21:38:39.8096705Z name (str): Event name. (E.g. Current action being performed). 2025-08-14T21:38:39.8096821Z hostname (str): Hostname of the node. 2025-08-14T21:38:39.8096961Z pid (Optional[int]): The process id of the node. 2025-08-14T21:38:39.8097194Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-08-14T21:38:39.8097461Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-08-14T21:38:39.8097617Z rank (Optional[int]): The rank of the node, if known. 2025-08-14T21:38:39.8097708Z Returns: 2025-08-14T21:38:39.8097787Z None 2025-08-14T21:38:39.8097866Z Example: 2025-08-14T21:38:39.8097994Z >>> # See DynamicRendezvousHandler class 2025-08-14T21:38:39.8098075Z >>> def _record( 2025-08-14T21:38:39.8098154Z ... self, 2025-08-14T21:38:39.8098250Z ... message: str, 2025-08-14T21:38:39.8098381Z ... node_state: NodeState = NodeState.RUNNING, 2025-08-14T21:38:39.8098479Z ... rank: Optional[int] = None, 2025-08-14T21:38:39.8098574Z ... ) -> None: 2025-08-14T21:38:39.8098686Z ... construct_and_record_rdzv_event( 2025-08-14T21:38:39.8098856Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-08-14T21:38:39.8098968Z ... run_id=self._settings.run_id, 2025-08-14T21:38:39.8099061Z ... message=message, 2025-08-14T21:38:39.8099167Z ... node_state=node_state, 2025-08-14T21:38:39.8099282Z ... hostname=self._this_node.addr, 2025-08-14T21:38:39.8099381Z ... pid=self._this_node.pid, 2025-08-14T21:38:39.8099506Z ... local_id=self._this_node.local_id, 2025-08-14T21:38:39.8099592Z ... rank=rank, 2025-08-14T21:38:39.8099670Z ... ) 2025-08-14T21:38:39.8099675Z 2025-08-14T21:38:39.8099932Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:39.8099937Z 2025-08-14T21:38:40.0018758Z 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. 2025-08-14T21:38:40.0019962Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.0019978Z 2025-08-14T21:38:40.0020289Z This configures FSDP-native mixed precision training. 2025-08-14T21:38:40.0020299Z 2025-08-14T21:38:40.0020468Z Attributes: 2025-08-14T21:38:40.0020871Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-08-14T21:38:40.0021252Z parameters during forward and backward and thus the dtype for 2025-08-14T21:38:40.0021603Z forward and backward computation. Outside forward and backward, the 2025-08-14T21:38:40.0021915Z *sharded* parameters are kept in full precision (e.g. for the 2025-08-14T21:38:40.0022302Z optimizer step), and for model checkpointing, the parameters are 2025-08-14T21:38:40.0022553Z always saved in full precision. (Default: ``None``) 2025-08-14T21:38:40.0022886Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-08-14T21:38:40.0023287Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-08-14T21:38:40.0023686Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-08-14T21:38:40.0024069Z the ``param_dtype`` value, still running gradient reduction in low 2025-08-14T21:38:40.0024441Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-08-14T21:38:40.0025013Z to force gradient reduction to run in full precision. (Default: 2025-08-14T21:38:40.0025172Z ``None``) 2025-08-14T21:38:40.0025553Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-08-14T21:38:40.0025922Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-08-14T21:38:40.0026291Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-08-14T21:38:40.0026675Z dtype thereafter. For model checkpointing, the buffers are saved 2025-08-14T21:38:40.0027010Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-08-14T21:38:40.0027156Z ``None``) 2025-08-14T21:38:40.0027515Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-08-14T21:38:40.0027922Z gradients to full precision after the backward pass in preparation 2025-08-14T21:38:40.0028289Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-08-14T21:38:40.0028644Z in the dtype used for gradient reduction, which can save memory if 2025-08-14T21:38:40.0029024Z using a custom optimizer that supports running in low precision. 2025-08-14T21:38:40.0029192Z (Default: ``False``) 2025-08-14T21:38:40.0029583Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-08-14T21:38:40.0029947Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-08-14T21:38:40.0030331Z that parameter and input dtypes match for forward computation, as 2025-08-14T21:38:40.0030703Z required by many ops. This may need to be set to ``True`` when only 2025-08-14T21:38:40.0031111Z applying mixed precision to some but not all FSDP modules, in which 2025-08-14T21:38:40.0031481Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-08-14T21:38:40.0031640Z (Default: ``False``) 2025-08-14T21:38:40.0031983Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-08-14T21:38:40.0032329Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-08-14T21:38:40.0032632Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-08-14T21:38:40.0032866Z this does not do anything. (Default: ``True``) 2025-08-14T21:38:40.0033263Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-08-14T21:38:40.0033577Z module classes to ignore for mixed precision when using an 2025-08-14T21:38:40.0033895Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-08-14T21:38:40.0034280Z applied to them separately with mixed precision disabled (meaning 2025-08-14T21:38:40.0034779Z that the final FSDP construction would deviate from the specified 2025-08-14T21:38:40.0035140Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-08-14T21:38:40.0035494Z not do anything. This API is experimental and subject to change. 2025-08-14T21:38:40.0035688Z (Default: ``(_BatchNorm,)``) 2025-08-14T21:38:40.0035698Z 2025-08-14T21:38:40.0036017Z .. note:: This API is experimental and subject to change. 2025-08-14T21:38:40.0036026Z 2025-08-14T21:38:40.0036430Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-08-14T21:38:40.0036438Z 2025-08-14T21:38:40.0036776Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-08-14T21:38:40.0036965Z precision, but buffers are not. 2025-08-14T21:38:40.0036974Z 2025-08-14T21:38:40.0037342Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-08-14T21:38:40.0037730Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-08-14T21:38:40.0038158Z Disabling FSDP's mixed precision for those norm modules only means that 2025-08-14T21:38:40.0038542Z the affine parameters are kept in ``float32``. However, this incurs 2025-08-14T21:38:40.0038983Z separate all-gathers and reduce-scatters for those norm modules, which 2025-08-14T21:38:40.0039480Z may be inefficient, so if the workload permits, the user should prefer 2025-08-14T21:38:40.0039751Z to still apply mixed precision to those modules. 2025-08-14T21:38:40.0039761Z 2025-08-14T21:38:40.0040121Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-08-14T21:38:40.0040469Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-08-14T21:38:40.0040857Z modules will have FSDP applied to them separately with mixed precision 2025-08-14T21:38:40.0041033Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-08-14T21:38:40.0041039Z 2025-08-14T21:38:40.0041259Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-08-14T21:38:40.0041478Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-08-14T21:38:40.0041647Z its ``cast_root_forward_inputs`` takes precedence over its 2025-08-14T21:38:40.0041833Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-08-14T21:38:40.0042045Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-08-14T21:38:40.0042255Z sufficient for the typical case where each FSDP instance has the same 2025-08-14T21:38:40.0042486Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-08-14T21:38:40.0042661Z ``param_dtype`` at the beginning of the model's forward pass. 2025-08-14T21:38:40.0042665Z 2025-08-14T21:38:40.0042875Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-08-14T21:38:40.0043103Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-08-14T21:38:40.0043302Z values to configure casting inputs or not before each instance's 2025-08-14T21:38:40.0043500Z forward. In such a case, since the casts happen before each FSDP 2025-08-14T21:38:40.0043706Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-08-14T21:38:40.0043929Z submodules run before its FSDP submodules to avoid the activation dtype 2025-08-14T21:38:40.0044145Z being changed due to a different ``MixedPrecision`` configuration. 2025-08-14T21:38:40.0044150Z 2025-08-14T21:38:40.0044236Z Example:: 2025-08-14T21:38:40.0044240Z 2025-08-14T21:38:40.0044376Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:40.0044537Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-08-14T21:38:40.0044631Z >>> model[1] = FSDP( 2025-08-14T21:38:40.0044728Z >>> model[1], 2025-08-14T21:38:40.0045028Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-08-14T21:38:40.0045121Z >>> ) 2025-08-14T21:38:40.0045301Z >>> model = FSDP( 2025-08-14T21:38:40.0045385Z >>> model, 2025-08-14T21:38:40.0045696Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-08-14T21:38:40.0045775Z >>> ) 2025-08-14T21:38:40.0045780Z 2025-08-14T21:38:40.0045990Z The above shows a working example. On the other hand, if ``model[1]`` 2025-08-14T21:38:40.0046201Z were replaced with ``model[0]``, meaning that the submodule using 2025-08-14T21:38:40.0046415Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-08-14T21:38:40.0046625Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-08-14T21:38:40.0046722Z ones. 2025-08-14T21:38:40.0046726Z 2025-08-14T21:38:40.0046730Z 2025-08-14T21:38:40.0046973Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.0046978Z 2025-08-14T21:38:40.0047599Z 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. 2025-08-14T21:38:40.0047850Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.0047855Z 2025-08-14T21:38:40.0048065Z ``FullStateDictConfig`` is a config class meant to be used with 2025-08-14T21:38:40.0048312Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-08-14T21:38:40.0048507Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-08-14T21:38:40.0048733Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-08-14T21:38:40.0048923Z is meant to be used via the :func:`state_dict_type` context manager as 2025-08-14T21:38:40.0049020Z follows: 2025-08-14T21:38:40.0049024Z 2025-08-14T21:38:40.0049145Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:40.0049374Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-14T21:38:40.0049511Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-08-14T21:38:40.0049710Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-08-14T21:38:40.0049919Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-08-14T21:38:40.0050037Z >>> state = fsdp.state_dict() 2025-08-14T21:38:40.0050434Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-08-14T21:38:40.0050853Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-08-14T21:38:40.0051265Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-08-14T21:38:40.0051443Z >>> if dist.get_rank() == 0: 2025-08-14T21:38:40.0051782Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-08-14T21:38:40.0052019Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-08-14T21:38:40.0052221Z >>> model.load_state_dict(state_dict) 2025-08-14T21:38:40.0052667Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-08-14T21:38:40.0053104Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-08-14T21:38:40.0053271Z >>> fsdp = FSDP( 2025-08-14T21:38:40.0053415Z ... model, 2025-08-14T21:38:40.0053627Z ... device_id=torch.cuda.current_device(), 2025-08-14T21:38:40.0053826Z ... auto_wrap_policy=..., 2025-08-14T21:38:40.0054006Z ... sync_module_states=True, 2025-08-14T21:38:40.0054148Z ... ) 2025-08-14T21:38:40.0054541Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-08-14T21:38:40.0054549Z 2025-08-14T21:38:40.0054703Z Attributes: 2025-08-14T21:38:40.0055026Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-08-14T21:38:40.0055384Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-08-14T21:38:40.0055639Z ranks save the full state dict. (Default: ``False``) 2025-08-14T21:38:40.0055649Z 2025-08-14T21:38:40.0056235Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.0056245Z 2025-08-14T21:38:40.0167802Z 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. 2025-08-14T21:38:40.0168370Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.0168786Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-08-14T21:38:40.0168797Z 2025-08-14T21:38:40.0169291Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-08-14T21:38:40.0169641Z The target module does not have to be a FSDP module. If the target 2025-08-14T21:38:40.0170016Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-08-14T21:38:40.0170025Z 2025-08-14T21:38:40.0170388Z .. note:: This API should be called for only the top-level (root) 2025-08-14T21:38:40.0170541Z module. 2025-08-14T21:38:40.0170551Z 2025-08-14T21:38:40.0170938Z .. note:: This API enables users to transparently use the conventional 2025-08-14T21:38:40.0171272Z ``state_dict`` API to take model checkpoints in cases where the 2025-08-14T21:38:40.0171828Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-08-14T21:38:40.0172188Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-08-14T21:38:40.0172582Z instances, while dispatching into `sharded_state_dict` implementation 2025-08-14T21:38:40.0172723Z for FSDP: 2025-08-14T21:38:40.0172733Z 2025-08-14T21:38:40.0172905Z Example:: 2025-08-14T21:38:40.0172913Z 2025-08-14T21:38:40.0173134Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:40.0173339Z >>> model = DDP(FSDP(...)) 2025-08-14T21:38:40.0173535Z >>> FSDP.set_state_dict_type( 2025-08-14T21:38:40.0173707Z >>> model, 2025-08-14T21:38:40.0173953Z >>> StateDictType.SHARDED_STATE_DICT, 2025-08-14T21:38:40.0174347Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-08-14T21:38:40.0174758Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-08-14T21:38:40.0174933Z >>> ) 2025-08-14T21:38:40.0175159Z >>> param_state_dict = model.state_dict() 2025-08-14T21:38:40.0175482Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-08-14T21:38:40.0175491Z 2025-08-14T21:38:40.0175638Z Args: 2025-08-14T21:38:40.0175861Z module (torch.nn.Module): Root module. 2025-08-14T21:38:40.0176303Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-08-14T21:38:40.0176740Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-08-14T21:38:40.0176938Z target ``state_dict_type``. 2025-08-14T21:38:40.0177411Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-08-14T21:38:40.0177615Z for the optimizer state dict. 2025-08-14T21:38:40.0177624Z 2025-08-14T21:38:40.0177790Z Returns: 2025-08-14T21:38:40.0178203Z A StateDictSettings that include the previous state_dict type and 2025-08-14T21:38:40.0178403Z configuration for the module. 2025-08-14T21:38:40.0178567Z 2025-08-14T21:38:40.0179028Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.0179037Z 2025-08-14T21:38:40.0180639Z 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. 2025-08-14T21:38:40.0181124Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.0181748Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-08-14T21:38:40.0181761Z 2025-08-14T21:38:40.0182365Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-08-14T21:38:40.0182590Z :meth:`set_state_dict_type` for the detail. 2025-08-14T21:38:40.0182606Z 2025-08-14T21:38:40.0182779Z Example:: 2025-08-14T21:38:40.0182787Z 2025-08-14T21:38:40.0183012Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:40.0183198Z >>> model = DDP(FSDP(...)) 2025-08-14T21:38:40.0183410Z >>> with FSDP.state_dict_type( 2025-08-14T21:38:40.0183667Z >>> model, 2025-08-14T21:38:40.0183890Z >>> StateDictType.SHARDED_STATE_DICT, 2025-08-14T21:38:40.0184051Z >>> ): 2025-08-14T21:38:40.0184260Z >>> checkpoint = model.state_dict() 2025-08-14T21:38:40.0184269Z 2025-08-14T21:38:40.0184430Z Args: 2025-08-14T21:38:40.0184649Z module (torch.nn.Module): Root module. 2025-08-14T21:38:40.0185074Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-08-14T21:38:40.0185511Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-08-14T21:38:40.0185893Z configuration for the target ``state_dict_type``. 2025-08-14T21:38:40.0186340Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-08-14T21:38:40.0186717Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-08-14T21:38:40.0186865Z 2025-08-14T21:38:40.0187346Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.0187354Z 2025-08-14T21:38:40.0233423Z 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. 2025-08-14T21:38:40.0233999Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.0234010Z 2025-08-14T21:38:40.0234450Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-08-14T21:38:40.0234460Z 2025-08-14T21:38:40.0234825Z The given state-dict can be transformed to one of three types: 2025-08-14T21:38:40.0235365Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-08-14T21:38:40.0235374Z 2025-08-14T21:38:40.0235795Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-08-14T21:38:40.0236199Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-08-14T21:38:40.0236345Z avoid OOM. 2025-08-14T21:38:40.0236353Z 2025-08-14T21:38:40.0236774Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-08-14T21:38:40.0237104Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-08-14T21:38:40.0237233Z memory. 2025-08-14T21:38:40.0237241Z 2025-08-14T21:38:40.0237610Z For local state_dict, no transformation will be performed. But a state 2025-08-14T21:38:40.0237951Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-08-14T21:38:40.0238112Z nature (this is not supported yet). 2025-08-14T21:38:40.0238127Z 2025-08-14T21:38:40.0238283Z Example:: 2025-08-14T21:38:40.0238290Z 2025-08-14T21:38:40.0238494Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:40.0238947Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-14T21:38:40.0239228Z >>> from torch.distributed.fsdp import StateDictType 2025-08-14T21:38:40.0239538Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-08-14T21:38:40.0239918Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-08-14T21:38:40.0240090Z >>> # Save a checkpoint 2025-08-14T21:38:40.0240272Z >>> model, optim = ... 2025-08-14T21:38:40.0240471Z >>> FSDP.set_state_dict_type( 2025-08-14T21:38:40.0240827Z >>> model, 2025-08-14T21:38:40.0241038Z >>> StateDictType.FULL_STATE_DICT, 2025-08-14T21:38:40.0241274Z >>> FullStateDictConfig(rank0_only=False), 2025-08-14T21:38:40.0241524Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-14T21:38:40.0241691Z >>> ) 2025-08-14T21:38:40.0241879Z >>> state_dict = model.state_dict() 2025-08-14T21:38:40.0242184Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-08-14T21:38:40.0242457Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-08-14T21:38:40.0242628Z >>> # Load a checkpoint 2025-08-14T21:38:40.0242804Z >>> model, optim = ... 2025-08-14T21:38:40.0243072Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-08-14T21:38:40.0243262Z >>> FSDP.set_state_dict_type( 2025-08-14T21:38:40.0243416Z >>> model, 2025-08-14T21:38:40.0243625Z >>> StateDictType.FULL_STATE_DICT, 2025-08-14T21:38:40.0243849Z >>> FullStateDictConfig(rank0_only=False), 2025-08-14T21:38:40.0244123Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-14T21:38:40.0244269Z >>> ) 2025-08-14T21:38:40.0244462Z >>> model.load_state_dict(state_dict) 2025-08-14T21:38:40.0244730Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-08-14T21:38:40.0245057Z >>> model, optim, optim_state_dict 2025-08-14T21:38:40.0245194Z >>> ) 2025-08-14T21:38:40.0245420Z >>> optim.load_state_dict(optim_state_dict) 2025-08-14T21:38:40.0245429Z 2025-08-14T21:38:40.0245567Z Args: 2025-08-14T21:38:40.0245914Z model (torch.nn.Module): Root module (which may or may not be a 2025-08-14T21:38:40.0246288Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-08-14T21:38:40.0246518Z were passed into the optimizer ``optim``. 2025-08-14T21:38:40.0246850Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-08-14T21:38:40.0247008Z parameters. 2025-08-14T21:38:40.0247396Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-08-14T21:38:40.0247784Z transform. If the value is None, optim.state_dict() will be used. ( 2025-08-14T21:38:40.0247950Z Default: ``None``) 2025-08-14T21:38:40.0248358Z group (dist.ProcessGroup): Model's process group across which parameters 2025-08-14T21:38:40.0248708Z are sharded or ``None`` if using the default process group. ( 2025-08-14T21:38:40.0248866Z Default: ``None``) 2025-08-14T21:38:40.0248875Z 2025-08-14T21:38:40.0249022Z Returns: 2025-08-14T21:38:40.0249326Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-08-14T21:38:40.0249551Z ``model``. The sharding of the optimizer state is based on 2025-08-14T21:38:40.0249665Z ``state_dict_type``. 2025-08-14T21:38:40.0249670Z 2025-08-14T21:38:40.0249919Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.0249924Z 2025-08-14T21:38:40.0250808Z 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. 2025-08-14T21:38:40.0251065Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.0251070Z 2025-08-14T21:38:40.0251433Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-08-14T21:38:40.0251438Z 2025-08-14T21:38:40.0251599Z Given a ``optim_state_dict`` that is transformed through 2025-08-14T21:38:40.0251805Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-08-14T21:38:40.0252018Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-08-14T21:38:40.0252200Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-08-14T21:38:40.0252205Z 2025-08-14T21:38:40.0252339Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:40.0252566Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-14T21:38:40.0252810Z >>> from torch.distributed.fsdp import StateDictType 2025-08-14T21:38:40.0252998Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-08-14T21:38:40.0253194Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-08-14T21:38:40.0253294Z >>> # Save a checkpoint 2025-08-14T21:38:40.0253404Z >>> model, optim = ... 2025-08-14T21:38:40.0253506Z >>> FSDP.set_state_dict_type( 2025-08-14T21:38:40.0253603Z >>> model, 2025-08-14T21:38:40.0253717Z >>> StateDictType.FULL_STATE_DICT, 2025-08-14T21:38:40.0253841Z >>> FullStateDictConfig(rank0_only=False), 2025-08-14T21:38:40.0253993Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-14T21:38:40.0254074Z >>> ) 2025-08-14T21:38:40.0254180Z >>> state_dict = model.state_dict() 2025-08-14T21:38:40.0254305Z >>> original_osd = optim.state_dict() 2025-08-14T21:38:40.0254426Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-08-14T21:38:40.0254511Z >>> model, 2025-08-14T21:38:40.0254604Z >>> optim, 2025-08-14T21:38:40.0254710Z >>> optim_state_dict=original_osd 2025-08-14T21:38:40.0254788Z >>> ) 2025-08-14T21:38:40.0254939Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-08-14T21:38:40.0255086Z >>> # Load a checkpoint 2025-08-14T21:38:40.0255193Z >>> model, optim = ... 2025-08-14T21:38:40.0255337Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-08-14T21:38:40.0255439Z >>> FSDP.set_state_dict_type( 2025-08-14T21:38:40.0255533Z >>> model, 2025-08-14T21:38:40.0255643Z >>> StateDictType.FULL_STATE_DICT, 2025-08-14T21:38:40.0255909Z >>> FullStateDictConfig(rank0_only=False), 2025-08-14T21:38:40.0256145Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-14T21:38:40.0256275Z >>> ) 2025-08-14T21:38:40.0256466Z >>> model.load_state_dict(state_dict) 2025-08-14T21:38:40.0256727Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-08-14T21:38:40.0256928Z >>> model, optim, optim_state_dict 2025-08-14T21:38:40.0257062Z >>> ) 2025-08-14T21:38:40.0257267Z >>> optim.load_state_dict(optim_state_dict) 2025-08-14T21:38:40.0257275Z 2025-08-14T21:38:40.0257399Z Args: 2025-08-14T21:38:40.0257742Z model (torch.nn.Module): Root module (which may or may not be a 2025-08-14T21:38:40.0258104Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-08-14T21:38:40.0258325Z were passed into the optimizer ``optim``. 2025-08-14T21:38:40.0258656Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-08-14T21:38:40.0258813Z parameters. 2025-08-14T21:38:40.0259181Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-08-14T21:38:40.0259546Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-08-14T21:38:40.0259885Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-08-14T21:38:40.0260218Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-08-14T21:38:40.0260565Z load_directly (bool): If this is set to True, this API will also 2025-08-14T21:38:40.0260923Z call optim.load_state_dict(result) before returning the result. 2025-08-14T21:38:40.0261315Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-08-14T21:38:40.0261487Z (Default: ``False``) 2025-08-14T21:38:40.0261913Z group (dist.ProcessGroup): Model's process group across which parameters 2025-08-14T21:38:40.0262254Z are sharded or ``None`` if using the default process group. ( 2025-08-14T21:38:40.0262417Z Default: ``None``) 2025-08-14T21:38:40.0262426Z 2025-08-14T21:38:40.0262900Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.0262909Z 2025-08-14T21:38:40.0798986Z 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. 2025-08-14T21:38:40.0801209Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.0801963Z 2025-08-14T21:38:40.0802377Z RemoteModule instance can only be created after RPC initialization. 2025-08-14T21:38:40.0803021Z 2025-08-14T21:38:40.0803390Z It creates a user-specified module on a specified remote node. 2025-08-14T21:38:40.0804402Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-08-14T21:38:40.0805207Z executed on the remote node. 2025-08-14T21:38:40.0805966Z It takes care of autograd recording to ensure the backward pass propagates 2025-08-14T21:38:40.0806850Z gradients back to the corresponding remote module. 2025-08-14T21:38:40.0807992Z It can be shared across processors using `RPC framework `__, 2025-08-14T21:38:40.0809228Z without incurring any overheads of copying the actual module, 2025-08-14T21:38:40.0810174Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-08-14T21:38:40.0810948Z pointing to the remote module. 2025-08-14T21:38:40.0811290Z 2025-08-14T21:38:40.0811607Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-08-14T21:38:40.0812474Z the ``forward`` method of the module returned by the ``module_cls``. 2025-08-14T21:38:40.0813255Z 2025-08-14T21:38:40.0813844Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-08-14T21:38:40.0814650Z 2025-08-14T21:38:40.0815099Z Particularly, to create a hybrid model, typically the local modules should be 2025-08-14T21:38:40.0816506Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-08-14T21:38:40.0817578Z Hybrid Example: 2025-08-14T21:38:40.0818021Z >>> class HybridModel(nn.Module): 2025-08-14T21:38:40.0818607Z >>> def __init__(self) -> None: 2025-08-14T21:38:40.0819175Z >>> nn.Module.__init__(self) 2025-08-14T21:38:40.0819839Z >>> self.remote_embedding = RemoteModule(...) 2025-08-14T21:38:40.0820492Z >>> self.local_linear = nn.Linear(...) 2025-08-14T21:38:40.0820936Z 2025-08-14T21:38:40.0821301Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-08-14T21:38:40.0822340Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-08-14T21:38:40.0823403Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-08-14T21:38:40.0824320Z ``def forward(input: Tensor) -> Tensor:`` and 2025-08-14T21:38:40.0825038Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-08-14T21:38:40.0825490Z 2025-08-14T21:38:40.0825656Z .. note:: 2025-08-14T21:38:40.0826055Z If the remote module is placed on a cuda device, 2025-08-14T21:38:40.0826872Z any input CPU tensors will be automatically moved to the same cuda device, 2025-08-14T21:38:40.0828246Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-08-14T21:38:40.0829209Z 2025-08-14T21:38:40.0829353Z Args: 2025-08-14T21:38:40.0830069Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-08-14T21:38:40.0830864Z The device can be a local device or a remote device specified by one of the following remote 2025-08-14T21:38:40.0831365Z formats: 2025-08-14T21:38:40.0831502Z 2025-08-14T21:38:40.0831642Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-08-14T21:38:40.0832042Z 2. "/" (ex: "trainer0/cuda:0"). 2025-08-14T21:38:40.0832295Z 2025-08-14T21:38:40.0832542Z In addition, the device field can be optional and the default value is "cpu". 2025-08-14T21:38:40.0833016Z module_cls (nn.Module): For example, 2025-08-14T21:38:40.0833328Z >>> class MyModule(nn.Module): 2025-08-14T21:38:40.0833641Z >>> def forward(input): 2025-08-14T21:38:40.0833938Z >>> return input + 1 2025-08-14T21:38:40.0834313Z >>> 2025-08-14T21:38:40.0834537Z >>> module_cls = MyModule 2025-08-14T21:38:40.0834926Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-08-14T21:38:40.0835414Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-08-14T21:38:40.0835999Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-08-14T21:38:40.0836964Z to be created. The type object should be decorated by @torch.jit.interface. 2025-08-14T21:38:40.0837964Z If not provided, the generated RemoteModule is not torchscript-able. 2025-08-14T21:38:40.0838968Z Warning, this is an experimental API and susceptible to frequent changes. 2025-08-14T21:38:40.0839628Z 2025-08-14T21:38:40.0839775Z Returns: 2025-08-14T21:38:40.0840429Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-08-14T21:38:40.0841473Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-08-14T21:38:40.0842618Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-08-14T21:38:40.0843605Z on the user-provided module on the remote side. 2025-08-14T21:38:40.0844074Z 2025-08-14T21:38:40.0844255Z Example:: 2025-08-14T21:38:40.0844736Z Run the following code in two different processes: 2025-08-14T21:38:40.0845383Z 2025-08-14T21:38:40.0845586Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:40.0846162Z >>> # On worker 0: 2025-08-14T21:38:40.0846561Z >>> import torch 2025-08-14T21:38:40.0847035Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:40.0847647Z >>> from torch import nn, Tensor 2025-08-14T21:38:40.0848401Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-08-14T21:38:40.0849171Z >>> 2025-08-14T21:38:40.0849619Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-14T21:38:40.0850243Z >>> remote_linear_module = RemoteModule( 2025-08-14T21:38:40.0850868Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-08-14T21:38:40.0851402Z >>> ) 2025-08-14T21:38:40.0851767Z >>> input = torch.randn(128, 20) 2025-08-14T21:38:40.0852373Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-08-14T21:38:40.0853035Z >>> ret = ret_fut.wait() 2025-08-14T21:38:40.0853460Z >>> rpc.shutdown() 2025-08-14T21:38:40.0853613Z 2025-08-14T21:38:40.0853700Z >>> # On worker 1: 2025-08-14T21:38:40.0853940Z >>> import torch 2025-08-14T21:38:40.0854211Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:40.0854513Z >>> 2025-08-14T21:38:40.0854764Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-14T21:38:40.0855101Z >>> rpc.shutdown() 2025-08-14T21:38:40.0855252Z 2025-08-14T21:38:40.0855509Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.0855868Z 2025-08-14T21:38:40.0856625Z 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. 2025-08-14T21:38:40.0857675Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.0858045Z 2025-08-14T21:38:40.0858357Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-08-14T21:38:40.0859078Z 2025-08-14T21:38:40.0859645Z This alternate initialization method can be particularly useful if we want to create multiple 2025-08-14T21:38:40.0861009Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-08-14T21:38:40.0861792Z 2025-08-14T21:38:40.0862191Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-08-14T21:38:40.0862753Z which is not supported. The recommended way is as follows: 2025-08-14T21:38:40.0863050Z 2025-08-14T21:38:40.0863165Z 1. the sender creates a RemoteModule; 2025-08-14T21:38:40.0863628Z 2. the sender sends its ``module_rref`` over RPC; 2025-08-14T21:38:40.0864345Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-08-14T21:38:40.0864787Z 2025-08-14T21:38:40.0864883Z Example:: 2025-08-14T21:38:40.0865156Z Run the following code in two different processes: 2025-08-14T21:38:40.0865414Z 2025-08-14T21:38:40.0865535Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:40.0865829Z >>> # On worker 0: 2025-08-14T21:38:40.0866077Z >>> import torch 2025-08-14T21:38:40.0866355Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:40.0866683Z >>> from torch import nn, Tensor 2025-08-14T21:38:40.0867106Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-08-14T21:38:40.0867522Z >>> 2025-08-14T21:38:40.0868025Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-14T21:38:40.0868631Z >>> remote_module = RemoteModule( 2025-08-14T21:38:40.0869213Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-08-14T21:38:40.0869789Z >>> ) 2025-08-14T21:38:40.0870139Z >>> 2025-08-14T21:38:40.0870528Z >>> remote_module1 = rpc.rpc_sync( 2025-08-14T21:38:40.0871043Z >>> "worker1/cpu", 2025-08-14T21:38:40.0871544Z >>> RemoteModule.init_from_module_rref, 2025-08-14T21:38:40.0872188Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-08-14T21:38:40.0872672Z >>> ) 2025-08-14T21:38:40.0872876Z >>> rpc.shutdown() 2025-08-14T21:38:40.0873043Z 2025-08-14T21:38:40.0873134Z >>> # On worker 1: 2025-08-14T21:38:40.0873375Z >>> import torch 2025-08-14T21:38:40.0873635Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:40.0873946Z >>> 2025-08-14T21:38:40.0874198Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-14T21:38:40.0874538Z >>> rpc.shutdown() 2025-08-14T21:38:40.0874694Z 2025-08-14T21:38:40.0874773Z Args: 2025-08-14T21:38:40.0875183Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-08-14T21:38:40.0875887Z The device can be a local device or a remote device specified by one of the following remote 2025-08-14T21:38:40.0876363Z formats: 2025-08-14T21:38:40.0876508Z 2025-08-14T21:38:40.0876645Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-08-14T21:38:40.0877044Z 2. "/" (ex: "trainer0/cuda:0"). 2025-08-14T21:38:40.0877295Z 2025-08-14T21:38:40.0877716Z In addition, the device field can be optional and the default value is "cpu". 2025-08-14T21:38:40.0878755Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-08-14T21:38:40.0879612Z the created remote module. 2025-08-14T21:38:40.0880465Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-08-14T21:38:40.0881613Z to be created. The type object should be decorated by @torch.jit.interface. 2025-08-14T21:38:40.0882670Z If not provided, the generated RemoteModule is not torchscript-able. 2025-08-14T21:38:40.0883734Z Warning, this is an experimental API and susceptible to frequent changes. 2025-08-14T21:38:40.0884378Z 2025-08-14T21:38:40.0884535Z Returns: 2025-08-14T21:38:40.0885166Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-08-14T21:38:40.0886213Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-08-14T21:38:40.0887368Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-08-14T21:38:40.0888342Z on the user-provided module on the remote side. 2025-08-14T21:38:40.0888817Z 2025-08-14T21:38:40.0889242Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.0889930Z 2025-08-14T21:38:40.0891066Z 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. 2025-08-14T21:38:40.0893320Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.0894033Z 2025-08-14T21:38:40.0894391Z A RemoteModule instance can only be created after RPC initialization. 2025-08-14T21:38:40.0894736Z 2025-08-14T21:38:40.0894930Z It creates a user-specified module on a specified remote node. 2025-08-14T21:38:40.0895468Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-08-14T21:38:40.0895921Z executed on the remote node. 2025-08-14T21:38:40.0896338Z It takes care of autograd recording to ensure the backward pass propagates 2025-08-14T21:38:40.0896842Z gradients back to the corresponding remote module. 2025-08-14T21:38:40.0897113Z 2025-08-14T21:38:40.0897326Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-08-14T21:38:40.0897867Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-08-14T21:38:40.0898425Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-08-14T21:38:40.0898983Z and ``forward`` are the same as the ``forward`` method of the module 2025-08-14T21:38:40.0899617Z returned by the ``module_cls``. 2025-08-14T21:38:40.0899954Z 2025-08-14T21:38:40.0900298Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-08-14T21:38:40.0901464Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-08-14T21:38:40.0902559Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-08-14T21:38:40.0903188Z 2025-08-14T21:38:40.0903426Z | ``def forward(input: Tensor) -> Tensor:`` 2025-08-14T21:38:40.0904216Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-08-14T21:38:40.0904723Z 2025-08-14T21:38:40.0904864Z Args: 2025-08-14T21:38:40.0905604Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-08-14T21:38:40.0907033Z The format should be "/", where the device field can be parsed as torch.device type. 2025-08-14T21:38:40.0908095Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-08-14T21:38:40.0908958Z In addition, the device field can be optional and the default value is "cpu". 2025-08-14T21:38:40.0910034Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-08-14T21:38:40.0910536Z 2025-08-14T21:38:40.0910649Z >>> class MyModule(nn.Module): 2025-08-14T21:38:40.0910969Z >>> def forward(input): 2025-08-14T21:38:40.0911264Z >>> return input + 1 2025-08-14T21:38:40.0911545Z >>> 2025-08-14T21:38:40.0911753Z >>> module_cls = MyModule 2025-08-14T21:38:40.0911951Z 2025-08-14T21:38:40.0912143Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-08-14T21:38:40.0912637Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-08-14T21:38:40.0912935Z 2025-08-14T21:38:40.0913017Z Returns: 2025-08-14T21:38:40.0913378Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-08-14T21:38:40.0913956Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-08-14T21:38:40.0914561Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-08-14T21:38:40.0915072Z on the user-provided module on the remote side. 2025-08-14T21:38:40.0915334Z 2025-08-14T21:38:40.0915427Z Example:: 2025-08-14T21:38:40.0915698Z Run the following code in two different processes: 2025-08-14T21:38:40.0915951Z 2025-08-14T21:38:40.0916061Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:40.0916548Z >>> # On worker 0: 2025-08-14T21:38:40.0916948Z >>> import torch 2025-08-14T21:38:40.0917388Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:40.0917978Z >>> from torch import nn, Tensor 2025-08-14T21:38:40.0918744Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-08-14T21:38:40.0919503Z >>> 2025-08-14T21:38:40.0920072Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-14T21:38:40.0920749Z >>> remote_linear_module = RemoteModule( 2025-08-14T21:38:40.0921371Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-08-14T21:38:40.0921937Z >>> ) 2025-08-14T21:38:40.0922325Z >>> input = torch.randn(128, 20) 2025-08-14T21:38:40.0922990Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-08-14T21:38:40.0923649Z >>> ret = ret_fut.wait() 2025-08-14T21:38:40.0924160Z >>> rpc.shutdown() 2025-08-14T21:38:40.0924436Z 2025-08-14T21:38:40.0924590Z >>> # On worker 1: 2025-08-14T21:38:40.0924999Z >>> import torch 2025-08-14T21:38:40.0925479Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:40.0926032Z >>> 2025-08-14T21:38:40.0926464Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-14T21:38:40.0927025Z >>> rpc.shutdown() 2025-08-14T21:38:40.0927295Z 2025-08-14T21:38:40.0927534Z Furthermore, a more practical example that is combined with 2025-08-14T21:38:40.0928332Z `DistributedDataParallel `__ (DDP) 2025-08-14T21:38:40.0929225Z can be found in this `tutorial `__. 2025-08-14T21:38:40.0929679Z 2025-08-14T21:38:40.0930027Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.0930398Z 2025-08-14T21:38:40.1081042Z 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. 2025-08-14T21:38:40.1082252Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.1082627Z 2025-08-14T21:38:40.1082881Z DistributedOptimizer takes remote references to parameters scattered 2025-08-14T21:38:40.1083457Z across workers and applies the given optimizer locally for each parameter. 2025-08-14T21:38:40.1083817Z 2025-08-14T21:38:40.1084056Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-08-14T21:38:40.1084554Z to retrieve the gradients for specific parameters. 2025-08-14T21:38:40.1084809Z 2025-08-14T21:38:40.1084913Z Concurrent calls to 2025-08-14T21:38:40.1085251Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-08-14T21:38:40.1085705Z either from the same or different clients, will 2025-08-14T21:38:40.1086175Z be serialized on each worker -- as each worker's optimizer can only work 2025-08-14T21:38:40.1086703Z on one set of gradients at a time. However, there is no guarantee that 2025-08-14T21:38:40.1087692Z the full forward-backward-optimizer sequence will execute for one client 2025-08-14T21:38:40.1088736Z at a time. This means that the gradients being applied may not correspond 2025-08-14T21:38:40.1089760Z to the latest forward pass executed on a given worker. Also, there is no 2025-08-14T21:38:40.1090587Z guaranteed ordering across workers. 2025-08-14T21:38:40.1090988Z 2025-08-14T21:38:40.1091458Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-08-14T21:38:40.1092532Z by default, so that optimizer updates are not blocked by the Python Global 2025-08-14T21:38:40.1093828Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-08-14T21:38:40.1094982Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-08-14T21:38:40.1096106Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-08-14T21:38:40.1096999Z for your own custom optimizers. 2025-08-14T21:38:40.1097356Z 2025-08-14T21:38:40.1097487Z Args: 2025-08-14T21:38:40.1098017Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-08-14T21:38:40.1098771Z instantiate on each worker. 2025-08-14T21:38:40.1099477Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-08-14T21:38:40.1100248Z to optimize. 2025-08-14T21:38:40.1100881Z args: arguments to pass to the optimizer constructor on each worker. 2025-08-14T21:38:40.1102097Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-08-14T21:38:40.1102743Z 2025-08-14T21:38:40.1102905Z Example:: 2025-08-14T21:38:40.1103291Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:40.1103998Z >>> import torch.distributed.autograd as dist_autograd 2025-08-14T21:38:40.1104686Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:40.1105271Z >>> from torch import optim 2025-08-14T21:38:40.1105949Z >>> from torch.distributed.optim import DistributedOptimizer 2025-08-14T21:38:40.1106632Z >>> 2025-08-14T21:38:40.1107072Z >>> with dist_autograd.context() as context_id: 2025-08-14T21:38:40.1107674Z >>> # Forward pass. 2025-08-14T21:38:40.1108299Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-08-14T21:38:40.1108901Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-08-14T21:38:40.1109347Z >>> loss = rref1.to_here() + rref2.to_here() 2025-08-14T21:38:40.1109679Z >>> 2025-08-14T21:38:40.1109881Z >>> # Backward pass. 2025-08-14T21:38:40.1110198Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-08-14T21:38:40.1110541Z >>> 2025-08-14T21:38:40.1110733Z >>> # Optimizer. 2025-08-14T21:38:40.1111177Z >>> dist_optim = DistributedOptimizer( 2025-08-14T21:38:40.1111501Z >>> optim.SGD, 2025-08-14T21:38:40.1111742Z >>> [rref1, rref2], 2025-08-14T21:38:40.1112004Z >>> lr=0.05, 2025-08-14T21:38:40.1112235Z >>> ) 2025-08-14T21:38:40.1112453Z >>> dist_optim.step(context_id) 2025-08-14T21:38:40.1112671Z 2025-08-14T21:38:40.1112831Z __ https://github.com/pytorch/tutorials/pull/1465 2025-08-14T21:38:40.1113099Z 2025-08-14T21:38:40.1113343Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.1113701Z 2025-08-14T21:38:40.1115017Z 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. 2025-08-14T21:38:40.1117013Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.1117708Z 2025-08-14T21:38:40.1118434Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-08-14T21:38:40.1119600Z This optimizer runs local optimizer at every step. 2025-08-14T21:38:40.1120692Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-08-14T21:38:40.1121503Z 2025-08-14T21:38:40.1121662Z Args: 2025-08-14T21:38:40.1122012Z optim: The local optimizer. 2025-08-14T21:38:40.1122755Z averager: A model averager instance to run post-localSGD algorithm. 2025-08-14T21:38:40.1123367Z 2025-08-14T21:38:40.1123541Z Example:: 2025-08-14T21:38:40.1123752Z 2025-08-14T21:38:40.1123946Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:40.1124515Z >>> import torch 2025-08-14T21:38:40.1124992Z >>> import torch.distributed as dist 2025-08-14T21:38:40.1125856Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-08-14T21:38:40.1126734Z >>> import torch.nn as nn 2025-08-14T21:38:40.1127412Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-08-14T21:38:40.1128202Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-08-14T21:38:40.1128684Z >>> PostLocalSGDState, 2025-08-14T21:38:40.1128973Z >>> post_localSGD_hook, 2025-08-14T21:38:40.1129239Z >>> ) 2025-08-14T21:38:40.1129434Z >>> 2025-08-14T21:38:40.1129702Z >>> model = nn.parallel.DistributedDataParallel( 2025-08-14T21:38:40.1130103Z >>> module, device_ids=[rank], output_device=rank 2025-08-14T21:38:40.1130422Z >>> ) 2025-08-14T21:38:40.1130621Z >>> 2025-08-14T21:38:40.1130880Z >>> # Register a post-localSGD communication hook. 2025-08-14T21:38:40.1131535Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-08-14T21:38:40.1132095Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-08-14T21:38:40.1132449Z >>> 2025-08-14T21:38:40.1132769Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-08-14T21:38:40.1133321Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-08-14T21:38:40.1133849Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-08-14T21:38:40.1134335Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-08-14T21:38:40.1134761Z >>> opt = PostLocalSGDOptimizer( 2025-08-14T21:38:40.1135079Z >>> optim=local_optim, 2025-08-14T21:38:40.1135511Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-08-14T21:38:40.1136236Z >>> ) 2025-08-14T21:38:40.1136559Z >>> 2025-08-14T21:38:40.1137166Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-08-14T21:38:40.1138340Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-08-14T21:38:40.1139751Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-08-14T21:38:40.1140973Z >>> for step in range(0, 200): 2025-08-14T21:38:40.1141508Z >>> opt.zero_grad() 2025-08-14T21:38:40.1141981Z >>> loss = loss_fn(output, labels) 2025-08-14T21:38:40.1142529Z >>> loss.backward() 2025-08-14T21:38:40.1142998Z >>> opt.step() 2025-08-14T21:38:40.1143269Z 2025-08-14T21:38:40.1143822Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.1144500Z 2025-08-14T21:38:40.1229057Z 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. 2025-08-14T21:38:40.1231254Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.1231987Z 2025-08-14T21:38:40.1232750Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-08-14T21:38:40.1233758Z 2025-08-14T21:38:40.1234215Z The sharing is done as described by `ZeRO `_. 2025-08-14T21:38:40.1234903Z 2025-08-14T21:38:40.1235178Z The local optimizer instance in each rank is only 2025-08-14T21:38:40.1236092Z responsible for updating approximately ``1 / world_size`` parameters and 2025-08-14T21:38:40.1237142Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-08-14T21:38:40.1238191Z parameters are updated locally, each rank will broadcast its parameters to 2025-08-14T21:38:40.1239201Z all other peers to keep all model replicas in the same state. 2025-08-14T21:38:40.1240090Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-08-14T21:38:40.1241130Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-08-14T21:38:40.1242009Z memory consumption. 2025-08-14T21:38:40.1242275Z 2025-08-14T21:38:40.1242758Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-08-14T21:38:40.1243902Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-08-14T21:38:40.1245012Z not divided among ranks. The partition is arbitrary and might not match the 2025-08-14T21:38:40.1245922Z the parameter registration or usage order. 2025-08-14T21:38:40.1246352Z 2025-08-14T21:38:40.1246506Z Arguments: 2025-08-14T21:38:40.1247070Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-08-14T21:38:40.1247990Z or :class:`dict` s giving all parameters, which will be sharded 2025-08-14T21:38:40.1248712Z across ranks. 2025-08-14T21:38:40.1249000Z 2025-08-14T21:38:40.1249157Z Keyword Args: 2025-08-14T21:38:40.1249796Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-08-14T21:38:40.1250588Z optimizer. 2025-08-14T21:38:40.1251424Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-08-14T21:38:40.1252410Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-08-14T21:38:40.1253267Z :meth:`torch.distributed.init_process_group`). 2025-08-14T21:38:40.1254154Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-08-14T21:38:40.1255193Z packed into buckets to speed up communication, and ``param.data`` 2025-08-14T21:38:40.1256184Z fields point to bucket views at different offsets; if ``False``, 2025-08-14T21:38:40.1257159Z each individual parameter is communicated separately, and each 2025-08-14T21:38:40.1258008Z ``params.data`` stays intact (default: ``False``). 2025-08-14T21:38:40.1258862Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-08-14T21:38:40.1259806Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-08-14T21:38:40.1260793Z synchronization; this requires (1) either a functional optimizer 2025-08-14T21:38:40.1261753Z for the ``optimizer_class`` argument or one with a functional 2025-08-14T21:38:40.1262637Z equivalent and (2) registering a DDP communication hook 2025-08-14T21:38:40.1263653Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-08-14T21:38:40.1264707Z parameters are packed into buckets matching those in 2025-08-14T21:38:40.1265509Z :class:`DistributedDataParallel`, meaning that the 2025-08-14T21:38:40.1266290Z ``parameters_as_bucket_view`` argument is ignored. 2025-08-14T21:38:40.1267100Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-08-14T21:38:40.1267834Z (per normal). 2025-08-14T21:38:40.1268285Z (default: ``False``) 2025-08-14T21:38:40.1268976Z **defaults: any trailing arguments, which are forwarded to the local 2025-08-14T21:38:40.1269746Z optimizer. 2025-08-14T21:38:40.1270017Z 2025-08-14T21:38:40.1270186Z Example:: 2025-08-14T21:38:40.1270397Z 2025-08-14T21:38:40.1270581Z >>> # xdoctest: +SKIP 2025-08-14T21:38:40.1271048Z >>> import torch.nn as nn 2025-08-14T21:38:40.1271750Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-08-14T21:38:40.1272711Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-08-14T21:38:40.1273700Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-08-14T21:38:40.1274555Z >>> ddp = DDP(model, device_ids=[rank]) 2025-08-14T21:38:40.1275183Z >>> opt = ZeroRedundancyOptimizer( 2025-08-14T21:38:40.1275758Z >>> ddp.parameters(), 2025-08-14T21:38:40.1276309Z >>> optimizer_class=torch.optim.Adam, 2025-08-14T21:38:40.1276882Z >>> lr=0.01 2025-08-14T21:38:40.1277278Z >>> ) 2025-08-14T21:38:40.1277666Z >>> ddp(inputs).sum().backward() 2025-08-14T21:38:40.1278200Z >>> opt.step() 2025-08-14T21:38:40.1278456Z 2025-08-14T21:38:40.1278623Z .. warning:: 2025-08-14T21:38:40.1279176Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-08-14T21:38:40.1279991Z passed-in parameters are the same dense type. 2025-08-14T21:38:40.1280400Z 2025-08-14T21:38:40.1280507Z .. warning:: 2025-08-14T21:38:40.1280834Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-08-14T21:38:40.1281371Z the way that overlapping :class:`DistributedDataParallel` with 2025-08-14T21:38:40.1281916Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-08-14T21:38:40.1282486Z two or three training iterations do not perform parameter updates in 2025-08-14T21:38:40.1282991Z the optimizer step, depending on if ``static_graph=False`` or 2025-08-14T21:38:40.1283475Z ``static_graph=True``, respectively. This is because it needs 2025-08-14T21:38:40.1283948Z information about the gradient bucketing strategy used by 2025-08-14T21:38:40.1284444Z :class:`DistributedDataParallel`, which is not finalized until the 2025-08-14T21:38:40.1285074Z second forward pass if ``static_graph=False`` or until the third 2025-08-14T21:38:40.1285591Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-08-14T21:38:40.1286021Z is to prepend dummy inputs. 2025-08-14T21:38:40.1286212Z 2025-08-14T21:38:40.1286462Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-08-14T21:38:40.1286835Z 2025-08-14T21:38:40.1287079Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.1287437Z 2025-08-14T21:38:40.1488102Z 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. 2025-08-14T21:38:40.1489998Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.1490675Z 2025-08-14T21:38:40.1491086Z Custom reducer class that can be used to specify a custom operation that 2025-08-14T21:38:40.1492043Z reduces losses of multiple microbatches into one value. 2025-08-14T21:38:40.1492576Z 2025-08-14T21:38:40.1492728Z Example: 2025-08-14T21:38:40.1493288Z >>> # xdoctest: +SKIP 2025-08-14T21:38:40.1493751Z >>> sum_reducer = _CustomReducer( 2025-08-14T21:38:40.1494292Z >>> torch.tensor(0.0), 2025-08-14T21:38:40.1494961Z >>> lambda a, b: a + b 2025-08-14T21:38:40.1495359Z >>> ) 2025-08-14T21:38:40.1495575Z 2025-08-14T21:38:40.1496025Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.1496713Z 2025-08-14T21:38:40.2011322Z 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. 2025-08-14T21:38:40.2012493Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.2012886Z 2025-08-14T21:38:40.2013125Z A decorator for a function indicating that the return value of the function 2025-08-14T21:38:40.2013703Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-08-14T21:38:40.2014247Z function can run asynchronously on the RPC callee. More specifically, the 2025-08-14T21:38:40.2014833Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-08-14T21:38:40.2015420Z function and installs subsequent processing steps as a callback to that 2025-08-14T21:38:40.2015981Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-08-14T21:38:40.2016525Z from the :class:`~torch.futures.Future` when completed and send the 2025-08-14T21:38:40.2017016Z value back as the RPC response. That also means the returned 2025-08-14T21:38:40.2017524Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-08-14T21:38:40.2018070Z sent through RPC. This decorator is useful when the wrapped function's 2025-08-14T21:38:40.2018598Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-08-14T21:38:40.2019129Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-08-14T21:38:40.2019458Z 2025-08-14T21:38:40.2019689Z .. note:: To enable asynchronous execution, applications must pass the 2025-08-14T21:38:40.2020222Z function object returned by this decorator to RPC APIs. If RPC detected 2025-08-14T21:38:40.2020786Z attributes installed by this decorator, it knows that this function 2025-08-14T21:38:40.2021299Z returns a ``Future`` object and will handle that accordingly. 2025-08-14T21:38:40.2021791Z However, this does not mean this decorator has to be outmost one when 2025-08-14T21:38:40.2022334Z defining a function. For example, when combined with ``@staticmethod`` 2025-08-14T21:38:40.2022883Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-08-14T21:38:40.2023424Z inner decorator to allow the target function be recognized as a static 2025-08-14T21:38:40.2024043Z or class function. This target function can still execute asynchronously 2025-08-14T21:38:40.2024816Z because, when accessed, the static or class method preserves attributes 2025-08-14T21:38:40.2025314Z installed by ``@rpc.functions.async_execution``. 2025-08-14T21:38:40.2025569Z 2025-08-14T21:38:40.2025574Z 2025-08-14T21:38:40.2025666Z Example:: 2025-08-14T21:38:40.2025990Z The returned :class:`~torch.futures.Future` object can come from 2025-08-14T21:38:40.2026434Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-08-14T21:38:40.2026892Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-08-14T21:38:40.2027390Z constructor. The example below shows directly using the 2025-08-14T21:38:40.2027808Z :class:`~torch.futures.Future` returned by 2025-08-14T21:38:40.2028162Z :meth:`~torch.futures.Future.then`. 2025-08-14T21:38:40.2028379Z 2025-08-14T21:38:40.2028495Z >>> from torch.distributed import rpc 2025-08-14T21:38:40.2028801Z >>> 2025-08-14T21:38:40.2029027Z >>> # omitting setup and shutdown RPC 2025-08-14T21:38:40.2029317Z >>> 2025-08-14T21:38:40.2029526Z >>> # On all workers 2025-08-14T21:38:40.2029805Z >>> @rpc.functions.async_execution 2025-08-14T21:38:40.2030129Z >>> def async_add_chained(to, x, y, z): 2025-08-14T21:38:40.2030521Z >>> # This function runs on "worker1" and returns immediately when 2025-08-14T21:38:40.2031114Z >>> # the callback is installed through the `then(cb)` API. In the 2025-08-14T21:38:40.2031602Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-08-14T21:38:40.2032044Z >>> # When the return value of that `rpc_async` arrives at 2025-08-14T21:38:40.2032495Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-08-14T21:38:40.2032978Z >>> # and set the value for the previously returned `Future`, which 2025-08-14T21:38:40.2033460Z >>> # will then trigger RPC to send the result back to "worker0". 2025-08-14T21:38:40.2033908Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-14T21:38:40.2034296Z >>> lambda fut: fut.wait() + z 2025-08-14T21:38:40.2034596Z >>> ) 2025-08-14T21:38:40.2034792Z >>> 2025-08-14T21:38:40.2034992Z >>> # On worker0 2025-08-14T21:38:40.2035234Z >>> # xdoctest: +SKIP 2025-08-14T21:38:40.2035483Z >>> ret = rpc.rpc_sync( 2025-08-14T21:38:40.2035746Z >>> "worker1", 2025-08-14T21:38:40.2035992Z >>> async_add_chained, 2025-08-14T21:38:40.2036274Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-08-14T21:38:40.2036586Z >>> ) 2025-08-14T21:38:40.2036822Z >>> print(ret) # prints tensor([3., 3.]) 2025-08-14T21:38:40.2037038Z 2025-08-14T21:38:40.2037257Z When combined with TorchScript decorators, this decorator must be the 2025-08-14T21:38:40.2037684Z outmost one. 2025-08-14T21:38:40.2037829Z 2025-08-14T21:38:40.2037930Z >>> from torch import Tensor 2025-08-14T21:38:40.2038232Z >>> from torch.futures import Future 2025-08-14T21:38:40.2038551Z >>> from torch.distributed import rpc 2025-08-14T21:38:40.2038860Z >>> 2025-08-14T21:38:40.2039087Z >>> # omitting setup and shutdown RPC 2025-08-14T21:38:40.2039373Z >>> 2025-08-14T21:38:40.2039577Z >>> # On all workers 2025-08-14T21:38:40.2039836Z >>> @torch.jit.script 2025-08-14T21:38:40.2040135Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-08-14T21:38:40.2040486Z >>> return x + y 2025-08-14T21:38:40.2040730Z >>> 2025-08-14T21:38:40.2040948Z >>> @rpc.functions.async_execution 2025-08-14T21:38:40.2041262Z >>> @torch.jit.script 2025-08-14T21:38:40.2041614Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-08-14T21:38:40.2042049Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-08-14T21:38:40.2042366Z >>> 2025-08-14T21:38:40.2042568Z >>> # On worker0 2025-08-14T21:38:40.2042815Z >>> ret = rpc.rpc_sync( 2025-08-14T21:38:40.2043067Z >>> "worker1", 2025-08-14T21:38:40.2043312Z >>> async_add, 2025-08-14T21:38:40.2043578Z >>> args=("worker2", torch.ones(2), 1) 2025-08-14T21:38:40.2043941Z >>> ) 2025-08-14T21:38:40.2044182Z >>> print(ret) # prints tensor([2., 2.]) 2025-08-14T21:38:40.2044402Z 2025-08-14T21:38:40.2044630Z When combined with static or class method, this decorator must be the 2025-08-14T21:38:40.2045040Z inner one. 2025-08-14T21:38:40.2045183Z 2025-08-14T21:38:40.2045299Z >>> from torch.distributed import rpc 2025-08-14T21:38:40.2045608Z >>> 2025-08-14T21:38:40.2045836Z >>> # omitting setup and shutdown RPC 2025-08-14T21:38:40.2046123Z >>> 2025-08-14T21:38:40.2046330Z >>> # On all workers 2025-08-14T21:38:40.2056927Z >>> class AsyncExecutionClass: 2025-08-14T21:38:40.2057230Z >>> 2025-08-14T21:38:40.2057450Z >>> @staticmethod 2025-08-14T21:38:40.2057737Z >>> @rpc.functions.async_execution 2025-08-14T21:38:40.2058067Z >>> def static_async_add(to, x, y, z): 2025-08-14T21:38:40.2058465Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-14T21:38:40.2058875Z >>> lambda fut: fut.wait() + z 2025-08-14T21:38:40.2059187Z >>> ) 2025-08-14T21:38:40.2059487Z >>> 2025-08-14T21:38:40.2059789Z >>> @classmethod 2025-08-14T21:38:40.2060085Z >>> @rpc.functions.async_execution 2025-08-14T21:38:40.2060586Z >>> def class_async_add(cls, to, x, y, z): 2025-08-14T21:38:40.2060941Z >>> ret_fut = torch.futures.Future() 2025-08-14T21:38:40.2061321Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-14T21:38:40.2061721Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-08-14T21:38:40.2062068Z >>> ) 2025-08-14T21:38:40.2062302Z >>> return ret_fut 2025-08-14T21:38:40.2062553Z >>> 2025-08-14T21:38:40.2062785Z >>> @rpc.functions.async_execution 2025-08-14T21:38:40.2063127Z >>> def bound_async_add(self, to, x, y, z): 2025-08-14T21:38:40.2063608Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-14T21:38:40.2063998Z >>> lambda fut: fut.wait() + z 2025-08-14T21:38:40.2064305Z >>> ) 2025-08-14T21:38:40.2064528Z >>> 2025-08-14T21:38:40.2064720Z >>> # On worker0 2025-08-14T21:38:40.2064969Z >>> ret = rpc.rpc_sync( 2025-08-14T21:38:40.2065238Z >>> "worker1", 2025-08-14T21:38:40.2065508Z >>> AsyncExecutionClass.static_async_add, 2025-08-14T21:38:40.2065635Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-08-14T21:38:40.2065717Z >>> ) 2025-08-14T21:38:40.2065832Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-14T21:38:40.2065927Z >>> 2025-08-14T21:38:40.2066021Z >>> ret = rpc.rpc_sync( 2025-08-14T21:38:40.2066106Z >>> "worker1", 2025-08-14T21:38:40.2066242Z >>> AsyncExecutionClass.class_async_add, 2025-08-14T21:38:40.2066354Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-08-14T21:38:40.2066451Z >>> ) 2025-08-14T21:38:40.2066561Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-14T21:38:40.2066568Z 2025-08-14T21:38:40.2066729Z This decorator also works with RRef helpers, i.e., . 2025-08-14T21:38:40.2066877Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-08-14T21:38:40.2067026Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-08-14T21:38:40.2067159Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-08-14T21:38:40.2067168Z 2025-08-14T21:38:40.2067292Z >>> from torch.distributed import rpc 2025-08-14T21:38:40.2067371Z >>> 2025-08-14T21:38:40.2067511Z >>> # reuse the AsyncExecutionClass class above 2025-08-14T21:38:40.2067657Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-08-14T21:38:40.2067857Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-08-14T21:38:40.2067981Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-14T21:38:40.2068060Z >>> 2025-08-14T21:38:40.2068204Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-08-14T21:38:40.2068523Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-08-14T21:38:40.2068637Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-14T21:38:40.2068716Z >>> 2025-08-14T21:38:40.2068874Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-08-14T21:38:40.2069098Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-08-14T21:38:40.2069225Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-14T21:38:40.2069230Z 2025-08-14T21:38:40.2069474Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.2069479Z 2025-08-14T21:38:40.2070215Z 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. 2025-08-14T21:38:40.2070470Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.2070474Z 2025-08-14T21:38:40.2070669Z Set device mapping between each RPC caller and callee pair. This 2025-08-14T21:38:40.2070862Z function can be called multiple times to incrementally add 2025-08-14T21:38:40.2070974Z device placement configurations. 2025-08-14T21:38:40.2070979Z 2025-08-14T21:38:40.2071070Z Args: 2025-08-14T21:38:40.2071167Z to (str): Callee name. 2025-08-14T21:38:40.2071416Z device_map (Dict of int, str, or torch.device): Device placement 2025-08-14T21:38:40.2071610Z mappings from this worker to the callee. This map must be 2025-08-14T21:38:40.2071698Z invertible. 2025-08-14T21:38:40.2071703Z 2025-08-14T21:38:40.2071786Z Example: 2025-08-14T21:38:40.2071913Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:40.2072002Z >>> # both workers 2025-08-14T21:38:40.2072090Z >>> def add(x, y): 2025-08-14T21:38:40.2072234Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-08-14T21:38:40.2072335Z >>> return x + y, (x + y).to(2) 2025-08-14T21:38:40.2072427Z >>> 2025-08-14T21:38:40.2072514Z >>> # on worker 0 2025-08-14T21:38:40.2072649Z >>> options = TensorPipeRpcBackendOptions( 2025-08-14T21:38:40.2072761Z >>> num_worker_threads=8, 2025-08-14T21:38:40.2072869Z >>> device_maps={"worker1": {0: 1}} 2025-08-14T21:38:40.2072993Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-08-14T21:38:40.2073096Z >>> ) 2025-08-14T21:38:40.2073218Z >>> options.set_device_map("worker1", {1: 2}) 2025-08-14T21:38:40.2073340Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-08-14T21:38:40.2073434Z >>> 2025-08-14T21:38:40.2073523Z >>> rpc.init_rpc( 2025-08-14T21:38:40.2073609Z >>> "worker0", 2025-08-14T21:38:40.2073703Z >>> rank=0, 2025-08-14T21:38:40.2073792Z >>> world_size=2, 2025-08-14T21:38:40.2073930Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-08-14T21:38:40.2074034Z >>> rpc_backend_options=options 2025-08-14T21:38:40.2074114Z >>> ) 2025-08-14T21:38:40.2074206Z >>> 2025-08-14T21:38:40.2074297Z >>> x = torch.ones(2) 2025-08-14T21:38:40.2074456Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-08-14T21:38:40.2074652Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-08-14T21:38:40.2074832Z >>> # sending the return value back, it will follow the invert of 2025-08-14T21:38:40.2075004Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-08-14T21:38:40.2075110Z >>> # cuda:1 on worker0 2025-08-14T21:38:40.2075251Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-08-14T21:38:40.2075401Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-08-14T21:38:40.2075405Z 2025-08-14T21:38:40.2075652Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.2075657Z 2025-08-14T21:38:40.2100091Z 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. 2025-08-14T21:38:40.2100551Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.2100558Z 2025-08-14T21:38:40.2100759Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-08-14T21:38:40.2101048Z except that it enables profiling on all threads running RPC server request callbacks. 2025-08-14T21:38:40.2101062Z 2025-08-14T21:38:40.2101334Z Context manager that manages autograd profiler state and holds a summary of results. 2025-08-14T21:38:40.2101572Z Under the hood it just records events of functions being executed in C++ and 2025-08-14T21:38:40.2101798Z exposes those events to Python. You can wrap any code into it and it will 2025-08-14T21:38:40.2101926Z only report runtime of PyTorch functions. 2025-08-14T21:38:40.2102200Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-08-14T21:38:40.2102205Z 2025-08-14T21:38:40.2102287Z Args: 2025-08-14T21:38:40.2102565Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-08-14T21:38:40.2102664Z Default: ``True``. 2025-08-14T21:38:40.2102669Z 2025-08-14T21:38:40.2102943Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-08-14T21:38:40.2103153Z Adds approximately 4us of overhead to each tensor operation. 2025-08-14T21:38:40.2103325Z Default: ``False`` 2025-08-14T21:38:40.2103330Z 2025-08-14T21:38:40.2103613Z record_shapes (bool, optional): If shapes recording is set, information 2025-08-14T21:38:40.2103850Z about input dimensions will be collected. This allows one to see which 2025-08-14T21:38:40.2104056Z dimensions have been used under the hood and further group by them 2025-08-14T21:38:40.2104282Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-08-14T21:38:40.2104500Z shape recording might skew your profiling data. It is recommended to 2025-08-14T21:38:40.2104729Z use separate runs with and without shape recording to validate the timing. 2025-08-14T21:38:40.2104969Z Most likely the skew will be negligible for bottom most events (in a case 2025-08-14T21:38:40.2105175Z of nested function calls). But for higher level functions the total 2025-08-14T21:38:40.2105375Z self cpu time might be artificially increased because of the shape 2025-08-14T21:38:40.2105483Z collection. 2025-08-14T21:38:40.2105488Z 2025-08-14T21:38:40.2105746Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-08-14T21:38:40.2105751Z 2025-08-14T21:38:40.2105859Z .. warning:: 2025-08-14T21:38:40.2106058Z Enabling memory profiling incurs additional profiler overhead 2025-08-14T21:38:40.2106063Z 2025-08-14T21:38:40.2106146Z .. warning:: 2025-08-14T21:38:40.2106447Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-08-14T21:38:40.2106726Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-08-14T21:38:40.2106971Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-08-14T21:38:40.2107148Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-08-14T21:38:40.2107153Z 2025-08-14T21:38:40.2107248Z Example: 2025-08-14T21:38:40.2107343Z >>> # xdoctest: +SKIP 2025-08-14T21:38:40.2107434Z >>> # On worker 0: 2025-08-14T21:38:40.2107535Z >>> import torch 2025-08-14T21:38:40.2107655Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:40.2107789Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-14T21:38:40.2107922Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-08-14T21:38:40.2108031Z >>> outer_profile_rref = rpc.remote( 2025-08-14T21:38:40.2108188Z ... dst_worker_name, rpc._server_process_global_profile 2025-08-14T21:38:40.2108284Z ... ) 2025-08-14T21:38:40.2108405Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-08-14T21:38:40.2108548Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-08-14T21:38:40.2108734Z >>> inner_profile_rref = rpc.remote( 2025-08-14T21:38:40.2108889Z ... dst_worker_name, rpc._server_process_global_profile 2025-08-14T21:38:40.2108981Z ... ) 2025-08-14T21:38:40.2109102Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-08-14T21:38:40.2109243Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-08-14T21:38:40.2109434Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-08-14T21:38:40.2109598Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-08-14T21:38:40.2109749Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-08-14T21:38:40.2109994Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-14T21:38:40.2110299Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-08-14T21:38:40.2110532Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-14T21:38:40.2110719Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-08-14T21:38:40.2110903Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-08-14T21:38:40.2111186Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-14T21:38:40.2111289Z Self CPU time total: 89.667us 2025-08-14T21:38:40.2111460Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-08-14T21:38:40.2111677Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-14T21:38:40.2111974Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-08-14T21:38:40.2112204Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-14T21:38:40.2112380Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-08-14T21:38:40.2112562Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-08-14T21:38:40.2112748Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-08-14T21:38:40.2112968Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-14T21:38:40.2113084Z Self CPU time total: 213.926us 2025-08-14T21:38:40.2113175Z >>> rpc.shutdown() 2025-08-14T21:38:40.2113180Z 2025-08-14T21:38:40.2113269Z >>> # On worker 1: 2025-08-14T21:38:40.2113406Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:40.2113541Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-14T21:38:40.2113696Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-08-14T21:38:40.2113800Z >>> rpc.shutdown() 2025-08-14T21:38:40.2113808Z 2025-08-14T21:38:40.2114056Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.2114061Z 2025-08-14T21:38:40.3379922Z 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. 2025-08-14T21:38:40.3380222Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.3380230Z 2025-08-14T21:38:40.3380507Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-08-14T21:38:40.3380780Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-08-14T21:38:40.3381040Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-08-14T21:38:40.3381209Z :class:`DTensor` according to the ``out_placements``. 2025-08-14T21:38:40.3381214Z 2025-08-14T21:38:40.3381296Z Args: 2025-08-14T21:38:40.3381723Z func (Callable): the function to be applied on each local shard of 2025-08-14T21:38:40.3381823Z :class:`DTensor` s. 2025-08-14T21:38:40.3382047Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-08-14T21:38:40.3382308Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-08-14T21:38:40.3382560Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-08-14T21:38:40.3382794Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-08-14T21:38:40.3383032Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-08-14T21:38:40.3383160Z mapping to the flattened ``output``. 2025-08-14T21:38:40.3383357Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-08-14T21:38:40.3383704Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-08-14T21:38:40.3383819Z should be `None`. 2025-08-14T21:38:40.3384046Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-08-14T21:38:40.3384273Z in. In this case, even if `out_placements` is not `None`, the result function 2025-08-14T21:38:40.3384519Z should ignore the desired placements because the function is not running with 2025-08-14T21:38:40.3384877Z :class:`DTensor` s. 2025-08-14T21:38:40.3385125Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-08-14T21:38:40.3385595Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-08-14T21:38:40.3386008Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-08-14T21:38:40.3386409Z placements of each :class:`DTensor` argument is the same as the required 2025-08-14T21:38:40.3386719Z placements or not. If the placements are not the same and 2025-08-14T21:38:40.3387174Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-08-14T21:38:40.3387606Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-08-14T21:38:40.3388085Z the required sharding placements before passing its local tensor to ``func``. 2025-08-14T21:38:40.3388495Z The only exception is when required placements are not ``None`` and the 2025-08-14T21:38:40.3388924Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-08-14T21:38:40.3389320Z will be skipped and the argument will be directly passed to ``func``. 2025-08-14T21:38:40.3389718Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-08-14T21:38:40.3389891Z Default: None 2025-08-14T21:38:40.3390215Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-08-14T21:38:40.3390592Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-08-14T21:38:40.3390996Z to the flattened input DTensor. This argument is the hint that user 2025-08-14T21:38:40.3391328Z can give to :meth:`to_local` in case the gradient layout of the 2025-08-14T21:38:40.3391717Z local tensor input does not match its :class:`DTensor` input layout. 2025-08-14T21:38:40.3392086Z If not specified, we will assume the gradient layout of the local 2025-08-14T21:38:40.3392462Z tensor input remains the same as the original :class:`DTensor` input 2025-08-14T21:38:40.3392736Z and use that for gradient computation. Default: None. 2025-08-14T21:38:40.3393138Z device_mesh (:class:`DeviceMesh`, optional): 2025-08-14T21:38:40.3393533Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-08-14T21:38:40.3393980Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-08-14T21:38:40.3394162Z mesh. Default: None. 2025-08-14T21:38:40.3394172Z 2025-08-14T21:38:40.3394324Z Keyword Args: 2025-08-14T21:38:40.3394742Z redistribute_inputs (bool, optional): 2025-08-14T21:38:40.3395218Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-08-14T21:38:40.3395672Z their placements are different from the required input placements. If this 2025-08-14T21:38:40.3396097Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-08-14T21:38:40.3396331Z an exception will be raised. Default: False. 2025-08-14T21:38:40.3396340Z 2025-08-14T21:38:40.3396502Z Returns: 2025-08-14T21:38:40.3396981Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-08-14T21:38:40.3397405Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-08-14T21:38:40.3397412Z 2025-08-14T21:38:40.3397548Z Raises: 2025-08-14T21:38:40.3397974Z AssertionError: For any non-DTensor output, we require its corresponding 2025-08-14T21:38:40.3398449Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-08-14T21:38:40.3398627Z if this is not the case. 2025-08-14T21:38:40.3398636Z 2025-08-14T21:38:40.3399065Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-08-14T21:38:40.3399350Z a redistribution according to ``in_placements``. 2025-08-14T21:38:40.3399521Z 2025-08-14T21:38:40.3399676Z Example: 2025-08-14T21:38:40.3399877Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:40.3400131Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-08-14T21:38:40.3400345Z >>> partial_sum_tensor = torch.mm(W, X) 2025-08-14T21:38:40.3400794Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-08-14T21:38:40.3400973Z >>> return reduced_tensor 2025-08-14T21:38:40.3401116Z >>> 2025-08-14T21:38:40.3401330Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-08-14T21:38:40.3401525Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-08-14T21:38:40.3401693Z >>> Y = torch.mm(W, X) 2025-08-14T21:38:40.3402051Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-08-14T21:38:40.3402373Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-08-14T21:38:40.3402519Z >>> 2025-08-14T21:38:40.3403036Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor conversion 2025-08-14T21:38:40.3403257Z >>> local_mm_allreduce_forward = local_map( 2025-08-14T21:38:40.3403447Z >>> mm_allreduce_forward, 2025-08-14T21:38:40.3403649Z >>> out_placements=[Replicate()], 2025-08-14T21:38:40.3403853Z >>> in_placements=[col_wise, row_wise], 2025-08-14T21:38:40.3404042Z >>> device_mesh=device_mesh, 2025-08-14T21:38:40.3404186Z >>> ) 2025-08-14T21:38:40.3404326Z >>> 2025-08-14T21:38:40.3404519Z >>> W_dt = distribute_tensor( 2025-08-14T21:38:40.3404699Z ... W, device_mesh, (col_wise) 2025-08-14T21:38:40.3404886Z ... ) # col-wisely sharded W tensor 2025-08-14T21:38:40.3405080Z >>> X_dt = distribute_tensor( 2025-08-14T21:38:40.3405243Z ... X, device_mesh, (row_wise) 2025-08-14T21:38:40.3405428Z ... ) # row-wisely sharded X tensor 2025-08-14T21:38:40.3405590Z >>> Y_dt = local_mm_allreduce_forward( 2025-08-14T21:38:40.3405751Z ... device_mesh, W_dt, X_dt 2025-08-14T21:38:40.3406001Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-08-14T21:38:40.3406010Z 2025-08-14T21:38:40.3406339Z .. note:: This API is currently experimental and subject to change 2025-08-14T21:38:40.3406349Z 2025-08-14T21:38:40.3406788Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.3406815Z 2025-08-14T21:38:40.3408040Z 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. 2025-08-14T21:38:40.3408305Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.3408422Z 2025-08-14T21:38:40.3408709Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-08-14T21:38:40.3408942Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-08-14T21:38:40.3409207Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-08-14T21:38:40.3409444Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-08-14T21:38:40.3409710Z when users would like to overwrite default sharding strategies of existing operators. 2025-08-14T21:38:40.3409714Z 2025-08-14T21:38:40.3409808Z Args: 2025-08-14T21:38:40.3409934Z op (Union[OpOverload, List[OpOverload]]): 2025-08-14T21:38:40.3410121Z An op or a list of ops to register the customized sharding function. 2025-08-14T21:38:40.3410144Z 2025-08-14T21:38:40.3410227Z Returns: 2025-08-14T21:38:40.3410488Z A function decorator which can be used to wrap a function that defines the sharding 2025-08-14T21:38:40.3410763Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-08-14T21:38:40.3411027Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-08-14T21:38:40.3411318Z already implemented the operator. The customized sharding function takes the same inputs 2025-08-14T21:38:40.3411628Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-08-14T21:38:40.3411890Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-08-14T21:38:40.3412166Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-08-14T21:38:40.3412280Z corresponding input placements. 2025-08-14T21:38:40.3412284Z 2025-08-14T21:38:40.3412366Z Example: 2025-08-14T21:38:40.3412490Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:40.3412633Z >>> @register_sharding(aten._softmax.default) 2025-08-14T21:38:40.3412800Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-08-14T21:38:40.3412937Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-08-14T21:38:40.3413043Z >>> acceptable_shardings = [] 2025-08-14T21:38:40.3413138Z >>> 2025-08-14T21:38:40.3413315Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-08-14T21:38:40.3413587Z >>> acceptable_shardings.append(all_replicate) 2025-08-14T21:38:40.3413732Z >>> 2025-08-14T21:38:40.3413913Z >>> for sharding_dim in range(x.ndim): 2025-08-14T21:38:40.3414107Z >>> if sharding_dim != softmax_dim: 2025-08-14T21:38:40.3414285Z >>> all_sharded = ( 2025-08-14T21:38:40.3414469Z >>> [Shard(sharding_dim)], 2025-08-14T21:38:40.3414684Z >>> [Shard(sharding_dim), None, None], 2025-08-14T21:38:40.3414815Z >>> ) 2025-08-14T21:38:40.3415042Z >>> acceptable_shardings.append(all_sharded) 2025-08-14T21:38:40.3415199Z >>> 2025-08-14T21:38:40.3415379Z >>> return acceptable_shardings 2025-08-14T21:38:40.3415388Z 2025-08-14T21:38:40.3415725Z .. note:: This API is currently experimental and subject to change 2025-08-14T21:38:40.3415734Z 2025-08-14T21:38:40.3416165Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.3416180Z 2025-08-14T21:38:40.3659529Z 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. 2025-08-14T21:38:40.3660032Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.3660043Z 2025-08-14T21:38:40.3660762Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-08-14T21:38:40.3661332Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-08-14T21:38:40.3661343Z 2025-08-14T21:38:40.3661704Z Keyword Args: 2025-08-14T21:38:40.3662089Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-14T21:38:40.3662695Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-08-14T21:38:40.3663349Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-08-14T21:38:40.3663639Z as a placeholder. default: None. 2025-08-14T21:38:40.3664055Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-14T21:38:40.3664770Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-14T21:38:40.3665513Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-08-14T21:38:40.3665744Z input_kwarg_layouts (Dict[str, Placement]): 2025-08-14T21:38:40.3666409Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-08-14T21:38:40.3666554Z default: None 2025-08-14T21:38:40.3666824Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-08-14T21:38:40.3667497Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-14T21:38:40.3667931Z have the desired DTensor layouts. default: None. 2025-08-14T21:38:40.3668152Z use_local_output (bool, optional): 2025-08-14T21:38:40.3668820Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-08-14T21:38:40.3668972Z Returns: 2025-08-14T21:38:40.3669561Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-08-14T21:38:40.3669572Z 2025-08-14T21:38:40.3669729Z Example:: 2025-08-14T21:38:40.3669931Z >>> # xdoctest: +SKIP(failing) 2025-08-14T21:38:40.3670513Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-08-14T21:38:40.3670867Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-08-14T21:38:40.3671026Z >>> ... 2025-08-14T21:38:40.3671593Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-08-14T21:38:40.3671825Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-08-14T21:38:40.3671989Z >>> 2025-08-14T21:38:40.3672576Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-08-14T21:38:40.3672844Z >>> # and then redistributed to Replicated DTensor. 2025-08-14T21:38:40.3673024Z >>> parallelize_module( 2025-08-14T21:38:40.3673240Z >>> block, # this can be a submodule or module 2025-08-14T21:38:40.3673404Z >>> tp_mesh, 2025-08-14T21:38:40.3673584Z >>> parallelize_plan={ 2025-08-14T21:38:40.3673769Z >>> "attn": PrepareModuleInput( 2025-08-14T21:38:40.3674015Z >>> input_layouts=(Shard(0), None, None, ...), 2025-08-14T21:38:40.3674311Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-08-14T21:38:40.3674458Z >>> ), 2025-08-14T21:38:40.3674616Z >>> } 2025-08-14T21:38:40.3674760Z >>> ) 2025-08-14T21:38:40.3674770Z 2025-08-14T21:38:40.3675245Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.3675254Z 2025-08-14T21:38:40.3676536Z 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. 2025-08-14T21:38:40.3677033Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.3677042Z 2025-08-14T21:38:40.3677776Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-08-14T21:38:40.3678543Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-08-14T21:38:40.3678554Z 2025-08-14T21:38:40.3678726Z Keyword Args: 2025-08-14T21:38:40.3679013Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-14T21:38:40.3679622Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-08-14T21:38:40.3680268Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-08-14T21:38:40.3680498Z ``None`` need to be specified as a placeholder. 2025-08-14T21:38:40.3680826Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-14T21:38:40.3681530Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-08-14T21:38:40.3681719Z have the desired DTensor layouts. 2025-08-14T21:38:40.3681905Z use_local_output (bool, optional): 2025-08-14T21:38:40.3682279Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-08-14T21:38:40.3682374Z Returns: 2025-08-14T21:38:40.3682663Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-08-14T21:38:40.3682761Z 2025-08-14T21:38:40.3682853Z Example:: 2025-08-14T21:38:40.3682967Z >>> # xdoctest: +SKIP(failing) 2025-08-14T21:38:40.3683273Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-08-14T21:38:40.3683461Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-08-14T21:38:40.3683554Z >>> ... 2025-08-14T21:38:40.3683853Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-08-14T21:38:40.3683988Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-08-14T21:38:40.3684069Z >>> 2025-08-14T21:38:40.3684467Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-08-14T21:38:40.3684611Z >>> # and then redistributed to Sharded DTensor. 2025-08-14T21:38:40.3684707Z >>> parallelize_module( 2025-08-14T21:38:40.3684836Z >>> block, # this can be a submodule or module 2025-08-14T21:38:40.3684941Z >>> tp_mesh, 2025-08-14T21:38:40.3685071Z >>> parallelize_plan = PrepareModuleOutput( 2025-08-14T21:38:40.3685194Z >>> output_layouts=Replicate(), 2025-08-14T21:38:40.3685309Z >>> desired_output_layouts=Shard(0) 2025-08-14T21:38:40.3685389Z >>> ) 2025-08-14T21:38:40.3685481Z >>> ) 2025-08-14T21:38:40.3685486Z 2025-08-14T21:38:40.3685737Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.3685742Z 2025-08-14T21:38:40.3686455Z 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. 2025-08-14T21:38:40.3686713Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.3686717Z 2025-08-14T21:38:40.3687136Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-08-14T21:38:40.3687558Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-08-14T21:38:40.3688275Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-08-14T21:38:40.3688632Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-08-14T21:38:40.3688642Z 2025-08-14T21:38:40.3688784Z Keyword Args: 2025-08-14T21:38:40.3689129Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-14T21:38:40.3689737Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-08-14T21:38:40.3690503Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-08-14T21:38:40.3690716Z as a placeholder. default: None. 2025-08-14T21:38:40.3691127Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-14T21:38:40.3691837Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-14T21:38:40.3692576Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-08-14T21:38:40.3692809Z input_kwarg_layouts (Dict[str, Placement]): 2025-08-14T21:38:40.3693680Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-08-14T21:38:40.3693856Z default: None 2025-08-14T21:38:40.3694140Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-08-14T21:38:40.3694849Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-14T21:38:40.3695109Z have the desired DTensor layouts. default: None. 2025-08-14T21:38:40.3695298Z use_local_input (bool, optional): 2025-08-14T21:38:40.3695969Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-08-14T21:38:40.3696430Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-14T21:38:40.3697064Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-08-14T21:38:40.3697789Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-08-14T21:38:40.3698039Z ``None`` need to be specified as a placeholder. 2025-08-14T21:38:40.3698407Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-14T21:38:40.3699122Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-08-14T21:38:40.3699322Z have the desired DTensor layouts. 2025-08-14T21:38:40.3699538Z use_local_output (bool, optional): 2025-08-14T21:38:40.3700138Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-08-14T21:38:40.3700304Z Returns: 2025-08-14T21:38:40.3700983Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-08-14T21:38:40.3700993Z 2025-08-14T21:38:40.3701148Z Example:: 2025-08-14T21:38:40.3701335Z >>> # xdoctest: +SKIP(failing) 2025-08-14T21:38:40.3701932Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-08-14T21:38:40.3702273Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-08-14T21:38:40.3702436Z >>> ... 2025-08-14T21:38:40.3702984Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-08-14T21:38:40.3703226Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-08-14T21:38:40.3703371Z >>> 2025-08-14T21:38:40.3704086Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-08-14T21:38:40.3704772Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-08-14T21:38:40.3705147Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-08-14T21:38:40.3705330Z >>> parallelize_module( 2025-08-14T21:38:40.3705579Z >>> block, # this can be a submodule or module 2025-08-14T21:38:40.3705727Z >>> tp_mesh, 2025-08-14T21:38:40.3705901Z >>> parallelize_plan={ 2025-08-14T21:38:40.3706151Z >>> "attn": PrepareModuleInputOutput( 2025-08-14T21:38:40.3706387Z >>> input_layouts=(Shard(0), None, None, ...), 2025-08-14T21:38:40.3706867Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-08-14T21:38:40.3707073Z >>> output_layouts=Replicate(), 2025-08-14T21:38:40.3707281Z >>> desired_output_layouts=Shard(0), 2025-08-14T21:38:40.3707436Z >>> ), 2025-08-14T21:38:40.3707571Z >>> } 2025-08-14T21:38:40.3707714Z >>> ) 2025-08-14T21:38:40.3707724Z 2025-08-14T21:38:40.3708190Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.3708198Z 2025-08-14T21:38:40.4404224Z 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. 2025-08-14T21:38:40.4404528Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.4404535Z 2025-08-14T21:38:40.4404833Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-08-14T21:38:40.4405035Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-08-14T21:38:40.4405052Z 2025-08-14T21:38:40.4405221Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-08-14T21:38:40.4405226Z 2025-08-14T21:38:40.4405312Z Example: 2025-08-14T21:38:40.4405460Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-08-14T21:38:40.4405752Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-14T21:38:40.4405882Z >>> m = LowRankMultivariateNormal( 2025-08-14T21:38:40.4406058Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-08-14T21:38:40.4406137Z ... ) 2025-08-14T21:38:40.4406432Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-08-14T21:38:40.4406532Z tensor([-0.2102, -0.5429]) 2025-08-14T21:38:40.4406537Z 2025-08-14T21:38:40.4406615Z Args: 2025-08-14T21:38:40.4406859Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-08-14T21:38:40.4407111Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-08-14T21:38:40.4407237Z `batch_shape + event_shape + (rank,)` 2025-08-14T21:38:40.4407485Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-08-14T21:38:40.4407588Z `batch_shape + event_shape` 2025-08-14T21:38:40.4407597Z 2025-08-14T21:38:40.4407688Z Note: 2025-08-14T21:38:40.4407947Z The computation for determinant and inverse of covariance matrix is avoided when 2025-08-14T21:38:40.4408181Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-08-14T21:38:40.4408413Z `_ and 2025-08-14T21:38:40.4408697Z `matrix determinant lemma `_. 2025-08-14T21:38:40.4408956Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-08-14T21:38:40.4409075Z the small size "capacitance" matrix:: 2025-08-14T21:38:40.4409080Z 2025-08-14T21:38:40.4409399Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-08-14T21:38:40.4409405Z 2025-08-14T21:38:40.4409842Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.4409850Z 2025-08-14T21:38:40.4430001Z 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. 2025-08-14T21:38:40.4430386Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.4430392Z 2025-08-14T21:38:40.4430613Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-08-14T21:38:40.4430865Z distribution where all component are from different parameterizations of 2025-08-14T21:38:40.4431138Z the same distribution type. It is parameterized by a `Categorical` 2025-08-14T21:38:40.4431329Z "selecting distribution" (over `k` component) and a component 2025-08-14T21:38:40.4431688Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-08-14T21:38:40.4431907Z (equal to `[k]`) which indexes each (batch of) component. 2025-08-14T21:38:40.4431913Z 2025-08-14T21:38:40.4432021Z Examples:: 2025-08-14T21:38:40.4432025Z 2025-08-14T21:38:40.4432140Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:40.4432344Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-08-14T21:38:40.4432465Z >>> # weighted normal distributions 2025-08-14T21:38:40.4432634Z >>> mix = D.Categorical(torch.ones(5,)) 2025-08-14T21:38:40.4432778Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-08-14T21:38:40.4432907Z >>> gmm = MixtureSameFamily(mix, comp) 2025-08-14T21:38:40.4432912Z 2025-08-14T21:38:40.4433107Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-08-14T21:38:40.4433250Z >>> # weighted bivariate normal distributions 2025-08-14T21:38:40.4433416Z >>> mix = D.Categorical(torch.ones(5,)) 2025-08-14T21:38:40.4433528Z >>> comp = D.Independent(D.Normal( 2025-08-14T21:38:40.4433662Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-08-14T21:38:40.4433776Z >>> gmm = MixtureSameFamily(mix, comp) 2025-08-14T21:38:40.4433780Z 2025-08-14T21:38:40.4433958Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-08-14T21:38:40.4434289Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-08-14T21:38:40.4434404Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-08-14T21:38:40.4434528Z >>> comp = D.Independent(D.Normal( 2025-08-14T21:38:40.4434655Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-08-14T21:38:40.4434902Z >>> gmm = MixtureSameFamily(mix, comp) 2025-08-14T21:38:40.4434910Z 2025-08-14T21:38:40.4435044Z Args: 2025-08-14T21:38:40.4435374Z mixture_distribution: `torch.distributions.Categorical`-like 2025-08-14T21:38:40.4435683Z instance. Manages the probability of selecting component. 2025-08-14T21:38:40.4435988Z The number of categories must match the rightmost batch 2025-08-14T21:38:40.4436320Z dimension of the `component_distribution`. Must have either 2025-08-14T21:38:40.4436586Z scalar `batch_shape` or `batch_shape` matching 2025-08-14T21:38:40.4436815Z `component_distribution.batch_shape[:-1]` 2025-08-14T21:38:40.4437217Z component_distribution: `torch.distributions.Distribution`-like 2025-08-14T21:38:40.4437548Z instance. Right-most batch dimension indexes component. 2025-08-14T21:38:40.4437557Z 2025-08-14T21:38:40.4438006Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.4438015Z 2025-08-14T21:38:40.4591565Z 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. 2025-08-14T21:38:40.4591849Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.4591855Z 2025-08-14T21:38:40.4592050Z Creates a RelaxedBernoulli distribution, parametrized by 2025-08-14T21:38:40.4592258Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-08-14T21:38:40.4592471Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-08-14T21:38:40.4592665Z so the values are in (0, 1), and has reparametrizable samples. 2025-08-14T21:38:40.4592676Z 2025-08-14T21:38:40.4592773Z Example:: 2025-08-14T21:38:40.4592778Z 2025-08-14T21:38:40.4593090Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-14T21:38:40.4593237Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-08-14T21:38:40.4593361Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-08-14T21:38:40.4593450Z >>> m.sample() 2025-08-14T21:38:40.4593574Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-08-14T21:38:40.4593578Z 2025-08-14T21:38:40.4593660Z Args: 2025-08-14T21:38:40.4593808Z temperature (Tensor): relaxation temperature 2025-08-14T21:38:40.4593973Z probs (Number, Tensor): the probability of sampling `1` 2025-08-14T21:38:40.4594292Z logits (Number, Tensor): the log-odds of sampling `1` 2025-08-14T21:38:40.4594297Z 2025-08-14T21:38:40.4594557Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.4594562Z 2025-08-14T21:38:40.4611775Z 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. 2025-08-14T21:38:40.4612069Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.4612076Z 2025-08-14T21:38:40.4612292Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-08-14T21:38:40.4612481Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-08-14T21:38:40.4612729Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-08-14T21:38:40.4612889Z its samples are on simplex, and are reparametrizable. 2025-08-14T21:38:40.4612894Z 2025-08-14T21:38:40.4613011Z Example:: 2025-08-14T21:38:40.4613015Z 2025-08-14T21:38:40.4613149Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-14T21:38:40.4613302Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-08-14T21:38:40.4613437Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-08-14T21:38:40.4613665Z >>> m.sample() 2025-08-14T21:38:40.4613790Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-08-14T21:38:40.4613795Z 2025-08-14T21:38:40.4613873Z Args: 2025-08-14T21:38:40.4614006Z temperature (Tensor): relaxation temperature 2025-08-14T21:38:40.4614132Z probs (Tensor): event probabilities 2025-08-14T21:38:40.4614312Z logits (Tensor): unnormalized log probability for each event 2025-08-14T21:38:40.4614316Z 2025-08-14T21:38:40.4614559Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.4614563Z 2025-08-14T21:38:40.8587273Z 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. 2025-08-14T21:38:40.8589300Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.8590339Z Return a new dict with new, potentially nested, key value pair 2025-08-14T21:38:40.8590886Z 2025-08-14T21:38:40.8591037Z >>> purchase = { 2025-08-14T21:38:40.8591485Z ... "name": "Alice", 2025-08-14T21:38:40.8592103Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-08-14T21:38:40.8593030Z ... "credit card": "5555-1234-1234-1234", 2025-08-14T21:38:40.8593587Z ... } 2025-08-14T21:38:40.8594145Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-08-14T21:38:40.8594943Z {'credit card': '5555-1234-1234-1234', 2025-08-14T21:38:40.8595462Z 'name': 'Alice', 2025-08-14T21:38:40.8595956Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-08-14T21:38:40.8596595Z 2025-08-14T21:38:40.8597253Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.8597916Z 2025-08-14T21:38:40.8599107Z 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. 2025-08-14T21:38:40.8600957Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.8601483Z Update value in a (potentially) nested dictionary 2025-08-14T21:38:40.8601737Z 2025-08-14T21:38:40.8601829Z inputs: 2025-08-14T21:38:40.8602071Z d - dictionary on which to operate 2025-08-14T21:38:40.8602489Z keys - list or tuple giving the location of the value to be changed in d 2025-08-14T21:38:40.8602942Z func - function to operate on that value 2025-08-14T21:38:40.8603164Z 2025-08-14T21:38:40.8603362Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-08-14T21:38:40.8604134Z original dictionary with v replaced by func(v), but does not mutate the 2025-08-14T21:38:40.8604586Z original dictionary. 2025-08-14T21:38:40.8604753Z 2025-08-14T21:38:40.8604971Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-08-14T21:38:40.8605505Z specified by the keys, with the innermost value set to func(default). 2025-08-14T21:38:40.8605834Z 2025-08-14T21:38:40.8605931Z >>> inc = lambda x: x + 1 2025-08-14T21:38:40.8606220Z >>> update_in({"a": 0}, ["a"], inc) 2025-08-14T21:38:40.8606512Z {'a': 1} 2025-08-14T21:38:40.8606632Z 2025-08-14T21:38:40.8606723Z >>> transaction = { 2025-08-14T21:38:40.8606977Z ... "name": "Alice", 2025-08-14T21:38:40.8607679Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-08-14T21:38:40.8608412Z ... "credit card": "5555-1234-1234-1234", 2025-08-14T21:38:40.8608966Z ... } 2025-08-14T21:38:40.8609572Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-08-14T21:38:40.8610382Z {'credit card': '5555-1234-1234-1234', 2025-08-14T21:38:40.8610915Z 'name': 'Alice', 2025-08-14T21:38:40.8611489Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-08-14T21:38:40.8612001Z 2025-08-14T21:38:40.8612226Z >>> # updating a value when k0 is not in d 2025-08-14T21:38:40.8613103Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-08-14T21:38:40.8613659Z {1: {2: {3: 'bar'}}} 2025-08-14T21:38:40.8614121Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-08-14T21:38:40.8614663Z {1: 'foo', 2: {3: {4: 1}}} 2025-08-14T21:38:40.8615127Z 2025-08-14T21:38:40.8615762Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.8616433Z 2025-08-14T21:38:40.8617659Z 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. 2025-08-14T21:38:40.8619552Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.8620527Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-08-14T21:38:40.8621000Z 2025-08-14T21:38:40.8621325Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-08-14T21:38:40.8622238Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-08-14T21:38:40.8622819Z 2025-08-14T21:38:40.8623197Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-08-14T21:38:40.8624108Z structures such as dictionaries and lists. 2025-08-14T21:38:40.8624563Z 2025-08-14T21:38:40.8624727Z >>> transaction = { 2025-08-14T21:38:40.8625178Z ... "name": "Alice", 2025-08-14T21:38:40.8625822Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-08-14T21:38:40.8626572Z ... "credit card": "5555-1234-1234-1234", 2025-08-14T21:38:40.8627099Z ... } 2025-08-14T21:38:40.8627553Z >>> get_in(["purchase", "items", 0], transaction) 2025-08-14T21:38:40.8628167Z 'Apple' 2025-08-14T21:38:40.8628575Z >>> get_in(["name"], transaction) 2025-08-14T21:38:40.8629069Z 'Alice' 2025-08-14T21:38:40.8629507Z >>> get_in(["purchase", "total"], transaction) 2025-08-14T21:38:40.8630215Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-08-14T21:38:40.8630910Z >>> get_in(["purchase", "items", 10], transaction) 2025-08-14T21:38:40.8631595Z >>> get_in(["purchase", "total"], transaction, 0) 2025-08-14T21:38:40.8632182Z 0 2025-08-14T21:38:40.8632551Z >>> get_in(["y"], {}, no_default=True) 2025-08-14T21:38:40.8633118Z Traceback (most recent call last): 2025-08-14T21:38:40.8633640Z ... 2025-08-14T21:38:40.8633998Z KeyError: 'y' 2025-08-14T21:38:40.8634259Z 2025-08-14T21:38:40.8634407Z See Also: 2025-08-14T21:38:40.8634779Z itertoolz.get 2025-08-14T21:38:40.8635218Z operator.getitem 2025-08-14T21:38:40.8635648Z 2025-08-14T21:38:40.8636458Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.8637147Z 2025-08-14T21:38:40.8638372Z 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. 2025-08-14T21:38:40.8640285Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:40.8641044Z Group a collection by a key function 2025-08-14T21:38:40.8641269Z 2025-08-14T21:38:40.8641430Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-08-14T21:38:40.8641831Z >>> groupby(len, names) # doctest: +SKIP 2025-08-14T21:38:40.8642204Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-08-14T21:38:40.8642485Z 2025-08-14T21:38:40.8642588Z >>> iseven = lambda x: x % 2 == 0 2025-08-14T21:38:40.8642957Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-08-14T21:38:40.8643332Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-08-14T21:38:40.8643564Z 2025-08-14T21:38:40.8643703Z Non-callable keys imply grouping on a member. 2025-08-14T21:38:40.8643960Z 2025-08-14T21:38:40.8644048Z >>> groupby( 2025-08-14T21:38:40.8644278Z ... "gender", 2025-08-14T21:38:40.8644507Z ... [ 2025-08-14T21:38:40.8644872Z ... {"name": "Alice", "gender": "F"}, 2025-08-14T21:38:40.8645214Z ... {"name": "Bob", "gender": "M"}, 2025-08-14T21:38:40.8645543Z ... {"name": "Charlie", "gender": "M"}, 2025-08-14T21:38:40.8645855Z ... ], 2025-08-14T21:38:40.8646082Z ... ) # doctest:+SKIP 2025-08-14T21:38:40.8646542Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-08-14T21:38:40.8647083Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-08-14T21:38:40.8647660Z {'gender': 'M', 'name': 'Charlie'}]} 2025-08-14T21:38:40.8648065Z 2025-08-14T21:38:40.8648322Z Not to be confused with ``itertools.groupby`` 2025-08-14T21:38:40.8648772Z 2025-08-14T21:38:40.8648917Z See Also: 2025-08-14T21:38:40.8649290Z countby 2025-08-14T21:38:40.8649670Z 2025-08-14T21:38:40.8650310Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:40.8651030Z 2025-08-14T21:38:41.1645924Z 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. 2025-08-14T21:38:41.1647652Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.1648741Z Return the recommended gain value for the given nonlinearity function. 2025-08-14T21:38:41.1649379Z 2025-08-14T21:38:41.1649580Z The values are as follows: 2025-08-14T21:38:41.1649902Z 2025-08-14T21:38:41.1650092Z ================= ==================================================== 2025-08-14T21:38:41.1650705Z nonlinearity gain 2025-08-14T21:38:41.1651214Z ================= ==================================================== 2025-08-14T21:38:41.1651843Z Linear / Identity :math:`1` 2025-08-14T21:38:41.1652353Z Conv{1,2,3}D :math:`1` 2025-08-14T21:38:41.1652850Z Sigmoid :math:`1` 2025-08-14T21:38:41.1653343Z Tanh :math:`\frac{5}{3}` 2025-08-14T21:38:41.1653889Z ReLU :math:`\sqrt{2}` 2025-08-14T21:38:41.1654603Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-08-14T21:38:41.1655349Z SELU :math:`\frac{3}{4}` 2025-08-14T21:38:41.1655912Z ================= ==================================================== 2025-08-14T21:38:41.1656352Z 2025-08-14T21:38:41.1656527Z .. warning:: 2025-08-14T21:38:41.1657109Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-08-14T21:38:41.1658096Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-08-14T21:38:41.1659042Z This gives the initial weights a variance of ``1 / N``, 2025-08-14T21:38:41.1659959Z which is necessary to induce a stable fixed point in the forward pass. 2025-08-14T21:38:41.1661293Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-08-14T21:38:41.1662250Z effect for more stable gradient flow in rectangular layers. 2025-08-14T21:38:41.1662807Z 2025-08-14T21:38:41.1662955Z Args: 2025-08-14T21:38:41.1663625Z nonlinearity: the non-linear function (`nn.functional` name) 2025-08-14T21:38:41.1664493Z param: optional parameter for the non-linear function 2025-08-14T21:38:41.1665007Z 2025-08-14T21:38:41.1665157Z Examples: 2025-08-14T21:38:41.1665582Z >>> gain = nn.init.calculate_gain( 2025-08-14T21:38:41.1666149Z ... "leaky_relu", 0.2 2025-08-14T21:38:41.1666679Z ... ) # leaky_relu with negative_slope=0.2 2025-08-14T21:38:41.1667127Z 2025-08-14T21:38:41.1668010Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-08-14T21:38:41.1669247Z 2025-08-14T21:38:41.1669924Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.1670616Z 2025-08-14T21:38:41.3857429Z 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. 2025-08-14T21:38:41.3859539Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.3860541Z Applies Batch Normalization over a N-Dimensional input. 2025-08-14T21:38:41.3861064Z 2025-08-14T21:38:41.3861657Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-08-14T21:38:41.3862875Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-08-14T21:38:41.3863924Z Internal Covariate Shift `__ . 2025-08-14T21:38:41.3864529Z 2025-08-14T21:38:41.3864706Z .. math:: 2025-08-14T21:38:41.3864935Z 2025-08-14T21:38:41.3865349Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-08-14T21:38:41.3865944Z 2025-08-14T21:38:41.3866326Z The mean and standard-deviation are calculated per-dimension over all 2025-08-14T21:38:41.3867386Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-08-14T21:38:41.3868489Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-08-14T21:38:41.3869500Z By default, the elements of :math:`\gamma` are sampled from 2025-08-14T21:38:41.3870368Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-08-14T21:38:41.3871405Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-08-14T21:38:41.3872282Z `torch.var(input, unbiased=False)`. 2025-08-14T21:38:41.3872679Z 2025-08-14T21:38:41.3873125Z Also by default, during training this layer keeps running estimates of its 2025-08-14T21:38:41.3874198Z computed mean and variance, which are then used for normalization during 2025-08-14T21:38:41.3875282Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-08-14T21:38:41.3876110Z of 0.1. 2025-08-14T21:38:41.3876327Z 2025-08-14T21:38:41.3876730Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-08-14T21:38:41.3877775Z keep running estimates, and batch statistics are instead used during 2025-08-14T21:38:41.3878560Z evaluation time as well. 2025-08-14T21:38:41.3878941Z 2025-08-14T21:38:41.3879113Z .. note:: 2025-08-14T21:38:41.3879727Z This :attr:`momentum` argument is different from one used in optimizer 2025-08-14T21:38:41.3880720Z classes and the conventional notion of momentum. Mathematically, the 2025-08-14T21:38:41.3881567Z update rule for running statistics here is 2025-08-14T21:38:41.3882483Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-08-14T21:38:41.3883473Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-08-14T21:38:41.3884471Z new observed value. 2025-08-14T21:38:41.3884796Z 2025-08-14T21:38:41.3885365Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-08-14T21:38:41.3886602Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-08-14T21:38:41.3887589Z Normalization or Spatio-temporal Batch Normalization. 2025-08-14T21:38:41.3888136Z 2025-08-14T21:38:41.3888390Z Currently :class:`SyncBatchNorm` only supports 2025-08-14T21:38:41.3889381Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-08-14T21:38:41.3890499Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-08-14T21:38:41.3891438Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-08-14T21:38:41.3892216Z Network with DDP. 2025-08-14T21:38:41.3892491Z 2025-08-14T21:38:41.3892649Z Args: 2025-08-14T21:38:41.3893372Z num_features: :math:`C` from an expected input of size 2025-08-14T21:38:41.3894054Z :math:`(N, C, +)` 2025-08-14T21:38:41.3894733Z eps: a value added to the denominator for numerical stability. 2025-08-14T21:38:41.3895444Z Default: ``1e-5`` 2025-08-14T21:38:41.3896316Z momentum: the value used for the running_mean and running_var 2025-08-14T21:38:41.3897262Z computation. Can be set to ``None`` for cumulative moving average 2025-08-14T21:38:41.3898089Z (i.e. simple average). Default: 0.1 2025-08-14T21:38:41.3898866Z affine: a boolean value that when set to ``True``, this module has 2025-08-14T21:38:41.3899671Z learnable affine parameters. Default: ``True`` 2025-08-14T21:38:41.3900534Z track_running_stats: a boolean value that when set to ``True``, this 2025-08-14T21:38:41.3901545Z module tracks the running mean and variance, and when set to ``False``, 2025-08-14T21:38:41.3902603Z this module does not track such statistics, and initializes statistics 2025-08-14T21:38:41.3903707Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-08-14T21:38:41.3904712Z When these buffers are ``None``, this module always uses batch statistics. 2025-08-14T21:38:41.3905625Z in both training and eval modes. Default: ``True`` 2025-08-14T21:38:41.3906576Z process_group: synchronization of stats happen within each process group 2025-08-14T21:38:41.3907664Z individually. Default behavior is synchronization across the whole 2025-08-14T21:38:41.3908440Z world 2025-08-14T21:38:41.3908699Z 2025-08-14T21:38:41.3908846Z Shape: 2025-08-14T21:38:41.3909242Z - Input: :math:`(N, C, +)` 2025-08-14T21:38:41.3909847Z - Output: :math:`(N, C, +)` (same shape as input) 2025-08-14T21:38:41.3910308Z 2025-08-14T21:38:41.3910466Z .. note:: 2025-08-14T21:38:41.3911139Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-08-14T21:38:41.3912205Z synchronization is disabled when ``model.eval()`` is set or if 2025-08-14T21:38:41.3913012Z ``self.training`` is otherwise ``False``. 2025-08-14T21:38:41.3913458Z 2025-08-14T21:38:41.3913612Z Examples:: 2025-08-14T21:38:41.3913844Z 2025-08-14T21:38:41.3914037Z >>> # xdoctest: +SKIP 2025-08-14T21:38:41.3914560Z >>> # With Learnable Parameters 2025-08-14T21:38:41.3915113Z >>> m = nn.SyncBatchNorm(100) 2025-08-14T21:38:41.3915608Z >>> # creating process group (optional) 2025-08-14T21:38:41.3916230Z >>> # ranks is a list of int identifying rank ids. 2025-08-14T21:38:41.3916854Z >>> ranks = list(range(8)) 2025-08-14T21:38:41.3917389Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-08-14T21:38:41.3918027Z >>> # Note: every rank calls into new_group for every 2025-08-14T21:38:41.3918755Z >>> # process group created, even if that rank is not 2025-08-14T21:38:41.3919427Z >>> # part of the group. 2025-08-14T21:38:41.3920413Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-08-14T21:38:41.3921477Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-08-14T21:38:41.3922269Z >>> # Without Learnable Parameters 2025-08-14T21:38:41.3923044Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-08-14T21:38:41.3923856Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-08-14T21:38:41.3924437Z >>> output = m(input) 2025-08-14T21:38:41.3924784Z 2025-08-14T21:38:41.3924988Z >>> # network is nn.BatchNorm layer 2025-08-14T21:38:41.3925887Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-08-14T21:38:41.3926922Z >>> # only single gpu per process is currently supported 2025-08-14T21:38:41.3927823Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-08-14T21:38:41.3928657Z >>> sync_bn_network, 2025-08-14T21:38:41.3929295Z >>> device_ids=[args.local_rank], 2025-08-14T21:38:41.3929955Z >>> output_device=args.local_rank) 2025-08-14T21:38:41.3930551Z 2025-08-14T21:38:41.3931217Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.3932053Z 2025-08-14T21:38:41.3933320Z 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. 2025-08-14T21:38:41.3935244Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.3936527Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-08-14T21:38:41.3937320Z 2025-08-14T21:38:41.3937480Z Args: 2025-08-14T21:38:41.3938150Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-08-14T21:38:41.3939237Z process_group (optional): process group to scope synchronization, 2025-08-14T21:38:41.3940075Z default is the whole world 2025-08-14T21:38:41.3940474Z 2025-08-14T21:38:41.3940638Z Returns: 2025-08-14T21:38:41.3941343Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-08-14T21:38:41.3942434Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-08-14T21:38:41.3943530Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-08-14T21:38:41.3944305Z instead. 2025-08-14T21:38:41.3944567Z 2025-08-14T21:38:41.3944734Z Example:: 2025-08-14T21:38:41.3945004Z 2025-08-14T21:38:41.3945214Z >>> # Network with nn.BatchNorm layer 2025-08-14T21:38:41.3945889Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-14T21:38:41.3946565Z >>> module = torch.nn.Sequential( 2025-08-14T21:38:41.3947147Z >>> torch.nn.Linear(20, 100), 2025-08-14T21:38:41.3947815Z >>> torch.nn.BatchNorm1d(100), 2025-08-14T21:38:41.3948446Z >>> ).cuda() 2025-08-14T21:38:41.3948937Z >>> # creating process group (optional) 2025-08-14T21:38:41.3949607Z >>> # ranks is a list of int identifying rank ids. 2025-08-14T21:38:41.3950233Z >>> ranks = list(range(8)) 2025-08-14T21:38:41.3950758Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-08-14T21:38:41.3951344Z >>> # Note: every rank calls into new_group for every 2025-08-14T21:38:41.3951997Z >>> # process group created, even if that rank is not 2025-08-14T21:38:41.3952606Z >>> # part of the group. 2025-08-14T21:38:41.3953130Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:41.3953971Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-08-14T21:38:41.3955123Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-08-14T21:38:41.3956153Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-08-14T21:38:41.3956731Z 2025-08-14T21:38:41.3956850Z 2025-08-14T21:38:41.3957491Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.3958173Z 2025-08-14T21:38:41.4163077Z 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. 2025-08-14T21:38:41.4164781Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.4165483Z 2025-08-14T21:38:41.4166053Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-08-14T21:38:41.4166868Z 2025-08-14T21:38:41.4167367Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-08-14T21:38:41.4168529Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-08-14T21:38:41.4169170Z 2025-08-14T21:38:41.4169755Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-08-14T21:38:41.4170966Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-08-14T21:38:41.4172167Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-08-14T21:38:41.4172691Z 2025-08-14T21:38:41.4172845Z Shape: 2025-08-14T21:38:41.4173424Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-08-14T21:38:41.4174488Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-08-14T21:38:41.4175591Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-08-14T21:38:41.4176434Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-08-14T21:38:41.4176866Z 2025-08-14T21:38:41.4177014Z Args: 2025-08-14T21:38:41.4177495Z dim (Union[int, str]): Dimension to be unflattened 2025-08-14T21:38:41.4178635Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-08-14T21:38:41.4179524Z 2025-08-14T21:38:41.4179691Z Examples: 2025-08-14T21:38:41.4180088Z >>> input = torch.randn(2, 50) 2025-08-14T21:38:41.4180633Z >>> # With tuple of ints 2025-08-14T21:38:41.4181136Z >>> m = nn.Sequential( 2025-08-14T21:38:41.4181601Z >>> nn.Linear(50, 50), 2025-08-14T21:38:41.4182111Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-08-14T21:38:41.4182617Z >>> ) 2025-08-14T21:38:41.4182952Z >>> output = m(input) 2025-08-14T21:38:41.4183412Z >>> output.size() 2025-08-14T21:38:41.4183936Z torch.Size([2, 2, 5, 5]) 2025-08-14T21:38:41.4184408Z >>> # With torch.Size 2025-08-14T21:38:41.4184877Z >>> m = nn.Sequential( 2025-08-14T21:38:41.4185344Z >>> nn.Linear(50, 50), 2025-08-14T21:38:41.4185875Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-08-14T21:38:41.4186426Z >>> ) 2025-08-14T21:38:41.4186799Z >>> output = m(input) 2025-08-14T21:38:41.4187239Z >>> output.size() 2025-08-14T21:38:41.4187694Z torch.Size([2, 2, 5, 5]) 2025-08-14T21:38:41.4188213Z >>> # With namedshape (tuple of tuples) 2025-08-14T21:38:41.4188881Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-08-14T21:38:41.4189691Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-08-14T21:38:41.4190469Z >>> output = unflatten(input) 2025-08-14T21:38:41.4191013Z >>> output.size() 2025-08-14T21:38:41.4191462Z torch.Size([2, 2, 5, 5]) 2025-08-14T21:38:41.4191803Z 2025-08-14T21:38:41.4192279Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.4193168Z 2025-08-14T21:38:41.4539773Z 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. 2025-08-14T21:38:41.4541975Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.4543085Z Creates a criterion that measures the triplet loss given input 2025-08-14T21:38:41.4544085Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-08-14T21:38:41.4545058Z positive, and negative examples, respectively), and a nonnegative, 2025-08-14T21:38:41.4546149Z real-valued function ("distance function") used to compute the relationship 2025-08-14T21:38:41.4547228Z between the anchor and positive example ("positive distance") and the 2025-08-14T21:38:41.4548149Z anchor and negative example ("negative distance"). 2025-08-14T21:38:41.4548658Z 2025-08-14T21:38:41.4549039Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-08-14T21:38:41.4549831Z can be described as: 2025-08-14T21:38:41.4550140Z 2025-08-14T21:38:41.4550303Z .. math:: 2025-08-14T21:38:41.4550772Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-08-14T21:38:41.4551459Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-08-14T21:38:41.4551925Z 2025-08-14T21:38:41.4552381Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-08-14T21:38:41.4553540Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-08-14T21:38:41.4554978Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-08-14T21:38:41.4556075Z between the positive and negative distances that is required for the loss to 2025-08-14T21:38:41.4557197Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-08-14T21:38:41.4558070Z that the distance function can handle. 2025-08-14T21:38:41.4558508Z 2025-08-14T21:38:41.4558714Z If :attr:`reduction` is not ``'none'`` 2025-08-14T21:38:41.4559289Z (default ``'mean'``), then: 2025-08-14T21:38:41.4559619Z 2025-08-14T21:38:41.4559773Z .. math:: 2025-08-14T21:38:41.4560148Z \ell(x, y) = 2025-08-14T21:38:41.4560579Z \begin{cases} 2025-08-14T21:38:41.4561200Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-08-14T21:38:41.4562063Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-08-14T21:38:41.4562724Z \end{cases} 2025-08-14T21:38:41.4562944Z 2025-08-14T21:38:41.4563343Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-08-14T21:38:41.4564360Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-08-14T21:38:41.4565038Z 2025-08-14T21:38:41.4565192Z Args: 2025-08-14T21:38:41.4565866Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-08-14T21:38:41.4566916Z quantifies the closeness of two tensors. If not specified, 2025-08-14T21:38:41.4567783Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-08-14T21:38:41.4568811Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-08-14T21:38:41.4570019Z between the positive and negative distances required for the loss to be 0. Larger 2025-08-14T21:38:41.4571244Z margins penalize cases where the negative examples are not distant enough from the 2025-08-14T21:38:41.4572239Z anchors, relative to the positives. Default: :math:`1`. 2025-08-14T21:38:41.4573204Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-08-14T21:38:41.4574321Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-08-14T21:38:41.4575447Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-08-14T21:38:41.4576565Z negative example than the anchor is, swaps the positive example and the anchor in 2025-08-14T21:38:41.4577516Z the loss computation. Default: ``False``. 2025-08-14T21:38:41.4578621Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-08-14T21:38:41.4579706Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-08-14T21:38:41.4580534Z ``'mean'``: the sum of the output will be divided by the number of 2025-08-14T21:38:41.4581526Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-08-14T21:38:41.4582151Z 2025-08-14T21:38:41.4582159Z 2025-08-14T21:38:41.4582304Z Shape: 2025-08-14T21:38:41.4582956Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-08-14T21:38:41.4583919Z as supported by the distance function. 2025-08-14T21:38:41.4584744Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-08-14T21:38:41.4585585Z otherwise. 2025-08-14T21:38:41.4585865Z 2025-08-14T21:38:41.4586016Z Examples: 2025-08-14T21:38:41.4586244Z 2025-08-14T21:38:41.4586437Z >>> # Initialize embeddings 2025-08-14T21:38:41.4586968Z >>> embedding = nn.Embedding(1000, 128) 2025-08-14T21:38:41.4587566Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-08-14T21:38:41.4588205Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-08-14T21:38:41.4588825Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-08-14T21:38:41.4589573Z >>> anchor = embedding(anchor_ids) 2025-08-14T21:38:41.4590097Z >>> positive = embedding(positive_ids) 2025-08-14T21:38:41.4590670Z >>> negative = embedding(negative_ids) 2025-08-14T21:38:41.4591208Z >>> 2025-08-14T21:38:41.4591581Z >>> # Built-in Distance Function 2025-08-14T21:38:41.4592081Z >>> triplet_loss = \ 2025-08-14T21:38:41.4593085Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-08-14T21:38:41.4594077Z >>> output = triplet_loss(anchor, positive, negative) 2025-08-14T21:38:41.4594728Z >>> output.backward() 2025-08-14T21:38:41.4595168Z >>> 2025-08-14T21:38:41.4595563Z >>> # Custom Distance Function 2025-08-14T21:38:41.4596112Z >>> def l_infinity(x1, x2): 2025-08-14T21:38:41.4596695Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-08-14T21:38:41.4597328Z >>> 2025-08-14T21:38:41.4597871Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-08-14T21:38:41.4598593Z >>> triplet_loss = ( 2025-08-14T21:38:41.4599413Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-08-14T21:38:41.4600419Z >>> output = triplet_loss(anchor, positive, negative) 2025-08-14T21:38:41.4601068Z >>> output.backward() 2025-08-14T21:38:41.4601488Z >>> 2025-08-14T21:38:41.4601893Z >>> # Custom Distance Function (Lambda) 2025-08-14T21:38:41.4602421Z >>> triplet_loss = ( 2025-08-14T21:38:41.4602934Z >>> nn.TripletMarginWithDistanceLoss( 2025-08-14T21:38:41.4603750Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-08-14T21:38:41.4604647Z >>> output = triplet_loss(anchor, positive, negative) 2025-08-14T21:38:41.4605282Z >>> output.backward() 2025-08-14T21:38:41.4605606Z 2025-08-14T21:38:41.4605761Z Reference: 2025-08-14T21:38:41.4606557Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-08-14T21:38:41.4607712Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-08-14T21:38:41.4608299Z 2025-08-14T21:38:41.4608913Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-08-14T21:38:41.4609598Z 2025-08-14T21:38:41.4610581Z 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. 2025-08-14T21:38:41.4612177Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.4613137Z The Connectionist Temporal Classification loss. 2025-08-14T21:38:41.4613608Z 2025-08-14T21:38:41.4614517Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-08-14T21:38:41.4616107Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-08-14T21:38:41.4617605Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-08-14T21:38:41.4618999Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-08-14T21:38:41.4619803Z 2025-08-14T21:38:41.4619963Z Args: 2025-08-14T21:38:41.4620458Z blank (int, optional): blank label. Default :math:`0`. 2025-08-14T21:38:41.4621381Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-08-14T21:38:41.4622369Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-08-14T21:38:41.4623256Z ``'mean'``: the output losses will be divided by the target lengths and 2025-08-14T21:38:41.4624364Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-08-14T21:38:41.4625231Z Default: ``'mean'`` 2025-08-14T21:38:41.4625769Z zero_infinity (bool, optional): 2025-08-14T21:38:41.4626507Z Whether to zero infinite losses and the associated gradients. 2025-08-14T21:38:41.4627432Z Default: ``False`` 2025-08-14T21:38:41.4628088Z Infinite losses mainly occur when the inputs are too short 2025-08-14T21:38:41.4628827Z to be aligned to the targets. 2025-08-14T21:38:41.4629227Z 2025-08-14T21:38:41.4629369Z Shape: 2025-08-14T21:38:41.4629926Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-08-14T21:38:41.4630685Z where :math:`T = \text{input length}`, 2025-08-14T21:38:41.4631337Z :math:`N = \text{batch size}`, and 2025-08-14T21:38:41.4632046Z :math:`C = \text{number of classes (including blank)}`. 2025-08-14T21:38:41.4632935Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-08-14T21:38:41.4633790Z :func:`torch.nn.functional.log_softmax`). 2025-08-14T21:38:41.4634464Z - Targets: Tensor of size :math:`(N, S)` or 2025-08-14T21:38:41.4635187Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-08-14T21:38:41.4635891Z where :math:`N = \text{batch size}` and 2025-08-14T21:38:41.4636594Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-08-14T21:38:41.4637463Z It represents the target sequences. Each element in the target 2025-08-14T21:38:41.4638475Z sequence is a class index. And the target index cannot be blank (default=0). 2025-08-14T21:38:41.4639433Z In the :math:`(N, S)` form, targets are padded to the 2025-08-14T21:38:41.4640160Z length of the longest sequence, and stacked. 2025-08-14T21:38:41.4640926Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-08-14T21:38:41.4641697Z the targets are assumed to be un-padded and 2025-08-14T21:38:41.4642207Z concatenated within 1 dimension. 2025-08-14T21:38:41.4642795Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-08-14T21:38:41.4643702Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-08-14T21:38:41.4644631Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-08-14T21:38:41.4645619Z for each sequence to achieve masking under the assumption that sequences 2025-08-14T21:38:41.4646449Z are padded to equal lengths. 2025-08-14T21:38:41.4647181Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-08-14T21:38:41.4648145Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-08-14T21:38:41.4649133Z Lengths are specified for each sequence to achieve masking under the 2025-08-14T21:38:41.4650316Z assumption that sequences are padded to equal lengths. If target shape is 2025-08-14T21:38:41.4651283Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-08-14T21:38:41.4652235Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-08-14T21:38:41.4653228Z each target in a batch. Lengths must each be :math:`\leq S` 2025-08-14T21:38:41.4654199Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-08-14T21:38:41.4655284Z targets, the target_lengths must add up to the total length of the tensor. 2025-08-14T21:38:41.4656280Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-08-14T21:38:41.4657230Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-08-14T21:38:41.4658233Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-08-14T21:38:41.4658820Z 2025-08-14T21:38:41.4658970Z Examples: 2025-08-14T21:38:41.4659196Z 2025-08-14T21:38:41.4659398Z >>> # Target are to be padded 2025-08-14T21:38:41.4659947Z >>> T = 50 # Input sequence length 2025-08-14T21:38:41.4660574Z >>> C = 20 # Number of classes (including blank) 2025-08-14T21:38:41.4661176Z >>> N = 16 # Batch size 2025-08-14T21:38:41.4662033Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-08-14T21:38:41.4662994Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-08-14T21:38:41.4663761Z >>> 2025-08-14T21:38:41.4664321Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-08-14T21:38:41.4665233Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-08-14T21:38:41.4665956Z >>> 2025-08-14T21:38:41.4666496Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-08-14T21:38:41.4667401Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-08-14T21:38:41.4668167Z >>> 2025-08-14T21:38:41.4668776Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-08-14T21:38:41.4669599Z >>> target_lengths = torch.randint( 2025-08-14T21:38:41.4670108Z ... low=S_min, 2025-08-14T21:38:41.4670562Z ... high=S, 2025-08-14T21:38:41.4670983Z ... size=(N,), 2025-08-14T21:38:41.4671431Z ... dtype=torch.long, 2025-08-14T21:38:41.4671923Z ... ) 2025-08-14T21:38:41.4672326Z >>> ctc_loss = nn.CTCLoss() 2025-08-14T21:38:41.4673008Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-08-14T21:38:41.4673743Z >>> loss.backward() 2025-08-14T21:38:41.4674206Z >>> 2025-08-14T21:38:41.4674551Z >>> 2025-08-14T21:38:41.4674960Z >>> # Target are to be un-padded 2025-08-14T21:38:41.4675554Z >>> T = 50 # Input sequence length 2025-08-14T21:38:41.4676165Z >>> C = 20 # Number of classes (including blank) 2025-08-14T21:38:41.4676795Z >>> N = 16 # Batch size 2025-08-14T21:38:41.4677278Z >>> 2025-08-14T21:38:41.4677857Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-08-14T21:38:41.4678806Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-08-14T21:38:41.4679808Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-08-14T21:38:41.4680580Z >>> 2025-08-14T21:38:41.4681133Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-08-14T21:38:41.4682150Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-08-14T21:38:41.4683012Z >>> target = torch.randint( 2025-08-14T21:38:41.4683527Z ... low=1, 2025-08-14T21:38:41.4683954Z ... high=C, 2025-08-14T21:38:41.4684431Z ... size=(sum(target_lengths),), 2025-08-14T21:38:41.4685011Z ... dtype=torch.long, 2025-08-14T21:38:41.4685668Z ... ) 2025-08-14T21:38:41.4686082Z >>> ctc_loss = nn.CTCLoss() 2025-08-14T21:38:41.4686780Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-08-14T21:38:41.4687503Z >>> loss.backward() 2025-08-14T21:38:41.4687971Z >>> 2025-08-14T21:38:41.4688343Z >>> 2025-08-14T21:38:41.4688874Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-08-14T21:38:41.4689616Z >>> T = 50 # Input sequence length 2025-08-14T21:38:41.4690231Z >>> C = 20 # Number of classes (including blank) 2025-08-14T21:38:41.4690800Z >>> 2025-08-14T21:38:41.4691346Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-08-14T21:38:41.4692148Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-08-14T21:38:41.4693121Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-08-14T21:38:41.4693960Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-08-14T21:38:41.4694589Z >>> 2025-08-14T21:38:41.4695145Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-08-14T21:38:41.4696117Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-08-14T21:38:41.4696912Z >>> target = torch.randint( 2025-08-14T21:38:41.4697582Z ... low=1, 2025-08-14T21:38:41.4697983Z ... high=C, 2025-08-14T21:38:41.4698441Z ... size=(target_lengths,), 2025-08-14T21:38:41.4698999Z ... dtype=torch.long, 2025-08-14T21:38:41.4699463Z ... ) 2025-08-14T21:38:41.4699851Z >>> ctc_loss = nn.CTCLoss() 2025-08-14T21:38:41.4700510Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-08-14T21:38:41.4701219Z >>> loss.backward() 2025-08-14T21:38:41.4701516Z 2025-08-14T21:38:41.4701666Z Reference: 2025-08-14T21:38:41.4702215Z A. Graves et al.: Connectionist Temporal Classification: 2025-08-14T21:38:41.4703156Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-08-14T21:38:41.4704122Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-08-14T21:38:41.4704607Z 2025-08-14T21:38:41.4704749Z Note: 2025-08-14T21:38:41.4705373Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-08-14T21:38:41.4706484Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-08-14T21:38:41.4707545Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-08-14T21:38:41.4708340Z dtype :attr:`torch.int32`. 2025-08-14T21:38:41.4708681Z 2025-08-14T21:38:41.4709166Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-08-14T21:38:41.4709869Z 2025-08-14T21:38:41.4709876Z 2025-08-14T21:38:41.4710035Z Note: 2025-08-14T21:38:41.4724311Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-08-14T21:38:41.4725506Z may select a nondeterministic algorithm to increase performance. If this is 2025-08-14T21:38:41.4726582Z undesirable, you can try to make the operation deterministic (potentially at 2025-08-14T21:38:41.4727561Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-08-14T21:38:41.4728282Z True``. 2025-08-14T21:38:41.4728823Z Please see the notes on :doc:`/notes/randomness` for background. 2025-08-14T21:38:41.4729482Z 2025-08-14T21:38:41.4730052Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.4730670Z 2025-08-14T21:38:41.5177225Z 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. 2025-08-14T21:38:41.5179000Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.5179944Z Computes a partial inverse of :class:`MaxPool2d`. 2025-08-14T21:38:41.5180398Z 2025-08-14T21:38:41.5181128Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-08-14T21:38:41.5181843Z 2025-08-14T21:38:41.5182262Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-08-14T21:38:41.5183324Z including the indices of the maximal values and computes a partial inverse 2025-08-14T21:38:41.5184335Z in which all non-maximal values are set to zero. 2025-08-14T21:38:41.5184812Z 2025-08-14T21:38:41.5184974Z Note: 2025-08-14T21:38:41.5185753Z This operation may behave nondeterministically when the input indices has repeat values. 2025-08-14T21:38:41.5187251Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-08-14T21:38:41.5188188Z 2025-08-14T21:38:41.5188636Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-08-14T21:38:41.5189594Z sizes. Hence, the inversion process can get ambiguous. 2025-08-14T21:38:41.5190492Z To accommodate this, you can provide the needed output size 2025-08-14T21:38:41.5191460Z as an additional argument :attr:`output_size` in the forward call. 2025-08-14T21:38:41.5192256Z See the Inputs and Example below. 2025-08-14T21:38:41.5193091Z 2025-08-14T21:38:41.5193239Z Args: 2025-08-14T21:38:41.5193797Z kernel_size (int or tuple): Size of the max pooling window. 2025-08-14T21:38:41.5194645Z stride (int or tuple): Stride of the max pooling window. 2025-08-14T21:38:41.5195350Z It is set to :attr:`kernel_size` by default. 2025-08-14T21:38:41.5196117Z padding (int or tuple): Padding that was added to the input 2025-08-14T21:38:41.5196665Z 2025-08-14T21:38:41.5196814Z Inputs: 2025-08-14T21:38:41.5197243Z - `input`: the input Tensor to invert 2025-08-14T21:38:41.5197985Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-08-14T21:38:41.5198894Z - `output_size` (optional): the targeted output size 2025-08-14T21:38:41.5199396Z 2025-08-14T21:38:41.5199563Z Shape: 2025-08-14T21:38:41.5200111Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-08-14T21:38:41.5201058Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-08-14T21:38:41.5201698Z 2025-08-14T21:38:41.5201865Z .. math:: 2025-08-14T21:38:41.5202604Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-08-14T21:38:41.5203292Z 2025-08-14T21:38:41.5203436Z .. math:: 2025-08-14T21:38:41.5204143Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-08-14T21:38:41.5204834Z 2025-08-14T21:38:41.5205129Z or as given by :attr:`output_size` in the call operator 2025-08-14T21:38:41.5205636Z 2025-08-14T21:38:41.5205805Z Example:: 2025-08-14T21:38:41.5206035Z 2025-08-14T21:38:41.5206334Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-08-14T21:38:41.5207031Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-08-14T21:38:41.5207683Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-08-14T21:38:41.5208279Z [ 5., 6., 7., 8.], 2025-08-14T21:38:41.5208877Z [ 9., 10., 11., 12.], 2025-08-14T21:38:41.5209454Z [13., 14., 15., 16.]]]]) 2025-08-14T21:38:41.5210045Z >>> output, indices = pool(input) 2025-08-14T21:38:41.5210642Z >>> unpool(output, indices) 2025-08-14T21:38:41.5211206Z tensor([[[[ 0., 0., 0., 0.], 2025-08-14T21:38:41.5211764Z [ 0., 6., 0., 8.], 2025-08-14T21:38:41.5212314Z [ 0., 0., 0., 0.], 2025-08-14T21:38:41.5212878Z [ 0., 14., 0., 16.]]]]) 2025-08-14T21:38:41.5213652Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-08-14T21:38:41.5214716Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-08-14T21:38:41.5215389Z [ 6., 7., 8., 9., 10.], 2025-08-14T21:38:41.5215986Z [11., 12., 13., 14., 15.], 2025-08-14T21:38:41.5216577Z [16., 17., 18., 19., 20.]]]]) 2025-08-14T21:38:41.5217147Z >>> output, indices = pool(input) 2025-08-14T21:38:41.5217861Z >>> # This call will not work without specifying output_size 2025-08-14T21:38:41.5218670Z >>> unpool(output, indices, output_size=input.size()) 2025-08-14T21:38:41.5219343Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-08-14T21:38:41.5219871Z [ 0., 7., 0., 9., 0.], 2025-08-14T21:38:41.5220412Z [ 0., 0., 0., 0., 0.], 2025-08-14T21:38:41.5220933Z [ 0., 17., 0., 19., 0.]]]]) 2025-08-14T21:38:41.5221318Z 2025-08-14T21:38:41.5221327Z 2025-08-14T21:38:41.5221467Z 2025-08-14T21:38:41.5222152Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.5222814Z 2025-08-14T21:38:41.5481355Z 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. 2025-08-14T21:38:41.5483335Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.5484672Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-08-14T21:38:41.5485493Z 2025-08-14T21:38:41.5486109Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-08-14T21:38:41.5487081Z and with 2D inputs, this class 2025-08-14T21:38:41.5487434Z 2025-08-14T21:38:41.5487977Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-08-14T21:38:41.5489348Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-08-14T21:38:41.5490690Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-08-14T21:38:41.5491463Z 2025-08-14T21:38:41.5492152Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-08-14T21:38:41.5493401Z operations. 2025-08-14T21:38:41.5493659Z 2025-08-14T21:38:41.5494112Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-08-14T21:38:41.5495216Z pass. This scales the output of the Embedding before performing a weighted 2025-08-14T21:38:41.5496271Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-08-14T21:38:41.5497389Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-08-14T21:38:41.5498194Z :attr:`per_sample_weights`. 2025-08-14T21:38:41.5498520Z 2025-08-14T21:38:41.5498676Z Args: 2025-08-14T21:38:41.5499201Z num_embeddings (int): size of the dictionary of embeddings 2025-08-14T21:38:41.5500031Z embedding_dim (int): the size of each embedding vector 2025-08-14T21:38:41.5501113Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-08-14T21:38:41.5502161Z is renormalized to have norm :attr:`max_norm`. 2025-08-14T21:38:41.5503281Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-08-14T21:38:41.5504842Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-08-14T21:38:41.5505966Z the words in the mini-batch. Default ``False``. 2025-08-14T21:38:41.5506799Z Note: this option is not supported when ``mode="max"``. 2025-08-14T21:38:41.5507967Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-08-14T21:38:41.5509034Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-08-14T21:38:41.5510045Z into consideration. ``"mean"`` computes the average of the values 2025-08-14T21:38:41.5510975Z in the bag, ``"max"`` computes the max value over each bag. 2025-08-14T21:38:41.5511700Z Default: ``"mean"`` 2025-08-14T21:38:41.5512707Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-08-14T21:38:41.5513984Z Notes for more details regarding sparse gradients. Note: this option is not 2025-08-14T21:38:41.5514897Z supported when ``mode="max"``. 2025-08-14T21:38:41.5515974Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-08-14T21:38:41.5517324Z is equivalent to the size of `indices`. This matches the CSR format. 2025-08-14T21:38:41.5518561Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-08-14T21:38:41.5520090Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-08-14T21:38:41.5521272Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-08-14T21:38:41.5522450Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-08-14T21:38:41.5523614Z zeros, but can be updated to another value to be used as the padding vector. 2025-08-14T21:38:41.5524762Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-08-14T21:38:41.5525637Z reduction. 2025-08-14T21:38:41.5526042Z 2025-08-14T21:38:41.5526214Z Attributes: 2025-08-14T21:38:41.5527030Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-08-14T21:38:41.5528104Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-08-14T21:38:41.5528579Z 2025-08-14T21:38:41.5528769Z Examples:: 2025-08-14T21:38:41.5529004Z 2025-08-14T21:38:41.5529320Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-08-14T21:38:41.5530112Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-08-14T21:38:41.5530821Z >>> # a batch of 2 samples of 4 indices each 2025-08-14T21:38:41.5531505Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-08-14T21:38:41.5532311Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-08-14T21:38:41.5533043Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-14T21:38:41.5533686Z >>> embedding_sum(input, offsets) 2025-08-14T21:38:41.5534267Z tensor([[-0.8861, -5.4350, -0.0523], 2025-08-14T21:38:41.5534835Z [ 1.1306, -2.5798, -1.0044]]) 2025-08-14T21:38:41.5535251Z 2025-08-14T21:38:41.5535439Z >>> # Example with padding_idx 2025-08-14T21:38:41.5536198Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-08-14T21:38:41.5537131Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-08-14T21:38:41.5537962Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-08-14T21:38:41.5538649Z >>> embedding_sum(input, offsets) 2025-08-14T21:38:41.5539228Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-08-14T21:38:41.5539794Z [-0.7082, 3.2145, -2.6251]]) 2025-08-14T21:38:41.5540178Z 2025-08-14T21:38:41.5540506Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-08-14T21:38:41.5541473Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-08-14T21:38:41.5542227Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-08-14T21:38:41.5542916Z embedding.weight, 2025-08-14T21:38:41.5543611Z padding_idx=embedding.padding_idx, 2025-08-14T21:38:41.5544224Z mode='sum') 2025-08-14T21:38:41.5544680Z 2025-08-14T21:38:41.5545349Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.5546050Z 2025-08-14T21:38:41.5580093Z 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. 2025-08-14T21:38:41.5581997Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.5582935Z Take in and process masked source/target sequences. 2025-08-14T21:38:41.5583514Z 2025-08-14T21:38:41.5583672Z .. note:: 2025-08-14T21:38:41.5583902Z 2025-08-14T21:38:41.5584650Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-08-14T21:38:41.5585841Z not allowed to participate in the attention, 2025-08-14T21:38:41.5586622Z which is the opposite of the definition for :attr:`attn_mask` 2025-08-14T21:38:41.5587682Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-08-14T21:38:41.5588236Z 2025-08-14T21:38:41.5588395Z Args: 2025-08-14T21:38:41.5588857Z src: the sequence to the encoder (required). 2025-08-14T21:38:41.5589546Z tgt: the sequence to the decoder (required). 2025-08-14T21:38:41.5590329Z src_mask: the additive mask for the src sequence (optional). 2025-08-14T21:38:41.5591177Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-08-14T21:38:41.5592133Z memory_mask: the additive mask for the encoder output (optional). 2025-08-14T21:38:41.5593367Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-08-14T21:38:41.5594421Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-08-14T21:38:41.5595478Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-08-14T21:38:41.5596570Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-08-14T21:38:41.5597416Z Default: ``None``; try to detect a causal mask. 2025-08-14T21:38:41.5598039Z Warning: 2025-08-14T21:38:41.5598586Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-08-14T21:38:41.5599414Z the causal mask. Providing incorrect hints can result in 2025-08-14T21:38:41.5600265Z incorrect execution, including forward and backward 2025-08-14T21:38:41.5600956Z compatibility. 2025-08-14T21:38:41.5601668Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-08-14T21:38:41.5602540Z Default: ``None``; try to detect a causal mask. 2025-08-14T21:38:41.5603160Z Warning: 2025-08-14T21:38:41.5603742Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-08-14T21:38:41.5604595Z the causal mask. Providing incorrect hints can result in 2025-08-14T21:38:41.5605454Z incorrect execution, including forward and backward 2025-08-14T21:38:41.5606151Z compatibility. 2025-08-14T21:38:41.5606805Z memory_is_causal: If specified, applies a causal mask as 2025-08-14T21:38:41.5607529Z ``memory_mask``. 2025-08-14T21:38:41.5608053Z Default: ``False``. 2025-08-14T21:38:41.5608559Z Warning: 2025-08-14T21:38:41.5609077Z ``memory_is_causal`` provides a hint that 2025-08-14T21:38:41.5609838Z ``memory_mask`` is the causal mask. Providing incorrect 2025-08-14T21:38:41.5610834Z hints can result in incorrect execution, including 2025-08-14T21:38:41.5611548Z forward and backward compatibility. 2025-08-14T21:38:41.5611959Z 2025-08-14T21:38:41.5612110Z Shape: 2025-08-14T21:38:41.5612780Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-08-14T21:38:41.5613624Z `(N, S, E)` if `batch_first=True`. 2025-08-14T21:38:41.5614451Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-08-14T21:38:41.5615315Z `(N, T, E)` if `batch_first=True`. 2025-08-14T21:38:41.5616053Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-08-14T21:38:41.5616953Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-08-14T21:38:41.5617719Z - memory_mask: :math:`(T, S)`. 2025-08-14T21:38:41.5618592Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-08-14T21:38:41.5619721Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-08-14T21:38:41.5620880Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-08-14T21:38:41.5621775Z 2025-08-14T21:38:41.5622316Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-08-14T21:38:41.5623555Z positions. If a BoolTensor is provided, positions with ``True`` 2025-08-14T21:38:41.5624625Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-08-14T21:38:41.5625605Z is provided, it will be added to the attention weight. 2025-08-14T21:38:41.5626661Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-08-14T21:38:41.5627835Z the attention. If a BoolTensor is provided, the positions with the 2025-08-14T21:38:41.5629028Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-08-14T21:38:41.5629849Z 2025-08-14T21:38:41.5630318Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-08-14T21:38:41.5631227Z `(N, T, E)` if `batch_first=True`. 2025-08-14T21:38:41.5631641Z 2025-08-14T21:38:41.5632084Z Note: Due to the multi-head attention architecture in the transformer model, 2025-08-14T21:38:41.5633189Z the output sequence length of a transformer is same as the input sequence 2025-08-14T21:38:41.5634057Z (i.e. target) length of the decoder. 2025-08-14T21:38:41.5634465Z 2025-08-14T21:38:41.5635036Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-08-14T21:38:41.5636011Z batch size, :math:`E` is the feature number 2025-08-14T21:38:41.5636438Z 2025-08-14T21:38:41.5636604Z Examples: 2025-08-14T21:38:41.5637024Z >>> # xdoctest: +SKIP 2025-08-14T21:38:41.5637551Z >>> output = transformer_model( 2025-08-14T21:38:41.5638190Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-08-14T21:38:41.5638815Z ... ) 2025-08-14T21:38:41.5639193Z 2025-08-14T21:38:41.5639844Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.5640547Z 2025-08-14T21:38:41.5871936Z 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. 2025-08-14T21:38:41.5873921Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.5874650Z 2025-08-14T21:38:41.5875102Z Context manager for training with uneven inputs across processes in DDP. 2025-08-14T21:38:41.5875766Z 2025-08-14T21:38:41.5876366Z This context manager will keep track of already-joined DDP processes, 2025-08-14T21:38:41.5877416Z and "shadow" the forward and backward passes by inserting collective 2025-08-14T21:38:41.5878501Z communication operations to match with the ones created by non-joined 2025-08-14T21:38:41.5879581Z DDP processes. This will ensure each collective call has a corresponding 2025-08-14T21:38:41.5880657Z call by already-joined DDP processes, preventing hangs or errors that 2025-08-14T21:38:41.5881629Z would otherwise happen when training with uneven inputs across 2025-08-14T21:38:41.5882606Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-08-14T21:38:41.5883623Z specified to be ``True``, all trainers will throw an error once one rank 2025-08-14T21:38:41.5884570Z runs out of inputs, allowing these errors to be caught and handled 2025-08-14T21:38:41.5885346Z according to application logic. 2025-08-14T21:38:41.5885674Z 2025-08-14T21:38:41.5886066Z Once all DDP processes have joined, the context manager will broadcast 2025-08-14T21:38:41.5887097Z the model corresponding to the last joined process to all processes to 2025-08-14T21:38:41.5887964Z ensure the model is the same across all processes 2025-08-14T21:38:41.5888615Z (which is guaranteed by DDP). 2025-08-14T21:38:41.5888959Z 2025-08-14T21:38:41.5889301Z To use this to enable training with uneven inputs across processes, 2025-08-14T21:38:41.5890451Z simply wrap this context manager around your training loop. No further 2025-08-14T21:38:41.5891386Z modifications to the model or data loading is required. 2025-08-14T21:38:41.5891921Z 2025-08-14T21:38:41.5892088Z .. warning:: 2025-08-14T21:38:41.5892692Z If the model or training loop this context manager is wrapped around 2025-08-14T21:38:41.5893782Z has additional distributed collective operations, such as 2025-08-14T21:38:41.5894662Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-08-14T21:38:41.5895544Z ``throw_on_early_termination`` must be enabled. This is because this 2025-08-14T21:38:41.5896513Z context manager is not aware of non-DDP collective communication. 2025-08-14T21:38:41.5897421Z This flag will cause all ranks to throw when any one rank 2025-08-14T21:38:41.5898309Z exhausts inputs, allowing these errors to be caught and recovered 2025-08-14T21:38:41.5899083Z from across all ranks. 2025-08-14T21:38:41.5899409Z 2025-08-14T21:38:41.5899552Z Args: 2025-08-14T21:38:41.5900060Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-08-14T21:38:41.5900976Z gradients by the initial ``world_size`` DDP training was launched 2025-08-14T21:38:41.5901848Z with. If ``False``, will compute the effective world size 2025-08-14T21:38:41.5902660Z (number of ranks that have not depleted their inputs yet) and 2025-08-14T21:38:41.5903536Z divide gradients by that during allreduce. Set 2025-08-14T21:38:41.5904291Z ``divide_by_initial_world_size=True`` to ensure every input 2025-08-14T21:38:41.5905179Z sample including the uneven inputs have equal weight in terms of 2025-08-14T21:38:41.5906087Z how much they contribute to the global gradient. This is 2025-08-14T21:38:41.5906938Z achieved by always dividing the gradient by the initial 2025-08-14T21:38:41.5907787Z ``world_size`` even when we encounter uneven inputs. If you set 2025-08-14T21:38:41.5908663Z this to ``False``, we divide the gradient by the remaining 2025-08-14T21:38:41.5909530Z number of nodes. This ensures parity with training on a smaller 2025-08-14T21:38:41.5910404Z ``world_size`` although it also means the uneven inputs would 2025-08-14T21:38:41.5911293Z contribute more towards the global gradient. Typically, you 2025-08-14T21:38:41.5912208Z would want to set this to ``True`` for cases where the last few 2025-08-14T21:38:41.5913092Z inputs of your training job are uneven. In extreme cases, where 2025-08-14T21:38:41.5914006Z there is a large discrepancy in the number of inputs, setting 2025-08-14T21:38:41.5915013Z this to ``False`` might provide better results. 2025-08-14T21:38:41.5915869Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-08-14T21:38:41.5916770Z in ``enable=False`` to disable in cases where you know that 2025-08-14T21:38:41.5917548Z inputs are even across participating processes. Default is 2025-08-14T21:38:41.5918191Z ``True``. 2025-08-14T21:38:41.5918739Z throw_on_early_termination (bool): Whether to throw an error 2025-08-14T21:38:41.5919568Z or continue training when at least one rank has exhausted 2025-08-14T21:38:41.5920441Z inputs. If ``True``, will throw upon the first rank reaching end 2025-08-14T21:38:41.5921308Z of data. If ``False``, will continue training with a smaller 2025-08-14T21:38:41.5922186Z effective world size until all ranks are joined. Note that if 2025-08-14T21:38:41.5922975Z this flag is specified, then the flag 2025-08-14T21:38:41.5923722Z ``divide_by_initial_world_size`` would be ignored. Default 2025-08-14T21:38:41.5924423Z is ``False``. 2025-08-14T21:38:41.5924695Z 2025-08-14T21:38:41.5924703Z 2025-08-14T21:38:41.5924866Z Example:: 2025-08-14T21:38:41.5925100Z 2025-08-14T21:38:41.5925294Z >>> # xdoctest: +SKIP("Distributed") 2025-08-14T21:38:41.5926059Z >>> import torch 2025-08-14T21:38:41.5926528Z >>> import torch.distributed as dist 2025-08-14T21:38:41.5927089Z >>> import os 2025-08-14T21:38:41.5927568Z >>> import torch.multiprocessing as mp 2025-08-14T21:38:41.5928155Z >>> import torch.nn as nn 2025-08-14T21:38:41.5928681Z >>> # On each spawned worker 2025-08-14T21:38:41.5929203Z >>> def worker(rank): 2025-08-14T21:38:41.5929780Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-08-14T21:38:41.5930493Z >>> torch.cuda.set_device(rank) 2025-08-14T21:38:41.5931076Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-08-14T21:38:41.5931800Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-08-14T21:38:41.5932550Z >>> model, device_ids=[rank], output_device=rank 2025-08-14T21:38:41.5933149Z >>> ) 2025-08-14T21:38:41.5933583Z >>> # Rank 1 gets one more input than rank 0. 2025-08-14T21:38:41.5934333Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-08-14T21:38:41.5935068Z >>> with model.join(): 2025-08-14T21:38:41.5935549Z >>> for _ in range(5): 2025-08-14T21:38:41.5936057Z >>> for inp in inputs: 2025-08-14T21:38:41.5936623Z >>> loss = model(inp).sum() 2025-08-14T21:38:41.5937203Z >>> loss.backward() 2025-08-14T21:38:41.5937913Z >>> # Without the join() API, the below synchronization will hang 2025-08-14T21:38:41.5938713Z >>> # blocking for rank 1's allreduce to complete. 2025-08-14T21:38:41.5939389Z >>> torch.cuda.synchronize(device=rank) 2025-08-14T21:38:41.5939829Z 2025-08-14T21:38:41.5940319Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.5941027Z 2025-08-14T21:38:41.5942450Z 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. 2025-08-14T21:38:41.5944541Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.5945228Z 2025-08-14T21:38:41.5945764Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-08-14T21:38:41.5946565Z 2025-08-14T21:38:41.5946932Z Registers an optimizer with DDP such that the optimization for a 2025-08-14T21:38:41.5947869Z parameter will run immediately when that parameter's gradient is 2025-08-14T21:38:41.5948789Z finished with reduction, instead of waiting for all parameters' 2025-08-14T21:38:41.5949706Z gradients to finish reduction. This can result in a training speedup 2025-08-14T21:38:41.5950827Z depending on your workload since the optimizer can run while gradient 2025-08-14T21:38:41.5951873Z reduction for other parameters are still ongoing. In addition, this has 2025-08-14T21:38:41.5952910Z the potential to reduce peak memory consumption during training, as it 2025-08-14T21:38:41.5953888Z only needs to load the per-parameter optimizer states of a single 2025-08-14T21:38:41.5954861Z parameter at a time, instead of loading all per-parameter optimizer 2025-08-14T21:38:41.5955607Z states at once. 2025-08-14T21:38:41.5955872Z 2025-08-14T21:38:41.5956013Z Args: 2025-08-14T21:38:41.5956577Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-08-14T21:38:41.5957317Z as a fused optimizer. 2025-08-14T21:38:41.5957899Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-08-14T21:38:41.5958814Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-08-14T21:38:41.5959823Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-08-14T21:38:41.5960825Z Optimizers. If this is omitted, all DDP model parameters will be 2025-08-14T21:38:41.5961586Z optimized. 2025-08-14T21:38:41.5962183Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-08-14T21:38:41.5962773Z 2025-08-14T21:38:41.5962938Z .. warning :: 2025-08-14T21:38:41.5963699Z _register_fused_optim should only be called once on a DDP instance, 2025-08-14T21:38:41.5964684Z and registering multiple fused optimizers for the same DDP model 2025-08-14T21:38:41.5965499Z is not currently supported. Please ping 2025-08-14T21:38:41.5966333Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-08-14T21:38:41.5967142Z for your use case. 2025-08-14T21:38:41.5967397Z 2025-08-14T21:38:41.5967553Z .. warning :: 2025-08-14T21:38:41.5968099Z _register_fused_optim and register_comm_hook currently do not 2025-08-14T21:38:41.5968990Z compose together, meaning that custom DDP communication hooks are 2025-08-14T21:38:41.5969864Z not supported with overlapped optimizers. Please ping 2025-08-14T21:38:41.5970782Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-08-14T21:38:41.5971581Z for your use case. 2025-08-14T21:38:41.5971880Z 2025-08-14T21:38:41.5972039Z .. warning :: 2025-08-14T21:38:41.5972691Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-08-14T21:38:41.5973526Z with overlapped optimizer. Please ping 2025-08-14T21:38:41.5974367Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-08-14T21:38:41.5975175Z for your use case. 2025-08-14T21:38:41.5975452Z 2025-08-14T21:38:41.5975606Z Example:: 2025-08-14T21:38:41.5975830Z 2025-08-14T21:38:41.5976052Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-08-14T21:38:41.5977040Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-08-14T21:38:41.5978198Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-08-14T21:38:41.5978925Z >>> lr = 1e-2 2025-08-14T21:38:41.5979346Z >>> betas = (0.9, 0.99) 2025-08-14T21:38:41.5979819Z >>> eps = 1e-6 2025-08-14T21:38:41.5980438Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-08-14T21:38:41.5981242Z >>> # Example with subset of parameters 2025-08-14T21:38:41.5981858Z >>> params_to_opt = [list(net.parameters())[0]] 2025-08-14T21:38:41.5982485Z >>> net._register_fused_optim( 2025-08-14T21:38:41.5983239Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-08-14T21:38:41.5984125Z ... ) 2025-08-14T21:38:41.5984335Z 2025-08-14T21:38:41.5984801Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.5985452Z 2025-08-14T21:38:41.6195354Z 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. 2025-08-14T21:38:41.6197519Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.6198594Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-08-14T21:38:41.6199192Z 2025-08-14T21:38:41.6199688Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-08-14T21:38:41.6200969Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-08-14T21:38:41.6202161Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-08-14T21:38:41.6203316Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-08-14T21:38:41.6204118Z 2025-08-14T21:38:41.6204275Z .. note:: 2025-08-14T21:38:41.6204925Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-08-14T21:38:41.6205941Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-08-14T21:38:41.6206936Z layer with 4d weight will be affected by ``model.to``, which does not 2025-08-14T21:38:41.6207932Z necessarily benefit from conversion to specified ``memory_format``. 2025-08-14T21:38:41.6208959Z One place we are confident in is that NHWC(channels_last) conversion for 2025-08-14T21:38:41.6209982Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-08-14T21:38:41.6211203Z even in cases where we have to apply permutation to input tensors. 2025-08-14T21:38:41.6211797Z 2025-08-14T21:38:41.6212222Z Hence our strategy here is to convert only the weight of convolution to 2025-08-14T21:38:41.6213033Z channels_last. This ensures that; 2025-08-14T21:38:41.6213832Z 1. Fast convolution kernels will be used, the benefit of which could 2025-08-14T21:38:41.6214881Z outweigh overhead of permutation (if input is not in the same format). 2025-08-14T21:38:41.6215959Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-08-14T21:38:41.6216815Z from memory_format conversion. 2025-08-14T21:38:41.6217212Z 2025-08-14T21:38:41.6217622Z The optimal case is that, layers between convolution layers are channels 2025-08-14T21:38:41.6218694Z last compatible. Input tensor would be permuted to channels last when it 2025-08-14T21:38:41.6219774Z encounters the first convolution layer and stay in that memory format. 2025-08-14T21:38:41.6220860Z Hence following convolutions will not need to permute its input tensor. 2025-08-14T21:38:41.6221473Z 2025-08-14T21:38:41.6221872Z In case where a channels last incompatible layer is between convolution 2025-08-14T21:38:41.6222892Z layers, we need to permute the input tensor back to contiguous format 2025-08-14T21:38:41.6224003Z for that layer. The input tensor will go through the remaining layers in 2025-08-14T21:38:41.6225057Z contiguous format and be permuted to channels last when it encounters 2025-08-14T21:38:41.6226103Z another convolution layer. There's no point in propagating that 2025-08-14T21:38:41.6227114Z permutation to an earlier layer, as most layers are quite agnostic to 2025-08-14T21:38:41.6227925Z ``memory_format``. 2025-08-14T21:38:41.6228250Z 2025-08-14T21:38:41.6228680Z This claim might change when PyTorch supports fusion of permutation, as 2025-08-14T21:38:41.6229755Z there might have been a better spot to fuse the permutation other than 2025-08-14T21:38:41.6230581Z immediately before a convolution. 2025-08-14T21:38:41.6231005Z 2025-08-14T21:38:41.6231154Z Args: 2025-08-14T21:38:41.6231751Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-08-14T21:38:41.6232525Z ``nn.Module`` 2025-08-14T21:38:41.6233181Z memory_format: user specified ``memory_format``, 2025-08-14T21:38:41.6233979Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-08-14T21:38:41.6234528Z 2025-08-14T21:38:41.6234687Z Returns: 2025-08-14T21:38:41.6235294Z The original module with updated ``nn.Conv2d`` 2025-08-14T21:38:41.6235759Z 2025-08-14T21:38:41.6235886Z Example: 2025-08-14T21:38:41.6236349Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-14T21:38:41.6237064Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-08-14T21:38:41.6237681Z >>> input = torch.randint( 2025-08-14T21:38:41.6238292Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-08-14T21:38:41.6238867Z ... ) 2025-08-14T21:38:41.6239267Z >>> model = nn.Sequential( 2025-08-14T21:38:41.6239810Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-08-14T21:38:41.6240387Z >>> # This is identical to: 2025-08-14T21:38:41.6241149Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-08-14T21:38:41.6242121Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-08-14T21:38:41.6242827Z ... model, torch.channels_last 2025-08-14T21:38:41.6243357Z ... ) 2025-08-14T21:38:41.6243754Z >>> out = model(input) 2025-08-14T21:38:41.6244214Z 2025-08-14T21:38:41.6244853Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.6245687Z 2025-08-14T21:38:41.6246852Z 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. 2025-08-14T21:38:41.6248789Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.6249920Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-08-14T21:38:41.6251063Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-08-14T21:38:41.6252306Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-08-14T21:38:41.6253530Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-08-14T21:38:41.6254749Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-08-14T21:38:41.6255512Z 2025-08-14T21:38:41.6255686Z .. note:: 2025-08-14T21:38:41.6256348Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-08-14T21:38:41.6257422Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-08-14T21:38:41.6258406Z layer with 4d weight will be affected by ``model.to``, which does not 2025-08-14T21:38:41.6259427Z necessarily benefit from conversion to specified ``memory_format``. 2025-08-14T21:38:41.6260476Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-08-14T21:38:41.6261523Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-08-14T21:38:41.6262543Z even in cases where we have to apply permutation to input tensors. 2025-08-14T21:38:41.6263145Z 2025-08-14T21:38:41.6263648Z Hence our strategy here is to convert only the weight of convolution to 2025-08-14T21:38:41.6264502Z channels_last_3d. This ensures that; 2025-08-14T21:38:41.6265275Z 1. Fast convolution kernels will be used, the benefit of which could 2025-08-14T21:38:41.6266307Z outweigh overhead of permutation (if input is not in the same format). 2025-08-14T21:38:41.6267381Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-08-14T21:38:41.6268209Z from memory_format conversion. 2025-08-14T21:38:41.6268588Z 2025-08-14T21:38:41.6269007Z The optimal case is that, layers between convolution layers are channels 2025-08-14T21:38:41.6270046Z last compatible. Input tensor would be permuted to channels last when it 2025-08-14T21:38:41.6271124Z encounters the first convolution layer and stay in that memory format. 2025-08-14T21:38:41.6272232Z Hence following convolutions will not need to permute its input tensor. 2025-08-14T21:38:41.6273052Z 2025-08-14T21:38:41.6273493Z In case where a channels last incompatible layer is between convolution 2025-08-14T21:38:41.6274526Z layers, we need to permute the input tensor back to contiguous format 2025-08-14T21:38:41.6275556Z for that layer. The input tensor will go through the remaining layers in 2025-08-14T21:38:41.6276632Z contiguous format and be permuted to channels last when it encounters 2025-08-14T21:38:41.6277638Z another convolution layer. There's no point in propagating that 2025-08-14T21:38:41.6278592Z permutation to an earlier layer, as most layers are quite agnostic to 2025-08-14T21:38:41.6279393Z ``memory_format``. 2025-08-14T21:38:41.6279700Z 2025-08-14T21:38:41.6280142Z This claim might change when PyTorch supports fusion of permutation, as 2025-08-14T21:38:41.6281158Z there might have been a better spot to fuse the permutation other than 2025-08-14T21:38:41.6281978Z immediately before a convolution. 2025-08-14T21:38:41.6282393Z 2025-08-14T21:38:41.6282542Z Args: 2025-08-14T21:38:41.6283122Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-08-14T21:38:41.6283872Z ``nn.Module`` 2025-08-14T21:38:41.6284664Z memory_format: user specified ``memory_format``, 2025-08-14T21:38:41.6285444Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-08-14T21:38:41.6285989Z 2025-08-14T21:38:41.6286133Z Returns: 2025-08-14T21:38:41.6286586Z The original module with updated ``nn.Conv3d`` 2025-08-14T21:38:41.6287032Z 2025-08-14T21:38:41.6287175Z Example: 2025-08-14T21:38:41.6287617Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-14T21:38:41.6288324Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-08-14T21:38:41.6288986Z >>> input = torch.randint( 2025-08-14T21:38:41.6289618Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-08-14T21:38:41.6290239Z ... ) 2025-08-14T21:38:41.6290653Z >>> model = nn.Sequential( 2025-08-14T21:38:41.6291186Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-08-14T21:38:41.6291752Z >>> # This is identical to: 2025-08-14T21:38:41.6292578Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-08-14T21:38:41.6293750Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-08-14T21:38:41.6294457Z ... model, torch.channels_last_3d 2025-08-14T21:38:41.6294993Z ... ) 2025-08-14T21:38:41.6295377Z >>> out = model(input) 2025-08-14T21:38:41.6295830Z 2025-08-14T21:38:41.6296464Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.6297160Z 2025-08-14T21:38:41.6346275Z 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. 2025-08-14T21:38:41.6347462Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.6347993Z Register a parametrization to a tensor in a module. 2025-08-14T21:38:41.6348254Z 2025-08-14T21:38:41.6348536Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-08-14T21:38:41.6349187Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-08-14T21:38:41.6349831Z If the original tensor requires a gradient, the backward pass will differentiate 2025-08-14T21:38:41.6350487Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-08-14T21:38:41.6350884Z 2025-08-14T21:38:41.6351195Z The first time that a module registers a parametrization, this function will add an attribute 2025-08-14T21:38:41.6351836Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-08-14T21:38:41.6352195Z 2025-08-14T21:38:41.6352620Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-08-14T21:38:41.6353113Z ``module.parametrizations.weight``. 2025-08-14T21:38:41.6353336Z 2025-08-14T21:38:41.6353480Z The original tensor will be accessible under 2025-08-14T21:38:41.6353852Z ``module.parametrizations.weight.original``. 2025-08-14T21:38:41.6354117Z 2025-08-14T21:38:41.6354372Z Parametrizations may be concatenated by registering several parametrizations 2025-08-14T21:38:41.6354847Z on the same attribute. 2025-08-14T21:38:41.6355017Z 2025-08-14T21:38:41.6355253Z The training mode of a registered parametrization is updated on registration 2025-08-14T21:38:41.6355735Z to match the training mode of the host module 2025-08-14T21:38:41.6355979Z 2025-08-14T21:38:41.6356275Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-08-14T21:38:41.6356813Z using the context manager :func:`cached`. 2025-08-14T21:38:41.6357038Z 2025-08-14T21:38:41.6357276Z A :attr:`parametrization` may optionally implement a method with signature 2025-08-14T21:38:41.6357633Z 2025-08-14T21:38:41.6357738Z .. code-block:: python 2025-08-14T21:38:41.6357906Z 2025-08-14T21:38:41.6358127Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-08-14T21:38:41.6358531Z 2025-08-14T21:38:41.6358797Z This method is called on the unparametrized tensor when the first parametrization 2025-08-14T21:38:41.6359367Z is registered to compute the initial value of the original tensor. 2025-08-14T21:38:41.6359988Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-08-14T21:38:41.6360401Z 2025-08-14T21:38:41.6360715Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-08-14T21:38:41.6361417Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-08-14T21:38:41.6361810Z 2025-08-14T21:38:41.6362036Z It is possible for the first parametrization to depend on several inputs. 2025-08-14T21:38:41.6362614Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-08-14T21:38:41.6363196Z (see the example implementation of a ``RankOne`` parametrization below). 2025-08-14T21:38:41.6363537Z 2025-08-14T21:38:41.6363873Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-08-14T21:38:41.6364431Z with names ``original0``, ``original1``,... 2025-08-14T21:38:41.6364671Z 2025-08-14T21:38:41.6364755Z .. note:: 2025-08-14T21:38:41.6364879Z 2025-08-14T21:38:41.6365149Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-08-14T21:38:41.6365663Z once to perform a number of consistency checks. 2025-08-14T21:38:41.6366175Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-08-14T21:38:41.6366669Z and nothing will be called otherwise. 2025-08-14T21:38:41.6366893Z 2025-08-14T21:38:41.6366987Z .. note:: 2025-08-14T21:38:41.6367106Z 2025-08-14T21:38:41.6367304Z In most situations, ``right_inverse`` will be a function such that 2025-08-14T21:38:41.6367732Z ``forward(right_inverse(X)) == X`` (see 2025-08-14T21:38:41.6368239Z `right inverse `_). 2025-08-14T21:38:41.6368867Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-08-14T21:38:41.6369320Z to relax this. 2025-08-14T21:38:41.6369480Z 2025-08-14T21:38:41.6369566Z .. warning:: 2025-08-14T21:38:41.6369697Z 2025-08-14T21:38:41.6369973Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-08-14T21:38:41.6370598Z will register a number of new parameters. If such parametrization is registered 2025-08-14T21:38:41.6371223Z after the optimizer is created, these new parameters will need to be added manually 2025-08-14T21:38:41.6371861Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-08-14T21:38:41.6372163Z 2025-08-14T21:38:41.6372254Z Args: 2025-08-14T21:38:41.6372564Z module (nn.Module): module on which to register the parametrization 2025-08-14T21:38:41.6373096Z tensor_name (str): name of the parameter or buffer on which to register 2025-08-14T21:38:41.6373524Z the parametrization 2025-08-14T21:38:41.6373899Z parametrization (nn.Module): the parametrization to register 2025-08-14T21:38:41.6374299Z Keyword args: 2025-08-14T21:38:41.6374652Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-08-14T21:38:41.6375152Z may change the dtype and shape of the tensor. Default: `False` 2025-08-14T21:38:41.6375717Z Warning: the parametrization is not checked for consistency upon registration. 2025-08-14T21:38:41.6376209Z Enable this flag at your own risk. 2025-08-14T21:38:41.6376425Z 2025-08-14T21:38:41.6376521Z Raises: 2025-08-14T21:38:41.6376912Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-08-14T21:38:41.6377318Z 2025-08-14T21:38:41.6377401Z Examples: 2025-08-14T21:38:41.6377679Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-08-14T21:38:41.6378086Z >>> import torch 2025-08-14T21:38:41.6378340Z >>> import torch.nn as nn 2025-08-14T21:38:41.6378665Z >>> import torch.nn.utils.parametrize as P 2025-08-14T21:38:41.6378996Z >>> 2025-08-14T21:38:41.6379211Z >>> class Symmetric(nn.Module): 2025-08-14T21:38:41.6379524Z >>> def forward(self, X): 2025-08-14T21:38:41.6379898Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-08-14T21:38:41.6380259Z >>> 2025-08-14T21:38:41.6380484Z >>> def right_inverse(self, A): 2025-08-14T21:38:41.6380794Z >>> return A.triu() 2025-08-14T21:38:41.6381057Z >>> 2025-08-14T21:38:41.6381292Z >>> m = nn.Linear(5, 5) 2025-08-14T21:38:41.6381643Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-08-14T21:38:41.6382145Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-08-14T21:38:41.6382590Z True 2025-08-14T21:38:41.6382817Z >>> A = torch.rand(5, 5) 2025-08-14T21:38:41.6383117Z >>> A = A + A.T # A is now symmetric 2025-08-14T21:38:41.6383612Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-08-14T21:38:41.6384054Z >>> print(torch.allclose(m.weight, A)) 2025-08-14T21:38:41.6384368Z True 2025-08-14T21:38:41.6384489Z 2025-08-14T21:38:41.6384593Z >>> class RankOne(nn.Module): 2025-08-14T21:38:41.6384906Z >>> def forward(self, x, y): 2025-08-14T21:38:41.6385260Z >>> # Form a rank 1 matrix multiplying two vectors 2025-08-14T21:38:41.6385638Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-08-14T21:38:41.6385976Z >>> 2025-08-14T21:38:41.6386209Z >>> def right_inverse(self, Z): 2025-08-14T21:38:41.6386545Z >>> # Project Z onto the rank 1 matrices 2025-08-14T21:38:41.6386912Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-08-14T21:38:41.6387303Z >>> # Return rescaled singular vectors 2025-08-14T21:38:41.6387645Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-08-14T21:38:41.6388003Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-08-14T21:38:41.6388347Z >>> 2025-08-14T21:38:41.6388619Z >>> linear_rank_one = P.register_parametrization( 2025-08-14T21:38:41.6388989Z ... nn.Linear(4, 4), "weight", RankOne() 2025-08-14T21:38:41.6389306Z ... ) 2025-08-14T21:38:41.6389634Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-08-14T21:38:41.6390028Z 1 2025-08-14T21:38:41.6390139Z 2025-08-14T21:38:41.6390221Z 2025-08-14T21:38:41.6390652Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.6391009Z 2025-08-14T21:38:41.6449348Z 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. 2025-08-14T21:38:41.6450250Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.6450909Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-08-14T21:38:41.6451330Z 2025-08-14T21:38:41.6451557Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-08-14T21:38:41.6452113Z by removing the specified ``amount`` of (currently unpruned) channels 2025-08-14T21:38:41.6452624Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-08-14T21:38:41.6453091Z Modifies module in place (and also return the modified module) 2025-08-14T21:38:41.6453576Z by: 2025-08-14T21:38:41.6453692Z 2025-08-14T21:38:41.6453910Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-08-14T21:38:41.6454436Z binary mask applied to the parameter ``name`` by the pruning method. 2025-08-14T21:38:41.6455111Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-08-14T21:38:41.6456219Z original (unpruned) parameter is stored in a new parameter named 2025-08-14T21:38:41.6456632Z ``name+'_orig'``. 2025-08-14T21:38:41.6456791Z 2025-08-14T21:38:41.6456871Z Args: 2025-08-14T21:38:41.6457167Z module (nn.Module): module containing the tensor to prune 2025-08-14T21:38:41.6457633Z name (str): parameter name within ``module`` on which pruning 2025-08-14T21:38:41.6458060Z will act. 2025-08-14T21:38:41.6458413Z amount (int or float): quantity of parameters to prune. 2025-08-14T21:38:41.6459183Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-08-14T21:38:41.6459915Z fraction of parameters to prune. If ``int``, it represents the 2025-08-14T21:38:41.6460344Z absolute number of parameters to prune. 2025-08-14T21:38:41.6460777Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-08-14T21:38:41.6461238Z entries for argument ``p`` in :func:`torch.norm`. 2025-08-14T21:38:41.6461692Z dim (int): index of the dim along which we define channels to prune. 2025-08-14T21:38:41.6462234Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-08-14T21:38:41.6462767Z shape as module parameter) used to compute mask for pruning. 2025-08-14T21:38:41.6463282Z The values in this tensor indicate the importance of the corresponding 2025-08-14T21:38:41.6463816Z elements in the parameter being pruned. 2025-08-14T21:38:41.6464281Z If unspecified or None, the module parameter will be used in its place. 2025-08-14T21:38:41.6464619Z 2025-08-14T21:38:41.6464721Z Returns: 2025-08-14T21:38:41.6465050Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-08-14T21:38:41.6465390Z 2025-08-14T21:38:41.6465475Z Examples: 2025-08-14T21:38:41.6465725Z >>> from torch.nn.utils import prune 2025-08-14T21:38:41.6466044Z >>> m = prune.ln_structured( 2025-08-14T21:38:41.6466426Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-08-14T21:38:41.6466799Z ... ) 2025-08-14T21:38:41.6467010Z 2025-08-14T21:38:41.6467355Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.6467727Z 2025-08-14T21:38:41.6468287Z 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. 2025-08-14T21:38:41.6469180Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.6469545Z 2025-08-14T21:38:41.6470095Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-08-14T21:38:41.6470629Z 2025-08-14T21:38:41.6470750Z Modifies modules in place by: 2025-08-14T21:38:41.6470933Z 2025-08-14T21:38:41.6471134Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-08-14T21:38:41.6471665Z binary mask applied to the parameter ``name`` by the pruning method. 2025-08-14T21:38:41.6472195Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-08-14T21:38:41.6472703Z original (unpruned) parameter is stored in a new parameter named 2025-08-14T21:38:41.6473111Z ``name+'_orig'``. 2025-08-14T21:38:41.6473271Z 2025-08-14T21:38:41.6473350Z Args: 2025-08-14T21:38:41.6473664Z parameters (Iterable of (module, name) tuples): parameters of 2025-08-14T21:38:41.6474149Z the model to prune in a global fashion, i.e. by aggregating all 2025-08-14T21:38:41.6474656Z weights prior to deciding which ones to prune. module must be of 2025-08-14T21:38:41.6475123Z type :class:`nn.Module`, and name must be a string. 2025-08-14T21:38:41.6475592Z pruning_method (function): a valid pruning function from this module, 2025-08-14T21:38:41.6476102Z or a custom one implemented by the user that satisfies the 2025-08-14T21:38:41.6476695Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-08-14T21:38:41.6477263Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-08-14T21:38:41.6477815Z the corresponding parameter's importance scores tensor. The tensor 2025-08-14T21:38:41.6478354Z should be the same shape as the parameter, and is used for computing 2025-08-14T21:38:41.6478770Z mask for pruning. 2025-08-14T21:38:41.6479129Z If unspecified or None, the parameter will be used in place of its 2025-08-14T21:38:41.6479550Z importance scores. 2025-08-14T21:38:41.6479843Z kwargs: other keyword arguments such as: 2025-08-14T21:38:41.6480266Z amount (int or float): quantity of parameters to prune across the 2025-08-14T21:38:41.6480670Z specified parameters. 2025-08-14T21:38:41.6481026Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-08-14T21:38:41.6481508Z fraction of parameters to prune. If ``int``, it represents the 2025-08-14T21:38:41.6481934Z absolute number of parameters to prune. 2025-08-14T21:38:41.6482179Z 2025-08-14T21:38:41.6482260Z Raises: 2025-08-14T21:38:41.6482531Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-08-14T21:38:41.6482785Z 2025-08-14T21:38:41.6482866Z Note: 2025-08-14T21:38:41.6483197Z Since global structured pruning doesn't make much sense unless the 2025-08-14T21:38:41.6483721Z norm is normalized by the size of the parameter, we now limit the 2025-08-14T21:38:41.6484182Z scope of global pruning to unstructured methods. 2025-08-14T21:38:41.6484431Z 2025-08-14T21:38:41.6484520Z Examples: 2025-08-14T21:38:41.6484764Z >>> from torch.nn.utils import prune 2025-08-14T21:38:41.6485107Z >>> from collections import OrderedDict 2025-08-14T21:38:41.6485422Z >>> net = nn.Sequential( 2025-08-14T21:38:41.6485699Z ... OrderedDict( 2025-08-14T21:38:41.6485952Z ... [ 2025-08-14T21:38:41.6486194Z ... ("first", nn.Linear(10, 4)), 2025-08-14T21:38:41.6486538Z ... ("second", nn.Linear(4, 1)), 2025-08-14T21:38:41.6486842Z ... ] 2025-08-14T21:38:41.6487044Z ... ) 2025-08-14T21:38:41.6487251Z ... ) 2025-08-14T21:38:41.6487470Z >>> parameters_to_prune = ( 2025-08-14T21:38:41.6487761Z ... (net.first, "weight"), 2025-08-14T21:38:41.6488043Z ... (net.second, "weight"), 2025-08-14T21:38:41.6488324Z ... ) 2025-08-14T21:38:41.6488550Z >>> prune.global_unstructured( 2025-08-14T21:38:41.6488839Z ... parameters_to_prune, 2025-08-14T21:38:41.6489156Z ... pruning_method=prune.L1Unstructured, 2025-08-14T21:38:41.6489477Z ... amount=10, 2025-08-14T21:38:41.6489758Z ... ) 2025-08-14T21:38:41.6490095Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-08-14T21:38:41.6490514Z tensor(10) 2025-08-14T21:38:41.6490640Z 2025-08-14T21:38:41.6490645Z 2025-08-14T21:38:41.6490889Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.6491261Z 2025-08-14T21:38:41.6491765Z 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. 2025-08-14T21:38:41.6492633Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.6493580Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-08-14T21:38:41.6494073Z 2025-08-14T21:38:41.6494294Z Modifies module in place (and also return the modified module) by: 2025-08-14T21:38:41.6494612Z 2025-08-14T21:38:41.6494819Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-08-14T21:38:41.6495355Z binary mask applied to the parameter ``name`` by the pruning method. 2025-08-14T21:38:41.6495893Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-08-14T21:38:41.6496497Z original (unpruned) parameter is stored in a new parameter named 2025-08-14T21:38:41.6496913Z ``name+'_orig'``. 2025-08-14T21:38:41.6497086Z 2025-08-14T21:38:41.6497167Z Args: 2025-08-14T21:38:41.6497459Z module (nn.Module): module containing the tensor to prune 2025-08-14T21:38:41.6497911Z name (str): parameter name within ``module`` on which pruning 2025-08-14T21:38:41.6498289Z will act. 2025-08-14T21:38:41.6498611Z mask (Tensor): binary mask to be applied to the parameter. 2025-08-14T21:38:41.6498891Z 2025-08-14T21:38:41.6498973Z Returns: 2025-08-14T21:38:41.6499314Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-08-14T21:38:41.6499640Z 2025-08-14T21:38:41.6499740Z Examples: 2025-08-14T21:38:41.6499977Z >>> from torch.nn.utils import prune 2025-08-14T21:38:41.6500309Z >>> m = prune.custom_from_mask( 2025-08-14T21:38:41.6500690Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-08-14T21:38:41.6501062Z ... ) 2025-08-14T21:38:41.6501274Z >>> print(m.bias_mask) 2025-08-14T21:38:41.6501552Z tensor([0., 1., 0.]) 2025-08-14T21:38:41.6501719Z 2025-08-14T21:38:41.6501811Z 2025-08-14T21:38:41.6502154Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.6502523Z 2025-08-14T21:38:41.6515153Z 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. 2025-08-14T21:38:41.6516073Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.6516588Z Pad a packed batch of variable length sequences. 2025-08-14T21:38:41.6516845Z 2025-08-14T21:38:41.6517031Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-08-14T21:38:41.6517312Z 2025-08-14T21:38:41.6517583Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-08-14T21:38:41.6518212Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-08-14T21:38:41.6518674Z sequence and ``B`` is the batch size. 2025-08-14T21:38:41.6518890Z 2025-08-14T21:38:41.6518973Z Example: 2025-08-14T21:38:41.6519339Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-08-14T21:38:41.6519838Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-08-14T21:38:41.6520189Z >>> lens = [2, 1, 3] 2025-08-14T21:38:41.6520462Z >>> packed = pack_padded_sequence( 2025-08-14T21:38:41.6520833Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-08-14T21:38:41.6521178Z ... ) 2025-08-14T21:38:41.6521513Z >>> packed 2025-08-14T21:38:41.6521890Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-08-14T21:38:41.6522459Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-08-14T21:38:41.6523033Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-08-14T21:38:41.6523471Z >>> seq_unpacked 2025-08-14T21:38:41.6523726Z tensor([[1, 2, 0], 2025-08-14T21:38:41.6523979Z [3, 0, 0], 2025-08-14T21:38:41.6524214Z [4, 5, 6]]) 2025-08-14T21:38:41.6524467Z >>> lens_unpacked 2025-08-14T21:38:41.6524722Z tensor([2, 1, 3]) 2025-08-14T21:38:41.6524878Z 2025-08-14T21:38:41.6524967Z .. note:: 2025-08-14T21:38:41.6525238Z :attr:`total_length` is useful to implement the 2025-08-14T21:38:41.6525709Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-08-14T21:38:41.6526252Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-08-14T21:38:41.6526813Z See :ref:`this FAQ section ` for 2025-08-14T21:38:41.6527243Z details. 2025-08-14T21:38:41.6527374Z 2025-08-14T21:38:41.6527529Z Args: 2025-08-14T21:38:41.6527762Z sequence (PackedSequence): batch to pad 2025-08-14T21:38:41.6528229Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-08-14T21:38:41.6528691Z format, ``T x B x *`` otherwise. 2025-08-14T21:38:41.6529084Z padding_value (float, optional): values for padded elements. 2025-08-14T21:38:41.6529612Z total_length (int, optional): if not ``None``, the output will be padded to 2025-08-14T21:38:41.6530190Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-08-14T21:38:41.6530736Z if :attr:`total_length` is less than the max sequence length in 2025-08-14T21:38:41.6531128Z :attr:`sequence`. 2025-08-14T21:38:41.6531315Z 2025-08-14T21:38:41.6531397Z Returns: 2025-08-14T21:38:41.6531714Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-08-14T21:38:41.6532191Z containing the list of lengths of each sequence in the batch. 2025-08-14T21:38:41.6532725Z Batch elements will be re-ordered as they were ordered originally when 2025-08-14T21:38:41.6533281Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-08-14T21:38:41.6533697Z 2025-08-14T21:38:41.6534045Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.6534415Z 2025-08-14T21:38:41.7166604Z msg = Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=808. 2025-08-14T21:38:41.7167520Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.7168235Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-08-14T21:38:41.7168672Z 2025-08-14T21:38:41.7169033Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-08-14T21:38:41.7169732Z intervals by which each scheduler should be called at a given epoch. 2025-08-14T21:38:41.7170076Z 2025-08-14T21:38:41.7170160Z Args: 2025-08-14T21:38:41.7170421Z optimizer (Optimizer): Wrapped optimizer. 2025-08-14T21:38:41.7170795Z schedulers (list): List of chained schedulers. 2025-08-14T21:38:41.7171264Z milestones (list): List of integers that reflects milestone points. 2025-08-14T21:38:41.7171758Z last_epoch (int): The index of last epoch. Default: -1. 2025-08-14T21:38:41.7172030Z 2025-08-14T21:38:41.7172114Z Example: 2025-08-14T21:38:41.7172343Z >>> # xdoctest: +SKIP 2025-08-14T21:38:41.7172670Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-08-14T21:38:41.7173271Z >>> # lr = 0.005 if epoch == 0 2025-08-14T21:38:41.7173587Z >>> # lr = 0.005 if epoch == 1 2025-08-14T21:38:41.7173900Z >>> # lr = 0.005 if epoch == 2 2025-08-14T21:38:41.7174188Z >>> # ... 2025-08-14T21:38:41.7174413Z >>> # lr = 0.05 if epoch == 20 2025-08-14T21:38:41.7174725Z >>> # lr = 0.045 if epoch == 21 2025-08-14T21:38:41.7175033Z >>> # lr = 0.0405 if epoch == 22 2025-08-14T21:38:41.7175422Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-08-14T21:38:41.7175902Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-08-14T21:38:41.7176271Z >>> scheduler = SequentialLR( 2025-08-14T21:38:41.7176562Z ... optimizer, 2025-08-14T21:38:41.7176861Z ... schedulers=[scheduler1, scheduler2], 2025-08-14T21:38:41.7177200Z ... milestones=[20], 2025-08-14T21:38:41.7177471Z ... ) 2025-08-14T21:38:41.7177696Z >>> for epoch in range(100): 2025-08-14T21:38:41.7177990Z >>> train(...) 2025-08-14T21:38:41.7178252Z >>> validate(...) 2025-08-14T21:38:41.7178520Z >>> scheduler.step() 2025-08-14T21:38:41.7178713Z 2025-08-14T21:38:41.7178892Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-08-14T21:38:41.7179333Z 2025-08-14T21:38:41.7179682Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.7180057Z 2025-08-14T21:38:41.7205438Z msg = Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1233. 2025-08-14T21:38:41.7206481Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:41.7207020Z Reduce learning rate when a metric has stopped improving. 2025-08-14T21:38:41.7207307Z 2025-08-14T21:38:41.7207517Z Models often benefit from reducing the learning rate by a factor 2025-08-14T21:38:41.7208013Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-08-14T21:38:41.7208501Z quantity and if no improvement is seen for a 'patience' number 2025-08-14T21:38:41.7208926Z of epochs, the learning rate is reduced. 2025-08-14T21:38:41.7209147Z 2025-08-14T21:38:41.7209230Z Args: 2025-08-14T21:38:41.7209487Z optimizer (Optimizer): Wrapped optimizer. 2025-08-14T21:38:41.7209880Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-08-14T21:38:41.7210288Z be reduced when the quantity monitored has stopped 2025-08-14T21:38:41.7210715Z decreasing; in `max` mode it will be reduced when the 2025-08-14T21:38:41.7211178Z quantity monitored has stopped increasing. Default: 'min'. 2025-08-14T21:38:41.7211651Z factor (float): Factor by which the learning rate will be 2025-08-14T21:38:41.7212055Z reduced. new_lr = lr * factor. Default: 0.1. 2025-08-14T21:38:41.7212518Z patience (int): The number of allowed epochs with no improvement after 2025-08-14T21:38:41.7212975Z which the learning rate will be reduced. 2025-08-14T21:38:41.7213454Z For example, consider the case of having no patience (`patience = 0`). 2025-08-14T21:38:41.7214522Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-08-14T21:38:41.7215639Z In the second epoch, if the performance is worse than the baseline, 2025-08-14T21:38:41.7216182Z we have what is considered an intolerable epoch. 2025-08-14T21:38:41.7216688Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-08-14T21:38:41.7217231Z the learning rate is reduced at the end of this epoch. 2025-08-14T21:38:41.7217855Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-08-14T21:38:41.7218742Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-08-14T21:38:41.7219290Z the learning rate is not adjusted. 2025-08-14T21:38:41.7219594Z Default: 10. 2025-08-14T21:38:41.7219944Z threshold (float): Threshold for measuring the new optimum, 2025-08-14T21:38:41.7220402Z to only focus on significant changes. Default: 1e-4. 2025-08-14T21:38:41.7220843Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-08-14T21:38:41.7221269Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-08-14T21:38:41.7221759Z mode or best * ( 1 - threshold ) in `min` mode. 2025-08-14T21:38:41.7222344Z In `abs` mode, dynamic_threshold = best + threshold in 2025-08-14T21:38:41.7222961Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-08-14T21:38:41.7223556Z cooldown (int): Number of epochs to wait before resuming 2025-08-14T21:38:41.7224015Z normal operation after lr has been reduced. Default: 0. 2025-08-14T21:38:41.7224470Z min_lr (float or list): A scalar or a list of scalars. A 2025-08-14T21:38:41.7224893Z lower bound on the learning rate of all param groups 2025-08-14T21:38:41.7225301Z or each group respectively. Default: 0. 2025-08-14T21:38:41.7225859Z eps (float): Minimal decay applied to lr. If the difference 2025-08-14T21:38:41.7226311Z between new and old lr is smaller than eps, the update is 2025-08-14T21:38:41.7226711Z ignored. Default: 1e-8. 2025-08-14T21:38:41.7226923Z 2025-08-14T21:38:41.7227007Z Example: 2025-08-14T21:38:41.7227233Z >>> # xdoctest: +SKIP 2025-08-14T21:38:41.7227625Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-08-14T21:38:41.7228121Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-08-14T21:38:41.7228492Z >>> for epoch in range(10): 2025-08-14T21:38:41.7228772Z >>> train(...) 2025-08-14T21:38:41.7229046Z >>> val_loss = validate(...) 2025-08-14T21:38:41.7229405Z >>> # Note that step should be called after validate() 2025-08-14T21:38:41.7229764Z >>> scheduler.step(val_loss) 2025-08-14T21:38:41.7229986Z 2025-08-14T21:38:41.7230185Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-08-14T21:38:41.7230579Z 2025-08-14T21:38:41.7241960Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-08-14T21:38:41.7242747Z 2025-08-14T21:38:41.7242880Z scheduler.step(val_loss) 2025-08-14T21:38:41.7243189Z ^ 2025-08-14T21:38:41.7243969Z msg = Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1430. 2025-08-14T21:38:41.7245198Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.7245891Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-08-14T21:38:41.7246343Z 2025-08-14T21:38:41.7246618Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-08-14T21:38:41.7247273Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-08-14T21:38:41.7247867Z The distance between the two boundaries can be scaled on a per-iteration 2025-08-14T21:38:41.7248309Z or per-cycle basis. 2025-08-14T21:38:41.7248482Z 2025-08-14T21:38:41.7248718Z Cyclical learning rate policy changes the learning rate after every batch. 2025-08-14T21:38:41.7249266Z `step` should be called after a batch has been used for training. 2025-08-14T21:38:41.7249566Z 2025-08-14T21:38:41.7249770Z This class has three built-in policies, as put forth in the paper: 2025-08-14T21:38:41.7250094Z 2025-08-14T21:38:41.7250297Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-08-14T21:38:41.7251013Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-08-14T21:38:41.7251742Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-08-14T21:38:41.7252269Z at each cycle iteration. 2025-08-14T21:38:41.7252465Z 2025-08-14T21:38:41.7252699Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-08-14T21:38:41.7253047Z 2025-08-14T21:38:41.7253142Z Args: 2025-08-14T21:38:41.7253383Z optimizer (Optimizer): Wrapped optimizer. 2025-08-14T21:38:41.7253806Z base_lr (float or list): Initial learning rate which is the 2025-08-14T21:38:41.7254261Z lower boundary in the cycle for each parameter group. 2025-08-14T21:38:41.7254734Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-08-14T21:38:41.7255163Z for each parameter group. Functionally, 2025-08-14T21:38:41.7255558Z it defines the cycle amplitude (max_lr - base_lr). 2025-08-14T21:38:41.7255953Z The lr at any cycle is the sum of base_lr 2025-08-14T21:38:41.7256317Z and some scaling of the amplitude; therefore 2025-08-14T21:38:41.7256708Z max_lr may not actually be reached depending on 2025-08-14T21:38:41.7257063Z scaling function. 2025-08-14T21:38:41.7257470Z step_size_up (int): Number of training iterations in the 2025-08-14T21:38:41.7257870Z increasing half of a cycle. Default: 2000 2025-08-14T21:38:41.7258287Z step_size_down (int): Number of training iterations in the 2025-08-14T21:38:41.7258745Z decreasing half of a cycle. If step_size_down is None, 2025-08-14T21:38:41.7259139Z it is set to step_size_up. Default: None 2025-08-14T21:38:41.7259542Z mode (str): One of {triangular, triangular2, exp_range}. 2025-08-14T21:38:41.7259961Z Values correspond to policies detailed above. 2025-08-14T21:38:41.7260354Z If scale_fn is not None, this argument is ignored. 2025-08-14T21:38:41.7260722Z Default: 'triangular' 2025-08-14T21:38:41.7261078Z gamma (float): Constant in 'exp_range' scaling function: 2025-08-14T21:38:41.7261462Z gamma**(cycle iterations) 2025-08-14T21:38:41.7261751Z Default: 1.0 2025-08-14T21:38:41.7262102Z scale_fn (function): Custom scaling policy defined by a single 2025-08-14T21:38:41.7262522Z argument lambda function, where 2025-08-14T21:38:41.7262843Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-08-14T21:38:41.7263184Z If specified, then 'mode' is ignored. 2025-08-14T21:38:41.7263599Z Default: None 2025-08-14T21:38:41.7263878Z scale_mode (str): {'cycle', 'iterations'}. 2025-08-14T21:38:41.7264239Z Defines whether scale_fn is evaluated on 2025-08-14T21:38:41.7264610Z cycle number or cycle iterations (training 2025-08-14T21:38:41.7264966Z iterations since start of cycle). 2025-08-14T21:38:41.7265271Z Default: 'cycle' 2025-08-14T21:38:41.7265645Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-08-14T21:38:41.7266135Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-08-14T21:38:41.7266513Z Default: True 2025-08-14T21:38:41.7266895Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-08-14T21:38:41.7267431Z for each parameter group. Note that momentum is cycled inversely 2025-08-14T21:38:41.7267897Z to learning rate; at the peak of a cycle, momentum is 2025-08-14T21:38:41.7268310Z 'base_momentum' and learning rate is 'max_lr'. 2025-08-14T21:38:41.7268653Z Default: 0.8 2025-08-14T21:38:41.7269027Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-08-14T21:38:41.7269468Z for each parameter group. Functionally, 2025-08-14T21:38:41.7269900Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-08-14T21:38:41.7270451Z The momentum at any cycle is the difference of max_momentum 2025-08-14T21:38:41.7270866Z and some scaling of the amplitude; therefore 2025-08-14T21:38:41.7271276Z base_momentum may not actually be reached depending on 2025-08-14T21:38:41.7271737Z scaling function. Note that momentum is cycled inversely 2025-08-14T21:38:41.7272239Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-08-14T21:38:41.7272665Z and learning rate is 'base_lr' 2025-08-14T21:38:41.7272978Z Default: 0.9 2025-08-14T21:38:41.7273362Z last_epoch (int): The index of the last batch. This parameter is used when 2025-08-14T21:38:41.7273901Z resuming a training job. Since `step()` should be invoked after each 2025-08-14T21:38:41.7274440Z batch instead of after each epoch, this number represents the total 2025-08-14T21:38:41.7274986Z number of *batches* computed, not the total number of epochs computed. 2025-08-14T21:38:41.7275514Z When last_epoch=-1, the schedule is started from the beginning. 2025-08-14T21:38:41.7275894Z Default: -1 2025-08-14T21:38:41.7276050Z 2025-08-14T21:38:41.7276132Z Example: 2025-08-14T21:38:41.7276361Z >>> # xdoctest: +SKIP 2025-08-14T21:38:41.7276808Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-08-14T21:38:41.7277298Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-08-14T21:38:41.7277656Z ... optimizer, 2025-08-14T21:38:41.7277904Z ... base_lr=0.01, 2025-08-14T21:38:41.7278175Z ... max_lr=0.1, 2025-08-14T21:38:41.7278446Z ... step_size_up=10, 2025-08-14T21:38:41.7278703Z ... ) 2025-08-14T21:38:41.7278984Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-08-14T21:38:41.7279353Z >>> for epoch in range(10): 2025-08-14T21:38:41.7279659Z >>> for batch in data_loader: 2025-08-14T21:38:41.7279965Z >>> train_batch(...) 2025-08-14T21:38:41.7280263Z >>> scheduler.step() 2025-08-14T21:38:41.7280458Z 2025-08-14T21:38:41.7280629Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-08-14T21:38:41.7280893Z 2025-08-14T21:38:41.7281198Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-08-14T21:38:41.7281780Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-08-14T21:38:41.7282128Z 2025-08-14T21:38:41.7282488Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.7282846Z 2025-08-14T21:38:41.7283507Z msg = Cannot scrape callname=CosineAnnealingWarmRestarts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1722. 2025-08-14T21:38:41.7284480Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.7285082Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-08-14T21:38:41.7285454Z 2025-08-14T21:38:41.7285629Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-08-14T21:38:41.7286148Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-08-14T21:38:41.7286624Z of epochs between two warm restarts in SGDR: 2025-08-14T21:38:41.7286860Z 2025-08-14T21:38:41.7286944Z .. math:: 2025-08-14T21:38:41.7287249Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-08-14T21:38:41.7287676Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-08-14T21:38:41.7287917Z 2025-08-14T21:38:41.7288078Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-08-14T21:38:41.7288537Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-08-14T21:38:41.7288842Z 2025-08-14T21:38:41.7288941Z It has been proposed in 2025-08-14T21:38:41.7289287Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-08-14T21:38:41.7289561Z 2025-08-14T21:38:41.7289702Z Args: 2025-08-14T21:38:41.7289961Z optimizer (Optimizer): Wrapped optimizer. 2025-08-14T21:38:41.7290352Z T_0 (int): Number of iterations until the first restart. 2025-08-14T21:38:41.7290901Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-08-14T21:38:41.7291466Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-08-14T21:38:41.7291973Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-08-14T21:38:41.7292306Z 2025-08-14T21:38:41.7292471Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-08-14T21:38:41.7293087Z https://arxiv.org/abs/1608.03983 2025-08-14T21:38:41.7293309Z 2025-08-14T21:38:41.7293394Z Example: 2025-08-14T21:38:41.7293619Z >>> # xdoctest: +SKIP 2025-08-14T21:38:41.7293982Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-08-14T21:38:41.7294495Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-08-14T21:38:41.7294937Z ... optimizer, T_0=20 2025-08-14T21:38:41.7295206Z ... ) 2025-08-14T21:38:41.7295421Z >>> for epoch in range(100): 2025-08-14T21:38:41.7295708Z >>> train(...) 2025-08-14T21:38:41.7296074Z >>> validate(...) 2025-08-14T21:38:41.7296349Z >>> scheduler.step() 2025-08-14T21:38:41.7296532Z 2025-08-14T21:38:41.7296765Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-08-14T21:38:41.7297189Z 2025-08-14T21:38:41.7297546Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.7297899Z 2025-08-14T21:38:41.7298408Z msg = Cannot scrape callname=OneCycleLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1872. 2025-08-14T21:38:41.7299282Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.7299950Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-08-14T21:38:41.7300357Z 2025-08-14T21:38:41.7300659Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-08-14T21:38:41.7301339Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-08-14T21:38:41.7301861Z lower than the initial learning rate. 2025-08-14T21:38:41.7302305Z This policy was initially described in the paper `Super-Convergence: 2025-08-14T21:38:41.7302846Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-08-14T21:38:41.7303160Z 2025-08-14T21:38:41.7303398Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-08-14T21:38:41.7304011Z `step` should be called after a batch has been used for training. 2025-08-14T21:38:41.7304310Z 2025-08-14T21:38:41.7304427Z This scheduler is not chainable. 2025-08-14T21:38:41.7304630Z 2025-08-14T21:38:41.7304877Z Note also that the total number of steps in the cycle can be determined in one 2025-08-14T21:38:41.7305348Z of two ways (listed in order of precedence): 2025-08-14T21:38:41.7305595Z 2025-08-14T21:38:41.7305739Z #. A value for total_steps is explicitly provided. 2025-08-14T21:38:41.7306173Z #. A number of epochs (epochs) and a number of steps per epoch 2025-08-14T21:38:41.7306560Z (steps_per_epoch) are provided. 2025-08-14T21:38:41.7306924Z In this case, the number of total steps is inferred by 2025-08-14T21:38:41.7307305Z total_steps = epochs * steps_per_epoch 2025-08-14T21:38:41.7307526Z 2025-08-14T21:38:41.7307767Z You must either provide a value for total_steps or provide a value for both 2025-08-14T21:38:41.7308209Z epochs and steps_per_epoch. 2025-08-14T21:38:41.7308405Z 2025-08-14T21:38:41.7308698Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-08-14T21:38:41.7309503Z claims that "unpublished work has shown even better results by using only two phases". To 2025-08-14T21:38:41.7310136Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-08-14T21:38:41.7310498Z 2025-08-14T21:38:41.7310579Z Args: 2025-08-14T21:38:41.7310833Z optimizer (Optimizer): Wrapped optimizer. 2025-08-14T21:38:41.7311281Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-08-14T21:38:41.7311690Z for each parameter group. 2025-08-14T21:38:41.7312099Z total_steps (int): The total number of steps in the cycle. Note that 2025-08-14T21:38:41.7312627Z if a value is not provided here, then it must be inferred by providing 2025-08-14T21:38:41.7313063Z a value for epochs and steps_per_epoch. 2025-08-14T21:38:41.7313390Z Default: None 2025-08-14T21:38:41.7313752Z epochs (int): The number of epochs to train for. This is used along 2025-08-14T21:38:41.7314302Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-08-14T21:38:41.7314775Z if a value for total_steps is not provided. 2025-08-14T21:38:41.7315106Z Default: None 2025-08-14T21:38:41.7315489Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-08-14T21:38:41.7316096Z used along with epochs in order to infer the total number of steps in the 2025-08-14T21:38:41.7316585Z cycle if a value for total_steps is not provided. 2025-08-14T21:38:41.7316937Z Default: None 2025-08-14T21:38:41.7317320Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-08-14T21:38:41.7317759Z increasing the learning rate. 2025-08-14T21:38:41.7318067Z Default: 0.3 2025-08-14T21:38:41.7318347Z anneal_strategy (str): {'cos', 'linear'} 2025-08-14T21:38:41.7318586Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-08-14T21:38:41.7318683Z linear annealing. 2025-08-14T21:38:41.7318785Z Default: 'cos' 2025-08-14T21:38:41.7318977Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-08-14T21:38:41.7319166Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-08-14T21:38:41.7319255Z Default: True 2025-08-14T21:38:41.7319474Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-08-14T21:38:41.7319667Z for each parameter group. Note that momentum is cycled inversely 2025-08-14T21:38:41.7319821Z to learning rate; at the peak of a cycle, momentum is 2025-08-14T21:38:41.7319966Z 'base_momentum' and learning rate is 'max_lr'. 2025-08-14T21:38:41.7320052Z Default: 0.85 2025-08-14T21:38:41.7320256Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-08-14T21:38:41.7320389Z for each parameter group. Functionally, 2025-08-14T21:38:41.7320580Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-08-14T21:38:41.7320698Z Note that momentum is cycled inversely 2025-08-14T21:38:41.7320912Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-08-14T21:38:41.7321016Z and learning rate is 'base_lr' 2025-08-14T21:38:41.7321119Z Default: 0.95 2025-08-14T21:38:41.7321294Z div_factor (float): Determines the initial learning rate via 2025-08-14T21:38:41.7321397Z initial_lr = max_lr/div_factor 2025-08-14T21:38:41.7321496Z Default: 25 2025-08-14T21:38:41.7321695Z final_div_factor (float): Determines the minimum learning rate via 2025-08-14T21:38:41.7321802Z min_lr = initial_lr/final_div_factor 2025-08-14T21:38:41.7321894Z Default: 1e4 2025-08-14T21:38:41.7322125Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-08-14T21:38:41.7322429Z learning rate according to 'final_div_factor' instead of modifying the second 2025-08-14T21:38:41.7322658Z phase (the first two phases will be symmetrical about the step indicated by 2025-08-14T21:38:41.7322744Z 'pct_start'). 2025-08-14T21:38:41.7322961Z last_epoch (int): The index of the last batch. This parameter is used when 2025-08-14T21:38:41.7323167Z resuming a training job. Since `step()` should be invoked after each 2025-08-14T21:38:41.7323369Z batch instead of after each epoch, this number represents the total 2025-08-14T21:38:41.7323590Z number of *batches* computed, not the total number of epochs computed. 2025-08-14T21:38:41.7323769Z When last_epoch=-1, the schedule is started from the beginning. 2025-08-14T21:38:41.7323864Z Default: -1 2025-08-14T21:38:41.7323868Z 2025-08-14T21:38:41.7323949Z Example: 2025-08-14T21:38:41.7324035Z >>> # xdoctest: +SKIP 2025-08-14T21:38:41.7324192Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-08-14T21:38:41.7324411Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-08-14T21:38:41.7324561Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-08-14T21:38:41.7324772Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-08-14T21:38:41.7324896Z ... ) 2025-08-14T21:38:41.7324998Z >>> for epoch in range(10): 2025-08-14T21:38:41.7325098Z >>> for batch in data_loader: 2025-08-14T21:38:41.7325189Z >>> train_batch(...) 2025-08-14T21:38:41.7325291Z >>> optimizer.step() 2025-08-14T21:38:41.7325385Z >>> scheduler.step() 2025-08-14T21:38:41.7325389Z 2025-08-14T21:38:41.7325552Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-08-14T21:38:41.7325556Z 2025-08-14T21:38:41.7325848Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-08-14T21:38:41.7325965Z https://arxiv.org/abs/1708.07120 2025-08-14T21:38:41.7326049Z 2025-08-14T21:38:41.7326290Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.7326294Z 2025-08-14T21:38:41.7368048Z 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=867. 2025-08-14T21:38:41.7368577Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.7368755Z Load the optimizer state. 2025-08-14T21:38:41.7368766Z 2025-08-14T21:38:41.7368914Z Args: 2025-08-14T21:38:41.7369272Z state_dict (dict): optimizer state. Should be an object returned 2025-08-14T21:38:41.7369475Z from a call to :meth:`state_dict`. 2025-08-14T21:38:41.7369483Z 2025-08-14T21:38:41.7369649Z .. warning:: 2025-08-14T21:38:41.7370277Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-08-14T21:38:41.7370666Z as calling it beforehand will overwrite the loaded learning rates. 2025-08-14T21:38:41.7370683Z 2025-08-14T21:38:41.7370831Z .. note:: 2025-08-14T21:38:41.7371364Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-08-14T21:38:41.7371661Z in :meth:`state_dict`) will not affect the loading process. 2025-08-14T21:38:41.7372240Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-08-14T21:38:41.7372489Z differ from those initialized in the optimizer), 2025-08-14T21:38:41.7373062Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-08-14T21:38:41.7373220Z accordingly. 2025-08-14T21:38:41.7373753Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-08-14T21:38:41.7374464Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-08-14T21:38:41.7374763Z the optimizer ``param_names`` will remain unchanged. 2025-08-14T21:38:41.7374773Z 2025-08-14T21:38:41.7374913Z Example: 2025-08-14T21:38:41.7375094Z >>> # xdoctest: +SKIP 2025-08-14T21:38:41.7375299Z >>> model = torch.nn.Linear(10, 10) 2025-08-14T21:38:41.7375582Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-08-14T21:38:41.7375852Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-08-14T21:38:41.7376011Z ... optim, 2025-08-14T21:38:41.7376180Z ... start_factor=0.1, 2025-08-14T21:38:41.7376337Z ... end_factor=1, 2025-08-14T21:38:41.7376513Z ... total_iters=20, 2025-08-14T21:38:41.7376654Z ... ) 2025-08-14T21:38:41.7376998Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-08-14T21:38:41.7377157Z ... optim, 2025-08-14T21:38:41.7377310Z ... T_max=80, 2025-08-14T21:38:41.7377478Z ... eta_min=3e-5, 2025-08-14T21:38:41.7377614Z ... ) 2025-08-14T21:38:41.7377857Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-08-14T21:38:41.7378118Z ... optim, 2025-08-14T21:38:41.7378340Z ... schedulers=[scheduler1, scheduler2], 2025-08-14T21:38:41.7378504Z ... milestones=[20], 2025-08-14T21:38:41.7378648Z ... ) 2025-08-14T21:38:41.7378908Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-08-14T21:38:41.7379278Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-08-14T21:38:41.7379574Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-08-14T21:38:41.7379584Z 2025-08-14T21:38:41.7379724Z 2025-08-14T21:38:41.7380193Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.7380209Z 2025-08-14T21:38:41.7536405Z 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. 2025-08-14T21:38:41.7536924Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.7537668Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-08-14T21:38:41.7537679Z 2025-08-14T21:38:41.7538140Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-08-14T21:38:41.7538527Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-08-14T21:38:41.7538887Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-08-14T21:38:41.7539048Z (UAI 2018). 2025-08-14T21:38:41.7539056Z 2025-08-14T21:38:41.7539427Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-08-14T21:38:41.7539860Z but using exponential weights instead of equal weights across iterations. 2025-08-14T21:38:41.7539870Z 2025-08-14T21:38:41.7540295Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-08-14T21:38:41.7540696Z on the device :attr:`device` and allows to compute running averages of the 2025-08-14T21:38:41.7540888Z parameters of the :attr:`model`. 2025-08-14T21:38:41.7540897Z 2025-08-14T21:38:41.7541028Z Args: 2025-08-14T21:38:41.7541276Z model (torch.nn.Module): model to use with SWA/EMA 2025-08-14T21:38:41.7541700Z device (torch.device, optional): if provided, the averaged model will be 2025-08-14T21:38:41.7541868Z stored on the :attr:`device` 2025-08-14T21:38:41.7542217Z avg_fn (function, optional): the averaging function used to update 2025-08-14T21:38:41.7542580Z parameters; the function must take in the current value of the 2025-08-14T21:38:41.7542972Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-08-14T21:38:41.7543633Z parameter, and the number of models already averaged; if None, 2025-08-14T21:38:41.7543927Z an equally weighted average is used (default: None) 2025-08-14T21:38:41.7544346Z multi_avg_fn (function, optional): the averaging function used to update 2025-08-14T21:38:41.7544801Z parameters inplace; the function must take in the current values of the 2025-08-14T21:38:41.7545287Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-08-14T21:38:41.7545713Z parameters as a list, and the number of models already averaged; if None, 2025-08-14T21:38:41.7545991Z an equally weighted average is used (default: None) 2025-08-14T21:38:41.7546353Z use_buffers (bool): if ``True``, it will compute running averages for 2025-08-14T21:38:41.7546779Z both the parameters and the buffers of the model. (default: ``False``) 2025-08-14T21:38:41.7546789Z 2025-08-14T21:38:41.7546931Z Example: 2025-08-14T21:38:41.7547143Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:41.7547350Z >>> loader, optimizer, model, loss_fn = ... 2025-08-14T21:38:41.7547642Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-08-14T21:38:41.7548041Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-08-14T21:38:41.7548365Z >>> T_max=300) 2025-08-14T21:38:41.7548526Z >>> swa_start = 160 2025-08-14T21:38:41.7548780Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-08-14T21:38:41.7548950Z >>> for i in range(300): 2025-08-14T21:38:41.7549117Z >>> for input, target in loader: 2025-08-14T21:38:41.7549303Z >>> optimizer.zero_grad() 2025-08-14T21:38:41.7549532Z >>> loss_fn(model(input), target).backward() 2025-08-14T21:38:41.7549726Z >>> optimizer.step() 2025-08-14T21:38:41.7549891Z >>> if i > swa_start: 2025-08-14T21:38:41.7550122Z >>> swa_model.update_parameters(model) 2025-08-14T21:38:41.7550320Z >>> swa_scheduler.step() 2025-08-14T21:38:41.7550460Z >>> else: 2025-08-14T21:38:41.7550634Z >>> scheduler.step() 2025-08-14T21:38:41.7550788Z >>> 2025-08-14T21:38:41.7551059Z >>> # Update bn statistics for the swa_model at the end 2025-08-14T21:38:41.7551325Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-08-14T21:38:41.7551334Z 2025-08-14T21:38:41.7551844Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-08-14T21:38:41.7552167Z If no averaging function is provided, the default is to compute 2025-08-14T21:38:41.7552422Z equally-weighted average of the weights (SWA). 2025-08-14T21:38:41.7552430Z 2025-08-14T21:38:41.7552561Z Example: 2025-08-14T21:38:41.7552769Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:41.7553137Z >>> # Compute exponential moving averages of the weights and buffers 2025-08-14T21:38:41.7553440Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-08-14T21:38:41.7553810Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-08-14T21:38:41.7553827Z 2025-08-14T21:38:41.7553972Z .. note:: 2025-08-14T21:38:41.7554333Z When using SWA/EMA with models containing Batch Normalization you may 2025-08-14T21:38:41.7554678Z need to update the activation statistics for Batch Normalization. 2025-08-14T21:38:41.7555082Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-08-14T21:38:41.7555481Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-08-14T21:38:41.7555927Z statistics in a post-training step by passing data through the model. The 2025-08-14T21:38:41.7556345Z second does it during the parameter update phase by averaging all buffers. 2025-08-14T21:38:41.7556947Z Empirical evidence has shown that updating the statistics in normalization 2025-08-14T21:38:41.7557323Z layers increases accuracy, but you may wish to empirically test which 2025-08-14T21:38:41.7557572Z approach yields the best results in your problem. 2025-08-14T21:38:41.7557592Z 2025-08-14T21:38:41.7557751Z .. note:: 2025-08-14T21:38:41.7558209Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-08-14T21:38:41.7558218Z 2025-08-14T21:38:41.7558374Z .. note:: 2025-08-14T21:38:41.7558739Z When :meth:`update_parameters` is called for the first time (i.e. 2025-08-14T21:38:41.7559072Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-08-14T21:38:41.7559442Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-08-14T21:38:41.7559782Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-08-14T21:38:41.7559967Z to update the parameters. 2025-08-14T21:38:41.7559982Z 2025-08-14T21:38:41.7560403Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-08-14T21:38:41.7560605Z https://arxiv.org/abs/1803.05407 2025-08-14T21:38:41.7561020Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-08-14T21:38:41.7561271Z Average: 2025-08-14T21:38:41.7561463Z https://arxiv.org/abs/1806.05594 2025-08-14T21:38:41.7561814Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-08-14T21:38:41.7562010Z https://arxiv.org/abs/1904.11943 2025-08-14T21:38:41.7562391Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-08-14T21:38:41.7562562Z Generalizes Well: 2025-08-14T21:38:41.7562753Z https://arxiv.org/abs/2001.02312 2025-08-14T21:38:41.7562914Z .. _Polyak averaging: 2025-08-14T21:38:41.7563195Z https://paperswithcode.com/method/polyak-averaging 2025-08-14T21:38:41.7563326Z 2025-08-14T21:38:41.7563785Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.7563795Z 2025-08-14T21:38:41.7564690Z 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. 2025-08-14T21:38:41.7565187Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:41.7565570Z Anneals the learning rate in each parameter group to a fixed value. 2025-08-14T21:38:41.7565579Z 2025-08-14T21:38:41.7565986Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-08-14T21:38:41.7566366Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-08-14T21:38:41.7566375Z 2025-08-14T21:38:41.7566516Z Args: 2025-08-14T21:38:41.7566820Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-08-14T21:38:41.7567190Z swa_lrs (float or list): the learning rate value for all param groups 2025-08-14T21:38:41.7567413Z together or separately for each group. 2025-08-14T21:38:41.7567776Z annealing_epochs (int): number of epochs in the annealing phase 2025-08-14T21:38:41.7567941Z (default: 10) 2025-08-14T21:38:41.7568333Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-08-14T21:38:41.7568728Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-08-14T21:38:41.7568901Z (default: "cos") 2025-08-14T21:38:41.7569226Z last_epoch (int): the index of the last epoch (default: -1) 2025-08-14T21:38:41.7569234Z 2025-08-14T21:38:41.7569565Z The :class:`SWALR` scheduler can be used together with other 2025-08-14T21:38:41.7569955Z schedulers to switch to a constant learning rate late in the training 2025-08-14T21:38:41.7570149Z as in the example below. 2025-08-14T21:38:41.7570158Z 2025-08-14T21:38:41.7570307Z Example: 2025-08-14T21:38:41.7570528Z >>> # xdoctest: +SKIP("Undefined variables") 2025-08-14T21:38:41.7570912Z >>> loader, optimizer, model = ... 2025-08-14T21:38:41.7571114Z >>> lr_lambda = lambda epoch: 0.9 2025-08-14T21:38:41.7571519Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-08-14T21:38:41.7571715Z >>> lr_lambda=lr_lambda) 2025-08-14T21:38:41.7572026Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-08-14T21:38:41.7572360Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-08-14T21:38:41.7572525Z >>> swa_start = 160 2025-08-14T21:38:41.7572697Z >>> for i in range(300): 2025-08-14T21:38:41.7572912Z >>> for input, target in loader: 2025-08-14T21:38:41.7573106Z >>> optimizer.zero_grad() 2025-08-14T21:38:41.7573345Z >>> loss_fn(model(input), target).backward() 2025-08-14T21:38:41.7573549Z >>> optimizer.step() 2025-08-14T21:38:41.7573720Z >>> if i > swa_start: 2025-08-14T21:38:41.7573914Z >>> swa_scheduler.step() 2025-08-14T21:38:41.7574081Z >>> else: 2025-08-14T21:38:41.7574264Z >>> scheduler.step() 2025-08-14T21:38:41.7574273Z 2025-08-14T21:38:41.7574683Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-08-14T21:38:41.7574964Z https://arxiv.org/abs/1803.05407 2025-08-14T21:38:41.7575085Z 2025-08-14T21:38:41.7575531Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:41.7575540Z 2025-08-14T21:38:42.2194632Z 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. 2025-08-14T21:38:42.2196265Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:42.2197224Z Asserts that ``actual`` and ``expected`` are close. 2025-08-14T21:38:42.2197684Z 2025-08-14T21:38:42.2198462Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-08-14T21:38:42.2199380Z 2025-08-14T21:38:42.2199553Z .. math:: 2025-08-14T21:38:42.2199800Z 2025-08-14T21:38:42.2200458Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-08-14T21:38:42.2201350Z 2025-08-14T21:38:42.2201996Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-08-14T21:38:42.2203214Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-08-14T21:38:42.2203819Z 2025-08-14T21:38:42.2204177Z In addition, they are only considered close if they have the same 2025-08-14T21:38:42.2204776Z 2025-08-14T21:38:42.2205080Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-08-14T21:38:42.2205873Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-08-14T21:38:42.2206547Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-08-14T21:38:42.2207240Z - stride (if ``check_stride`` is ``True``). 2025-08-14T21:38:42.2207664Z 2025-08-14T21:38:42.2208224Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-08-14T21:38:42.2209003Z 2025-08-14T21:38:42.2209685Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-08-14T21:38:42.2211296Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-08-14T21:38:42.2212545Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-08-14T21:38:42.2213902Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-08-14T21:38:42.2214858Z 2025-08-14T21:38:42.2215372Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-08-14T21:38:42.2217077Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-08-14T21:38:42.2218139Z definition above. 2025-08-14T21:38:42.2218396Z 2025-08-14T21:38:42.2218918Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-08-14T21:38:42.2220466Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-08-14T21:38:42.2222093Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-08-14T21:38:42.2223828Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-08-14T21:38:42.2225212Z their elements are considered close according to the above definition. 2025-08-14T21:38:42.2225874Z 2025-08-14T21:38:42.2226047Z .. note:: 2025-08-14T21:38:42.2226278Z 2025-08-14T21:38:42.2226870Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-08-14T21:38:42.2228242Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-08-14T21:38:42.2229592Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-08-14T21:38:42.2230552Z 2025-08-14T21:38:42.2230690Z Args: 2025-08-14T21:38:42.2231073Z actual (Any): Actual input. 2025-08-14T21:38:42.2231621Z expected (Any): Expected input. 2025-08-14T21:38:42.2232631Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-08-14T21:38:42.2233777Z are allowed. Otherwise type equality is required. 2025-08-14T21:38:42.2234951Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-08-14T21:38:42.2236269Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-08-14T21:38:42.2237624Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-08-14T21:38:42.2239000Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-08-14T21:38:42.2240078Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-08-14T21:38:42.2241229Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-08-14T21:38:42.2242371Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-08-14T21:38:42.2243437Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-08-14T21:38:42.2244691Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-08-14T21:38:42.2246208Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-08-14T21:38:42.2247297Z :func:`torch.promote_types`) before being compared. 2025-08-14T21:38:42.2248453Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-08-14T21:38:42.2249968Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-08-14T21:38:42.2250958Z compared. 2025-08-14T21:38:42.2251892Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-08-14T21:38:42.2253468Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-08-14T21:38:42.2254983Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-08-14T21:38:42.2256083Z should return the new message. 2025-08-14T21:38:42.2256481Z 2025-08-14T21:38:42.2256794Z Raises: 2025-08-14T21:38:42.2257445Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-08-14T21:38:42.2258386Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-08-14T21:38:42.2259480Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-08-14T21:38:42.2260995Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-08-14T21:38:42.2262081Z different types. 2025-08-14T21:38:42.2263021Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-08-14T21:38:42.2264723Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-08-14T21:38:42.2266200Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-08-14T21:38:42.2267582Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-08-14T21:38:42.2268556Z :attr:`~torch.Tensor.layout`. 2025-08-14T21:38:42.2269357Z AssertionError: If only one of corresponding tensors is quantized. 2025-08-14T21:38:42.2270850Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-08-14T21:38:42.2272319Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-08-14T21:38:42.2273299Z :attr:`~torch.Tensor.device`. 2025-08-14T21:38:42.2274315Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-08-14T21:38:42.2275855Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-08-14T21:38:42.2277443Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-08-14T21:38:42.2278375Z 2025-08-14T21:38:42.2278952Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-08-14T21:38:42.2280136Z ``dtype``'s, the maximum of both tolerances is used. 2025-08-14T21:38:42.2280624Z 2025-08-14T21:38:42.2280858Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2281493Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-08-14T21:38:42.2282112Z +===========================+============+==========+ 2025-08-14T21:38:42.2282763Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-08-14T21:38:42.2283406Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2284079Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-08-14T21:38:42.2284756Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2285428Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:42.2286093Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2286759Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-08-14T21:38:42.2287412Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2288064Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-08-14T21:38:42.2288716Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2289364Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:42.2290023Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2290671Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-08-14T21:38:42.2291329Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2291986Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:42.2292632Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2293687Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:42.2294366Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2295024Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:42.2295700Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2296381Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:42.2297040Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2297697Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:42.2298365Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2299007Z | other | ``0.0`` | ``0.0`` | 2025-08-14T21:38:42.2299642Z +---------------------------+------------+----------+ 2025-08-14T21:38:42.2300098Z 2025-08-14T21:38:42.2300258Z .. note:: 2025-08-14T21:38:42.2300502Z 2025-08-14T21:38:42.2301231Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-08-14T21:38:42.2302847Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-08-14T21:38:42.2304301Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-08-14T21:38:42.2305208Z 2025-08-14T21:38:42.2305381Z >>> import functools 2025-08-14T21:38:42.2306186Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-08-14T21:38:42.2307087Z >>> assert_equal(1e-9, 1e-10) 2025-08-14T21:38:42.2307653Z Traceback (most recent call last): 2025-08-14T21:38:42.2308220Z ... 2025-08-14T21:38:42.2308656Z AssertionError: Scalars are not equal! 2025-08-14T21:38:42.2309238Z 2025-08-14T21:38:42.2309692Z Expected 1e-10 but got 1e-09. 2025-08-14T21:38:42.2310295Z Absolute difference: 9.000000000000001e-10 2025-08-14T21:38:42.2310891Z Relative difference: 9.0 2025-08-14T21:38:42.2311263Z 2025-08-14T21:38:42.2311423Z Examples: 2025-08-14T21:38:42.2311855Z >>> # tensor to tensor comparison 2025-08-14T21:38:42.2312477Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-08-14T21:38:42.2313167Z >>> actual = torch.acos(torch.cos(expected)) 2025-08-14T21:38:42.2313889Z >>> torch.testing.assert_close(actual, expected) 2025-08-14T21:38:42.2314352Z 2025-08-14T21:38:42.2314564Z >>> # scalar to scalar comparison 2025-08-14T21:38:42.2315108Z >>> import math 2025-08-14T21:38:42.2315576Z >>> expected = math.sqrt(2.0) 2025-08-14T21:38:42.2316135Z >>> actual = 2.0 / math.sqrt(2.0) 2025-08-14T21:38:42.2316761Z >>> torch.testing.assert_close(actual, expected) 2025-08-14T21:38:42.2317242Z 2025-08-14T21:38:42.2317440Z >>> # numpy array to numpy array comparison 2025-08-14T21:38:42.2318052Z >>> import numpy as np 2025-08-14T21:38:42.2318632Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-08-14T21:38:42.2319261Z >>> actual = np.arccos(np.cos(expected)) 2025-08-14T21:38:42.2319922Z >>> torch.testing.assert_close(actual, expected) 2025-08-14T21:38:42.2320364Z 2025-08-14T21:38:42.2320574Z >>> # sequence to sequence comparison 2025-08-14T21:38:42.2321138Z >>> import numpy as np 2025-08-14T21:38:42.2321826Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-08-14T21:38:42.2322669Z >>> # length and their elements have to match. 2025-08-14T21:38:42.2323328Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-08-14T21:38:42.2323978Z >>> actual = tuple(expected) 2025-08-14T21:38:42.2324573Z >>> torch.testing.assert_close(actual, expected) 2025-08-14T21:38:42.2325017Z 2025-08-14T21:38:42.2325231Z >>> # mapping to mapping comparison 2025-08-14T21:38:42.2325757Z >>> from collections import OrderedDict 2025-08-14T21:38:42.2326221Z >>> import numpy as np 2025-08-14T21:38:42.2326836Z >>> foo = torch.tensor(1.0) 2025-08-14T21:38:42.2327349Z >>> bar = 2.0 2025-08-14T21:38:42.2327782Z >>> baz = np.array(3.0) 2025-08-14T21:38:42.2328577Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-08-14T21:38:42.2329651Z >>> # have to have the same set of keys and their elements have to match. 2025-08-14T21:38:42.2330598Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-08-14T21:38:42.2331426Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-08-14T21:38:42.2332133Z >>> torch.testing.assert_close(actual, expected) 2025-08-14T21:38:42.2332597Z 2025-08-14T21:38:42.2332809Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-08-14T21:38:42.2333384Z >>> actual = expected.clone() 2025-08-14T21:38:42.2334026Z >>> # By default, directly related instances can be compared 2025-08-14T21:38:42.2334885Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-08-14T21:38:42.2335808Z >>> # This check can be made more strict with allow_subclasses=False 2025-08-14T21:38:42.2336553Z >>> torch.testing.assert_close( 2025-08-14T21:38:42.2337299Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-08-14T21:38:42.2338162Z ... ) 2025-08-14T21:38:42.2338604Z Traceback (most recent call last): 2025-08-14T21:38:42.2339164Z ... 2025-08-14T21:38:42.2339709Z TypeError: No comparison pair was able to handle inputs of type 2025-08-14T21:38:42.2340703Z and . 2025-08-14T21:38:42.2341731Z >>> # If the inputs are not directly related, they are never considered close 2025-08-14T21:38:42.2342683Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-08-14T21:38:42.2343392Z Traceback (most recent call last): 2025-08-14T21:38:42.2344042Z ... 2025-08-14T21:38:42.2344823Z TypeError: No comparison pair was able to handle inputs of type 2025-08-14T21:38:42.2345773Z and . 2025-08-14T21:38:42.2346643Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-08-14T21:38:42.2347588Z >>> # their type if check_dtype=False. 2025-08-14T21:38:42.2348302Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-08-14T21:38:42.2348845Z 2025-08-14T21:38:42.2349020Z >>> # NaN != NaN by default. 2025-08-14T21:38:42.2349611Z >>> expected = torch.tensor(float("Nan")) 2025-08-14T21:38:42.2350244Z >>> actual = expected.clone() 2025-08-14T21:38:42.2350820Z >>> torch.testing.assert_close(actual, expected) 2025-08-14T21:38:42.2351497Z Traceback (most recent call last): 2025-08-14T21:38:42.2352062Z ... 2025-08-14T21:38:42.2352495Z AssertionError: Scalars are not close! 2025-08-14T21:38:42.2353084Z 2025-08-14T21:38:42.2353530Z Expected nan but got nan. 2025-08-14T21:38:42.2354126Z Absolute difference: nan (up to 1e-05 allowed) 2025-08-14T21:38:42.2354860Z Relative difference: nan (up to 1.3e-06 allowed) 2025-08-14T21:38:42.2355692Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-08-14T21:38:42.2356292Z 2025-08-14T21:38:42.2356527Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-08-14T21:38:42.2357158Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-08-14T21:38:42.2357830Z >>> # The default error message can be overwritten. 2025-08-14T21:38:42.2358504Z >>> torch.testing.assert_close( 2025-08-14T21:38:42.2359214Z ... actual, expected, msg="Argh, the tensors are not close!" 2025-08-14T21:38:42.2359915Z ... ) 2025-08-14T21:38:42.2360347Z Traceback (most recent call last): 2025-08-14T21:38:42.2360898Z ... 2025-08-14T21:38:42.2361377Z AssertionError: Argh, the tensors are not close! 2025-08-14T21:38:42.2362412Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-08-14T21:38:42.2363226Z >>> # extra information 2025-08-14T21:38:42.2363747Z >>> torch.testing.assert_close( 2025-08-14T21:38:42.2364481Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-08-14T21:38:42.2365121Z ... ) 2025-08-14T21:38:42.2365537Z Traceback (most recent call last): 2025-08-14T21:38:42.2366085Z ... 2025-08-14T21:38:42.2366497Z AssertionError: Header 2025-08-14T21:38:42.2366975Z 2025-08-14T21:38:42.2367421Z Tensor-likes are not close! 2025-08-14T21:38:42.2367956Z 2025-08-14T21:38:42.2368401Z Mismatched elements: 2 / 3 (66.7%) 2025-08-14T21:38:42.2369220Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-08-14T21:38:42.2370293Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-08-14T21:38:42.2371131Z 2025-08-14T21:38:42.2371531Z Footer 2025-08-14T21:38:42.2371912Z 2025-08-14T21:38:42.2372578Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:42.2373274Z 2025-08-14T21:38:43.3175190Z 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. 2025-08-14T21:38:43.3176968Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.3177963Z Register a container-like type as pytree node. 2025-08-14T21:38:43.3178429Z 2025-08-14T21:38:43.3178590Z Args: 2025-08-14T21:38:43.3179151Z cls (type): A Python type to treat as an internal pytree node. 2025-08-14T21:38:43.3180200Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-08-14T21:38:43.3181431Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-08-14T21:38:43.3182573Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-08-14T21:38:43.3183639Z passed to the ``unflatten_fn``. 2025-08-14T21:38:43.3184551Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-08-14T21:38:43.3185792Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-08-14T21:38:43.3186775Z The function should return an instance of ``cls``. 2025-08-14T21:38:43.3187743Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-08-14T21:38:43.3188744Z qualified name used when serializing the tree spec. 2025-08-14T21:38:43.3189754Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-08-14T21:38:43.3191126Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-08-14T21:38:43.3192356Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-08-14T21:38:43.3193827Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-08-14T21:38:43.3195135Z how to convert the custom json dumpable representation of the context back to the 2025-08-14T21:38:43.3196325Z original context. This is used for json deserialization, which is being used in 2025-08-14T21:38:43.3197236Z :mod:`torch.export` right now. 2025-08-14T21:38:43.3197636Z 2025-08-14T21:38:43.3197844Z Example:: 2025-08-14T21:38:43.3198074Z 2025-08-14T21:38:43.3198251Z >>> # xdoctest: +SKIP 2025-08-14T21:38:43.3198826Z >>> # Registry a Python type with lambda functions 2025-08-14T21:38:43.3199473Z >>> register_pytree_node( 2025-08-14T21:38:43.3199962Z ... set, 2025-08-14T21:38:43.3200447Z ... lambda s: (sorted(s), None, None), 2025-08-14T21:38:43.3201427Z ... lambda children, _: set(children), 2025-08-14T21:38:43.3202022Z ... ) 2025-08-14T21:38:43.3202390Z 2025-08-14T21:38:43.3203063Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.3203728Z 2025-08-14T21:38:43.3728123Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1218. 2025-08-14T21:38:43.3730093Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.3730775Z 2025-08-14T21:38:43.3731152Z Context passed to policy function during selective checkpointing. 2025-08-14T21:38:43.3731751Z 2025-08-14T21:38:43.3732167Z This class is used to pass relevant metadata to the policy function during 2025-08-14T21:38:43.3733271Z selective checkpointing. The metadata includes whether the current invocation 2025-08-14T21:38:43.3734305Z of the policy function is during recomputation or not. 2025-08-14T21:38:43.3734823Z 2025-08-14T21:38:43.3734975Z Example: 2025-08-14T21:38:43.3735371Z >>> # xdoctest: +SKIP(stub) 2025-08-14T21:38:43.3735807Z >>> 2025-08-14T21:38:43.3736166Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-08-14T21:38:43.3737028Z >>> print(ctx.is_recompute) 2025-08-14T21:38:43.3737559Z >>> 2025-08-14T21:38:43.3738259Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-08-14T21:38:43.3739140Z >>> 2025-08-14T21:38:43.3739593Z >>> out = torch.utils.checkpoint.checkpoint( 2025-08-14T21:38:43.3740211Z >>> fn, x, y, 2025-08-14T21:38:43.3740655Z >>> use_reentrant=False, 2025-08-14T21:38:43.3741178Z >>> context_fn=context_fn, 2025-08-14T21:38:43.3741654Z >>> ) 2025-08-14T21:38:43.3741872Z 2025-08-14T21:38:43.3742315Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.3742974Z 2025-08-14T21:38:43.3744313Z 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=1358. 2025-08-14T21:38:43.3746184Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.3746904Z 2025-08-14T21:38:43.3747339Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-08-14T21:38:43.3747991Z 2025-08-14T21:38:43.3748380Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-08-14T21:38:43.3749262Z operations are recomputed during the backward pass. 2025-08-14T21:38:43.3749772Z 2025-08-14T21:38:43.3749914Z Args: 2025-08-14T21:38:43.3750328Z policy_fn_or_list (Callable or List): 2025-08-14T21:38:43.3751023Z - If a policy function is provided, it should accept a 2025-08-14T21:38:43.3751975Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-08-14T21:38:43.3752990Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-08-14T21:38:43.3754026Z indicating whether the execution of the op should be recomputed or not. 2025-08-14T21:38:43.3755066Z - If a list of operations is provided, it is equivalent to a policy 2025-08-14T21:38:43.3755984Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-08-14T21:38:43.3756939Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-08-14T21:38:43.3757711Z operations. 2025-08-14T21:38:43.3758359Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-08-14T21:38:43.3759358Z raised if any tensors cached by selective activation checkpoint are 2025-08-14T21:38:43.3760343Z mutated in order to ensure correctness. If set to `True`, this check 2025-08-14T21:38:43.3761086Z is disabled. 2025-08-14T21:38:43.3761503Z Returns: 2025-08-14T21:38:43.3761899Z A tuple of two context managers. 2025-08-14T21:38:43.3762284Z 2025-08-14T21:38:43.3762430Z Example: 2025-08-14T21:38:43.3763070Z >>> # xdoctest: +REQUIRES(LINUX) 2025-08-14T21:38:43.3763611Z >>> import functools 2025-08-14T21:38:43.3764065Z >>> 2025-08-14T21:38:43.3764493Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-08-14T21:38:43.3765148Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-08-14T21:38:43.3765717Z >>> 2025-08-14T21:38:43.3766087Z >>> ops_to_save = [ 2025-08-14T21:38:43.3766577Z >>> torch.ops.aten.mm.default, 2025-08-14T21:38:43.3767108Z >>> ] 2025-08-14T21:38:43.3767459Z >>> 2025-08-14T21:38:43.3767874Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-08-14T21:38:43.3768463Z >>> if op in ops_to_save: 2025-08-14T21:38:43.3769018Z >>> return CheckpointPolicy.MUST_SAVE 2025-08-14T21:38:43.3769595Z >>> else: 2025-08-14T21:38:43.3770065Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-08-14T21:38:43.3770666Z >>> 2025-08-14T21:38:43.3771358Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-08-14T21:38:43.3772234Z >>> 2025-08-14T21:38:43.3772603Z >>> # or equivalently 2025-08-14T21:38:43.3773386Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-08-14T21:38:43.3774270Z >>> 2025-08-14T21:38:43.3774829Z >>> def fn(x, y): 2025-08-14T21:38:43.3775478Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-08-14T21:38:43.3776215Z >>> 2025-08-14T21:38:43.3776592Z >>> out = torch.utils.checkpoint.checkpoint( 2025-08-14T21:38:43.3777186Z >>> fn, x, y, 2025-08-14T21:38:43.3777636Z >>> use_reentrant=False, 2025-08-14T21:38:43.3778140Z >>> context_fn=context_fn, 2025-08-14T21:38:43.3778636Z >>> ) 2025-08-14T21:38:43.3778842Z 2025-08-14T21:38:43.3779344Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.3780042Z 2025-08-14T21:38:43.3987604Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1147. 2025-08-14T21:38:43.3989393Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.3990125Z 2025-08-14T21:38:43.3990384Z Create a :class:`setuptools.Extension` for C++. 2025-08-14T21:38:43.3990858Z 2025-08-14T21:38:43.3991282Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-08-14T21:38:43.3992317Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-08-14T21:38:43.3993097Z 2025-08-14T21:38:43.3993432Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-08-14T21:38:43.3994310Z constructor. Full list arguments can be found at 2025-08-14T21:38:43.3995398Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-08-14T21:38:43.3996233Z 2025-08-14T21:38:43.3996432Z .. warning:: 2025-08-14T21:38:43.3997036Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-08-14T21:38:43.3997960Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-08-14T21:38:43.3998826Z the user's responsibility in their library to not use APIs from 2025-08-14T21:38:43.3999848Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-08-14T21:38:43.4000912Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-08-14T21:38:43.4001935Z example, to give access to custom ops from python, the library should 2025-08-14T21:38:43.4002745Z register the ops through the dispatcher. 2025-08-14T21:38:43.4003158Z 2025-08-14T21:38:43.4003541Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-08-14T21:38:43.4004534Z as a compile flag when py_limited_api is specified as an option for 2025-08-14T21:38:43.4005527Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-08-14T21:38:43.4006483Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-08-14T21:38:43.4007628Z safety, and sanity in order to encourage best practices. To target a 2025-08-14T21:38:43.4008629Z different version, set min_supported_cpython to the hexcode of the 2025-08-14T21:38:43.4009430Z CPython version of choice. 2025-08-14T21:38:43.4009777Z 2025-08-14T21:38:43.4009938Z Example: 2025-08-14T21:38:43.4010321Z >>> # xdoctest: +SKIP 2025-08-14T21:38:43.4010903Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-14T21:38:43.4011579Z >>> from setuptools import setup 2025-08-14T21:38:43.4012383Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-08-14T21:38:43.4013180Z >>> setup( 2025-08-14T21:38:43.4013582Z ... name='extension', 2025-08-14T21:38:43.4014085Z ... ext_modules=[ 2025-08-14T21:38:43.4014562Z ... CppExtension( 2025-08-14T21:38:43.4015073Z ... name='extension', 2025-08-14T21:38:43.4015653Z ... sources=['extension.cpp'], 2025-08-14T21:38:43.4016298Z ... extra_compile_args=['-g'], 2025-08-14T21:38:43.4016947Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-08-14T21:38:43.4017551Z ... ], 2025-08-14T21:38:43.4017968Z ... cmdclass={ 2025-08-14T21:38:43.4018417Z ... 'build_ext': BuildExtension 2025-08-14T21:38:43.4019188Z ... }) 2025-08-14T21:38:43.4019435Z 2025-08-14T21:38:43.4019902Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.4020615Z 2025-08-14T21:38:43.4021739Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1217. 2025-08-14T21:38:43.4023313Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.4023981Z 2025-08-14T21:38:43.4024225Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-08-14T21:38:43.4024673Z 2025-08-14T21:38:43.4025120Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-08-14T21:38:43.4026129Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-08-14T21:38:43.4027166Z extension. This includes the CUDA include path, library path and runtime 2025-08-14T21:38:43.4027995Z library. 2025-08-14T21:38:43.4028207Z 2025-08-14T21:38:43.4028610Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-08-14T21:38:43.4029465Z constructor. Full list arguments can be found at 2025-08-14T21:38:43.4030555Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-08-14T21:38:43.4031398Z 2025-08-14T21:38:43.4031576Z .. warning:: 2025-08-14T21:38:43.4032217Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-08-14T21:38:43.4033203Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-08-14T21:38:43.4034116Z the user's responsibility in their library to not use APIs from 2025-08-14T21:38:43.4035101Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-08-14T21:38:43.4036159Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-08-14T21:38:43.4037189Z example, to give access to custom ops from python, the library should 2025-08-14T21:38:43.4038044Z register the ops through the dispatcher. 2025-08-14T21:38:43.4038471Z 2025-08-14T21:38:43.4039055Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-08-14T21:38:43.4040037Z as a compile flag when py_limited_api is specified as an option for 2025-08-14T21:38:43.4040911Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-08-14T21:38:43.4041831Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-08-14T21:38:43.4042797Z safety, and sanity in order to encourage best practices. To target a 2025-08-14T21:38:43.4043815Z different version, set min_supported_cpython to the hexcode of the 2025-08-14T21:38:43.4044627Z CPython version of choice. 2025-08-14T21:38:43.4044967Z 2025-08-14T21:38:43.4045267Z Example: 2025-08-14T21:38:43.4045658Z >>> # xdoctest: +SKIP 2025-08-14T21:38:43.4046236Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-14T21:38:43.4046922Z >>> from setuptools import setup 2025-08-14T21:38:43.4047709Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-08-14T21:38:43.4048509Z >>> setup( 2025-08-14T21:38:43.4048927Z ... name='cuda_extension', 2025-08-14T21:38:43.4049409Z ... ext_modules=[ 2025-08-14T21:38:43.4049858Z ... CUDAExtension( 2025-08-14T21:38:43.4050335Z ... name='cuda_extension', 2025-08-14T21:38:43.4050954Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-08-14T21:38:43.4051679Z ... extra_compile_args={'cxx': ['-g'], 2025-08-14T21:38:43.4052290Z ... 'nvcc': ['-O2']}, 2025-08-14T21:38:43.4052971Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-08-14T21:38:43.4053576Z ... ], 2025-08-14T21:38:43.4053961Z ... cmdclass={ 2025-08-14T21:38:43.4054411Z ... 'build_ext': BuildExtension 2025-08-14T21:38:43.4054925Z ... }) 2025-08-14T21:38:43.4055151Z 2025-08-14T21:38:43.4055480Z Compute capabilities: 2025-08-14T21:38:43.4055753Z 2025-08-14T21:38:43.4056287Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-08-14T21:38:43.4057532Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-08-14T21:38:43.4058772Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-08-14T21:38:43.4060087Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-08-14T21:38:43.4061387Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-08-14T21:38:43.4062345Z support (see below for details on PTX). 2025-08-14T21:38:43.4062781Z 2025-08-14T21:38:43.4063363Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-08-14T21:38:43.4064465Z CCs you want the extension to support: 2025-08-14T21:38:43.4064870Z 2025-08-14T21:38:43.4065227Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-08-14T21:38:43.4066227Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-08-14T21:38:43.4066860Z 2025-08-14T21:38:43.4067437Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-08-14T21:38:43.4068874Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-08-14T21:38:43.4070274Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-08-14T21:38:43.4071616Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-08-14T21:38:43.4072972Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-08-14T21:38:43.4074270Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-08-14T21:38:43.4075614Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-08-14T21:38:43.4076998Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-08-14T21:38:43.4077945Z "8.0 8.6" would be better. 2025-08-14T21:38:43.4078252Z 2025-08-14T21:38:43.4078812Z Note that while it's possible to include all supported archs, the more archs get included the 2025-08-14T21:38:43.4080101Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-08-14T21:38:43.4080878Z 2025-08-14T21:38:43.4081494Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-08-14T21:38:43.4082880Z To workaround the issue, move python binding logic to pure C++ file. 2025-08-14T21:38:43.4083496Z 2025-08-14T21:38:43.4083671Z Example use: 2025-08-14T21:38:43.4084066Z #include 2025-08-14T21:38:43.4084639Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-08-14T21:38:43.4085102Z 2025-08-14T21:38:43.4085260Z Instead of: 2025-08-14T21:38:43.4085658Z #include 2025-08-14T21:38:43.4086244Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-08-14T21:38:43.4086702Z 2025-08-14T21:38:43.4087206Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-08-14T21:38:43.4088878Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-08-14T21:38:43.4090044Z 2025-08-14T21:38:43.4090217Z Relocatable device code linking: 2025-08-14T21:38:43.4090579Z 2025-08-14T21:38:43.4091095Z If you want to reference device symbols across compilation units (across object files), 2025-08-14T21:38:43.4092287Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-08-14T21:38:43.4093837Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-08-14T21:38:43.4095335Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-08-14T21:38:43.4096956Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-08-14T21:38:43.4098058Z helps reduce the protentional perf degradation of `-rdc`. 2025-08-14T21:38:43.4098873Z Note that it needs to be used at both steps to be useful. 2025-08-14T21:38:43.4099384Z 2025-08-14T21:38:43.4100009Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-08-14T21:38:43.4101191Z There is also a case where `-dlink` is used without `-rdc`: 2025-08-14T21:38:43.4102149Z when an extension is linked against a static lib containing rdc-compiled objects 2025-08-14T21:38:43.4103220Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-08-14T21:38:43.4103914Z 2025-08-14T21:38:43.4104269Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-08-14T21:38:43.4104853Z 2025-08-14T21:38:43.4104998Z Example: 2025-08-14T21:38:43.4105380Z >>> # xdoctest: +SKIP 2025-08-14T21:38:43.4105915Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-14T21:38:43.4106537Z >>> CUDAExtension( 2025-08-14T21:38:43.4106998Z ... name='cuda_extension', 2025-08-14T21:38:43.4107626Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-08-14T21:38:43.4108243Z ... dlink=True, 2025-08-14T21:38:43.4108739Z ... dlink_libraries=["dlink_lib"], 2025-08-14T21:38:43.4109350Z ... extra_compile_args={'cxx': ['-g'], 2025-08-14T21:38:43.4109970Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-08-14T21:38:43.4110391Z 2025-08-14T21:38:43.4110877Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.4111563Z 2025-08-14T21:38:43.4112594Z msg = Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1408. 2025-08-14T21:38:43.4114346Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.4115067Z 2025-08-14T21:38:43.4115346Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-08-14T21:38:43.4115863Z 2025-08-14T21:38:43.4116265Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-08-14T21:38:43.4117289Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-08-14T21:38:43.4118017Z extension. 2025-08-14T21:38:43.4118249Z 2025-08-14T21:38:43.4118618Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-08-14T21:38:43.4119376Z constructor. 2025-08-14T21:38:43.4119614Z 2025-08-14T21:38:43.4119776Z .. warning:: 2025-08-14T21:38:43.4120606Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-08-14T21:38:43.4121632Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-08-14T21:38:43.4122584Z the user's responsibility in their library to not use APIs from 2025-08-14T21:38:43.4123572Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-08-14T21:38:43.4124612Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-08-14T21:38:43.4125602Z example, to give access to custom ops from python, the library should 2025-08-14T21:38:43.4126421Z register the ops through the dispatcher. 2025-08-14T21:38:43.4126869Z 2025-08-14T21:38:43.4127283Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-08-14T21:38:43.4128303Z as a compile flag when py_limited_api is specified as an option for 2025-08-14T21:38:43.4129258Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-08-14T21:38:43.4130240Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-08-14T21:38:43.4131234Z safety, and sanity in order to encourage best practices. To target a 2025-08-14T21:38:43.4132149Z different version, set min_supported_cpython to the hexcode of the 2025-08-14T21:38:43.4133087Z CPython version of choice. 2025-08-14T21:38:43.4133438Z 2025-08-14T21:38:43.4133583Z Example: 2025-08-14T21:38:43.4133970Z >>> # xdoctest: +SKIP 2025-08-14T21:38:43.4134540Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-14T21:38:43.4135395Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-08-14T21:38:43.4136195Z >>> setup( 2025-08-14T21:38:43.4136615Z ... name='xpu_extension', 2025-08-14T21:38:43.4137123Z ... ext_modules=[ 2025-08-14T21:38:43.4137592Z ... SyclExtension( 2025-08-14T21:38:43.4138093Z ... name='xpu_extension', 2025-08-14T21:38:43.4138783Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-08-14T21:38:43.4139630Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-08-14T21:38:43.4140333Z ... ], 2025-08-14T21:38:43.4140700Z ... cmdclass={ 2025-08-14T21:38:43.4141169Z ... 'build_ext': BuildExtension 2025-08-14T21:38:43.4141724Z ... }) 2025-08-14T21:38:43.4141952Z 2025-08-14T21:38:43.4142520Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-08-14T21:38:43.4143854Z building process of the extension. If down the road a new card is installed the 2025-08-14T21:38:43.4145028Z extension may need to be recompiled. You can override the default behavior using 2025-08-14T21:38:43.4146302Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-08-14T21:38:43.4147246Z to support: 2025-08-14T21:38:43.4147486Z 2025-08-14T21:38:43.4147855Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-08-14T21:38:43.4148447Z 2025-08-14T21:38:43.4149019Z Note that while it's possible to include all supported archs, the more archs get included the 2025-08-14T21:38:43.4150358Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-08-14T21:38:43.4151126Z 2025-08-14T21:38:43.4151381Z Note: Ninja is required to build SyclExtension. 2025-08-14T21:38:43.4151850Z 2025-08-14T21:38:43.4152310Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.4152995Z 2025-08-14T21:38:43.4154038Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1585. 2025-08-14T21:38:43.4155668Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.4156336Z 2025-08-14T21:38:43.4156558Z Load a PyTorch C++ extension just-in-time (JIT). 2025-08-14T21:38:43.4156988Z 2025-08-14T21:38:43.4157510Z To load an extension, a Ninja build file is emitted, which is used to 2025-08-14T21:38:43.4158444Z compile the given sources into a dynamic library. This library is 2025-08-14T21:38:43.4159408Z subsequently loaded into the current Python process as a module and 2025-08-14T21:38:43.4160091Z returned from this function, ready for use. 2025-08-14T21:38:43.4160514Z 2025-08-14T21:38:43.4160904Z By default, the directory to which the build file is emitted and the 2025-08-14T21:38:43.4161913Z resulting library compiled to is ``/torch_extensions/``, where 2025-08-14T21:38:43.4162872Z ```` is the temporary folder on the current platform and ```` 2025-08-14T21:38:43.4163824Z the name of the extension. This location can be overridden in two ways. 2025-08-14T21:38:43.4164790Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-08-14T21:38:43.4165785Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-08-14T21:38:43.4166795Z into subfolders of this directory. Second, if the ``build_directory`` 2025-08-14T21:38:43.4179441Z argument to this function is supplied, it overrides the entire path, i.e. 2025-08-14T21:38:43.4180515Z the library will be compiled into that folder directly. 2025-08-14T21:38:43.4181027Z 2025-08-14T21:38:43.4181415Z To compile the sources, the default system compiler (``c++``) is used, 2025-08-14T21:38:43.4182620Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-08-14T21:38:43.4183799Z additional arguments to the compilation process, ``extra_cflags`` or 2025-08-14T21:38:43.4184804Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-08-14T21:38:43.4185748Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-08-14T21:38:43.4186627Z ``extra_cflags`` to pass further include directories. 2025-08-14T21:38:43.4187113Z 2025-08-14T21:38:43.4187564Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-08-14T21:38:43.4188552Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-08-14T21:38:43.4189578Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-08-14T21:38:43.4190637Z passing the CUDA lib64 directory as a library directory, and linking 2025-08-14T21:38:43.4191497Z ``cudart``. You can pass additional flags to nvcc via 2025-08-14T21:38:43.4192366Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-08-14T21:38:43.4193569Z heuristics for finding the CUDA install directory are used, which usually 2025-08-14T21:38:43.4194628Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-08-14T21:38:43.4195385Z safest option. 2025-08-14T21:38:43.4195644Z 2025-08-14T21:38:43.4196078Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-08-14T21:38:43.4197120Z files (``.sycl``) along with other sources. Such files will be detected 2025-08-14T21:38:43.4198107Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-08-14T21:38:43.4199117Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-08-14T21:38:43.4200083Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-08-14T21:38:43.4201040Z SYCL compiler is expected to be found via system PATH environment 2025-08-14T21:38:43.4201744Z variable. 2025-08-14T21:38:43.4201960Z 2025-08-14T21:38:43.4202093Z Args: 2025-08-14T21:38:43.4202634Z name: The name of the extension to build. This MUST be the same as the 2025-08-14T21:38:43.4203378Z name of the pybind11 module! 2025-08-14T21:38:43.4204098Z sources: A list of relative or absolute paths to C++ source files. 2025-08-14T21:38:43.4205094Z extra_cflags: optional list of compiler flags to forward to the build. 2025-08-14T21:38:43.4206129Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-08-14T21:38:43.4206933Z when building CUDA sources. 2025-08-14T21:38:43.4207677Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-08-14T21:38:43.4208718Z compiler when building SYCL sources. 2025-08-14T21:38:43.4209535Z extra_ldflags: optional list of linker flags to forward to the build. 2025-08-14T21:38:43.4210565Z extra_include_paths: optional list of include directories to forward 2025-08-14T21:38:43.4211332Z to the build. 2025-08-14T21:38:43.4211924Z build_directory: optional path to use as build workspace. 2025-08-14T21:38:43.4212792Z verbose: If ``True``, turns on verbose logging of load steps. 2025-08-14T21:38:43.4213747Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-08-14T21:38:43.4214665Z the build. If set to ``None`` (default), this value is 2025-08-14T21:38:43.4215527Z automatically determined based on the existence of ``.cu`` or 2025-08-14T21:38:43.4216438Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-08-14T21:38:43.4217171Z and libraries to be included. 2025-08-14T21:38:43.4217963Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-08-14T21:38:43.4218899Z the build. If set to ``None`` (default), this value is 2025-08-14T21:38:43.4219809Z automatically determined based on the existence of ``.sycl`` in 2025-08-14T21:38:43.4220727Z ``sources``. Set it to `True`` to force SYCL headers and 2025-08-14T21:38:43.4221613Z libraries to be included. 2025-08-14T21:38:43.4222338Z is_python_module: If ``True`` (default), imports the produced shared 2025-08-14T21:38:43.4223268Z library as a Python module. If ``False``, behavior depends on 2025-08-14T21:38:43.4224055Z ``is_standalone``. 2025-08-14T21:38:43.4224685Z is_standalone: If ``False`` (default) loads the constructed extension 2025-08-14T21:38:43.4225631Z into the process as a plain dynamic library. If ``True``, build a 2025-08-14T21:38:43.4226402Z standalone executable. 2025-08-14T21:38:43.4226736Z 2025-08-14T21:38:43.4226885Z Returns: 2025-08-14T21:38:43.4227286Z If ``is_python_module`` is ``True``: 2025-08-14T21:38:43.4228011Z Returns the loaded PyTorch extension as a Python module. 2025-08-14T21:38:43.4228524Z 2025-08-14T21:38:43.4228876Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-08-14T21:38:43.4229827Z Returns nothing. (The shared library is loaded into the process as 2025-08-14T21:38:43.4230611Z a side effect.) 2025-08-14T21:38:43.4230895Z 2025-08-14T21:38:43.4231103Z If ``is_standalone`` is ``True``. 2025-08-14T21:38:43.4231843Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-08-14T21:38:43.4232720Z added to the PATH environment variable as a side effect.) 2025-08-14T21:38:43.4233236Z 2025-08-14T21:38:43.4233390Z Example: 2025-08-14T21:38:43.4233729Z >>> # xdoctest: +SKIP 2025-08-14T21:38:43.4234246Z >>> from torch.utils.cpp_extension import load 2025-08-14T21:38:43.4234826Z >>> module = load( 2025-08-14T21:38:43.4235253Z ... name='extension', 2025-08-14T21:38:43.4235856Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-08-14T21:38:43.4236524Z ... extra_cflags=['-O2'], 2025-08-14T21:38:43.4237038Z ... verbose=True) 2025-08-14T21:38:43.4237339Z 2025-08-14T21:38:43.4237801Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.4238502Z 2025-08-14T21:38:43.4239495Z 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=1890. 2025-08-14T21:38:43.4241187Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.4241901Z 2025-08-14T21:38:43.4242300Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-08-14T21:38:43.4242903Z 2025-08-14T21:38:43.4243333Z This function behaves exactly like :func:`load`, but takes its sources as 2025-08-14T21:38:43.4244407Z strings rather than filenames. These strings are stored to files in the 2025-08-14T21:38:43.4245580Z build directory, after which the behavior of :func:`load_inline` is 2025-08-14T21:38:43.4246357Z identical to :func:`load`. 2025-08-14T21:38:43.4246697Z 2025-08-14T21:38:43.4246847Z See `the 2025-08-14T21:38:43.4247678Z tests `_ 2025-08-14T21:38:43.4248755Z for good examples of using this function. 2025-08-14T21:38:43.4249182Z 2025-08-14T21:38:43.4249613Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-08-14T21:38:43.4250735Z the necessary header includes, as well as the (pybind11) binding code. More 2025-08-14T21:38:43.4251861Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-08-14T21:38:43.4252833Z single ``.cpp`` file. This file is then prepended with ``#include 2025-08-14T21:38:43.4253556Z `` 2025-08-14T21:38:43.4253849Z 2025-08-14T21:38:43.4254219Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-08-14T21:38:43.4255250Z automatically generated for each function specified. ``functions`` can 2025-08-14T21:38:43.4256280Z either be a list of function names, or a dictionary mapping from function 2025-08-14T21:38:43.4257287Z names to docstrings. If a list is given, the name of each function is used 2025-08-14T21:38:43.4258241Z as its docstring. 2025-08-14T21:38:43.4258497Z 2025-08-14T21:38:43.4258894Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-08-14T21:38:43.4259826Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-08-14T21:38:43.4260714Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-08-14T21:38:43.4261655Z separately, but ultimately linked into a single library. Note that no 2025-08-14T21:38:43.4262653Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-08-14T21:38:43.4263731Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-08-14T21:38:43.4264749Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-08-14T21:38:43.4265542Z include its name in ``functions``). 2025-08-14T21:38:43.4265918Z 2025-08-14T21:38:43.4266317Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-08-14T21:38:43.4267326Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-08-14T21:38:43.4268302Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-08-14T21:38:43.4269273Z linked into a single library. Note that no bindings are generated for 2025-08-14T21:38:43.4270289Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-08-14T21:38:43.4271311Z create a C++ function that calls it, and either declare or define this 2025-08-14T21:38:43.4272237Z C++ function in one of the ``cpp_sources`` (and include its name 2025-08-14T21:38:43.4272893Z in ``functions``). 2025-08-14T21:38:43.4273164Z 2025-08-14T21:38:43.4273173Z 2025-08-14T21:38:43.4273180Z 2025-08-14T21:38:43.4273516Z See :func:`load` for a description of arguments omitted below. 2025-08-14T21:38:43.4274050Z 2025-08-14T21:38:43.4274207Z Args: 2025-08-14T21:38:43.4274782Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-08-14T21:38:43.4275740Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-08-14T21:38:43.4276801Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-08-14T21:38:43.4277818Z functions: A list of function names for which to generate function 2025-08-14T21:38:43.4278795Z bindings. If a dictionary is given, it should map function names to 2025-08-14T21:38:43.4279715Z docstrings (which are otherwise just the function names). 2025-08-14T21:38:43.4280622Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-08-14T21:38:43.4281475Z the build. If set to ``None`` (default), this value is 2025-08-14T21:38:43.4282334Z automatically determined based on whether ``cuda_sources`` is 2025-08-14T21:38:43.4283371Z provided. Set it to ``True`` to force CUDA headers 2025-08-14T21:38:43.4284030Z and libraries to be included. 2025-08-14T21:38:43.4284809Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-08-14T21:38:43.4285722Z the build. If set to ``None`` (default), this value is 2025-08-14T21:38:43.4286582Z automatically determined based on whether ``sycl_sources`` is 2025-08-14T21:38:43.4287459Z provided. Set it to ``True`` to force SYCL headers 2025-08-14T21:38:43.4288130Z and libraries to be included. 2025-08-14T21:38:43.4288880Z with_pytorch_error_handling: Determines whether pytorch error and 2025-08-14T21:38:43.4289869Z warning macros are handled by pytorch instead of pybind. To do 2025-08-14T21:38:43.4290872Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-08-14T21:38:43.4291849Z function. This redirection might cause issues in obscure cases 2025-08-14T21:38:43.4292720Z of cpp. This flag should be set to ``False`` when this redirect 2025-08-14T21:38:43.4293652Z causes issues. 2025-08-14T21:38:43.4294358Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-08-14T21:38:43.4295413Z ``#include `` and ``#include `` lines. 2025-08-14T21:38:43.4296511Z Use this option to improve cold start times when you 2025-08-14T21:38:43.4297486Z already include the necessary headers in your source code. Default: ``False``. 2025-08-14T21:38:43.4298184Z 2025-08-14T21:38:43.4298344Z Example: 2025-08-14T21:38:43.4298813Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-14T21:38:43.4299550Z >>> from torch.utils.cpp_extension import load_inline 2025-08-14T21:38:43.4300175Z >>> source = """ 2025-08-14T21:38:43.4300667Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-08-14T21:38:43.4301252Z return x.sin() + y.sin(); 2025-08-14T21:38:43.4301699Z } 2025-08-14T21:38:43.4302027Z """ 2025-08-14T21:38:43.4302465Z >>> module = load_inline(name='inline_extension', 2025-08-14T21:38:43.4303121Z ... cpp_sources=[source], 2025-08-14T21:38:43.4303838Z ... functions=['sin_add']) 2025-08-14T21:38:43.4304272Z 2025-08-14T21:38:43.4304427Z .. note:: 2025-08-14T21:38:43.4305076Z Since load_inline will just-in-time compile the source code, please ensure 2025-08-14T21:38:43.4306133Z that you have the right toolchains installed in the runtime. For example, 2025-08-14T21:38:43.4307158Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-08-14T21:38:43.4308232Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-08-14T21:38:43.4309346Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-08-14T21:38:43.4310466Z are not included when you install torch and must be additionally installed. 2025-08-14T21:38:43.4311152Z 2025-08-14T21:38:43.4311625Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-08-14T21:38:43.4312744Z the extension. This may use up too many resources on some systems. One 2025-08-14T21:38:43.4313792Z can control the number of workers by setting the `MAX_JOBS` environment 2025-08-14T21:38:43.4314598Z variable to a non-negative number. 2025-08-14T21:38:43.4314994Z 2025-08-14T21:38:43.4315427Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.4316103Z 2025-08-14T21:38:43.4336545Z 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. 2025-08-14T21:38:43.4338484Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.4339207Z 2025-08-14T21:38:43.4339749Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-08-14T21:38:43.4340663Z 2025-08-14T21:38:43.4341223Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-08-14T21:38:43.4342413Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-08-14T21:38:43.4343621Z server like load. It can emulate multiple calling threads to a single module 2025-08-14T21:38:43.4344739Z provided. In the future we plan to enhance this component to support inter and 2025-08-14T21:38:43.4345839Z intra-op parallelism as well as multiple models running in a single process. 2025-08-14T21:38:43.4346535Z 2025-08-14T21:38:43.4347002Z Please note that even though nn.Module is supported, it might incur an overhead 2025-08-14T21:38:43.4348097Z from the need to hold GIL every time we execute Python code or pass around 2025-08-14T21:38:43.4349150Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-08-14T21:38:43.4350246Z model for inference deployment it is better to switch to using it in this 2025-08-14T21:38:43.4351076Z benchmark. 2025-08-14T21:38:43.4351301Z 2025-08-14T21:38:43.4351474Z Example:: 2025-08-14T21:38:43.4351685Z 2025-08-14T21:38:43.4351895Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:43.4352570Z >>> from torch.utils import ThroughputBenchmark 2025-08-14T21:38:43.4353395Z >>> bench = ThroughputBenchmark(my_module) 2025-08-14T21:38:43.4354110Z >>> # Pre-populate benchmark's data set with the inputs 2025-08-14T21:38:43.4354768Z >>> for input in inputs: 2025-08-14T21:38:43.4355502Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-08-14T21:38:43.4356365Z ... bench.add_input(input[0], x2=input[1]) 2025-08-14T21:38:43.4357143Z >>> # Inputs supplied above are randomly used during the execution 2025-08-14T21:38:43.4357908Z >>> stats = bench.benchmark( 2025-08-14T21:38:43.4358436Z ... num_calling_threads=4, 2025-08-14T21:38:43.4358968Z ... num_warmup_iters = 100, 2025-08-14T21:38:43.4359495Z ... num_iters = 1000, 2025-08-14T21:38:43.4359960Z ... ) 2025-08-14T21:38:43.4360484Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-08-14T21:38:43.4361313Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-08-14T21:38:43.4361873Z 2025-08-14T21:38:43.4362265Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.4362943Z 2025-08-14T21:38:43.5251042Z 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. 2025-08-14T21:38:43.5252871Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.5253945Z Sampler that restricts data loading to a subset of the dataset. 2025-08-14T21:38:43.5254522Z 2025-08-14T21:38:43.5254761Z It is especially useful in conjunction with 2025-08-14T21:38:43.5255641Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-08-14T21:38:43.5256806Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-08-14T21:38:43.5257914Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-08-14T21:38:43.5258777Z original dataset that is exclusive to it. 2025-08-14T21:38:43.5259241Z 2025-08-14T21:38:43.5259403Z .. note:: 2025-08-14T21:38:43.5260063Z Dataset is assumed to be of constant size and that any instance of it always 2025-08-14T21:38:43.5260935Z returns the same elements in the same order. 2025-08-14T21:38:43.5261398Z 2025-08-14T21:38:43.5261546Z Args: 2025-08-14T21:38:43.5261946Z dataset: Dataset used for sampling. 2025-08-14T21:38:43.5262701Z num_replicas (int, optional): Number of processes participating in 2025-08-14T21:38:43.5263863Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-08-14T21:38:43.5264724Z current distributed group. 2025-08-14T21:38:43.5265804Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-08-14T21:38:43.5266829Z By default, :attr:`rank` is retrieved from the current distributed 2025-08-14T21:38:43.5267572Z group. 2025-08-14T21:38:43.5268203Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-08-14T21:38:43.5268980Z indices. 2025-08-14T21:38:43.5269530Z seed (int, optional): random seed used to shuffle the sampler if 2025-08-14T21:38:43.5270396Z :attr:`shuffle=True`. This number should be identical across all 2025-08-14T21:38:43.5271255Z processes in the distributed group. Default: ``0``. 2025-08-14T21:38:43.5272128Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-08-14T21:38:43.5273093Z tail of the data to make it evenly divisible across the number of 2025-08-14T21:38:43.5274038Z replicas. If ``False``, the sampler will add extra indices to make 2025-08-14T21:38:43.5274989Z the data evenly divisible across the replicas. Default: ``False``. 2025-08-14T21:38:43.5275598Z 2025-08-14T21:38:43.5275757Z .. warning:: 2025-08-14T21:38:43.5276339Z In distributed mode, calling the :meth:`set_epoch` method at 2025-08-14T21:38:43.5277570Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-08-14T21:38:43.5278743Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-08-14T21:38:43.5279670Z the same ordering will be always used. 2025-08-14T21:38:43.5280098Z 2025-08-14T21:38:43.5280246Z Example:: 2025-08-14T21:38:43.5280474Z 2025-08-14T21:38:43.5280652Z >>> # xdoctest: +SKIP 2025-08-14T21:38:43.5281316Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-08-14T21:38:43.5282226Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-08-14T21:38:43.5282964Z ... sampler=sampler) 2025-08-14T21:38:43.5283577Z >>> for epoch in range(start_epoch, n_epochs): 2025-08-14T21:38:43.5284178Z ... if is_distributed: 2025-08-14T21:38:43.5284716Z ... sampler.set_epoch(epoch) 2025-08-14T21:38:43.5285271Z ... train(loader) 2025-08-14T21:38:43.5285741Z 2025-08-14T21:38:43.5286398Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.5287088Z 2025-08-14T21:38:43.5328446Z 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. 2025-08-14T21:38:43.5330233Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.5331345Z Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights). 2025-08-14T21:38:43.5332037Z 2025-08-14T21:38:43.5332177Z Args: 2025-08-14T21:38:43.5332808Z weights (sequence) : a sequence of weights, not necessary summing up to one 2025-08-14T21:38:43.5333684Z num_samples (int): number of samples to draw 2025-08-14T21:38:43.5334469Z replacement (bool): if ``True``, samples are drawn with replacement. 2025-08-14T21:38:43.5335452Z If not, they are drawn without replacement, which means that when a 2025-08-14T21:38:43.5336418Z sample index is drawn for a row, it cannot be drawn again for that row. 2025-08-14T21:38:43.5337301Z generator (Generator): Generator used in sampling. 2025-08-14T21:38:43.5337776Z 2025-08-14T21:38:43.5337927Z Example: 2025-08-14T21:38:43.5338386Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-14T21:38:43.5338934Z >>> list( 2025-08-14T21:38:43.5339351Z ... WeightedRandomSampler( 2025-08-14T21:38:43.5339982Z ... [0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True 2025-08-14T21:38:43.5340564Z ... ) 2025-08-14T21:38:43.5340946Z ... ) 2025-08-14T21:38:43.5341509Z [4, 4, 1, 4, 5] 2025-08-14T21:38:43.5341907Z >>> list( 2025-08-14T21:38:43.5342336Z ... WeightedRandomSampler( 2025-08-14T21:38:43.5342970Z ... [0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False 2025-08-14T21:38:43.5343659Z ... ) 2025-08-14T21:38:43.5344047Z ... ) 2025-08-14T21:38:43.5344411Z [0, 1, 4, 3, 2] 2025-08-14T21:38:43.5344814Z 2025-08-14T21:38:43.5345452Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.5346134Z 2025-08-14T21:38:43.5347160Z 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. 2025-08-14T21:38:43.5348867Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.5349874Z Wraps another sampler to yield a mini-batch of indices. 2025-08-14T21:38:43.5350357Z 2025-08-14T21:38:43.5350501Z Args: 2025-08-14T21:38:43.5351128Z sampler (Sampler or Iterable): Base sampler. Can be any iterable object 2025-08-14T21:38:43.5351971Z batch_size (int): Size of mini-batch. 2025-08-14T21:38:43.5352732Z drop_last (bool): If ``True``, the sampler will drop the last batch if 2025-08-14T21:38:43.5353717Z its size would be less than ``batch_size`` 2025-08-14T21:38:43.5354161Z 2025-08-14T21:38:43.5354319Z Example: 2025-08-14T21:38:43.5354677Z >>> list( 2025-08-14T21:38:43.5355091Z ... BatchSampler( 2025-08-14T21:38:43.5355791Z ... SequentialSampler(range(10)), batch_size=3, drop_last=False 2025-08-14T21:38:43.5356557Z ... ) 2025-08-14T21:38:43.5356943Z ... ) 2025-08-14T21:38:43.5357354Z [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] 2025-08-14T21:38:43.5357898Z >>> list( 2025-08-14T21:38:43.5358610Z ... BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True) 2025-08-14T21:38:43.5359485Z ... ) 2025-08-14T21:38:43.5359898Z [[0, 1, 2], [3, 4, 5], [6, 7, 8]] 2025-08-14T21:38:43.5360401Z 2025-08-14T21:38:43.5361044Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.5361716Z 2025-08-14T21:38:43.5527921Z 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. 2025-08-14T21:38:43.5529842Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.5530525Z 2025-08-14T21:38:43.5530725Z Iterable-style DataPipe. 2025-08-14T21:38:43.5531023Z 2025-08-14T21:38:43.5531473Z All DataPipes that represent an iterable of data samples should subclass this. 2025-08-14T21:38:43.5532655Z This style of DataPipes is particularly useful when data come from a stream, or 2025-08-14T21:38:43.5534009Z when the number of samples is too large to fit them all in memory. ``IterDataPipe`` is lazily initialized and its 2025-08-14T21:38:43.5535389Z elements are computed only when ``next()`` is called on the iterator of an ``IterDataPipe``. 2025-08-14T21:38:43.5536131Z 2025-08-14T21:38:43.5536549Z All subclasses should overwrite :meth:`__iter__`, which would return an 2025-08-14T21:38:43.5537806Z iterator of samples in this DataPipe. Calling ``__iter__`` of an ``IterDataPipe`` automatically invokes its 2025-08-14T21:38:43.5539356Z method ``reset()``, which by default performs no operation. When writing a custom ``IterDataPipe``, users should 2025-08-14T21:38:43.5540760Z override ``reset()`` if necessary. The common usages include resetting buffers, pointers, 2025-08-14T21:38:43.5541831Z and various state variables within the custom ``IterDataPipe``. 2025-08-14T21:38:43.5542406Z 2025-08-14T21:38:43.5542536Z Note: 2025-08-14T21:38:43.5543103Z Only `one` iterator can be valid for each ``IterDataPipe`` at a time, 2025-08-14T21:38:43.5544374Z and the creation a second iterator will invalidate the first one. This constraint is necessary because 2025-08-14T21:38:43.5546159Z some ``IterDataPipe`` have internal buffers, whose states can become invalid if there are multiple iterators. 2025-08-14T21:38:43.5547592Z The code example below presents details on how this constraint looks in practice. 2025-08-14T21:38:43.5548982Z If you have any feedback related to this constraint, please see `GitHub IterDataPipe Single Iterator Issue`_. 2025-08-14T21:38:43.5549901Z 2025-08-14T21:38:43.5550430Z These DataPipes can be invoked in two ways, using the class constructor or applying their 2025-08-14T21:38:43.5551843Z functional form onto an existing ``IterDataPipe`` (recommended, available to most but not all DataPipes). 2025-08-14T21:38:43.5553317Z You can chain multiple `IterDataPipe` together to form a pipeline that will perform multiple 2025-08-14T21:38:43.5554316Z operations in succession. 2025-08-14T21:38:43.5554637Z 2025-08-14T21:38:43.5554873Z .. _GitHub IterDataPipe Single Iterator Issue: 2025-08-14T21:38:43.5555572Z https://github.com/pytorch/data/issues/45 2025-08-14T21:38:43.5556030Z 2025-08-14T21:38:43.5556188Z Note: 2025-08-14T21:38:43.5556783Z When a subclass is used with :class:`~torch.utils.data.DataLoader`, each 2025-08-14T21:38:43.5557909Z item in the DataPipe will be yielded from the :class:`~torch.utils.data.DataLoader` 2025-08-14T21:38:43.5559205Z iterator. When :attr:`num_workers > 0`, each worker process will have a 2025-08-14T21:38:43.5560258Z different copy of the DataPipe object, so it is often desired to configure 2025-08-14T21:38:43.5561355Z each copy independently to avoid having duplicate data returned from the 2025-08-14T21:38:43.5562467Z workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker 2025-08-14T21:38:43.5563614Z process, returns information about the worker. It can be used in either the 2025-08-14T21:38:43.5564770Z dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's 2025-08-14T21:38:43.5565780Z :attr:`worker_init_fn` option to modify each copy's behavior. 2025-08-14T21:38:43.5566355Z 2025-08-14T21:38:43.5566507Z Examples: 2025-08-14T21:38:43.5566889Z General Usage: 2025-08-14T21:38:43.5567308Z >>> # xdoctest: +SKIP 2025-08-14T21:38:43.5568024Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-08-14T21:38:43.5568850Z >>> dp = IterableWrapper(range(10)) 2025-08-14T21:38:43.5569579Z >>> map_dp_1 = Mapper(dp, lambda x: x + 1) # Using class constructor 2025-08-14T21:38:43.5570283Z >>> map_dp_2 = dp.map( 2025-08-14T21:38:43.5570789Z ... lambda x: x + 1 2025-08-14T21:38:43.5571290Z ... ) # Using functional form (recommended) 2025-08-14T21:38:43.5571886Z >>> list(map_dp_1) 2025-08-14T21:38:43.5572358Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-08-14T21:38:43.5572898Z >>> list(map_dp_2) 2025-08-14T21:38:43.5573355Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-08-14T21:38:43.5573939Z >>> filter_dp = map_dp_1.filter(lambda x: x % 2 == 0) 2025-08-14T21:38:43.5574558Z >>> list(filter_dp) 2025-08-14T21:38:43.5575001Z [2, 4, 6, 8, 10] 2025-08-14T21:38:43.5575482Z Single Iterator Constraint Example: 2025-08-14T21:38:43.5576228Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-08-14T21:38:43.5577056Z >>> source_dp = IterableWrapper(range(10)) 2025-08-14T21:38:43.5577684Z >>> it1 = iter(source_dp) 2025-08-14T21:38:43.5578194Z >>> list(it1) 2025-08-14T21:38:43.5578630Z [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 2025-08-14T21:38:43.5579147Z >>> it1 = iter(source_dp) 2025-08-14T21:38:43.5579648Z >>> it2 = iter( 2025-08-14T21:38:43.5580080Z ... source_dp 2025-08-14T21:38:43.5580625Z ... ) # The creation of a new iterator invalidates `it1` 2025-08-14T21:38:43.5581275Z >>> next(it2) 2025-08-14T21:38:43.5581674Z 0 2025-08-14T21:38:43.5582175Z >>> next(it1) # Further usage of `it1` will raise a `RunTimeError` 2025-08-14T21:38:43.5582716Z 2025-08-14T21:38:43.5583316Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.5584125Z 2025-08-14T21:38:43.5759081Z 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. 2025-08-14T21:38:43.5760170Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.5760542Z 2025-08-14T21:38:43.5760997Z Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``). 2025-08-14T21:38:43.5761552Z 2025-08-14T21:38:43.5761790Z A list of the child DataPipes is returned from this operation. 2025-08-14T21:38:43.5762154Z 2025-08-14T21:38:43.5762239Z Args: 2025-08-14T21:38:43.5762551Z datapipe: Iterable DataPipe being filtered 2025-08-14T21:38:43.5762967Z num_instances: number of instances of the DataPipe to create 2025-08-14T21:38:43.5763647Z classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None`` 2025-08-14T21:38:43.5764445Z drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None`` 2025-08-14T21:38:43.5765189Z buffer_size: this defines the maximum number of inputs that the buffer can hold across all child 2025-08-14T21:38:43.5766013Z DataPipes while waiting for their values to be yielded. 2025-08-14T21:38:43.5766492Z Defaults to ``1000``. Use ``-1`` for the unlimited buffer. 2025-08-14T21:38:43.5766830Z 2025-08-14T21:38:43.5766919Z Examples: 2025-08-14T21:38:43.5767166Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-08-14T21:38:43.5767576Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-14T21:38:43.5767946Z >>> def odd_or_even(n): 2025-08-14T21:38:43.5768215Z ... return n % 2 2025-08-14T21:38:43.5768504Z >>> source_dp = IterableWrapper(range(5)) 2025-08-14T21:38:43.5768951Z >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even) 2025-08-14T21:38:43.5769352Z >>> list(dp1) 2025-08-14T21:38:43.5769576Z [0, 2, 4] 2025-08-14T21:38:43.5769812Z >>> list(dp2) 2025-08-14T21:38:43.5770020Z [1, 3] 2025-08-14T21:38:43.5770383Z >>> # It can also filter out any element that gets `None` from the `classifier_fn` 2025-08-14T21:38:43.5770836Z >>> def odd_or_even_no_zero(n): 2025-08-14T21:38:43.5771147Z ... return n % 2 if n != 0 else None 2025-08-14T21:38:43.5771458Z >>> dp1, dp2 = source_dp.demux( 2025-08-14T21:38:43.5771874Z ... num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True 2025-08-14T21:38:43.5772284Z ... ) 2025-08-14T21:38:43.5772473Z >>> list(dp1) 2025-08-14T21:38:43.5772696Z [2, 4] 2025-08-14T21:38:43.5772899Z >>> list(dp2) 2025-08-14T21:38:43.5773105Z [1, 3] 2025-08-14T21:38:43.5773229Z 2025-08-14T21:38:43.5773472Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.5773832Z 2025-08-14T21:38:43.5774520Z 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. 2025-08-14T21:38:43.5775552Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.5775921Z 2025-08-14T21:38:43.5776218Z Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). 2025-08-14T21:38:43.5776641Z 2025-08-14T21:38:43.5776976Z As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, 2025-08-14T21:38:43.5777612Z and so on. It ends when the shortest input DataPipe is exhausted. 2025-08-14T21:38:43.5777896Z 2025-08-14T21:38:43.5777992Z Args: 2025-08-14T21:38:43.5778493Z datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted 2025-08-14T21:38:43.5779010Z 2025-08-14T21:38:43.5779188Z Example: 2025-08-14T21:38:43.5779432Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-08-14T21:38:43.5779828Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-14T21:38:43.5780213Z >>> dp1, dp2, dp3 = ( 2025-08-14T21:38:43.5780495Z ... IterableWrapper(range(3)), 2025-08-14T21:38:43.5780836Z ... IterableWrapper(range(10, 15)), 2025-08-14T21:38:43.5781158Z ... IterableWrapper(range(20, 25)), 2025-08-14T21:38:43.5781463Z ... ) 2025-08-14T21:38:43.5781681Z >>> list(dp1.mux(dp2, dp3)) 2025-08-14T21:38:43.5781945Z [0, 10, 20, 1, 11, 21, 2, 12, 22] 2025-08-14T21:38:43.5782142Z 2025-08-14T21:38:43.5782384Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.5782742Z 2025-08-14T21:38:43.5783396Z 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. 2025-08-14T21:38:43.5784587Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.5785015Z 2025-08-14T21:38:43.5785370Z Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). 2025-08-14T21:38:43.5785906Z 2025-08-14T21:38:43.5786257Z The output is stopped as soon as the shortest input DataPipe is exhausted. 2025-08-14T21:38:43.5786613Z 2025-08-14T21:38:43.5786707Z Args: 2025-08-14T21:38:43.5786973Z *datapipes: Iterable DataPipes being aggregated 2025-08-14T21:38:43.5787288Z 2025-08-14T21:38:43.5787369Z Example: 2025-08-14T21:38:43.5787608Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-08-14T21:38:43.5788074Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-14T21:38:43.5788437Z >>> dp1, dp2, dp3 = ( 2025-08-14T21:38:43.5788778Z ... IterableWrapper(range(5)), 2025-08-14T21:38:43.5789103Z ... IterableWrapper(range(10, 15)), 2025-08-14T21:38:43.5789485Z ... IterableWrapper(range(20, 25)), 2025-08-14T21:38:43.5789790Z ... ) 2025-08-14T21:38:43.5790010Z >>> list(dp1.zip(dp2, dp3)) 2025-08-14T21:38:43.5790375Z [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)] 2025-08-14T21:38:43.5790624Z 2025-08-14T21:38:43.5790881Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.5791305Z 2025-08-14T21:38:43.5792056Z 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. 2025-08-14T21:38:43.5793479Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.5793849Z 2025-08-14T21:38:43.5794283Z Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``). 2025-08-14T21:38:43.5794771Z 2025-08-14T21:38:43.5794885Z Args: 2025-08-14T21:38:43.5795166Z datapipe: Iterable datapipe that provides pathnames 2025-08-14T21:38:43.5795664Z mode: An optional string that specifies the mode in which 2025-08-14T21:38:43.5796156Z the file is opened by ``open()``. It defaults to ``r``, other options are 2025-08-14T21:38:43.5796682Z ``b`` for reading in binary mode and ``t`` for text mode. 2025-08-14T21:38:43.5797201Z encoding: An optional string that specifies the encoding of the 2025-08-14T21:38:43.5797766Z underlying file. It defaults to ``None`` to match the default encoding of ``open``. 2025-08-14T21:38:43.5798306Z length: Nominal length of the datapipe 2025-08-14T21:38:43.5798531Z 2025-08-14T21:38:43.5798657Z Note: 2025-08-14T21:38:43.5799048Z The opened file handles will be closed by Python's GC periodically. Users can choose 2025-08-14T21:38:43.5799587Z to close them explicitly. 2025-08-14T21:38:43.5799770Z 2025-08-14T21:38:43.5799850Z Example: 2025-08-14T21:38:43.5800059Z >>> # xdoctest: +SKIP 2025-08-14T21:38:43.5800412Z >>> from torchdata.datapipes.iter import ( 2025-08-14T21:38:43.5800845Z ... FileLister, 2025-08-14T21:38:43.5801154Z ... FileOpener, 2025-08-14T21:38:43.5801402Z ... StreamReader, 2025-08-14T21:38:43.5801653Z ... ) 2025-08-14T21:38:43.5802024Z >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith(".txt")) 2025-08-14T21:38:43.5802508Z >>> dp = FileOpener(dp) 2025-08-14T21:38:43.5802772Z >>> dp = StreamReader(dp) 2025-08-14T21:38:43.5803039Z >>> list(dp) 2025-08-14T21:38:43.5803323Z [('./abc.txt', 'abc')] 2025-08-14T21:38:43.5803484Z 2025-08-14T21:38:43.5803743Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.5804159Z 2025-08-14T21:38:43.5818205Z 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. 2025-08-14T21:38:43.5819280Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:43.5819659Z 2025-08-14T21:38:43.5820057Z Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``. 2025-08-14T21:38:43.5820557Z 2025-08-14T21:38:43.5820674Z (functional name: ``groupby``). 2025-08-14T21:38:43.5820860Z 2025-08-14T21:38:43.5821232Z The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group 2025-08-14T21:38:43.5822143Z will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full, 2025-08-14T21:38:43.5822842Z the DataPipe will yield the largest batch with the same key, provided that its size is larger 2025-08-14T21:38:43.5823655Z than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``. 2025-08-14T21:38:43.5824084Z 2025-08-14T21:38:43.5824466Z After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity 2025-08-14T21:38:43.5825256Z will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``. 2025-08-14T21:38:43.5825688Z 2025-08-14T21:38:43.5825766Z Args: 2025-08-14T21:38:43.5826012Z datapipe: Iterable datapipe to be grouped 2025-08-14T21:38:43.5826506Z group_key_fn: Function used to generate group key from the data of the source datapipe 2025-08-14T21:38:43.5827120Z keep_key: Option to yield the matching key along with the items in a tuple, 2025-08-14T21:38:43.5827638Z resulting in `(key, [items])` otherwise returning [items] 2025-08-14T21:38:43.5828073Z buffer_size: The size of buffer for ungrouped data 2025-08-14T21:38:43.5828591Z group_size: The max size of each group, a batch is yielded as soon as it reaches this size 2025-08-14T21:38:43.5829309Z guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full 2025-08-14T21:38:43.5830102Z drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer 2025-08-14T21:38:43.5830669Z when the buffer is full 2025-08-14T21:38:43.5830851Z 2025-08-14T21:38:43.5830934Z Example: 2025-08-14T21:38:43.5831142Z >>> import os 2025-08-14T21:38:43.5831383Z >>> # xdoctest: +SKIP 2025-08-14T21:38:43.5831715Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-14T21:38:43.5832096Z >>> def group_fn(file): 2025-08-14T21:38:43.5832405Z ... return os.path.basename(file).split(".")[0] 2025-08-14T21:38:43.5832815Z >>> source_dp = IterableWrapper( 2025-08-14T21:38:43.5833177Z ... ["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"] 2025-08-14T21:38:43.5833524Z ... ) 2025-08-14T21:38:43.5833774Z >>> dp0 = source_dp.groupby(group_key_fn=group_fn) 2025-08-14T21:38:43.5834113Z >>> list(dp0) 2025-08-14T21:38:43.5834415Z [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']] 2025-08-14T21:38:43.5834865Z >>> # A group is yielded as soon as its size equals to `group_size` 2025-08-14T21:38:43.5835398Z >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2) 2025-08-14T21:38:43.5835775Z >>> list(dp1) 2025-08-14T21:38:43.5836071Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-08-14T21:38:43.5836667Z >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size` 2025-08-14T21:38:43.5837226Z >>> dp2 = source_dp.groupby( 2025-08-14T21:38:43.5837523Z ... group_key_fn=group_fn, 2025-08-14T21:38:43.5837800Z ... buffer_size=3, 2025-08-14T21:38:43.5838060Z ... group_size=3, 2025-08-14T21:38:43.5838326Z ... guaranteed_group_size=2, 2025-08-14T21:38:43.5838614Z ... ) 2025-08-14T21:38:43.5838804Z >>> list(dp2) 2025-08-14T21:38:43.5839098Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-08-14T21:38:43.5839366Z 2025-08-14T21:38:43.5839621Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:43.5839976Z 2025-08-14T21:38:43.8760689Z gathering tests 2025-08-14T21:38:43.8772520Z running 732 test(s) 2025-08-14T21:38:43.8790822Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0, line 1082 <- wrt source file 2025-08-14T21:38:43.8799946Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0 2025-08-14T21:38:43.8801311Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0, line 1118 <- wrt source file 2025-08-14T21:38:43.8804750Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0 2025-08-14T21:38:43.8805937Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0, line 1203 <- wrt source file 2025-08-14T21:38:43.8807432Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0 2025-08-14T21:38:43.8808718Z * 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 2025-08-14T21:38:43.8810164Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0 2025-08-14T21:38:43.8811916Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0, line 1289 <- wrt source file 2025-08-14T21:38:43.8813109Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0 2025-08-14T21:38:43.8814315Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1444 <- wrt source file 2025-08-14T21:38:43.8815800Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2025-08-14T21:38:43.8817148Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0, line 2567 <- wrt source file 2025-08-14T21:38:43.8818423Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0 2025-08-14T21:38:43.8820650Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2840 <- wrt source file 2025-08-14T21:38:43.8822189Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2025-08-14T21:38:43.8823554Z * 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 2025-08-14T21:38:43.8824879Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0 2025-08-14T21:38:43.8826343Z * 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 2025-08-14T21:38:43.8827691Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0 2025-08-14T21:38:43.8828886Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2025-08-14T21:38:43.8829999Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0 2025-08-14T21:38:43.8831050Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0, line 138 <- wrt source file 2025-08-14T21:38:43.8832117Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0 2025-08-14T21:38:43.8833213Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0, line 208 <- wrt source file 2025-08-14T21:38:43.9224046Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0 2025-08-14T21:38:43.9225419Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2025-08-14T21:38:43.9226872Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0 2025-08-14T21:38:43.9228072Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 649 <- wrt source file 2025-08-14T21:38:43.9236273Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0 2025-08-14T21:38:43.9237564Z * 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 2025-08-14T21:38:43.9255093Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2025-08-14T21:38:43.9256375Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1333 <- wrt source file 2025-08-14T21:38:43.9364708Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0 2025-08-14T21:38:43.9367811Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1378 <- wrt source file 2025-08-14T21:38:43.9372514Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0 2025-08-14T21:38:43.9373629Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0, line 1451 <- wrt source file 2025-08-14T21:38:43.9379530Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0 2025-08-14T21:38:43.9380665Z * 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 2025-08-14T21:38:43.9385042Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2025-08-14T21:38:43.9386194Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2025-08-14T21:38:43.9404464Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0 2025-08-14T21:38:43.9405830Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0, line 64 <- wrt source file 2025-08-14T21:38:43.9411138Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0 2025-08-14T21:38:43.9412317Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0, line 92 <- wrt source file 2025-08-14T21:38:43.9413972Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0 2025-08-14T21:38:43.9415086Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0, line 193 <- wrt source file 2025-08-14T21:38:43.9426223Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0 2025-08-14T21:38:43.9427300Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0, line 307 <- wrt source file 2025-08-14T21:38:43.9443335Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0 2025-08-14T21:38:43.9444504Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1041 <- wrt source file 2025-08-14T21:38:43.9455331Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0 2025-08-14T21:38:43.9456521Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0, line 1316 <- wrt source file 2025-08-14T21:38:43.9465922Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0 2025-08-14T21:38:43.9467054Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0, line 1400 <- wrt source file 2025-08-14T21:38:43.9473314Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0 2025-08-14T21:38:43.9474469Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0, line 1434 <- wrt source file 2025-08-14T21:38:43.9482845Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0 2025-08-14T21:38:43.9483956Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0, line 1490 <- wrt source file 2025-08-14T21:38:43.9496501Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0 2025-08-14T21:38:43.9497606Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0, line 1531 <- wrt source file 2025-08-14T21:38:43.9513442Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0 2025-08-14T21:38:43.9514573Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0, line 1569 <- wrt source file 2025-08-14T21:38:43.9530803Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0 2025-08-14T21:38:43.9531932Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0, line 1609 <- wrt source file 2025-08-14T21:38:43.9552434Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0 2025-08-14T21:38:43.9553522Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0, line 1784 <- wrt source file 2025-08-14T21:38:43.9584526Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0 2025-08-14T21:38:43.9585821Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0, line 1952 <- wrt source file 2025-08-14T21:38:43.9610907Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0 2025-08-14T21:38:43.9612416Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0, line 2052 <- wrt source file 2025-08-14T21:38:43.9614073Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0 2025-08-14T21:38:43.9615243Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0, line 2153 <- wrt source file 2025-08-14T21:38:43.9616358Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0 2025-08-14T21:38:43.9617442Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0, line 473 <- wrt source file 2025-08-14T21:38:43.9618729Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0 2025-08-14T21:38:43.9619699Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0, line 533 <- wrt source file 2025-08-14T21:38:43.9620846Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0 2025-08-14T21:38:43.9621908Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0, line 153 <- wrt source file 2025-08-14T21:38:43.9623042Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0 2025-08-14T21:38:43.9624327Z * 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 2025-08-14T21:38:43.9629921Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2025-08-14T21:38:43.9631123Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0, line 307 <- wrt source file 2025-08-14T21:38:43.9634933Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0 2025-08-14T21:38:43.9636071Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0, line 512 <- wrt source file 2025-08-14T21:38:43.9654836Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0 2025-08-14T21:38:43.9655887Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0, line 618 <- wrt source file 2025-08-14T21:38:43.9670890Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0 2025-08-14T21:38:43.9672374Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0, line 799 <- wrt source file 2025-08-14T21:38:43.9673622Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0 2025-08-14T21:38:43.9674744Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0, line 867 <- wrt source file 2025-08-14T21:38:43.9675922Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0 2025-08-14T21:38:43.9677105Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0, line 1232 <- wrt source file 2025-08-14T21:38:43.9752137Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0 2025-08-14T21:38:43.9753473Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0, line 1321 <- wrt source file 2025-08-14T21:38:43.9896458Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0 2025-08-14T21:38:43.9897636Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0, line 116 <- wrt source file 2025-08-14T21:38:43.9902827Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0 2025-08-14T21:38:43.9904118Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0, line 422 <- wrt source file 2025-08-14T21:38:43.9939039Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0 2025-08-14T21:38:43.9940264Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0, line 1577 <- wrt source file 2025-08-14T21:38:43.9942406Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0 2025-08-14T21:38:43.9943680Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0, line 1712 <- wrt source file 2025-08-14T21:38:43.9945739Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0 2025-08-14T21:38:43.9946985Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1960 <- wrt source file 2025-08-14T21:38:43.9971360Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2025-08-14T21:38:43.9972586Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0, line 1979 <- wrt source file 2025-08-14T21:38:43.9979036Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0 2025-08-14T21:38:43.9980478Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2025-08-14T21:38:43.9981949Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0 2025-08-14T21:38:43.9983366Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0, line 299 <- wrt source file 2025-08-14T21:38:43.9984676Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0 2025-08-14T21:38:43.9985838Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0, line 324 <- wrt source file 2025-08-14T21:38:43.9987304Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0 2025-08-14T21:38:43.9988999Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0, line 400 <- wrt source file 2025-08-14T21:38:43.9990253Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0 2025-08-14T21:38:43.9991498Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0, line 472 <- wrt source file 2025-08-14T21:38:43.9993265Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0 2025-08-14T21:38:43.9994419Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0, line 950 <- wrt source file 2025-08-14T21:38:43.9995683Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0 2025-08-14T21:38:43.9996808Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0, line 19 <- wrt source file 2025-08-14T21:38:43.9997969Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0 2025-08-14T21:38:43.9999163Z * 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 2025-08-14T21:38:44.0000413Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2025-08-14T21:38:44.0001609Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0, line 357 <- wrt source file 2025-08-14T21:38:44.0006110Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0 2025-08-14T21:38:44.0007364Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 113 <- wrt source file 2025-08-14T21:38:44.0011680Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0 2025-08-14T21:38:44.0013539Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::device_index:0, line 249 <- wrt source file 2025-08-14T21:38:44.0014805Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::device_index:0 2025-08-14T21:38:44.0015996Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 125 <- wrt source file 2025-08-14T21:38:44.0017219Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2025-08-14T21:38:44.0018448Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 181 <- wrt source file 2025-08-14T21:38:44.6748652Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0 2025-08-14T21:38:44.6749940Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 411 <- wrt source file 2025-08-14T21:38:44.6751222Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2025-08-14T21:38:44.6752571Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0, line 443 <- wrt source file 2025-08-14T21:38:44.6753810Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0 2025-08-14T21:38:44.6755151Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 464 <- wrt source file 2025-08-14T21:38:44.6756567Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2025-08-14T21:38:44.6757969Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0, line 482 <- wrt source file 2025-08-14T21:38:44.6760226Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0 2025-08-14T21:38:44.6762453Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0, line 497 <- wrt source file 2025-08-14T21:38:44.6764733Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0 2025-08-14T21:38:44.6766435Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0, line 512 <- wrt source file 2025-08-14T21:38:44.6768263Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0 2025-08-14T21:38:44.6769750Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py::_compile_kernel:0, line 1760 <- wrt source file 2025-08-14T21:38:44.6771298Z * SKIPPED: 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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 2025-08-14T21:38:44.6780862Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2025-08-14T21:38:44.6782131Z * 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 2025-08-14T21:38:44.6783471Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0 2025-08-14T21:38:44.6784688Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0, line 295 <- wrt source file 2025-08-14T21:38:44.6786026Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0 2025-08-14T21:38:44.6787264Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0, line 147 <- wrt source file 2025-08-14T21:38:44.6788379Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0 2025-08-14T21:38:44.6789596Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 22 <- wrt source file 2025-08-14T21:38:44.6790933Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0 2025-08-14T21:38:44.6792161Z * 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 2025-08-14T21:38:44.6812276Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::as_nested_tensor:0 2025-08-14T21:38:44.6813481Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0, line 240 <- wrt source file 2025-08-14T21:38:44.6818026Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0 2025-08-14T21:38:44.6819354Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0, line 315 <- wrt source file 2025-08-14T21:38:44.6880757Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0 2025-08-14T21:38:44.6882000Z * 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 2025-08-14T21:38:44.6906585Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2025-08-14T21:38:44.6908396Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0, line 481 <- wrt source file 2025-08-14T21:38:44.6927718Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0 2025-08-14T21:38:44.6928991Z * 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 2025-08-14T21:38:44.6951426Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2025-08-14T21:38:44.6954129Z * 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 2025-08-14T21:38:44.6956206Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0 2025-08-14T21:38:44.6958186Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/eval_frame.py::fullgraph_capture:0, line 2328 <- wrt source file 2025-08-14T21:38:44.6959511Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/eval_frame.py::fullgraph_capture:0 2025-08-14T21:38:44.6960857Z * 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 2025-08-14T21:38:44.6962305Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2025-08-14T21:38:44.6963696Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0, line 1386 <- wrt source file 2025-08-14T21:38:44.6965085Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0 2025-08-14T21:38:44.6966572Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0, line 157 <- wrt source file 2025-08-14T21:38:44.6968131Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0 2025-08-14T21:38:44.6969500Z * 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 2025-08-14T21:38:44.7346959Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2025-08-14T21:38:44.7349881Z * 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 2025-08-14T21:38:44.7352803Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2025-08-14T21:38:44.7355483Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 233 <- wrt source file 2025-08-14T21:38:44.7398673Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2025-08-14T21:38:44.7401098Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 475 <- wrt source file 2025-08-14T21:38:44.7472926Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2025-08-14T21:38:44.7475314Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file 2025-08-14T21:38:44.8067245Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2025-08-14T21:38:44.8069666Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file 2025-08-14T21:38:44.8133691Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2025-08-14T21:38:44.8136121Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file 2025-08-14T21:38:44.8152762Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2025-08-14T21:38:44.8155292Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file 2025-08-14T21:38:44.8157935Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2025-08-14T21:38:44.8160458Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1704 <- wrt source file 2025-08-14T21:38:44.8328757Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2025-08-14T21:38:44.8331300Z * 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 2025-08-14T21:38:44.8333944Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0 2025-08-14T21:38:44.8336394Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2025-08-14T21:38:44.8338770Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2025-08-14T21:38:44.8341475Z * 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 2025-08-14T21:38:44.8344553Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0 2025-08-14T21:38:44.8347507Z * 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 2025-08-14T21:38:44.8350514Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::InductorWrapper.post_compile:0 2025-08-14T21:38:44.8353310Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 133 <- wrt source file 2025-08-14T21:38:44.8356087Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2025-08-14T21:38:44.8359116Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0, line 269 <- wrt source file 2025-08-14T21:38:44.8362062Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0 2025-08-14T21:38:44.8364591Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 105 <- wrt source file 2025-08-14T21:38:44.8366874Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0 2025-08-14T21:38:44.8369308Z * 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 2025-08-14T21:38:44.8372007Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0 2025-08-14T21:38:44.8374411Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/map.py::map:0, line 77 <- wrt source file 2025-08-14T21:38:44.8376676Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/map.py::map:0 2025-08-14T21:38:44.8379048Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 155 <- wrt source file 2025-08-14T21:38:44.8381339Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0 2025-08-14T21:38:44.8383779Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0, line 423 <- wrt source file 2025-08-14T21:38:44.8386318Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0 2025-08-14T21:38:44.8388825Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/codecache.py::WritableTempFile:0, line 371 <- wrt source file 2025-08-14T21:38:44.8391345Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/codecache.py::WritableTempFile:0 2025-08-14T21:38:44.8394299Z * 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 1501 <- wrt source file 2025-08-14T21:38:44.8397241Z * 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 2025-08-14T21:38:44.8400034Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0, line 3349 <- wrt source file 2025-08-14T21:38:44.8402783Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0 2025-08-14T21:38:44.8405533Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/template_registry.py::register_template_heuristic:0, line 45 <- wrt source file 2025-08-14T21:38:44.8408425Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/template_registry.py::register_template_heuristic:0 2025-08-14T21:38:44.8410969Z * 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 2025-08-14T21:38:44.8921563Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0 2025-08-14T21:38:44.8923031Z * 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 2025-08-14T21:38:44.9009977Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2025-08-14T21:38:44.9011368Z * 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 2025-08-14T21:38:44.9012768Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2025-08-14T21:38:44.9014129Z * 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 2025-08-14T21:38:44.9182514Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2025-08-14T21:38:44.9183949Z * 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 2025-08-14T21:38:44.9352269Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2025-08-14T21:38:44.9354431Z * 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 2025-08-14T21:38:44.9355867Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0 2025-08-14T21:38:44.9357217Z * 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 2025-08-14T21:38:44.9358599Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2025-08-14T21:38:44.9359954Z * 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 2025-08-14T21:38:44.9431191Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2025-08-14T21:38:44.9432493Z * 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 2025-08-14T21:38:44.9437653Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0 2025-08-14T21:38:44.9438876Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0, line 459 <- wrt source file 2025-08-14T21:38:44.9440056Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0 2025-08-14T21:38:44.9441258Z * 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 2025-08-14T21:38:44.9475853Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2025-08-14T21:38:44.9477098Z * 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 2025-08-14T21:38:44.9531615Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2025-08-14T21:38:44.9532983Z * 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 2025-08-14T21:38:44.9534449Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2025-08-14T21:38:44.9535712Z * 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 2025-08-14T21:38:44.9583971Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2025-08-14T21:38:44.9585326Z * 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 2025-08-14T21:38:44.9588009Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2025-08-14T21:38:44.9589358Z * 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 2025-08-14T21:38:44.9592652Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2025-08-14T21:38:44.9594390Z * 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 2025-08-14T21:38:44.9595781Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2025-08-14T21:38:44.9597023Z * 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 2025-08-14T21:38:44.9599417Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2025-08-14T21:38:44.9601029Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 95 <- wrt source file 2025-08-14T21:38:44.9602929Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0 2025-08-14T21:38:44.9605173Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 64 <- wrt source file 2025-08-14T21:38:44.9606453Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2025-08-14T21:38:44.9607764Z * 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 2025-08-14T21:38:44.9609223Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2025-08-14T21:38:44.9610754Z * 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 2025-08-14T21:38:44.9612404Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2025-08-14T21:38:44.9613952Z * 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 2025-08-14T21:38:44.9615479Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2025-08-14T21:38:44.9617012Z * 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 2025-08-14T21:38:44.9618605Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2025-08-14T21:38:44.9620265Z * 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 2025-08-14T21:38:44.9621822Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2025-08-14T21:38:44.9623210Z * 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 2025-08-14T21:38:44.9626090Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2025-08-14T21:38:44.9627833Z * 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 2025-08-14T21:38:44.9657377Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2025-08-14T21:38:44.9659776Z * 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 2025-08-14T21:38:44.9662098Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2025-08-14T21:38:44.9663631Z * 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 2025-08-14T21:38:44.9664913Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2025-08-14T21:38:44.9666159Z * 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 2025-08-14T21:38:44.9667958Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2025-08-14T21:38:44.9669237Z * 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 2025-08-14T21:38:44.9670576Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2025-08-14T21:38:44.9672636Z * 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 2025-08-14T21:38:44.9674651Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2025-08-14T21:38:44.9676334Z * 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 2025-08-14T21:38:44.9678484Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2025-08-14T21:38:44.9680651Z * 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 2025-08-14T21:38:44.9682157Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2025-08-14T21:38:44.9683522Z * 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 2025-08-14T21:38:44.9685477Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2025-08-14T21:38:44.9687267Z * 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 2025-08-14T21:38:44.9689302Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2025-08-14T21:38:44.9690889Z * 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 2025-08-14T21:38:44.9692386Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2025-08-14T21:38:44.9694009Z * 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 2025-08-14T21:38:44.9695514Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2025-08-14T21:38:44.9704048Z * 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 2025-08-14T21:38:44.9705669Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2025-08-14T21:38:44.9707052Z * 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 2025-08-14T21:38:44.9708584Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2025-08-14T21:38:44.9709915Z * 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 2025-08-14T21:38:44.9711270Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2025-08-14T21:38:44.9712615Z * 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 2025-08-14T21:38:44.9714017Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2025-08-14T21:38:44.9715397Z * 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 2025-08-14T21:38:44.9716814Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2025-08-14T21:38:44.9718284Z * 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 2025-08-14T21:38:44.9720101Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2025-08-14T21:38:44.9721460Z * 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 2025-08-14T21:38:44.9722824Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2025-08-14T21:38:44.9724117Z * 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 2025-08-14T21:38:44.9725412Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2025-08-14T21:38:44.9726676Z * 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 2025-08-14T21:38:44.9727963Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2025-08-14T21:38:44.9729799Z * 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 2025-08-14T21:38:44.9731508Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2025-08-14T21:38:44.9732903Z * 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 2025-08-14T21:38:44.9734373Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2025-08-14T21:38:44.9736245Z * 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 2025-08-14T21:38:44.9737669Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2025-08-14T21:38:44.9739254Z * 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 2025-08-14T21:38:44.9741401Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2025-08-14T21:38:44.9743602Z * 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 2025-08-14T21:38:44.9745099Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2025-08-14T21:38:44.9746579Z * 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 2025-08-14T21:38:44.9748149Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2025-08-14T21:38:44.9749662Z * 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 2025-08-14T21:38:44.9751203Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2025-08-14T21:38:44.9752574Z * 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 2025-08-14T21:38:44.9753894Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2025-08-14T21:38:44.9755416Z * 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 2025-08-14T21:38:44.9757162Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2025-08-14T21:38:44.9758699Z * 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 2025-08-14T21:38:44.9768947Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2025-08-14T21:38:44.9771797Z * 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 2025-08-14T21:38:44.9774753Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2025-08-14T21:38:44.9777625Z * 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 2025-08-14T21:38:44.9780290Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2025-08-14T21:38:44.9782985Z * 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 2025-08-14T21:38:44.9785888Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2025-08-14T21:38:44.9788706Z * 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 2025-08-14T21:38:44.9791644Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2025-08-14T21:38:44.9794690Z * 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 2025-08-14T21:38:44.9797567Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2025-08-14T21:38:44.9800664Z * 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 2025-08-14T21:38:44.9803721Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2025-08-14T21:38:44.9806440Z * 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 2025-08-14T21:38:44.9808950Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0 2025-08-14T21:38:44.9811526Z * 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 2025-08-14T21:38:44.9814248Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2025-08-14T21:38:44.9816804Z * 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 2025-08-14T21:38:44.9819317Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2025-08-14T21:38:44.9821832Z * 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 2025-08-14T21:38:44.9824494Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2025-08-14T21:38:44.9827288Z * 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 2025-08-14T21:38:44.9829988Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2025-08-14T21:38:44.9832563Z * 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 2025-08-14T21:38:44.9835137Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2025-08-14T21:38:44.9837942Z * 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 2025-08-14T21:38:44.9840808Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2025-08-14T21:38:44.9843744Z * 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 2025-08-14T21:38:44.9846835Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2025-08-14T21:38:44.9849652Z * 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 2025-08-14T21:38:44.9852336Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2025-08-14T21:38:44.9855009Z * 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 2025-08-14T21:38:44.9857764Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2025-08-14T21:38:44.9860489Z * 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 2025-08-14T21:38:44.9863168Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2025-08-14T21:38:44.9865827Z * 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 2025-08-14T21:38:44.9868422Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2025-08-14T21:38:44.9870999Z * 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 2025-08-14T21:38:44.9873611Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2025-08-14T21:38:44.9876218Z * 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 2025-08-14T21:38:44.9878899Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2025-08-14T21:38:44.9881501Z * 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 2025-08-14T21:38:44.9884119Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2025-08-14T21:38:44.9886699Z * 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 2025-08-14T21:38:44.9889295Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2025-08-14T21:38:44.9891836Z * 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 2025-08-14T21:38:44.9894528Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2025-08-14T21:38:44.9897331Z * 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 2025-08-14T21:38:44.9900551Z * 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 2025-08-14T21:38:44.9903683Z * 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 2025-08-14T21:38:44.9906801Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0 2025-08-14T21:38:44.9909822Z * 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 2025-08-14T21:38:44.9912883Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0 2025-08-14T21:38:44.9915846Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0, line 188 <- wrt source file 2025-08-14T21:38:44.9918833Z * 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 2025-08-14T21:38:44.9921980Z * 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 2025-08-14T21:38:44.9925130Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2025-08-14T21:38:44.9927886Z * 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 2025-08-14T21:38:44.9930420Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0 2025-08-14T21:38:44.9932795Z * 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 2025-08-14T21:38:44.9935125Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0 2025-08-14T21:38:44.9937472Z * 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 2025-08-14T21:38:44.9939876Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0 2025-08-14T21:38:44.9942218Z * 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 2025-08-14T21:38:44.9944644Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0 2025-08-14T21:38:44.9947179Z * 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 2025-08-14T21:38:44.9949946Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0 2025-08-14T21:38:44.9952655Z * 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 2025-08-14T21:38:44.9955408Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0 2025-08-14T21:38:44.9958034Z * 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 2025-08-14T21:38:44.9960743Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0 2025-08-14T21:38:44.9963471Z * 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 2025-08-14T21:38:44.9966374Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0 2025-08-14T21:38:44.9969187Z * 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 2025-08-14T21:38:44.9972044Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2025-08-14T21:38:44.9974579Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0, line 485 <- wrt source file 2025-08-14T21:38:44.9976901Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0 2025-08-14T21:38:44.9979165Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0, line 293 <- wrt source file 2025-08-14T21:38:44.9981500Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0 2025-08-14T21:38:44.9983814Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0, line 395 <- wrt source file 2025-08-14T21:38:44.9986084Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0 2025-08-14T21:38:44.9988382Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0, line 630 <- wrt source file 2025-08-14T21:38:44.9990765Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0 2025-08-14T21:38:44.9993203Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0, line 894 <- wrt source file 2025-08-14T21:38:44.9995569Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0 2025-08-14T21:38:44.9997863Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0, line 1010 <- wrt source file 2025-08-14T21:38:45.0000151Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0 2025-08-14T21:38:45.0002385Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0, line 1109 <- wrt source file 2025-08-14T21:38:45.0004660Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0 2025-08-14T21:38:45.0006875Z * 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 2025-08-14T21:38:45.0009154Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0 2025-08-14T21:38:45.0011439Z * 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 2025-08-14T21:38:45.0013815Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2025-08-14T21:38:45.0016188Z * 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 2025-08-14T21:38:45.0018670Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0 2025-08-14T21:38:45.0021233Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 238 <- wrt source file 2025-08-14T21:38:45.0023835Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0 2025-08-14T21:38:45.0026159Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0, line 53 <- wrt source file 2025-08-14T21:38:45.0028431Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0 2025-08-14T21:38:45.0030759Z * 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 2025-08-14T21:38:45.0033213Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0 2025-08-14T21:38:45.0035919Z * 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 2025-08-14T21:38:45.0038474Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2025-08-14T21:38:45.0041046Z * 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 2025-08-14T21:38:45.0043684Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0 2025-08-14T21:38:45.0046281Z * 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 2025-08-14T21:38:45.0048608Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0 2025-08-14T21:38:45.0051050Z * 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 2025-08-14T21:38:45.0053695Z * SKIPPED: 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2025-08-14T21:38:45.0085253Z * 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 2025-08-14T21:38:45.0087600Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0 2025-08-14T21:38:45.0089798Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0, line 85 <- wrt source file 2025-08-14T21:38:45.0091881Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0 2025-08-14T21:38:45.0094214Z * 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 2025-08-14T21:38:45.0096557Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0 2025-08-14T21:38:45.0098863Z * 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 2025-08-14T21:38:45.0101342Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1 2025-08-14T21:38:45.0103716Z * 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 2025-08-14T21:38:45.0106062Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2 2025-08-14T21:38:45.0108505Z * 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 2025-08-14T21:38:45.0111134Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0 2025-08-14T21:38:45.0113513Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file 2025-08-14T21:38:45.0115740Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0 2025-08-14T21:38:45.0118072Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 431 <- wrt source file 2025-08-14T21:38:45.0120599Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0 2025-08-14T21:38:45.0123210Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0, line 976 <- wrt source file 2025-08-14T21:38:45.0126013Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0 2025-08-14T21:38:45.0128688Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0, line 1122 <- wrt source file 2025-08-14T21:38:45.0131310Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0 2025-08-14T21:38:45.0134002Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0, line 2574 <- wrt source file 2025-08-14T21:38:45.0136822Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0 2025-08-14T21:38:45.0139633Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0, line 2676 <- wrt source file 2025-08-14T21:38:45.0142351Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0 2025-08-14T21:38:45.0145196Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 3143 <- wrt source file 2025-08-14T21:38:45.0147965Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0 2025-08-14T21:38:45.0150665Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0, line 3372 <- wrt source file 2025-08-14T21:38:45.0153410Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0 2025-08-14T21:38:45.0156066Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0, line 3474 <- wrt source file 2025-08-14T21:38:45.0158805Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0 2025-08-14T21:38:45.0161635Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0, line 3588 <- wrt source file 2025-08-14T21:38:45.0164485Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0 2025-08-14T21:38:45.0167277Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3711 <- wrt source file 2025-08-14T21:38:45.0170070Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0 2025-08-14T21:38:45.0172842Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3920 <- wrt source file 2025-08-14T21:38:45.0175719Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0 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line 54 <- wrt source file 2025-08-14T21:38:45.0560069Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0 2025-08-14T21:38:45.0563109Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0, line 224 <- wrt source file 2025-08-14T21:38:45.0565964Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0 2025-08-14T21:38:45.0568838Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0, line 134 <- wrt source file 2025-08-14T21:38:45.0571855Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0 2025-08-14T21:38:45.0575185Z * DOCTEST : 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wrt source file 2025-08-14T21:38:45.0691794Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0 2025-08-14T21:38:45.0694548Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 30 <- wrt source file 2025-08-14T21:38:45.0697042Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2025-08-14T21:38:45.0699360Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0, line 21 <- wrt source file 2025-08-14T21:38:45.0701631Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0 2025-08-14T21:38:45.0703993Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0, line 31 <- wrt source file 2025-08-14T21:38:45.0706442Z * SUCCESS: 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2025-08-14T21:38:45.0723383Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0, line 166 <- wrt source file 2025-08-14T21:38:45.0725998Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0 2025-08-14T21:38:45.0728636Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 187 <- wrt source file 2025-08-14T21:38:45.0731396Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::_DependentProperty:0 2025-08-14T21:38:45.0734210Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 35 <- wrt source file 2025-08-14T21:38:45.0737242Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0 2025-08-14T21:38:45.0739902Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 42 <- wrt source file 2025-08-14T21:38:45.0742395Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/dirichlet.py::Dirichlet:0 2025-08-14T21:38:45.0744964Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/exponential.py::Exponential:0, line 20 <- wrt source file 2025-08-14T21:38:45.0747558Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/exponential.py::Exponential:0 2025-08-14T21:38:45.0750192Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 21 <- wrt source file 2025-08-14T21:38:45.0752952Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0 2025-08-14T21:38:45.0755398Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gamma.py::Gamma:0, line 24 <- wrt source file 2025-08-14T21:38:45.0757682Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gamma.py::Gamma:0 2025-08-14T21:38:45.0760244Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0, line 26 <- wrt source file 2025-08-14T21:38:45.0763141Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0 2025-08-14T21:38:45.0765782Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0, line 36 <- wrt source file 2025-08-14T21:38:45.0768264Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0 2025-08-14T21:38:45.0770613Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 23 <- wrt source file 2025-08-14T21:38:45.0772959Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gumbel.py::Gumbel:0 2025-08-14T21:38:45.0775369Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 24 <- wrt source file 2025-08-14T21:38:45.0777998Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0 2025-08-14T21:38:45.0780512Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 24 <- wrt source file 2025-08-14T21:38:45.0783052Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_normal.py::HalfNormal:0 2025-08-14T21:38:45.0785631Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/independent.py::Independent:0, line 27 <- wrt source file 2025-08-14T21:38:45.0788215Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/independent.py::Independent:0 2025-08-14T21:38:45.0790770Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 24 <- wrt source file 2025-08-14T21:38:45.0793542Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0 2025-08-14T21:38:45.0796116Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 30 <- wrt source file 2025-08-14T21:38:45.0798792Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0 2025-08-14T21:38:45.0801218Z * DOCTEST : 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source file 2025-08-14T21:38:45.1050708Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0 2025-08-14T21:38:45.1053596Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0, line 416 <- wrt source file 2025-08-14T21:38:45.1056546Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0 2025-08-14T21:38:45.1059351Z * 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 2025-08-14T21:38:45.1062200Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0 2025-08-14T21:38:45.1064980Z * 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 2025-08-14T21:38:45.1067723Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0 2025-08-14T21:38:45.1070421Z * 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 2025-08-14T21:38:45.1073211Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0 2025-08-14T21:38:45.1075882Z * 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 2025-08-14T21:38:45.1078559Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::freeze:0 2025-08-14T21:38:45.1081270Z * 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 2025-08-14T21:38:45.1084238Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/variable.py::variables:0 2025-08-14T21:38:45.1087227Z * 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 2025-08-14T21:38:45.1090378Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0 2025-08-14T21:38:45.1093681Z * 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 2025-08-14T21:38:45.1097009Z * SKIPPED: 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/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 2025-08-14T21:38:45.1156979Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0 2025-08-14T21:38:45.1160660Z * 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 2025-08-14T21:38:45.1164360Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0 2025-08-14T21:38:45.1168005Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 47 <- wrt source file 2025-08-14T21:38:45.1171834Z * SKIPPED: 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file 2025-08-14T21:38:45.1191851Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0 2025-08-14T21:38:45.1194800Z * 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 2025-08-14T21:38:45.1197699Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0 2025-08-14T21:38:45.1201165Z * 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 2025-08-14T21:38:45.1205195Z * 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 2025-08-14T21:38:45.1208665Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file 2025-08-14T21:38:45.1211803Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0 2025-08-14T21:38:45.1214818Z * 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 2025-08-14T21:38:45.1217935Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0 2025-08-14T21:38:45.1221071Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0, line 122 <- wrt source file 2025-08-14T21:38:45.1224418Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0 2025-08-14T21:38:45.1227471Z * DOCTEST : 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 460 <- wrt source file 2025-08-14T21:38:45.1248275Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0 2025-08-14T21:38:45.1251445Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 579 <- wrt source file 2025-08-14T21:38:45.1797926Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0 2025-08-14T21:38:45.1820020Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2174 <- wrt source file 2025-08-14T21:38:45.1829689Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0 2025-08-14T21:38:45.1832425Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0, line 2478 <- wrt source file 2025-08-14T21:38:45.1837972Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0 2025-08-14T21:38:45.1840240Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0, line 2618 <- wrt source file 2025-08-14T21:38:45.1848403Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0 2025-08-14T21:38:45.1850665Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0, line 3051 <- wrt source file 2025-08-14T21:38:45.1866018Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0 2025-08-14T21:38:45.1868197Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0, line 3121 <- wrt source file 2025-08-14T21:38:45.1872991Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0 2025-08-14T21:38:45.1875618Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0, line 3430 <- wrt source file 2025-08-14T21:38:45.1882537Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0 2025-08-14T21:38:45.1885297Z * 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 2025-08-14T21:38:45.1889622Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2025-08-14T21:38:45.1892272Z * 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 2025-08-14T21:38:45.1897434Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2025-08-14T21:38:45.1900212Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0, line 5263 <- wrt source file 2025-08-14T21:38:45.1906830Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0 2025-08-14T21:38:45.1908916Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2025-08-14T21:38:45.1915242Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0 2025-08-14T21:38:45.1917375Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2025-08-14T21:38:45.1920594Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0 2025-08-14T21:38:45.1922727Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2025-08-14T21:38:45.1928411Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0 2025-08-14T21:38:45.1930646Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2025-08-14T21:38:45.1933550Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0 2025-08-14T21:38:45.1935681Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2025-08-14T21:38:45.1966681Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0 2025-08-14T21:38:45.1969605Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2025-08-14T21:38:45.2017282Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0 2025-08-14T21:38:45.2019749Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0, line 230 <- wrt source file 2025-08-14T21:38:45.2023362Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0 2025-08-14T21:38:45.2025488Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0, line 257 <- wrt source file 2025-08-14T21:38:45.2027564Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0 2025-08-14T21:38:45.2029644Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0, line 292 <- wrt source file 2025-08-14T21:38:45.2049680Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0 2025-08-14T21:38:45.2051731Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0, line 306 <- wrt source file 2025-08-14T21:38:45.2053839Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0 2025-08-14T21:38:45.2055842Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0, line 323 <- wrt source file 2025-08-14T21:38:45.2057992Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0 2025-08-14T21:38:45.2060046Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0, line 336 <- wrt source file 2025-08-14T21:38:45.2062296Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0 2025-08-14T21:38:45.2064418Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0, line 352 <- wrt source file 2025-08-14T21:38:45.2066408Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0 2025-08-14T21:38:45.2068388Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0, line 374 <- wrt source file 2025-08-14T21:38:45.2070775Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0 2025-08-14T21:38:45.2072851Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0, line 460 <- wrt source file 2025-08-14T21:38:45.2075046Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0 2025-08-14T21:38:45.2077191Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0, line 492 <- wrt source file 2025-08-14T21:38:45.2079352Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0 2025-08-14T21:38:45.2081547Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 543 <- wrt source file 2025-08-14T21:38:45.2083759Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0 2025-08-14T21:38:45.2085928Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0, line 608 <- wrt source file 2025-08-14T21:38:45.2088084Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0 2025-08-14T21:38:45.2090197Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0, line 647 <- wrt source file 2025-08-14T21:38:45.2092335Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0 2025-08-14T21:38:45.2094531Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0, line 700 <- wrt source file 2025-08-14T21:38:45.2096611Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0 2025-08-14T21:38:45.2098786Z * 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 2025-08-14T21:38:45.2101182Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2025-08-14T21:38:45.2103576Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 95 <- wrt source file 2025-08-14T21:38:45.2105924Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0 2025-08-14T21:38:45.2108235Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0, line 72 <- wrt source file 2025-08-14T21:38:45.2110636Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0 2025-08-14T21:38:45.2112902Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0, line 120 <- wrt source file 2025-08-14T21:38:45.2115189Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0 2025-08-14T21:38:45.2117443Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0, line 185 <- wrt source file 2025-08-14T21:38:45.2119840Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0 2025-08-14T21:38:45.2122222Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 247 <- wrt source file 2025-08-14T21:38:45.2124606Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2025-08-14T21:38:45.2126884Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 318 <- wrt source file 2025-08-14T21:38:45.2129179Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0 2025-08-14T21:38:45.2131477Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 349 <- wrt source file 2025-08-14T21:38:45.2133924Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2025-08-14T21:38:45.2136311Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 384 <- wrt source file 2025-08-14T21:38:45.2138830Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2025-08-14T21:38:45.2141153Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0, line 420 <- wrt source file 2025-08-14T21:38:45.2143534Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0 2025-08-14T21:38:45.2145786Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0, line 456 <- wrt source file 2025-08-14T21:38:45.2148166Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0 2025-08-14T21:38:45.2150436Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0, line 501 <- wrt source file 2025-08-14T21:38:45.2152725Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0 2025-08-14T21:38:45.2155060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 552 <- wrt source file 2025-08-14T21:38:45.2157466Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0 2025-08-14T21:38:45.2159864Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0, line 598 <- wrt source file 2025-08-14T21:38:45.2162158Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0 2025-08-14T21:38:45.2164399Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0, line 646 <- wrt source file 2025-08-14T21:38:45.2167648Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0 2025-08-14T21:38:45.2169902Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0, line 705 <- wrt source file 2025-08-14T21:38:45.2173914Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0 2025-08-14T21:38:45.2176209Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0, line 751 <- wrt source file 2025-08-14T21:38:45.2185838Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0 2025-08-14T21:38:45.2188284Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0, line 799 <- wrt source file 2025-08-14T21:38:45.2195827Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0 2025-08-14T21:38:45.2198166Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 848 <- wrt source file 2025-08-14T21:38:45.2201970Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2025-08-14T21:38:45.2204420Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 903 <- wrt source file 2025-08-14T21:38:45.2207910Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2025-08-14T21:38:45.2210333Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 945 <- wrt source file 2025-08-14T21:38:45.2213271Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2025-08-14T21:38:45.2215860Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0, line 981 <- wrt source file 2025-08-14T21:38:45.2225060Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0 2025-08-14T21:38:45.2227505Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 1030 <- wrt source file 2025-08-14T21:38:45.2230396Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0 2025-08-14T21:38:45.2232941Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1144 <- wrt source file 2025-08-14T21:38:45.2235627Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2025-08-14T21:38:45.2238076Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1609 <- wrt source file 2025-08-14T21:38:45.2240440Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0 2025-08-14T21:38:45.2242778Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1660 <- wrt source file 2025-08-14T21:38:45.2245195Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0 2025-08-14T21:38:45.2247577Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1686 <- wrt source file 2025-08-14T21:38:45.2250054Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2025-08-14T21:38:45.2252440Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1724 <- wrt source file 2025-08-14T21:38:45.2254799Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0 2025-08-14T21:38:45.2257397Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1788 <- wrt source file 2025-08-14T21:38:45.2260132Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0 2025-08-14T21:38:45.2263111Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1835 <- wrt source file 2025-08-14T21:38:45.2265683Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2025-08-14T21:38:45.2268022Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1874 <- wrt source file 2025-08-14T21:38:45.2270474Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2025-08-14T21:38:45.2272846Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 332 <- wrt source file 2025-08-14T21:38:45.2275284Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2025-08-14T21:38:45.2277642Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 443 <- wrt source file 2025-08-14T21:38:45.2500485Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2025-08-14T21:38:45.2503146Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 554 <- wrt source file 2025-08-14T21:38:45.4957051Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2025-08-14T21:38:45.5093980Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2025-08-14T21:38:45.5117969Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2025-08-14T21:38:45.5121703Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0, line 81 <- wrt source file 2025-08-14T21:38:45.5123883Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0 2025-08-14T21:38:45.5126139Z * 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 2025-08-14T21:38:45.5130859Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.append:0 2025-08-14T21:38:45.5133443Z * 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 2025-08-14T21:38:45.5139259Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.insert:0 2025-08-14T21:38:45.5141863Z * 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 2025-08-14T21:38:45.5148045Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.extend:0 2025-08-14T21:38:45.5150536Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0, line 343 <- wrt source file 2025-08-14T21:38:45.5153010Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0 2025-08-14T21:38:45.5155409Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 523 <- wrt source file 2025-08-14T21:38:45.5157856Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0 2025-08-14T21:38:45.5160605Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0, line 653 <- wrt source file 2025-08-14T21:38:45.5163186Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0 2025-08-14T21:38:45.5165656Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 808 <- wrt source file 2025-08-14T21:38:45.5168180Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0 2025-08-14T21:38:45.5170664Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2025-08-14T21:38:45.5173109Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2025-08-14T21:38:45.5175615Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 81 <- wrt source file 2025-08-14T21:38:45.5178289Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2025-08-14T21:38:45.5180646Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2025-08-14T21:38:45.5201120Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0 2025-08-14T21:38:45.5203844Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 108 <- wrt source file 2025-08-14T21:38:45.5206481Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2025-08-14T21:38:45.5209055Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 163 <- wrt source file 2025-08-14T21:38:45.5211447Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2025-08-14T21:38:45.5213763Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 211 <- wrt source file 2025-08-14T21:38:45.5274154Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2025-08-14T21:38:45.5276911Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 257 <- wrt source file 2025-08-14T21:38:45.5279536Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2025-08-14T21:38:45.5282060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 309 <- wrt source file 2025-08-14T21:38:45.5357971Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2025-08-14T21:38:45.5360510Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2025-08-14T21:38:45.5366594Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0 2025-08-14T21:38:45.5368660Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0, line 224 <- wrt source file 2025-08-14T21:38:45.5373705Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0 2025-08-14T21:38:45.5375886Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0, line 395 <- wrt source file 2025-08-14T21:38:45.5389234Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0 2025-08-14T21:38:45.5391706Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2025-08-14T21:38:45.5402733Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2025-08-14T21:38:45.5405282Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2025-08-14T21:38:45.5596669Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2025-08-14T21:38:45.5599606Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2025-08-14T21:38:45.7996714Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2025-08-14T21:38:45.8133442Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 77 <- wrt source file 2025-08-14T21:38:45.8136557Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2025-08-14T21:38:45.8138898Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2025-08-14T21:38:45.8144546Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0 2025-08-14T21:38:45.8146790Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0, line 83 <- wrt source file 2025-08-14T21:38:45.8153632Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0 2025-08-14T21:38:45.8155894Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 191 <- wrt source file 2025-08-14T21:38:45.8177719Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0 2025-08-14T21:38:45.8179964Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2025-08-14T21:38:45.8186284Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0 2025-08-14T21:38:45.8188496Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 215 <- wrt source file 2025-08-14T21:38:45.8222803Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2025-08-14T21:38:45.8225525Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 329 <- wrt source file 2025-08-14T21:38:45.8232459Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2025-08-14T21:38:45.8235072Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 418 <- wrt source file 2025-08-14T21:38:45.8248902Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2025-08-14T21:38:45.8251267Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 535 <- wrt source file 2025-08-14T21:38:45.8257594Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2025-08-14T21:38:45.8260246Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 617 <- wrt source file 2025-08-14T21:38:45.8264433Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0 2025-08-14T21:38:45.8266638Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 703 <- wrt source file 2025-08-14T21:38:45.8271927Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0 2025-08-14T21:38:45.8274284Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 778 <- wrt source file 2025-08-14T21:38:45.8284713Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2025-08-14T21:38:45.8287320Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1, line 826 <- wrt source file 2025-08-14T21:38:45.8291264Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1 2025-08-14T21:38:45.8294469Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 974 <- wrt source file 2025-08-14T21:38:45.8301675Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2025-08-14T21:38:45.8304194Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1306 <- wrt source file 2025-08-14T21:38:45.8310329Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2025-08-14T21:38:45.8313166Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:1, line 1333 <- wrt source file 2025-08-14T21:38:45.8315829Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:1 2025-08-14T21:38:45.8318292Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1495 <- wrt source file 2025-08-14T21:38:45.8322868Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2025-08-14T21:38:45.8325351Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1562 <- wrt source file 2025-08-14T21:38:45.8331066Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2025-08-14T21:38:45.8333511Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1643 <- wrt source file 2025-08-14T21:38:45.8340129Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2025-08-14T21:38:45.8342572Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1745 <- wrt source file 2025-08-14T21:38:45.8352596Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2025-08-14T21:38:45.8355123Z * 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 2025-08-14T21:38:45.8357731Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2025-08-14T21:38:45.8360389Z * 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 2025-08-14T21:38:45.8369502Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0 2025-08-14T21:38:45.8371833Z * 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 2025-08-14T21:38:45.8376877Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0 2025-08-14T21:38:45.8379270Z * 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 2025-08-14T21:38:45.8381786Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2025-08-14T21:38:45.8384457Z * 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 2025-08-14T21:38:45.8386991Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0 2025-08-14T21:38:45.8389639Z * 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 2025-08-14T21:38:45.8392283Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2025-08-14T21:38:45.8394936Z * 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 2025-08-14T21:38:45.8397397Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0 2025-08-14T21:38:45.8399865Z * 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 2025-08-14T21:38:45.8402442Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2025-08-14T21:38:45.8404977Z * 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 2025-08-14T21:38:45.8407564Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0 2025-08-14T21:38:45.8410038Z * 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 2025-08-14T21:38:45.8412357Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0 2025-08-14T21:38:45.8414821Z * 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 2025-08-14T21:38:45.8417386Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2025-08-14T21:38:45.8419931Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2025-08-14T21:38:45.8440241Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2025-08-14T21:38:45.8443491Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 163 <- wrt source file 2025-08-14T21:38:45.8450017Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2025-08-14T21:38:45.8452950Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 274 <- wrt source file 2025-08-14T21:38:45.8458476Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2025-08-14T21:38:45.8461313Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 367 <- wrt source file 2025-08-14T21:38:45.8464269Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2025-08-14T21:38:45.8466671Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 70 <- wrt source file 2025-08-14T21:38:45.8470977Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2025-08-14T21:38:45.8473398Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 122 <- wrt source file 2025-08-14T21:38:45.8493705Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2025-08-14T21:38:45.8496228Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 187 <- wrt source file 2025-08-14T21:38:46.4621935Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2025-08-14T21:38:46.4875059Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 241 <- wrt source file 2025-08-14T21:38:46.4884964Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2025-08-14T21:38:46.4887412Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 294 <- wrt source file 2025-08-14T21:38:46.4891529Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2025-08-14T21:38:46.4894209Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 350 <- wrt source file 2025-08-14T21:38:46.4915819Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2025-08-14T21:38:46.4918312Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 395 <- wrt source file 2025-08-14T21:38:46.4922633Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2025-08-14T21:38:46.4925127Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 439 <- wrt source file 2025-08-14T21:38:46.4928738Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2025-08-14T21:38:46.4931194Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 497 <- wrt source file 2025-08-14T21:38:46.4933716Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2025-08-14T21:38:46.4936196Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 556 <- wrt source file 2025-08-14T21:38:46.4940119Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2025-08-14T21:38:46.4942679Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 600 <- wrt source file 2025-08-14T21:38:46.4946403Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2025-08-14T21:38:46.4948908Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 658 <- wrt source file 2025-08-14T21:38:47.0019597Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2025-08-14T21:38:47.0273424Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 692 <- wrt source file 2025-08-14T21:38:47.0282883Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2025-08-14T21:38:47.0285209Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 750 <- wrt source file 2025-08-14T21:38:47.0289513Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2025-08-14T21:38:47.0292124Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 812 <- wrt source file 2025-08-14T21:38:47.0314376Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2025-08-14T21:38:47.0316912Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2025-08-14T21:38:47.0319634Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2025-08-14T21:38:47.0322208Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 99 <- wrt source file 2025-08-14T21:38:47.0326091Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2025-08-14T21:38:47.0328538Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 129 <- wrt source file 2025-08-14T21:38:47.0332312Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2025-08-14T21:38:47.0334646Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 207 <- wrt source file 2025-08-14T21:38:47.0385070Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2025-08-14T21:38:47.0387457Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 291 <- wrt source file 2025-08-14T21:38:47.2576826Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2025-08-14T21:38:47.2629268Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 366 <- wrt source file 2025-08-14T21:38:47.2641145Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2025-08-14T21:38:47.2643524Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 550 <- wrt source file 2025-08-14T21:38:47.3423813Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2025-08-14T21:38:47.3426549Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 642 <- wrt source file 2025-08-14T21:38:47.3449976Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2025-08-14T21:38:47.3452396Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 738 <- wrt source file 2025-08-14T21:38:47.3497637Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2025-08-14T21:38:47.3500010Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 855 <- wrt source file 2025-08-14T21:38:47.5170393Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2025-08-14T21:38:47.5218421Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 946 <- wrt source file 2025-08-14T21:38:47.5265671Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2025-08-14T21:38:47.5268577Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1033 <- wrt source file 2025-08-14T21:38:47.5990322Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2025-08-14T21:38:47.5992959Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1152 <- wrt source file 2025-08-14T21:38:47.5999562Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2025-08-14T21:38:47.6002302Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1204 <- wrt source file 2025-08-14T21:38:47.6052769Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2025-08-14T21:38:47.6055817Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1264 <- wrt source file 2025-08-14T21:38:47.8187261Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2025-08-14T21:38:47.8235852Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1320 <- wrt source file 2025-08-14T21:38:47.8251284Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2025-08-14T21:38:47.8253676Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1355 <- wrt source file 2025-08-14T21:38:47.8262702Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2025-08-14T21:38:47.8265357Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1399 <- wrt source file 2025-08-14T21:38:47.8288902Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2025-08-14T21:38:47.8291474Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1447 <- wrt source file 2025-08-14T21:38:47.8304673Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2025-08-14T21:38:47.8307516Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1481 <- wrt source file 2025-08-14T21:38:47.8312864Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2025-08-14T21:38:47.8315420Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1521 <- wrt source file 2025-08-14T21:38:47.8332594Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2025-08-14T21:38:47.8334905Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0, line 595 <- wrt source file 2025-08-14T21:38:47.8344805Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0 2025-08-14T21:38:47.8346971Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 953 <- wrt source file 2025-08-14T21:38:47.8690358Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0 2025-08-14T21:38:47.8693318Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1288 <- wrt source file 2025-08-14T21:38:47.8707115Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0 2025-08-14T21:38:47.8709303Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1537 <- wrt source file 2025-08-14T21:38:47.8719995Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2025-08-14T21:38:47.8722448Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1659 <- wrt source file 2025-08-14T21:38:47.8732080Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2025-08-14T21:38:47.8734338Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1773 <- wrt source file 2025-08-14T21:38:47.8746266Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2025-08-14T21:38:47.8748536Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 71 <- wrt source file 2025-08-14T21:38:47.8760631Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0 2025-08-14T21:38:47.8763132Z * 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 2025-08-14T21:38:47.8766327Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2025-08-14T21:38:47.8769030Z * 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 2025-08-14T21:38:47.8773329Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2025-08-14T21:38:47.8775936Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file 2025-08-14T21:38:48.6878383Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0 2025-08-14T21:38:48.6894327Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 336 <- wrt source file 2025-08-14T21:38:48.7999923Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2025-08-14T21:38:48.8006210Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 562 <- wrt source file 2025-08-14T21:38:49.0436739Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2025-08-14T21:38:49.0446582Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 686 <- wrt source file 2025-08-14T21:38:49.0730826Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2025-08-14T21:38:49.0765632Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 995 <- wrt source file 2025-08-14T21:38:49.1302331Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2025-08-14T21:38:49.1305271Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2025-08-14T21:38:49.1327950Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2025-08-14T21:38:49.1330510Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 229 <- wrt source file 2025-08-14T21:38:49.1341432Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2025-08-14T21:38:49.1344204Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 279 <- wrt source file 2025-08-14T21:38:49.1350773Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2025-08-14T21:38:49.1353432Z * 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 2025-08-14T21:38:49.1356154Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2025-08-14T21:38:49.1358857Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 642 <- wrt source file 2025-08-14T21:38:49.1361698Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2025-08-14T21:38:49.1364599Z * 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 2025-08-14T21:38:49.1367624Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2025-08-14T21:38:49.1370720Z * 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 2025-08-14T21:38:49.1373958Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2025-08-14T21:38:49.1377132Z * 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 2025-08-14T21:38:49.1380658Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2025-08-14T21:38:49.1384073Z * 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 2025-08-14T21:38:49.1387471Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2025-08-14T21:38:49.1390436Z * 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 2025-08-14T21:38:49.1393301Z * 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 2025-08-14T21:38:49.1395770Z * 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 2025-08-14T21:38:49.1398033Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0 2025-08-14T21:38:49.1400430Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2025-08-14T21:38:49.1403046Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2025-08-14T21:38:49.1405558Z * 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 2025-08-14T21:38:49.1408141Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2025-08-14T21:38:49.1410694Z * 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 2025-08-14T21:38:49.1413333Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2025-08-14T21:38:49.1415700Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0, line 849 <- wrt source file 2025-08-14T21:38:49.1417954Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0 2025-08-14T21:38:49.1420282Z * 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 2025-08-14T21:38:49.1422769Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2025-08-14T21:38:49.1425207Z * 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 2025-08-14T21:38:49.1427602Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2025-08-14T21:38:49.1429973Z * 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 2025-08-14T21:38:49.1432406Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_structured:0 2025-08-14T21:38:49.1434679Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0, line 1197 <- wrt source file 2025-08-14T21:38:49.1436904Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0 2025-08-14T21:38:49.1439231Z * 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 2025-08-14T21:38:49.1441574Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0 2025-08-14T21:38:49.1443765Z * 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 2025-08-14T21:38:49.1446135Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2025-08-14T21:38:49.1448385Z * 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 2025-08-14T21:38:49.1451276Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2025-08-14T21:38:49.1453809Z * 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 2025-08-14T21:38:49.1459595Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2025-08-14T21:38:49.1461937Z * 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 2025-08-14T21:38:49.1476816Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2025-08-14T21:38:49.1479245Z * 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 2025-08-14T21:38:49.1484424Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2025-08-14T21:38:49.1486998Z * 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 2025-08-14T21:38:49.1492047Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2025-08-14T21:38:49.1494737Z * 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 2025-08-14T21:38:49.1497281Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0 2025-08-14T21:38:49.1499691Z * 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 2025-08-14T21:38:49.1503939Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2025-08-14T21:38:49.1506388Z * 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 2025-08-14T21:38:49.1509528Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2025-08-14T21:38:49.1512152Z * 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 2025-08-14T21:38:49.1514888Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2025-08-14T21:38:49.1517901Z * 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 2025-08-14T21:38:49.1521331Z * 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 2025-08-14T21:38:49.1524326Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 280 <- wrt source file 2025-08-14T21:38:49.1526734Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2025-08-14T21:38:49.1529169Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 388 <- wrt source file 2025-08-14T21:38:49.1531707Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2025-08-14T21:38:49.1534048Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 491 <- wrt source file 2025-08-14T21:38:49.1536344Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2025-08-14T21:38:49.1538663Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 547 <- wrt source file 2025-08-14T21:38:49.1541072Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2025-08-14T21:38:49.1543464Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 608 <- wrt source file 2025-08-14T21:38:49.1545918Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2025-08-14T21:38:49.1548227Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 683 <- wrt source file 2025-08-14T21:38:49.1550555Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2025-08-14T21:38:49.1552905Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0, line 773 <- wrt source file 2025-08-14T21:38:49.1555358Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0 2025-08-14T21:38:49.1557753Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 971 <- wrt source file 2025-08-14T21:38:49.1560199Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2025-08-14T21:38:49.1562677Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0, line 1062 <- wrt source file 2025-08-14T21:38:49.1565221Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0 2025-08-14T21:38:49.1567736Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1134 <- wrt source file 2025-08-14T21:38:49.1570274Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2025-08-14T21:38:49.1572989Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1803 <- wrt source file 2025-08-14T21:38:49.1575893Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2025-08-14T21:38:49.1578765Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1819 <- wrt source file 2025-08-14T21:38:49.1581689Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2025-08-14T21:38:49.1584390Z * 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 2025-08-14T21:38:49.1586710Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0 2025-08-14T21:38:49.1588995Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0, line 22 <- wrt source file 2025-08-14T21:38:49.1591377Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0 2025-08-14T21:38:49.1594211Z * 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 2025-08-14T21:38:49.1597239Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0 2025-08-14T21:38:49.1599840Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0, line 617 <- wrt source file 2025-08-14T21:38:49.1602186Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0 2025-08-14T21:38:49.1604777Z * 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 2025-08-14T21:38:49.1607582Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0 2025-08-14T21:38:49.1610087Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_creation.py::make_tensor:0, line 114 <- wrt source file 2025-08-14T21:38:49.1612445Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_creation.py::make_tensor:0 2025-08-14T21:38:49.1614947Z * 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 2025-08-14T21:38:49.1617606Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::parametrize:0 2025-08-14T21:38:49.1620269Z * 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 2025-08-14T21:38:49.1622953Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0 2025-08-14T21:38:49.1625625Z * 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 2025-08-14T21:38:49.1628269Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0 2025-08-14T21:38:49.1631046Z * 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 2025-08-14T21:38:49.1634027Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0 2025-08-14T21:38:49.1636932Z * 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 2025-08-14T21:38:49.1639909Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2025-08-14T21:38:49.1642813Z * 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 2025-08-14T21:38:49.1645868Z * SKIPPED: 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<- wrt source file 2025-08-14T21:38:49.1663471Z * 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 2025-08-14T21:38:49.1667259Z * 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 2025-08-14T21:38:49.1670635Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0 2025-08-14T21:38:49.1673389Z * 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 2025-08-14T21:38:49.1675749Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0 2025-08-14T21:38:49.1678067Z * 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 2025-08-14T21:38:49.1680425Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2025-08-14T21:38:49.1682778Z * 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 2025-08-14T21:38:49.1685183Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2025-08-14T21:38:49.1687476Z * 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 2025-08-14T21:38:49.1689776Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2025-08-14T21:38:49.1692067Z * 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 2025-08-14T21:38:49.1694574Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2025-08-14T21:38:49.1696899Z * 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 2025-08-14T21:38:49.1699315Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2025-08-14T21:38:49.1701605Z * 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 2025-08-14T21:38:49.1703937Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2025-08-14T21:38:49.1706411Z * 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 2025-08-14T21:38:49.1708937Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2025-08-14T21:38:49.1711358Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0, line 303 <- wrt source file 2025-08-14T21:38:49.1713795Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0 2025-08-14T21:38:49.1716166Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0, line 419 <- wrt source file 2025-08-14T21:38:49.1718568Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0 2025-08-14T21:38:49.1720878Z * 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 2025-08-14T21:38:49.1723161Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_is_leaf:0 2025-08-14T21:38:49.1725438Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0, line 1345 <- wrt source file 2025-08-14T21:38:49.1727645Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0 2025-08-14T21:38:49.1730178Z * 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 2025-08-14T21:38:49.1733079Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2025-08-14T21:38:49.1736085Z * 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 2025-08-14T21:38:49.1739254Z * SKIPPED: 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SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0 2025-08-14T21:38:49.1757878Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 93 <- wrt source file 2025-08-14T21:38:49.1760140Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0 2025-08-14T21:38:49.1762680Z * 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 2025-08-14T21:38:49.2127544Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0 2025-08-14T21:38:49.2130851Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 94 <- wrt source file 2025-08-14T21:38:49.2133913Z * SKIPPED: 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2025-08-14T21:38:49.2150860Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 39 <- wrt source file 2025-08-14T21:38:49.2153512Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2025-08-14T21:38:49.2156022Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 137 <- wrt source file 2025-08-14T21:38:49.2158481Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0 2025-08-14T21:38:49.2160962Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 364 <- wrt source file 2025-08-14T21:38:49.2163599Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_collate:0 2025-08-14T21:38:49.2166247Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 268 <- wrt source file 2025-08-14T21:38:49.2168966Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0 2025-08-14T21:38:49.2171759Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 53 <- wrt source file 2025-08-14T21:38:49.2174771Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0 2025-08-14T21:38:49.2177756Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 201 <- wrt source file 2025-08-14T21:38:49.2180819Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0 2025-08-14T21:38:49.2183928Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0, line 90 <- wrt source file 2025-08-14T21:38:49.2187109Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0 2025-08-14T21:38:49.2190257Z * 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 2025-08-14T21:38:49.2193541Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2025-08-14T21:38:49.2196553Z * 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 2025-08-14T21:38:49.2199597Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 25 <- wrt source file 2025-08-14T21:38:49.2236208Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2025-08-14T21:38:49.2239352Z * 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 2025-08-14T21:38:49.2242496Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2025-08-14T21:38:49.2245458Z * 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 2025-08-14T21:38:49.2248429Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2025-08-14T21:38:49.2251458Z * 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 2025-08-14T21:38:49.2254745Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2025-08-14T21:38:49.2257868Z * 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 2025-08-14T21:38:49.2260897Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2025-08-14T21:38:49.2263888Z * 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 2025-08-14T21:38:49.2266894Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2025-08-14T21:38:49.2269829Z * 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 2025-08-14T21:38:49.2272803Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2025-08-14T21:38:49.2275804Z * 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 2025-08-14T21:38:49.2278952Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2025-08-14T21:38:49.2281901Z * 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 2025-08-14T21:38:49.2284844Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2025-08-14T21:38:49.2287704Z * 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 2025-08-14T21:38:49.2291598Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2025-08-14T21:38:49.2294470Z * 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 2025-08-14T21:38:49.2297194Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2025-08-14T21:38:49.2299916Z * 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 2025-08-14T21:38:49.2302720Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2025-08-14T21:38:49.2305531Z * 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 2025-08-14T21:38:49.2308321Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2025-08-14T21:38:49.2311097Z * 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 2025-08-14T21:38:49.2313985Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2025-08-14T21:38:49.2316815Z * 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 2025-08-14T21:38:49.2319820Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2025-08-14T21:38:49.2322677Z * 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 2025-08-14T21:38:49.2325558Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2025-08-14T21:38:49.2328373Z * 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 2025-08-14T21:38:49.2331251Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2025-08-14T21:38:49.2334084Z * 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 2025-08-14T21:38:49.2337006Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2025-08-14T21:38:49.2339921Z * 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 2025-08-14T21:38:49.2342984Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2025-08-14T21:38:49.2345914Z * 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 2025-08-14T21:38:49.2348739Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2025-08-14T21:38:49.2351553Z * 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 2025-08-14T21:38:49.2354411Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2025-08-14T21:38:49.2357181Z * 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 2025-08-14T21:38:49.2359987Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2025-08-14T21:38:49.2362806Z * 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 2025-08-14T21:38:49.2365807Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2025-08-14T21:38:49.2368666Z * 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 2025-08-14T21:38:49.2371569Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2025-08-14T21:38:49.2374666Z * 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 2025-08-14T21:38:49.2377985Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2025-08-14T21:38:49.2381222Z * 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 2025-08-14T21:38:49.2384631Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2025-08-14T21:38:49.2387758Z * 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 2025-08-14T21:38:49.2390791Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2025-08-14T21:38:49.2393797Z * 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 2025-08-14T21:38:49.2396627Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2025-08-14T21:38:49.2398076Z ============ 2025-08-14T21:38:49.2398554Z Finished doctests 2025-08-14T21:38:49.2398984Z 338 / 732 passed 2025-08-14T21:38:49.2399423Z  2025-08-14T21:38:49.2399957Z === Found 146 parse-time warnings === 2025-08-14T21:38:49.2400728Z --- Parse Warning: 1 / 146 --- 2025-08-14T21:38:49.2402918Z /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. 2025-08-14T21:38:49.2405314Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.2406184Z 2025-08-14T21:38:49.2406606Z dim_order(ambiguity_check=False) -> tuple 2025-08-14T21:38:49.2407192Z 2025-08-14T21:38:49.2407824Z Returns the uniquely determined tuple of int describing the dim order or 2025-08-14T21:38:49.2408702Z physical layout of :attr:`self`. 2025-08-14T21:38:49.2409239Z 2025-08-14T21:38:49.2409936Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-08-14T21:38:49.2410970Z starting from the outermost to the innermost dimension. 2025-08-14T21:38:49.2411661Z 2025-08-14T21:38:49.2412207Z Note that the dim order may not always be uniquely determined. 2025-08-14T21:38:49.2413520Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-08-14T21:38:49.2415274Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-08-14T21:38:49.2416760Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-08-14T21:38:49.2418092Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-08-14T21:38:49.2419143Z Otherwise, it will raise TypeError. 2025-08-14T21:38:49.2419706Z 2025-08-14T21:38:49.2420053Z Args: 2025-08-14T21:38:49.2420874Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-08-14T21:38:49.2421875Z 2025-08-14T21:38:49.2422235Z Examples:: 2025-08-14T21:38:49.2422610Z 2025-08-14T21:38:49.2423019Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-08-14T21:38:49.2423662Z (0, 1, 2, 3) 2025-08-14T21:38:49.2424203Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-08-14T21:38:49.2424875Z (0, 2, 1, 3) 2025-08-14T21:38:49.2425515Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-08-14T21:38:49.2426296Z (0, 2, 3, 1) 2025-08-14T21:38:49.2426748Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-08-14T21:38:49.2427325Z (0, 1, 2, 3) 2025-08-14T21:38:49.2427743Z >>> try: 2025-08-14T21:38:49.2428293Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-08-14T21:38:49.2429041Z ... except RuntimeError as e: 2025-08-14T21:38:49.2429735Z ... print(e) 2025-08-14T21:38:49.2430659Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-08-14T21:38:49.2431693Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-08-14T21:38:49.2432507Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-08-14T21:38:49.2433381Z ... ) # It can be mapped to contiguous format 2025-08-14T21:38:49.2433976Z (0, 1, 2, 3) 2025-08-14T21:38:49.2434395Z >>> try: 2025-08-14T21:38:49.2434978Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-08-14T21:38:49.2435718Z ... except TypeError as e: 2025-08-14T21:38:49.2436254Z ... print(e) 2025-08-14T21:38:49.2436987Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-08-14T21:38:49.2437794Z 2025-08-14T21:38:49.2438161Z .. warning:: 2025-08-14T21:38:49.2438786Z The dim_order tensor API is experimental and subject to change. 2025-08-14T21:38:49.2439526Z 2025-08-14T21:38:49.2440198Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.2441057Z 2025-08-14T21:38:49.2441424Z warnings.warn(msg) 2025-08-14T21:38:49.2441870Z 2025-08-14T21:38:49.2442485Z --- Parse Warning: 2 / 146 --- 2025-08-14T21:38:49.2444562Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=446. 2025-08-14T21:38:49.2446906Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.2448097Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-08-14T21:38:49.2448926Z 2025-08-14T21:38:49.2449450Z This is helpful when you want to visualize data over some 2025-08-14T21:38:49.2450279Z range of inputs. See below for a plotting example. 2025-08-14T21:38:49.2450929Z 2025-08-14T21:38:49.2451426Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-08-14T21:38:49.2452284Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-08-14T21:38:49.2453211Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-08-14T21:38:49.2454080Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-08-14T21:38:49.2454925Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-08-14T21:38:49.2455683Z to the result shape. 2025-08-14T21:38:49.2456186Z 2025-08-14T21:38:49.2456543Z .. note:: 2025-08-14T21:38:49.2457096Z 0D inputs are treated equivalently to 1D inputs of a 2025-08-14T21:38:49.2457791Z single element. 2025-08-14T21:38:49.2458272Z 2025-08-14T21:38:49.2458627Z .. warning:: 2025-08-14T21:38:49.2459262Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-08-14T21:38:49.2460124Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-08-14T21:38:49.2460783Z 2025-08-14T21:38:49.2461259Z In the future `torch.meshgrid` will transition to 2025-08-14T21:38:49.2461976Z `indexing='xy'` as the default. 2025-08-14T21:38:49.2462538Z 2025-08-14T21:38:49.2463085Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-08-14T21:38:49.2464052Z this issue with the goal of migrating to NumPy's behavior. 2025-08-14T21:38:49.2464768Z 2025-08-14T21:38:49.2465118Z .. seealso:: 2025-08-14T21:38:49.2465559Z 2025-08-14T21:38:49.2466072Z :func:`torch.cartesian_prod` has the same effect but it 2025-08-14T21:38:49.2466845Z collects the data in a tensor of vectors. 2025-08-14T21:38:49.2467456Z 2025-08-14T21:38:49.2467815Z Args: 2025-08-14T21:38:49.2468634Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-08-14T21:38:49.2469704Z treated as tensors of size :math:`(1,)` automatically 2025-08-14T21:38:49.2470383Z 2025-08-14T21:38:49.2470895Z indexing: (str, optional): the indexing mode, either "xy" 2025-08-14T21:38:49.2471765Z or "ij", defaults to "ij". See warning for future changes. 2025-08-14T21:38:49.2472447Z 2025-08-14T21:38:49.2472924Z If "xy" is selected, the first dimension corresponds 2025-08-14T21:38:49.2473715Z to the cardinality of the second input and the second 2025-08-14T21:38:49.2474564Z dimension corresponds to the cardinality of the first 2025-08-14T21:38:49.2475270Z input. 2025-08-14T21:38:49.2475699Z 2025-08-14T21:38:49.2476164Z If "ij" is selected, the dimensions are in the same 2025-08-14T21:38:49.2476907Z order as the cardinality of the inputs. 2025-08-14T21:38:49.2477494Z 2025-08-14T21:38:49.2477847Z Returns: 2025-08-14T21:38:49.2478408Z seq (sequence of Tensors): If the input has :math:`N` 2025-08-14T21:38:49.2479220Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-08-14T21:38:49.2480087Z output will also have :math:`N` tensors, where each tensor 2025-08-14T21:38:49.2480871Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-08-14T21:38:49.2481453Z 2025-08-14T21:38:49.2481799Z Example:: 2025-08-14T21:38:49.2482217Z 2025-08-14T21:38:49.2482613Z >>> x = torch.tensor([1, 2, 3]) 2025-08-14T21:38:49.2483212Z >>> y = torch.tensor([4, 5, 6]) 2025-08-14T21:38:49.2483779Z 2025-08-14T21:38:49.2484321Z Observe the element-wise pairings across the grid, (1, 4), 2025-08-14T21:38:49.2485125Z (1, 5), ..., (3, 6). This is the same thing as the 2025-08-14T21:38:49.2485785Z cartesian product. 2025-08-14T21:38:49.2486445Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-08-14T21:38:49.2487133Z >>> grid_x 2025-08-14T21:38:49.2487592Z tensor([[1, 1, 1], 2025-08-14T21:38:49.2488121Z [2, 2, 2], 2025-08-14T21:38:49.2488628Z [3, 3, 3]]) 2025-08-14T21:38:49.2489129Z >>> grid_y 2025-08-14T21:38:49.2489598Z tensor([[4, 5, 6], 2025-08-14T21:38:49.2490118Z [4, 5, 6], 2025-08-14T21:38:49.2490612Z [4, 5, 6]]) 2025-08-14T21:38:49.2491106Z 2025-08-14T21:38:49.2491611Z This correspondence can be seen when these grids are 2025-08-14T21:38:49.2492315Z stacked properly. 2025-08-14T21:38:49.2493197Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-08-14T21:38:49.2494048Z ... torch.cartesian_prod(x, y)) 2025-08-14T21:38:49.2494666Z True 2025-08-14T21:38:49.2495061Z 2025-08-14T21:38:49.2495593Z `torch.meshgrid` is commonly used to produce a grid for 2025-08-14T21:38:49.2496322Z plotting. 2025-08-14T21:38:49.2496846Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-08-14T21:38:49.2497541Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-08-14T21:38:49.2498230Z >>> import matplotlib.pyplot as plt 2025-08-14T21:38:49.2498880Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-08-14T21:38:49.2499561Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-08-14T21:38:49.2500247Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-08-14T21:38:49.2500953Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-08-14T21:38:49.2501613Z >>> ax = plt.axes(projection='3d') 2025-08-14T21:38:49.2502480Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-08-14T21:38:49.2503204Z >>> plt.show() 2025-08-14T21:38:49.2503770Z 2025-08-14T21:38:49.2504186Z .. image:: ../_static/img/meshgrid.png 2025-08-14T21:38:49.2504787Z :width: 512 2025-08-14T21:38:49.2505229Z 2025-08-14T21:38:49.2505581Z 2025-08-14T21:38:49.2506295Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.2507141Z 2025-08-14T21:38:49.2507513Z warnings.warn(msg) 2025-08-14T21:38:49.2507968Z 2025-08-14T21:38:49.2508531Z --- Parse Warning: 3 / 146 --- 2025-08-14T21:38:49.2510620Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=842. 2025-08-14T21:38:49.2513010Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.2514479Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-08-14T21:38:49.2515588Z 2025-08-14T21:38:49.2516060Z Returns the unique elements of the input tensor. 2025-08-14T21:38:49.2516701Z 2025-08-14T21:38:49.2517508Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-08-14T21:38:49.2518668Z this function also eliminates non-consecutive duplicate values. 2025-08-14T21:38:49.2519437Z 2025-08-14T21:38:49.2520055Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-08-14T21:38:49.2521251Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-08-14T21:38:49.2522572Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-08-14T21:38:49.2523677Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-08-14T21:38:49.2524390Z 2025-08-14T21:38:49.2524731Z Args: 2025-08-14T21:38:49.2525168Z input (Tensor): the input tensor 2025-08-14T21:38:49.2525992Z sorted (bool): Whether to sort the unique elements in ascending order 2025-08-14T21:38:49.2526815Z before returning as output. 2025-08-14T21:38:49.2527621Z return_inverse (bool): Whether to also return the indices for where 2025-08-14T21:38:49.2528663Z elements in the original input ended up in the returned unique list. 2025-08-14T21:38:49.2529736Z return_counts (bool): Whether to also return the counts for each unique 2025-08-14T21:38:49.2530540Z element. 2025-08-14T21:38:49.2531212Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-08-14T21:38:49.2532218Z unique of the flattened input is returned. Otherwise, each of the 2025-08-14T21:38:49.2533211Z tensors indexed by the given dimension is treated as one of the 2025-08-14T21:38:49.2534229Z elements to apply the unique operation upon. See examples for more 2025-08-14T21:38:49.2535066Z details. Default: ``None`` 2025-08-14T21:38:49.2535603Z 2025-08-14T21:38:49.2535957Z Returns: 2025-08-14T21:38:49.2536739Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-08-14T21:38:49.2537660Z 2025-08-14T21:38:49.2538206Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-08-14T21:38:49.2539054Z - **inverse_indices** (*Tensor*): (optional) if 2025-08-14T21:38:49.2539862Z :attr:`return_inverse` is True, there will be an additional 2025-08-14T21:38:49.2540791Z returned tensor (same shape as input) representing the indices 2025-08-14T21:38:49.2541768Z for where elements in the original input map to in the output; 2025-08-14T21:38:49.2542786Z otherwise, this function will only return a single tensor. 2025-08-14T21:38:49.2543694Z - **counts** (*Tensor*): (optional) if 2025-08-14T21:38:49.2544438Z :attr:`return_counts` is True, there will be an additional 2025-08-14T21:38:49.2545346Z returned tensor (same shape as output or output.size(dim), 2025-08-14T21:38:49.2546271Z if dim was specified) representing the number of occurrences 2025-08-14T21:38:49.2547046Z for each unique value or tensor. 2025-08-14T21:38:49.2547622Z 2025-08-14T21:38:49.2547986Z Example:: 2025-08-14T21:38:49.2548375Z 2025-08-14T21:38:49.2548963Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-08-14T21:38:49.2549764Z >>> output 2025-08-14T21:38:49.2550211Z tensor([1, 2, 3]) 2025-08-14T21:38:49.2550678Z 2025-08-14T21:38:49.2551109Z >>> output, inverse_indices = torch.unique( 2025-08-14T21:38:49.2551999Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-08-14T21:38:49.2552823Z >>> output 2025-08-14T21:38:49.2553266Z tensor([1, 2, 3]) 2025-08-14T21:38:49.2553767Z >>> inverse_indices 2025-08-14T21:38:49.2554297Z tensor([0, 2, 1, 2]) 2025-08-14T21:38:49.2554786Z 2025-08-14T21:38:49.2555212Z >>> output, inverse_indices = torch.unique( 2025-08-14T21:38:49.2556089Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-08-14T21:38:49.2556925Z >>> output 2025-08-14T21:38:49.2557373Z tensor([1, 2, 3]) 2025-08-14T21:38:49.2557874Z >>> inverse_indices 2025-08-14T21:38:49.2558360Z tensor([[0, 2], 2025-08-14T21:38:49.2558830Z [1, 2]]) 2025-08-14T21:38:49.2559293Z 2025-08-14T21:38:49.2559661Z >>> a = torch.tensor([ 2025-08-14T21:38:49.2560178Z ... [ 2025-08-14T21:38:49.2560614Z ... [1, 1, 0, 0], 2025-08-14T21:38:49.2561128Z ... [1, 1, 0, 0], 2025-08-14T21:38:49.2561652Z ... [0, 0, 1, 1], 2025-08-14T21:38:49.2562168Z ... ], 2025-08-14T21:38:49.2562588Z ... [ 2025-08-14T21:38:49.2563015Z ... [0, 0, 1, 1], 2025-08-14T21:38:49.2563543Z ... [0, 0, 1, 1], 2025-08-14T21:38:49.2564053Z ... [1, 1, 1, 1], 2025-08-14T21:38:49.2564561Z ... ], 2025-08-14T21:38:49.2564988Z ... [ 2025-08-14T21:38:49.2565407Z ... [1, 1, 0, 0], 2025-08-14T21:38:49.2565935Z ... [1, 1, 0, 0], 2025-08-14T21:38:49.2566461Z ... [0, 0, 1, 1], 2025-08-14T21:38:49.2566977Z ... ], 2025-08-14T21:38:49.2567396Z ... ]) 2025-08-14T21:38:49.2567795Z 2025-08-14T21:38:49.2568391Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-08-14T21:38:49.2569390Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-08-14T21:38:49.2615973Z >>> # each other, so one of them will be removed. 2025-08-14T21:38:49.2616772Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-08-14T21:38:49.2617353Z tensor(True) 2025-08-14T21:38:49.2617883Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-08-14T21:38:49.2618500Z >>> a_unique_dim0 2025-08-14T21:38:49.2618986Z tensor([[[0, 0, 1, 1], 2025-08-14T21:38:49.2619512Z [0, 0, 1, 1], 2025-08-14T21:38:49.2620025Z [1, 1, 1, 1]], 2025-08-14T21:38:49.2620523Z [[1, 1, 0, 0], 2025-08-14T21:38:49.2621034Z [1, 1, 0, 0], 2025-08-14T21:38:49.2621542Z [0, 0, 1, 1]]]) 2025-08-14T21:38:49.2622028Z 2025-08-14T21:38:49.2622819Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-08-14T21:38:49.2623762Z >>> # `a_unique_dim0`: 2025-08-14T21:38:49.2624320Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-08-14T21:38:49.2624935Z tensor(True) 2025-08-14T21:38:49.2625439Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-08-14T21:38:49.2626033Z tensor(True) 2025-08-14T21:38:49.2626452Z 2025-08-14T21:38:49.2627012Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-08-14T21:38:49.2627935Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-08-14T21:38:49.2628669Z >>> # them will be removed. 2025-08-14T21:38:49.2629219Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-08-14T21:38:49.2629766Z tensor(True) 2025-08-14T21:38:49.2630229Z >>> torch.unique(a, dim=1) 2025-08-14T21:38:49.2630767Z tensor([[[0, 0, 1, 1], 2025-08-14T21:38:49.2631264Z [1, 1, 0, 0]], 2025-08-14T21:38:49.2631758Z [[1, 1, 1, 1], 2025-08-14T21:38:49.2632244Z [0, 0, 1, 1]], 2025-08-14T21:38:49.2632744Z [[0, 0, 1, 1], 2025-08-14T21:38:49.2633252Z [1, 1, 0, 0]]]) 2025-08-14T21:38:49.2633797Z 2025-08-14T21:38:49.2634383Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-08-14T21:38:49.2635289Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-08-14T21:38:49.2636117Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-08-14T21:38:49.2636887Z >>> # sub-tensors will be removed. 2025-08-14T21:38:49.2637492Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-08-14T21:38:49.2638042Z tensor(True) 2025-08-14T21:38:49.2638513Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-08-14T21:38:49.2639082Z tensor(True) 2025-08-14T21:38:49.2639573Z >>> torch.unique(a, dim=2) 2025-08-14T21:38:49.2640111Z tensor([[[0, 1], 2025-08-14T21:38:49.2640599Z [0, 1], 2025-08-14T21:38:49.2641077Z [1, 0]], 2025-08-14T21:38:49.2641539Z [[1, 0], 2025-08-14T21:38:49.2642013Z [1, 0], 2025-08-14T21:38:49.2642484Z [1, 1]], 2025-08-14T21:38:49.2642946Z [[0, 1], 2025-08-14T21:38:49.2643413Z [0, 1], 2025-08-14T21:38:49.2643889Z [1, 0]]]) 2025-08-14T21:38:49.2644343Z 2025-08-14T21:38:49.2645036Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.2645907Z 2025-08-14T21:38:49.2646261Z warnings.warn(msg) 2025-08-14T21:38:49.2646720Z 2025-08-14T21:38:49.2647325Z --- Parse Warning: 4 / 146 --- 2025-08-14T21:38:49.2649305Z /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. 2025-08-14T21:38:49.2651583Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.2652474Z 2025-08-14T21:38:49.2652972Z Load a model from a github repo or a local directory. 2025-08-14T21:38:49.2653622Z 2025-08-14T21:38:49.2654247Z Note: Loading a model is the typical use case, but this can also be used to 2025-08-14T21:38:49.2655309Z for loading other objects such as tokenizers, loss functions, etc. 2025-08-14T21:38:49.2656071Z 2025-08-14T21:38:49.2656573Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-08-14T21:38:49.2657405Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-08-14T21:38:49.2658119Z ref (a tag or a branch). 2025-08-14T21:38:49.2658579Z 2025-08-14T21:38:49.2659168Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-08-14T21:38:49.2659925Z path to a local directory. 2025-08-14T21:38:49.2660400Z 2025-08-14T21:38:49.2660754Z Args: 2025-08-14T21:38:49.2661212Z repo_or_dir (str): If ``source`` is 'github', 2025-08-14T21:38:49.2662199Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-08-14T21:38:49.2663633Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-08-14T21:38:49.2664934Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-08-14T21:38:49.2666031Z If ``source`` is 'local' then it should be a path to a local directory. 2025-08-14T21:38:49.2666987Z model (str): the name of a callable (entrypoint) defined in the 2025-08-14T21:38:49.2667764Z repo/dir's ``hubconf.py``. 2025-08-14T21:38:49.2668522Z *args (optional): the corresponding args for callable ``model``. 2025-08-14T21:38:49.2669438Z source (str, optional): 'github' or 'local'. Specifies how 2025-08-14T21:38:49.2670295Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-08-14T21:38:49.2671224Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-08-14T21:38:49.2672330Z This parameter was introduced in v1.12 and helps ensuring that users 2025-08-14T21:38:49.2673213Z only run code from repos that they trust. 2025-08-14T21:38:49.2673818Z 2025-08-14T21:38:49.2674376Z - If ``False``, a prompt will ask the user whether the repo should 2025-08-14T21:38:49.2675122Z be trusted. 2025-08-14T21:38:49.2675772Z - If ``True``, the repo will be added to the trusted list and loaded 2025-08-14T21:38:49.2676600Z without requiring explicit confirmation. 2025-08-14T21:38:49.2677363Z - If ``"check"``, the repo will be checked against the list of 2025-08-14T21:38:49.2678306Z trusted repos in the cache. If it is not present in that list, the 2025-08-14T21:38:49.2679305Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-08-14T21:38:49.2680244Z - If ``None``: this will raise a warning, inviting the user to set 2025-08-14T21:38:49.2681146Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-08-14T21:38:49.2682114Z is only present for backward compatibility and will be removed in 2025-08-14T21:38:49.2682894Z v2.0. 2025-08-14T21:38:49.2683281Z 2025-08-14T21:38:49.2683869Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-08-14T21:38:49.2684891Z force_reload (bool, optional): whether to force a fresh download of 2025-08-14T21:38:49.2685870Z the github repo unconditionally. Does not have any effect if 2025-08-14T21:38:49.2686668Z ``source = 'local'``. Default is ``False``. 2025-08-14T21:38:49.2687505Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-08-14T21:38:49.2688505Z local caches. Note that the message about first download cannot be 2025-08-14T21:38:49.2689430Z muted. Does not have any effect if ``source = 'local'``. 2025-08-14T21:38:49.2690154Z Default is ``True``. 2025-08-14T21:38:49.2691052Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-08-14T21:38:49.2692386Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-08-14T21:38:49.2693842Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-08-14T21:38:49.2694925Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-08-14T21:38:49.2695899Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-08-14T21:38:49.2696660Z 2025-08-14T21:38:49.2697012Z Returns: 2025-08-14T21:38:49.2697713Z The output of the ``model`` callable when called with the given 2025-08-14T21:38:49.2698519Z ``*args`` and ``**kwargs``. 2025-08-14T21:38:49.2699015Z 2025-08-14T21:38:49.2699369Z Example: 2025-08-14T21:38:49.2699841Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-08-14T21:38:49.2700472Z >>> # from a github repo 2025-08-14T21:38:49.2701007Z >>> repo = "pytorch/vision" 2025-08-14T21:38:49.2701558Z >>> model = torch.hub.load( 2025-08-14T21:38:49.2702252Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-08-14T21:38:49.2702967Z ... ) 2025-08-14T21:38:49.2703439Z >>> # from a local directory 2025-08-14T21:38:49.2704040Z >>> path = "/some/local/path/pytorch/vision" 2025-08-14T21:38:49.2704683Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.2705453Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-08-14T21:38:49.2706285Z 2025-08-14T21:38:49.2706978Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.2707833Z 2025-08-14T21:38:49.2708194Z warnings.warn(msg) 2025-08-14T21:38:49.2708655Z 2025-08-14T21:38:49.2709218Z --- Parse Warning: 5 / 146 --- 2025-08-14T21:38:49.2711294Z /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. 2025-08-14T21:38:49.2713607Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.2714490Z 2025-08-14T21:38:49.2715007Z Load a model from a local directory with a ``hubconf.py``. 2025-08-14T21:38:49.2715683Z 2025-08-14T21:38:49.2716026Z Args: 2025-08-14T21:38:49.2716580Z hubconf_dir (str): path to a local directory that contains a 2025-08-14T21:38:49.2717299Z ``hubconf.py``. 2025-08-14T21:38:49.2717956Z model (str): name of an entrypoint defined in the directory's 2025-08-14T21:38:49.2718689Z ``hubconf.py``. 2025-08-14T21:38:49.2719359Z *args (optional): the corresponding args for callable ``model``. 2025-08-14T21:38:49.2720347Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-08-14T21:38:49.2721129Z 2025-08-14T21:38:49.2721470Z Returns: 2025-08-14T21:38:49.2721997Z a single model with corresponding pretrained weights. 2025-08-14T21:38:49.2722671Z 2025-08-14T21:38:49.2723030Z Example: 2025-08-14T21:38:49.2723465Z >>> # xdoctest: +SKIP("stub local path") 2025-08-14T21:38:49.2724135Z >>> path = "/some/local/path/pytorch/vision" 2025-08-14T21:38:49.2724769Z >>> model = _load_local( 2025-08-14T21:38:49.2725253Z ... path, 2025-08-14T21:38:49.2725688Z ... "resnet50", 2025-08-14T21:38:49.2726252Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-08-14T21:38:49.2726859Z ... ) 2025-08-14T21:38:49.2727236Z 2025-08-14T21:38:49.2727910Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.2728752Z 2025-08-14T21:38:49.2729129Z warnings.warn(msg) 2025-08-14T21:38:49.2729588Z 2025-08-14T21:38:49.2730101Z --- Parse Warning: 6 / 146 --- 2025-08-14T21:38:49.2732213Z /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. 2025-08-14T21:38:49.2734576Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.2735585Z Download object at the given URL to a local path. 2025-08-14T21:38:49.2736214Z 2025-08-14T21:38:49.2736555Z Args: 2025-08-14T21:38:49.2737007Z url (str): URL of the object to download 2025-08-14T21:38:49.2737957Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-08-14T21:38:49.2739335Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-08-14T21:38:49.2740367Z Default: None 2025-08-14T21:38:49.2741172Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-08-14T21:38:49.2742038Z Default: True 2025-08-14T21:38:49.2742511Z 2025-08-14T21:38:49.2742873Z Example: 2025-08-14T21:38:49.2743353Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-08-14T21:38:49.2744097Z >>> # xdoctest: +REQUIRES(POSIX) 2025-08-14T21:38:49.2744727Z >>> torch.hub.download_url_to_file( 2025-08-14T21:38:49.2745559Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-08-14T21:38:49.2746400Z ... "/tmp/temporary_file", 2025-08-14T21:38:49.2746950Z ... ) 2025-08-14T21:38:49.2747349Z 2025-08-14T21:38:49.2747691Z 2025-08-14T21:38:49.2748371Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.2749232Z 2025-08-14T21:38:49.2749588Z warnings.warn(msg) 2025-08-14T21:38:49.2750041Z 2025-08-14T21:38:49.2750611Z --- Parse Warning: 7 / 146 --- 2025-08-14T21:38:49.2752729Z /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. 2025-08-14T21:38:49.2755134Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.2756132Z Loads the Torch serialized object at the given URL. 2025-08-14T21:38:49.2756773Z 2025-08-14T21:38:49.2757299Z If downloaded file is a zip file, it will be automatically 2025-08-14T21:38:49.2757993Z decompressed. 2025-08-14T21:38:49.2758414Z 2025-08-14T21:38:49.2759007Z If the object is already present in `model_dir`, it's deserialized and 2025-08-14T21:38:49.2759778Z returned. 2025-08-14T21:38:49.2760414Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-08-14T21:38:49.2761393Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-08-14T21:38:49.2762109Z 2025-08-14T21:38:49.2762449Z Args: 2025-08-14T21:38:49.2762898Z url (str): URL of the object to download 2025-08-14T21:38:49.2763683Z model_dir (str, optional): directory in which to save the object 2025-08-14T21:38:49.2764937Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-08-14T21:38:49.2766314Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-08-14T21:38:49.2767194Z Default: True 2025-08-14T21:38:49.2768122Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-08-14T21:38:49.2769370Z ``filename-.ext`` where ```` is the first eight or more 2025-08-14T21:38:49.2770425Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-08-14T21:38:49.2771436Z ensure unique names and to verify the contents of the file. 2025-08-14T21:38:49.2772177Z Default: False 2025-08-14T21:38:49.2773136Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-08-14T21:38:49.2774607Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-08-14T21:38:49.2775930Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-08-14T21:38:49.2776786Z 2025-08-14T21:38:49.2777122Z Example: 2025-08-14T21:38:49.2777674Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-08-14T21:38:49.2778417Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-08-14T21:38:49.2779301Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-08-14T21:38:49.2780070Z ... ) 2025-08-14T21:38:49.2780442Z 2025-08-14T21:38:49.2780778Z 2025-08-14T21:38:49.2781447Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.2782275Z 2025-08-14T21:38:49.2782638Z warnings.warn(msg) 2025-08-14T21:38:49.2783080Z 2025-08-14T21:38:49.2783662Z --- Parse Warning: 8 / 146 --- 2025-08-14T21:38:49.2785766Z /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. 2025-08-14T21:38:49.2788122Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:49.2789270Z Registers the function implementation as the fallback for the given key. 2025-08-14T21:38:49.2790091Z 2025-08-14T21:38:49.2790685Z This function only works for a library with global namespace ("_"). 2025-08-14T21:38:49.2791505Z 2025-08-14T21:38:49.2791829Z Args: 2025-08-14T21:38:49.2792594Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-08-14T21:38:49.2793695Z to register a fallthrough. 2025-08-14T21:38:49.2794727Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-08-14T21:38:49.2795860Z the dispatch key that the library was created with. 2025-08-14T21:38:49.2797101Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-08-14T21:38:49.2798670Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-08-14T21:38:49.2799639Z 2025-08-14T21:38:49.2800007Z Example:: 2025-08-14T21:38:49.2800426Z 2025-08-14T21:38:49.2800807Z >>> my_lib = Library("_", "IMPL") 2025-08-14T21:38:49.2801449Z >>> def fallback_kernel(op, *args, **kwargs): 2025-08-14T21:38:49.2802136Z >>> # Handle all autocast ops generically 2025-08-14T21:38:49.2802739Z >>> # ... 2025-08-14T21:38:49.2803290Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-08-14T21:38:49.2803925Z 2025-08-14T21:38:49.2805364Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-08-14T21:38:49.2806941Z 2025-08-14T21:38:49.2807368Z my_lib.fallback(fallback_kernel, "Autocast") 2025-08-14T21:38:49.2807957Z ^ 2025-08-14T21:38:49.2808336Z warnings.warn(msg) 2025-08-14T21:38:49.2808765Z 2025-08-14T21:38:49.2809286Z --- Parse Warning: 9 / 146 --- 2025-08-14T21:38:49.2811356Z /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. 2025-08-14T21:38:49.2813673Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:49.2814793Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-08-14T21:38:49.2815584Z 2025-08-14T21:38:49.2816096Z Also sometimes known as a "meta kernel", "abstract impl". 2025-08-14T21:38:49.2816777Z 2025-08-14T21:38:49.2817424Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-08-14T21:38:49.2818518Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-08-14T21:38:49.2819716Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-08-14T21:38:49.2820699Z what the properties of the output Tensors are. 2025-08-14T21:38:49.2821309Z 2025-08-14T21:38:49.2821928Z The FakeTensor implementation has the same signature as the operator. 2025-08-14T21:38:49.2822975Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-08-14T21:38:49.2824068Z implementation, assume that all Tensor inputs to the operator are 2025-08-14T21:38:49.2825090Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-08-14T21:38:49.2826063Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-08-14T21:38:49.2827103Z The FakeTensor implementation must consist of only PyTorch operations 2025-08-14T21:38:49.2828124Z (and may not directly access the storage or data of any input or 2025-08-14T21:38:49.2828875Z intermediate Tensors). 2025-08-14T21:38:49.2829356Z 2025-08-14T21:38:49.2829823Z This API may be used as a decorator (see examples). 2025-08-14T21:38:49.2830456Z 2025-08-14T21:38:49.2830889Z For a detailed guide on custom ops, please see 2025-08-14T21:38:49.2831780Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-08-14T21:38:49.2832635Z 2025-08-14T21:38:49.2832956Z Args: 2025-08-14T21:38:49.2833590Z op_name: Operator name (along with the overload) or OpOverload object. 2025-08-14T21:38:49.2834453Z func: Fake tensor implementation. 2025-08-14T21:38:49.2835235Z lib (Optional[Library]): Library to register the fake tensor to. 2025-08-14T21:38:49.2836172Z allow_override: Flag controlling if we want to override an 2025-08-14T21:38:49.2837060Z existing registered fake impl. This is by default off, 2025-08-14T21:38:49.2837925Z and will error you're trying to register a fake impl to 2025-08-14T21:38:49.2838795Z an operator that already has a fake impl. This also only 2025-08-14T21:38:49.2839654Z applies if the custom operator was not created via 2025-08-14T21:38:49.2840527Z torch.library.custom_op, as overriding and existing fake 2025-08-14T21:38:49.2841311Z impl is already allowed. 2025-08-14T21:38:49.2841882Z 2025-08-14T21:38:49.2842231Z Examples: 2025-08-14T21:38:49.2842636Z >>> import torch 2025-08-14T21:38:49.2843133Z >>> import numpy as np 2025-08-14T21:38:49.2843685Z >>> from torch import Tensor 2025-08-14T21:38:49.2844215Z >>> 2025-08-14T21:38:49.2844791Z >>> # Example 1: an operator without data-dependent output shape 2025-08-14T21:38:49.2845759Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-08-14T21:38:49.2846789Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-08-14T21:38:49.2847747Z >>> raise NotImplementedError("Implementation goes here") 2025-08-14T21:38:49.2848449Z >>> 2025-08-14T21:38:49.2848980Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-08-14T21:38:49.2849670Z >>> def _(x, weight, bias): 2025-08-14T21:38:49.2850229Z >>> assert x.dim() == 2 2025-08-14T21:38:49.2850817Z >>> assert weight.dim() == 2 2025-08-14T21:38:49.2851414Z >>> assert bias.dim() == 1 2025-08-14T21:38:49.2852012Z >>> assert x.shape[1] == weight.shape[1] 2025-08-14T21:38:49.2852686Z >>> assert weight.shape[0] == bias.shape[0] 2025-08-14T21:38:49.2853362Z >>> assert x.device == weight.device 2025-08-14T21:38:49.2853929Z >>> 2025-08-14T21:38:49.2854369Z >>> return (x @ weight.t()) + bias 2025-08-14T21:38:49.2854937Z >>> 2025-08-14T21:38:49.2855545Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-08-14T21:38:49.2856306Z >>> x = torch.randn(2, 3) 2025-08-14T21:38:49.2856888Z >>> w = torch.randn(3, 3) 2025-08-14T21:38:49.2857465Z >>> b = torch.randn(3) 2025-08-14T21:38:49.2858081Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-08-14T21:38:49.2858704Z >>> 2025-08-14T21:38:49.2859133Z >>> assert y.shape == (2, 3) 2025-08-14T21:38:49.2859666Z >>> 2025-08-14T21:38:49.2860217Z >>> # Example 2: an operator with data-dependent output shape 2025-08-14T21:38:49.2861170Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-08-14T21:38:49.2862027Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-08-14T21:38:49.2862679Z >>> x_np = x.numpy(force=True) 2025-08-14T21:38:49.2863327Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-08-14T21:38:49.2864127Z >>> return torch.tensor(res, device=x.device) 2025-08-14T21:38:49.2864745Z >>> 2025-08-14T21:38:49.2865303Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-08-14T21:38:49.2866018Z >>> def _(x): 2025-08-14T21:38:49.2866600Z >>> # Number of nonzero-elements is data-dependent. 2025-08-14T21:38:49.2867372Z >>> # Since we cannot peek at the data in an fake impl, 2025-08-14T21:38:49.2868167Z >>> # we use the ctx object to construct a new symint that 2025-08-14T21:38:49.2868890Z >>> # represents the data-dependent size. 2025-08-14T21:38:49.2869552Z >>> ctx = torch.library.get_ctx() 2025-08-14T21:38:49.2870185Z >>> nnz = ctx.new_dynamic_size() 2025-08-14T21:38:49.2870788Z >>> shape = [nnz, x.dim()] 2025-08-14T21:38:49.2871437Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-08-14T21:38:49.2872098Z >>> return result 2025-08-14T21:38:49.2872603Z >>> 2025-08-14T21:38:49.2873161Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-08-14T21:38:49.2873877Z >>> 2025-08-14T21:38:49.2874318Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-08-14T21:38:49.2875186Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-08-14T21:38:49.2876074Z >>> trace.print_readable() 2025-08-14T21:38:49.2876611Z >>> 2025-08-14T21:38:49.2877267Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-08-14T21:38:49.2878054Z 2025-08-14T21:38:49.2878403Z 2025-08-14T21:38:49.2879650Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-08-14T21:38:49.2881050Z 2025-08-14T21:38:49.2881398Z _._ = None 2025-08-14T21:38:49.2881782Z ^ 2025-08-14T21:38:49.2882147Z warnings.warn(msg) 2025-08-14T21:38:49.2882595Z 2025-08-14T21:38:49.2883159Z --- Parse Warning: 10 / 146 --- 2025-08-14T21:38:49.2885292Z /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. 2025-08-14T21:38:49.2887716Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.2888707Z Register a backward formula for this custom op. 2025-08-14T21:38:49.2889341Z 2025-08-14T21:38:49.2889896Z In order for an operator to work with autograd, you need to register 2025-08-14T21:38:49.2890685Z a backward formula: 2025-08-14T21:38:49.2891382Z 1. You must tell us how to compute gradients during the backward pass 2025-08-14T21:38:49.2892198Z by providing us a "backward" function. 2025-08-14T21:38:49.2893311Z 2. If you need any values from the forward to compute gradients, you can 2025-08-14T21:38:49.2894273Z use `setup_context` to save values for backward. 2025-08-14T21:38:49.2894895Z 2025-08-14T21:38:49.2895499Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-08-14T21:38:49.2896515Z - ``grads`` is one or more gradients. The number of gradients matches 2025-08-14T21:38:49.2897323Z the number of outputs of the operator. 2025-08-14T21:38:49.2898139Z The ``ctx`` object is `the same ctx object `_ used by 2025-08-14T21:38:49.2899226Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-08-14T21:38:49.2900195Z same as :meth:`torch.autograd.Function.backward`. 2025-08-14T21:38:49.2900829Z 2025-08-14T21:38:49.2901425Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-08-14T21:38:49.2902486Z Please save quantities needed for backward onto the ``ctx`` object via 2025-08-14T21:38:49.2903645Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-08-14T21:38:49.2904672Z or assigning them as attributes of ``ctx``. If your custom op has 2025-08-14T21:38:49.2905661Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-08-14T21:38:49.2906674Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-08-14T21:38:49.2907381Z 2025-08-14T21:38:49.2907966Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-08-14T21:38:49.2909020Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-08-14T21:38:49.2910094Z not depend on or mutate global state. If you need a non-traceable backward, 2025-08-14T21:38:49.2911155Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-08-14T21:38:49.2911950Z 2025-08-14T21:38:49.2912533Z If you need different autograd behavior on different devices, then we 2025-08-14T21:38:49.2913600Z recommend creating two different custom operators, one for each device 2025-08-14T21:38:49.2914682Z that needs different behavior, and switching between them at runtime. 2025-08-14T21:38:49.2915464Z 2025-08-14T21:38:49.2915825Z Examples: 2025-08-14T21:38:49.2916230Z >>> import torch 2025-08-14T21:38:49.2916727Z >>> import numpy as np 2025-08-14T21:38:49.2917284Z >>> from torch import Tensor 2025-08-14T21:38:49.2917814Z >>> 2025-08-14T21:38:49.2918404Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-08-14T21:38:49.2919202Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-08-14T21:38:49.2919803Z >>> x_np = x.cpu().numpy() 2025-08-14T21:38:49.2920370Z >>> y_np = np.sin(x_np) 2025-08-14T21:38:49.2921029Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-08-14T21:38:49.2921663Z >>> 2025-08-14T21:38:49.2922162Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-08-14T21:38:49.2922810Z >>> x, = inputs 2025-08-14T21:38:49.2923313Z >>> ctx.save_for_backward(x) 2025-08-14T21:38:49.2923847Z >>> 2025-08-14T21:38:49.2924268Z >>> def backward(ctx, grad): 2025-08-14T21:38:49.2924833Z >>> x, = ctx.saved_tensors 2025-08-14T21:38:49.2925388Z >>> return grad * x.cos() 2025-08-14T21:38:49.2925914Z >>> 2025-08-14T21:38:49.2926363Z >>> torch.library.register_autograd( 2025-08-14T21:38:49.2927103Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-08-14T21:38:49.2927797Z ... ) 2025-08-14T21:38:49.2928184Z >>> 2025-08-14T21:38:49.2928627Z >>> x = torch.randn(3, requires_grad=True) 2025-08-14T21:38:49.2929235Z >>> y = numpy_sin(x) 2025-08-14T21:38:49.2929948Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-08-14T21:38:49.2930731Z >>> assert torch.allclose(grad_x, x.cos()) 2025-08-14T21:38:49.2931306Z >>> 2025-08-14T21:38:49.2931741Z >>> # Example with a keyword-only arg 2025-08-14T21:38:49.2932527Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-08-14T21:38:49.2933384Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-08-14T21:38:49.2934054Z >>> x_np = x.cpu().numpy() 2025-08-14T21:38:49.2934602Z >>> y_np = x_np * val 2025-08-14T21:38:49.2935231Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-08-14T21:38:49.2935877Z >>> 2025-08-14T21:38:49.2936508Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-08-14T21:38:49.2937376Z >>> ctx.val = keyword_only_inputs["val"] 2025-08-14T21:38:49.2937949Z >>> 2025-08-14T21:38:49.2938367Z >>> def backward(ctx, grad): 2025-08-14T21:38:49.2938946Z >>> return grad * ctx.val 2025-08-14T21:38:49.2939469Z >>> 2025-08-14T21:38:49.2939916Z >>> torch.library.register_autograd( 2025-08-14T21:38:49.2940671Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-08-14T21:38:49.2941396Z ... ) 2025-08-14T21:38:49.2941793Z >>> 2025-08-14T21:38:49.2942252Z >>> x = torch.randn(3, requires_grad=True) 2025-08-14T21:38:49.2942875Z >>> y = numpy_mul(x, val=3.14) 2025-08-14T21:38:49.2943632Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-08-14T21:38:49.2944496Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-08-14T21:38:49.2945195Z 2025-08-14T21:38:49.2945522Z 2025-08-14T21:38:49.2946204Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.2947066Z 2025-08-14T21:38:49.2947430Z warnings.warn(msg) 2025-08-14T21:38:49.2947882Z 2025-08-14T21:38:49.2948441Z --- Parse Warning: 11 / 146 --- 2025-08-14T21:38:49.2950490Z /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. 2025-08-14T21:38:49.2952797Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:49.2953888Z Returns the computed kernel for a given operator and dispatch key. 2025-08-14T21:38:49.2954655Z 2025-08-14T21:38:49.2955262Z This function retrieves the kernel that would be executed for a given 2025-08-14T21:38:49.2956350Z operator and dispatch key combination. The returned SafeKernelFunction 2025-08-14T21:38:49.2957383Z can be used to call the kernel in a boxed fashion. The intended use 2025-08-14T21:38:49.2958376Z case for this function is to retrieve the original kernel for a given 2025-08-14T21:38:49.2959409Z dispatch key and then register another kernel to the same dispatch key 2025-08-14T21:38:49.2960361Z that calls into the original kernel for certain cases. 2025-08-14T21:38:49.2961027Z 2025-08-14T21:38:49.2961355Z Args: 2025-08-14T21:38:49.2961954Z op: Operator name (along with the overload) or OpOverload object 2025-08-14T21:38:49.2962970Z Can be a string (e.g., "aten::add.Tensor"), an OpOverload, or a CustomOpDef. 2025-08-14T21:38:49.2964082Z dispatch_key (str | torch.DispatchKey): The dispatch key to get the kernel for. 2025-08-14T21:38:49.2965157Z Can be a string (e.g., "CPU", "CUDA") or a DispatchKey enum value. 2025-08-14T21:38:49.2965897Z 2025-08-14T21:38:49.2966244Z Returns: 2025-08-14T21:38:49.2966924Z torch._C._SafeKernelFunction: A safe kernel function that can be used to 2025-08-14T21:38:49.2967783Z call the kernel. 2025-08-14T21:38:49.2968362Z 2025-08-14T21:38:49.2968735Z Raises: 2025-08-14T21:38:49.2969230Z RuntimeError: If the operator does not exist. 2025-08-14T21:38:49.2969848Z 2025-08-14T21:38:49.2970180Z Example: 2025-08-14T21:38:49.2970635Z >>> # Get the CPU kernel for torch.add 2025-08-14T21:38:49.2971425Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", "CPU") 2025-08-14T21:38:49.2972144Z >>> 2025-08-14T21:38:49.2972596Z >>> # You can also use DispatchKey enum 2025-08-14T21:38:49.2973512Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", torch.DispatchKey.CPU) 2025-08-14T21:38:49.2974373Z >>> 2025-08-14T21:38:49.2974800Z >>> # Or use an OpOverload directly 2025-08-14T21:38:49.2975633Z >>> kernel = torch.library.get_kernel(torch.ops.aten.add.Tensor, "CPU") 2025-08-14T21:38:49.2976433Z >>> 2025-08-14T21:38:49.2977056Z >>> # Example: Using get_kernel in a custom op with conditional dispatch 2025-08-14T21:38:49.2977915Z >>> # Get the original kernel for torch.sin 2025-08-14T21:38:49.2978760Z >>> original_sin_kernel = torch.library.get_kernel("aten::sin", "CPU") 2025-08-14T21:38:49.2979543Z >>> 2025-08-14T21:38:49.2980223Z >>> # If input has negative values, use original sin, otherwise return zeros 2025-08-14T21:38:49.2981139Z >>> def conditional_sin_impl(dispatch_keys, x): 2025-08-14T21:38:49.2981790Z >>> if (x < 0).any(): 2025-08-14T21:38:49.2982472Z >>> return original_sin_kernel.call_boxed(dispatch_keys, x) 2025-08-14T21:38:49.2983201Z >>> else: 2025-08-14T21:38:49.2983766Z >>> return torch.zeros_like(x) 2025-08-14T21:38:49.2984325Z >>> 2025-08-14T21:38:49.2984810Z >>> lib = torch.library.Library("aten", "IMPL") 2025-08-14T21:38:49.2985765Z >>> # with_keyset=True so the first argument to the impl is the current DispatchKeySet 2025-08-14T21:38:49.2986836Z >>> which needs to be the first argument to ``kernel.call_boxed`` 2025-08-14T21:38:49.2987749Z >>> lib.impl("sin", conditional_sin_impl, "CPU", with_keyset=True) 2025-08-14T21:38:49.2988473Z >>> 2025-08-14T21:38:49.2988907Z >>> # Test the conditional behavior 2025-08-14T21:38:49.2989532Z >>> x_positive = torch.tensor([1.0, 2.0]) 2025-08-14T21:38:49.2990164Z >>> x_mixed = torch.tensor([-1.0, 2.0]) 2025-08-14T21:38:49.2990773Z >>> torch.sin(x_positive) 2025-08-14T21:38:49.2991310Z tensor([0., 0.]) 2025-08-14T21:38:49.2991804Z >>> torch.sin(x_mixed) 2025-08-14T21:38:49.2992333Z tensor([-0.8415, 0.9093]) 2025-08-14T21:38:49.2993003Z 2025-08-14T21:38:49.2994118Z Original Error: SyntaxError('invalid syntax', ('', 23, 7, 'which needs to be the first argument to ``kernel.call_boxed``\n', 23, 12)) 2025-08-14T21:38:49.2995410Z 2025-08-14T21:38:49.2995941Z which needs to be the first argument to ``kernel.call_boxed`` 2025-08-14T21:38:49.2996631Z ^ 2025-08-14T21:38:49.2997017Z warnings.warn(msg) 2025-08-14T21:38:49.2997470Z 2025-08-14T21:38:49.2998010Z --- Parse Warning: 12 / 146 --- 2025-08-14T21:38:49.3000062Z /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. 2025-08-14T21:38:49.3002388Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3003523Z Given an operator and some sample arguments, tests if the operator is 2025-08-14T21:38:49.3004322Z registered correctly. 2025-08-14T21:38:49.3004808Z 2025-08-14T21:38:49.3005387Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-08-14T21:38:49.3006582Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-08-14T21:38:49.3007749Z and these APIs require that the functions you pass them satisfy certain 2025-08-14T21:38:49.3008829Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-08-14T21:38:49.3009760Z ``opcheck`` tests these metadata and properties. 2025-08-14T21:38:49.3010381Z 2025-08-14T21:38:49.3010783Z Concretely, we test the following: 2025-08-14T21:38:49.3011341Z 2025-08-14T21:38:49.3011848Z - test_schema: If the schema matches the implementation of 2025-08-14T21:38:49.3012807Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-08-14T21:38:49.3013187Z then we check the implementation mutates the Tensor. If the schema 2025-08-14T21:38:49.3013550Z specifies that we return a new Tensor, then we check that the 2025-08-14T21:38:49.3013967Z implementation returns a new Tensor (instead of an existing one or 2025-08-14T21:38:49.3014168Z a view of an existing one). 2025-08-14T21:38:49.3014532Z - test_autograd_registration: If the operator supports training 2025-08-14T21:38:49.3014893Z (autograd): we check that its autograd formula is registered via 2025-08-14T21:38:49.3015342Z torch.library.register_autograd or a manual registration to one 2025-08-14T21:38:49.3015712Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-08-14T21:38:49.3015962Z registrations may lead to undefined behavior. 2025-08-14T21:38:49.3016297Z - test_faketensor: If the operator has a FakeTensor kernel 2025-08-14T21:38:49.3016609Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-08-14T21:38:49.3017027Z but not sufficient) for the operator to work with PyTorch compilation 2025-08-14T21:38:49.3017414Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-08-14T21:38:49.3017778Z (also sometimes known as a meta kernel) was registered for the 2025-08-14T21:38:49.3018144Z operator and that it is correct. This test takes the result of 2025-08-14T21:38:49.3018499Z running the operator on real tensors and the result of running 2025-08-14T21:38:49.3018853Z the operator on FakeTensors and checks that they have the same 2025-08-14T21:38:49.3019139Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-08-14T21:38:49.3019507Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-08-14T21:38:49.3019841Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-08-14T21:38:49.3020225Z This checks that the outputs (and gradients, if applicable) are the 2025-08-14T21:38:49.3020483Z same under eager-mode PyTorch and torch.compile. 2025-08-14T21:38:49.3020847Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-08-14T21:38:49.3021143Z other things it tests are that the operator supports 2025-08-14T21:38:49.3021549Z functionalization and that the backward pass (if it exists) also 2025-08-14T21:38:49.3021805Z supports FakeTensor and functionalization. 2025-08-14T21:38:49.3021944Z 2025-08-14T21:38:49.3022308Z For best results, please call ``opcheck`` multiple times with a 2025-08-14T21:38:49.3022637Z representative set of inputs. If your operator supports 2025-08-14T21:38:49.3023067Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-08-14T21:38:49.3023557Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-08-14T21:38:49.3023836Z use ``opcheck`` with inputs on all supported devices. 2025-08-14T21:38:49.3023974Z 2025-08-14T21:38:49.3024127Z Args: 2025-08-14T21:38:49.3024447Z op: The operator. Must either be a function decorated with 2025-08-14T21:38:49.3024985Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-08-14T21:38:49.3025411Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-08-14T21:38:49.3025601Z args: The args to the operator 2025-08-14T21:38:49.3025814Z kwargs: The kwargs to the operator 2025-08-14T21:38:49.3026145Z test_utils: Tests that we should run. Default: all of them. 2025-08-14T21:38:49.3026380Z Example: ("test_schema", "test_faketensor") 2025-08-14T21:38:49.3026747Z raise_exception: If we should raise an exception on the first 2025-08-14T21:38:49.3027049Z error. If False, we will return a dict with information 2025-08-14T21:38:49.3027249Z on if each test passed or not. 2025-08-14T21:38:49.3027704Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-08-14T21:38:49.3027946Z If specified ``atol`` must also be specified. 2025-08-14T21:38:49.3028305Z If omitted, default values based on the ``dtype`` are selected 2025-08-14T21:38:49.3028602Z (see the table in :func:`torch.testing.assert_close`). 2025-08-14T21:38:49.3029040Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-08-14T21:38:49.3029334Z If specified ``rtol`` must also be specified. 2025-08-14T21:38:49.3029677Z If omitted, default values based on the ``dtype`` are selected 2025-08-14T21:38:49.3029974Z (see the table in :func:`torch.testing.assert_close`). 2025-08-14T21:38:49.3030129Z 2025-08-14T21:38:49.3030287Z .. warning:: 2025-08-14T21:38:49.3030444Z 2025-08-14T21:38:49.3030837Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-08-14T21:38:49.3031221Z opcheck tests if your usage of torch.library APIs is correct while 2025-08-14T21:38:49.3031621Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-08-14T21:38:49.3032027Z mathematically correct. Use both to test custom ops that support 2025-08-14T21:38:49.3032212Z gradient computation. 2025-08-14T21:38:49.3032369Z 2025-08-14T21:38:49.3032522Z Example: 2025-08-14T21:38:49.3032658Z 2025-08-14T21:38:49.3032918Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-14T21:38:49.3033282Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-08-14T21:38:49.3033544Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-08-14T21:38:49.3033739Z >>> x_np = x.numpy(force=True) 2025-08-14T21:38:49.3033908Z >>> z_np = x_np * y 2025-08-14T21:38:49.3034148Z >>> return torch.from_numpy(z_np).to(x.device) 2025-08-14T21:38:49.3034293Z >>> 2025-08-14T21:38:49.3034481Z >>> @numpy_mul.register_fake 2025-08-14T21:38:49.3034656Z >>> def _(x, y): 2025-08-14T21:38:49.3034853Z >>> return torch.empty_like(x) 2025-08-14T21:38:49.3035004Z >>> 2025-08-14T21:38:49.3035239Z >>> def setup_context(ctx, inputs, output): 2025-08-14T21:38:49.3035404Z >>> y, = inputs 2025-08-14T21:38:49.3035582Z >>> ctx.y = y 2025-08-14T21:38:49.3035731Z >>> 2025-08-14T21:38:49.3035925Z >>> def backward(ctx, grad): 2025-08-14T21:38:49.3036130Z >>> return grad * ctx.y, None 2025-08-14T21:38:49.3036274Z >>> 2025-08-14T21:38:49.3036681Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-08-14T21:38:49.3036843Z >>> 2025-08-14T21:38:49.3037024Z >>> sample_inputs = [ 2025-08-14T21:38:49.3037213Z >>> (torch.randn(3), 3.14), 2025-08-14T21:38:49.3037453Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-08-14T21:38:49.3037710Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-08-14T21:38:49.3038140Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-08-14T21:38:49.3038324Z >>> ] 2025-08-14T21:38:49.3038473Z >>> 2025-08-14T21:38:49.3038684Z >>> for args in sample_inputs: 2025-08-14T21:38:49.3038920Z >>> torch.library.opcheck(numpy_mul, args) 2025-08-14T21:38:49.3039071Z 2025-08-14T21:38:49.3039233Z 2025-08-14T21:38:49.3039706Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3039854Z 2025-08-14T21:38:49.3040036Z warnings.warn(msg) 2025-08-14T21:38:49.3040172Z 2025-08-14T21:38:49.3040524Z --- Parse Warning: 13 / 146 --- 2025-08-14T21:38:49.3042177Z /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. 2025-08-14T21:38:49.3042672Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3043316Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-08-14T21:38:49.3043461Z 2025-08-14T21:38:49.3043780Z Loads an object saved with :func:`torch.save` from a file. 2025-08-14T21:38:49.3043981Z 2025-08-14T21:38:49.3044410Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-08-14T21:38:49.3044846Z which underlie tensors, specially. They are first deserialized on the 2025-08-14T21:38:49.3045248Z CPU and are then moved to the device they were saved from. If this fails 2025-08-14T21:38:49.3045676Z (e.g. because the run time system doesn't have certain devices), an exception 2025-08-14T21:38:49.3046125Z is raised. However, storages can be dynamically remapped to an alternative 2025-08-14T21:38:49.3046425Z set of devices using the :attr:`map_location` argument. 2025-08-14T21:38:49.3046571Z 2025-08-14T21:38:49.3047040Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-08-14T21:38:49.3047462Z storage with two arguments: storage and location. The storage argument 2025-08-14T21:38:49.3047907Z will be the initial deserialization of the storage, residing on the CPU. 2025-08-14T21:38:49.3048304Z Each serialized storage has a location tag associated with it which 2025-08-14T21:38:49.3048699Z identifies the device it was saved from, and this tag is the second 2025-08-14T21:38:49.3049189Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-08-14T21:38:49.3049598Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-08-14T21:38:49.3049971Z :attr:`map_location` should return either ``None`` or a storage. If 2025-08-14T21:38:49.3050457Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-08-14T21:38:49.3050912Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-08-14T21:38:49.3051383Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-08-14T21:38:49.3051530Z 2025-08-14T21:38:49.3051959Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-08-14T21:38:49.3052404Z a device tag, it indicates the location where all tensors should be loaded. 2025-08-14T21:38:49.3052540Z 2025-08-14T21:38:49.3053019Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-08-14T21:38:49.3053413Z appearing in the file (keys), to ones that specify where to put the 2025-08-14T21:38:49.3053586Z storages (values). 2025-08-14T21:38:49.3053742Z 2025-08-14T21:38:49.3054156Z User extensions can register their own location tags and tagging and 2025-08-14T21:38:49.3054642Z deserialization methods using :func:`torch.serialization.register_package`. 2025-08-14T21:38:49.3054903Z 2025-08-14T21:38:49.3055356Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-08-14T21:38:49.3055503Z 2025-08-14T21:38:49.3055660Z Args: 2025-08-14T21:38:49.3056263Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-08-14T21:38:49.3056573Z or a string or os.PathLike object containing a file name 2025-08-14T21:38:49.3057193Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-08-14T21:38:49.3057351Z locations 2025-08-14T21:38:49.3057789Z pickle_module: module used for unpickling metadata and objects (has to 2025-08-14T21:38:49.3058098Z match the :attr:`pickle_module` used to serialize file) 2025-08-14T21:38:49.3058489Z weights_only: Indicates whether unpickler should be restricted to 2025-08-14T21:38:49.3058796Z loading only tensors, primitive types, dictionaries 2025-08-14T21:38:49.3059199Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-08-14T21:38:49.3059429Z See :ref:`weights-only` for more details. 2025-08-14T21:38:49.3060115Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-08-14T21:38:49.3060757Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-08-14T21:38:49.3061415Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-08-14T21:38:49.3062033Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-08-14T21:38:49.3062667Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-08-14T21:38:49.3062958Z will be lazily loaded when their data is accessed. 2025-08-14T21:38:49.3063485Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-08-14T21:38:49.3063908Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-08-14T21:38:49.3064084Z :attr:`errors=...`. 2025-08-14T21:38:49.3064228Z 2025-08-14T21:38:49.3064404Z .. warning:: 2025-08-14T21:38:49.3064791Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-08-14T21:38:49.3065152Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-08-14T21:38:49.3065683Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-08-14T21:38:49.3066128Z during unpickling. Never load data that could have come from an untrusted 2025-08-14T21:38:49.3066698Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-08-14T21:38:49.3066847Z 2025-08-14T21:38:49.3067000Z .. note:: 2025-08-14T21:38:49.3067497Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-08-14T21:38:49.3067976Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-08-14T21:38:49.3068484Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-08-14T21:38:49.3068643Z 2025-08-14T21:38:49.3068798Z .. note:: 2025-08-14T21:38:49.3069276Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-08-14T21:38:49.3069672Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-08-14T21:38:49.3070052Z when loading files saved by Python 2 in Python 3. If this default 2025-08-14T21:38:49.3070610Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-08-14T21:38:49.3071089Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-08-14T21:38:49.3071530Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-08-14T21:38:49.3071977Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-08-14T21:38:49.3072122Z 2025-08-14T21:38:49.3072285Z Example: 2025-08-14T21:38:49.3072513Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-08-14T21:38:49.3072764Z >>> torch.load("tensors.pt", weights_only=True) 2025-08-14T21:38:49.3072979Z # Load all tensors onto the CPU 2025-08-14T21:38:49.3073145Z >>> torch.load( 2025-08-14T21:38:49.3073317Z ... "tensors.pt", 2025-08-14T21:38:49.3073554Z ... map_location=torch.device("cpu"), 2025-08-14T21:38:49.3073737Z ... weights_only=True, 2025-08-14T21:38:49.3073884Z ... ) 2025-08-14T21:38:49.3074162Z # Load all tensors onto the CPU, using a function 2025-08-14T21:38:49.3074335Z >>> torch.load( 2025-08-14T21:38:49.3074517Z ... "tensors.pt", 2025-08-14T21:38:49.3074760Z ... map_location=lambda storage, loc: storage, 2025-08-14T21:38:49.3074988Z ... weights_only=True, 2025-08-14T21:38:49.3075157Z ... ) 2025-08-14T21:38:49.3075352Z # Load all tensors onto GPU 1 2025-08-14T21:38:49.3075518Z >>> torch.load( 2025-08-14T21:38:49.3075706Z ... "tensors.pt", 2025-08-14T21:38:49.3075995Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-08-14T21:38:49.3076176Z ... weights_only=True, 2025-08-14T21:38:49.3076391Z ... ) # type: ignore[attr-defined] 2025-08-14T21:38:49.3076590Z # Map tensors from GPU 1 to GPU 0 2025-08-14T21:38:49.3076771Z >>> torch.load( 2025-08-14T21:38:49.3076948Z ... "tensors.pt", 2025-08-14T21:38:49.3077157Z ... map_location={"cuda:1": "cuda:0"}, 2025-08-14T21:38:49.3077347Z ... weights_only=True, 2025-08-14T21:38:49.3077490Z ... ) 2025-08-14T21:38:49.3077696Z # Load tensor from io.BytesIO object 2025-08-14T21:38:49.3078176Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-08-14T21:38:49.3078382Z >>> with open("tensor.pt", "rb") as f: 2025-08-14T21:38:49.3078576Z ... buffer = io.BytesIO(f.read()) 2025-08-14T21:38:49.3078810Z >>> torch.load(buffer, weights_only=False) 2025-08-14T21:38:49.3079083Z # Load a module with 'ascii' encoding for unpickling 2025-08-14T21:38:49.3079553Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-08-14T21:38:49.3079902Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-08-14T21:38:49.3080038Z 2025-08-14T21:38:49.3080534Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3080679Z 2025-08-14T21:38:49.3080852Z warnings.warn(msg) 2025-08-14T21:38:49.3081014Z 2025-08-14T21:38:49.3081368Z --- Parse Warning: 14 / 146 --- 2025-08-14T21:38:49.3083333Z /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=1851. 2025-08-14T21:38:49.3083823Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3084226Z Computes the minimum storage size to hold the given tensor geometry. 2025-08-14T21:38:49.3084384Z 2025-08-14T21:38:49.3084536Z Example 2025-08-14T21:38:49.3084682Z ======= 2025-08-14T21:38:49.3084836Z 2025-08-14T21:38:49.3085331Z This is the size of a newly allocated tensor's storage, in units of elements 2025-08-14T21:38:49.3085505Z 2025-08-14T21:38:49.3085698Z >>> t = torch.empty((10, 20)) 2025-08-14T21:38:49.3086152Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-08-14T21:38:49.3086319Z 200 2025-08-14T21:38:49.3086458Z 2025-08-14T21:38:49.3086649Z >>> # xdoctest: +SKIP(failing) 2025-08-14T21:38:49.3086892Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-08-14T21:38:49.3087117Z >>> size = compute_required_storage_length( 2025-08-14T21:38:49.3087353Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-08-14T21:38:49.3087514Z ... ) 2025-08-14T21:38:49.3087695Z >>> size == t.storage().size() 2025-08-14T21:38:49.3087845Z True 2025-08-14T21:38:49.3088004Z 2025-08-14T21:38:49.3088366Z A valid tensor may have a larger storage size, but never smaller 2025-08-14T21:38:49.3088512Z 2025-08-14T21:38:49.3088725Z >>> slice = torch.empty(100)[20:40] 2025-08-14T21:38:49.3088909Z >>> slice.storage().size() 2025-08-14T21:38:49.3089070Z 100 2025-08-14T21:38:49.3089213Z 2025-08-14T21:38:49.3089416Z >>> compute_required_storage_length( 2025-08-14T21:38:49.3089721Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-08-14T21:38:49.3089917Z ... ) 2025-08-14T21:38:49.3090060Z 40 2025-08-14T21:38:49.3090216Z 2025-08-14T21:38:49.3090361Z 2025-08-14T21:38:49.3090835Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3090987Z 2025-08-14T21:38:49.3091155Z warnings.warn(msg) 2025-08-14T21:38:49.3091300Z 2025-08-14T21:38:49.3091639Z --- Parse Warning: 15 / 146 --- 2025-08-14T21:38:49.3093524Z /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. 2025-08-14T21:38:49.3094003Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:49.3094477Z Check if the current accelerator is available at runtime: it was build, all the 2025-08-14T21:38:49.3094876Z required drivers are available and at least one device is visible. 2025-08-14T21:38:49.3095153Z See :ref:`accelerator` for details. 2025-08-14T21:38:49.3095301Z 2025-08-14T21:38:49.3095455Z Returns: 2025-08-14T21:38:49.3095989Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-08-14T21:38:49.3096136Z 2025-08-14T21:38:49.3096601Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-08-14T21:38:49.3097094Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-08-14T21:38:49.3097564Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-08-14T21:38:49.3097826Z :ref:`multiprocessing-poison-fork-note`. 2025-08-14T21:38:49.3097974Z 2025-08-14T21:38:49.3098132Z Example:: 2025-08-14T21:38:49.3098282Z 2025-08-14T21:38:49.3098777Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-14T21:38:49.3098938Z 2025-08-14T21:38:49.3100010Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-08-14T21:38:49.3100154Z 2025-08-14T21:38:49.3100661Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-14T21:38:49.3100827Z ^ 2025-08-14T21:38:49.3100998Z warnings.warn(msg) 2025-08-14T21:38:49.3101156Z 2025-08-14T21:38:49.3101488Z --- Parse Warning: 16 / 146 --- 2025-08-14T21:38:49.3103371Z /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. 2025-08-14T21:38:49.3103977Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:49.3104360Z Wait for all kernels in all streams on the given device to complete. 2025-08-14T21:38:49.3104510Z 2025-08-14T21:38:49.3104660Z Args: 2025-08-14T21:38:49.3105248Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-08-14T21:38:49.3105679Z the current :ref:`accelerator` device type. If not given, 2025-08-14T21:38:49.3106024Z use :func:`torch.accelerator.current_device_index` by default. 2025-08-14T21:38:49.3106176Z 2025-08-14T21:38:49.3106766Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-08-14T21:38:49.3106913Z 2025-08-14T21:38:49.3107084Z Example:: 2025-08-14T21:38:49.3107231Z 2025-08-14T21:38:49.3107473Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-14T21:38:49.3107978Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-14T21:38:49.3108278Z >>> start_event = torch.Event(enable_timing=True) 2025-08-14T21:38:49.3108525Z >>> end_event = torch.Event(enable_timing=True) 2025-08-14T21:38:49.3108721Z >>> start_event.record() 2025-08-14T21:38:49.3109168Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-08-14T21:38:49.3109364Z >>> sum = torch.sum(tensor) 2025-08-14T21:38:49.3109543Z >>> end_event.record() 2025-08-14T21:38:49.3109761Z >>> torch.accelerator.synchronize() 2025-08-14T21:38:49.3110081Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-08-14T21:38:49.3110229Z 2025-08-14T21:38:49.3111315Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-08-14T21:38:49.3111472Z 2025-08-14T21:38:49.3111961Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-14T21:38:49.3112138Z ^ 2025-08-14T21:38:49.3112309Z warnings.warn(msg) 2025-08-14T21:38:49.3112450Z 2025-08-14T21:38:49.3112788Z --- Parse Warning: 17 / 146 --- 2025-08-14T21:38:49.3114415Z /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. 2025-08-14T21:38:49.3114869Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:49.3115090Z Retrieves the CUDA runtime API module. 2025-08-14T21:38:49.3115234Z 2025-08-14T21:38:49.3115372Z 2025-08-14T21:38:49.3115856Z This function initializes the CUDA runtime environment if it is not already 2025-08-14T21:38:49.3116281Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-08-14T21:38:49.3116710Z runtime API module provides access to various CUDA runtime functions. 2025-08-14T21:38:49.3116848Z 2025-08-14T21:38:49.3116991Z Args: 2025-08-14T21:38:49.3117157Z ``None`` 2025-08-14T21:38:49.3117296Z 2025-08-14T21:38:49.3117444Z Returns: 2025-08-14T21:38:49.3117710Z module: The CUDA runtime API module (_cudart). 2025-08-14T21:38:49.3117854Z 2025-08-14T21:38:49.3117998Z Raises: 2025-08-14T21:38:49.3118438Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-08-14T21:38:49.3119197Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-08-14T21:38:49.3119382Z 2025-08-14T21:38:49.3119618Z Example of CUDA operations with profiling: 2025-08-14T21:38:49.3119786Z >>> import torch 2025-08-14T21:38:49.3120039Z >>> from torch.cuda import cudart, check_error 2025-08-14T21:38:49.3120199Z >>> import os 2025-08-14T21:38:49.3120337Z >>> 2025-08-14T21:38:49.3120559Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-08-14T21:38:49.3120703Z >>> 2025-08-14T21:38:49.3120941Z >>> def perform_cuda_operations_with_streams(): 2025-08-14T21:38:49.3121153Z >>> stream = torch.cuda.Stream() 2025-08-14T21:38:49.3121363Z >>> with torch.cuda.stream(stream): 2025-08-14T21:38:49.3121588Z >>> x = torch.randn(100, 100, device='cuda') 2025-08-14T21:38:49.3121804Z >>> y = torch.randn(100, 100, device='cuda') 2025-08-14T21:38:49.3121978Z >>> z = torch.mul(x, y) 2025-08-14T21:38:49.3122154Z >>> return z 2025-08-14T21:38:49.3122300Z >>> 2025-08-14T21:38:49.3122493Z >>> torch.cuda.synchronize() 2025-08-14T21:38:49.3122734Z >>> print("====== Start nsys profiling ======") 2025-08-14T21:38:49.3122971Z >>> check_error(cudart().cudaProfilerStart()) 2025-08-14T21:38:49.3123251Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-08-14T21:38:49.3123536Z >>> result = perform_cuda_operations_with_streams() 2025-08-14T21:38:49.3123758Z >>> print("CUDA operations completed.") 2025-08-14T21:38:49.3124059Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-08-14T21:38:49.3124280Z >>> print("====== End nsys profiling ======") 2025-08-14T21:38:49.3124421Z 2025-08-14T21:38:49.3124795Z To run this example and save the profiling information, execute: 2025-08-14T21:38:49.3125472Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-08-14T21:38:49.3125613Z 2025-08-14T21:38:49.3126079Z This command profiles the CUDA operations in the provided script and saves 2025-08-14T21:38:49.3126438Z the profiling information to a file named `trace_name.prof`. 2025-08-14T21:38:49.3126881Z The `--profile-from-start off` option ensures that profiling starts only 2025-08-14T21:38:49.3127154Z after the `cudaProfilerStart` call in the script. 2025-08-14T21:38:49.3127569Z The `--csv` and `--print-summary` options format the profiling output as a 2025-08-14T21:38:49.3127816Z CSV file and print a summary, respectively. 2025-08-14T21:38:49.3128269Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-08-14T21:38:49.3128551Z overwrite of the output file if it already exists. 2025-08-14T21:38:49.3128706Z 2025-08-14T21:38:49.3129957Z 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)) 2025-08-14T21:38:49.3130120Z 2025-08-14T21:38:49.3130777Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-08-14T21:38:49.3130920Z ^ 2025-08-14T21:38:49.3131107Z warnings.warn(msg) 2025-08-14T21:38:49.3131253Z 2025-08-14T21:38:49.3131587Z --- Parse Warning: 18 / 146 --- 2025-08-14T21:38:49.3133291Z /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. 2025-08-14T21:38:49.3133785Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3133944Z 2025-08-14T21:38:49.3134367Z Append the given callback function to this ``Future``, which will be run 2025-08-14T21:38:49.3134808Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-08-14T21:38:49.3135252Z the same ``Future``, but the order in which they will be executed cannot 2025-08-14T21:38:49.3135584Z be guaranteed (to enforce a certain order consider chaining: 2025-08-14T21:38:49.3135948Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-08-14T21:38:49.3136343Z is the reference to this ``Future``. The callback function can use the 2025-08-14T21:38:49.3136722Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-08-14T21:38:49.3137158Z already completed, the given callback will be run immediately inline. 2025-08-14T21:38:49.3137302Z 2025-08-14T21:38:49.3137650Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-08-14T21:38:49.3138063Z callback might be invoked while the async kernels that are populating 2025-08-14T21:38:49.3138488Z those tensors haven't yet finished executing on the device. However, the 2025-08-14T21:38:49.3138873Z callback will be invoked with some dedicated streams set as current 2025-08-14T21:38:49.3139257Z (fetched from a global pool) which will be synchronized with those 2025-08-14T21:38:49.3139678Z kernels. Hence any operation performed by the callback on these tensors 2025-08-14T21:38:49.3140119Z will be scheduled on the device after the kernels complete. In other 2025-08-14T21:38:49.3140483Z words, as long as the callback doesn't switch streams, it can safely 2025-08-14T21:38:49.3140910Z manipulate the result without any additional synchronization. This is 2025-08-14T21:38:49.3141209Z similar to the non-blocking behavior of :meth:`wait`. 2025-08-14T21:38:49.3141348Z 2025-08-14T21:38:49.3141742Z Similarly, if the callback returns a value that contains tensors that 2025-08-14T21:38:49.3142107Z reside on a GPU, it can do so even if the kernels that are producing 2025-08-14T21:38:49.3142503Z these tensors are still running on the device, as long as the callback 2025-08-14T21:38:49.3142877Z didn't change streams during its execution. If one wants to change 2025-08-14T21:38:49.3143285Z streams, one must be careful to re-synchronize them with the original 2025-08-14T21:38:49.3143760Z streams, that is, those that were current when the callback was invoked. 2025-08-14T21:38:49.3143918Z 2025-08-14T21:38:49.3144061Z Args: 2025-08-14T21:38:49.3144424Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-08-14T21:38:49.3144620Z the only argument. 2025-08-14T21:38:49.3144762Z 2025-08-14T21:38:49.3144904Z Returns: 2025-08-14T21:38:49.3145212Z A new ``Future`` object that holds the return value of the 2025-08-14T21:38:49.3145527Z ``callback`` and will be marked as completed when the given 2025-08-14T21:38:49.3145693Z ``callback`` finishes. 2025-08-14T21:38:49.3145843Z 2025-08-14T21:38:49.3146156Z .. note:: Note that if the callback function throws, either 2025-08-14T21:38:49.3146556Z through the original future being completed with an exception and 2025-08-14T21:38:49.3146908Z calling ``fut.wait()``, or through other code in the callback, the 2025-08-14T21:38:49.3147281Z future returned by ``then`` will be marked appropriately with the 2025-08-14T21:38:49.3147657Z encountered error. However, if this callback later completes 2025-08-14T21:38:49.3148056Z additional futures, those futures are not marked as completed with 2025-08-14T21:38:49.3148428Z an error and the user is responsible for handling completion/waiting 2025-08-14T21:38:49.3148638Z on those futures independently. 2025-08-14T21:38:49.3148783Z 2025-08-14T21:38:49.3148950Z Example:: 2025-08-14T21:38:49.3149089Z 2025-08-14T21:38:49.3149350Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-08-14T21:38:49.3149535Z >>> def callback(fut): 2025-08-14T21:38:49.3149849Z ... print(f"RPC return value is {fut.wait()}.") 2025-08-14T21:38:49.3150080Z >>> fut = torch.futures.Future() 2025-08-14T21:38:49.3150392Z >>> # The inserted callback will print the return value when 2025-08-14T21:38:49.3150607Z >>> # receiving the response from "worker1" 2025-08-14T21:38:49.3150802Z >>> cb_fut = fut.then(callback) 2025-08-14T21:38:49.3150996Z >>> chain_cb_fut = cb_fut.then( 2025-08-14T21:38:49.3151263Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-08-14T21:38:49.3151416Z ... ) 2025-08-14T21:38:49.3151580Z >>> fut.set_result(5) 2025-08-14T21:38:49.3151756Z RPC return value is 5. 2025-08-14T21:38:49.3151936Z Chained cb done. None 2025-08-14T21:38:49.3152077Z 2025-08-14T21:38:49.3152547Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3152698Z 2025-08-14T21:38:49.3152863Z warnings.warn(msg) 2025-08-14T21:38:49.3153002Z 2025-08-14T21:38:49.3153360Z --- Parse Warning: 19 / 146 --- 2025-08-14T21:38:49.3155111Z /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. 2025-08-14T21:38:49.3155653Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3155797Z 2025-08-14T21:38:49.3156170Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-08-14T21:38:49.3156592Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-08-14T21:38:49.3156788Z cannot be marked completed twice. 2025-08-14T21:38:49.3156929Z 2025-08-14T21:38:49.3157341Z If the result contains tensors that reside on GPUs, this method can be 2025-08-14T21:38:49.3157714Z called even if the asynchronous kernels that are populating those 2025-08-14T21:38:49.3158143Z tensors haven't yet completed running on the device, provided that the 2025-08-14T21:38:49.3158568Z streams on which those kernels were enqueued are set as the current ones 2025-08-14T21:38:49.3158960Z when this method is called. Put simply, it's safe to call this method 2025-08-14T21:38:49.3159378Z immediately after launching those kernels, without any additional 2025-08-14T21:38:49.3159801Z synchronization, as long as one doesn't change streams in between. This 2025-08-14T21:38:49.3160205Z method will record events on all the relevant current streams and will 2025-08-14T21:38:49.3160587Z use them to ensure proper scheduling for all the consumers of this 2025-08-14T21:38:49.3160739Z ``Future``. 2025-08-14T21:38:49.3160887Z 2025-08-14T21:38:49.3161053Z Args: 2025-08-14T21:38:49.3161345Z result (object): the result object of this ``Future``. 2025-08-14T21:38:49.3161504Z 2025-08-14T21:38:49.3161655Z Example:: 2025-08-14T21:38:49.3161794Z 2025-08-14T21:38:49.3162074Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-08-14T21:38:49.3162252Z >>> import threading 2025-08-14T21:38:49.3162411Z >>> import time 2025-08-14T21:38:49.3162625Z >>> def slow_set_future(fut, value): 2025-08-14T21:38:49.3162801Z ... time.sleep(0.5) 2025-08-14T21:38:49.3162985Z ... fut.set_result(value) 2025-08-14T21:38:49.3163195Z >>> fut = torch.futures.Future() 2025-08-14T21:38:49.3163374Z >>> t = threading.Thread( 2025-08-14T21:38:49.3163552Z ... target=slow_set_future, 2025-08-14T21:38:49.3163756Z ... args=(fut, torch.ones(2) * 3) 2025-08-14T21:38:49.3163905Z ... ) 2025-08-14T21:38:49.3164070Z >>> t.start() 2025-08-14T21:38:49.3164245Z >>> print(fut.wait()) 2025-08-14T21:38:49.3164409Z tensor([3., 3.]) 2025-08-14T21:38:49.3164572Z >>> t.join() 2025-08-14T21:38:49.3164714Z 2025-08-14T21:38:49.3165253Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3165442Z 2025-08-14T21:38:49.3165614Z warnings.warn(msg) 2025-08-14T21:38:49.3165761Z 2025-08-14T21:38:49.3166102Z --- Parse Warning: 20 / 146 --- 2025-08-14T21:38:49.3167794Z /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. 2025-08-14T21:38:49.3168306Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3168718Z Compiles compute shader from source and allows one to invoke kernels 2025-08-14T21:38:49.3168987Z defined there from the comfort of Python runtime 2025-08-14T21:38:49.3169161Z Example:: 2025-08-14T21:38:49.3169304Z 2025-08-14T21:38:49.3169546Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-08-14T21:38:49.3169779Z >>> lib = torch.mps.compile_shader( 2025-08-14T21:38:49.3170510Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-08-14T21:38:49.3170676Z ... ) 2025-08-14T21:38:49.3170880Z >>> x = torch.zeros(16, device="mps") 2025-08-14T21:38:49.3171080Z >>> lib.full(x, 3.14) 2025-08-14T21:38:49.3171243Z 2025-08-14T21:38:49.3171711Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3171853Z 2025-08-14T21:38:49.3172036Z warnings.warn(msg) 2025-08-14T21:38:49.3172177Z 2025-08-14T21:38:49.3172505Z --- Parse Warning: 21 / 146 --- 2025-08-14T21:38:49.3174129Z /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. 2025-08-14T21:38:49.3174622Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3174929Z Return the sum of each row of the given sparse tensor. 2025-08-14T21:38:49.3175074Z 2025-08-14T21:38:49.3175489Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-08-14T21:38:49.3175858Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-08-14T21:38:49.3176248Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-08-14T21:38:49.3176520Z returns a dense tensor instead of a sparse tensor. 2025-08-14T21:38:49.3176675Z 2025-08-14T21:38:49.3177156Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-08-14T21:38:49.3177521Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-08-14T21:38:49.3177661Z 2025-08-14T21:38:49.3178074Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-08-14T21:38:49.3178540Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-08-14T21:38:49.3178687Z 2025-08-14T21:38:49.3178838Z Args: 2025-08-14T21:38:49.3179067Z input (Tensor): the input sparse tensor 2025-08-14T21:38:49.3179574Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-08-14T21:38:49.3179749Z over all dims. 2025-08-14T21:38:49.3180237Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-08-14T21:38:49.3180441Z Default: dtype of :attr:`input`. 2025-08-14T21:38:49.3180601Z 2025-08-14T21:38:49.3180759Z Example:: 2025-08-14T21:38:49.3180906Z 2025-08-14T21:38:49.3181070Z >>> nnz = 3 2025-08-14T21:38:49.3181240Z >>> dims = [5, 5, 2, 3] 2025-08-14T21:38:49.3181523Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-08-14T21:38:49.3181946Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-08-14T21:38:49.3182190Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-08-14T21:38:49.3182371Z >>> size = torch.Size(dims) 2025-08-14T21:38:49.3182634Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-14T21:38:49.3182861Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-08-14T21:38:49.3183026Z >>> S 2025-08-14T21:38:49.3183227Z tensor(indices=tensor([[2, 0, 3], 2025-08-14T21:38:49.3183460Z [2, 4, 1]]), 2025-08-14T21:38:49.3183704Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-08-14T21:38:49.3183895Z [ 0.3411, 0.0918, -0.2312]], 2025-08-14T21:38:49.3184040Z 2025-08-14T21:38:49.3184250Z [[ 0.5348, 0.0634, -2.0494], 2025-08-14T21:38:49.3184444Z [-0.7125, -1.0646, 2.1844]], 2025-08-14T21:38:49.3184590Z 2025-08-14T21:38:49.3184800Z [[ 0.1276, 0.1874, -0.6334], 2025-08-14T21:38:49.3185000Z [-1.9682, -0.5340, 0.7483]]]), 2025-08-14T21:38:49.3185271Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-08-14T21:38:49.3185456Z 2025-08-14T21:38:49.3185808Z # when sum over only part of sparse_dims, return a sparse tensor 2025-08-14T21:38:49.3186019Z >>> torch.sparse.sum(S, [1, 3]) 2025-08-14T21:38:49.3186224Z tensor(indices=tensor([[0, 2, 3]]), 2025-08-14T21:38:49.3186423Z values=tensor([[-1.4512, 0.4073], 2025-08-14T21:38:49.3186617Z [-0.8901, 0.2017], 2025-08-14T21:38:49.3186802Z [-0.3183, -1.7539]]), 2025-08-14T21:38:49.3187038Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-08-14T21:38:49.3187199Z 2025-08-14T21:38:49.3187479Z # when sum over all sparse dim, return a dense tensor 2025-08-14T21:38:49.3187687Z # with summed dims squeezed 2025-08-14T21:38:49.3187886Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-08-14T21:38:49.3188061Z tensor([-2.6596, -1.1450]) 2025-08-14T21:38:49.3188214Z 2025-08-14T21:38:49.3188696Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3188835Z 2025-08-14T21:38:49.3189022Z warnings.warn(msg) 2025-08-14T21:38:49.3189163Z 2025-08-14T21:38:49.3189495Z --- Parse Warning: 22 / 146 --- 2025-08-14T21:38:49.3191258Z /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. 2025-08-14T21:38:49.3191752Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3192115Z Decorate function, to extend gradcheck for sparse tensors. 2025-08-14T21:38:49.3192261Z 2025-08-14T21:38:49.3192654Z Decorator for torch.autograd.gradcheck or its functools.partial 2025-08-14T21:38:49.3195570Z variants that extends the gradcheck function with support to input 2025-08-14T21:38:49.3195909Z functions that operate on or/and return sparse tensors. 2025-08-14T21:38:49.3196055Z 2025-08-14T21:38:49.3196461Z The specified gradcheck function itself is guaranteed to operate 2025-08-14T21:38:49.3196646Z on strided tensors only. 2025-08-14T21:38:49.3196803Z 2025-08-14T21:38:49.3196961Z For example: 2025-08-14T21:38:49.3197098Z 2025-08-14T21:38:49.3197555Z >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck) 2025-08-14T21:38:49.3197705Z >>> x = ( 2025-08-14T21:38:49.3197985Z ... torch.tensor([[0, 1], [2, 3]], dtype=torch.float64) 2025-08-14T21:38:49.3198170Z ... .to_sparse_coo() 2025-08-14T21:38:49.3198509Z ... .requires_grad_(True) 2025-08-14T21:38:49.3198696Z ... ) 2025-08-14T21:38:49.3198941Z >>> gradcheck(lambda x: x.to_sparse_csr(), x) 2025-08-14T21:38:49.3199091Z True 2025-08-14T21:38:49.3199235Z 2025-08-14T21:38:49.3199724Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3199868Z 2025-08-14T21:38:49.3200048Z warnings.warn(msg) 2025-08-14T21:38:49.3200189Z 2025-08-14T21:38:49.3200545Z --- Parse Warning: 23 / 146 --- 2025-08-14T21:38:49.3202182Z /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. 2025-08-14T21:38:49.3202669Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3202813Z 2025-08-14T21:38:49.3203237Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-08-14T21:38:49.3203603Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-08-14T21:38:49.3204024Z pushes the map into PyTorch operations called by ``func``, effectively 2025-08-14T21:38:49.3204219Z vectorizing those operations. 2025-08-14T21:38:49.3204414Z 2025-08-14T21:38:49.3204824Z vmap is useful for handling batch dimensions: one can write a function 2025-08-14T21:38:49.3205193Z ``func`` that runs on examples and then lift it to a function that can 2025-08-14T21:38:49.3205586Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-08-14T21:38:49.3205911Z compute batched gradients when composed with autograd. 2025-08-14T21:38:49.3206058Z 2025-08-14T21:38:49.3206212Z .. note:: 2025-08-14T21:38:49.3206555Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-08-14T21:38:49.3206781Z convenience. Use whichever one you'd like. 2025-08-14T21:38:49.3206942Z 2025-08-14T21:38:49.3207098Z Args: 2025-08-14T21:38:49.3207488Z func (function): A Python function that takes one or more arguments. 2025-08-14T21:38:49.3207705Z Must return one or more Tensors. 2025-08-14T21:38:49.3208067Z in_dims (int or nested structure): Specifies which dimension of the 2025-08-14T21:38:49.3208387Z inputs should be mapped over. ``in_dims`` should have a 2025-08-14T21:38:49.3208754Z structure like the inputs. If the ``in_dim`` for a particular 2025-08-14T21:38:49.3209092Z input is None, then that indicates there is no map dimension. 2025-08-14T21:38:49.3209272Z Default: 0. 2025-08-14T21:38:49.3209622Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-08-14T21:38:49.3209970Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-08-14T21:38:49.3210251Z it should have one element per output. Default: 0. 2025-08-14T21:38:49.3223626Z randomness (str): Specifies whether the randomness in this 2025-08-14T21:38:49.3224033Z vmap should be the same or different across batches. If 'different', 2025-08-14T21:38:49.3224420Z the randomness for each batch will be different. If 'same', the 2025-08-14T21:38:49.3224822Z randomness will be the same across batches. If 'error', any calls to 2025-08-14T21:38:49.3225216Z random functions will error. Default: 'error'. WARNING: this flag 2025-08-14T21:38:49.3225616Z only applies to random PyTorch operations and does not apply to 2025-08-14T21:38:49.3225857Z Python's random module or numpy randomness. 2025-08-14T21:38:49.3226288Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-08-14T21:38:49.3226689Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-08-14T21:38:49.3227166Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-08-14T21:38:49.3227817Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-08-14T21:38:49.3228033Z 2025-08-14T21:38:49.3228184Z Returns: 2025-08-14T21:38:49.3228543Z Returns a new "batched" function. It takes the same inputs as 2025-08-14T21:38:49.3228883Z ``func``, except each input has an extra dimension at the index 2025-08-14T21:38:49.3229226Z specified by ``in_dims``. It takes returns the same outputs as 2025-08-14T21:38:49.3229577Z ``func``, except each output has an extra dimension at the index 2025-08-14T21:38:49.3229760Z specified by ``out_dims``. 2025-08-14T21:38:49.3229913Z 2025-08-14T21:38:49.3230063Z .. warning: 2025-08-14T21:38:49.3230428Z :func:`vmap` works best with functional-style code. Please do not 2025-08-14T21:38:49.3230778Z perform any side-effects in ``func``, with the exception of 2025-08-14T21:38:49.3231211Z in-place PyTorch operations. Examples of side-effects include mutating 2025-08-14T21:38:49.3231630Z Python data structures and assigning values to variables not captured 2025-08-14T21:38:49.3231799Z in ``func``. 2025-08-14T21:38:49.3231944Z 2025-08-14T21:38:49.3232392Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-08-14T21:38:49.3232833Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-08-14T21:38:49.3233243Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-08-14T21:38:49.3233397Z 2025-08-14T21:38:49.3233585Z >>> torch.dot # [D], [D] -> [] 2025-08-14T21:38:49.3233955Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-08-14T21:38:49.3234188Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-08-14T21:38:49.3234360Z >>> batched_dot(x, y) 2025-08-14T21:38:49.3234503Z 2025-08-14T21:38:49.3234941Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-08-14T21:38:49.3235136Z model authoring experience. 2025-08-14T21:38:49.3235301Z 2025-08-14T21:38:49.3235501Z >>> batch_size, feature_size = 3, 5 2025-08-14T21:38:49.3235817Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-08-14T21:38:49.3235977Z >>> 2025-08-14T21:38:49.3236154Z >>> def model(feature_vec): 2025-08-14T21:38:49.3236384Z >>> # Very simple linear model with activation 2025-08-14T21:38:49.3236618Z >>> return feature_vec.dot(weights).relu() 2025-08-14T21:38:49.3236762Z >>> 2025-08-14T21:38:49.3237025Z >>> examples = torch.randn(batch_size, feature_size) 2025-08-14T21:38:49.3237249Z >>> result = torch.vmap(model)(examples) 2025-08-14T21:38:49.3237394Z 2025-08-14T21:38:49.3237859Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-08-14T21:38:49.3238306Z or impossible to batch. One example is higher-order gradient computation. 2025-08-14T21:38:49.3238741Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-08-14T21:38:49.3239191Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-08-14T21:38:49.3239650Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-08-14T21:38:49.3240093Z we can vectorize the whole computation, computing the Jacobian in a single 2025-08-14T21:38:49.3240293Z call to ``autograd.grad``. 2025-08-14T21:38:49.3240437Z 2025-08-14T21:38:49.3240595Z >>> # Setup 2025-08-14T21:38:49.3240758Z >>> N = 5 2025-08-14T21:38:49.3240925Z >>> f = lambda x: x**2 2025-08-14T21:38:49.3241150Z >>> x = torch.randn(N, requires_grad=True) 2025-08-14T21:38:49.3241300Z >>> y = f(x) 2025-08-14T21:38:49.3241471Z >>> I_N = torch.eye(N) 2025-08-14T21:38:49.3241632Z >>> 2025-08-14T21:38:49.3241817Z >>> # Sequential approach 2025-08-14T21:38:49.3242272Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-08-14T21:38:49.3242517Z >>> for v in I_N.unbind()] 2025-08-14T21:38:49.3242737Z >>> jacobian = torch.stack(jacobian_rows) 2025-08-14T21:38:49.3242883Z >>> 2025-08-14T21:38:49.3243109Z >>> # vectorized gradient computation 2025-08-14T21:38:49.3243279Z >>> def get_vjp(v): 2025-08-14T21:38:49.3243494Z >>> return torch.autograd.grad(y, x, v) 2025-08-14T21:38:49.3243719Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-08-14T21:38:49.3243865Z 2025-08-14T21:38:49.3244370Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-08-14T21:38:49.3244513Z 2025-08-14T21:38:49.3244699Z >>> torch.dot # [D], [D] -> [] 2025-08-14T21:38:49.3244899Z >>> batched_dot = torch.vmap( 2025-08-14T21:38:49.3245086Z ... torch.vmap(torch.dot) 2025-08-14T21:38:49.3245288Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-08-14T21:38:49.3245545Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-08-14T21:38:49.3245771Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-08-14T21:38:49.3245925Z 2025-08-14T21:38:49.3246386Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-08-14T21:38:49.3246692Z the dimension that each inputs are batched along as 2025-08-14T21:38:49.3246849Z 2025-08-14T21:38:49.3247030Z >>> torch.dot # [N], [N] -> [] 2025-08-14T21:38:49.3247432Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-08-14T21:38:49.3247667Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-08-14T21:38:49.3247831Z >>> batched_dot( 2025-08-14T21:38:49.3247979Z ... x, y 2025-08-14T21:38:49.3248340Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-08-14T21:38:49.3248483Z 2025-08-14T21:38:49.3248970Z If there are multiple inputs each of which is batched along different dimensions, 2025-08-14T21:38:49.3249342Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-08-14T21:38:49.3249481Z 2025-08-14T21:38:49.3249676Z >>> torch.dot # [D], [D] -> [] 2025-08-14T21:38:49.3250108Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-08-14T21:38:49.3250321Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-08-14T21:38:49.3250496Z >>> batched_dot( 2025-08-14T21:38:49.3250643Z ... x, y 2025-08-14T21:38:49.3250986Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-08-14T21:38:49.3251141Z 2025-08-14T21:38:49.3251579Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-08-14T21:38:49.3251776Z matching the shape of the input: 2025-08-14T21:38:49.3251927Z 2025-08-14T21:38:49.3252172Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-08-14T21:38:49.3252404Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-08-14T21:38:49.3252580Z >>> input = {"x": x, "y": y} 2025-08-14T21:38:49.3252896Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-08-14T21:38:49.3253086Z >>> batched_dot(input) 2025-08-14T21:38:49.3253233Z 2025-08-14T21:38:49.3253741Z By default, the output is batched along the first dimension. However, it can be batched 2025-08-14T21:38:49.3253975Z along any dimension by using ``out_dims`` 2025-08-14T21:38:49.3254121Z 2025-08-14T21:38:49.3254291Z >>> f = lambda x: x**2 2025-08-14T21:38:49.3254481Z >>> x = torch.randn(2, 5) 2025-08-14T21:38:49.3254699Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-08-14T21:38:49.3254892Z >>> batched_pow(x) # [5, 2] 2025-08-14T21:38:49.3255034Z 2025-08-14T21:38:49.3255591Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-08-14T21:38:49.3255767Z accept kwargs 2025-08-14T21:38:49.3255979Z 2025-08-14T21:38:49.3256186Z >>> x = torch.randn([2, 5]) 2025-08-14T21:38:49.3256371Z >>> def fn(x, scale=4.): 2025-08-14T21:38:49.3256539Z >>> return x * scale 2025-08-14T21:38:49.3256686Z >>> 2025-08-14T21:38:49.3256894Z >>> batched_pow = torch.vmap(fn) 2025-08-14T21:38:49.3257146Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-08-14T21:38:49.3257559Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-08-14T21:38:49.3257712Z 2025-08-14T21:38:49.3257874Z .. note:: 2025-08-14T21:38:49.3258296Z vmap does not provide general autobatching or handle variable-length 2025-08-14T21:38:49.3258479Z sequences out of the box. 2025-08-14T21:38:49.3258624Z 2025-08-14T21:38:49.3259115Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3259253Z 2025-08-14T21:38:49.3259428Z warnings.warn(msg) 2025-08-14T21:38:49.3259590Z 2025-08-14T21:38:49.3259984Z --- Parse Warning: 24 / 146 --- 2025-08-14T21:38:49.3261611Z /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. 2025-08-14T21:38:49.3262158Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3262592Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-08-14T21:38:49.3262970Z input(s) specified by ``argnums``. This operator can be nested to 2025-08-14T21:38:49.3263175Z compute higher-order gradients. 2025-08-14T21:38:49.3263325Z 2025-08-14T21:38:49.3263578Z Args: 2025-08-14T21:38:49.3263967Z func (Callable): A Python function that takes one or more arguments. 2025-08-14T21:38:49.3264465Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-08-14T21:38:49.3264979Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-08-14T21:38:49.3265153Z ``(output, aux)``. 2025-08-14T21:38:49.3265684Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-08-14T21:38:49.3266062Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-08-14T21:38:49.3266462Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-08-14T21:38:49.3266780Z auxiliary objects: ``(output, aux)``. Default: False. 2025-08-14T21:38:49.3266926Z 2025-08-14T21:38:49.3267075Z Returns: 2025-08-14T21:38:49.3267622Z Function to compute gradients with respect to its inputs. By default, the output of 2025-08-14T21:38:49.3268063Z the function is the gradient tensor(s) with respect to the first argument. 2025-08-14T21:38:49.3268583Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-08-14T21:38:49.3269047Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-08-14T21:38:49.3269305Z respect to each ``argnums`` value is returned. 2025-08-14T21:38:49.3269469Z 2025-08-14T21:38:49.3269653Z Example of using ``grad``: 2025-08-14T21:38:49.3269802Z 2025-08-14T21:38:49.3269985Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3270182Z >>> from torch.func import grad 2025-08-14T21:38:49.3270372Z >>> x = torch.randn([]) 2025-08-14T21:38:49.3270596Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-08-14T21:38:49.3270815Z >>> assert torch.allclose(cos_x, x.cos()) 2025-08-14T21:38:49.3270975Z >>> 2025-08-14T21:38:49.3271164Z >>> # Second-order gradients 2025-08-14T21:38:49.3271424Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-08-14T21:38:49.3271763Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-08-14T21:38:49.3271941Z 2025-08-14T21:38:49.3272400Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-08-14T21:38:49.3272552Z 2025-08-14T21:38:49.3272731Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3272943Z >>> from torch.func import grad, vmap 2025-08-14T21:38:49.3273161Z >>> batch_size, feature_size = 3, 5 2025-08-14T21:38:49.3273310Z >>> 2025-08-14T21:38:49.3273535Z >>> def model(weights, feature_vec): 2025-08-14T21:38:49.3273766Z >>> # Very simple linear model with activation 2025-08-14T21:38:49.3273972Z >>> assert feature_vec.dim() == 1 2025-08-14T21:38:49.3274210Z >>> return feature_vec.dot(weights).relu() 2025-08-14T21:38:49.3274362Z >>> 2025-08-14T21:38:49.3274611Z >>> def compute_loss(weights, example, target): 2025-08-14T21:38:49.3274825Z >>> y = model(weights, example) 2025-08-14T21:38:49.3275055Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-08-14T21:38:49.3275201Z >>> 2025-08-14T21:38:49.3275524Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-08-14T21:38:49.3275869Z >>> examples = torch.randn(batch_size, feature_size) 2025-08-14T21:38:49.3276093Z >>> targets = torch.randn(batch_size) 2025-08-14T21:38:49.3276309Z >>> inputs = (weights, examples, targets) 2025-08-14T21:38:49.3276738Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-08-14T21:38:49.3276908Z ... *inputs 2025-08-14T21:38:49.3277057Z ... ) 2025-08-14T21:38:49.3277200Z 2025-08-14T21:38:49.3277530Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-08-14T21:38:49.3277672Z 2025-08-14T21:38:49.3277844Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3278056Z >>> from torch.func import grad 2025-08-14T21:38:49.3278249Z >>> def my_loss_func(y, y_pred): 2025-08-14T21:38:49.3278490Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-08-14T21:38:49.3278701Z >>> loss = loss_per_sample.mean() 2025-08-14T21:38:49.3278925Z >>> return loss, (y_pred, loss_per_sample) 2025-08-14T21:38:49.3279082Z >>> 2025-08-14T21:38:49.3279352Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-08-14T21:38:49.3279532Z >>> y_true = torch.rand(4) 2025-08-14T21:38:49.3279782Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-08-14T21:38:49.3279971Z >>> out = fn(y_true, y_preds) 2025-08-14T21:38:49.3280430Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-08-14T21:38:49.3280573Z 2025-08-14T21:38:49.3280731Z .. note:: 2025-08-14T21:38:49.3281062Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-08-14T21:38:49.3281214Z 2025-08-14T21:38:49.3281477Z Case 1: Using ``torch.no_grad`` inside a function: 2025-08-14T21:38:49.3281636Z 2025-08-14T21:38:49.3281820Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3281984Z >>> def f(x): 2025-08-14T21:38:49.3282184Z >>> with torch.no_grad(): 2025-08-14T21:38:49.3282349Z >>> c = x ** 2 2025-08-14T21:38:49.3282519Z >>> return x - c 2025-08-14T21:38:49.3282677Z 2025-08-14T21:38:49.3283038Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-08-14T21:38:49.3283198Z 2025-08-14T21:38:49.3283529Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-08-14T21:38:49.3283675Z 2025-08-14T21:38:49.3283869Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3284051Z >>> with torch.no_grad(): 2025-08-14T21:38:49.3284280Z >>> grad(f)(x) 2025-08-14T21:38:49.3284472Z 2025-08-14T21:38:49.3284880Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-08-14T21:38:49.3285286Z outer one. This is because ``grad`` is a "function transform": its result 2025-08-14T21:38:49.3285711Z should not depend on the result of a context manager outside of ``f``. 2025-08-14T21:38:49.3285852Z 2025-08-14T21:38:49.3286013Z 2025-08-14T21:38:49.3286489Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3286631Z 2025-08-14T21:38:49.3286818Z warnings.warn(msg) 2025-08-14T21:38:49.3286965Z 2025-08-14T21:38:49.3287318Z --- Parse Warning: 25 / 146 --- 2025-08-14T21:38:49.3289220Z /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. 2025-08-14T21:38:49.3289683Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:49.3290022Z Register a FakeTensor implementation for this custom op. 2025-08-14T21:38:49.3290164Z 2025-08-14T21:38:49.3290672Z This is necessary to get the operator to work efficiently with torch.compile. 2025-08-14T21:38:49.3290810Z 2025-08-14T21:38:49.3291217Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-08-14T21:38:49.3291656Z specifies the behavior of this operator on Tensors that carry no data. 2025-08-14T21:38:49.3291925Z Given some input Tensors with certain properties 2025-08-14T21:38:49.3292417Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-08-14T21:38:49.3292600Z the output Tensors are. 2025-08-14T21:38:49.3292748Z 2025-08-14T21:38:49.3293345Z Please see :func:`torch.library.register_fake` for more details. 2025-08-14T21:38:49.3293494Z 2025-08-14T21:38:49.3293639Z Args: 2025-08-14T21:38:49.3293957Z fn (Callable): The function to register as the FakeTensor 2025-08-14T21:38:49.3294141Z implementation. 2025-08-14T21:38:49.3294289Z 2025-08-14T21:38:49.3294457Z Examples: 2025-08-14T21:38:49.3294628Z >>> import torch 2025-08-14T21:38:49.3294812Z >>> import numpy as np 2025-08-14T21:38:49.3295020Z >>> from torch import Tensor 2025-08-14T21:38:49.3295166Z >>> 2025-08-14T21:38:49.3295525Z >>> # Example 1: an operator without data-dependent output shape 2025-08-14T21:38:49.3295875Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-08-14T21:38:49.3296227Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-08-14T21:38:49.3296443Z >>> return (x @ weight.t()) + bias 2025-08-14T21:38:49.3296598Z >>> 2025-08-14T21:38:49.3296784Z >>> @linear.register_fake 2025-08-14T21:38:49.3296980Z >>> def _(x, weight, bias): 2025-08-14T21:38:49.3297159Z >>> assert x.dim() == 2 2025-08-14T21:38:49.3297370Z >>> assert weight.dim() == 2 2025-08-14T21:38:49.3297564Z >>> assert bias.dim() == 1 2025-08-14T21:38:49.3297782Z >>> assert x.shape[1] == weight.shape[1] 2025-08-14T21:38:49.3298025Z >>> assert weight.shape[0] == bias.shape[0] 2025-08-14T21:38:49.3298237Z >>> assert x.device == weight.device 2025-08-14T21:38:49.3298498Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-08-14T21:38:49.3298660Z >>> 2025-08-14T21:38:49.3298845Z >>> x = torch.randn(2, 2) 2025-08-14T21:38:49.3299074Z >>> weight = torch.randn(2, 2) 2025-08-14T21:38:49.3299384Z >>> bias = torch.randn(2) 2025-08-14T21:38:49.3299673Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-14T21:38:49.3300030Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-08-14T21:38:49.3300268Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-14T21:38:49.3300723Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-08-14T21:38:49.3300879Z >>> 2025-08-14T21:38:49.3301200Z >>> # Example 2: an operator with data-dependent output shape 2025-08-14T21:38:49.3301555Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-08-14T21:38:49.3301776Z >>> def nonzero(x: Tensor) -> Tensor: 2025-08-14T21:38:49.3301962Z >>> x_np = x.cpu().numpy() 2025-08-14T21:38:49.3302213Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-08-14T21:38:49.3302453Z >>> return torch.tensor(res, device=x.device) 2025-08-14T21:38:49.3302603Z >>> 2025-08-14T21:38:49.3302812Z >>> @nonzero.register_fake 2025-08-14T21:38:49.3302976Z >>> def _(x): 2025-08-14T21:38:49.3303245Z >>> # Number of nonzero-elements is data-dependent. 2025-08-14T21:38:49.3303678Z >>> # Since we cannot peek at the data in an abstract impl, 2025-08-14T21:38:49.3303953Z >>> # we use the ctx object to construct a new symint that 2025-08-14T21:38:49.3304192Z >>> # represents the data-dependent size. 2025-08-14T21:38:49.3304400Z >>> ctx = torch.library.get_ctx() 2025-08-14T21:38:49.3304603Z >>> nnz = ctx.new_dynamic_size() 2025-08-14T21:38:49.3304812Z >>> shape = [nnz, x.dim()] 2025-08-14T21:38:49.3305077Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-08-14T21:38:49.3305253Z >>> return result 2025-08-14T21:38:49.3305420Z >>> 2025-08-14T21:38:49.3305626Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-08-14T21:38:49.3305865Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-14T21:38:49.3306140Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-08-14T21:38:49.3306381Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-14T21:38:49.3306632Z >>> assert torch.allclose(out, x.nonzero()) 2025-08-14T21:38:49.3306773Z 2025-08-14T21:38:49.3306918Z 2025-08-14T21:38:49.3307962Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-08-14T21:38:49.3308110Z 2025-08-14T21:38:49.3308254Z _._ = None 2025-08-14T21:38:49.3308409Z ^ 2025-08-14T21:38:49.3308581Z warnings.warn(msg) 2025-08-14T21:38:49.3308722Z 2025-08-14T21:38:49.3309104Z --- Parse Warning: 26 / 146 --- 2025-08-14T21:38:49.3311003Z /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. 2025-08-14T21:38:49.3311515Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3311658Z 2025-08-14T21:38:49.3312083Z Registers a fake kernel based on the given operator profiles. This fake 2025-08-14T21:38:49.3312561Z kernel registration will override any existing fake kernel registrations. 2025-08-14T21:38:49.3312706Z 2025-08-14T21:38:49.3313099Z The input is a dictionary mapping operator names to a set of operator 2025-08-14T21:38:49.3313535Z profiles, which we will use to generate fake kernels. The operator profiles 2025-08-14T21:38:49.3313902Z are a record of the input and output tensor metadata. Based on this 2025-08-14T21:38:49.3314434Z information we will match a given input to the recorded profile, and return 2025-08-14T21:38:49.3314883Z an output with the same metadata as in the recorded profile. If a profile 2025-08-14T21:38:49.3315130Z doesn't exist then an exception will be thrown. 2025-08-14T21:38:49.3315291Z 2025-08-14T21:38:49.3315709Z The fake kernel generation is considered unsafe because it relies on the 2025-08-14T21:38:49.3316131Z rigid, pre-defined operator profiles that do not account for potential 2025-08-14T21:38:49.3316614Z variations in output behavior. Specifically, the generated kernels assume a 2025-08-14T21:38:49.3317092Z fixed relationship between input and output ranks. However, in reality, it's 2025-08-14T21:38:49.3317555Z possible that data-dependent operations may produce outputs of different 2025-08-14T21:38:49.3317970Z ranks even when given inputs of the same rank. The generated fake kernels 2025-08-14T21:38:49.3318371Z are inflexible and unable to accommodate these nuances, making them 2025-08-14T21:38:49.3318560Z potentially unsafe. 2025-08-14T21:38:49.3318708Z 2025-08-14T21:38:49.3318859Z Args: 2025-08-14T21:38:49.3319266Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-08-14T21:38:49.3319683Z name to a set of operator profiles from which we will generate fake 2025-08-14T21:38:49.3319832Z kernels. 2025-08-14T21:38:49.3319990Z 2025-08-14T21:38:49.3320143Z Examples: 2025-08-14T21:38:49.3320294Z 2025-08-14T21:38:49.3320595Z >>> # Example: Registering an op-profile from draft-export 2025-08-14T21:38:49.3320757Z >>> import torch 2025-08-14T21:38:49.3321060Z >>> from torch.export._draft_export import draft_export 2025-08-14T21:38:49.3321203Z >>> 2025-08-14T21:38:49.3321522Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-08-14T21:38:49.3321753Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-08-14T21:38:49.3321921Z >>> return x + y 2025-08-14T21:38:49.3322065Z >>> 2025-08-14T21:38:49.3322260Z >>> class M(torch.nn.Module): 2025-08-14T21:38:49.3322445Z >>> def forward(self, a, b): 2025-08-14T21:38:49.3322707Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-08-14T21:38:49.3322875Z >>> return res 2025-08-14T21:38:49.3323019Z >>> 2025-08-14T21:38:49.3323330Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-08-14T21:38:49.3323473Z >>> 2025-08-14T21:38:49.3324033Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-08-14T21:38:49.3324256Z >>> decomp = ep.run_decompositions() 2025-08-14T21:38:49.3324398Z 2025-08-14T21:38:49.3324544Z 2025-08-14T21:38:49.3325022Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3325171Z 2025-08-14T21:38:49.3325347Z warnings.warn(msg) 2025-08-14T21:38:49.3325498Z 2025-08-14T21:38:49.3325839Z --- Parse Warning: 27 / 146 --- 2025-08-14T21:38:49.3327513Z /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. 2025-08-14T21:38:49.3328008Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3328484Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-08-14T21:38:49.3328625Z 2025-08-14T21:38:49.3329002Z This is a more structured way of using triton kernels with PyTorch. 2025-08-14T21:38:49.3329484Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-08-14T21:38:49.3329942Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-08-14T21:38:49.3330117Z that is simpler; 2025-08-14T21:38:49.3330648Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-08-14T21:38:49.3331098Z want to create an operator that behaves like PyTorch built-in operators. 2025-08-14T21:38:49.3331488Z For example, you may use a ``torch.library`` wrapper API to define the 2025-08-14T21:38:49.3331890Z behavior of the triton kernel when passed a tensor subclass or under 2025-08-14T21:38:49.3332067Z a TorchDispatchMode. 2025-08-14T21:38:49.3332219Z 2025-08-14T21:38:49.3332680Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-08-14T21:38:49.3332871Z when the implementation 2025-08-14T21:38:49.3333262Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-08-14T21:38:49.3333563Z custom operators as opaque (:func:`torch.compile` and 2025-08-14T21:38:49.3334003Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-08-14T21:38:49.3334416Z makes the implementation visible to these subsystems, allowing them 2025-08-14T21:38:49.3334616Z to optimize the triton kernel(s). 2025-08-14T21:38:49.3334769Z 2025-08-14T21:38:49.3335113Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-08-14T21:38:49.3335571Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-08-14T21:38:49.3335918Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-08-14T21:38:49.3336062Z 2025-08-14T21:38:49.3336218Z Args: 2025-08-14T21:38:49.3336630Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-08-14T21:38:49.3337011Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-08-14T21:38:49.3337314Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-08-14T21:38:49.3337741Z To avoid name collisions, please use your project name as the namespace; 2025-08-14T21:38:49.3338138Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-08-14T21:38:49.3338651Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-08-14T21:38:49.3339087Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-08-14T21:38:49.3339598Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-08-14T21:38:49.3339930Z schema (None | str): A schema string for the operator. If None 2025-08-14T21:38:49.3340303Z (recommended) we'll infer a schema for the operator from its type 2025-08-14T21:38:49.3340682Z annotations. We recommend letting us infer a schema unless you 2025-08-14T21:38:49.3340885Z have a specific reason not to. 2025-08-14T21:38:49.3341160Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-08-14T21:38:49.3341302Z 2025-08-14T21:38:49.3341459Z Example:: 2025-08-14T21:38:49.3341608Z 2025-08-14T21:38:49.3341848Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-14T21:38:49.3342015Z >>> import torch 2025-08-14T21:38:49.3342292Z >>> from torch.library import triton_op, wrap_triton 2025-08-14T21:38:49.3342436Z >>> 2025-08-14T21:38:49.3342600Z >>> import triton 2025-08-14T21:38:49.3342811Z >>> from triton import language as tl 2025-08-14T21:38:49.3342955Z >>> 2025-08-14T21:38:49.3343114Z >>> @triton.jit 2025-08-14T21:38:49.3343288Z >>> def add_kernel( 2025-08-14T21:38:49.3343512Z >>> in_ptr0, 2025-08-14T21:38:49.3343673Z >>> in_ptr1, 2025-08-14T21:38:49.3343825Z >>> out_ptr, 2025-08-14T21:38:49.3343987Z >>> n_elements, 2025-08-14T21:38:49.3344196Z >>> BLOCK_SIZE: "tl.constexpr", 2025-08-14T21:38:49.3344340Z >>> ): 2025-08-14T21:38:49.3344603Z >>> pid = tl.program_id(axis=0) 2025-08-14T21:38:49.3344835Z >>> block_start = pid * BLOCK_SIZE 2025-08-14T21:38:49.3345099Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-08-14T21:38:49.3345300Z >>> mask = offsets < n_elements 2025-08-14T21:38:49.3345531Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-08-14T21:38:49.3345749Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-08-14T21:38:49.3345925Z >>> output = x + y 2025-08-14T21:38:49.3346173Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-08-14T21:38:49.3346321Z >>> 2025-08-14T21:38:49.3346557Z >>> @triton_op("mylib::add", mutates_args={}) 2025-08-14T21:38:49.3346882Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-08-14T21:38:49.3347080Z >>> output = torch.empty_like(x) 2025-08-14T21:38:49.3347288Z >>> n_elements = output.numel() 2025-08-14T21:38:49.3347434Z >>> 2025-08-14T21:38:49.3347602Z >>> def grid(meta): 2025-08-14T21:38:49.3347918Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-08-14T21:38:49.3348102Z >>> 2025-08-14T21:38:49.3348443Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-08-14T21:38:49.3348775Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-08-14T21:38:49.3348942Z >>> return output 2025-08-14T21:38:49.3349095Z >>> 2025-08-14T21:38:49.3349264Z >>> @torch.compile 2025-08-14T21:38:49.3349425Z >>> def f(x, y): 2025-08-14T21:38:49.3349601Z >>> return add(x, y) 2025-08-14T21:38:49.3349740Z >>> 2025-08-14T21:38:49.3349940Z >>> x = torch.randn(3, device="cuda") 2025-08-14T21:38:49.3350143Z >>> y = torch.randn(3, device="cuda") 2025-08-14T21:38:49.3350295Z >>> 2025-08-14T21:38:49.3350464Z >>> z = f(x, y) 2025-08-14T21:38:49.3350666Z >>> assert torch.allclose(z, x + y) 2025-08-14T21:38:49.3350806Z 2025-08-14T21:38:49.3350957Z 2025-08-14T21:38:49.3351427Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3351569Z 2025-08-14T21:38:49.3351743Z warnings.warn(msg) 2025-08-14T21:38:49.3351892Z 2025-08-14T21:38:49.3352243Z --- Parse Warning: 28 / 146 --- 2025-08-14T21:38:49.3353948Z /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. 2025-08-14T21:38:49.3354438Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3354790Z Allows capture of a triton kernel into a graph via make_fx or 2025-08-14T21:38:49.3354982Z non-strict ``torch.export``. 2025-08-14T21:38:49.3355124Z 2025-08-14T21:38:49.3355460Z These technologies perform Dispatcher-based tracing (via 2025-08-14T21:38:49.3355822Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-08-14T21:38:49.3356181Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-08-14T21:38:49.3356395Z can actually be traced into a graph. 2025-08-14T21:38:49.3356535Z 2025-08-14T21:38:49.3356920Z Please use this API together with :func:`torch.library.triton_op`. 2025-08-14T21:38:49.3357058Z 2025-08-14T21:38:49.3357212Z Examples: 2025-08-14T21:38:49.3357358Z 2025-08-14T21:38:49.3357530Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3357695Z >>> import torch 2025-08-14T21:38:49.3357862Z >>> import triton 2025-08-14T21:38:49.3358075Z >>> from triton import language as tl 2025-08-14T21:38:49.3358460Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-08-14T21:38:49.3358722Z >>> from torch.library import wrap_triton 2025-08-14T21:38:49.3358866Z >>> 2025-08-14T21:38:49.3359031Z >>> @triton.jit 2025-08-14T21:38:49.3359198Z >>> def add_kernel( 2025-08-14T21:38:49.3359359Z >>> in_ptr0, 2025-08-14T21:38:49.3359521Z >>> in_ptr1, 2025-08-14T21:38:49.3359676Z >>> out_ptr, 2025-08-14T21:38:49.3359838Z >>> n_elements, 2025-08-14T21:38:49.3360041Z >>> BLOCK_SIZE: "tl.constexpr", 2025-08-14T21:38:49.3360181Z >>> ): 2025-08-14T21:38:49.3360374Z >>> pid = tl.program_id(axis=0) 2025-08-14T21:38:49.3360575Z >>> block_start = pid * BLOCK_SIZE 2025-08-14T21:38:49.3360840Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-08-14T21:38:49.3361031Z >>> mask = offsets < n_elements 2025-08-14T21:38:49.3361257Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-08-14T21:38:49.3361471Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-08-14T21:38:49.3361647Z >>> output = x + y 2025-08-14T21:38:49.3361899Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-08-14T21:38:49.3362079Z >>> 2025-08-14T21:38:49.3362253Z >>> def add(x, y): 2025-08-14T21:38:49.3362446Z >>> output = torch.empty_like(x) 2025-08-14T21:38:49.3362635Z >>> n_elements = output.numel() 2025-08-14T21:38:49.3362790Z >>> 2025-08-14T21:38:49.3362962Z >>> def grid_fn(meta): 2025-08-14T21:38:49.3363263Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-08-14T21:38:49.3363410Z >>> 2025-08-14T21:38:49.3363757Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-08-14T21:38:49.3363928Z >>> return output 2025-08-14T21:38:49.3364072Z >>> 2025-08-14T21:38:49.3364272Z >>> x = torch.randn(3, device="cuda") 2025-08-14T21:38:49.3364474Z >>> y = torch.randn(3, device="cuda") 2025-08-14T21:38:49.3364649Z >>> gm = make_fx(add)(x, y) 2025-08-14T21:38:49.3364817Z >>> print(gm.code) 2025-08-14T21:38:49.3365014Z >>> # def forward(self, x_1, y_1): 2025-08-14T21:38:49.3365442Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-08-14T21:38:49.3365891Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-08-14T21:38:49.3366116Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-08-14T21:38:49.3366308Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-08-14T21:38:49.3366587Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-08-14T21:38:49.3366805Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-08-14T21:38:49.3366963Z >>> # }) 2025-08-14T21:38:49.3367147Z >>> # return empty_like 2025-08-14T21:38:49.3367292Z 2025-08-14T21:38:49.3367439Z 2025-08-14T21:38:49.3367917Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3368064Z 2025-08-14T21:38:49.3368233Z warnings.warn(msg) 2025-08-14T21:38:49.3368387Z 2025-08-14T21:38:49.3368720Z --- Parse Warning: 29 / 146 --- 2025-08-14T21:38:49.3370524Z /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. 2025-08-14T21:38:49.3371007Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3371150Z 2025-08-14T21:38:49.3371631Z Test if two objects are equal, and print an error message if test fails. 2025-08-14T21:38:49.3371826Z 2025-08-14T21:38:49.3372085Z The test is performed with ``actual == desired``. 2025-08-14T21:38:49.3372233Z 2025-08-14T21:38:49.3372381Z Parameters 2025-08-14T21:38:49.3372529Z ---------- 2025-08-14T21:38:49.3372710Z test_string : str 2025-08-14T21:38:49.3372935Z The message supplied to AssertionError. 2025-08-14T21:38:49.3373113Z actual : object 2025-08-14T21:38:49.3373383Z The object to test for equality against `desired`. 2025-08-14T21:38:49.3373545Z desired : object 2025-08-14T21:38:49.3373734Z The expected result. 2025-08-14T21:38:49.3373877Z 2025-08-14T21:38:49.3374033Z Examples 2025-08-14T21:38:49.3374199Z -------- 2025-08-14T21:38:49.3374406Z >>> np.testing.print_assert_equal( 2025-08-14T21:38:49.3374609Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-08-14T21:38:49.3374792Z ... ) # doctest: +SKIP 2025-08-14T21:38:49.3374993Z >>> np.testing.print_assert_equal( 2025-08-14T21:38:49.3375200Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-08-14T21:38:49.3375390Z ... ) # doctest: +SKIP 2025-08-14T21:38:49.3375587Z Traceback (most recent call last): 2025-08-14T21:38:49.3375749Z ... 2025-08-14T21:38:49.3375982Z AssertionError: Test XYZ of func xyz failed 2025-08-14T21:38:49.3376169Z ACTUAL: 2025-08-14T21:38:49.3376332Z [0, 1] 2025-08-14T21:38:49.3376480Z DESIRED: 2025-08-14T21:38:49.3376626Z [0, 2] 2025-08-14T21:38:49.3376785Z 2025-08-14T21:38:49.3376929Z 2025-08-14T21:38:49.3377400Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3377559Z 2025-08-14T21:38:49.3377734Z warnings.warn(msg) 2025-08-14T21:38:49.3377878Z 2025-08-14T21:38:49.3378223Z --- Parse Warning: 30 / 146 --- 2025-08-14T21:38:49.3380031Z /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. 2025-08-14T21:38:49.3380544Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3380691Z 2025-08-14T21:38:49.3381076Z Raises an AssertionError if two items are not equal up to desired 2025-08-14T21:38:49.3381254Z precision. 2025-08-14T21:38:49.3381400Z 2025-08-14T21:38:49.3381712Z .. note:: It is recommended to use one of `assert_allclose`, 2025-08-14T21:38:49.3382060Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-08-14T21:38:49.3382405Z instead of this function for more consistent floating point 2025-08-14T21:38:49.3382572Z comparisons. 2025-08-14T21:38:49.3382724Z 2025-08-14T21:38:49.3383125Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-08-14T21:38:49.3383286Z 2025-08-14T21:38:49.3383637Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-08-14T21:38:49.3383787Z 2025-08-14T21:38:49.3384227Z That is a looser test than originally documented, but agrees with what the 2025-08-14T21:38:49.3384662Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-08-14T21:38:49.3385105Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-08-14T21:38:49.3385333Z delegates to assert_array_almost_equal 2025-08-14T21:38:49.3385475Z 2025-08-14T21:38:49.3385636Z Parameters 2025-08-14T21:38:49.3385800Z ---------- 2025-08-14T21:38:49.3385969Z actual : array_like 2025-08-14T21:38:49.3386162Z The object to check. 2025-08-14T21:38:49.3386331Z desired : array_like 2025-08-14T21:38:49.3386508Z The expected object. 2025-08-14T21:38:49.3386701Z decimal : int, optional 2025-08-14T21:38:49.3386906Z Desired precision, default is 7. 2025-08-14T21:38:49.3387079Z err_msg : str, optional 2025-08-14T21:38:49.3387449Z The error message to be printed in case of failure. 2025-08-14T21:38:49.3387655Z verbose : bool, optional 2025-08-14T21:38:49.3388024Z If True, the conflicting values are appended to the error message. 2025-08-14T21:38:49.3388179Z 2025-08-14T21:38:49.3388321Z Raises 2025-08-14T21:38:49.3388479Z ------ 2025-08-14T21:38:49.3388655Z AssertionError 2025-08-14T21:38:49.3389013Z If actual and desired are not equal up to specified precision. 2025-08-14T21:38:49.3389166Z 2025-08-14T21:38:49.3389315Z See Also 2025-08-14T21:38:49.3389464Z -------- 2025-08-14T21:38:49.3389923Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-14T21:38:49.3390146Z relative and/or absolute precision. 2025-08-14T21:38:49.3390532Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-14T21:38:49.3390691Z 2025-08-14T21:38:49.3390841Z Examples 2025-08-14T21:38:49.3390990Z -------- 2025-08-14T21:38:49.3391302Z >>> from torch._numpy.testing import assert_almost_equal 2025-08-14T21:38:49.3391542Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-08-14T21:38:49.3391863Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-08-14T21:38:49.3392075Z Traceback (most recent call last): 2025-08-14T21:38:49.3392259Z ... 2025-08-14T21:38:49.3392434Z AssertionError: 2025-08-14T21:38:49.3392653Z Arrays are not almost equal to 10 decimals 2025-08-14T21:38:49.3392819Z ACTUAL: 2.3333333333333 2025-08-14T21:38:49.3393134Z DESIRED: 2.33333334 2025-08-14T21:38:49.3393277Z 2025-08-14T21:38:49.3393450Z >>> assert_almost_equal( 2025-08-14T21:38:49.3393826Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-08-14T21:38:49.3393976Z ... ) 2025-08-14T21:38:49.3394174Z Traceback (most recent call last): 2025-08-14T21:38:49.3394339Z ... 2025-08-14T21:38:49.3394504Z AssertionError: 2025-08-14T21:38:49.3394725Z Arrays are not almost equal to 9 decimals 2025-08-14T21:38:49.3394896Z 2025-08-14T21:38:49.3395086Z Mismatched elements: 1 / 2 (50%) 2025-08-14T21:38:49.3395331Z Max absolute difference: 6.666699636781459e-09 2025-08-14T21:38:49.3395563Z Max relative difference: 2.8571569790287484e-09 2025-08-14T21:38:49.3395808Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-08-14T21:38:49.3396059Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-08-14T21:38:49.3396202Z 2025-08-14T21:38:49.3396348Z 2025-08-14T21:38:49.3396836Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3396977Z 2025-08-14T21:38:49.3397146Z warnings.warn(msg) 2025-08-14T21:38:49.3397301Z 2025-08-14T21:38:49.3397646Z --- Parse Warning: 31 / 146 --- 2025-08-14T21:38:49.3399475Z /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. 2025-08-14T21:38:49.3399970Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3400118Z 2025-08-14T21:38:49.3400550Z Raises an AssertionError if two items are not equal up to significant 2025-08-14T21:38:49.3400696Z digits. 2025-08-14T21:38:49.3400839Z 2025-08-14T21:38:49.3401156Z .. note:: It is recommended to use one of `assert_allclose`, 2025-08-14T21:38:49.3401482Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-08-14T21:38:49.3401841Z instead of this function for more consistent floating point 2025-08-14T21:38:49.3402004Z comparisons. 2025-08-14T21:38:49.3402146Z 2025-08-14T21:38:49.3402496Z Given two numbers, check that they are approximately equal. 2025-08-14T21:38:49.3402898Z Approximately equal is defined as the number of significant digits 2025-08-14T21:38:49.3403166Z that agree. 2025-08-14T21:38:49.3403378Z 2025-08-14T21:38:49.3403534Z Parameters 2025-08-14T21:38:49.3403680Z ---------- 2025-08-14T21:38:49.3403851Z actual : scalar 2025-08-14T21:38:49.3404019Z The object to check. 2025-08-14T21:38:49.3404182Z desired : scalar 2025-08-14T21:38:49.3404370Z The expected object. 2025-08-14T21:38:49.3404550Z significant : int, optional 2025-08-14T21:38:49.3404747Z Desired precision, default is 7. 2025-08-14T21:38:49.3404936Z err_msg : str, optional 2025-08-14T21:38:49.3405212Z The error message to be printed in case of failure. 2025-08-14T21:38:49.3405400Z verbose : bool, optional 2025-08-14T21:38:49.3405767Z If True, the conflicting values are appended to the error message. 2025-08-14T21:38:49.3405903Z 2025-08-14T21:38:49.3406064Z Raises 2025-08-14T21:38:49.3406212Z ------ 2025-08-14T21:38:49.3406371Z AssertionError 2025-08-14T21:38:49.3406737Z If actual and desired are not equal up to specified precision. 2025-08-14T21:38:49.3406883Z 2025-08-14T21:38:49.3407028Z See Also 2025-08-14T21:38:49.3407190Z -------- 2025-08-14T21:38:49.3407630Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-14T21:38:49.3407898Z relative and/or absolute precision. 2025-08-14T21:38:49.3408294Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-14T21:38:49.3408437Z 2025-08-14T21:38:49.3408598Z Examples 2025-08-14T21:38:49.3408748Z -------- 2025-08-14T21:38:49.3408952Z >>> np.testing.assert_approx_equal( 2025-08-14T21:38:49.3409150Z ... 0.12345677777777e-20, 0.1234567e-20 2025-08-14T21:38:49.3409319Z ... ) # doctest: +SKIP 2025-08-14T21:38:49.3409525Z >>> np.testing.assert_approx_equal( 2025-08-14T21:38:49.3409699Z ... 0.12345670e-20, 2025-08-14T21:38:49.3409892Z ... 0.12345671e-20, # doctest: +SKIP 2025-08-14T21:38:49.3410064Z ... significant=8, 2025-08-14T21:38:49.3410227Z ... ) 2025-08-14T21:38:49.3410431Z >>> np.testing.assert_approx_equal( 2025-08-14T21:38:49.3410587Z ... 0.12345670e-20, 2025-08-14T21:38:49.3410790Z ... 0.12345672e-20, # doctest: +SKIP 2025-08-14T21:38:49.3410955Z ... significant=8, 2025-08-14T21:38:49.3411113Z ... ) 2025-08-14T21:38:49.3411310Z Traceback (most recent call last): 2025-08-14T21:38:49.3411455Z ... 2025-08-14T21:38:49.3411636Z AssertionError: 2025-08-14T21:38:49.3411870Z Items are not equal to 8 significant digits: 2025-08-14T21:38:49.3412034Z ACTUAL: 1.234567e-21 2025-08-14T21:38:49.3412214Z DESIRED: 1.2345672e-21 2025-08-14T21:38:49.3412360Z 2025-08-14T21:38:49.3412649Z the evaluated condition that raises the exception is 2025-08-14T21:38:49.3412806Z 2025-08-14T21:38:49.3413140Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-08-14T21:38:49.3413290Z True 2025-08-14T21:38:49.3413444Z 2025-08-14T21:38:49.3413594Z 2025-08-14T21:38:49.3414083Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3414226Z 2025-08-14T21:38:49.3414392Z warnings.warn(msg) 2025-08-14T21:38:49.3414550Z 2025-08-14T21:38:49.3414893Z --- Parse Warning: 32 / 146 --- 2025-08-14T21:38:49.3416699Z /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. 2025-08-14T21:38:49.3417209Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3417348Z 2025-08-14T21:38:49.3417747Z Raises an AssertionError if two array_like objects are not equal. 2025-08-14T21:38:49.3417891Z 2025-08-14T21:38:49.3418269Z Given two array_like objects, check that the shape is equal and all 2025-08-14T21:38:49.3418772Z elements of these objects are equal (but see the Notes for the special 2025-08-14T21:38:49.3419172Z handling of a scalar). An exception is raised at shape mismatch or 2025-08-14T21:38:49.3419585Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-08-14T21:38:49.3420010Z are compared like numbers, no assertion is raised if both objects have 2025-08-14T21:38:49.3420199Z NaNs in the same positions. 2025-08-14T21:38:49.3420346Z 2025-08-14T21:38:49.3420784Z The usual caution for verifying equality with floating point numbers is 2025-08-14T21:38:49.3420934Z advised. 2025-08-14T21:38:49.3421088Z 2025-08-14T21:38:49.3421246Z Parameters 2025-08-14T21:38:49.3421401Z ---------- 2025-08-14T21:38:49.3421573Z x : array_like 2025-08-14T21:38:49.3421762Z The actual object to check. 2025-08-14T21:38:49.3421918Z y : array_like 2025-08-14T21:38:49.3422124Z The desired, expected object. 2025-08-14T21:38:49.3422300Z err_msg : str, optional 2025-08-14T21:38:49.3422581Z The error message to be printed in case of failure. 2025-08-14T21:38:49.3422778Z verbose : bool, optional 2025-08-14T21:38:49.3423152Z If True, the conflicting values are appended to the error message. 2025-08-14T21:38:49.3423324Z strict : bool, optional 2025-08-14T21:38:49.3423802Z If True, raise an AssertionError when either the shape or the data 2025-08-14T21:38:49.3424120Z type of the array_like objects does not match. The special 2025-08-14T21:38:49.3424503Z handling for scalars mentioned in the Notes section is disabled. 2025-08-14T21:38:49.3424650Z 2025-08-14T21:38:49.3424798Z Raises 2025-08-14T21:38:49.3424960Z ------ 2025-08-14T21:38:49.3425117Z AssertionError 2025-08-14T21:38:49.3425356Z If actual and desired objects are not equal. 2025-08-14T21:38:49.3425515Z 2025-08-14T21:38:49.3425665Z See Also 2025-08-14T21:38:49.3425817Z -------- 2025-08-14T21:38:49.3426279Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-14T21:38:49.3426498Z relative and/or absolute precision. 2025-08-14T21:38:49.3426889Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-14T21:38:49.3427051Z 2025-08-14T21:38:49.3427203Z Notes 2025-08-14T21:38:49.3427367Z ----- 2025-08-14T21:38:49.3427697Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-08-14T21:38:49.3428116Z function checks that each element of the array_like object is equal to 2025-08-14T21:38:49.3428540Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-08-14T21:38:49.3428682Z 2025-08-14T21:38:49.3428828Z Examples 2025-08-14T21:38:49.3428990Z -------- 2025-08-14T21:38:49.3429227Z The first assert does not raise an exception: 2025-08-14T21:38:49.3429369Z 2025-08-14T21:38:49.3429585Z >>> np.testing.assert_array_equal( 2025-08-14T21:38:49.3429840Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-08-14T21:38:49.3430006Z ... ) 2025-08-14T21:38:49.3430155Z 2025-08-14T21:38:49.3430571Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-08-14T21:38:49.3430779Z functions for these cases instead: 2025-08-14T21:38:49.3430928Z 2025-08-14T21:38:49.3431122Z >>> np.testing.assert_allclose( 2025-08-14T21:38:49.3431480Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-08-14T21:38:49.3431631Z ... ) 2025-08-14T21:38:49.3431775Z 2025-08-14T21:38:49.3432165Z As mentioned in the Notes section, `assert_array_equal` has special 2025-08-14T21:38:49.3432585Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-08-14T21:38:49.3432727Z 2025-08-14T21:38:49.3432930Z >>> x = np.full((2, 5), fill_value=3) 2025-08-14T21:38:49.3433140Z >>> np.testing.assert_array_equal(x, 3) 2025-08-14T21:38:49.3433293Z 2025-08-14T21:38:49.3433773Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-08-14T21:38:49.3433949Z array: 2025-08-14T21:38:49.3434100Z 2025-08-14T21:38:49.3434362Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-08-14T21:38:49.3434561Z Traceback (most recent call last): 2025-08-14T21:38:49.3434718Z ... 2025-08-14T21:38:49.3434878Z AssertionError: 2025-08-14T21:38:49.3435050Z Arrays are not equal 2025-08-14T21:38:49.3435216Z 2025-08-14T21:38:49.3435394Z (shapes (2, 5), () mismatch) 2025-08-14T21:38:49.3435576Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-08-14T21:38:49.3435744Z [3, 3, 3, 3, 3]]) 2025-08-14T21:38:49.3435915Z y: torch.ndarray(3) 2025-08-14T21:38:49.3436067Z 2025-08-14T21:38:49.3436463Z The `strict` parameter also ensures that the array data types match: 2025-08-14T21:38:49.3436607Z 2025-08-14T21:38:49.3436788Z >>> x = np.array([2, 2, 2]) 2025-08-14T21:38:49.3437019Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-08-14T21:38:49.3437288Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-08-14T21:38:49.3437503Z Traceback (most recent call last): 2025-08-14T21:38:49.3437649Z ... 2025-08-14T21:38:49.3437814Z AssertionError: 2025-08-14T21:38:49.3438028Z Arrays are not equal 2025-08-14T21:38:49.3438179Z 2025-08-14T21:38:49.3438437Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-08-14T21:38:49.3438629Z x: torch.ndarray([2, 2, 2]) 2025-08-14T21:38:49.3438814Z y: torch.ndarray([2., 2., 2.]) 2025-08-14T21:38:49.3438968Z 2025-08-14T21:38:49.3439441Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3439583Z 2025-08-14T21:38:49.3439764Z warnings.warn(msg) 2025-08-14T21:38:49.3439907Z 2025-08-14T21:38:49.3440252Z --- Parse Warning: 33 / 146 --- 2025-08-14T21:38:49.3442134Z /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. 2025-08-14T21:38:49.3442623Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3442774Z 2025-08-14T21:38:49.3443183Z Raises an AssertionError if two objects are not equal up to desired 2025-08-14T21:38:49.3443341Z precision. 2025-08-14T21:38:49.3443495Z 2025-08-14T21:38:49.3443798Z .. note:: It is recommended to use one of `assert_allclose`, 2025-08-14T21:38:49.3444127Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-08-14T21:38:49.3444483Z instead of this function for more consistent floating point 2025-08-14T21:38:49.3444653Z comparisons. 2025-08-14T21:38:49.3444798Z 2025-08-14T21:38:49.3445259Z The test verifies identical shapes and that the elements of ``actual`` and 2025-08-14T21:38:49.3445431Z ``desired`` satisfy. 2025-08-14T21:38:49.3445571Z 2025-08-14T21:38:49.3445815Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-08-14T21:38:49.3445957Z 2025-08-14T21:38:49.3446395Z That is a looser test than originally documented, but agrees with what the 2025-08-14T21:38:49.3446851Z actual implementation did up to rounding vagaries. An exception is raised 2025-08-14T21:38:49.3447286Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-08-14T21:38:49.3447698Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-08-14T21:38:49.3447913Z objects have NaNs in the same positions. 2025-08-14T21:38:49.3448054Z 2025-08-14T21:38:49.3448224Z Parameters 2025-08-14T21:38:49.3448375Z ---------- 2025-08-14T21:38:49.3448534Z x : array_like 2025-08-14T21:38:49.3448735Z The actual object to check. 2025-08-14T21:38:49.3448891Z y : array_like 2025-08-14T21:38:49.3449094Z The desired, expected object. 2025-08-14T21:38:49.3449338Z decimal : int, optional 2025-08-14T21:38:49.3449573Z Desired precision, default is 6. 2025-08-14T21:38:49.3449754Z err_msg : str, optional 2025-08-14T21:38:49.3450035Z The error message to be printed in case of failure. 2025-08-14T21:38:49.3450217Z verbose : bool, optional 2025-08-14T21:38:49.3450601Z If True, the conflicting values are appended to the error message. 2025-08-14T21:38:49.3450749Z 2025-08-14T21:38:49.3450899Z Raises 2025-08-14T21:38:49.3451057Z ------ 2025-08-14T21:38:49.3451228Z AssertionError 2025-08-14T21:38:49.3451590Z If actual and desired are not equal up to specified precision. 2025-08-14T21:38:49.3451746Z 2025-08-14T21:38:49.3451896Z See Also 2025-08-14T21:38:49.3452061Z -------- 2025-08-14T21:38:49.3452503Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-14T21:38:49.3452725Z relative and/or absolute precision. 2025-08-14T21:38:49.3453122Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-14T21:38:49.3453267Z 2025-08-14T21:38:49.3453414Z Examples 2025-08-14T21:38:49.3453575Z -------- 2025-08-14T21:38:49.3453810Z the first assert does not raise an exception 2025-08-14T21:38:49.3453985Z 2025-08-14T21:38:49.3454464Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-08-14T21:38:49.3454606Z 2025-08-14T21:38:49.3454824Z >>> np.testing.assert_array_almost_equal( 2025-08-14T21:38:49.3455099Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-08-14T21:38:49.3455243Z ... ) 2025-08-14T21:38:49.3455452Z Traceback (most recent call last): 2025-08-14T21:38:49.3455594Z ... 2025-08-14T21:38:49.3455756Z AssertionError: 2025-08-14T21:38:49.3455976Z Arrays are not almost equal to 5 decimals 2025-08-14T21:38:49.3456130Z 2025-08-14T21:38:49.3456310Z Mismatched elements: 1 / 3 (33.3%) 2025-08-14T21:38:49.3456546Z Max absolute difference: 5.999999999994898e-05 2025-08-14T21:38:49.3456782Z Max relative difference: 2.5713661239633743e-05 2025-08-14T21:38:49.3457067Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-08-14T21:38:49.3457363Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-08-14T21:38:49.3457507Z 2025-08-14T21:38:49.3457724Z >>> np.testing.assert_array_almost_equal( 2025-08-14T21:38:49.3457956Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-08-14T21:38:49.3458099Z ... ) 2025-08-14T21:38:49.3458305Z Traceback (most recent call last): 2025-08-14T21:38:49.3458453Z ... 2025-08-14T21:38:49.3458616Z AssertionError: 2025-08-14T21:38:49.3458842Z Arrays are not almost equal to 5 decimals 2025-08-14T21:38:49.3458997Z 2025-08-14T21:38:49.3459180Z x and y nan location mismatch: 2025-08-14T21:38:49.3459479Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-08-14T21:38:49.3459769Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-08-14T21:38:49.3459908Z 2025-08-14T21:38:49.3460063Z 2025-08-14T21:38:49.3460533Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3460696Z 2025-08-14T21:38:49.3460869Z warnings.warn(msg) 2025-08-14T21:38:49.3461007Z 2025-08-14T21:38:49.3461352Z --- Parse Warning: 34 / 146 --- 2025-08-14T21:38:49.3463208Z /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. 2025-08-14T21:38:49.3463779Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3464188Z Context manager that resets warning registry for catching warnings 2025-08-14T21:38:49.3464331Z 2025-08-14T21:38:49.3464860Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-08-14T21:38:49.3465329Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-08-14T21:38:49.3465779Z it impossible to retrigger the warning in this module, whatever you put in 2025-08-14T21:38:49.3466251Z the warnings filters. This context manager accepts a sequence of `modules` 2025-08-14T21:38:49.3466497Z as a keyword argument to its constructor and: 2025-08-14T21:38:49.3466643Z 2025-08-14T21:38:49.3467069Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-08-14T21:38:49.3467221Z on entry; 2025-08-14T21:38:49.3467561Z * resets ``__warningregistry__`` to its previous state on exit. 2025-08-14T21:38:49.3467716Z 2025-08-14T21:38:49.3468126Z This makes it possible to trigger any warning afresh inside the context 2025-08-14T21:38:49.3468475Z manager without disturbing the state of warnings outside. 2025-08-14T21:38:49.3468620Z 2025-08-14T21:38:49.3469057Z For compatibility with Python 3.0, please consider all arguments to be 2025-08-14T21:38:49.3469227Z keyword-only. 2025-08-14T21:38:49.3469370Z 2025-08-14T21:38:49.3469531Z Parameters 2025-08-14T21:38:49.3469732Z ---------- 2025-08-14T21:38:49.3469905Z record : bool, optional 2025-08-14T21:38:49.3470246Z Specifies whether warnings should be captured by a custom 2025-08-14T21:38:49.3470696Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-08-14T21:38:49.3471078Z returned by the context manager. Otherwise None is returned by the 2025-08-14T21:38:49.3471511Z context manager. The objects appended to the list are arguments whose 2025-08-14T21:38:49.3471816Z attributes mirror the arguments to ``showwarning()``. 2025-08-14T21:38:49.3472000Z modules : sequence, optional 2025-08-14T21:38:49.3472424Z Sequence of modules for which to reset warnings registry on entry and 2025-08-14T21:38:49.3472778Z restore on exit. To work correctly, all 'ignore' filters should 2025-08-14T21:38:49.3472973Z filter by one of these modules. 2025-08-14T21:38:49.3473127Z 2025-08-14T21:38:49.3473281Z Examples 2025-08-14T21:38:49.3473442Z -------- 2025-08-14T21:38:49.3473602Z >>> import warnings 2025-08-14T21:38:49.3473938Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-08-14T21:38:49.3474149Z ... modules=[np.core.fromnumeric] 2025-08-14T21:38:49.3474295Z ... ): 2025-08-14T21:38:49.3474513Z ... warnings.simplefilter("always") 2025-08-14T21:38:49.3474929Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-08-14T21:38:49.3475240Z ... # do something that raises a warning but ignore those in 2025-08-14T21:38:49.3475426Z ... # np.core.fromnumeric 2025-08-14T21:38:49.3475584Z 2025-08-14T21:38:49.3476064Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3476210Z 2025-08-14T21:38:49.3476378Z warnings.warn(msg) 2025-08-14T21:38:49.3476522Z 2025-08-14T21:38:49.3476871Z --- Parse Warning: 35 / 146 --- 2025-08-14T21:38:49.3478668Z /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. 2025-08-14T21:38:49.3479153Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3479553Z Applies a 1D convolution over a quantized input signal composed of 2025-08-14T21:38:49.3479754Z several quantized input planes. 2025-08-14T21:38:49.3479898Z 2025-08-14T21:38:49.3480302Z For details on input arguments, parameters, and implementation see 2025-08-14T21:38:49.3480557Z :class:`~torch.nn.Conv1d`. 2025-08-14T21:38:49.3480746Z 2025-08-14T21:38:49.3480896Z .. note:: 2025-08-14T21:38:49.3481252Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-08-14T21:38:49.3481404Z 2025-08-14T21:38:49.3481560Z .. note:: 2025-08-14T21:38:49.3481875Z Only `torch.quint8` is supported for the input data type. 2025-08-14T21:38:49.3482036Z 2025-08-14T21:38:49.3482173Z 2025-08-14T21:38:49.3482332Z Attributes: 2025-08-14T21:38:49.3482726Z weight (Tensor): packed tensor derived from the learnable weight 2025-08-14T21:38:49.3482906Z parameter. 2025-08-14T21:38:49.3483174Z scale (Tensor): scalar for the output scale 2025-08-14T21:38:49.3483470Z zero_point (Tensor): scalar for the output zero point 2025-08-14T21:38:49.3483615Z 2025-08-14T21:38:49.3483903Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-08-14T21:38:49.3484049Z 2025-08-14T21:38:49.3484206Z Examples:: 2025-08-14T21:38:49.3484359Z 2025-08-14T21:38:49.3484622Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-08-14T21:38:49.3484855Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-08-14T21:38:49.3485100Z >>> input = torch.randn(20, 16, 100) 2025-08-14T21:38:49.3485293Z >>> # quantize input to quint8 2025-08-14T21:38:49.3485468Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3485865Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-08-14T21:38:49.3486083Z ... dtype=torch.quint8) 2025-08-14T21:38:49.3486265Z >>> output = m(q_input) 2025-08-14T21:38:49.3486405Z 2025-08-14T21:38:49.3486542Z 2025-08-14T21:38:49.3487024Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3487169Z 2025-08-14T21:38:49.3487345Z warnings.warn(msg) 2025-08-14T21:38:49.3487491Z 2025-08-14T21:38:49.3487814Z --- Parse Warning: 36 / 146 --- 2025-08-14T21:38:49.3489555Z /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. 2025-08-14T21:38:49.3490061Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3490286Z A quantized long short-term memory (LSTM). 2025-08-14T21:38:49.3490441Z 2025-08-14T21:38:49.3490965Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-08-14T21:38:49.3491104Z 2025-08-14T21:38:49.3491276Z Attributes: 2025-08-14T21:38:49.3491493Z layers : instances of the `_LSTMLayer` 2025-08-14T21:38:49.3491638Z 2025-08-14T21:38:49.3491797Z .. note:: 2025-08-14T21:38:49.3492199Z To access the weights and biases, you need to access them per layer. 2025-08-14T21:38:49.3492520Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-08-14T21:38:49.3492665Z 2025-08-14T21:38:49.3492956Z Examples:: 2025-08-14T21:38:49.3493150Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3493337Z >>> custom_module_config = { 2025-08-14T21:38:49.3493577Z ... 'float_to_observed_custom_module_class': { 2025-08-14T21:38:49.3493807Z ... nn.LSTM: nn.quantizable.LSTM, 2025-08-14T21:38:49.3493953Z ... }, 2025-08-14T21:38:49.3494213Z ... 'observed_to_quantized_custom_module_class': { 2025-08-14T21:38:49.3494475Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-08-14T21:38:49.3494622Z ... } 2025-08-14T21:38:49.3494786Z ... } 2025-08-14T21:38:49.3495193Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-08-14T21:38:49.3495724Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-08-14T21:38:49.3495926Z 2025-08-14T21:38:49.3496398Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3496537Z 2025-08-14T21:38:49.3496721Z warnings.warn(msg) 2025-08-14T21:38:49.3496863Z 2025-08-14T21:38:49.3497201Z --- Parse Warning: 37 / 146 --- 2025-08-14T21:38:49.3499537Z /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. 2025-08-14T21:38:49.3500023Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3500177Z 2025-08-14T21:38:49.3500670Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-08-14T21:38:49.3501090Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-08-14T21:38:49.3501567Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-08-14T21:38:49.3501738Z input by the user. 2025-08-14T21:38:49.3502202Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-08-14T21:38:49.3502492Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-08-14T21:38:49.3502626Z 2025-08-14T21:38:49.3502760Z Note:: 2025-08-14T21:38:49.3503082Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-08-14T21:38:49.3503156Z 2025-08-14T21:38:49.3503244Z Args: 2025-08-14T21:38:49.3503337Z model (nn.Module): 2025-08-14T21:38:49.3503618Z The model whose layers will be sparsified. The layers that needs to be 2025-08-14T21:38:49.3503868Z sparsified should be added separately using the register_layer() function 2025-08-14T21:38:49.3503986Z aggregate_fn (Optional, Callable): 2025-08-14T21:38:49.3504237Z default aggregate_fn that is used if not specified while registering the layer. 2025-08-14T21:38:49.3504414Z specifies how inputs should be aggregated over time. 2025-08-14T21:38:49.3504692Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-08-14T21:38:49.3504790Z Example 2025-08-14T21:38:49.3504960Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-08-14T21:38:49.3505071Z reduce_fn (Optional, Callable): 2025-08-14T21:38:49.3505320Z default reduce_fn that is used if not specified while registering the layer. 2025-08-14T21:38:49.3505564Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-08-14T21:38:49.3505676Z calling agg_fn() on all inputs. 2025-08-14T21:38:49.3505776Z Example 2025-08-14T21:38:49.3505967Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-08-14T21:38:49.3506087Z mask_fn (Optional, Callable): 2025-08-14T21:38:49.3506388Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-08-14T21:38:49.3506633Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-08-14T21:38:49.3506741Z register_layer(). 2025-08-14T21:38:49.3507076Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-08-14T21:38:49.3507176Z arguments. 2025-08-14T21:38:49.3507282Z features (Optional, list): 2025-08-14T21:38:49.3507406Z default selected features to sparsify. 2025-08-14T21:38:49.3507732Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-08-14T21:38:49.3507881Z For example, 2025-08-14T21:38:49.3508125Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-08-14T21:38:49.3508243Z feature_dim (Optional, int): 2025-08-14T21:38:49.3508511Z default dimension of input features. Again, features along this dim will be chosen 2025-08-14T21:38:49.3508620Z for sparsification. 2025-08-14T21:38:49.3508720Z sparse_config (Dict): 2025-08-14T21:38:49.3508930Z Default configuration for the mask_fn. This config will be passed 2025-08-14T21:38:49.3509042Z with the mask_fn() 2025-08-14T21:38:49.3509118Z 2025-08-14T21:38:49.3509195Z Example: 2025-08-14T21:38:49.3509299Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3509392Z >>> model = SomeModel() 2025-08-14T21:38:49.3509629Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-08-14T21:38:49.3509746Z >>> # Initialize aggregate_fn 2025-08-14T21:38:49.3509835Z >>> def agg_fn(x, y): 2025-08-14T21:38:49.3509924Z >>> return x + y 2025-08-14T21:38:49.3510012Z >>> 2025-08-14T21:38:49.3510134Z >>> # Initialize reduce_fn 2025-08-14T21:38:49.3510234Z >>> def reduce_fn(x): 2025-08-14T21:38:49.3510340Z >>> return torch.mean(x, dim=0) 2025-08-14T21:38:49.3510416Z >>> 2025-08-14T21:38:49.3510523Z >>> # Initialize mask_fn 2025-08-14T21:38:49.3510615Z >>> def mask_fn(data): 2025-08-14T21:38:49.3510754Z >>> return torch.eye(data.shape).to(data.device) 2025-08-14T21:38:49.3510847Z >>> 2025-08-14T21:38:49.3510923Z >>> 2025-08-14T21:38:49.3511035Z >>> act_sparsifier.register_layer( 2025-08-14T21:38:49.3511143Z ... model.some_layer, 2025-08-14T21:38:49.3511238Z ... aggregate_fn=agg_fn, 2025-08-14T21:38:49.3511335Z ... reduce_fn=reduce_fn, 2025-08-14T21:38:49.3511438Z ... mask_fn=mask_fn, 2025-08-14T21:38:49.3511515Z ... ) 2025-08-14T21:38:49.3511605Z >>> 2025-08-14T21:38:49.3511704Z >>> # start training process 2025-08-14T21:38:49.3511787Z >>> for _ in [...]: 2025-08-14T21:38:49.3511889Z >>> # epoch starts 2025-08-14T21:38:49.3512058Z >>> # model.forward(), compute_loss() and model.backwards() 2025-08-14T21:38:49.3512147Z >>> # epoch ends 2025-08-14T21:38:49.3512264Z >>> act_sparsifier.step() 2025-08-14T21:38:49.3512358Z >>> # end training process 2025-08-14T21:38:49.3512459Z >>> sparsifier.squash_mask() 2025-08-14T21:38:49.3512545Z 2025-08-14T21:38:49.3512789Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3512864Z 2025-08-14T21:38:49.3512966Z warnings.warn(msg) 2025-08-14T21:38:49.3513039Z 2025-08-14T21:38:49.3513254Z --- Parse Warning: 38 / 146 --- 2025-08-14T21:38:49.3514430Z /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. 2025-08-14T21:38:49.3514686Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3514773Z 2025-08-14T21:38:49.3514969Z Abstract method that needs to be implemented by the child class. 2025-08-14T21:38:49.3515216Z The expected return type should is a dictionary of name to schedule_param value 2025-08-14T21:38:49.3515499Z The returned values will be updated in sparsifier when the scheduler step() function 2025-08-14T21:38:49.3515584Z is called. 2025-08-14T21:38:49.3515679Z 2025-08-14T21:38:49.3515763Z Example: 2025-08-14T21:38:49.3515874Z >>> def get_schedule_param(self): 2025-08-14T21:38:49.3516033Z ... new_param = {} 2025-08-14T21:38:49.3516215Z ... for name in self.sparsifier.data_groups.keys(): 2025-08-14T21:38:49.3516316Z ... new_param[name] = ( 2025-08-14T21:38:49.3516535Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-08-14T21:38:49.3516620Z ... ) 2025-08-14T21:38:49.3516715Z ... return new_param 2025-08-14T21:38:49.3516808Z 2025-08-14T21:38:49.3517142Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-08-14T21:38:49.3517243Z would be halved 2025-08-14T21:38:49.3517325Z 2025-08-14T21:38:49.3517571Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3517664Z 2025-08-14T21:38:49.3517761Z warnings.warn(msg) 2025-08-14T21:38:49.3517839Z 2025-08-14T21:38:49.3518037Z --- Parse Warning: 39 / 146 --- 2025-08-14T21:38:49.3519079Z /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. 2025-08-14T21:38:49.3519378Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3519550Z Squashes the sparse masks into the appropriate tensors. 2025-08-14T21:38:49.3519629Z 2025-08-14T21:38:49.3519848Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-08-14T21:38:49.3520029Z the module will have a `sparse_params` dict attached to it. 2025-08-14T21:38:49.3520108Z 2025-08-14T21:38:49.3520207Z Args: 2025-08-14T21:38:49.3520390Z params_to_keep: List of keys to save in the module or a dict 2025-08-14T21:38:49.3520549Z representing the modules and keys that will have 2025-08-14T21:38:49.3520686Z sparsity parameters saved 2025-08-14T21:38:49.3520896Z params_to_keep_per_layer: Dict to specify the params that should be 2025-08-14T21:38:49.3521058Z saved for specific layers. The keys in the dict 2025-08-14T21:38:49.3521210Z should be the module fqn, while the values should 2025-08-14T21:38:49.3521366Z be a list of strings with the names of the variables 2025-08-14T21:38:49.3521500Z to save in the `sparse_params` 2025-08-14T21:38:49.3521577Z 2025-08-14T21:38:49.3521662Z Examples: 2025-08-14T21:38:49.3521803Z >>> # xdoctest: +SKIP("locals are undefined") 2025-08-14T21:38:49.3521914Z >>> # Don't save any sparse params 2025-08-14T21:38:49.3522042Z >>> sparsifier.squash_mask() 2025-08-14T21:38:49.3522181Z >>> hasattr(model.submodule1, "sparse_params") 2025-08-14T21:38:49.3522265Z False 2025-08-14T21:38:49.3522353Z 2025-08-14T21:38:49.3522467Z >>> # Keep sparse params per layer 2025-08-14T21:38:49.3522571Z >>> sparsifier.squash_mask( 2025-08-14T21:38:49.3522696Z ... params_to_keep_per_layer={ 2025-08-14T21:38:49.3522828Z ... "submodule1.linear1": ("foo", "bar"), 2025-08-14T21:38:49.3522954Z ... "submodule2.linear42": ("baz",), 2025-08-14T21:38:49.3523049Z ... } 2025-08-14T21:38:49.3523130Z ... ) 2025-08-14T21:38:49.3523293Z >>> print(model.submodule1.linear1.sparse_params) 2025-08-14T21:38:49.3523388Z {'foo': 42, 'bar': 24} 2025-08-14T21:38:49.3523549Z >>> print(model.submodule2.linear42.sparse_params) 2025-08-14T21:38:49.3523649Z {'baz': 0.1} 2025-08-14T21:38:49.3523726Z 2025-08-14T21:38:49.3523897Z >>> # Keep sparse params for all layers 2025-08-14T21:38:49.3524100Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-08-14T21:38:49.3524250Z >>> print(model.submodule1.linear1.sparse_params) 2025-08-14T21:38:49.3524345Z {'foo': 42, 'bar': 24} 2025-08-14T21:38:49.3524511Z >>> print(model.submodule2.linear42.sparse_params) 2025-08-14T21:38:49.3524605Z {'foo': 42, 'bar': 24} 2025-08-14T21:38:49.3524695Z 2025-08-14T21:38:49.3524887Z >>> # Keep some sparse params for all layers, and specific ones for 2025-08-14T21:38:49.3524989Z >>> # some other layers 2025-08-14T21:38:49.3525111Z >>> sparsifier.squash_mask( 2025-08-14T21:38:49.3525228Z ... params_to_keep=("foo", "bar"), 2025-08-14T21:38:49.3525416Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-08-14T21:38:49.3525510Z ... ) 2025-08-14T21:38:49.3525664Z >>> print(model.submodule1.linear1.sparse_params) 2025-08-14T21:38:49.3525762Z {'foo': 42, 'bar': 24} 2025-08-14T21:38:49.3525928Z >>> print(model.submodule2.linear42.sparse_params) 2025-08-14T21:38:49.3526033Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-08-14T21:38:49.3526164Z 2025-08-14T21:38:49.3526412Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3526490Z 2025-08-14T21:38:49.3526598Z warnings.warn(msg) 2025-08-14T21:38:49.3526675Z 2025-08-14T21:38:49.3526862Z --- Parse Warning: 40 / 146 --- 2025-08-14T21:38:49.3527891Z /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. 2025-08-14T21:38:49.3528151Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3528246Z 2025-08-14T21:38:49.3528491Z Config object that specifies the supported data types passed as arguments to 2025-08-14T21:38:49.3528724Z quantize ops in the reference model spec, for input and output activations, 2025-08-14T21:38:49.3528832Z weights, and biases. 2025-08-14T21:38:49.3528911Z 2025-08-14T21:38:49.3529064Z For example, consider the following reference model: 2025-08-14T21:38:49.3529155Z 2025-08-14T21:38:49.3529306Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-08-14T21:38:49.3529385Z 2025-08-14T21:38:49.3529602Z The pattern in the square brackets refers to the reference pattern of 2025-08-14T21:38:49.3529833Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-08-14T21:38:49.3530067Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-08-14T21:38:49.3530290Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-08-14T21:38:49.3530507Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-08-14T21:38:49.3530626Z the second quantize op (quant2). 2025-08-14T21:38:49.3530704Z 2025-08-14T21:38:49.3530919Z Note that the dtype here does not refer to the interface dtypes of the 2025-08-14T21:38:49.3531133Z op. For example, the "input dtype" here is not the dtype of the input 2025-08-14T21:38:49.3531342Z tensor passed to the quantized linear op. Though it can still be the 2025-08-14T21:38:49.3531546Z same as the interface dtype, this is not always the case, e.g. the 2025-08-14T21:38:49.3531757Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-08-14T21:38:49.3531967Z specified in the DTypeConfig would still be quint8. The semantics of 2025-08-14T21:38:49.3532183Z dtypes here are the same as the semantics of the dtypes specified in 2025-08-14T21:38:49.3532269Z the observers. 2025-08-14T21:38:49.3532348Z 2025-08-14T21:38:49.3532611Z These dtypes are matched against the ones specified in the user's 2025-08-14T21:38:49.3532844Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-08-14T21:38:49.3533056Z specified in the DTypeConfig (if any), then we will quantize the given 2025-08-14T21:38:49.3533288Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-08-14T21:38:49.3533398Z the pattern will not be quantized. 2025-08-14T21:38:49.3533490Z 2025-08-14T21:38:49.3533587Z Example usage:: 2025-08-14T21:38:49.3533664Z 2025-08-14T21:38:49.3533780Z >>> # xdoctest: +SKIP(failing) 2025-08-14T21:38:49.3533887Z >>> dtype_config1 = DTypeConfig( 2025-08-14T21:38:49.3533993Z ... input_dtype=torch.quint8, 2025-08-14T21:38:49.3534115Z ... output_dtype=torch.quint8, 2025-08-14T21:38:49.3534221Z ... weight_dtype=torch.qint8, 2025-08-14T21:38:49.3534322Z ... bias_dtype=torch.float) 2025-08-14T21:38:49.3534414Z 2025-08-14T21:38:49.3534519Z >>> dtype_config2 = DTypeConfig( 2025-08-14T21:38:49.3534656Z ... input_dtype=DTypeWithConstraints( 2025-08-14T21:38:49.3534756Z ... dtype=torch.quint8, 2025-08-14T21:38:49.3534862Z ... quant_min_lower_bound=0, 2025-08-14T21:38:49.3535008Z ... quant_max_upper_bound=255, 2025-08-14T21:38:49.3535088Z ... ), 2025-08-14T21:38:49.3535216Z ... output_dtype=DTypeWithConstraints( 2025-08-14T21:38:49.3535327Z ... dtype=torch.quint8, 2025-08-14T21:38:49.3535430Z ... quant_min_lower_bound=0, 2025-08-14T21:38:49.3535538Z ... quant_max_upper_bound=255, 2025-08-14T21:38:49.3535630Z ... ), 2025-08-14T21:38:49.3535755Z ... weight_dtype=DTypeWithConstraints( 2025-08-14T21:38:49.3535854Z ... dtype=torch.qint8, 2025-08-14T21:38:49.3535978Z ... quant_min_lower_bound=-128, 2025-08-14T21:38:49.3536088Z ... quant_max_upper_bound=127, 2025-08-14T21:38:49.3536179Z ... ), 2025-08-14T21:38:49.3536280Z ... bias_dtype=torch.float) 2025-08-14T21:38:49.3536356Z 2025-08-14T21:38:49.3536473Z >>> dtype_config1.input_dtype 2025-08-14T21:38:49.3536561Z torch.quint8 2025-08-14T21:38:49.3536638Z 2025-08-14T21:38:49.3536752Z >>> dtype_config2.input_dtype 2025-08-14T21:38:49.3536839Z torch.quint8 2025-08-14T21:38:49.3536915Z 2025-08-14T21:38:49.3537062Z >>> dtype_config2.input_dtype_with_constraints 2025-08-14T21:38:49.3537586Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-08-14T21:38:49.3537676Z 2025-08-14T21:38:49.3537921Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3537998Z 2025-08-14T21:38:49.3538103Z warnings.warn(msg) 2025-08-14T21:38:49.3538185Z 2025-08-14T21:38:49.3538374Z --- Parse Warning: 41 / 146 --- 2025-08-14T21:38:49.3539389Z /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. 2025-08-14T21:38:49.3539645Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3539734Z 2025-08-14T21:38:49.3540032Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-08-14T21:38:49.3540322Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-08-14T21:38:49.3540631Z though the requirements on the type of model more-so depends on the specific report the user 2025-08-14T21:38:49.3540974Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-08-14T21:38:49.3541269Z a set of Detector classes, each of which generate reports on quantization configuration 2025-08-14T21:38:49.3541382Z issues a use might have. 2025-08-14T21:38:49.3541464Z 2025-08-14T21:38:49.3541594Z Currently supports generating reports on: 2025-08-14T21:38:49.3541834Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-08-14T21:38:49.3542098Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-08-14T21:38:49.3542396Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-08-14T21:38:49.3542602Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-08-14T21:38:49.3542679Z 2025-08-14T21:38:49.3543086Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-08-14T21:38:49.3543537Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-08-14T21:38:49.3543921Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-08-14T21:38:49.3544129Z has the capability to remove all the observers it inserted as well. 2025-08-14T21:38:49.3544245Z 2025-08-14T21:38:49.3544540Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-08-14T21:38:49.3544620Z 2025-08-14T21:38:49.3544985Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-08-14T21:38:49.3545305Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-08-14T21:38:49.3545382Z 2025-08-14T21:38:49.3545677Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-08-14T21:38:49.3545900Z This set is generated by calling the get_detector_name() of each detector 2025-08-14T21:38:49.3545979Z 2025-08-14T21:38:49.3546320Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-08-14T21:38:49.3546630Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-08-14T21:38:49.3546744Z can be removed at the end if desired 2025-08-14T21:38:49.3546836Z 2025-08-14T21:38:49.3547148Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-08-14T21:38:49.3547392Z This is to ensure we only insert observers once with the ModelReport instance 2025-08-14T21:38:49.3547471Z 2025-08-14T21:38:49.3547719Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-08-14T21:38:49.3548017Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-08-14T21:38:49.3548335Z instance. This also allows the functionality where we can generate the report multiple times 2025-08-14T21:38:49.3548481Z as long as we haven't removed the observers yet. 2025-08-14T21:38:49.3548574Z 2025-08-14T21:38:49.3548656Z Note: 2025-08-14T21:38:49.3548948Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-08-14T21:38:49.3549271Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-08-14T21:38:49.3549560Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-08-14T21:38:49.3549686Z the QuantizationTracer class. 2025-08-14T21:38:49.3549769Z 2025-08-14T21:38:49.3549871Z General Flow for Fx workflow: 2025-08-14T21:38:49.3550269Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-08-14T21:38:49.3550387Z 2.) Prepare your model with prepare_fx 2025-08-14T21:38:49.3550675Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-08-14T21:38:49.3550816Z 4.) Calibrate your model with data 2025-08-14T21:38:49.3551168Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-08-14T21:38:49.3551266Z Optional 2025-08-14T21:38:49.3551532Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-08-14T21:38:49.3551770Z 7.) To help in parsing report information and debugging, view report info as a: 2025-08-14T21:38:49.3551872Z - Table 2025-08-14T21:38:49.3551960Z - Histogram 2025-08-14T21:38:49.3552051Z - Line plot 2025-08-14T21:38:49.3552376Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-08-14T21:38:49.3552456Z 2025-08-14T21:38:49.3552582Z Example (with QuantizationTracer): 2025-08-14T21:38:49.3552678Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3552789Z >>> # get the necessary qconfig 2025-08-14T21:38:49.3552916Z >>> config = PrepareCustomConfig() 2025-08-14T21:38:49.3553069Z >>> skipped_module_names, skipped_module_classes = ( 2025-08-14T21:38:49.3553227Z ... get_skipped_module_name_and_classes(config, False) 2025-08-14T21:38:49.3553348Z ... ) 2025-08-14T21:38:49.3553428Z 2025-08-14T21:38:49.3553558Z >>> # initialize our model and get GraphModule 2025-08-14T21:38:49.3553662Z >>> model = SomeModel() 2025-08-14T21:38:49.3553911Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-08-14T21:38:49.3554095Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-08-14T21:38:49.3554173Z 2025-08-14T21:38:49.3554324Z >>> # get our set of detectors and ModelReport instance 2025-08-14T21:38:49.3554434Z >>> detector_set = set( 2025-08-14T21:38:49.3554514Z ... [ 2025-08-14T21:38:49.3554648Z ... DynamicStaticDetector(tolerance=0.5), 2025-08-14T21:38:49.3554849Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-08-14T21:38:49.3554929Z ... ] 2025-08-14T21:38:49.3555008Z ... ) 2025-08-14T21:38:49.3555221Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-08-14T21:38:49.3555305Z 2025-08-14T21:38:49.3555456Z >>> # now we insert the observers and calibrate the model 2025-08-14T21:38:49.3555717Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-08-14T21:38:49.3555844Z >>> for i in range(num_callibration_batches): 2025-08-14T21:38:49.3555986Z >>> example_input = get_callibration_input() 2025-08-14T21:38:49.3556118Z >>> tracer_model_with_observers(example_input) 2025-08-14T21:38:49.3556197Z 2025-08-14T21:38:49.3556462Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-08-14T21:38:49.3556612Z >>> reports = tracer_reporter.generate_model_report( 2025-08-14T21:38:49.3556726Z ... remove_inserted_observers=True 2025-08-14T21:38:49.3556817Z ... ) 2025-08-14T21:38:49.3556895Z 2025-08-14T21:38:49.3557124Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-08-14T21:38:49.3557282Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-08-14T21:38:49.3557359Z 2025-08-14T21:38:49.3557608Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-08-14T21:38:49.3557781Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-08-14T21:38:49.3557863Z 2025-08-14T21:38:49.3558149Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-08-14T21:38:49.3558350Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-08-14T21:38:49.3558429Z 2025-08-14T21:38:49.3558517Z 2025-08-14T21:38:49.3558811Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3558935Z 2025-08-14T21:38:49.3559030Z warnings.warn(msg) 2025-08-14T21:38:49.3559106Z 2025-08-14T21:38:49.3559325Z --- Parse Warning: 42 / 146 --- 2025-08-14T21:38:49.3560553Z /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. 2025-08-14T21:38:49.3560808Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3560899Z 2025-08-14T21:38:49.3561160Z Takes in optional filter values and generates two tables with desired information. 2025-08-14T21:38:49.3561254Z 2025-08-14T21:38:49.3561457Z The generated tables are presented in both a list-of-lists format 2025-08-14T21:38:49.3561541Z 2025-08-14T21:38:49.3561754Z The reason for the two tables are that they handle different things: 2025-08-14T21:38:49.3561911Z 1.) the first table handles all tensor level information 2025-08-14T21:38:49.3562121Z 2.) the second table handles and displays all channel based information 2025-08-14T21:38:49.3562240Z 2025-08-14T21:38:49.3562552Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-08-14T21:38:49.3562874Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-08-14T21:38:49.3563234Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-08-14T21:38:49.3563312Z 2025-08-14T21:38:49.3563419Z Tensor table columns: 2025-08-14T21:38:49.3563606Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-08-14T21:38:49.3563760Z ---- --------- --------- --------- --------- --------- 2025-08-14T21:38:49.3563850Z 2025-08-14T21:38:49.3563956Z Per-Channel table columns: 2025-08-14T21:38:49.3564171Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-08-14T21:38:49.3564348Z ---- --------- ------- --------- --------- --------- --------- 2025-08-14T21:38:49.3564428Z 2025-08-14T21:38:49.3564523Z Args: 2025-08-14T21:38:49.3564778Z feature_filter (str, optional): Filters the features presented to only those that 2025-08-14T21:38:49.3564889Z contain this filter substring 2025-08-14T21:38:49.3565061Z Default = "", results in all the features being printed 2025-08-14T21:38:49.3565314Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-14T21:38:49.3565556Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-14T21:38:49.3565646Z 2025-08-14T21:38:49.3565758Z Returns a dictionary with two keys: 2025-08-14T21:38:49.3565931Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-08-14T21:38:49.3566066Z "tensor_level_info", "channel_level_info" 2025-08-14T21:38:49.3566173Z Each key maps to a tuple with: 2025-08-14T21:38:49.3566302Z A list of the headers of each table 2025-08-14T21:38:49.3566478Z A list of lists containing the table information row by row 2025-08-14T21:38:49.3566647Z The 0th index row will contain the headers of the columns 2025-08-14T21:38:49.3566781Z The rest of the rows will contain data 2025-08-14T21:38:49.3566862Z 2025-08-14T21:38:49.3566947Z Example Use: 2025-08-14T21:38:49.3567079Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:49.3567224Z >>> mod_report_visualizer.generate_filtered_tables( 2025-08-14T21:38:49.3567476Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-14T21:38:49.3567767Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-08-14T21:38:49.3567844Z 2025-08-14T21:38:49.3568100Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3568177Z 2025-08-14T21:38:49.3568272Z warnings.warn(msg) 2025-08-14T21:38:49.3568361Z 2025-08-14T21:38:49.3568548Z --- Parse Warning: 43 / 146 --- 2025-08-14T21:38:49.3569787Z /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. 2025-08-14T21:38:49.3570055Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3570133Z 2025-08-14T21:38:49.3570414Z Takes in optional filter values and prints out formatted tables of the information. 2025-08-14T21:38:49.3570493Z 2025-08-14T21:38:49.3570826Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-08-14T21:38:49.3570997Z 1.) the first table handles all tensor level information 2025-08-14T21:38:49.3571239Z 2.) the second table handles and displays all channel based information 2025-08-14T21:38:49.3571318Z 2025-08-14T21:38:49.3571647Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-08-14T21:38:49.3571969Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-08-14T21:38:49.3572329Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-08-14T21:38:49.3572406Z 2025-08-14T21:38:49.3572501Z Tensor table columns: 2025-08-14T21:38:49.3572703Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-08-14T21:38:49.3572850Z ---- --------- --------- --------- --------- --------- 2025-08-14T21:38:49.3572926Z 2025-08-14T21:38:49.3573040Z Per-Channel table columns: 2025-08-14T21:38:49.3573118Z 2025-08-14T21:38:49.3573342Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-08-14T21:38:49.3573498Z ---- --------- ------- --------- --------- --------- --------- 2025-08-14T21:38:49.3573575Z 2025-08-14T21:38:49.3573668Z Args: 2025-08-14T21:38:49.3573922Z feature_filter (str, optional): Filters the features presented to only those that 2025-08-14T21:38:49.3574028Z contain this filter substring 2025-08-14T21:38:49.3574198Z Default = "", results in all the features being printed 2025-08-14T21:38:49.3574449Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-14T21:38:49.3574693Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-14T21:38:49.3574788Z 2025-08-14T21:38:49.3574874Z Example Use: 2025-08-14T21:38:49.3575010Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:49.3575172Z >>> mod_report_visualizer.generate_table_visualization( 2025-08-14T21:38:49.3575365Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-14T21:38:49.3575458Z ... ) 2025-08-14T21:38:49.3575644Z >>> # prints out neatly formatted table with per_channel_min info 2025-08-14T21:38:49.3575766Z >>> # for all modules in block 1 of the model 2025-08-14T21:38:49.3575855Z 2025-08-14T21:38:49.3576104Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3576179Z 2025-08-14T21:38:49.3576285Z warnings.warn(msg) 2025-08-14T21:38:49.3576365Z 2025-08-14T21:38:49.3576560Z --- Parse Warning: 44 / 146 --- 2025-08-14T21:38:49.3577844Z /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. 2025-08-14T21:38:49.3578124Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3578214Z 2025-08-14T21:38:49.3578444Z Takes in a feature and optional module_filter and plots of the desired data. 2025-08-14T21:38:49.3578521Z 2025-08-14T21:38:49.3578800Z For per channel features, it averages the value across the channels and plots a point 2025-08-14T21:38:49.3579052Z per module. The reason for this is that for models with hundreds of channels, it can 2025-08-14T21:38:49.3579331Z be hard to differentiate one channel line from another, and so the point of generating 2025-08-14T21:38:49.3579595Z a single average point per module is to give a sense of general trends that encourage 2025-08-14T21:38:49.3579692Z further deep dives. 2025-08-14T21:38:49.3579777Z 2025-08-14T21:38:49.3579860Z Note: 2025-08-14T21:38:49.3580118Z Only features in the report that have tensor value data are plottable by this class 2025-08-14T21:38:49.3580318Z When the tensor information is plotted, it will plot: 2025-08-14T21:38:49.3580448Z idx as the x val, feature value as the y_val 2025-08-14T21:38:49.3580622Z When the channel information is plotted, it will plot: 2025-08-14T21:38:49.3580882Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-08-14T21:38:49.3581104Z The reason for this is that we want to be able to compare values across the 2025-08-14T21:38:49.3581340Z channels for same layer, and it will be hard if values are staggered by idx 2025-08-14T21:38:49.3581520Z This means each module is represented by only 1 x value 2025-08-14T21:38:49.3581607Z Args: 2025-08-14T21:38:49.3581839Z feature_filter (str): Filters the features presented to only those that 2025-08-14T21:38:49.3581948Z contain this filter substring 2025-08-14T21:38:49.3582217Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-14T21:38:49.3582459Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-14T21:38:49.3582541Z 2025-08-14T21:38:49.3582643Z Example Use: 2025-08-14T21:38:49.3582770Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:49.3582930Z >>> mod_report_visualizer.generate_plot_visualization( 2025-08-14T21:38:49.3583133Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-14T21:38:49.3583216Z ... ) 2025-08-14T21:38:49.3583470Z >>> # outputs line plot of per_channel_min information for all 2025-08-14T21:38:49.3583673Z >>> # modules in block1 of model each channel gets it's own line, 2025-08-14T21:38:49.3583846Z >>> # and it's plotted across the in-order modules on the x-axis 2025-08-14T21:38:49.3583940Z 2025-08-14T21:38:49.3584187Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3584271Z 2025-08-14T21:38:49.3584383Z warnings.warn(msg) 2025-08-14T21:38:49.3584462Z 2025-08-14T21:38:49.3584654Z --- Parse Warning: 45 / 146 --- 2025-08-14T21:38:49.3585932Z /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. 2025-08-14T21:38:49.3586191Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3586284Z 2025-08-14T21:38:49.3586625Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-08-14T21:38:49.3586735Z 2025-08-14T21:38:49.3586836Z Note: 2025-08-14T21:38:49.3587097Z Only features in the report that have tensor value data can be viewed as a histogram 2025-08-14T21:38:49.3587357Z If you want to plot a histogram from all the channel values of a specific feature for 2025-08-14T21:38:49.3587617Z a specific model, make sure to specify both the model and the feature properly 2025-08-14T21:38:49.3587857Z in the filters and you should be able to see a distribution of the channel data 2025-08-14T21:38:49.3587949Z 2025-08-14T21:38:49.3588032Z Args: 2025-08-14T21:38:49.3588289Z feature_filter (str, optional): Filters the features presented to only those that 2025-08-14T21:38:49.3588408Z contain this filter substring 2025-08-14T21:38:49.3588570Z Default = "", results in all the features being printed 2025-08-14T21:38:49.3588825Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-14T21:38:49.3589079Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-14T21:38:49.3589298Z num_bins (int, optional): The number of bins to create the histogram with 2025-08-14T21:38:49.3589519Z Default = 10, the values will be split into 10 equal sized bins 2025-08-14T21:38:49.3589597Z 2025-08-14T21:38:49.3589684Z Example Use: 2025-08-14T21:38:49.3589792Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3590087Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-08-14T21:38:49.3590273Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-14T21:38:49.3590365Z ... ) 2025-08-14T21:38:49.3590635Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-08-14T21:38:49.3590899Z information is gathered across all channels for all modules in block 1 for the 2025-08-14T21:38:49.3591111Z per_channel_min and is displayed in a histogram of equally sized bins 2025-08-14T21:38:49.3591187Z 2025-08-14T21:38:49.3591442Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3591521Z 2025-08-14T21:38:49.3591613Z warnings.warn(msg) 2025-08-14T21:38:49.3591702Z 2025-08-14T21:38:49.3591885Z --- Parse Warning: 46 / 146 --- 2025-08-14T21:38:49.3592769Z /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. 2025-08-14T21:38:49.3593193Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3593572Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-08-14T21:38:49.3593770Z Label will only appear if CPU activity tracing is enabled. 2025-08-14T21:38:49.3593848Z 2025-08-14T21:38:49.3593976Z It is useful when tracing the code profile. 2025-08-14T21:38:49.3594067Z 2025-08-14T21:38:49.3594149Z Args: 2025-08-14T21:38:49.3594290Z name (str): Label assigned to the block of code. 2025-08-14T21:38:49.3594484Z node_id (int): ID of node, for distributed profiling. Unset in 2025-08-14T21:38:49.3594591Z non-distributed cases. 2025-08-14T21:38:49.3594684Z 2025-08-14T21:38:49.3594769Z Example: 2025-08-14T21:38:49.3594947Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-08-14T21:38:49.3595089Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-08-14T21:38:49.3595250Z >>> with torch.autograd.profiler.profile() as prof: 2025-08-14T21:38:49.3595340Z ... y = x**2 2025-08-14T21:38:49.3595643Z ... with torch.autograd.profiler.record_function( 2025-08-14T21:38:49.3595771Z ... "label-z" 2025-08-14T21:38:49.3595869Z ... ): # label the block 2025-08-14T21:38:49.3595974Z ... z = y**3 2025-08-14T21:38:49.3596065Z ... y.backward() 2025-08-14T21:38:49.3596169Z >>> # xdoctest: +IGNORE_WANT 2025-08-14T21:38:49.3596316Z >>> # NOTE: some columns were removed for brevity 2025-08-14T21:38:49.3596515Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-08-14T21:38:49.3596719Z ----------------------------------- --------------- --------------- --------------- 2025-08-14T21:38:49.3596898Z Name Self CPU total % CPU time avg Number of Calls 2025-08-14T21:38:49.3597083Z ----------------------------------- --------------- --------------- --------------- 2025-08-14T21:38:49.3597232Z pow 60.77% 47.470us 3 2025-08-14T21:38:49.3597360Z mul 21.73% 25.465us 2 2025-08-14T21:38:49.3597541Z PowBackward0 12.03% 121.891us 1 2025-08-14T21:38:49.3597798Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-08-14T21:38:49.3597939Z label-z 2.13% 12.421us 1 2025-08-14T21:38:49.3598145Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-08-14T21:38:49.3598331Z ----------------------------------- --------------- --------------- --------------- 2025-08-14T21:38:49.3598452Z Self CPU time total: 234.344us 2025-08-14T21:38:49.3598550Z CUDA time total: 0.000us 2025-08-14T21:38:49.3598629Z 2025-08-14T21:38:49.3598720Z 2025-08-14T21:38:49.3598974Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3599054Z 2025-08-14T21:38:49.3599163Z warnings.warn(msg) 2025-08-14T21:38:49.3599242Z 2025-08-14T21:38:49.3599432Z --- Parse Warning: 47 / 146 --- 2025-08-14T21:38:49.3600379Z /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=722. 2025-08-14T21:38:49.3600623Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:49.3600714Z 2025-08-14T21:38:49.3600978Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-08-14T21:38:49.3601235Z The submesh created consists of the dimensions and the communicators indicated by 2025-08-14T21:38:49.3601337Z ``mesh_dim_names`` 2025-08-14T21:38:49.3601418Z 2025-08-14T21:38:49.3601498Z Args: 2025-08-14T21:38:49.3601744Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-08-14T21:38:49.3601928Z mesh dimension of the DeviceMesh to create the submesh for. 2025-08-14T21:38:49.3602009Z Returns: 2025-08-14T21:38:49.3602126Z A :class:`DeviceMesh` object 2025-08-14T21:38:49.3602202Z 2025-08-14T21:38:49.3602492Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-08-14T21:38:49.3602587Z In the first example: 2025-08-14T21:38:49.3602833Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-08-14T21:38:49.3603088Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-08-14T21:38:49.3603305Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-08-14T21:38:49.3603520Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-08-14T21:38:49.3603796Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-08-14T21:38:49.3604032Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-08-14T21:38:49.3604122Z 2025-08-14T21:38:49.3604219Z In the second example: 2025-08-14T21:38:49.3604479Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-08-14T21:38:49.3604747Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-08-14T21:38:49.3604999Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-08-14T21:38:49.3605251Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-08-14T21:38:49.3605340Z 2025-08-14T21:38:49.3605429Z Example:: 2025-08-14T21:38:49.3605518Z 2025-08-14T21:38:49.3605627Z >>> # xdoctest: +SKIP("no rank") 2025-08-14T21:38:49.3605797Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-08-14T21:38:49.3605889Z >>> 2025-08-14T21:38:49.3606086Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-08-14T21:38:49.3606250Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-08-14T21:38:49.3606507Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-08-14T21:38:49.3606607Z >>> tp_mesh = mesh_2d["tp"] 2025-08-14T21:38:49.3606704Z >>> dp_mesh = mesh_2d["dp"] 2025-08-14T21:38:49.3606795Z >>> 2025-08-14T21:38:49.3606891Z >>> # Initialize a 3D mesh. 2025-08-14T21:38:49.3607170Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-08-14T21:38:49.3607467Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-08-14T21:38:49.3607581Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-08-14T21:38:49.3607702Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-08-14T21:38:49.3607779Z 2025-08-14T21:38:49.3608443Z 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)) 2025-08-14T21:38:49.3608536Z 2025-08-14T21:38:49.3608774Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-08-14T21:38:49.3608901Z ^ 2025-08-14T21:38:49.3608998Z warnings.warn(msg) 2025-08-14T21:38:49.3609076Z 2025-08-14T21:38:49.3609274Z --- Parse Warning: 48 / 146 --- 2025-08-14T21:38:49.3610219Z /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=2706. 2025-08-14T21:38:49.3610478Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3610566Z 2025-08-14T21:38:49.3610810Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-08-14T21:38:49.3610901Z 2025-08-14T21:38:49.3611143Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-08-14T21:38:49.3611333Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-08-14T21:38:49.3611425Z 2025-08-14T21:38:49.3611506Z Args: 2025-08-14T21:38:49.3611724Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-08-14T21:38:49.3611961Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-08-14T21:38:49.3612165Z matters and it needs to match with corresponding isend/irecv on the 2025-08-14T21:38:49.3612268Z remote end. 2025-08-14T21:38:49.3612402Z 2025-08-14T21:38:49.3612507Z Returns: 2025-08-14T21:38:49.3612758Z A list of distributed request objects returned by calling the corresponding 2025-08-14T21:38:49.3612852Z op in the op_list. 2025-08-14T21:38:49.3612930Z 2025-08-14T21:38:49.3613028Z Examples: 2025-08-14T21:38:49.3613135Z >>> # xdoctest: +SKIP("no rank") 2025-08-14T21:38:49.3613319Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-08-14T21:38:49.3613477Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-08-14T21:38:49.3613685Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-08-14T21:38:49.3613784Z >>> recv_op = dist.P2POp( 2025-08-14T21:38:49.3613980Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-08-14T21:38:49.3614062Z ... ) 2025-08-14T21:38:49.3614208Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-08-14T21:38:49.3619728Z >>> for req in reqs: 2025-08-14T21:38:49.3619871Z >>> req.wait() 2025-08-14T21:38:49.3619978Z >>> recv_tensor 2025-08-14T21:38:49.3620075Z tensor([2, 3]) # Rank 0 2025-08-14T21:38:49.3620180Z tensor([0, 1]) # Rank 1 2025-08-14T21:38:49.3620330Z 2025-08-14T21:38:49.3620591Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-08-14T21:38:49.3620824Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-08-14T21:38:49.3620933Z lead to unexpected hang issues. 2025-08-14T21:38:49.3621008Z 2025-08-14T21:38:49.3621227Z In addition, if this API is the first collective call in the ``group`` 2025-08-14T21:38:49.3621444Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-08-14T21:38:49.3621660Z this API call; otherwise, the behavior is undefined. If this API call is 2025-08-14T21:38:49.3621890Z not the first collective call in the ``group``, batched P2P operations 2025-08-14T21:38:49.3622080Z involving only a subset of ranks of the ``group`` are allowed. 2025-08-14T21:38:49.3622168Z 2025-08-14T21:38:49.3622412Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3622492Z 2025-08-14T21:38:49.3622600Z warnings.warn(msg) 2025-08-14T21:38:49.3622679Z 2025-08-14T21:38:49.3622915Z --- Parse Warning: 49 / 146 --- 2025-08-14T21:38:49.3623947Z /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=2838. 2025-08-14T21:38:49.3624204Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3624294Z 2025-08-14T21:38:49.3624557Z Reduces the tensor data across all machines in a way that all get the final result. 2025-08-14T21:38:49.3624638Z 2025-08-14T21:38:49.3624876Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-08-14T21:38:49.3624953Z 2025-08-14T21:38:49.3625059Z Complex tensors are supported. 2025-08-14T21:38:49.3625150Z 2025-08-14T21:38:49.3625234Z Args: 2025-08-14T21:38:49.3625431Z tensor (Tensor): Input and output of the collective. The function 2025-08-14T21:38:49.3625543Z operates in-place. 2025-08-14T21:38:49.3625656Z op (optional): One of the values from 2025-08-14T21:38:49.3625783Z ``torch.distributed.ReduceOp`` 2025-08-14T21:38:49.3625981Z enum. Specifies an operation used for element-wise reductions. 2025-08-14T21:38:49.3626204Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-14T21:38:49.3626342Z the default process group will be used. 2025-08-14T21:38:49.3626531Z async_op (bool, optional): Whether this op should be an async op 2025-08-14T21:38:49.3626674Z 2025-08-14T21:38:49.3626814Z Returns: 2025-08-14T21:38:49.3626953Z Async work handle, if async_op is set to True. 2025-08-14T21:38:49.3627096Z None, if not async_op or if not part of the group 2025-08-14T21:38:49.3627193Z 2025-08-14T21:38:49.3627280Z Examples: 2025-08-14T21:38:49.3627399Z >>> # xdoctest: +SKIP("no rank") 2025-08-14T21:38:49.3627532Z >>> # All tensors below are of torch.int64 type. 2025-08-14T21:38:49.3627648Z >>> # We have 2 process groups, 2 ranks. 2025-08-14T21:38:49.3627777Z >>> device = torch.device(f"cuda:{rank}") 2025-08-14T21:38:49.3627999Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-08-14T21:38:49.3628081Z >>> tensor 2025-08-14T21:38:49.3628206Z tensor([1, 2], device='cuda:0') # Rank 0 2025-08-14T21:38:49.3628317Z tensor([3, 4], device='cuda:1') # Rank 1 2025-08-14T21:38:49.3628449Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-08-14T21:38:49.3628546Z >>> tensor 2025-08-14T21:38:49.3628656Z tensor([4, 6], device='cuda:0') # Rank 0 2025-08-14T21:38:49.3628777Z tensor([4, 6], device='cuda:1') # Rank 1 2025-08-14T21:38:49.3628856Z 2025-08-14T21:38:49.3628987Z >>> # All tensors below are of torch.cfloat type. 2025-08-14T21:38:49.3629138Z >>> # We have 2 process groups, 2 ranks. 2025-08-14T21:38:49.3629240Z >>> tensor = torch.tensor( 2025-08-14T21:38:49.3629384Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-08-14T21:38:49.3629492Z ... ) + 2 * rank * (1 + 1j) 2025-08-14T21:38:49.3629575Z >>> tensor 2025-08-14T21:38:49.3629710Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-08-14T21:38:49.3629859Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-08-14T21:38:49.3629988Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-08-14T21:38:49.3630082Z >>> tensor 2025-08-14T21:38:49.3630219Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-08-14T21:38:49.3630351Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-08-14T21:38:49.3630443Z 2025-08-14T21:38:49.3630523Z 2025-08-14T21:38:49.3630772Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3630868Z 2025-08-14T21:38:49.3630964Z warnings.warn(msg) 2025-08-14T21:38:49.3631041Z 2025-08-14T21:38:49.3631251Z --- Parse Warning: 50 / 146 --- 2025-08-14T21:38:49.3632184Z /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=3198. 2025-08-14T21:38:49.3632453Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3632533Z 2025-08-14T21:38:49.3632755Z Gathers picklable objects from the whole group in a single process. 2025-08-14T21:38:49.3632846Z 2025-08-14T21:38:49.3633075Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-08-14T21:38:49.3633219Z object must be picklable in order to be gathered. 2025-08-14T21:38:49.3633317Z 2025-08-14T21:38:49.3633402Z Args: 2025-08-14T21:38:49.3633528Z obj (Any): Input object. Must be picklable. 2025-08-14T21:38:49.3633745Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-08-14T21:38:49.3633926Z should be correctly sized as the size of the group for this 2025-08-14T21:38:49.3634147Z collective and will contain the output. Must be ``None`` on non-dst 2025-08-14T21:38:49.3634251Z ranks. (default is ``None``) 2025-08-14T21:38:49.3634564Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-08-14T21:38:49.3634764Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-08-14T21:38:49.3635039Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-08-14T21:38:49.3635246Z the default process group will be used. Default is ``None``. 2025-08-14T21:38:49.3635594Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-08-14T21:38:49.3635675Z 2025-08-14T21:38:49.3635763Z Returns: 2025-08-14T21:38:49.3635939Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-08-14T21:38:49.3636041Z output of the collective. 2025-08-14T21:38:49.3636129Z 2025-08-14T21:38:49.3636336Z .. note:: Note that this API differs slightly from the gather collective 2025-08-14T21:38:49.3636546Z since it does not provide an async_op handle and thus will be a blocking 2025-08-14T21:38:49.3636638Z call. 2025-08-14T21:38:49.3636716Z 2025-08-14T21:38:49.3636954Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-08-14T21:38:49.3637160Z of objects must be moved to the GPU device before communication takes 2025-08-14T21:38:49.3637302Z place. In this case, the device used is given by 2025-08-14T21:38:49.3637534Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-08-14T21:38:49.3637760Z ensure that this is set so that each rank has an individual GPU, via 2025-08-14T21:38:49.3637865Z ``torch.cuda.set_device()``. 2025-08-14T21:38:49.3637953Z 2025-08-14T21:38:49.3638039Z .. warning:: 2025-08-14T21:38:49.3638266Z Object collectives have a number of serious performance and scalability 2025-08-14T21:38:49.3638445Z limitations. See :ref:`object_collectives` for details. 2025-08-14T21:38:49.3638524Z 2025-08-14T21:38:49.3638619Z .. warning:: 2025-08-14T21:38:49.3638810Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-08-14T21:38:49.3639028Z known to be insecure. It is possible to construct malicious pickle data 2025-08-14T21:38:49.3639253Z which will execute arbitrary code during unpickling. Only call this 2025-08-14T21:38:49.3639357Z function with data you trust. 2025-08-14T21:38:49.3639438Z 2025-08-14T21:38:49.3639534Z .. warning:: 2025-08-14T21:38:49.3639743Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-08-14T21:38:49.3639960Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-08-14T21:38:49.3640137Z pickled. Please consider using :func:`gather` instead. 2025-08-14T21:38:49.3640213Z 2025-08-14T21:38:49.3640311Z Example:: 2025-08-14T21:38:49.3640444Z >>> # xdoctest: +SKIP("need process group init") 2025-08-14T21:38:49.3640620Z >>> # Note: Process group initialization omitted on each rank. 2025-08-14T21:38:49.3640749Z >>> import torch.distributed as dist 2025-08-14T21:38:49.3640852Z >>> # Assumes world_size of 3. 2025-08-14T21:38:49.3641027Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-08-14T21:38:49.3641157Z >>> output = [None for _ in gather_objects] 2025-08-14T21:38:49.3641252Z >>> dist.gather_object( 2025-08-14T21:38:49.3641382Z ... gather_objects[dist.get_rank()], 2025-08-14T21:38:49.3641511Z ... output if dist.get_rank() == 0 else None, 2025-08-14T21:38:49.3641595Z ... dst=0 2025-08-14T21:38:49.3641684Z ... ) 2025-08-14T21:38:49.3641769Z >>> # On rank 0 2025-08-14T21:38:49.3641856Z >>> output 2025-08-14T21:38:49.3641949Z ['foo', 12, {1: 2}] 2025-08-14T21:38:49.3642026Z 2025-08-14T21:38:49.3642273Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3642363Z 2025-08-14T21:38:49.3642455Z warnings.warn(msg) 2025-08-14T21:38:49.3642534Z 2025-08-14T21:38:49.3642731Z --- Parse Warning: 51 / 146 --- 2025-08-14T21:38:49.3643702Z /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=3794. 2025-08-14T21:38:49.3644001Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3644081Z 2025-08-14T21:38:49.3644220Z Gathers tensors from the whole group in a list. 2025-08-14T21:38:49.3644309Z 2025-08-14T21:38:49.3644449Z Complex and uneven sized tensors are supported. 2025-08-14T21:38:49.3644525Z 2025-08-14T21:38:49.3644618Z Args: 2025-08-14T21:38:49.3644792Z tensor_list (list[Tensor]): Output list. It should contain 2025-08-14T21:38:49.3645002Z correctly-sized tensors to be used for output of the collective. 2025-08-14T21:38:49.3645135Z Uneven sized tensors are supported. 2025-08-14T21:38:49.3645318Z tensor (Tensor): Tensor to be broadcast from current process. 2025-08-14T21:38:49.3645560Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-14T21:38:49.3645686Z the default process group will be used. 2025-08-14T21:38:49.3645875Z async_op (bool, optional): Whether this op should be an async op 2025-08-14T21:38:49.3645991Z 2025-08-14T21:38:49.3646073Z Returns: 2025-08-14T21:38:49.3646206Z Async work handle, if async_op is set to True. 2025-08-14T21:38:49.3646364Z None, if not async_op or if not part of the group 2025-08-14T21:38:49.3646443Z 2025-08-14T21:38:49.3646526Z Examples: 2025-08-14T21:38:49.3646670Z >>> # xdoctest: +SKIP("need process group init") 2025-08-14T21:38:49.3646802Z >>> # All tensors below are of torch.int64 dtype. 2025-08-14T21:38:49.3646930Z >>> # We have 2 process groups, 2 ranks. 2025-08-14T21:38:49.3647049Z >>> device = torch.device(f"cuda:{rank}") 2025-08-14T21:38:49.3647140Z >>> tensor_list = [ 2025-08-14T21:38:49.3647359Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-08-14T21:38:49.3647441Z ... ] 2025-08-14T21:38:49.3647530Z >>> tensor_list 2025-08-14T21:38:49.3647735Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-08-14T21:38:49.3647926Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-08-14T21:38:49.3648154Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-08-14T21:38:49.3648237Z >>> tensor 2025-08-14T21:38:49.3648351Z tensor([1, 2], device='cuda:0') # Rank 0 2025-08-14T21:38:49.3648473Z tensor([3, 4], device='cuda:1') # Rank 1 2025-08-14T21:38:49.3648588Z >>> dist.all_gather(tensor_list, tensor) 2025-08-14T21:38:49.3648673Z >>> tensor_list 2025-08-14T21:38:49.3648876Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-08-14T21:38:49.3649066Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-08-14T21:38:49.3649144Z 2025-08-14T21:38:49.3649293Z >>> # All tensors below are of torch.cfloat dtype. 2025-08-14T21:38:49.3649405Z >>> # We have 2 process groups, 2 ranks. 2025-08-14T21:38:49.3649505Z >>> tensor_list = [ 2025-08-14T21:38:49.3649711Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-08-14T21:38:49.3649791Z ... ] 2025-08-14T21:38:49.3649888Z >>> tensor_list 2025-08-14T21:38:49.3650129Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-08-14T21:38:49.3650362Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-08-14T21:38:49.3650474Z >>> tensor = torch.tensor( 2025-08-14T21:38:49.3650619Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-08-14T21:38:49.3650709Z ... ) + 2 * rank * (1 + 1j) 2025-08-14T21:38:49.3650886Z >>> tensor 2025-08-14T21:38:49.3651046Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-08-14T21:38:49.3651190Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-08-14T21:38:49.3651303Z >>> dist.all_gather(tensor_list, tensor) 2025-08-14T21:38:49.3651390Z >>> tensor_list 2025-08-14T21:38:49.3651634Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-08-14T21:38:49.3651863Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-08-14T21:38:49.3651941Z 2025-08-14T21:38:49.3652031Z 2025-08-14T21:38:49.3652277Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3652353Z 2025-08-14T21:38:49.3652457Z warnings.warn(msg) 2025-08-14T21:38:49.3652536Z 2025-08-14T21:38:49.3652733Z --- Parse Warning: 52 / 146 --- 2025-08-14T21:38:49.3653676Z /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=4500. 2025-08-14T21:38:49.3653935Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3654050Z 2025-08-14T21:38:49.3654298Z Split input tensor and then scatter the split list to all processes in a group. 2025-08-14T21:38:49.3654375Z 2025-08-14T21:38:49.3654638Z Later the received tensors are concatenated from all the processes in the group 2025-08-14T21:38:49.3654754Z and returned as a single output tensor. 2025-08-14T21:38:49.3654847Z 2025-08-14T21:38:49.3654953Z Complex tensors are supported. 2025-08-14T21:38:49.3655032Z 2025-08-14T21:38:49.3655121Z Args: 2025-08-14T21:38:49.3655286Z output (Tensor): Gathered concatenated output tensor. 2025-08-14T21:38:49.3655412Z input (Tensor): Input tensor to scatter. 2025-08-14T21:38:49.3655640Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-08-14T21:38:49.3655839Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-08-14T21:38:49.3655945Z equally by ``world_size``. 2025-08-14T21:38:49.3656160Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-08-14T21:38:49.3656355Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-08-14T21:38:49.3656468Z equally by ``world_size``. 2025-08-14T21:38:49.3656696Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-14T21:38:49.3656820Z the default process group will be used. 2025-08-14T21:38:49.3657021Z async_op (bool, optional): Whether this op should be an async op. 2025-08-14T21:38:49.3657099Z 2025-08-14T21:38:49.3657183Z Returns: 2025-08-14T21:38:49.3657333Z Async work handle, if async_op is set to True. 2025-08-14T21:38:49.3657478Z None, if not async_op or if not part of the group. 2025-08-14T21:38:49.3657558Z 2025-08-14T21:38:49.3657654Z .. warning:: 2025-08-14T21:38:49.3657821Z `all_to_all_single` is experimental and subject to change. 2025-08-14T21:38:49.3657909Z 2025-08-14T21:38:49.3657994Z Examples: 2025-08-14T21:38:49.3658119Z >>> # xdoctest: +SKIP("Undefined rank") 2025-08-14T21:38:49.3658230Z >>> input = torch.arange(4) + rank * 4 2025-08-14T21:38:49.3658310Z >>> input 2025-08-14T21:38:49.3658416Z tensor([0, 1, 2, 3]) # Rank 0 2025-08-14T21:38:49.3658513Z tensor([4, 5, 6, 7]) # Rank 1 2025-08-14T21:38:49.3658616Z tensor([8, 9, 10, 11]) # Rank 2 2025-08-14T21:38:49.3658708Z tensor([12, 13, 14, 15]) # Rank 3 2025-08-14T21:38:49.3658843Z >>> output = torch.empty([4], dtype=torch.int64) 2025-08-14T21:38:49.3658973Z >>> dist.all_to_all_single(output, input) 2025-08-14T21:38:49.3659104Z >>> output 2025-08-14T21:38:49.3659222Z tensor([0, 4, 8, 12]) # Rank 0 2025-08-14T21:38:49.3659321Z tensor([1, 5, 9, 13]) # Rank 1 2025-08-14T21:38:49.3659413Z tensor([2, 6, 10, 14]) # Rank 2 2025-08-14T21:38:49.3659505Z tensor([3, 7, 11, 15]) # Rank 3 2025-08-14T21:38:49.3659590Z 2025-08-14T21:38:49.3659746Z >>> # Essentially, it is similar to following operation: 2025-08-14T21:38:49.3659890Z >>> scatter_list = list(input.chunk(world_size)) 2025-08-14T21:38:49.3660022Z >>> gather_list = list(output.chunk(world_size)) 2025-08-14T21:38:49.3660123Z >>> for i in range(world_size): 2025-08-14T21:38:49.3660355Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-08-14T21:38:49.3660436Z 2025-08-14T21:38:49.3660552Z >>> # Another example with uneven split 2025-08-14T21:38:49.3660637Z >>> input 2025-08-14T21:38:49.3660788Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-08-14T21:38:49.3660944Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-08-14T21:38:49.3661099Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-08-14T21:38:49.3661273Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-08-14T21:38:49.3661372Z >>> input_splits 2025-08-14T21:38:49.3661490Z [2, 2, 1, 1] # Rank 0 2025-08-14T21:38:49.3661603Z [3, 2, 2, 2] # Rank 1 2025-08-14T21:38:49.3661726Z [2, 1, 1, 1] # Rank 2 2025-08-14T21:38:49.3661840Z [2, 2, 2, 1] # Rank 3 2025-08-14T21:38:49.3661930Z >>> output_splits 2025-08-14T21:38:49.3662057Z [2, 3, 2, 2] # Rank 0 2025-08-14T21:38:49.3662173Z [2, 2, 1, 2] # Rank 1 2025-08-14T21:38:49.3662296Z [1, 2, 1, 2] # Rank 2 2025-08-14T21:38:49.3662412Z [1, 2, 1, 1] # Rank 3 2025-08-14T21:38:49.3662501Z >>> output = ... 2025-08-14T21:38:49.3662711Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-08-14T21:38:49.3662791Z >>> output 2025-08-14T21:38:49.3662944Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-08-14T21:38:49.3663096Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-08-14T21:38:49.3663245Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-08-14T21:38:49.3663492Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-08-14T21:38:49.3663575Z 2025-08-14T21:38:49.3663656Z 2025-08-14T21:38:49.3663827Z >>> # Another example with tensors of torch.cfloat type. 2025-08-14T21:38:49.3663925Z >>> input = torch.tensor( 2025-08-14T21:38:49.3664059Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-08-14T21:38:49.3664161Z ... ) + 4 * rank * (1 + 1j) 2025-08-14T21:38:49.3664244Z >>> input 2025-08-14T21:38:49.3664413Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-08-14T21:38:49.3664588Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-08-14T21:38:49.3664762Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-08-14T21:38:49.3664943Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-08-14T21:38:49.3665079Z >>> output = torch.empty([4], dtype=torch.int64) 2025-08-14T21:38:49.3665254Z >>> dist.all_to_all_single(output, input) 2025-08-14T21:38:49.3665363Z >>> output 2025-08-14T21:38:49.3665529Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-08-14T21:38:49.3665696Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-08-14T21:38:49.3665874Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-08-14T21:38:49.3666041Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-08-14T21:38:49.3666129Z 2025-08-14T21:38:49.3666373Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3666450Z 2025-08-14T21:38:49.3666553Z warnings.warn(msg) 2025-08-14T21:38:49.3666635Z 2025-08-14T21:38:49.3666845Z --- Parse Warning: 53 / 146 --- 2025-08-14T21:38:49.3667775Z /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=4642. 2025-08-14T21:38:49.3668035Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3668126Z 2025-08-14T21:38:49.3668486Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-08-14T21:38:49.3668595Z 2025-08-14T21:38:49.3668718Z Complex tensors are supported. 2025-08-14T21:38:49.3668797Z 2025-08-14T21:38:49.3668881Z Args: 2025-08-14T21:38:49.3669107Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-08-14T21:38:49.3669192Z per rank. 2025-08-14T21:38:49.3669414Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-08-14T21:38:49.3669648Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-14T21:38:49.3669772Z the default process group will be used. 2025-08-14T21:38:49.3669977Z async_op (bool, optional): Whether this op should be an async op. 2025-08-14T21:38:49.3670053Z 2025-08-14T21:38:49.3670136Z Returns: 2025-08-14T21:38:49.3670279Z Async work handle, if async_op is set to True. 2025-08-14T21:38:49.3670421Z None, if not async_op or if not part of the group. 2025-08-14T21:38:49.3670501Z 2025-08-14T21:38:49.3670596Z .. warning:: 2025-08-14T21:38:49.3670739Z `all_to_all` is experimental and subject to change. 2025-08-14T21:38:49.3670815Z 2025-08-14T21:38:49.3670906Z Examples: 2025-08-14T21:38:49.3671022Z >>> # xdoctest: +SKIP("Undefined rank") 2025-08-14T21:38:49.3671132Z >>> input = torch.arange(4) + rank * 4 2025-08-14T21:38:49.3671247Z >>> input = list(input.chunk(4)) 2025-08-14T21:38:49.3671328Z >>> input 2025-08-14T21:38:49.3671502Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-08-14T21:38:49.3671664Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-08-14T21:38:49.3671821Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-08-14T21:38:49.3671991Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-08-14T21:38:49.3672166Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-08-14T21:38:49.3672274Z >>> dist.all_to_all(output, input) 2025-08-14T21:38:49.3672363Z >>> output 2025-08-14T21:38:49.3672521Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-08-14T21:38:49.3672684Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-08-14T21:38:49.3672839Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-08-14T21:38:49.3672993Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-08-14T21:38:49.3673078Z 2025-08-14T21:38:49.3673236Z >>> # Essentially, it is similar to following operation: 2025-08-14T21:38:49.3673387Z >>> scatter_list = input 2025-08-14T21:38:49.3673516Z >>> gather_list = output 2025-08-14T21:38:49.3673618Z >>> for i in range(world_size): 2025-08-14T21:38:49.3673831Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-08-14T21:38:49.3673919Z 2025-08-14T21:38:49.3673999Z >>> input 2025-08-14T21:38:49.3674157Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-08-14T21:38:49.3674308Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-08-14T21:38:49.3674454Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-08-14T21:38:49.3674614Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-08-14T21:38:49.3674702Z >>> input_splits 2025-08-14T21:38:49.3674817Z [2, 2, 1, 1] # Rank 0 2025-08-14T21:38:49.3674946Z [3, 2, 2, 2] # Rank 1 2025-08-14T21:38:49.3675063Z [2, 1, 1, 1] # Rank 2 2025-08-14T21:38:49.3675186Z [2, 2, 2, 1] # Rank 3 2025-08-14T21:38:49.3675300Z >>> output_splits 2025-08-14T21:38:49.3675414Z [2, 3, 2, 2] # Rank 0 2025-08-14T21:38:49.3675535Z [2, 2, 1, 2] # Rank 1 2025-08-14T21:38:49.3675652Z [1, 2, 1, 2] # Rank 2 2025-08-14T21:38:49.3675772Z [1, 2, 1, 1] # Rank 3 2025-08-14T21:38:49.3675895Z >>> input = list(input.split(input_splits)) 2025-08-14T21:38:49.3675984Z >>> input 2025-08-14T21:38:49.3676181Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-08-14T21:38:49.3676375Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-08-14T21:38:49.3676579Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-08-14T21:38:49.3676770Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-08-14T21:38:49.3676857Z >>> output = ... 2025-08-14T21:38:49.3676974Z >>> dist.all_to_all(output, input) 2025-08-14T21:38:49.3677052Z >>> output 2025-08-14T21:38:49.3677256Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-08-14T21:38:49.3677443Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-08-14T21:38:49.3677631Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-08-14T21:38:49.3677832Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-08-14T21:38:49.3677907Z 2025-08-14T21:38:49.3678061Z >>> # Another example with tensors of torch.cfloat type. 2025-08-14T21:38:49.3678166Z >>> input = torch.tensor( 2025-08-14T21:38:49.3678299Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-08-14T21:38:49.3678396Z ... ) + 4 * rank * (1 + 1j) 2025-08-14T21:38:49.3678498Z >>> input = list(input.chunk(4)) 2025-08-14T21:38:49.3678575Z >>> input 2025-08-14T21:38:49.3678791Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-08-14T21:38:49.3678989Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-08-14T21:38:49.3679196Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-08-14T21:38:49.3679418Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-08-14T21:38:49.3679653Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-08-14T21:38:49.3679794Z >>> dist.all_to_all(output, input) 2025-08-14T21:38:49.3679870Z >>> output 2025-08-14T21:38:49.3680068Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-08-14T21:38:49.3680280Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-08-14T21:38:49.3680475Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-08-14T21:38:49.3680674Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-08-14T21:38:49.3680759Z 2025-08-14T21:38:49.3680832Z 2025-08-14T21:38:49.3681076Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3681158Z 2025-08-14T21:38:49.3681248Z warnings.warn(msg) 2025-08-14T21:38:49.3681332Z 2025-08-14T21:38:49.3681523Z --- Parse Warning: 54 / 146 --- 2025-08-14T21:38:49.3682355Z /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. 2025-08-14T21:38:49.3682649Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3682724Z 2025-08-14T21:38:49.3682837Z Module ``torch.distributed.launch``. 2025-08-14T21:38:49.3682927Z 2025-08-14T21:38:49.3683168Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-08-14T21:38:49.3683327Z training processes on each of the training nodes. 2025-08-14T21:38:49.3683396Z 2025-08-14T21:38:49.3683480Z .. warning:: 2025-08-14T21:38:49.3683567Z 2025-08-14T21:38:49.3683813Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-08-14T21:38:49.3683887Z 2025-08-14T21:38:49.3684126Z The utility can be used for single-node distributed training, in which one or 2025-08-14T21:38:49.3684350Z more processes per node will be spawned. The utility can be used for either 2025-08-14T21:38:49.3684559Z CPU training or GPU training. If the utility is used for GPU training, 2025-08-14T21:38:49.3684804Z each distributed process will be operating on a single GPU. This can achieve 2025-08-14T21:38:49.3685023Z well-improved single-node training performance. It can also be used in 2025-08-14T21:38:49.3685286Z multi-node distributed training, by spawning up multiple processes on each node 2025-08-14T21:38:49.3685505Z for well-improved multi-node distributed training performance as well. 2025-08-14T21:38:49.3685726Z This will especially be beneficial for systems with multiple Infiniband 2025-08-14T21:38:49.3685975Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-08-14T21:38:49.3686081Z aggregated communication bandwidth. 2025-08-14T21:38:49.3686157Z 2025-08-14T21:38:49.3686389Z In both cases of single-node distributed training or multi-node distributed 2025-08-14T21:38:49.3686611Z training, this utility will launch the given number of processes per node 2025-08-14T21:38:49.3686832Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-08-14T21:38:49.3687044Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-08-14T21:38:49.3687236Z and each process will be operating on a single GPU from *GPU 0 to 2025-08-14T21:38:49.3687344Z GPU (nproc_per_node - 1)*. 2025-08-14T21:38:49.3687417Z 2025-08-14T21:38:49.3687512Z **How to use this module:** 2025-08-14T21:38:49.3687589Z 2025-08-14T21:38:49.3687730Z 1. Single-Node multi-process distributed training 2025-08-14T21:38:49.3687802Z 2025-08-14T21:38:49.3687885Z :: 2025-08-14T21:38:49.3687957Z 2025-08-14T21:38:49.3688182Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-08-14T21:38:49.3688415Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-08-14T21:38:49.3688555Z arguments of your training script) 2025-08-14T21:38:49.3688634Z 2025-08-14T21:38:49.3688835Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-08-14T21:38:49.3688910Z 2025-08-14T21:38:49.3688993Z 2025-08-14T21:38:49.3689129Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-08-14T21:38:49.3689201Z 2025-08-14T21:38:49.3689282Z :: 2025-08-14T21:38:49.3689356Z 2025-08-14T21:38:49.3689584Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-08-14T21:38:49.3689742Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-08-14T21:38:49.3689942Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-08-14T21:38:49.3690092Z and all other arguments of your training script) 2025-08-14T21:38:49.3690169Z 2025-08-14T21:38:49.3690248Z Node 2: 2025-08-14T21:38:49.3690332Z 2025-08-14T21:38:49.3690407Z :: 2025-08-14T21:38:49.3690479Z 2025-08-14T21:38:49.3690708Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-08-14T21:38:49.3690885Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-08-14T21:38:49.3691082Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-08-14T21:38:49.3691233Z and all other arguments of your training script) 2025-08-14T21:38:49.3691306Z 2025-08-14T21:38:49.3691468Z 3. To look up what optional arguments this module offers: 2025-08-14T21:38:49.3691543Z 2025-08-14T21:38:49.3691619Z :: 2025-08-14T21:38:49.3691699Z 2025-08-14T21:38:49.3691830Z python -m torch.distributed.launch --help 2025-08-14T21:38:49.3691905Z 2025-08-14T21:38:49.3691988Z 2025-08-14T21:38:49.3692084Z **Important Notices:** 2025-08-14T21:38:49.3692162Z 2025-08-14T21:38:49.3692352Z 1. This utility and multi-process distributed (single-node or 2025-08-14T21:38:49.3692593Z multi-node) GPU training currently only achieves the best performance using 2025-08-14T21:38:49.3692989Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-08-14T21:38:49.3693093Z use for GPU training. 2025-08-14T21:38:49.3693169Z 2025-08-14T21:38:49.3693385Z 2. In your training program, you must parse the command-line argument: 2025-08-14T21:38:49.3693611Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-08-14T21:38:49.3693833Z If your training program uses GPUs, you should ensure that your code only 2025-08-14T21:38:49.3694026Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-08-14T21:38:49.3694109Z 2025-08-14T21:38:49.3694211Z Parsing the local_rank argument 2025-08-14T21:38:49.3694296Z 2025-08-14T21:38:49.3694375Z :: 2025-08-14T21:38:49.3694455Z 2025-08-14T21:38:49.3694558Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3694647Z >>> import argparse 2025-08-14T21:38:49.3694766Z >>> parser = argparse.ArgumentParser() 2025-08-14T21:38:49.3694962Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-08-14T21:38:49.3695066Z >>> args = parser.parse_args() 2025-08-14T21:38:49.3695145Z 2025-08-14T21:38:49.3695261Z Set your device to local rank using either 2025-08-14T21:38:49.3695335Z 2025-08-14T21:38:49.3695421Z :: 2025-08-14T21:38:49.3695497Z 2025-08-14T21:38:49.3695691Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-08-14T21:38:49.3695775Z 2025-08-14T21:38:49.3695850Z or 2025-08-14T21:38:49.3695922Z 2025-08-14T21:38:49.3696008Z :: 2025-08-14T21:38:49.3696082Z 2025-08-14T21:38:49.3696208Z >>> with torch.cuda.device(args.local_rank): 2025-08-14T21:38:49.3696310Z >>> # your code to run 2025-08-14T21:38:49.3696484Z >>> ... 2025-08-14T21:38:49.3696601Z 2025-08-14T21:38:49.3696700Z .. versionchanged:: 2.0.0 2025-08-14T21:38:49.3696772Z 2025-08-14T21:38:49.3697025Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-08-14T21:38:49.3697258Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-08-14T21:38:49.3697394Z previously used underscored ``--local_rank``. 2025-08-14T21:38:49.3697481Z 2025-08-14T21:38:49.3697709Z For backward compatibility, it may be necessary for users to handle both 2025-08-14T21:38:49.3697964Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-08-14T21:38:49.3698182Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-08-14T21:38:49.3698424Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-08-14T21:38:49.3698657Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-08-14T21:38:49.3698800Z including ``"--local-rank"`` should be sufficient. 2025-08-14T21:38:49.3698872Z 2025-08-14T21:38:49.3699105Z 3. In your training program, you are supposed to call the following function 2025-08-14T21:38:49.3699377Z at the beginning to start the distributed backend. It is strongly recommended 2025-08-14T21:38:49.3699586Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-08-14T21:38:49.3699778Z but ``env://`` is the one that is officially supported by this module. 2025-08-14T21:38:49.3699850Z 2025-08-14T21:38:49.3699933Z :: 2025-08-14T21:38:49.3700006Z 2025-08-14T21:38:49.3700200Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-08-14T21:38:49.3700341Z >>> init_method='env://') 2025-08-14T21:38:49.3700420Z 2025-08-14T21:38:49.3700649Z 4. In your training program, you can either use regular distributed functions 2025-08-14T21:38:49.3700887Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-08-14T21:38:49.3701089Z training program uses GPUs for training and you would like to use 2025-08-14T21:38:49.3701272Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-08-14T21:38:49.3701380Z here is how to configure it. 2025-08-14T21:38:49.3701454Z 2025-08-14T21:38:49.3701540Z :: 2025-08-14T21:38:49.3701615Z 2025-08-14T21:38:49.3701798Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-08-14T21:38:49.3701936Z >>> device_ids=[args.local_rank], 2025-08-14T21:38:49.3702066Z >>> output_device=args.local_rank) 2025-08-14T21:38:49.3702141Z 2025-08-14T21:38:49.3702391Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-08-14T21:38:49.3702621Z that your code will be operating on. This is generally the local rank of the 2025-08-14T21:38:49.3702855Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-08-14T21:38:49.3703077Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-08-14T21:38:49.3703162Z utility 2025-08-14T21:38:49.3703252Z 2025-08-14T21:38:49.3703596Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-08-14T21:38:49.3703813Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-08-14T21:38:49.3704041Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-08-14T21:38:49.3704231Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-08-14T21:38:49.3704403Z will not pass ``--local-rank`` when you specify this flag. 2025-08-14T21:38:49.3704497Z 2025-08-14T21:38:49.3704586Z .. warning:: 2025-08-14T21:38:49.3704679Z 2025-08-14T21:38:49.3704938Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-08-14T21:38:49.3705146Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-08-14T21:38:49.3705278Z write to a networked filesystem. See 2025-08-14T21:38:49.3705499Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-08-14T21:38:49.3705662Z how things can go wrong if you don't do this correctly. 2025-08-14T21:38:49.3705757Z 2025-08-14T21:38:49.3705837Z 2025-08-14T21:38:49.3705917Z 2025-08-14T21:38:49.3706012Z 2025-08-14T21:38:49.3706260Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3706339Z 2025-08-14T21:38:49.3706450Z warnings.warn(msg) 2025-08-14T21:38:49.3706530Z 2025-08-14T21:38:49.3706759Z --- Parse Warning: 55 / 146 --- 2025-08-14T21:38:49.3707784Z /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. 2025-08-14T21:38:49.3708043Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3709313Z 2025-08-14T21:38:49.3709549Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-08-14T21:38:49.3709708Z Needs to be called on all ranks in an SPMD fashion. 2025-08-14T21:38:49.3709786Z 2025-08-14T21:38:49.3709868Z Args: 2025-08-14T21:38:49.3710145Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-08-14T21:38:49.3710311Z of shards that represent the local shards on this rank. 2025-08-14T21:38:49.3710531Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-08-14T21:38:49.3710666Z shape of the overall sharded tensor. 2025-08-14T21:38:49.3710748Z 2025-08-14T21:38:49.3710836Z Keyword args: 2025-08-14T21:38:49.3711103Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-08-14T21:38:49.3711225Z the default process group will be used. 2025-08-14T21:38:49.3711408Z init_rrefs (bool, optional): Whether or not to initialize 2025-08-14T21:38:49.3711613Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-08-14T21:38:49.3711800Z Need to initialize the RPC Framework if specified as ``True``. 2025-08-14T21:38:49.3711908Z Default: ``False``. 2025-08-14T21:38:49.3711987Z 2025-08-14T21:38:49.3712067Z Returns: 2025-08-14T21:38:49.3712228Z A :class:`ShardedTensor` object handle on this rank 2025-08-14T21:38:49.3712306Z 2025-08-14T21:38:49.3712383Z 2025-08-14T21:38:49.3712478Z Examples: 2025-08-14T21:38:49.3712723Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-08-14T21:38:49.3712918Z each shard have a (5, 5) local tensor, we can do it like below: 2025-08-14T21:38:49.3712997Z 2025-08-14T21:38:49.3713079Z on rank 0: 2025-08-14T21:38:49.3713210Z >>> # xdoctest: +SKIP("not distributed") 2025-08-14T21:38:49.3713331Z >>> local_shard_metadata = ShardMetadata( 2025-08-14T21:38:49.3713434Z >>> shard_offsets=[0, 0], 2025-08-14T21:38:49.3713547Z >>> shard_lengths=[5, 5], 2025-08-14T21:38:49.3713653Z >>> placement="rank:0/cuda:0" 2025-08-14T21:38:49.3713737Z >>> ) 2025-08-14T21:38:49.3713936Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-08-14T21:38:49.3714124Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-08-14T21:38:49.3714214Z 2025-08-14T21:38:49.3714297Z on rank 1: 2025-08-14T21:38:49.3714413Z >>> # xdoctest: +SKIP("not distributed") 2025-08-14T21:38:49.3714593Z >>> local_shard_metadata = ShardMetadata( 2025-08-14T21:38:49.3714721Z >>> shard_offsets=[5, 0], 2025-08-14T21:38:49.3714822Z >>> shard_lengths=[5, 5], 2025-08-14T21:38:49.3714941Z >>> placement="rank:1/cuda:1" 2025-08-14T21:38:49.3715019Z >>> ) 2025-08-14T21:38:49.3715212Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-08-14T21:38:49.3715414Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-08-14T21:38:49.3715492Z 2025-08-14T21:38:49.3715740Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3715833Z 2025-08-14T21:38:49.3715928Z warnings.warn(msg) 2025-08-14T21:38:49.3716024Z 2025-08-14T21:38:49.3716220Z --- Parse Warning: 56 / 146 --- 2025-08-14T21:38:49.3717291Z /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. 2025-08-14T21:38:49.3717562Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3717639Z 2025-08-14T21:38:49.3717891Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-08-14T21:38:49.3718043Z size and sharding spec on each rank. 2025-08-14T21:38:49.3718121Z 2025-08-14T21:38:49.3718217Z Args: 2025-08-14T21:38:49.3718436Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-08-14T21:38:49.3718691Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-08-14T21:38:49.3718871Z The specification describing how to shard the Tensor. 2025-08-14T21:38:49.3719037Z global_size (Sequence[int]): Size of the sharded tensor. 2025-08-14T21:38:49.3719282Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-08-14T21:38:49.3719389Z Default: None 2025-08-14T21:38:49.3719554Z init_rrefs (bool, optional): Whether or not to initialize 2025-08-14T21:38:49.3719767Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-08-14T21:38:49.3719956Z Need to initialize the RPC Framework if specified as ``True``. 2025-08-14T21:38:49.3720050Z Default: ``False``. 2025-08-14T21:38:49.3720140Z 2025-08-14T21:38:49.3720223Z Returns: 2025-08-14T21:38:49.3720461Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-08-14T21:38:49.3720588Z tensor stored in the current rank. 2025-08-14T21:38:49.3720667Z 2025-08-14T21:38:49.3720756Z Examples: 2025-08-14T21:38:49.3720861Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3720995Z >>> # All tensors below are of torch.int64 type. 2025-08-14T21:38:49.3721110Z >>> # We have 2 process groups, 2 ranks. 2025-08-14T21:38:49.3721297Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-08-14T21:38:49.3721496Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-08-14T21:38:49.3721599Z >>> local_tensor 2025-08-14T21:38:49.3721697Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-08-14T21:38:49.3721795Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-08-14T21:38:49.3721902Z >>> sharding_dim = 0 2025-08-14T21:38:49.3722021Z >>> sharding_spec = ChunkShardingSpec( 2025-08-14T21:38:49.3722116Z dim=sharding_dim, 2025-08-14T21:38:49.3722224Z placements=[ 2025-08-14T21:38:49.3722317Z "rank:0/cuda:0", 2025-08-14T21:38:49.3722409Z "rank:1/cuda:1", 2025-08-14T21:38:49.3722501Z ], 2025-08-14T21:38:49.3722582Z ) 2025-08-14T21:38:49.3722724Z >>> st = ShardedTensor._init_from_local_tensor( 2025-08-14T21:38:49.3722845Z ... local_tensor, sharding_spec, [2, 4] 2025-08-14T21:38:49.3722972Z ... ) 2025-08-14T21:38:49.3723091Z >>> st 2025-08-14T21:38:49.3723182Z ShardedTensor( 2025-08-14T21:38:49.3723286Z ShardedTensorMetadata( 2025-08-14T21:38:49.3723393Z shards_metadata=[ 2025-08-14T21:38:49.3723658Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-08-14T21:38:49.3723915Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-08-14T21:38:49.3724007Z ], 2025-08-14T21:38:49.3724108Z size=torch.Size([2, 4]) 2025-08-14T21:38:49.3724201Z ) 2025-08-14T21:38:49.3724295Z >>> st.local_tensor() 2025-08-14T21:38:49.3724393Z tensor([1, 2, 3, 4]) # Rank 0 2025-08-14T21:38:49.3724502Z tensor([3, 4, 5, 6]) # Rank 1 2025-08-14T21:38:49.3724580Z 2025-08-14T21:38:49.3724844Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-08-14T21:38:49.3725092Z rank validations, and we only validate the local shard on the current rank. 2025-08-14T21:38:49.3725303Z We fully rely on the user to ensure local tensor is sharded based on the 2025-08-14T21:38:49.3725397Z sharding spec. 2025-08-14T21:38:49.3725513Z 2025-08-14T21:38:49.3725761Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3725849Z 2025-08-14T21:38:49.3725943Z warnings.warn(msg) 2025-08-14T21:38:49.3726020Z 2025-08-14T21:38:49.3726222Z --- Parse Warning: 57 / 146 --- 2025-08-14T21:38:49.3727239Z /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. 2025-08-14T21:38:49.3727494Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3727589Z 2025-08-14T21:38:49.3727835Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-08-14T21:38:49.3727940Z single local shard. 2025-08-14T21:38:49.3728018Z 2025-08-14T21:38:49.3728232Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-08-14T21:38:49.3728481Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-08-14T21:38:49.3728586Z we swap local shards directly. 2025-08-14T21:38:49.3728839Z For more generic cases, we merge different shards across different ranks and split 2025-08-14T21:38:49.3729095Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-08-14T21:38:49.3729172Z 2025-08-14T21:38:49.3729253Z Args: 2025-08-14T21:38:49.3729546Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-08-14T21:38:49.3729708Z specification describing how the tensor is sharded. 2025-08-14T21:38:49.3729802Z 2025-08-14T21:38:49.3729886Z Returns: 2025-08-14T21:38:49.3730083Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-08-14T21:38:49.3730174Z 2025-08-14T21:38:49.3730259Z Examples: 2025-08-14T21:38:49.3730356Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3730489Z >>> # We have 2 process groups, 2 ranks. 2025-08-14T21:38:49.3730662Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-08-14T21:38:49.3730782Z >>> tensor = torch.stack([tensor, tensor]) 2025-08-14T21:38:49.3730878Z >>> tensor 2025-08-14T21:38:49.3730996Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-08-14T21:38:49.3731125Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-08-14T21:38:49.3731242Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-08-14T21:38:49.3731363Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-08-14T21:38:49.3731475Z >>> sharding_dim = 0 2025-08-14T21:38:49.3731635Z >>> spec = ChunkShardingSpec( 2025-08-14T21:38:49.3731754Z dim=sharding_dim, 2025-08-14T21:38:49.3731857Z placements=[ 2025-08-14T21:38:49.3731951Z "rank:0/cuda:0", 2025-08-14T21:38:49.3732042Z "rank:1/cuda:1", 2025-08-14T21:38:49.3732146Z "rank:2/cuda:2", 2025-08-14T21:38:49.3732235Z "rank:3/cuda:3", 2025-08-14T21:38:49.3732318Z ], 2025-08-14T21:38:49.3732410Z ) 2025-08-14T21:38:49.3732511Z >>> current_offsets = [0] * 2 2025-08-14T21:38:49.3732627Z >>> current_offsets[0] = rank * 2 2025-08-14T21:38:49.3732738Z >>> shard_metadata = ShardMetadata( 2025-08-14T21:38:49.3732880Z shard_offsets=copy.deepcopy(current_offsets), 2025-08-14T21:38:49.3732998Z shard_sizes=tensor.size(), 2025-08-14T21:38:49.3733117Z placement=spec.placements[rank], 2025-08-14T21:38:49.3733194Z ) 2025-08-14T21:38:49.3733299Z >>> local_shards = [ 2025-08-14T21:38:49.3733381Z Shard( 2025-08-14T21:38:49.3733470Z tensor=tensor, 2025-08-14T21:38:49.3733591Z metadata=shard_metadata, 2025-08-14T21:38:49.3733696Z ) 2025-08-14T21:38:49.3733773Z ] 2025-08-14T21:38:49.3734012Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-08-14T21:38:49.3734101Z >>> sharding_dim = 1 2025-08-14T21:38:49.3734234Z >>> resharding_spec = ChunkShardingSpec( 2025-08-14T21:38:49.3734331Z dim=sharding_dim, 2025-08-14T21:38:49.3734419Z placements=[ 2025-08-14T21:38:49.3734519Z "rank:0/cuda:0", 2025-08-14T21:38:49.3734606Z "rank:1/cuda:1", 2025-08-14T21:38:49.3734696Z "rank:2/cuda:2", 2025-08-14T21:38:49.3734798Z "rank:3/cuda:3", 2025-08-14T21:38:49.3734874Z ], 2025-08-14T21:38:49.3734956Z ) 2025-08-14T21:38:49.3735074Z >>> st.reshard(resharding_spec) 2025-08-14T21:38:49.3735184Z >>> tensor = st.local_shards()[0].tensor 2025-08-14T21:38:49.3735263Z >>> tensor 2025-08-14T21:38:49.3735412Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-08-14T21:38:49.3735543Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-08-14T21:38:49.3735686Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-08-14T21:38:49.3735817Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-08-14T21:38:49.3735894Z 2025-08-14T21:38:49.3736152Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3736228Z 2025-08-14T21:38:49.3736322Z warnings.warn(msg) 2025-08-14T21:38:49.3736407Z 2025-08-14T21:38:49.3736595Z --- Parse Warning: 58 / 146 --- 2025-08-14T21:38:49.3737573Z /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. 2025-08-14T21:38:49.3737827Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3737904Z 2025-08-14T21:38:49.3738129Z Representation of a sharding plan, describes how to shard a module 2025-08-14T21:38:49.3738391Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-08-14T21:38:49.3738669Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-08-14T21:38:49.3738919Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-08-14T21:38:49.3738993Z 2025-08-14T21:38:49.3739087Z Args: 2025-08-14T21:38:49.3739346Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-08-14T21:38:49.3739561Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-08-14T21:38:49.3739876Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-08-14T21:38:49.3740120Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-08-14T21:38:49.3740239Z a parameter to a `ShardingSpec`. 2025-08-14T21:38:49.3740496Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-08-14T21:38:49.3740595Z to a `Sharder` object. 2025-08-14T21:38:49.3740927Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-08-14T21:38:49.3741178Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-08-14T21:38:49.3741404Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-08-14T21:38:49.3741511Z Default: `None` 2025-08-14T21:38:49.3741756Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-08-14T21:38:49.3741985Z a module's sharded output to be returned as a Tensor from its local shards to 2025-08-14T21:38:49.3742271Z ensure further processing in a data parallel fashion. ("" in list means the 2025-08-14T21:38:49.3742355Z root module). 2025-08-14T21:38:49.3742453Z Default: None 2025-08-14T21:38:49.3742535Z Example: 2025-08-14T21:38:49.3742807Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-08-14T21:38:49.3743094Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-08-14T21:38:49.3743172Z 2025-08-14T21:38:49.3743335Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-08-14T21:38:49.3743534Z >>> class MyModule(nn.Module): 2025-08-14T21:38:49.3743642Z >>> def __init__(self) -> None: 2025-08-14T21:38:49.3743749Z >>> super().__init__() 2025-08-14T21:38:49.3743849Z >>> self.fc1 = nn.Linear() 2025-08-14T21:38:49.3743943Z >>> self.gelu = nn.GELU() 2025-08-14T21:38:49.3744049Z >>> self.fc2 = nn.Linear() 2025-08-14T21:38:49.3744151Z >>> self.relu = nn.Linear() 2025-08-14T21:38:49.3744225Z >>> 2025-08-14T21:38:49.3744335Z >>> def forward(self, input): 2025-08-14T21:38:49.3744501Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-08-14T21:38:49.3744574Z 2025-08-14T21:38:49.3744659Z 2025-08-14T21:38:49.3744785Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-08-14T21:38:49.3744892Z >>> sharding_plan = ShardingPlan( 2025-08-14T21:38:49.3744986Z >>> plan={ 2025-08-14T21:38:49.3745079Z >>> "fc1.weight": spec1, 2025-08-14T21:38:49.3745182Z >>> "fc2.weight": spec2 2025-08-14T21:38:49.3745263Z >>> }, 2025-08-14T21:38:49.3745354Z >>> output_plan={ 2025-08-14T21:38:49.3745463Z >>> "fc2": output_spec 2025-08-14T21:38:49.3745538Z >>> }, 2025-08-14T21:38:49.3745638Z >>> return_local_tensor=["fc2"] 2025-08-14T21:38:49.3745730Z >>> ) 2025-08-14T21:38:49.3745803Z 2025-08-14T21:38:49.3746048Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3746134Z 2025-08-14T21:38:49.3746226Z warnings.warn(msg) 2025-08-14T21:38:49.3746302Z 2025-08-14T21:38:49.3746505Z --- Parse Warning: 59 / 146 --- 2025-08-14T21:38:49.3747586Z /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. 2025-08-14T21:38:49.3747912Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3748011Z 2025-08-14T21:38:49.3748115Z Run post-localSGD algorithm. 2025-08-14T21:38:49.3748205Z 2025-08-14T21:38:49.3748433Z This DDP communication hook is used for running post-localSGD algorithm, 2025-08-14T21:38:49.3748589Z by combining with a model averaging component (e.g., 2025-08-14T21:38:49.3748919Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-08-14T21:38:49.3749023Z that runs after the optimizer step. 2025-08-14T21:38:49.3749113Z 2025-08-14T21:38:49.3749191Z Args: 2025-08-14T21:38:49.3749408Z state (PostLocalSGDState): State information to run post-localSGD. 2025-08-14T21:38:49.3749687Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-08-14T21:38:49.3750100Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-08-14T21:38:49.3750340Z Note that since DDP comm hook only supports single process single device mode, 2025-08-14T21:38:49.3750496Z only exactly one tensor is stored in this bucket. 2025-08-14T21:38:49.3750570Z 2025-08-14T21:38:49.3750686Z Returns: 2025-08-14T21:38:49.3750919Z Future handler of the communication, which updates the gradients in place. 2025-08-14T21:38:49.3750992Z 2025-08-14T21:38:49.3751092Z Example:: 2025-08-14T21:38:49.3751183Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3751423Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-08-14T21:38:49.3751547Z start_localSGD_iter=10) 2025-08-14T21:38:49.3751711Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-08-14T21:38:49.3752037Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-08-14T21:38:49.3752391Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-08-14T21:38:49.3752467Z 2025-08-14T21:38:49.3752725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3752801Z 2025-08-14T21:38:49.3752893Z warnings.warn(msg) 2025-08-14T21:38:49.3752983Z 2025-08-14T21:38:49.3753163Z --- Parse Warning: 60 / 146 --- 2025-08-14T21:38:49.3754202Z /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. 2025-08-14T21:38:49.3754472Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3754545Z 2025-08-14T21:38:49.3754665Z Implement PowerSGD algorithm. 2025-08-14T21:38:49.3754742Z 2025-08-14T21:38:49.3754961Z This DDP communication hook implements PowerSGD gradient compression 2025-08-14T21:38:49.3755210Z algorithm described in the `paper `_. 2025-08-14T21:38:49.3755443Z Once gradient tensors are aggregated across all workers, this hook applies 2025-08-14T21:38:49.3755541Z compression as follows: 2025-08-14T21:38:49.3755632Z 2025-08-14T21:38:49.3756057Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-08-14T21:38:49.3756131Z 2025-08-14T21:38:49.3756551Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-08-14T21:38:49.3756626Z 2025-08-14T21:38:49.3757032Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-08-14T21:38:49.3757109Z 2025-08-14T21:38:49.3757211Z 2. Handles uncompressed tensors: 2025-08-14T21:38:49.3757352Z 2025-08-14T21:38:49.3757892Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-08-14T21:38:49.3757967Z 2025-08-14T21:38:49.3758307Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-08-14T21:38:49.3758385Z 2025-08-14T21:38:49.3758620Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-08-14T21:38:49.3758695Z 2025-08-14T21:38:49.3758923Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-08-14T21:38:49.3759236Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-08-14T21:38:49.3759313Z 2025-08-14T21:38:49.3759449Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-08-14T21:38:49.3759535Z 2025-08-14T21:38:49.3759637Z 3.3. Allreduces Ps as a batch; 2025-08-14T21:38:49.3759714Z 2025-08-14T21:38:49.3759833Z 3.4. Orthogonalizes each P in Ps; 2025-08-14T21:38:49.3759906Z 2025-08-14T21:38:49.3760104Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-08-14T21:38:49.3760180Z 2025-08-14T21:38:49.3760304Z 3.6. Allreduces Qs as a batch; 2025-08-14T21:38:49.3760388Z 2025-08-14T21:38:49.3760676Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-08-14T21:38:49.3760753Z 2025-08-14T21:38:49.3761165Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-08-14T21:38:49.3761435Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-08-14T21:38:49.3761857Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-08-14T21:38:49.3761946Z 2025-08-14T21:38:49.3762026Z Args: 2025-08-14T21:38:49.3762462Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-08-14T21:38:49.3762809Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-08-14T21:38:49.3762915Z and ``min_compression_rate``. 2025-08-14T21:38:49.3763329Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-08-14T21:38:49.3763567Z Note that since DDP comm hook only supports single process single device mode, 2025-08-14T21:38:49.3763722Z only exactly one tensor is stored in this bucket. 2025-08-14T21:38:49.3763797Z 2025-08-14T21:38:49.3763874Z Returns: 2025-08-14T21:38:49.3764121Z Future handler of the communication, which updates the gradients in place. 2025-08-14T21:38:49.3764196Z 2025-08-14T21:38:49.3764280Z Example:: 2025-08-14T21:38:49.3764395Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3764660Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-08-14T21:38:49.3764813Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-08-14T21:38:49.3764975Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-08-14T21:38:49.3765051Z 2025-08-14T21:38:49.3765299Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3765389Z 2025-08-14T21:38:49.3765480Z warnings.warn(msg) 2025-08-14T21:38:49.3765569Z 2025-08-14T21:38:49.3765755Z --- Parse Warning: 61 / 146 --- 2025-08-14T21:38:49.3766840Z /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. 2025-08-14T21:38:49.3767157Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3767255Z 2025-08-14T21:38:49.3767439Z Averages parameters periodically after the warm-up stage. 2025-08-14T21:38:49.3767526Z 2025-08-14T21:38:49.3767774Z This can be used for running `post-local SGD `_, 2025-08-14T21:38:49.3767971Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-08-14T21:38:49.3768195Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-08-14T21:38:49.3768269Z 2025-08-14T21:38:49.3768358Z Args: 2025-08-14T21:38:49.3768515Z period (int): The number of steps per model averaging. 2025-08-14T21:38:49.3768777Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-08-14T21:38:49.3768914Z Otherwise, only DDP needs to be used. 2025-08-14T21:38:49.3769119Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-08-14T21:38:49.3769249Z model averaging is skipped. 2025-08-14T21:38:49.3769429Z process_group: The process group to be used for all-reduce. 2025-08-14T21:38:49.3769565Z If ``None``, the default process group, which 2025-08-14T21:38:49.3769787Z is created by :func:`torch.distributed.init_process_group`, 2025-08-14T21:38:49.3769898Z will be used. (default: ``None``) 2025-08-14T21:38:49.3769975Z 2025-08-14T21:38:49.3770072Z Example:: 2025-08-14T21:38:49.3770146Z 2025-08-14T21:38:49.3770270Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:49.3770372Z >>> import torch 2025-08-14T21:38:49.3770480Z >>> import torch.distributed as dist 2025-08-14T21:38:49.3770801Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-08-14T21:38:49.3771066Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-08-14T21:38:49.3771165Z >>> import torch.nn as nn 2025-08-14T21:38:49.3771254Z >>> 2025-08-14T21:38:49.3771423Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-08-14T21:38:49.3771529Z >>> torch.cuda.set_device(rank) 2025-08-14T21:38:49.3771662Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-08-14T21:38:49.3771808Z >>> model = nn.parallel.DistributedDataParallel( 2025-08-14T21:38:49.3771941Z >>> module, device_ids=[rank], output_device=rank 2025-08-14T21:38:49.3772028Z >>> ) 2025-08-14T21:38:49.3772164Z >>> # Register a post-localSGD communication hook. 2025-08-14T21:38:49.3772463Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-08-14T21:38:49.3772620Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-08-14T21:38:49.3772695Z >>> 2025-08-14T21:38:49.3772966Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-08-14T21:38:49.3773114Z >>> # After 100 steps, run model averaging every 4 steps. 2025-08-14T21:38:49.3773421Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-08-14T21:38:49.3773676Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-08-14T21:38:49.3773774Z >>> for step in range(0, 200): 2025-08-14T21:38:49.3773885Z >>> optimizer.zero_grad() 2025-08-14T21:38:49.3773991Z >>> loss = loss_fn(output, labels) 2025-08-14T21:38:49.3774084Z >>> loss.backward() 2025-08-14T21:38:49.3774190Z >>> optimizer.step() 2025-08-14T21:38:49.3774373Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-08-14T21:38:49.3774568Z >>> # inter-node communication only occurs every 4 iterations after 2025-08-14T21:38:49.3774763Z >>> # the initial ``warmup_steps`` period. 2025-08-14T21:38:49.3774941Z >>> averager.average_parameters(model.parameters()) 2025-08-14T21:38:49.3775030Z 2025-08-14T21:38:49.3775277Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3775352Z 2025-08-14T21:38:49.3775457Z warnings.warn(msg) 2025-08-14T21:38:49.3775537Z 2025-08-14T21:38:49.3775722Z --- Parse Warning: 62 / 146 --- 2025-08-14T21:38:49.3776921Z /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. 2025-08-14T21:38:49.3777173Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3777256Z 2025-08-14T21:38:49.3777587Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-08-14T21:38:49.3777663Z 2025-08-14T21:38:49.3777967Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-08-14T21:38:49.3778166Z by using different periods concurrently after the warm-up stage. 2025-08-14T21:38:49.3778589Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-08-14T21:38:49.3778931Z that supports `post-local SGD `_, which essentially only supports 2025-08-14T21:38:49.3779228Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-08-14T21:38:49.3779582Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-08-14T21:38:49.3779876Z Similarly, the process groups within this class do not have such an intra-machine process 2025-08-14T21:38:49.3780141Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-08-14T21:38:49.3780228Z 2025-08-14T21:38:49.3780307Z Args: 2025-08-14T21:38:49.3780555Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-08-14T21:38:49.3780757Z process group size, used for initializing process groups of 2025-08-14T21:38:49.3780974Z different sizes in a hierarchy to average parameters concurrently. 2025-08-14T21:38:49.3781188Z Particularly, at each iteration, there will be at most a single 2025-08-14T21:38:49.3781412Z process group that runs averaging -- the period of such group should 2025-08-14T21:38:49.3781615Z have the largest period which the current step can be divided by. 2025-08-14T21:38:49.3781783Z For example, if the dict has three keys: 2, 4, and 8, 2025-08-14T21:38:49.3781988Z then this means totally three process groups will be created to 2025-08-14T21:38:49.3782201Z average parameters every 2, 4, and 8 iterations, respectively. 2025-08-14T21:38:49.3782384Z At the 4th iteration, only the second process group will run 2025-08-14T21:38:49.3782558Z averaging, because the first process group should be a 2025-08-14T21:38:49.3782781Z subset of the second process group, and no need to execute the first 2025-08-14T21:38:49.3782902Z process group redundantly. 2025-08-14T21:38:49.3783093Z On the other hand, the third process group can only be triggered 2025-08-14T21:38:49.3783320Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-08-14T21:38:49.3783799Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-08-14T21:38:49.3784272Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-08-14T21:38:49.3784445Z If ``None``, the default process group, which is created 2025-08-14T21:38:49.3784650Z by :func:`torch.distributed.init_process_group`, will be used. 2025-08-14T21:38:49.3784780Z (default: ``None``) 2025-08-14T21:38:49.3784859Z 2025-08-14T21:38:49.3784950Z Example:: 2025-08-14T21:38:49.3785076Z >>> # xdoctest: +SKIP('undefined rank') 2025-08-14T21:38:49.3785198Z >>> from collections import OrderedDict 2025-08-14T21:38:49.3785295Z >>> import torch 2025-08-14T21:38:49.3785412Z >>> import torch.distributed as dist 2025-08-14T21:38:49.3785684Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-08-14T21:38:49.3785797Z >>> PostLocalSGDState, 2025-08-14T21:38:49.3785899Z >>> post_localSGD_hook, 2025-08-14T21:38:49.3785981Z >>> ) 2025-08-14T21:38:49.3786355Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-08-14T21:38:49.3786478Z >>> import torch.nn as nn 2025-08-14T21:38:49.3786562Z >>> 2025-08-14T21:38:49.3786733Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-08-14T21:38:49.3786836Z >>> torch.cuda.set_device(rank) 2025-08-14T21:38:49.3786970Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-08-14T21:38:49.3787117Z >>> model = nn.parallel.DistributedDataParallel( 2025-08-14T21:38:49.3787253Z >>> module, device_ids=[rank], output_device=rank 2025-08-14T21:38:49.3787336Z >>> ) 2025-08-14T21:38:49.3787476Z >>> # Register a post-localSGD communication hook. 2025-08-14T21:38:49.3787748Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-08-14T21:38:49.3787874Z >>> subgroup, _ = dist.new_subgroups() 2025-08-14T21:38:49.3788175Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-08-14T21:38:49.3788342Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-08-14T21:38:49.3788420Z >>> 2025-08-14T21:38:49.3788695Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-08-14T21:38:49.3788818Z >>> # the 16 processes every 16 iterations. 2025-08-14T21:38:49.3788997Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-08-14T21:38:49.3789220Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-08-14T21:38:49.3789534Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-08-14T21:38:49.3789797Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-08-14T21:38:49.3789953Z >>> # After 100 steps, run model averaging at two levels. 2025-08-14T21:38:49.3790053Z >>> for step in range(0, 200): 2025-08-14T21:38:49.3790157Z >>> optimizer.zero_grad() 2025-08-14T21:38:49.3790272Z >>> loss = loss_fn(output, labels) 2025-08-14T21:38:49.3790365Z >>> loss.backward() 2025-08-14T21:38:49.3790459Z >>> optimizer.step() 2025-08-14T21:38:49.3790621Z >>> # Average parameters after ``optimizer.step()``. 2025-08-14T21:38:49.3790901Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-08-14T21:38:49.3791061Z >>> averager.average_parameters(model.parameters()) 2025-08-14T21:38:49.3791138Z 2025-08-14T21:38:49.3791219Z .. warning :: 2025-08-14T21:38:49.3791519Z The last group size in the dict must be the size of the provided ``process_group``, 2025-08-14T21:38:49.3791770Z which indicates model averaging at the highest level of the hierarchy. 2025-08-14T21:38:49.3792064Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-08-14T21:38:49.3792148Z 2025-08-14T21:38:49.3792232Z .. warning :: 2025-08-14T21:38:49.3792457Z `HierarchicalModelAverager` is experimental and subject to change. 2025-08-14T21:38:49.3792540Z 2025-08-14T21:38:49.3792785Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3793054Z 2025-08-14T21:38:49.3793149Z warnings.warn(msg) 2025-08-14T21:38:49.3793226Z 2025-08-14T21:38:49.3793437Z --- Parse Warning: 63 / 146 --- 2025-08-14T21:38:49.3794494Z /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. 2025-08-14T21:38:49.3794751Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3794837Z 2025-08-14T21:38:49.3795123Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-08-14T21:38:49.3795432Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-08-14T21:38:49.3795506Z 2025-08-14T21:38:49.3795665Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-08-14T21:38:49.3795751Z 2025-08-14T21:38:49.3795838Z .. warning:: 2025-08-14T21:38:49.3796004Z Current implementation only supports loading Tensors. 2025-08-14T21:38:49.3796089Z 2025-08-14T21:38:49.3796198Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:49.3796291Z >>> sd = {"mode": model} 2025-08-14T21:38:49.3796386Z >>> dcp.load( 2025-08-14T21:38:49.3796465Z >>> sd, 2025-08-14T21:38:49.3796613Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-08-14T21:38:49.3796744Z >>> planner=DynamicMetaLoadPlanner(), 2025-08-14T21:38:49.3796852Z >>> checkpoint_id="path_to_model.pt" 2025-08-14T21:38:49.3796936Z >>> ) 2025-08-14T21:38:49.3797016Z 2025-08-14T21:38:49.3797261Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3797343Z 2025-08-14T21:38:49.3797434Z warnings.warn(msg) 2025-08-14T21:38:49.3797505Z 2025-08-14T21:38:49.3797692Z --- Parse Warning: 64 / 146 --- 2025-08-14T21:38:49.3798711Z /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. 2025-08-14T21:38:49.3798971Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3799052Z 2025-08-14T21:38:49.3799412Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-08-14T21:38:49.3799741Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-08-14T21:38:49.3799855Z metadata file, like Torch Save files. 2025-08-14T21:38:49.3799932Z 2025-08-14T21:38:49.3800121Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-08-14T21:38:49.3800199Z 2025-08-14T21:38:49.3800283Z .. warning:: 2025-08-14T21:38:49.3800461Z Current implementation only supports loading Tensors. 2025-08-14T21:38:49.3800539Z 2025-08-14T21:38:49.3800661Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:49.3800752Z >>> sd = {"mode": model} 2025-08-14T21:38:49.3800837Z >>> dcp.load( 2025-08-14T21:38:49.3800927Z >>> sd, 2025-08-14T21:38:49.3801074Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-08-14T21:38:49.3801263Z >>> planner=DynamicMetaLoadPlanner(), 2025-08-14T21:38:49.3801413Z >>> checkpoint_id="path_to_model.pt" 2025-08-14T21:38:49.3801493Z >>> ) 2025-08-14T21:38:49.3801569Z 2025-08-14T21:38:49.3801830Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3801913Z 2025-08-14T21:38:49.3802004Z warnings.warn(msg) 2025-08-14T21:38:49.3802096Z 2025-08-14T21:38:49.3802273Z --- Parse Warning: 65 / 146 --- 2025-08-14T21:38:49.3803310Z /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. 2025-08-14T21:38:49.3803563Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3803640Z 2025-08-14T21:38:49.3803877Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-08-14T21:38:49.3803957Z 2025-08-14T21:38:49.3804118Z This is the current recommended way to checkpoint FSDP. 2025-08-14T21:38:49.3804223Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3804374Z >>> import torch.distributed.checkpoint as dist_cp 2025-08-14T21:38:49.3804490Z >>> # Save 2025-08-14T21:38:49.3804587Z >>> model: torch.nn.Model 2025-08-14T21:38:49.3804701Z >>> optim_params = model.parameters() 2025-08-14T21:38:49.3804852Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-08-14T21:38:49.3804933Z >>> # Save 2025-08-14T21:38:49.3805143Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-08-14T21:38:49.3805246Z >>> state_dict = { 2025-08-14T21:38:49.3805395Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-08-14T21:38:49.3805500Z >>> "model": model.state_dict() 2025-08-14T21:38:49.3805589Z >>> } 2025-08-14T21:38:49.3805696Z >>> dist_cp.save_state_dict( 2025-08-14T21:38:49.3805794Z >>> state_dict=optim_state, 2025-08-14T21:38:49.3805978Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-08-14T21:38:49.3806107Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-08-14T21:38:49.3806198Z >>> ) 2025-08-14T21:38:49.3806276Z >>> 2025-08-14T21:38:49.3806354Z >>> # Load 2025-08-14T21:38:49.3806583Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-08-14T21:38:49.3806705Z >>> model_state_dict = model_tp.state_dict() 2025-08-14T21:38:49.3806795Z >>> checkpoint = { 2025-08-14T21:38:49.3806902Z >>> "model": model_state_dict 2025-08-14T21:38:49.3806979Z >>> } 2025-08-14T21:38:49.3807077Z >>> dist_cp.load_state_dict( 2025-08-14T21:38:49.3807184Z >>> state_dict=checkpoint, 2025-08-14T21:38:49.3807372Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-08-14T21:38:49.3807512Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-08-14T21:38:49.3807592Z >>> ) 2025-08-14T21:38:49.3807736Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-08-14T21:38:49.3807826Z >>> 2025-08-14T21:38:49.3807993Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-08-14T21:38:49.3808090Z >>> model_state_dict, 2025-08-14T21:38:49.3808207Z >>> optimizer_key="optimizer", 2025-08-14T21:38:49.3808380Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-08-14T21:38:49.3808460Z >>> ) 2025-08-14T21:38:49.3808544Z >>> 2025-08-14T21:38:49.3808684Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-08-14T21:38:49.3808822Z >>> model, optim, optim_state["optimizer"] 2025-08-14T21:38:49.3808899Z >>> ) 2025-08-14T21:38:49.3808978Z >>> 2025-08-14T21:38:49.3809105Z >>> optim.load_state_dict(flattened_osd) 2025-08-14T21:38:49.3809179Z 2025-08-14T21:38:49.3809488Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3809600Z 2025-08-14T21:38:49.3809695Z warnings.warn(msg) 2025-08-14T21:38:49.3809772Z 2025-08-14T21:38:49.3809958Z --- Parse Warning: 66 / 146 --- 2025-08-14T21:38:49.3810897Z /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. 2025-08-14T21:38:49.3811161Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3811236Z 2025-08-14T21:38:49.3811517Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-08-14T21:38:49.3811602Z 2025-08-14T21:38:49.3811886Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-08-14T21:38:49.3811962Z 2025-08-14T21:38:49.3812243Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-08-14T21:38:49.3812354Z will be visible to the whole process. 2025-08-14T21:38:49.3812431Z 2025-08-14T21:38:49.3812706Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-08-14T21:38:49.3812812Z 2025-08-14T21:38:49.3812939Z 1) set_up_planner - called on all ranks. 2025-08-14T21:38:49.3813061Z Signals the start of a checkpoint save. 2025-08-14T21:38:49.3813137Z 2025-08-14T21:38:49.3813268Z 2) create_local_plan - called on all ranks. 2025-08-14T21:38:49.3813548Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-08-14T21:38:49.3813625Z 2025-08-14T21:38:49.3813813Z 3) create_global_plan - called on the coordinator rank only. 2025-08-14T21:38:49.3814005Z Takes the SavePlan from all ranks and make any global decision. 2025-08-14T21:38:49.3814082Z 2025-08-14T21:38:49.3814202Z 4) finish_plan - called on all ranks. 2025-08-14T21:38:49.3814415Z This gives each rank a chance to adjust to global planning decisions. 2025-08-14T21:38:49.3814506Z 2025-08-14T21:38:49.3814652Z 5) resolve_data - called multiple times on each rank 2025-08-14T21:38:49.3814850Z Lookups a value on the `state_dict` for the storage layer to write. 2025-08-14T21:38:49.3814938Z 2025-08-14T21:38:49.3815235Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-08-14T21:38:49.3815414Z most changes can be expressed by changes in a single method. 2025-08-14T21:38:49.3815496Z 2025-08-14T21:38:49.3815615Z There are 3 usual patterns of extension: 2025-08-14T21:38:49.3815691Z 2025-08-14T21:38:49.3815941Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-08-14T21:38:49.3816161Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-08-14T21:38:49.3816247Z 2025-08-14T21:38:49.3816360Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:49.3816491Z >>> class RenamePlanner(DefaultSavePlanner): 2025-08-14T21:38:49.3816600Z >>> def set_up_planner( 2025-08-14T21:38:49.3816681Z >>> self, 2025-08-14T21:38:49.3816789Z >>> state_dict: STATE_DICT_TYPE, 2025-08-14T21:38:49.3816920Z >>> storage_meta: Optional[StorageMeta], 2025-08-14T21:38:49.3817021Z >>> is_coordinator: bool, 2025-08-14T21:38:49.3817109Z >>> ) -> None: 2025-08-14T21:38:49.3817218Z >>> # prefix all keys with `foo_`` 2025-08-14T21:38:49.3817504Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-08-14T21:38:49.3817591Z 2025-08-14T21:38:49.3817912Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-08-14T21:38:49.3817988Z 2025-08-14T21:38:49.3818108Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:49.3818282Z >>> class FP16Planner(DefaultSavePlanner): 2025-08-14T21:38:49.3818407Z >>> def create_local_plan(self): 2025-08-14T21:38:49.3818529Z >>> plan = super().create_local_plan() 2025-08-14T21:38:49.3818623Z >>> for p in plan: 2025-08-14T21:38:49.3818735Z >>> if p.tensor_data is not None: 2025-08-14T21:38:49.3818896Z >>> p.tensor_data.properties.dtype = torch.float16 2025-08-14T21:38:49.3818987Z >>> return plan 2025-08-14T21:38:49.3819072Z >>> 2025-08-14T21:38:49.3819186Z >>> def resolve_data(self, write_item): 2025-08-14T21:38:49.3819306Z >>> item = super().resolve_data(write_item) 2025-08-14T21:38:49.3819583Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-08-14T21:38:49.3819659Z 2025-08-14T21:38:49.3819992Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-08-14T21:38:49.3820080Z 2025-08-14T21:38:49.3820193Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:49.3820302Z >>> from itertools import zip_longest 2025-08-14T21:38:49.3820416Z >>> from dataclasses import replace 2025-08-14T21:38:49.3820582Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-08-14T21:38:49.3820884Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-08-14T21:38:49.3821009Z >>> # This sample doesn't handle ShardedTensors 2025-08-14T21:38:49.3821131Z >>> def create_global_plan(self, all_plans): 2025-08-14T21:38:49.3821288Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-08-14T21:38:49.3821385Z >>> items_per_rank = [ 2025-08-14T21:38:49.3821516Z >>> [item for item in items if item is not None] 2025-08-14T21:38:49.3821678Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-08-14T21:38:49.3821757Z >>> ] 2025-08-14T21:38:49.3821863Z >>> all_plans = [ 2025-08-14T21:38:49.3821974Z >>> replace(plan, items=items) 2025-08-14T21:38:49.3822156Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-08-14T21:38:49.3822244Z >>> ] 2025-08-14T21:38:49.3822379Z >>> return super().create_global_plan(all_plans) 2025-08-14T21:38:49.3822454Z 2025-08-14T21:38:49.3822725Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-08-14T21:38:49.3822984Z accomplished by having each rank contribute their data items in the local plan and 2025-08-14T21:38:49.3823092Z the global planner aggregate them: 2025-08-14T21:38:49.3823177Z 2025-08-14T21:38:49.3823284Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:49.3823548Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-08-14T21:38:49.3823673Z >>> def create_local_plan(self) -> SavePlan: 2025-08-14T21:38:49.3823793Z >>> plan = super().create_local_plan() 2025-08-14T21:38:49.3823968Z >>> return replace(plan, planner_data="per-rank-data") 2025-08-14T21:38:49.3824043Z >>> 2025-08-14T21:38:49.3824333Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-08-14T21:38:49.3824534Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-08-14T21:38:49.3824686Z >>> merged_data = [p.planner_data for p in global_plan] 2025-08-14T21:38:49.3824852Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-08-14T21:38:49.3824968Z >>> return global_plan, metadata 2025-08-14T21:38:49.3825047Z 2025-08-14T21:38:49.3825303Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3825381Z 2025-08-14T21:38:49.3825473Z warnings.warn(msg) 2025-08-14T21:38:49.3825557Z 2025-08-14T21:38:49.3825809Z --- Parse Warning: 67 / 146 --- 2025-08-14T21:38:49.3826773Z /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. 2025-08-14T21:38:49.3827039Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3827116Z 2025-08-14T21:38:49.3827408Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-08-14T21:38:49.3827484Z 2025-08-14T21:38:49.3827760Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-08-14T21:38:49.3827850Z 2025-08-14T21:38:49.3828123Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-08-14T21:38:49.3828235Z will be visible to the whole process. 2025-08-14T21:38:49.3828326Z 2025-08-14T21:38:49.3828602Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-08-14T21:38:49.3828680Z 2025-08-14T21:38:49.3828805Z 1) set_up_planner - called on all ranks. 2025-08-14T21:38:49.3828932Z Signals the start of loading a checkpoint. 2025-08-14T21:38:49.3829021Z 2025-08-14T21:38:49.3829167Z 2) create_local_plan - called on all ranks. 2025-08-14T21:38:49.3829445Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-08-14T21:38:49.3829532Z 2025-08-14T21:38:49.3829707Z 3) create_global_plan - called on the coordinator rank only. 2025-08-14T21:38:49.3829902Z Takes the LoadPlan from all ranks and make any global decision. 2025-08-14T21:38:49.3829986Z 2025-08-14T21:38:49.3830122Z 4) load_bytes - called multiple times on each rank 2025-08-14T21:38:49.3830285Z This is called once per non-tensor value in state_dict. 2025-08-14T21:38:49.3830377Z 2025-08-14T21:38:49.3830599Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-08-14T21:38:49.3830788Z They are called in pair for each Tensor value in state_dict. 2025-08-14T21:38:49.3830865Z 2025-08-14T21:38:49.3831163Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-08-14T21:38:49.3831357Z most changes can be expressed by changes in a single method. 2025-08-14T21:38:49.3831431Z 2025-08-14T21:38:49.3831556Z There are two usual patterns of extension: 2025-08-14T21:38:49.3831646Z 2025-08-14T21:38:49.3831890Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-08-14T21:38:49.3832137Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-08-14T21:38:49.3832367Z to keep a reference to the original state_dict as load happens in place so 2025-08-14T21:38:49.3832488Z we need to be able to perform it in place 2025-08-14T21:38:49.3832577Z 2025-08-14T21:38:49.3832691Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:49.3832822Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-08-14T21:38:49.3832934Z >>> def set_up_planner( 2025-08-14T21:38:49.3833012Z >>> self, 2025-08-14T21:38:49.3833119Z >>> state_dict: STATE_DICT_TYPE, 2025-08-14T21:38:49.3833230Z >>> metadata: Metadata, 2025-08-14T21:38:49.3833330Z >>> is_coordinator: bool, 2025-08-14T21:38:49.3833410Z >>> ) -> None: 2025-08-14T21:38:49.3833539Z >>> self.original_state_dict = state_dict 2025-08-14T21:38:49.3833704Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-08-14T21:38:49.3833789Z >>> 2025-08-14T21:38:49.3833902Z >>> if self.flatten_sharded_tensors: 2025-08-14T21:38:49.3834049Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-08-14T21:38:49.3834134Z >>> 2025-08-14T21:38:49.3834240Z >>> if self.flatten_state_dict: 2025-08-14T21:38:49.3834476Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-08-14T21:38:49.3834586Z >>> 2025-08-14T21:38:49.3834692Z >>> self.state_dict = state_dict 2025-08-14T21:38:49.3834795Z >>> self.metadata = metadata 2025-08-14T21:38:49.3834928Z >>> self.is_coordinator = is_coordinator 2025-08-14T21:38:49.3835008Z >>> 2025-08-14T21:38:49.3835133Z >>> def load_bytes(self, read_item, value): 2025-08-14T21:38:49.3835230Z >>> # Remove the "foo_" prefix 2025-08-14T21:38:49.3835540Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-08-14T21:38:49.3835630Z 2025-08-14T21:38:49.3835705Z 2025-08-14T21:38:49.3835957Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-08-14T21:38:49.3836042Z 2025-08-14T21:38:49.3836150Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:49.3836297Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-08-14T21:38:49.3836425Z >>> def resolve_tensor(self, read_item): 2025-08-14T21:38:49.3836556Z >>> tensor = super().resolve_tensor(read_item) 2025-08-14T21:38:49.3836701Z >>> return torch.empty_like(tensor, device="cpu") 2025-08-14T21:38:49.3836812Z >>> 2025-08-14T21:38:49.3836940Z >>> def commit_tensor(self, read_item, tensor): 2025-08-14T21:38:49.3837104Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-08-14T21:38:49.3837179Z 2025-08-14T21:38:49.3837425Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3837508Z 2025-08-14T21:38:49.3837599Z warnings.warn(msg) 2025-08-14T21:38:49.3837682Z 2025-08-14T21:38:49.3837879Z --- Parse Warning: 68 / 146 --- 2025-08-14T21:38:49.3838843Z /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. 2025-08-14T21:38:49.3839116Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3839195Z 2025-08-14T21:38:49.3839356Z Return the model state_dict and optimizers state_dict. 2025-08-14T21:38:49.3839449Z 2025-08-14T21:38:49.3839663Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-08-14T21:38:49.3839905Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-08-14T21:38:49.3840154Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-08-14T21:38:49.3840361Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-08-14T21:38:49.3840576Z with a different number of trainers and/or different parallelisms. 2025-08-14T21:38:49.3840815Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-08-14T21:38:49.3840899Z these APIs. 2025-08-14T21:38:49.3841029Z 3.) sanity checking the result state_dict. 2025-08-14T21:38:49.3841105Z 2025-08-14T21:38:49.3841307Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-08-14T21:38:49.3841544Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-08-14T21:38:49.3841772Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-08-14T21:38:49.3841971Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-08-14T21:38:49.3842179Z ``module.named_buffers()`` when the module is not distributed by any 2025-08-14T21:38:49.3842423Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-08-14T21:38:49.3842645Z a parameter, there will be a conversion from the parameter IDs to the 2025-08-14T21:38:49.3842756Z canonical FQNs when calling this API. 2025-08-14T21:38:49.3842832Z 2025-08-14T21:38:49.3843106Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-08-14T21:38:49.3843345Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-08-14T21:38:49.3843481Z optimizer parameter IDs to the canonical FQNs. 2025-08-14T21:38:49.3843565Z 2025-08-14T21:38:49.3843647Z Example: 2025-08-14T21:38:49.3843749Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3843834Z >>> import torch 2025-08-14T21:38:49.3844062Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-14T21:38:49.3844265Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-08-14T21:38:49.3844483Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-08-14T21:38:49.3844560Z 2025-08-14T21:38:49.3844696Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-08-14T21:38:49.3844876Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-08-14T21:38:49.3844996Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-08-14T21:38:49.3845176Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-08-14T21:38:49.3845251Z 2025-08-14T21:38:49.3845339Z 2025-08-14T21:38:49.3845561Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-08-14T21:38:49.3845751Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-08-14T21:38:49.3845866Z ... fsdp_model, fsdp_optim 2025-08-14T21:38:49.3845943Z ... ) 2025-08-14T21:38:49.3846023Z 2025-08-14T21:38:49.3846237Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-08-14T21:38:49.3846333Z >>> # the asserts will fail. 2025-08-14T21:38:49.3846455Z >>> assert ddp_state_dict == fsdp_state_dict 2025-08-14T21:38:49.3846608Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-08-14T21:38:49.3846682Z 2025-08-14T21:38:49.3846767Z 2025-08-14T21:38:49.3846844Z Args: 2025-08-14T21:38:49.3846980Z model (nn.Module): the nn.Module to the model. 2025-08-14T21:38:49.3847172Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-08-14T21:38:49.3847325Z The optimizers that are used to optimize ``model``. 2025-08-14T21:38:49.3847596Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-08-14T21:38:49.3847717Z that belong to the submodules. 2025-08-14T21:38:49.3847882Z options (StateDictOptions): the options to control how 2025-08-14T21:38:49.3848084Z model state_dict and optimizer state_dict should be returned. See 2025-08-14T21:38:49.3848209Z `StateDictOptions` for the details. 2025-08-14T21:38:49.3848287Z 2025-08-14T21:38:49.3848376Z Returns: 2025-08-14T21:38:49.3848564Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-08-14T21:38:49.3848641Z 2025-08-14T21:38:49.3848872Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-08-14T21:38:49.3848953Z 2025-08-14T21:38:49.3849202Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3849286Z 2025-08-14T21:38:49.3849378Z warnings.warn(msg) 2025-08-14T21:38:49.3849454Z 2025-08-14T21:38:49.3849645Z --- Parse Warning: 69 / 146 --- 2025-08-14T21:38:49.3850592Z /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. 2025-08-14T21:38:49.3850858Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3850934Z 2025-08-14T21:38:49.3851122Z Load a checkpoint into a distributed state dict in SPMD style. 2025-08-14T21:38:49.3851206Z 2025-08-14T21:38:49.3851419Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-08-14T21:38:49.3851693Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-08-14T21:38:49.3851936Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-08-14T21:38:49.3852014Z costs). 2025-08-14T21:38:49.3852091Z 2025-08-14T21:38:49.3852274Z Each rank will try to read the least amount of data necessary 2025-08-14T21:38:49.3852502Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-08-14T21:38:49.3852752Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-08-14T21:38:49.3852830Z 2025-08-14T21:38:49.3853082Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-08-14T21:38:49.3853338Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-08-14T21:38:49.3853500Z ``load_state_dict`` once the deserialization is complete. 2025-08-14T21:38:49.3853749Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-08-14T21:38:49.3853907Z it in the ``state_dict`` with the deserialized object. 2025-08-14T21:38:49.3853985Z 2025-08-14T21:38:49.3854084Z .. warning:: 2025-08-14T21:38:49.3854244Z All tensors in ``state_dict`` must be allocated on their 2025-08-14T21:38:49.3854459Z destination device *prior to* calling this function. 2025-08-14T21:38:49.3854553Z 2025-08-14T21:38:49.3854772Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-08-14T21:38:49.3854859Z on state_dict. 2025-08-14T21:38:49.3854949Z 2025-08-14T21:38:49.3855033Z .. warning:: 2025-08-14T21:38:49.3855229Z Users must call `load_state_dict` on the root module to ensure load 2025-08-14T21:38:49.3855419Z pos-processing and non-tensor data properly propagates. 2025-08-14T21:38:49.3855496Z 2025-08-14T21:38:49.3855588Z .. note: 2025-08-14T21:38:49.3855808Z If no process group is initialized, this function will assume the intent 2025-08-14T21:38:49.3856022Z is to load a checkpoint into the local process. This can be useful in the 2025-08-14T21:38:49.3856269Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-08-14T21:38:49.3856364Z or ShardedTensor) 2025-08-14T21:38:49.3856442Z 2025-08-14T21:38:49.3856532Z .. note: 2025-08-14T21:38:49.3856665Z Rank 0 is assumed to be the coordinator rank. 2025-08-14T21:38:49.3856740Z 2025-08-14T21:38:49.3856828Z Args: 2025-08-14T21:38:49.3857033Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-08-14T21:38:49.3857183Z checkpoint_id (Union[str, os.PathLike, None]): 2025-08-14T21:38:49.3857384Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-08-14T21:38:49.3857582Z depends on the storage. It can be a path to a folder or to a file. 2025-08-14T21:38:49.3857756Z It can also be a key if the storage is a key-value store. 2025-08-14T21:38:49.3857859Z (Default: ``None``) 2025-08-14T21:38:49.3857984Z storage_reader (Optional[StorageReader]): 2025-08-14T21:38:49.3858199Z Instance of StorageWriter used to perform reads. If this is not 2025-08-14T21:38:49.3858398Z specified, DCP will automatically infer the reader based on the 2025-08-14T21:38:49.3858605Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-08-14T21:38:49.3858713Z be raised. (Default: ``None``) 2025-08-14T21:38:49.3858826Z planner (Optional[LoadPlanner]): 2025-08-14T21:38:49.3859035Z Instance of LoadPlanner. If this is not specified, the default 2025-08-14T21:38:49.3859162Z planner will be used. (Default: ``None``) 2025-08-14T21:38:49.3859289Z process_group (Optional[ProcessGroup]): 2025-08-14T21:38:49.3859481Z ProcessGroup to be used for cross-rank synchronization. 2025-08-14T21:38:49.3859624Z (Default: ``None``) 2025-08-14T21:38:49.3859855Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-08-14T21:38:49.3860118Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-08-14T21:38:49.3860201Z Returns: 2025-08-14T21:38:49.3860296Z None. 2025-08-14T21:38:49.3860376Z 2025-08-14T21:38:49.3860460Z Examples 2025-08-14T21:38:49.3860571Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3860671Z >>> my_model = MyModule() 2025-08-14T21:38:49.3860805Z >>> optimizer = Adagrad(my_model.parameters()) 2025-08-14T21:38:49.3860945Z >>> model_state_dict = my_model.state_dict() 2025-08-14T21:38:49.3861173Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-08-14T21:38:49.3861269Z ... "/checkpoint/1" 2025-08-14T21:38:49.3861367Z ... ) 2025-08-14T21:38:49.3861450Z 2025-08-14T21:38:49.3861603Z >>> torch.distributed.checkpoint.load_state_dict( 2025-08-14T21:38:49.3861730Z >>> state_dict=model_state_dict, 2025-08-14T21:38:49.3861846Z >>> storage_reader=fs_storage_reader, 2025-08-14T21:38:49.3861942Z >>> ) 2025-08-14T21:38:49.3862023Z 2025-08-14T21:38:49.3862212Z >>> # module.load_state_dict() function might have customized steps 2025-08-14T21:38:49.3862377Z >>> # to flush the state_dict, must call it to 2025-08-14T21:38:49.3862478Z >>> # ensure correct behavior. 2025-08-14T21:38:49.3862606Z >>> my_model.load_state_dict(model_state_dict) 2025-08-14T21:38:49.3862694Z 2025-08-14T21:38:49.3862778Z .. note:: 2025-08-14T21:38:49.3862981Z load_state_dict uses collectives to coordinate reads across ranks. 2025-08-14T21:38:49.3863204Z For NCCL-based process groups, internal tensor representations of 2025-08-14T21:38:49.3863527Z objects must be moved to the GPU device before communication takes place. 2025-08-14T21:38:49.3863766Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-08-14T21:38:49.3863989Z and it is the user's responsibility to ensure that this is set so that each 2025-08-14T21:38:49.3864170Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-08-14T21:38:49.3864265Z 2025-08-14T21:38:49.3864510Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3864585Z 2025-08-14T21:38:49.3864692Z warnings.warn(msg) 2025-08-14T21:38:49.3864767Z 2025-08-14T21:38:49.3864965Z --- Parse Warning: 70 / 146 --- 2025-08-14T21:38:49.3865915Z /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. 2025-08-14T21:38:49.3866168Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3866260Z 2025-08-14T21:38:49.3866379Z Save a distributed model in SPMD style. 2025-08-14T21:38:49.3866455Z 2025-08-14T21:38:49.3866657Z This function is different from ``torch.save()`` as it handles 2025-08-14T21:38:49.3866905Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-08-14T21:38:49.3866997Z 2025-08-14T21:38:49.3867245Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-08-14T21:38:49.3867389Z save will call ``state_dict`` before serialization. 2025-08-14T21:38:49.3867477Z 2025-08-14T21:38:49.3867563Z .. warning:: 2025-08-14T21:38:49.3867791Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-08-14T21:38:49.3867903Z for saved state_dicts. 2025-08-14T21:38:49.3867980Z 2025-08-14T21:38:49.3868065Z .. warning:: 2025-08-14T21:38:49.3868278Z If using the `process_group` argument, make sure that only its ranks 2025-08-14T21:38:49.3868534Z call `save_state_dict` and that all data in state_dict belong to it. 2025-08-14T21:38:49.3868647Z 2025-08-14T21:38:49.3868740Z .. note:: 2025-08-14T21:38:49.3868994Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-08-14T21:38:49.3869259Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-08-14T21:38:49.3869363Z group needs to be passed in. 2025-08-14T21:38:49.3869441Z 2025-08-14T21:38:49.3869536Z .. note:: 2025-08-14T21:38:49.3869796Z If no process group is available, this function assumes the intention is to save the 2025-08-14T21:38:49.3869904Z state_dict in the local process. 2025-08-14T21:38:49.3869995Z 2025-08-14T21:38:49.3870074Z .. note: 2025-08-14T21:38:49.3870206Z Rank 0 is assumed to be the coordinator rank. 2025-08-14T21:38:49.3870296Z 2025-08-14T21:38:49.3870373Z 2025-08-14T21:38:49.3870462Z Args: 2025-08-14T21:38:49.3870615Z state_dict (Dict[str, Any]): The state_dict to save. 2025-08-14T21:38:49.3870755Z checkpoint_id (Union[str, os.PathLike, None]): 2025-08-14T21:38:49.3870973Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-08-14T21:38:49.3871197Z depends on the storage. It can be a path to a folder or to a file. 2025-08-14T21:38:49.3871361Z It can also be a key if the storage is a key-value store. 2025-08-14T21:38:49.3871467Z (Default: ``None``) 2025-08-14T21:38:49.3871594Z storage_writer (Optional[StorageWriter]): 2025-08-14T21:38:49.3871796Z Instance of StorageWriter used to perform writes. If this is not 2025-08-14T21:38:49.3872006Z specified, DCP will automatically infer the writer based on the 2025-08-14T21:38:49.3872202Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-08-14T21:38:49.3872319Z be raised. (Default: ``None``) 2025-08-14T21:38:49.3872436Z planner (Optional[SavePlanner]): 2025-08-14T21:38:49.3872628Z Instance of SavePlanner. If this is not specified, the default 2025-08-14T21:38:49.3872762Z planner will be used. (Default: ``None``) 2025-08-14T21:38:49.3872884Z process_group (Optional[ProcessGroup]): 2025-08-14T21:38:49.3873065Z ProcessGroup to be used for cross-rank synchronization. 2025-08-14T21:38:49.3873172Z (Default: ``None``) 2025-08-14T21:38:49.3873260Z no_dist (bool): 2025-08-14T21:38:49.3873438Z If ``True``, this function will assume the intent is to load 2025-08-14T21:38:49.3873561Z a checkpoint on a single rank/process. 2025-08-14T21:38:49.3873653Z (Default: ``False``) 2025-08-14T21:38:49.3873914Z use_collectives (bool): If ``False``, this function will assume the intent is to save 2025-08-14T21:38:49.3874085Z a checkpoint without using cross-rank synchronization. 2025-08-14T21:38:49.3874179Z (Default: ``True``) 2025-08-14T21:38:49.3874413Z This configuration is experimental and should be used with caution. 2025-08-14T21:38:49.3874673Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-08-14T21:38:49.3874762Z 2025-08-14T21:38:49.3874844Z Returns: 2025-08-14T21:38:49.3874998Z Metadata: Metadata object for the saved checkpoint. 2025-08-14T21:38:49.3875087Z 2025-08-14T21:38:49.3875169Z Example: 2025-08-14T21:38:49.3875264Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3875372Z >>> my_model = MyModule() 2025-08-14T21:38:49.3875448Z 2025-08-14T21:38:49.3875554Z >>> state_dict = {"model": my_model} 2025-08-14T21:38:49.3875642Z 2025-08-14T21:38:49.3875867Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-08-14T21:38:49.3875960Z ... "/checkpoint/1" 2025-08-14T21:38:49.3876049Z ... ) 2025-08-14T21:38:49.3876221Z >>> torch.distributed.checkpoint.save( 2025-08-14T21:38:49.3876345Z >>> state_dict=state_dict, 2025-08-14T21:38:49.3876472Z >>> storage_writer=fs_storage_writer, 2025-08-14T21:38:49.3876552Z >>> ) 2025-08-14T21:38:49.3876642Z 2025-08-14T21:38:49.3876724Z .. note:: 2025-08-14T21:38:49.3876933Z save_state_dict uses collectives to coordinate writes across ranks. 2025-08-14T21:38:49.3877154Z For NCCL-based process groups, internal tensor representations of 2025-08-14T21:38:49.3877376Z objects must be moved to the GPU device before communication takes place. 2025-08-14T21:38:49.3877587Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-08-14T21:38:49.3877798Z and it is the user's responsibility to ensure that this is set so that 2025-08-14T21:38:49.3877988Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-08-14T21:38:49.3878075Z 2025-08-14T21:38:49.3878321Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3878403Z 2025-08-14T21:38:49.3878509Z warnings.warn(msg) 2025-08-14T21:38:49.3878586Z 2025-08-14T21:38:49.3878773Z --- Parse Warning: 71 / 146 --- 2025-08-14T21:38:49.3879749Z /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. 2025-08-14T21:38:49.3880033Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3880302Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-08-14T21:38:49.3880581Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-08-14T21:38:49.3880658Z 2025-08-14T21:38:49.3880756Z .. warning:: 2025-08-14T21:38:49.3880914Z This feature is experimental and subject to change. 2025-08-14T21:38:49.3881054Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-08-14T21:38:49.3881141Z 2025-08-14T21:38:49.3881221Z Args: 2025-08-14T21:38:49.3881373Z state_dict (Dict[str, Any]): The state_dict to save. 2025-08-14T21:38:49.3881527Z checkpoint_id (Union[str, os.PathLike, None]): 2025-08-14T21:38:49.3881732Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-08-14T21:38:49.3881944Z depends on the storage. It can be a path to a folder or to a file. 2025-08-14T21:38:49.3882108Z It can also be a key if the storage is a key-value store. 2025-08-14T21:38:49.3882202Z (Default: ``None``) 2025-08-14T21:38:49.3882346Z storage_writer (Optional[StorageWriter]): 2025-08-14T21:38:49.3882554Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-08-14T21:38:49.3882787Z this is not specified, DCP will automatically infer the writer based on the 2025-08-14T21:38:49.3882998Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-08-14T21:38:49.3883104Z be raised. (Default: ``None``) 2025-08-14T21:38:49.3883235Z planner (Optional[SavePlanner]): 2025-08-14T21:38:49.3883431Z Instance of SavePlanner. If this is not specified, the default 2025-08-14T21:38:49.3883561Z planner will be used. (Default: ``None``) 2025-08-14T21:38:49.3883702Z process_group (Optional[ProcessGroup]): 2025-08-14T21:38:49.3883880Z ProcessGroup to be used for cross-rank synchronization. 2025-08-14T21:38:49.3883978Z (Default: ``None``) 2025-08-14T21:38:49.3884147Z async_checkpointer_type (AsyncCheckpointerType): 2025-08-14T21:38:49.3884316Z whether to do checkpoint in separate thread or process 2025-08-14T21:38:49.3884517Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-08-14T21:38:49.3884647Z async_stager (AsyncStager): 2025-08-14T21:38:49.3884898Z provides staging implementation. If storage_writer implements AsyncStager 2025-08-14T21:38:49.3885117Z and async_stager is provided, async_stager will be used for staging 2025-08-14T21:38:49.3885211Z no_dist (bool): 2025-08-14T21:38:49.3885377Z If ``True``, this function will assume the intent is to save 2025-08-14T21:38:49.3885516Z a checkpoint on a single rank/process. 2025-08-14T21:38:49.3885610Z (Default: ``False``) 2025-08-14T21:38:49.3885926Z use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``) 2025-08-14T21:38:49.3886144Z This configuration is experimental and should be used with caution. 2025-08-14T21:38:49.3886406Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-08-14T21:38:49.3892078Z 2025-08-14T21:38:49.3892188Z Returns: 2025-08-14T21:38:49.3892426Z Future: A future holding the resultant Metadata object from `save`. 2025-08-14T21:38:49.3892503Z 2025-08-14T21:38:49.3892585Z Example: 2025-08-14T21:38:49.3892773Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.3893100Z >>> my_model = MyModule() 2025-08-14T21:38:49.3893175Z 2025-08-14T21:38:49.3893300Z >>> state_dict = {"model": my_model} 2025-08-14T21:38:49.3893377Z 2025-08-14T21:38:49.3893607Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-08-14T21:38:49.3893718Z ... "/checkpoint/1" 2025-08-14T21:38:49.3893798Z ... ) 2025-08-14T21:38:49.3894003Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-08-14T21:38:49.3894115Z >>> state_dict=state_dict, 2025-08-14T21:38:49.3894232Z >>> storage_writer=fs_storage_writer, 2025-08-14T21:38:49.3894330Z >>> ) 2025-08-14T21:38:49.3894406Z >>> 2025-08-14T21:38:49.3894502Z >>> # ... do some work ... 2025-08-14T21:38:49.3894595Z >>> 2025-08-14T21:38:49.3894702Z >>> checkpoint_future.result() 2025-08-14T21:38:49.3894779Z 2025-08-14T21:38:49.3894870Z 2025-08-14T21:38:49.3895120Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3895199Z 2025-08-14T21:38:49.3895306Z warnings.warn(msg) 2025-08-14T21:38:49.3895380Z 2025-08-14T21:38:49.3895615Z --- Parse Warning: 72 / 146 --- 2025-08-14T21:38:49.3896663Z /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. 2025-08-14T21:38:49.3896928Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3897022Z 2025-08-14T21:38:49.3897215Z Initialize rendezvous event object and record its operations. 2025-08-14T21:38:49.3897294Z 2025-08-14T21:38:49.3897383Z Args: 2025-08-14T21:38:49.3897510Z run_id (str): The run id of the rendezvous. 2025-08-14T21:38:49.3897658Z message (str): The message describing the event. 2025-08-14T21:38:49.3897920Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-08-14T21:38:49.3898100Z name (str): Event name. (E.g. Current action being performed). 2025-08-14T21:38:49.3898226Z hostname (str): Hostname of the node. 2025-08-14T21:38:49.3898366Z pid (Optional[int]): The process id of the node. 2025-08-14T21:38:49.3898601Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-08-14T21:38:49.3898877Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-08-14T21:38:49.3899140Z rank (Optional[int]): The rank of the node, if known. 2025-08-14T21:38:49.3899282Z Returns: 2025-08-14T21:38:49.3899375Z None 2025-08-14T21:38:49.3899455Z Example: 2025-08-14T21:38:49.3899581Z >>> # See DynamicRendezvousHandler class 2025-08-14T21:38:49.3899683Z >>> def _record( 2025-08-14T21:38:49.3899765Z ... self, 2025-08-14T21:38:49.3899867Z ... message: str, 2025-08-14T21:38:49.3900006Z ... node_state: NodeState = NodeState.RUNNING, 2025-08-14T21:38:49.3900114Z ... rank: Optional[int] = None, 2025-08-14T21:38:49.3900211Z ... ) -> None: 2025-08-14T21:38:49.3900322Z ... construct_and_record_rdzv_event( 2025-08-14T21:38:49.3900483Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-08-14T21:38:49.3900615Z ... run_id=self._settings.run_id, 2025-08-14T21:38:49.3900708Z ... message=message, 2025-08-14T21:38:49.3900812Z ... node_state=node_state, 2025-08-14T21:38:49.3900937Z ... hostname=self._this_node.addr, 2025-08-14T21:38:49.3901042Z ... pid=self._this_node.pid, 2025-08-14T21:38:49.3901174Z ... local_id=self._this_node.local_id, 2025-08-14T21:38:49.3901298Z ... rank=rank, 2025-08-14T21:38:49.3901374Z ... ) 2025-08-14T21:38:49.3901461Z 2025-08-14T21:38:49.3901706Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3901781Z 2025-08-14T21:38:49.3901885Z warnings.warn(msg) 2025-08-14T21:38:49.3901962Z 2025-08-14T21:38:49.3902148Z --- Parse Warning: 73 / 146 --- 2025-08-14T21:38:49.3903060Z /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. 2025-08-14T21:38:49.3903317Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3903491Z 2025-08-14T21:38:49.3903656Z This configures FSDP-native mixed precision training. 2025-08-14T21:38:49.3903732Z 2025-08-14T21:38:49.3903831Z Attributes: 2025-08-14T21:38:49.3904062Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-08-14T21:38:49.3904255Z parameters during forward and backward and thus the dtype for 2025-08-14T21:38:49.3904483Z forward and backward computation. Outside forward and backward, the 2025-08-14T21:38:49.3904665Z *sharded* parameters are kept in full precision (e.g. for the 2025-08-14T21:38:49.3904881Z optimizer step), and for model checkpointing, the parameters are 2025-08-14T21:38:49.3905032Z always saved in full precision. (Default: ``None``) 2025-08-14T21:38:49.3905235Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-08-14T21:38:49.3905460Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-08-14T21:38:49.3905631Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-08-14T21:38:49.3905824Z the ``param_dtype`` value, still running gradient reduction in low 2025-08-14T21:38:49.3906042Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-08-14T21:38:49.3906231Z to force gradient reduction to run in full precision. (Default: 2025-08-14T21:38:49.3906324Z ``None``) 2025-08-14T21:38:49.3906526Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-08-14T21:38:49.3906718Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-08-14T21:38:49.3906918Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-08-14T21:38:49.3907118Z dtype thereafter. For model checkpointing, the buffers are saved 2025-08-14T21:38:49.3907362Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-08-14T21:38:49.3907477Z ``None``) 2025-08-14T21:38:49.3907663Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-08-14T21:38:49.3907884Z gradients to full precision after the backward pass in preparation 2025-08-14T21:38:49.3908080Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-08-14T21:38:49.3908270Z in the dtype used for gradient reduction, which can save memory if 2025-08-14T21:38:49.3908479Z using a custom optimizer that supports running in low precision. 2025-08-14T21:38:49.3908571Z (Default: ``False``) 2025-08-14T21:38:49.3908773Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-08-14T21:38:49.3908978Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-08-14T21:38:49.3909184Z that parameter and input dtypes match for forward computation, as 2025-08-14T21:38:49.3909396Z required by many ops. This may need to be set to ``True`` when only 2025-08-14T21:38:49.3909606Z applying mixed precision to some but not all FSDP modules, in which 2025-08-14T21:38:49.3909803Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-08-14T21:38:49.3909936Z (Default: ``False``) 2025-08-14T21:38:49.3910146Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-08-14T21:38:49.3910336Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-08-14T21:38:49.3910526Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-08-14T21:38:49.3910657Z this does not do anything. (Default: ``True``) 2025-08-14T21:38:49.3910874Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-08-14T21:38:49.3911052Z module classes to ignore for mixed precision when using an 2025-08-14T21:38:49.3911228Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-08-14T21:38:49.3911448Z applied to them separately with mixed precision disabled (meaning 2025-08-14T21:38:49.3911645Z that the final FSDP construction would deviate from the specified 2025-08-14T21:38:49.3911828Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-08-14T21:38:49.3912029Z not do anything. This API is experimental and subject to change. 2025-08-14T21:38:49.3912131Z (Default: ``(_BatchNorm,)``) 2025-08-14T21:38:49.3912217Z 2025-08-14T21:38:49.3912386Z .. note:: This API is experimental and subject to change. 2025-08-14T21:38:49.3912464Z 2025-08-14T21:38:49.3912692Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-08-14T21:38:49.3912770Z 2025-08-14T21:38:49.3912945Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-08-14T21:38:49.3913068Z precision, but buffers are not. 2025-08-14T21:38:49.3913143Z 2025-08-14T21:38:49.3913346Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-08-14T21:38:49.3913552Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-08-14T21:38:49.3913774Z Disabling FSDP's mixed precision for those norm modules only means that 2025-08-14T21:38:49.3913990Z the affine parameters are kept in ``float32``. However, this incurs 2025-08-14T21:38:49.3914219Z separate all-gathers and reduce-scatters for those norm modules, which 2025-08-14T21:38:49.3914427Z may be inefficient, so if the workload permits, the user should prefer 2025-08-14T21:38:49.3914586Z to still apply mixed precision to those modules. 2025-08-14T21:38:49.3914666Z 2025-08-14T21:38:49.3914870Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-08-14T21:38:49.3915116Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-08-14T21:38:49.3915361Z modules will have FSDP applied to them separately with mixed precision 2025-08-14T21:38:49.3915540Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-08-14T21:38:49.3915618Z 2025-08-14T21:38:49.3915819Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-08-14T21:38:49.3916038Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-08-14T21:38:49.3916206Z its ``cast_root_forward_inputs`` takes precedence over its 2025-08-14T21:38:49.3916386Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-08-14T21:38:49.3916596Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-08-14T21:38:49.3916808Z sufficient for the typical case where each FSDP instance has the same 2025-08-14T21:38:49.3917036Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-08-14T21:38:49.3917216Z ``param_dtype`` at the beginning of the model's forward pass. 2025-08-14T21:38:49.3917294Z 2025-08-14T21:38:49.3917500Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-08-14T21:38:49.3917728Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-08-14T21:38:49.3917987Z values to configure casting inputs or not before each instance's 2025-08-14T21:38:49.3918173Z forward. In such a case, since the casts happen before each FSDP 2025-08-14T21:38:49.3918383Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-08-14T21:38:49.3918615Z submodules run before its FSDP submodules to avoid the activation dtype 2025-08-14T21:38:49.3918816Z being changed due to a different ``MixedPrecision`` configuration. 2025-08-14T21:38:49.3918895Z 2025-08-14T21:38:49.3918996Z Example:: 2025-08-14T21:38:49.3919074Z 2025-08-14T21:38:49.3919208Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:49.3919388Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-08-14T21:38:49.3919484Z >>> model[1] = FSDP( 2025-08-14T21:38:49.3919583Z >>> model[1], 2025-08-14T21:38:49.3919886Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-08-14T21:38:49.3919970Z >>> ) 2025-08-14T21:38:49.3920070Z >>> model = FSDP( 2025-08-14T21:38:49.3920155Z >>> model, 2025-08-14T21:38:49.3920455Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-08-14T21:38:49.3920541Z >>> ) 2025-08-14T21:38:49.3920617Z 2025-08-14T21:38:49.3920825Z The above shows a working example. On the other hand, if ``model[1]`` 2025-08-14T21:38:49.3921029Z were replaced with ``model[0]``, meaning that the submodule using 2025-08-14T21:38:49.3921249Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-08-14T21:38:49.3921477Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-08-14T21:38:49.3921560Z ones. 2025-08-14T21:38:49.3921636Z 2025-08-14T21:38:49.3921732Z 2025-08-14T21:38:49.3921980Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3922053Z 2025-08-14T21:38:49.3922155Z warnings.warn(msg) 2025-08-14T21:38:49.3922232Z 2025-08-14T21:38:49.3922430Z --- Parse Warning: 74 / 146 --- 2025-08-14T21:38:49.3923373Z /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. 2025-08-14T21:38:49.3923627Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3923714Z 2025-08-14T21:38:49.3923963Z ``FullStateDictConfig`` is a config class meant to be used with 2025-08-14T21:38:49.3924182Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-08-14T21:38:49.3924382Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-08-14T21:38:49.3924596Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-08-14T21:38:49.3924801Z is meant to be used via the :func:`state_dict_type` context manager as 2025-08-14T21:38:49.3924883Z follows: 2025-08-14T21:38:49.3924960Z 2025-08-14T21:38:49.3925091Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:49.3925322Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-14T21:38:49.3925447Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-08-14T21:38:49.3925656Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-08-14T21:38:49.3925867Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-08-14T21:38:49.3925975Z >>> state = fsdp.state_dict() 2025-08-14T21:38:49.3926190Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-08-14T21:38:49.3926423Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-08-14T21:38:49.3926684Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-08-14T21:38:49.3926783Z >>> if dist.get_rank() == 0: 2025-08-14T21:38:49.3926961Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-08-14T21:38:49.3927111Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-08-14T21:38:49.3927226Z >>> model.load_state_dict(state_dict) 2025-08-14T21:38:49.3927453Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-08-14T21:38:49.3927693Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-08-14T21:38:49.3927785Z >>> fsdp = FSDP( 2025-08-14T21:38:49.3927878Z ... model, 2025-08-14T21:38:49.3928007Z ... device_id=torch.cuda.current_device(), 2025-08-14T21:38:49.3928107Z ... auto_wrap_policy=..., 2025-08-14T21:38:49.3928218Z ... sync_module_states=True, 2025-08-14T21:38:49.3928299Z ... ) 2025-08-14T21:38:49.3928506Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-08-14T21:38:49.3928592Z 2025-08-14T21:38:49.3928676Z Attributes: 2025-08-14T21:38:49.3928870Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-08-14T21:38:49.3929073Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-08-14T21:38:49.3929224Z ranks save the full state dict. (Default: ``False``) 2025-08-14T21:38:49.3929307Z 2025-08-14T21:38:49.3929553Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3929630Z 2025-08-14T21:38:49.3929730Z warnings.warn(msg) 2025-08-14T21:38:49.3929809Z 2025-08-14T21:38:49.3929996Z --- Parse Warning: 75 / 146 --- 2025-08-14T21:38:49.3931163Z /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. 2025-08-14T21:38:49.3931423Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3931674Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-08-14T21:38:49.3931752Z 2025-08-14T21:38:49.3932009Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-08-14T21:38:49.3932218Z The target module does not have to be a FSDP module. If the target 2025-08-14T21:38:49.3932473Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-08-14T21:38:49.3932576Z 2025-08-14T21:38:49.3932769Z .. note:: This API should be called for only the top-level (root) 2025-08-14T21:38:49.3932850Z module. 2025-08-14T21:38:49.3932937Z 2025-08-14T21:38:49.3933140Z .. note:: This API enables users to transparently use the conventional 2025-08-14T21:38:49.3933330Z ``state_dict`` API to take model checkpoints in cases where the 2025-08-14T21:38:49.3933530Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-08-14T21:38:49.3933733Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-08-14T21:38:49.3933967Z instances, while dispatching into `sharded_state_dict` implementation 2025-08-14T21:38:49.3934056Z for FSDP: 2025-08-14T21:38:49.3934141Z 2025-08-14T21:38:49.3934227Z Example:: 2025-08-14T21:38:49.3934304Z 2025-08-14T21:38:49.3934448Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:49.3934552Z >>> model = DDP(FSDP(...)) 2025-08-14T21:38:49.3934658Z >>> FSDP.set_state_dict_type( 2025-08-14T21:38:49.3934752Z >>> model, 2025-08-14T21:38:49.3934907Z >>> StateDictType.SHARDED_STATE_DICT, 2025-08-14T21:38:49.3935117Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-08-14T21:38:49.3935345Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-08-14T21:38:49.3935424Z >>> ) 2025-08-14T21:38:49.3935554Z >>> param_state_dict = model.state_dict() 2025-08-14T21:38:49.3935719Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-08-14T21:38:49.3935795Z 2025-08-14T21:38:49.3935882Z Args: 2025-08-14T21:38:49.3936004Z module (torch.nn.Module): Root module. 2025-08-14T21:38:49.3936230Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-08-14T21:38:49.3936469Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-08-14T21:38:49.3936576Z target ``state_dict_type``. 2025-08-14T21:38:49.3936827Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-08-14T21:38:49.3936937Z for the optimizer state dict. 2025-08-14T21:38:49.3937013Z 2025-08-14T21:38:49.3937102Z Returns: 2025-08-14T21:38:49.3937318Z A StateDictSettings that include the previous state_dict type and 2025-08-14T21:38:49.3937428Z configuration for the module. 2025-08-14T21:38:49.3937516Z 2025-08-14T21:38:49.3937761Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3937838Z 2025-08-14T21:38:49.3937932Z warnings.warn(msg) 2025-08-14T21:38:49.3938013Z 2025-08-14T21:38:49.3938205Z --- Parse Warning: 76 / 146 --- 2025-08-14T21:38:49.3939346Z /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. 2025-08-14T21:38:49.3939603Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3939851Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-08-14T21:38:49.3939926Z 2025-08-14T21:38:49.3940234Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-08-14T21:38:49.3940364Z :meth:`set_state_dict_type` for the detail. 2025-08-14T21:38:49.3940441Z 2025-08-14T21:38:49.3940540Z Example:: 2025-08-14T21:38:49.3940616Z 2025-08-14T21:38:49.3940810Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:49.3940949Z >>> model = DDP(FSDP(...)) 2025-08-14T21:38:49.3941058Z >>> with FSDP.state_dict_type( 2025-08-14T21:38:49.3941148Z >>> model, 2025-08-14T21:38:49.3941285Z >>> StateDictType.SHARDED_STATE_DICT, 2025-08-14T21:38:49.3941368Z >>> ): 2025-08-14T21:38:49.3941488Z >>> checkpoint = model.state_dict() 2025-08-14T21:38:49.3941569Z 2025-08-14T21:38:49.3941650Z Args: 2025-08-14T21:38:49.3941779Z module (torch.nn.Module): Root module. 2025-08-14T21:38:49.3942004Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-08-14T21:38:49.3942227Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-08-14T21:38:49.3942387Z configuration for the target ``state_dict_type``. 2025-08-14T21:38:49.3942615Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-08-14T21:38:49.3942807Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-08-14T21:38:49.3942891Z 2025-08-14T21:38:49.3943161Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3943250Z 2025-08-14T21:38:49.3943341Z warnings.warn(msg) 2025-08-14T21:38:49.3943502Z 2025-08-14T21:38:49.3943699Z --- Parse Warning: 77 / 146 --- 2025-08-14T21:38:49.3944852Z /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. 2025-08-14T21:38:49.3945110Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3945201Z 2025-08-14T21:38:49.3945439Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-08-14T21:38:49.3945530Z 2025-08-14T21:38:49.3945718Z The given state-dict can be transformed to one of three types: 2025-08-14T21:38:49.3946012Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-08-14T21:38:49.3946105Z 2025-08-14T21:38:49.3946331Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-08-14T21:38:49.3946540Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-08-14T21:38:49.3946632Z avoid OOM. 2025-08-14T21:38:49.3946709Z 2025-08-14T21:38:49.3946936Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-08-14T21:38:49.3947143Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-08-14T21:38:49.3947222Z memory. 2025-08-14T21:38:49.3947308Z 2025-08-14T21:38:49.3947520Z For local state_dict, no transformation will be performed. But a state 2025-08-14T21:38:49.3947749Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-08-14T21:38:49.3947869Z nature (this is not supported yet). 2025-08-14T21:38:49.3947947Z 2025-08-14T21:38:49.3948035Z Example:: 2025-08-14T21:38:49.3948120Z 2025-08-14T21:38:49.3948241Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:49.3948472Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-14T21:38:49.3948633Z >>> from torch.distributed.fsdp import StateDictType 2025-08-14T21:38:49.3948807Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-08-14T21:38:49.3949010Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-08-14T21:38:49.3949103Z >>> # Save a checkpoint 2025-08-14T21:38:49.3949198Z >>> model, optim = ... 2025-08-14T21:38:49.3949311Z >>> FSDP.set_state_dict_type( 2025-08-14T21:38:49.3949460Z >>> model, 2025-08-14T21:38:49.3949601Z >>> StateDictType.FULL_STATE_DICT, 2025-08-14T21:38:49.3949736Z >>> FullStateDictConfig(rank0_only=False), 2025-08-14T21:38:49.3949876Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-14T21:38:49.3949960Z >>> ) 2025-08-14T21:38:49.3950080Z >>> state_dict = model.state_dict() 2025-08-14T21:38:49.3950242Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-08-14T21:38:49.3950391Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-08-14T21:38:49.3950486Z >>> # Load a checkpoint 2025-08-14T21:38:49.3950579Z >>> model, optim = ... 2025-08-14T21:38:49.3950739Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-08-14T21:38:49.3950842Z >>> FSDP.set_state_dict_type( 2025-08-14T21:38:49.3950925Z >>> model, 2025-08-14T21:38:49.3951050Z >>> StateDictType.FULL_STATE_DICT, 2025-08-14T21:38:49.3951176Z >>> FullStateDictConfig(rank0_only=False), 2025-08-14T21:38:49.3951313Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-14T21:38:49.3951396Z >>> ) 2025-08-14T21:38:49.3951507Z >>> model.load_state_dict(state_dict) 2025-08-14T21:38:49.3951685Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-08-14T21:38:49.3951796Z >>> model, optim, optim_state_dict 2025-08-14T21:38:49.3951876Z >>> ) 2025-08-14T21:38:49.3952002Z >>> optim.load_state_dict(optim_state_dict) 2025-08-14T21:38:49.3952079Z 2025-08-14T21:38:49.3952161Z Args: 2025-08-14T21:38:49.3952358Z model (torch.nn.Module): Root module (which may or may not be a 2025-08-14T21:38:49.3952552Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-08-14T21:38:49.3952681Z were passed into the optimizer ``optim``. 2025-08-14T21:38:49.3952866Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-08-14T21:38:49.3952959Z parameters. 2025-08-14T21:38:49.3953171Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-08-14T21:38:49.3953374Z transform. If the value is None, optim.state_dict() will be used. ( 2025-08-14T21:38:49.3953474Z Default: ``None``) 2025-08-14T21:38:49.3953701Z group (dist.ProcessGroup): Model's process group across which parameters 2025-08-14T21:38:49.3953880Z are sharded or ``None`` if using the default process group. ( 2025-08-14T21:38:49.3953979Z Default: ``None``) 2025-08-14T21:38:49.3954054Z 2025-08-14T21:38:49.3954134Z Returns: 2025-08-14T21:38:49.3954324Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-08-14T21:38:49.3954484Z ``model``. The sharding of the optimizer state is based on 2025-08-14T21:38:49.3954575Z ``state_dict_type``. 2025-08-14T21:38:49.3954657Z 2025-08-14T21:38:49.3954904Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3954988Z 2025-08-14T21:38:49.3955077Z warnings.warn(msg) 2025-08-14T21:38:49.3955153Z 2025-08-14T21:38:49.3955345Z --- Parse Warning: 78 / 146 --- 2025-08-14T21:38:49.3956515Z /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. 2025-08-14T21:38:49.3956771Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3956854Z 2025-08-14T21:38:49.3957198Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-08-14T21:38:49.3957281Z 2025-08-14T21:38:49.3957439Z Given a ``optim_state_dict`` that is transformed through 2025-08-14T21:38:49.3957697Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-08-14T21:38:49.3957934Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-08-14T21:38:49.3958115Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-08-14T21:38:49.3958191Z 2025-08-14T21:38:49.3958323Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:49.3958553Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-14T21:38:49.3958710Z >>> from torch.distributed.fsdp import StateDictType 2025-08-14T21:38:49.3958879Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-08-14T21:38:49.3959071Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-08-14T21:38:49.3959172Z >>> # Save a checkpoint 2025-08-14T21:38:49.3959263Z >>> model, optim = ... 2025-08-14T21:38:49.3959365Z >>> FSDP.set_state_dict_type( 2025-08-14T21:38:49.3959455Z >>> model, 2025-08-14T21:38:49.3959570Z >>> StateDictType.FULL_STATE_DICT, 2025-08-14T21:38:49.3959696Z >>> FullStateDictConfig(rank0_only=False), 2025-08-14T21:38:49.3959840Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-14T21:38:49.3959916Z >>> ) 2025-08-14T21:38:49.3960053Z >>> state_dict = model.state_dict() 2025-08-14T21:38:49.3960160Z >>> original_osd = optim.state_dict() 2025-08-14T21:38:49.3960284Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-08-14T21:38:49.3960373Z >>> model, 2025-08-14T21:38:49.3960449Z >>> optim, 2025-08-14T21:38:49.3960554Z >>> optim_state_dict=original_osd 2025-08-14T21:38:49.3960640Z >>> ) 2025-08-14T21:38:49.3960776Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-08-14T21:38:49.3960864Z >>> # Load a checkpoint 2025-08-14T21:38:49.3960963Z >>> model, optim = ... 2025-08-14T21:38:49.3961108Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-08-14T21:38:49.3961215Z >>> FSDP.set_state_dict_type( 2025-08-14T21:38:49.3961290Z >>> model, 2025-08-14T21:38:49.3961400Z >>> StateDictType.FULL_STATE_DICT, 2025-08-14T21:38:49.3961525Z >>> FullStateDictConfig(rank0_only=False), 2025-08-14T21:38:49.3961660Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-14T21:38:49.3961735Z >>> ) 2025-08-14T21:38:49.3961849Z >>> model.load_state_dict(state_dict) 2025-08-14T21:38:49.3961987Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-08-14T21:38:49.3962091Z >>> model, optim, optim_state_dict 2025-08-14T21:38:49.3962172Z >>> ) 2025-08-14T21:38:49.3962286Z >>> optim.load_state_dict(optim_state_dict) 2025-08-14T21:38:49.3962359Z 2025-08-14T21:38:49.3962444Z Args: 2025-08-14T21:38:49.3962631Z model (torch.nn.Module): Root module (which may or may not be a 2025-08-14T21:38:49.3962836Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-08-14T21:38:49.3962962Z were passed into the optimizer ``optim``. 2025-08-14T21:38:49.3963131Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-08-14T21:38:49.3963221Z parameters. 2025-08-14T21:38:49.3963422Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-08-14T21:38:49.3963613Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-08-14T21:38:49.3963808Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-08-14T21:38:49.3963972Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-08-14T21:38:49.3964166Z load_directly (bool): If this is set to True, this API will also 2025-08-14T21:38:49.3964355Z call optim.load_state_dict(result) before returning the result. 2025-08-14T21:38:49.3964564Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-08-14T21:38:49.3964711Z (Default: ``False``) 2025-08-14T21:38:49.3964961Z group (dist.ProcessGroup): Model's process group across which parameters 2025-08-14T21:38:49.3965135Z are sharded or ``None`` if using the default process group. ( 2025-08-14T21:38:49.3965230Z Default: ``None``) 2025-08-14T21:38:49.3965305Z 2025-08-14T21:38:49.3965554Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3965626Z 2025-08-14T21:38:49.3965714Z warnings.warn(msg) 2025-08-14T21:38:49.3965795Z 2025-08-14T21:38:49.3965974Z --- Parse Warning: 79 / 146 --- 2025-08-14T21:38:49.3966947Z /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. 2025-08-14T21:38:49.3967211Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3967286Z 2025-08-14T21:38:49.3967501Z RemoteModule instance can only be created after RPC initialization. 2025-08-14T21:38:49.3967583Z 2025-08-14T21:38:49.3967766Z It creates a user-specified module on a specified remote node. 2025-08-14T21:38:49.3968038Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-08-14T21:38:49.3968137Z executed on the remote node. 2025-08-14T21:38:49.3968361Z It takes care of autograd recording to ensure the backward pass propagates 2025-08-14T21:38:49.3968519Z gradients back to the corresponding remote module. 2025-08-14T21:38:49.3968868Z It can be shared across processors using `RPC framework `__, 2025-08-14T21:38:49.3969053Z without incurring any overheads of copying the actual module, 2025-08-14T21:38:49.3969250Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-08-14T21:38:49.3969356Z pointing to the remote module. 2025-08-14T21:38:49.3969447Z 2025-08-14T21:38:49.3969637Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-08-14T21:38:49.3969832Z the ``forward`` method of the module returned by the ``module_cls``. 2025-08-14T21:38:49.3969917Z 2025-08-14T21:38:49.3970216Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-08-14T21:38:49.3970292Z 2025-08-14T21:38:49.3970537Z Particularly, to create a hybrid model, typically the local modules should be 2025-08-14T21:38:49.3970896Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-08-14T21:38:49.3970989Z Hybrid Example: 2025-08-14T21:38:49.3971097Z >>> class HybridModel(nn.Module): 2025-08-14T21:38:49.3971202Z >>> def __init__(self) -> None: 2025-08-14T21:38:49.3971315Z >>> nn.Module.__init__(self) 2025-08-14T21:38:49.3971453Z >>> self.remote_embedding = RemoteModule(...) 2025-08-14T21:38:49.3971571Z >>> self.local_linear = nn.Linear(...) 2025-08-14T21:38:49.3971656Z 2025-08-14T21:38:49.3971846Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-08-14T21:38:49.3972090Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-08-14T21:38:49.3972297Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-08-14T21:38:49.3972418Z ``def forward(input: Tensor) -> Tensor:`` and 2025-08-14T21:38:49.3972583Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-08-14T21:38:49.3972658Z 2025-08-14T21:38:49.3972738Z .. note:: 2025-08-14T21:38:49.3972882Z If the remote module is placed on a cuda device, 2025-08-14T21:38:49.3973109Z any input CPU tensors will be automatically moved to the same cuda device, 2025-08-14T21:38:49.3973545Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-08-14T21:38:49.3973652Z 2025-08-14T21:38:49.3973728Z Args: 2025-08-14T21:38:49.3974012Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-08-14T21:38:49.3974298Z The device can be a local device or a remote device specified by one of the following remote 2025-08-14T21:38:49.3974374Z formats: 2025-08-14T21:38:49.3974457Z 2025-08-14T21:38:49.3974588Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-08-14T21:38:49.3974728Z 2. "/" (ex: "trainer0/cuda:0"). 2025-08-14T21:38:49.3974810Z 2025-08-14T21:38:49.3975045Z In addition, the device field can be optional and the default value is "cpu". 2025-08-14T21:38:49.3975150Z module_cls (nn.Module): For example, 2025-08-14T21:38:49.3975266Z >>> class MyModule(nn.Module): 2025-08-14T21:38:49.3975362Z >>> def forward(input): 2025-08-14T21:38:49.3975470Z >>> return input + 1 2025-08-14T21:38:49.3975546Z >>> 2025-08-14T21:38:49.3975640Z >>> module_cls = MyModule 2025-08-14T21:38:49.3975835Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-08-14T21:38:49.3976041Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-08-14T21:38:49.3976304Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-08-14T21:38:49.3976535Z to be created. The type object should be decorated by @torch.jit.interface. 2025-08-14T21:38:49.3976741Z If not provided, the generated RemoteModule is not torchscript-able. 2025-08-14T21:38:49.3976966Z Warning, this is an experimental API and susceptible to frequent changes. 2025-08-14T21:38:49.3977049Z 2025-08-14T21:38:49.3977128Z Returns: 2025-08-14T21:38:49.3977373Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-08-14T21:38:49.3977595Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-08-14T21:38:49.3977858Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-08-14T21:38:49.3978011Z on the user-provided module on the remote side. 2025-08-14T21:38:49.3978083Z 2025-08-14T21:38:49.3978167Z Example:: 2025-08-14T21:38:49.3978322Z Run the following code in two different processes: 2025-08-14T21:38:49.3978393Z 2025-08-14T21:38:49.3978506Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:49.3978602Z >>> # On worker 0: 2025-08-14T21:38:49.3978685Z >>> import torch 2025-08-14T21:38:49.3978814Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:49.3978913Z >>> from torch import nn, Tensor 2025-08-14T21:38:49.3979119Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-08-14T21:38:49.3979207Z >>> 2025-08-14T21:38:49.3979340Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-14T21:38:49.3979453Z >>> remote_linear_module = RemoteModule( 2025-08-14T21:38:49.3979580Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-08-14T21:38:49.3979658Z >>> ) 2025-08-14T21:38:49.3979757Z >>> input = torch.randn(128, 20) 2025-08-14T21:38:49.3979910Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-08-14T21:38:49.3980000Z >>> ret = ret_fut.wait() 2025-08-14T21:38:49.3980096Z >>> rpc.shutdown() 2025-08-14T21:38:49.3980169Z 2025-08-14T21:38:49.3980253Z >>> # On worker 1: 2025-08-14T21:38:49.3980344Z >>> import torch 2025-08-14T21:38:49.3980463Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:49.3980537Z >>> 2025-08-14T21:38:49.3980672Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-14T21:38:49.3980759Z >>> rpc.shutdown() 2025-08-14T21:38:49.3980835Z 2025-08-14T21:38:49.3981167Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3981246Z 2025-08-14T21:38:49.3981341Z warnings.warn(msg) 2025-08-14T21:38:49.3981431Z 2025-08-14T21:38:49.3981620Z --- Parse Warning: 80 / 146 --- 2025-08-14T21:38:49.3982676Z /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. 2025-08-14T21:38:49.3982934Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3983015Z 2025-08-14T21:38:49.3983346Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-08-14T21:38:49.3983521Z 2025-08-14T21:38:49.3983848Z This alternate initialization method can be particularly useful if we want to create multiple 2025-08-14T21:38:49.3984174Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-08-14T21:38:49.3984255Z 2025-08-14T21:38:49.3984543Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-08-14T21:38:49.3984757Z which is not supported. The recommended way is as follows: 2025-08-14T21:38:49.3984836Z 2025-08-14T21:38:49.3984968Z 1. the sender creates a RemoteModule; 2025-08-14T21:38:49.3985108Z 2. the sender sends its ``module_rref`` over RPC; 2025-08-14T21:38:49.3985437Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-08-14T21:38:49.3985530Z 2025-08-14T21:38:49.3985620Z Example:: 2025-08-14T21:38:49.3985784Z Run the following code in two different processes: 2025-08-14T21:38:49.3985867Z 2025-08-14T21:38:49.3985981Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:49.3986090Z >>> # On worker 0: 2025-08-14T21:38:49.3986182Z >>> import torch 2025-08-14T21:38:49.3986306Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:49.3986426Z >>> from torch import nn, Tensor 2025-08-14T21:38:49.3986642Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-08-14T21:38:49.3986726Z >>> 2025-08-14T21:38:49.3986875Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-14T21:38:49.3986984Z >>> remote_module = RemoteModule( 2025-08-14T21:38:49.3987109Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-08-14T21:38:49.3987205Z >>> ) 2025-08-14T21:38:49.3987288Z >>> 2025-08-14T21:38:49.3987411Z >>> remote_module1 = rpc.rpc_sync( 2025-08-14T21:38:49.3987507Z >>> "worker1/cpu", 2025-08-14T21:38:49.3987629Z >>> RemoteModule.init_from_module_rref, 2025-08-14T21:38:49.3987796Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-08-14T21:38:49.3987880Z >>> ) 2025-08-14T21:38:49.3987973Z >>> rpc.shutdown() 2025-08-14T21:38:49.3988064Z 2025-08-14T21:38:49.3988153Z >>> # On worker 1: 2025-08-14T21:38:49.3988239Z >>> import torch 2025-08-14T21:38:49.3988380Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:49.3988464Z >>> 2025-08-14T21:38:49.3988596Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-14T21:38:49.3988697Z >>> rpc.shutdown() 2025-08-14T21:38:49.3988778Z 2025-08-14T21:38:49.3988870Z Args: 2025-08-14T21:38:49.3989158Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-08-14T21:38:49.3989439Z The device can be a local device or a remote device specified by one of the following remote 2025-08-14T21:38:49.3989537Z formats: 2025-08-14T21:38:49.3989617Z 2025-08-14T21:38:49.3989754Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-08-14T21:38:49.3989963Z 2. "/" (ex: "trainer0/cuda:0"). 2025-08-14T21:38:49.3990065Z 2025-08-14T21:38:49.3990303Z In addition, the device field can be optional and the default value is "cpu". 2025-08-14T21:38:49.3990556Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-08-14T21:38:49.3990662Z the created remote module. 2025-08-14T21:38:49.3990945Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-08-14T21:38:49.3991170Z to be created. The type object should be decorated by @torch.jit.interface. 2025-08-14T21:38:49.3991385Z If not provided, the generated RemoteModule is not torchscript-able. 2025-08-14T21:38:49.3991623Z Warning, this is an experimental API and susceptible to frequent changes. 2025-08-14T21:38:49.3991702Z 2025-08-14T21:38:49.3991785Z Returns: 2025-08-14T21:38:49.3992033Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-08-14T21:38:49.3992262Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-08-14T21:38:49.3992540Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-08-14T21:38:49.3992705Z on the user-provided module on the remote side. 2025-08-14T21:38:49.3992783Z 2025-08-14T21:38:49.3993203Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.3993283Z 2025-08-14T21:38:49.3993375Z warnings.warn(msg) 2025-08-14T21:38:49.3993465Z 2025-08-14T21:38:49.3993668Z --- Parse Warning: 81 / 146 --- 2025-08-14T21:38:49.3994641Z /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. 2025-08-14T21:38:49.3994902Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.3994985Z 2025-08-14T21:38:49.3995226Z A RemoteModule instance can only be created after RPC initialization. 2025-08-14T21:38:49.3995304Z 2025-08-14T21:38:49.3995498Z It creates a user-specified module on a specified remote node. 2025-08-14T21:38:49.3995741Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-08-14T21:38:49.3995843Z executed on the remote node. 2025-08-14T21:38:49.3996089Z It takes care of autograd recording to ensure the backward pass propagates 2025-08-14T21:38:49.3996247Z gradients back to the corresponding remote module. 2025-08-14T21:38:49.3996324Z 2025-08-14T21:38:49.3996549Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-08-14T21:38:49.3996755Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-08-14T21:38:49.3997003Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-08-14T21:38:49.3997209Z and ``forward`` are the same as the ``forward`` method of the module 2025-08-14T21:38:49.3997315Z returned by the ``module_cls``. 2025-08-14T21:38:49.3997391Z 2025-08-14T21:38:49.3997599Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-08-14T21:38:49.3997841Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-08-14T21:38:49.3998071Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-08-14T21:38:49.3998149Z 2025-08-14T21:38:49.3998269Z | ``def forward(input: Tensor) -> Tensor:`` 2025-08-14T21:38:49.3998441Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-08-14T21:38:49.3998518Z 2025-08-14T21:38:49.3998598Z Args: 2025-08-14T21:38:49.3998899Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-08-14T21:38:49.3999330Z The format should be "/", where the device field can be parsed as torch.device type. 2025-08-14T21:38:49.3999536Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-08-14T21:38:49.3999776Z In addition, the device field can be optional and the default value is "cpu". 2025-08-14T21:38:49.4000024Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-08-14T21:38:49.4000117Z 2025-08-14T21:38:49.4000228Z >>> class MyModule(nn.Module): 2025-08-14T21:38:49.4000329Z >>> def forward(input): 2025-08-14T21:38:49.4000443Z >>> return input + 1 2025-08-14T21:38:49.4000524Z >>> 2025-08-14T21:38:49.4000624Z >>> module_cls = MyModule 2025-08-14T21:38:49.4000718Z 2025-08-14T21:38:49.4000913Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-08-14T21:38:49.4001117Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-08-14T21:38:49.4001195Z 2025-08-14T21:38:49.4001277Z Returns: 2025-08-14T21:38:49.4001528Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-08-14T21:38:49.4001750Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-08-14T21:38:49.4002046Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-08-14T21:38:49.4002197Z on the user-provided module on the remote side. 2025-08-14T21:38:49.4002275Z 2025-08-14T21:38:49.4002363Z Example:: 2025-08-14T21:38:49.4002522Z Run the following code in two different processes: 2025-08-14T21:38:49.4002598Z 2025-08-14T21:38:49.4002721Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:49.4002810Z >>> # On worker 0: 2025-08-14T21:38:49.4002900Z >>> import torch 2025-08-14T21:38:49.4003035Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:49.4003143Z >>> from torch import nn, Tensor 2025-08-14T21:38:49.4003359Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-08-14T21:38:49.4003451Z >>> 2025-08-14T21:38:49.4003585Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-14T21:38:49.4003703Z >>> remote_linear_module = RemoteModule( 2025-08-14T21:38:49.4003838Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-08-14T21:38:49.4003919Z >>> ) 2025-08-14T21:38:49.4004033Z >>> input = torch.randn(128, 20) 2025-08-14T21:38:49.4004183Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-08-14T21:38:49.4004279Z >>> ret = ret_fut.wait() 2025-08-14T21:38:49.4004383Z >>> rpc.shutdown() 2025-08-14T21:38:49.4004460Z 2025-08-14T21:38:49.4004549Z >>> # On worker 1: 2025-08-14T21:38:49.4004650Z >>> import torch 2025-08-14T21:38:49.4004772Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:49.4004856Z >>> 2025-08-14T21:38:49.4005003Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-14T21:38:49.4005094Z >>> rpc.shutdown() 2025-08-14T21:38:49.4005171Z 2025-08-14T21:38:49.4005371Z Furthermore, a more practical example that is combined with 2025-08-14T21:38:49.4005839Z `DistributedDataParallel `__ (DDP) 2025-08-14T21:38:49.4006175Z can be found in this `tutorial `__. 2025-08-14T21:38:49.4006252Z 2025-08-14T21:38:49.4006498Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4006585Z 2025-08-14T21:38:49.4006682Z warnings.warn(msg) 2025-08-14T21:38:49.4006758Z 2025-08-14T21:38:49.4006965Z --- Parse Warning: 82 / 146 --- 2025-08-14T21:38:49.4007993Z /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. 2025-08-14T21:38:49.4008289Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4008370Z 2025-08-14T21:38:49.4008605Z DistributedOptimizer takes remote references to parameters scattered 2025-08-14T21:38:49.4008851Z across workers and applies the given optimizer locally for each parameter. 2025-08-14T21:38:49.4008931Z 2025-08-14T21:38:49.4009158Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-08-14T21:38:49.4009317Z to retrieve the gradients for specific parameters. 2025-08-14T21:38:49.4009397Z 2025-08-14T21:38:49.4009489Z Concurrent calls to 2025-08-14T21:38:49.4009701Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-08-14T21:38:49.4009840Z either from the same or different clients, will 2025-08-14T21:38:49.4010075Z be serialized on each worker -- as each worker's optimizer can only work 2025-08-14T21:38:49.4010280Z on one set of gradients at a time. However, there is no guarantee that 2025-08-14T21:38:49.4010518Z the full forward-backward-optimizer sequence will execute for one client 2025-08-14T21:38:49.4010766Z at a time. This means that the gradients being applied may not correspond 2025-08-14T21:38:49.4010983Z to the latest forward pass executed on a given worker. Also, there is no 2025-08-14T21:38:49.4011094Z guaranteed ordering across workers. 2025-08-14T21:38:49.4011186Z 2025-08-14T21:38:49.4011443Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-08-14T21:38:49.4011677Z by default, so that optimizer updates are not blocked by the Python Global 2025-08-14T21:38:49.4011917Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-08-14T21:38:49.4012157Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-08-14T21:38:49.4012416Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-08-14T21:38:49.4012522Z for your own custom optimizers. 2025-08-14T21:38:49.4012598Z 2025-08-14T21:38:49.4012691Z Args: 2025-08-14T21:38:49.4012884Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-08-14T21:38:49.4013000Z instantiate on each worker. 2025-08-14T21:38:49.4013203Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-08-14T21:38:49.4013292Z to optimize. 2025-08-14T21:38:49.4013508Z args: arguments to pass to the optimizer constructor on each worker. 2025-08-14T21:38:49.4013723Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-08-14T21:38:49.4013802Z 2025-08-14T21:38:49.4013899Z Example:: 2025-08-14T21:38:49.4014011Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:49.4014172Z >>> import torch.distributed.autograd as dist_autograd 2025-08-14T21:38:49.4014310Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:49.4014408Z >>> from torch import optim 2025-08-14T21:38:49.4014601Z >>> from torch.distributed.optim import DistributedOptimizer 2025-08-14T21:38:49.4014682Z >>> 2025-08-14T21:38:49.4014812Z >>> with dist_autograd.context() as context_id: 2025-08-14T21:38:49.4014915Z >>> # Forward pass. 2025-08-14T21:38:49.4015106Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-08-14T21:38:49.4015294Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-08-14T21:38:49.4015430Z >>> loss = rref1.to_here() + rref2.to_here() 2025-08-14T21:38:49.4015509Z >>> 2025-08-14T21:38:49.4015604Z >>> # Backward pass. 2025-08-14T21:38:49.4015756Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-08-14T21:38:49.4015835Z >>> 2025-08-14T21:38:49.4015977Z >>> # Optimizer. 2025-08-14T21:38:49.4016119Z >>> dist_optim = DistributedOptimizer( 2025-08-14T21:38:49.4016204Z >>> optim.SGD, 2025-08-14T21:38:49.4016305Z >>> [rref1, rref2], 2025-08-14T21:38:49.4016387Z >>> lr=0.05, 2025-08-14T21:38:49.4016468Z >>> ) 2025-08-14T21:38:49.4016581Z >>> dist_optim.step(context_id) 2025-08-14T21:38:49.4016658Z 2025-08-14T21:38:49.4016808Z __ https://github.com/pytorch/tutorials/pull/1465 2025-08-14T21:38:49.4016892Z 2025-08-14T21:38:49.4017136Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4017212Z 2025-08-14T21:38:49.4017311Z warnings.warn(msg) 2025-08-14T21:38:49.4017388Z 2025-08-14T21:38:49.4017574Z --- Parse Warning: 83 / 146 --- 2025-08-14T21:38:49.4018629Z /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. 2025-08-14T21:38:49.4018886Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4018998Z 2025-08-14T21:38:49.4019376Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-08-14T21:38:49.4019522Z This optimizer runs local optimizer at every step. 2025-08-14T21:38:49.4019858Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-08-14T21:38:49.4019933Z 2025-08-14T21:38:49.4020024Z Args: 2025-08-14T21:38:49.4020123Z optim: The local optimizer. 2025-08-14T21:38:49.4020332Z averager: A model averager instance to run post-localSGD algorithm. 2025-08-14T21:38:49.4020418Z 2025-08-14T21:38:49.4020501Z Example:: 2025-08-14T21:38:49.4020578Z 2025-08-14T21:38:49.4020710Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:49.4020800Z >>> import torch 2025-08-14T21:38:49.4020915Z >>> import torch.distributed as dist 2025-08-14T21:38:49.4021186Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-08-14T21:38:49.4021283Z >>> import torch.nn as nn 2025-08-14T21:38:49.4021483Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-08-14T21:38:49.4021741Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-08-14T21:38:49.4021839Z >>> PostLocalSGDState, 2025-08-14T21:38:49.4021948Z >>> post_localSGD_hook, 2025-08-14T21:38:49.4022027Z >>> ) 2025-08-14T21:38:49.4022105Z >>> 2025-08-14T21:38:49.4022261Z >>> model = nn.parallel.DistributedDataParallel( 2025-08-14T21:38:49.4022393Z >>> module, device_ids=[rank], output_device=rank 2025-08-14T21:38:49.4022472Z >>> ) 2025-08-14T21:38:49.4022561Z >>> 2025-08-14T21:38:49.4022702Z >>> # Register a post-localSGD communication hook. 2025-08-14T21:38:49.4023002Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-08-14T21:38:49.4023155Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-08-14T21:38:49.4023231Z >>> 2025-08-14T21:38:49.4023511Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-08-14T21:38:49.4023762Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-08-14T21:38:49.4023922Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-08-14T21:38:49.4024131Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-08-14T21:38:49.4024241Z >>> opt = PostLocalSGDOptimizer( 2025-08-14T21:38:49.4024338Z >>> optim=local_optim, 2025-08-14T21:38:49.4024643Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-08-14T21:38:49.4024752Z >>> ) 2025-08-14T21:38:49.4024844Z >>> 2025-08-14T21:38:49.4025058Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-08-14T21:38:49.4025346Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-08-14T21:38:49.4025724Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-08-14T21:38:49.4025821Z >>> for step in range(0, 200): 2025-08-14T21:38:49.4025911Z >>> opt.zero_grad() 2025-08-14T21:38:49.4026025Z >>> loss = loss_fn(output, labels) 2025-08-14T21:38:49.4026118Z >>> loss.backward() 2025-08-14T21:38:49.4026216Z >>> opt.step() 2025-08-14T21:38:49.4026288Z 2025-08-14T21:38:49.4026533Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4026619Z 2025-08-14T21:38:49.4026711Z warnings.warn(msg) 2025-08-14T21:38:49.4026788Z 2025-08-14T21:38:49.4026990Z --- Parse Warning: 84 / 146 --- 2025-08-14T21:38:49.4028058Z /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. 2025-08-14T21:38:49.4028348Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4028419Z 2025-08-14T21:38:49.4028811Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-08-14T21:38:49.4028898Z 2025-08-14T21:38:49.4029131Z The sharing is done as described by `ZeRO `_. 2025-08-14T21:38:49.4029204Z 2025-08-14T21:38:49.4029356Z The local optimizer instance in each rank is only 2025-08-14T21:38:49.4029595Z responsible for updating approximately ``1 / world_size`` parameters and 2025-08-14T21:38:49.4029791Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-08-14T21:38:49.4030036Z parameters are updated locally, each rank will broadcast its parameters to 2025-08-14T21:38:49.4030213Z all other peers to keep all model replicas in the same state. 2025-08-14T21:38:49.4030410Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-08-14T21:38:49.4030656Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-08-14T21:38:49.4030746Z memory consumption. 2025-08-14T21:38:49.4030834Z 2025-08-14T21:38:49.4031082Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-08-14T21:38:49.4031303Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-08-14T21:38:49.4031548Z not divided among ranks. The partition is arbitrary and might not match the 2025-08-14T21:38:49.4031675Z the parameter registration or usage order. 2025-08-14T21:38:49.4031765Z 2025-08-14T21:38:49.4031848Z Arguments: 2025-08-14T21:38:49.4032030Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-08-14T21:38:49.4032220Z or :class:`dict` s giving all parameters, which will be sharded 2025-08-14T21:38:49.4032306Z across ranks. 2025-08-14T21:38:49.4032378Z 2025-08-14T21:38:49.4032476Z Keyword Args: 2025-08-14T21:38:49.4032686Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-08-14T21:38:49.4032767Z optimizer. 2025-08-14T21:38:49.4032978Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-08-14T21:38:49.4033165Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-08-14T21:38:49.4033316Z :meth:`torch.distributed.init_process_group`). 2025-08-14T21:38:49.4033597Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-08-14T21:38:49.4033823Z packed into buckets to speed up communication, and ``param.data`` 2025-08-14T21:38:49.4034030Z fields point to bucket views at different offsets; if ``False``, 2025-08-14T21:38:49.4034225Z each individual parameter is communicated separately, and each 2025-08-14T21:38:49.4034372Z ``params.data`` stays intact (default: ``False``). 2025-08-14T21:38:49.4034567Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-08-14T21:38:49.4034753Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-08-14T21:38:49.4034969Z synchronization; this requires (1) either a functional optimizer 2025-08-14T21:38:49.4035145Z for the ``optimizer_class`` argument or one with a functional 2025-08-14T21:38:49.4035311Z equivalent and (2) registering a DDP communication hook 2025-08-14T21:38:49.4035515Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-08-14T21:38:49.4035674Z parameters are packed into buckets matching those in 2025-08-14T21:38:49.4035823Z :class:`DistributedDataParallel`, meaning that the 2025-08-14T21:38:49.4035977Z ``parameters_as_bucket_view`` argument is ignored. 2025-08-14T21:38:49.4036177Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-08-14T21:38:49.4036277Z (per normal). 2025-08-14T21:38:49.4036370Z (default: ``False``) 2025-08-14T21:38:49.4036567Z **defaults: any trailing arguments, which are forwarded to the local 2025-08-14T21:38:49.4036661Z optimizer. 2025-08-14T21:38:49.4036738Z 2025-08-14T21:38:49.4036818Z Example:: 2025-08-14T21:38:49.4036904Z 2025-08-14T21:38:49.4036995Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4037086Z >>> import torch.nn as nn 2025-08-14T21:38:49.4037297Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-08-14T21:38:49.4037489Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-08-14T21:38:49.4037719Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-08-14T21:38:49.4037833Z >>> ddp = DDP(model, device_ids=[rank]) 2025-08-14T21:38:49.4037944Z >>> opt = ZeroRedundancyOptimizer( 2025-08-14T21:38:49.4038051Z >>> ddp.parameters(), 2025-08-14T21:38:49.4038170Z >>> optimizer_class=torch.optim.Adam, 2025-08-14T21:38:49.4038247Z >>> lr=0.01 2025-08-14T21:38:49.4038336Z >>> ) 2025-08-14T21:38:49.4038436Z >>> ddp(inputs).sum().backward() 2025-08-14T21:38:49.4038523Z >>> opt.step() 2025-08-14T21:38:49.4038611Z 2025-08-14T21:38:49.4038689Z .. warning:: 2025-08-14T21:38:49.4038890Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-08-14T21:38:49.4039038Z passed-in parameters are the same dense type. 2025-08-14T21:38:49.4039109Z 2025-08-14T21:38:49.4039205Z .. warning:: 2025-08-14T21:38:49.4039408Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-08-14T21:38:49.4039601Z the way that overlapping :class:`DistributedDataParallel` with 2025-08-14T21:38:49.4039837Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-08-14T21:38:49.4040042Z two or three training iterations do not perform parameter updates in 2025-08-14T21:38:49.4040222Z the optimizer step, depending on if ``static_graph=False`` or 2025-08-14T21:38:49.4040408Z ``static_graph=True``, respectively. This is because it needs 2025-08-14T21:38:49.4040582Z information about the gradient bucketing strategy used by 2025-08-14T21:38:49.4040799Z :class:`DistributedDataParallel`, which is not finalized until the 2025-08-14T21:38:49.4040990Z second forward pass if ``static_graph=False`` or until the third 2025-08-14T21:38:49.4041236Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-08-14T21:38:49.4041376Z is to prepend dummy inputs. 2025-08-14T21:38:49.4041448Z 2025-08-14T21:38:49.4041691Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-08-14T21:38:49.4041775Z 2025-08-14T21:38:49.4042018Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4042092Z 2025-08-14T21:38:49.4042193Z warnings.warn(msg) 2025-08-14T21:38:49.4042266Z 2025-08-14T21:38:49.4042459Z --- Parse Warning: 85 / 146 --- 2025-08-14T21:38:49.4043422Z /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. 2025-08-14T21:38:49.4043672Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4043755Z 2025-08-14T21:38:49.4043977Z Custom reducer class that can be used to specify a custom operation that 2025-08-14T21:38:49.4044145Z reduces losses of multiple microbatches into one value. 2025-08-14T21:38:49.4044231Z 2025-08-14T21:38:49.4044310Z Example: 2025-08-14T21:38:49.4044440Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4044546Z >>> sum_reducer = _CustomReducer( 2025-08-14T21:38:49.4044642Z >>> torch.tensor(0.0), 2025-08-14T21:38:49.4044741Z >>> lambda a, b: a + b 2025-08-14T21:38:49.4044818Z >>> ) 2025-08-14T21:38:49.4044892Z 2025-08-14T21:38:49.4045150Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4045225Z 2025-08-14T21:38:49.4045314Z warnings.warn(msg) 2025-08-14T21:38:49.4045403Z 2025-08-14T21:38:49.4045579Z --- Parse Warning: 86 / 146 --- 2025-08-14T21:38:49.4046481Z /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. 2025-08-14T21:38:49.4046747Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4046822Z 2025-08-14T21:38:49.4047065Z A decorator for a function indicating that the return value of the function 2025-08-14T21:38:49.4047265Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-08-14T21:38:49.4047496Z function can run asynchronously on the RPC callee. More specifically, the 2025-08-14T21:38:49.4047732Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-08-14T21:38:49.4047957Z function and installs subsequent processing steps as a callback to that 2025-08-14T21:38:49.4048195Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-08-14T21:38:49.4048389Z from the :class:`~torch.futures.Future` when completed and send the 2025-08-14T21:38:49.4048566Z value back as the RPC response. That also means the returned 2025-08-14T21:38:49.4048800Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-08-14T21:38:49.4049015Z sent through RPC. This decorator is useful when the wrapped function's 2025-08-14T21:38:49.4049205Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-08-14T21:38:49.4049430Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-08-14T21:38:49.4049505Z 2025-08-14T21:38:49.4049714Z .. note:: To enable asynchronous execution, applications must pass the 2025-08-14T21:38:49.4049937Z function object returned by this decorator to RPC APIs. If RPC detected 2025-08-14T21:38:49.4050144Z attributes installed by this decorator, it knows that this function 2025-08-14T21:38:49.4050332Z returns a ``Future`` object and will handle that accordingly. 2025-08-14T21:38:49.4050587Z However, this does not mean this decorator has to be outmost one when 2025-08-14T21:38:49.4050857Z defining a function. For example, when combined with ``@staticmethod`` 2025-08-14T21:38:49.4051072Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-08-14T21:38:49.4051289Z inner decorator to allow the target function be recognized as a static 2025-08-14T21:38:49.4051515Z or class function. This target function can still execute asynchronously 2025-08-14T21:38:49.4051733Z because, when accessed, the static or class method preserves attributes 2025-08-14T21:38:49.4051881Z installed by ``@rpc.functions.async_execution``. 2025-08-14T21:38:49.4051963Z 2025-08-14T21:38:49.4052036Z 2025-08-14T21:38:49.4052116Z Example:: 2025-08-14T21:38:49.4052315Z The returned :class:`~torch.futures.Future` object can come from 2025-08-14T21:38:49.4052440Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-08-14T21:38:49.4052659Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-08-14T21:38:49.4052840Z constructor. The example below shows directly using the 2025-08-14T21:38:49.4052967Z :class:`~torch.futures.Future` returned by 2025-08-14T21:38:49.4053086Z :meth:`~torch.futures.Future.then`. 2025-08-14T21:38:49.4053190Z 2025-08-14T21:38:49.4053309Z >>> from torch.distributed import rpc 2025-08-14T21:38:49.4053396Z >>> 2025-08-14T21:38:49.4053510Z >>> # omitting setup and shutdown RPC 2025-08-14T21:38:49.4053585Z >>> 2025-08-14T21:38:49.4053683Z >>> # On all workers 2025-08-14T21:38:49.4053791Z >>> @rpc.functions.async_execution 2025-08-14T21:38:49.4053896Z >>> def async_add_chained(to, x, y, z): 2025-08-14T21:38:49.4054090Z >>> # This function runs on "worker1" and returns immediately when 2025-08-14T21:38:49.4054273Z >>> # the callback is installed through the `then(cb)` API. In the 2025-08-14T21:38:49.4054461Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-08-14T21:38:49.4054614Z >>> # When the return value of that `rpc_async` arrives at 2025-08-14T21:38:49.4054793Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-08-14T21:38:49.4054980Z >>> # and set the value for the previously returned `Future`, which 2025-08-14T21:38:49.4055153Z >>> # will then trigger RPC to send the result back to "worker0". 2025-08-14T21:38:49.4055313Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-14T21:38:49.4055425Z >>> lambda fut: fut.wait() + z 2025-08-14T21:38:49.4055502Z >>> ) 2025-08-14T21:38:49.4055584Z >>> 2025-08-14T21:38:49.4055666Z >>> # On worker0 2025-08-14T21:38:49.4055757Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4055856Z >>> ret = rpc.rpc_sync( 2025-08-14T21:38:49.4055939Z >>> "worker1", 2025-08-14T21:38:49.4056036Z >>> async_add_chained, 2025-08-14T21:38:49.4056165Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-08-14T21:38:49.4056250Z >>> ) 2025-08-14T21:38:49.4056365Z >>> print(ret) # prints tensor([3., 3.]) 2025-08-14T21:38:49.4056445Z 2025-08-14T21:38:49.4056667Z When combined with TorchScript decorators, this decorator must be the 2025-08-14T21:38:49.4056747Z outmost one. 2025-08-14T21:38:49.4056829Z 2025-08-14T21:38:49.4056928Z >>> from torch import Tensor 2025-08-14T21:38:49.4057042Z >>> from torch.futures import Future 2025-08-14T21:38:49.4057155Z >>> from torch.distributed import rpc 2025-08-14T21:38:49.4057231Z >>> 2025-08-14T21:38:49.4057345Z >>> # omitting setup and shutdown RPC 2025-08-14T21:38:49.4057423Z >>> 2025-08-14T21:38:49.4057508Z >>> # On all workers 2025-08-14T21:38:49.4057607Z >>> @torch.jit.script 2025-08-14T21:38:49.4057745Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-08-14T21:38:49.4057902Z >>> return x + y 2025-08-14T21:38:49.4057984Z >>> 2025-08-14T21:38:49.4058092Z >>> @rpc.functions.async_execution 2025-08-14T21:38:49.4058183Z >>> @torch.jit.script 2025-08-14T21:38:49.4058373Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-08-14T21:38:49.4058509Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-08-14T21:38:49.4058594Z >>> 2025-08-14T21:38:49.4058677Z >>> # On worker0 2025-08-14T21:38:49.4058767Z >>> ret = rpc.rpc_sync( 2025-08-14T21:38:49.4058861Z >>> "worker1", 2025-08-14T21:38:49.4058943Z >>> async_add, 2025-08-14T21:38:49.4059052Z >>> args=("worker2", torch.ones(2), 1) 2025-08-14T21:38:49.4059134Z >>> ) 2025-08-14T21:38:49.4059247Z >>> print(ret) # prints tensor([2., 2.]) 2025-08-14T21:38:49.4059322Z 2025-08-14T21:38:49.4059545Z When combined with static or class method, this decorator must be the 2025-08-14T21:38:49.4059624Z inner one. 2025-08-14T21:38:49.4059706Z 2025-08-14T21:38:49.4059822Z >>> from torch.distributed import rpc 2025-08-14T21:38:49.4059896Z >>> 2025-08-14T21:38:49.4060021Z >>> # omitting setup and shutdown RPC 2025-08-14T21:38:49.4060124Z >>> 2025-08-14T21:38:49.4060214Z >>> # On all workers 2025-08-14T21:38:49.4060334Z >>> class AsyncExecutionClass: 2025-08-14T21:38:49.4060411Z >>> 2025-08-14T21:38:49.4060499Z >>> @staticmethod 2025-08-14T21:38:49.4060620Z >>> @rpc.functions.async_execution 2025-08-14T21:38:49.4060727Z >>> def static_async_add(to, x, y, z): 2025-08-14T21:38:49.4060887Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-14T21:38:49.4061007Z >>> lambda fut: fut.wait() + z 2025-08-14T21:38:49.4061084Z >>> ) 2025-08-14T21:38:49.4061175Z >>> 2025-08-14T21:38:49.4061267Z >>> @classmethod 2025-08-14T21:38:49.4061380Z >>> @rpc.functions.async_execution 2025-08-14T21:38:49.4061507Z >>> def class_async_add(cls, to, x, y, z): 2025-08-14T21:38:49.4061623Z >>> ret_fut = torch.futures.Future() 2025-08-14T21:38:49.4061764Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-14T21:38:49.4061916Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-08-14T21:38:49.4061991Z >>> ) 2025-08-14T21:38:49.4062078Z >>> return ret_fut 2025-08-14T21:38:49.4062167Z >>> 2025-08-14T21:38:49.4062275Z >>> @rpc.functions.async_execution 2025-08-14T21:38:49.4062403Z >>> def bound_async_add(self, to, x, y, z): 2025-08-14T21:38:49.4062563Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-14T21:38:49.4062667Z >>> lambda fut: fut.wait() + z 2025-08-14T21:38:49.4062757Z >>> ) 2025-08-14T21:38:49.4062834Z >>> 2025-08-14T21:38:49.4062917Z >>> # On worker0 2025-08-14T21:38:49.4063022Z >>> ret = rpc.rpc_sync( 2025-08-14T21:38:49.4063102Z >>> "worker1", 2025-08-14T21:38:49.4063227Z >>> AsyncExecutionClass.static_async_add, 2025-08-14T21:38:49.4063352Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-08-14T21:38:49.4063523Z >>> ) 2025-08-14T21:38:49.4063639Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-14T21:38:49.4063730Z >>> 2025-08-14T21:38:49.4063821Z >>> ret = rpc.rpc_sync( 2025-08-14T21:38:49.4063921Z >>> "worker1", 2025-08-14T21:38:49.4064044Z >>> AsyncExecutionClass.class_async_add, 2025-08-14T21:38:49.4064154Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-08-14T21:38:49.4064244Z >>> ) 2025-08-14T21:38:49.4064355Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-14T21:38:49.4064430Z 2025-08-14T21:38:49.4064666Z This decorator also works with RRef helpers, i.e., . 2025-08-14T21:38:49.4064822Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-08-14T21:38:49.4064969Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-08-14T21:38:49.4065112Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-08-14T21:38:49.4065192Z 2025-08-14T21:38:49.4065316Z >>> from torch.distributed import rpc 2025-08-14T21:38:49.4065388Z >>> 2025-08-14T21:38:49.4065516Z >>> # reuse the AsyncExecutionClass class above 2025-08-14T21:38:49.4065672Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-08-14T21:38:49.4065871Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-08-14T21:38:49.4065984Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-14T21:38:49.4066077Z >>> 2025-08-14T21:38:49.4066222Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-08-14T21:38:49.4066446Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-08-14T21:38:49.4066569Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-14T21:38:49.4066646Z >>> 2025-08-14T21:38:49.4066803Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-08-14T21:38:49.4067027Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-08-14T21:38:49.4067163Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-14T21:38:49.4067249Z 2025-08-14T21:38:49.4067490Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4067565Z 2025-08-14T21:38:49.4067670Z warnings.warn(msg) 2025-08-14T21:38:49.4067744Z 2025-08-14T21:38:49.4067947Z --- Parse Warning: 87 / 146 --- 2025-08-14T21:38:49.4069006Z /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. 2025-08-14T21:38:49.4069262Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4069350Z 2025-08-14T21:38:49.4069544Z Set device mapping between each RPC caller and callee pair. This 2025-08-14T21:38:49.4069721Z function can be called multiple times to incrementally add 2025-08-14T21:38:49.4069839Z device placement configurations. 2025-08-14T21:38:49.4069912Z 2025-08-14T21:38:49.4069989Z Args: 2025-08-14T21:38:49.4070090Z to (str): Callee name. 2025-08-14T21:38:49.4070279Z device_map (Dict of int, str, or torch.device): Device placement 2025-08-14T21:38:49.4070458Z mappings from this worker to the callee. This map must be 2025-08-14T21:38:49.4070541Z invertible. 2025-08-14T21:38:49.4070613Z 2025-08-14T21:38:49.4070700Z Example: 2025-08-14T21:38:49.4070805Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:49.4070894Z >>> # both workers 2025-08-14T21:38:49.4070990Z >>> def add(x, y): 2025-08-14T21:38:49.4071117Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-08-14T21:38:49.4071224Z >>> return x + y, (x + y).to(2) 2025-08-14T21:38:49.4071310Z >>> 2025-08-14T21:38:49.4071399Z >>> # on worker 0 2025-08-14T21:38:49.4071535Z >>> options = TensorPipeRpcBackendOptions( 2025-08-14T21:38:49.4071644Z >>> num_worker_threads=8, 2025-08-14T21:38:49.4071752Z >>> device_maps={"worker1": {0: 1}} 2025-08-14T21:38:49.4071890Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-08-14T21:38:49.4071969Z >>> ) 2025-08-14T21:38:49.4072097Z >>> options.set_device_map("worker1", {1: 2}) 2025-08-14T21:38:49.4072232Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-08-14T21:38:49.4072312Z >>> 2025-08-14T21:38:49.4072404Z >>> rpc.init_rpc( 2025-08-14T21:38:49.4072499Z >>> "worker0", 2025-08-14T21:38:49.4072628Z >>> rank=0, 2025-08-14T21:38:49.4072742Z >>> world_size=2, 2025-08-14T21:38:49.4072875Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-08-14T21:38:49.4072984Z >>> rpc_backend_options=options 2025-08-14T21:38:49.4073075Z >>> ) 2025-08-14T21:38:49.4073153Z >>> 2025-08-14T21:38:49.4073244Z >>> x = torch.ones(2) 2025-08-14T21:38:49.4073408Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-08-14T21:38:49.4073589Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-08-14T21:38:49.4073769Z >>> # sending the return value back, it will follow the invert of 2025-08-14T21:38:49.4073946Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-08-14T21:38:49.4074038Z >>> # cuda:1 on worker0 2025-08-14T21:38:49.4074180Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-08-14T21:38:49.4074334Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-08-14T21:38:49.4074415Z 2025-08-14T21:38:49.4074673Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4074751Z 2025-08-14T21:38:49.4074846Z warnings.warn(msg) 2025-08-14T21:38:49.4074954Z 2025-08-14T21:38:49.4075135Z --- Parse Warning: 88 / 146 --- 2025-08-14T21:38:49.4076213Z /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. 2025-08-14T21:38:49.4076485Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4076563Z 2025-08-14T21:38:49.4076767Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-08-14T21:38:49.4077039Z except that it enables profiling on all threads running RPC server request callbacks. 2025-08-14T21:38:49.4077120Z 2025-08-14T21:38:49.4077404Z Context manager that manages autograd profiler state and holds a summary of results. 2025-08-14T21:38:49.4077629Z Under the hood it just records events of functions being executed in C++ and 2025-08-14T21:38:49.4077851Z exposes those events to Python. You can wrap any code into it and it will 2025-08-14T21:38:49.4077983Z only report runtime of PyTorch functions. 2025-08-14T21:38:49.4078242Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-08-14T21:38:49.4078321Z 2025-08-14T21:38:49.4078412Z Args: 2025-08-14T21:38:49.4078673Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-08-14T21:38:49.4078776Z Default: ``True``. 2025-08-14T21:38:49.4078850Z 2025-08-14T21:38:49.4079125Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-08-14T21:38:49.4079331Z Adds approximately 4us of overhead to each tensor operation. 2025-08-14T21:38:49.4079425Z Default: ``False`` 2025-08-14T21:38:49.4079499Z 2025-08-14T21:38:49.4079720Z record_shapes (bool, optional): If shapes recording is set, information 2025-08-14T21:38:49.4079939Z about input dimensions will be collected. This allows one to see which 2025-08-14T21:38:49.4080152Z dimensions have been used under the hood and further group by them 2025-08-14T21:38:49.4080362Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-08-14T21:38:49.4080577Z shape recording might skew your profiling data. It is recommended to 2025-08-14T21:38:49.4080813Z use separate runs with and without shape recording to validate the timing. 2025-08-14T21:38:49.4081031Z Most likely the skew will be negligible for bottom most events (in a case 2025-08-14T21:38:49.4081286Z of nested function calls). But for higher level functions the total 2025-08-14T21:38:49.4081514Z self cpu time might be artificially increased because of the shape 2025-08-14T21:38:49.4081601Z collection. 2025-08-14T21:38:49.4081690Z 2025-08-14T21:38:49.4081949Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-08-14T21:38:49.4082029Z 2025-08-14T21:38:49.4082125Z .. warning:: 2025-08-14T21:38:49.4082323Z Enabling memory profiling incurs additional profiler overhead 2025-08-14T21:38:49.4082402Z 2025-08-14T21:38:49.4082493Z .. warning:: 2025-08-14T21:38:49.4082773Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-08-14T21:38:49.4082966Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-08-14T21:38:49.4083200Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-08-14T21:38:49.4083361Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-08-14T21:38:49.4083454Z 2025-08-14T21:38:49.4083534Z Example: 2025-08-14T21:38:49.4083627Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4083723Z >>> # On worker 0: 2025-08-14T21:38:49.4083809Z >>> import torch 2025-08-14T21:38:49.4083956Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:49.4084096Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-14T21:38:49.4084213Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-08-14T21:38:49.4084323Z >>> outer_profile_rref = rpc.remote( 2025-08-14T21:38:49.4084490Z ... dst_worker_name, rpc._server_process_global_profile 2025-08-14T21:38:49.4084569Z ... ) 2025-08-14T21:38:49.4084700Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-08-14T21:38:49.4084841Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-08-14T21:38:49.4084952Z >>> inner_profile_rref = rpc.remote( 2025-08-14T21:38:49.4085123Z ... dst_worker_name, rpc._server_process_global_profile 2025-08-14T21:38:49.4085201Z ... ) 2025-08-14T21:38:49.4085323Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-08-14T21:38:49.4085474Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-08-14T21:38:49.4085640Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-08-14T21:38:49.4085807Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-08-14T21:38:49.4085966Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-08-14T21:38:49.4086195Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-14T21:38:49.4086509Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-08-14T21:38:49.4086730Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-14T21:38:49.4086919Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-08-14T21:38:49.4087115Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-08-14T21:38:49.4087335Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-14T21:38:49.4087454Z Self CPU time total: 89.667us 2025-08-14T21:38:49.4087609Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-08-14T21:38:49.4087825Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-14T21:38:49.4088132Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-08-14T21:38:49.4088347Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-14T21:38:49.4088583Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-08-14T21:38:49.4088787Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-08-14T21:38:49.4088961Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-08-14T21:38:49.4089191Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-14T21:38:49.4089291Z Self CPU time total: 213.926us 2025-08-14T21:38:49.4089383Z >>> rpc.shutdown() 2025-08-14T21:38:49.4089470Z 2025-08-14T21:38:49.4089554Z >>> # On worker 1: 2025-08-14T21:38:49.4089688Z >>> import torch.distributed.rpc as rpc 2025-08-14T21:38:49.4089820Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-14T21:38:49.4089973Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-08-14T21:38:49.4090080Z >>> rpc.shutdown() 2025-08-14T21:38:49.4090155Z 2025-08-14T21:38:49.4090400Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4090487Z 2025-08-14T21:38:49.4090575Z warnings.warn(msg) 2025-08-14T21:38:49.4090682Z 2025-08-14T21:38:49.4090884Z --- Parse Warning: 89 / 146 --- 2025-08-14T21:38:49.4091856Z /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. 2025-08-14T21:38:49.4092123Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4092197Z 2025-08-14T21:38:49.4092453Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-08-14T21:38:49.4092741Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-08-14T21:38:49.4093222Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-08-14T21:38:49.4093383Z :class:`DTensor` according to the ``out_placements``. 2025-08-14T21:38:49.4093476Z 2025-08-14T21:38:49.4093555Z Args: 2025-08-14T21:38:49.4093772Z func (Callable): the function to be applied on each local shard of 2025-08-14T21:38:49.4093866Z :class:`DTensor` s. 2025-08-14T21:38:49.4094086Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-08-14T21:38:49.4094349Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-08-14T21:38:49.4094583Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-08-14T21:38:49.4094818Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-08-14T21:38:49.4095074Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-08-14T21:38:49.4095195Z mapping to the flattened ``output``. 2025-08-14T21:38:49.4095404Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-08-14T21:38:49.4095665Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-08-14T21:38:49.4095758Z should be `None`. 2025-08-14T21:38:49.4095996Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-08-14T21:38:49.4096209Z in. In this case, even if `out_placements` is not `None`, the result function 2025-08-14T21:38:49.4096453Z should ignore the desired placements because the function is not running with 2025-08-14T21:38:49.4096557Z :class:`DTensor` s. 2025-08-14T21:38:49.4096713Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-08-14T21:38:49.4096995Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-08-14T21:38:49.4097323Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-08-14T21:38:49.4097578Z placements of each :class:`DTensor` argument is the same as the required 2025-08-14T21:38:49.4097768Z placements or not. If the placements are not the same and 2025-08-14T21:38:49.4098005Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-08-14T21:38:49.4098239Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-08-14T21:38:49.4098503Z the required sharding placements before passing its local tensor to ``func``. 2025-08-14T21:38:49.4098721Z The only exception is when required placements are not ``None`` and the 2025-08-14T21:38:49.4098967Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-08-14T21:38:49.4099177Z will be skipped and the argument will be directly passed to ``func``. 2025-08-14T21:38:49.4099395Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-08-14T21:38:49.4099500Z Default: None 2025-08-14T21:38:49.4099674Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-08-14T21:38:49.4099919Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-08-14T21:38:49.4100123Z to the flattened input DTensor. This argument is the hint that user 2025-08-14T21:38:49.4100298Z can give to :meth:`to_local` in case the gradient layout of the 2025-08-14T21:38:49.4100515Z local tensor input does not match its :class:`DTensor` input layout. 2025-08-14T21:38:49.4100706Z If not specified, we will assume the gradient layout of the local 2025-08-14T21:38:49.4100911Z tensor input remains the same as the original :class:`DTensor` input 2025-08-14T21:38:49.4101080Z and use that for gradient computation. Default: None. 2025-08-14T21:38:49.4101218Z device_mesh (:class:`DeviceMesh`, optional): 2025-08-14T21:38:49.4101447Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-08-14T21:38:49.4101691Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-08-14T21:38:49.4101785Z mesh. Default: None. 2025-08-14T21:38:49.4101871Z 2025-08-14T21:38:49.4101957Z Keyword Args: 2025-08-14T21:38:49.4102073Z redistribute_inputs (bool, optional): 2025-08-14T21:38:49.4102327Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-08-14T21:38:49.4102568Z their placements are different from the required input placements. If this 2025-08-14T21:38:49.4102798Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-08-14T21:38:49.4102929Z an exception will be raised. Default: False. 2025-08-14T21:38:49.4103003Z 2025-08-14T21:38:49.4103095Z Returns: 2025-08-14T21:38:49.4103343Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-08-14T21:38:49.4103646Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-08-14T21:38:49.4103739Z 2025-08-14T21:38:49.4103819Z Raises: 2025-08-14T21:38:49.4104052Z AssertionError: For any non-DTensor output, we require its corresponding 2025-08-14T21:38:49.4104319Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-08-14T21:38:49.4104413Z if this is not the case. 2025-08-14T21:38:49.4104501Z 2025-08-14T21:38:49.4104757Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-08-14T21:38:49.4104904Z a redistribution according to ``in_placements``. 2025-08-14T21:38:49.4104992Z 2025-08-14T21:38:49.4105075Z Example: 2025-08-14T21:38:49.4105239Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:49.4105408Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-08-14T21:38:49.4105530Z >>> partial_sum_tensor = torch.mm(W, X) 2025-08-14T21:38:49.4105762Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-08-14T21:38:49.4105875Z >>> return reduced_tensor 2025-08-14T21:38:49.4105952Z >>> 2025-08-14T21:38:49.4106084Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-08-14T21:38:49.4106206Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-08-14T21:38:49.4106297Z >>> Y = torch.mm(W, X) 2025-08-14T21:38:49.4106488Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-08-14T21:38:49.4106667Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-08-14T21:38:49.4106743Z >>> 2025-08-14T21:38:49.4107018Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor conversion 2025-08-14T21:38:49.4107141Z >>> local_mm_allreduce_forward = local_map( 2025-08-14T21:38:49.4107242Z >>> mm_allreduce_forward, 2025-08-14T21:38:49.4107369Z >>> out_placements=[Replicate()], 2025-08-14T21:38:49.4107485Z >>> in_placements=[col_wise, row_wise], 2025-08-14T21:38:49.4107621Z >>> device_mesh=device_mesh, 2025-08-14T21:38:49.4107703Z >>> ) 2025-08-14T21:38:49.4107784Z >>> 2025-08-14T21:38:49.4107895Z >>> W_dt = distribute_tensor( 2025-08-14T21:38:49.4107998Z ... W, device_mesh, (col_wise) 2025-08-14T21:38:49.4108105Z ... ) # col-wisely sharded W tensor 2025-08-14T21:38:49.4108216Z >>> X_dt = distribute_tensor( 2025-08-14T21:38:49.4108312Z ... X, device_mesh, (row_wise) 2025-08-14T21:38:49.4108419Z ... ) # row-wisely sharded X tensor 2025-08-14T21:38:49.4108539Z >>> Y_dt = local_mm_allreduce_forward( 2025-08-14T21:38:49.4108632Z ... device_mesh, W_dt, X_dt 2025-08-14T21:38:49.4108785Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-08-14T21:38:49.4108863Z 2025-08-14T21:38:49.4109053Z .. note:: This API is currently experimental and subject to change 2025-08-14T21:38:49.4109140Z 2025-08-14T21:38:49.4109387Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4109461Z 2025-08-14T21:38:49.4109564Z warnings.warn(msg) 2025-08-14T21:38:49.4109637Z 2025-08-14T21:38:49.4109852Z --- Parse Warning: 90 / 146 --- 2025-08-14T21:38:49.4110921Z /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. 2025-08-14T21:38:49.4111172Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4111256Z 2025-08-14T21:38:49.4111531Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-08-14T21:38:49.4111760Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-08-14T21:38:49.4112013Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-08-14T21:38:49.4112249Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-08-14T21:38:49.4112530Z when users would like to overwrite default sharding strategies of existing operators. 2025-08-14T21:38:49.4112606Z 2025-08-14T21:38:49.4112681Z Args: 2025-08-14T21:38:49.4112820Z op (Union[OpOverload, List[OpOverload]]): 2025-08-14T21:38:49.4113009Z An op or a list of ops to register the customized sharding function. 2025-08-14T21:38:49.4113085Z 2025-08-14T21:38:49.4113180Z Returns: 2025-08-14T21:38:49.4113436Z A function decorator which can be used to wrap a function that defines the sharding 2025-08-14T21:38:49.4113746Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-08-14T21:38:49.4114045Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-08-14T21:38:49.4114340Z already implemented the operator. The customized sharding function takes the same inputs 2025-08-14T21:38:49.4114587Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-08-14T21:38:49.4114850Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-08-14T21:38:49.4115114Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-08-14T21:38:49.4115239Z corresponding input placements. 2025-08-14T21:38:49.4115316Z 2025-08-14T21:38:49.4115394Z Example: 2025-08-14T21:38:49.4115515Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:49.4115644Z >>> @register_sharding(aten._softmax.default) 2025-08-14T21:38:49.4115806Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-08-14T21:38:49.4115942Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-08-14T21:38:49.4116045Z >>> acceptable_shardings = [] 2025-08-14T21:38:49.4116156Z >>> 2025-08-14T21:38:49.4116327Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-08-14T21:38:49.4116462Z >>> acceptable_shardings.append(all_replicate) 2025-08-14T21:38:49.4116555Z >>> 2025-08-14T21:38:49.4116668Z >>> for sharding_dim in range(x.ndim): 2025-08-14T21:38:49.4116779Z >>> if sharding_dim != softmax_dim: 2025-08-14T21:38:49.4116884Z >>> all_sharded = ( 2025-08-14T21:38:49.4116988Z >>> [Shard(sharding_dim)], 2025-08-14T21:38:49.4117116Z >>> [Shard(sharding_dim), None, None], 2025-08-14T21:38:49.4117195Z >>> ) 2025-08-14T21:38:49.4117331Z >>> acceptable_shardings.append(all_sharded) 2025-08-14T21:38:49.4117419Z >>> 2025-08-14T21:38:49.4117524Z >>> return acceptable_shardings 2025-08-14T21:38:49.4117600Z 2025-08-14T21:38:49.4117797Z .. note:: This API is currently experimental and subject to change 2025-08-14T21:38:49.4117873Z 2025-08-14T21:38:49.4118115Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4118200Z 2025-08-14T21:38:49.4118292Z warnings.warn(msg) 2025-08-14T21:38:49.4118369Z 2025-08-14T21:38:49.4118563Z --- Parse Warning: 91 / 146 --- 2025-08-14T21:38:49.4119560Z /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. 2025-08-14T21:38:49.4119827Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4119904Z 2025-08-14T21:38:49.4120278Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-08-14T21:38:49.4120602Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-08-14T21:38:49.4120680Z 2025-08-14T21:38:49.4120774Z Keyword Args: 2025-08-14T21:38:49.4120967Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-14T21:38:49.4121286Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-08-14T21:38:49.4121643Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-08-14T21:38:49.4121755Z as a placeholder. default: None. 2025-08-14T21:38:49.4121982Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-14T21:38:49.4122411Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-14T21:38:49.4122827Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-08-14T21:38:49.4122968Z input_kwarg_layouts (Dict[str, Placement]): 2025-08-14T21:38:49.4123332Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-08-14T21:38:49.4123421Z default: None 2025-08-14T21:38:49.4123586Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-08-14T21:38:49.4123941Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-14T21:38:49.4124082Z have the desired DTensor layouts. default: None. 2025-08-14T21:38:49.4124198Z use_local_output (bool, optional): 2025-08-14T21:38:49.4124548Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-08-14T21:38:49.4124643Z Returns: 2025-08-14T21:38:49.4124949Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-08-14T21:38:49.4125052Z 2025-08-14T21:38:49.4125150Z Example:: 2025-08-14T21:38:49.4125252Z >>> # xdoctest: +SKIP(failing) 2025-08-14T21:38:49.4125556Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-08-14T21:38:49.4125750Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-08-14T21:38:49.4125826Z >>> ... 2025-08-14T21:38:49.4126137Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-08-14T21:38:49.4126259Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-08-14T21:38:49.4126337Z >>> 2025-08-14T21:38:49.4126678Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-08-14T21:38:49.4126820Z >>> # and then redistributed to Replicated DTensor. 2025-08-14T21:38:49.4126916Z >>> parallelize_module( 2025-08-14T21:38:49.4127052Z >>> block, # this can be a submodule or module 2025-08-14T21:38:49.4127140Z >>> tp_mesh, 2025-08-14T21:38:49.4127239Z >>> parallelize_plan={ 2025-08-14T21:38:49.4127370Z >>> "attn": PrepareModuleInput( 2025-08-14T21:38:49.4127505Z >>> input_layouts=(Shard(0), None, None, ...), 2025-08-14T21:38:49.4127675Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-08-14T21:38:49.4127753Z >>> ), 2025-08-14T21:38:49.4127830Z >>> } 2025-08-14T21:38:49.4127918Z >>> ) 2025-08-14T21:38:49.4127995Z 2025-08-14T21:38:49.4128238Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4128327Z 2025-08-14T21:38:49.4128423Z warnings.warn(msg) 2025-08-14T21:38:49.4128498Z 2025-08-14T21:38:49.4128688Z --- Parse Warning: 92 / 146 --- 2025-08-14T21:38:49.4129686Z /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. 2025-08-14T21:38:49.4129952Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4130029Z 2025-08-14T21:38:49.4130409Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-08-14T21:38:49.4130743Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-08-14T21:38:49.4130819Z 2025-08-14T21:38:49.4130905Z Keyword Args: 2025-08-14T21:38:49.4131120Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-14T21:38:49.4131472Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-08-14T21:38:49.4131849Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-08-14T21:38:49.4131989Z ``None`` need to be specified as a placeholder. 2025-08-14T21:38:49.4132180Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-14T21:38:49.4132570Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-08-14T21:38:49.4132683Z have the desired DTensor layouts. 2025-08-14T21:38:49.4132807Z use_local_output (bool, optional): 2025-08-14T21:38:49.4133156Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-08-14T21:38:49.4133239Z Returns: 2025-08-14T21:38:49.4133544Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-08-14T21:38:49.4133621Z 2025-08-14T21:38:49.4133704Z Example:: 2025-08-14T21:38:49.4133820Z >>> # xdoctest: +SKIP(failing) 2025-08-14T21:38:49.4134151Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-08-14T21:38:49.4134337Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-08-14T21:38:49.4134431Z >>> ... 2025-08-14T21:38:49.4134727Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-08-14T21:38:49.4134859Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-08-14T21:38:49.4134936Z >>> 2025-08-14T21:38:49.4135329Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-08-14T21:38:49.4135474Z >>> # and then redistributed to Sharded DTensor. 2025-08-14T21:38:49.4135572Z >>> parallelize_module( 2025-08-14T21:38:49.4135701Z >>> block, # this can be a submodule or module 2025-08-14T21:38:49.4135794Z >>> tp_mesh, 2025-08-14T21:38:49.4135923Z >>> parallelize_plan = PrepareModuleOutput( 2025-08-14T21:38:49.4136047Z >>> output_layouts=Replicate(), 2025-08-14T21:38:49.4136162Z >>> desired_output_layouts=Shard(0) 2025-08-14T21:38:49.4136240Z >>> ) 2025-08-14T21:38:49.4136328Z >>> ) 2025-08-14T21:38:49.4136403Z 2025-08-14T21:38:49.4136650Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4136737Z 2025-08-14T21:38:49.4136835Z warnings.warn(msg) 2025-08-14T21:38:49.4136910Z 2025-08-14T21:38:49.4137104Z --- Parse Warning: 93 / 146 --- 2025-08-14T21:38:49.4138134Z /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. 2025-08-14T21:38:49.4138409Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4138489Z 2025-08-14T21:38:49.4138912Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-08-14T21:38:49.4139340Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-08-14T21:38:49.4139709Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-08-14T21:38:49.4139910Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-08-14T21:38:49.4139999Z 2025-08-14T21:38:49.4140086Z Keyword Args: 2025-08-14T21:38:49.4140358Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-14T21:38:49.4140704Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-08-14T21:38:49.4141051Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-08-14T21:38:49.4141182Z as a placeholder. default: None. 2025-08-14T21:38:49.4141409Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-14T21:38:49.4141788Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-14T21:38:49.4142171Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-08-14T21:38:49.4142300Z input_kwarg_layouts (Dict[str, Placement]): 2025-08-14T21:38:49.4142679Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-08-14T21:38:49.4142773Z default: None 2025-08-14T21:38:49.4142930Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-08-14T21:38:49.4143299Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-14T21:38:49.4143554Z have the desired DTensor layouts. default: None. 2025-08-14T21:38:49.4143682Z use_local_input (bool, optional): 2025-08-14T21:38:49.4144035Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-08-14T21:38:49.4144196Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-14T21:38:49.4144534Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-08-14T21:38:49.4144904Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-08-14T21:38:49.4145050Z ``None`` need to be specified as a placeholder. 2025-08-14T21:38:49.4145254Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-14T21:38:49.4145629Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-08-14T21:38:49.4145754Z have the desired DTensor layouts. 2025-08-14T21:38:49.4145868Z use_local_output (bool, optional): 2025-08-14T21:38:49.4146220Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-08-14T21:38:49.4146312Z Returns: 2025-08-14T21:38:49.4146668Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-08-14T21:38:49.4146758Z 2025-08-14T21:38:49.4146843Z Example:: 2025-08-14T21:38:49.4146945Z >>> # xdoctest: +SKIP(failing) 2025-08-14T21:38:49.4147291Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-08-14T21:38:49.4147478Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-08-14T21:38:49.4147557Z >>> ... 2025-08-14T21:38:49.4147861Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-08-14T21:38:49.4147985Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-08-14T21:38:49.4148063Z >>> 2025-08-14T21:38:49.4148395Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-08-14T21:38:49.4148740Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-08-14T21:38:49.4148954Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-08-14T21:38:49.4149052Z >>> parallelize_module( 2025-08-14T21:38:49.4149234Z >>> block, # this can be a submodule or module 2025-08-14T21:38:49.4149358Z >>> tp_mesh, 2025-08-14T21:38:49.4149457Z >>> parallelize_plan={ 2025-08-14T21:38:49.4149586Z >>> "attn": PrepareModuleInputOutput( 2025-08-14T21:38:49.4149727Z >>> input_layouts=(Shard(0), None, None, ...), 2025-08-14T21:38:49.4149900Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-08-14T21:38:49.4150026Z >>> output_layouts=Replicate(), 2025-08-14T21:38:49.4150144Z >>> desired_output_layouts=Shard(0), 2025-08-14T21:38:49.4150227Z >>> ), 2025-08-14T21:38:49.4150317Z >>> } 2025-08-14T21:38:49.4150394Z >>> ) 2025-08-14T21:38:49.4150474Z 2025-08-14T21:38:49.4150732Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4150809Z 2025-08-14T21:38:49.4150904Z warnings.warn(msg) 2025-08-14T21:38:49.4150992Z 2025-08-14T21:38:49.4151192Z --- Parse Warning: 94 / 146 --- 2025-08-14T21:38:49.4152275Z /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. 2025-08-14T21:38:49.4152987Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4153066Z 2025-08-14T21:38:49.4153373Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-08-14T21:38:49.4153554Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-08-14T21:38:49.4153631Z 2025-08-14T21:38:49.4153814Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-08-14T21:38:49.4153893Z 2025-08-14T21:38:49.4153973Z Example: 2025-08-14T21:38:49.4154125Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-08-14T21:38:49.4154265Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-14T21:38:49.4154393Z >>> m = LowRankMultivariateNormal( 2025-08-14T21:38:49.4154567Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-08-14T21:38:49.4154648Z ... ) 2025-08-14T21:38:49.4154945Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-08-14T21:38:49.4155039Z tensor([-0.2102, -0.5429]) 2025-08-14T21:38:49.4155116Z 2025-08-14T21:38:49.4155207Z Args: 2025-08-14T21:38:49.4155438Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-08-14T21:38:49.4155702Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-08-14T21:38:49.4155816Z `batch_shape + event_shape + (rank,)` 2025-08-14T21:38:49.4156064Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-08-14T21:38:49.4156183Z `batch_shape + event_shape` 2025-08-14T21:38:49.4156259Z 2025-08-14T21:38:49.4156338Z Note: 2025-08-14T21:38:49.4156612Z The computation for determinant and inverse of covariance matrix is avoided when 2025-08-14T21:38:49.4156843Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-08-14T21:38:49.4157054Z `_ and 2025-08-14T21:38:49.4157351Z `matrix determinant lemma `_. 2025-08-14T21:38:49.4157594Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-08-14T21:38:49.4157726Z the small size "capacitance" matrix:: 2025-08-14T21:38:49.4157802Z 2025-08-14T21:38:49.4157981Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-08-14T21:38:49.4158066Z 2025-08-14T21:38:49.4158377Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4158480Z 2025-08-14T21:38:49.4158585Z warnings.warn(msg) 2025-08-14T21:38:49.4158662Z 2025-08-14T21:38:49.4158847Z --- Parse Warning: 95 / 146 --- 2025-08-14T21:38:49.4159841Z /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. 2025-08-14T21:38:49.4160102Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4160195Z 2025-08-14T21:38:49.4160413Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-08-14T21:38:49.4160651Z distribution where all component are from different parameterizations of 2025-08-14T21:38:49.4160867Z the same distribution type. It is parameterized by a `Categorical` 2025-08-14T21:38:49.4161060Z "selecting distribution" (over `k` component) and a component 2025-08-14T21:38:49.4161276Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-08-14T21:38:49.4161431Z (equal to `[k]`) which indexes each (batch of) component. 2025-08-14T21:38:49.4161509Z 2025-08-14T21:38:49.4161612Z Examples:: 2025-08-14T21:38:49.4161719Z 2025-08-14T21:38:49.4161834Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:49.4162043Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-08-14T21:38:49.4162153Z >>> # weighted normal distributions 2025-08-14T21:38:49.4162267Z >>> mix = D.Categorical(torch.ones(5,)) 2025-08-14T21:38:49.4162420Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-08-14T21:38:49.4162533Z >>> gmm = MixtureSameFamily(mix, comp) 2025-08-14T21:38:49.4162621Z 2025-08-14T21:38:49.4162813Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-08-14T21:38:49.4162943Z >>> # weighted bivariate normal distributions 2025-08-14T21:38:49.4163070Z >>> mix = D.Categorical(torch.ones(5,)) 2025-08-14T21:38:49.4163179Z >>> comp = D.Independent(D.Normal( 2025-08-14T21:38:49.4163303Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-08-14T21:38:49.4163430Z >>> gmm = MixtureSameFamily(mix, comp) 2025-08-14T21:38:49.4163507Z 2025-08-14T21:38:49.4163684Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-08-14T21:38:49.4163893Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-08-14T21:38:49.4164007Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-08-14T21:38:49.4164126Z >>> comp = D.Independent(D.Normal( 2025-08-14T21:38:49.4164254Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-08-14T21:38:49.4164370Z >>> gmm = MixtureSameFamily(mix, comp) 2025-08-14T21:38:49.4164457Z 2025-08-14T21:38:49.4164534Z Args: 2025-08-14T21:38:49.4164735Z mixture_distribution: `torch.distributions.Categorical`-like 2025-08-14T21:38:49.4164929Z instance. Manages the probability of selecting component. 2025-08-14T21:38:49.4165093Z The number of categories must match the rightmost batch 2025-08-14T21:38:49.4165278Z dimension of the `component_distribution`. Must have either 2025-08-14T21:38:49.4165428Z scalar `batch_shape` or `batch_shape` matching 2025-08-14T21:38:49.4165560Z `component_distribution.batch_shape[:-1]` 2025-08-14T21:38:49.4165785Z component_distribution: `torch.distributions.Distribution`-like 2025-08-14T21:38:49.4165958Z instance. Right-most batch dimension indexes component. 2025-08-14T21:38:49.4166033Z 2025-08-14T21:38:49.4166292Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4166369Z 2025-08-14T21:38:49.4166464Z warnings.warn(msg) 2025-08-14T21:38:49.4166552Z 2025-08-14T21:38:49.4166787Z --- Parse Warning: 96 / 146 --- 2025-08-14T21:38:49.4167789Z /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. 2025-08-14T21:38:49.4168048Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4168121Z 2025-08-14T21:38:49.4168310Z Creates a RelaxedBernoulli distribution, parametrized by 2025-08-14T21:38:49.4168493Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-08-14T21:38:49.4168707Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-08-14T21:38:49.4168899Z so the values are in (0, 1), and has reparametrizable samples. 2025-08-14T21:38:49.4168975Z 2025-08-14T21:38:49.4169059Z Example:: 2025-08-14T21:38:49.4169157Z 2025-08-14T21:38:49.4169294Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-14T21:38:49.4169442Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-08-14T21:38:49.4169564Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-08-14T21:38:49.4169650Z >>> m.sample() 2025-08-14T21:38:49.4169777Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-08-14T21:38:49.4169881Z 2025-08-14T21:38:49.4169961Z Args: 2025-08-14T21:38:49.4170105Z temperature (Tensor): relaxation temperature 2025-08-14T21:38:49.4170271Z probs (Number, Tensor): the probability of sampling `1` 2025-08-14T21:38:49.4170425Z logits (Number, Tensor): the log-odds of sampling `1` 2025-08-14T21:38:49.4170513Z 2025-08-14T21:38:49.4170756Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4170843Z 2025-08-14T21:38:49.4170937Z warnings.warn(msg) 2025-08-14T21:38:49.4171013Z 2025-08-14T21:38:49.4171211Z --- Parse Warning: 97 / 146 --- 2025-08-14T21:38:49.4172234Z /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. 2025-08-14T21:38:49.4172492Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4172583Z 2025-08-14T21:38:49.4178036Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-08-14T21:38:49.4178279Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-08-14T21:38:49.4178520Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-08-14T21:38:49.4178679Z its samples are on simplex, and are reparametrizable. 2025-08-14T21:38:49.4178767Z 2025-08-14T21:38:49.4178861Z Example:: 2025-08-14T21:38:49.4178934Z 2025-08-14T21:38:49.4179078Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-14T21:38:49.4179244Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-08-14T21:38:49.4179381Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-08-14T21:38:49.4179466Z >>> m.sample() 2025-08-14T21:38:49.4179581Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-08-14T21:38:49.4179673Z 2025-08-14T21:38:49.4179754Z Args: 2025-08-14T21:38:49.4179890Z temperature (Tensor): relaxation temperature 2025-08-14T21:38:49.4180014Z probs (Tensor): event probabilities 2025-08-14T21:38:49.4180196Z logits (Tensor): unnormalized log probability for each event 2025-08-14T21:38:49.4180269Z 2025-08-14T21:38:49.4180523Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4180603Z 2025-08-14T21:38:49.4180701Z warnings.warn(msg) 2025-08-14T21:38:49.4180776Z 2025-08-14T21:38:49.4181003Z --- Parse Warning: 98 / 146 --- 2025-08-14T21:38:49.4182121Z /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. 2025-08-14T21:38:49.4182407Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4182595Z Return a new dict with new, potentially nested, key value pair 2025-08-14T21:38:49.4182680Z 2025-08-14T21:38:49.4182772Z >>> purchase = { 2025-08-14T21:38:49.4182873Z ... "name": "Alice", 2025-08-14T21:38:49.4183044Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-08-14T21:38:49.4183160Z ... "credit card": "5555-1234-1234-1234", 2025-08-14T21:38:49.4183247Z ... } 2025-08-14T21:38:49.4183551Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-08-14T21:38:49.4183658Z {'credit card': '5555-1234-1234-1234', 2025-08-14T21:38:49.4183761Z 'name': 'Alice', 2025-08-14T21:38:49.4183925Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-08-14T21:38:49.4184003Z 2025-08-14T21:38:49.4184261Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4184372Z 2025-08-14T21:38:49.4184472Z warnings.warn(msg) 2025-08-14T21:38:49.4184548Z 2025-08-14T21:38:49.4184740Z --- Parse Warning: 99 / 146 --- 2025-08-14T21:38:49.4185742Z /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. 2025-08-14T21:38:49.4185998Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4186144Z Update value in a (potentially) nested dictionary 2025-08-14T21:38:49.4186233Z 2025-08-14T21:38:49.4186322Z inputs: 2025-08-14T21:38:49.4186434Z d - dictionary on which to operate 2025-08-14T21:38:49.4186660Z keys - list or tuple giving the location of the value to be changed in d 2025-08-14T21:38:49.4186781Z func - function to operate on that value 2025-08-14T21:38:49.4186869Z 2025-08-14T21:38:49.4187055Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-08-14T21:38:49.4187280Z original dictionary with v replaced by func(v), but does not mutate the 2025-08-14T21:38:49.4187385Z original dictionary. 2025-08-14T21:38:49.4187462Z 2025-08-14T21:38:49.4187662Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-08-14T21:38:49.4187878Z specified by the keys, with the innermost value set to func(default). 2025-08-14T21:38:49.4187954Z 2025-08-14T21:38:49.4188053Z >>> inc = lambda x: x + 1 2025-08-14T21:38:49.4188158Z >>> update_in({"a": 0}, ["a"], inc) 2025-08-14T21:38:49.4188243Z {'a': 1} 2025-08-14T21:38:49.4188329Z 2025-08-14T21:38:49.4188420Z >>> transaction = { 2025-08-14T21:38:49.4188512Z ... "name": "Alice", 2025-08-14T21:38:49.4188700Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-08-14T21:38:49.4188816Z ... "credit card": "5555-1234-1234-1234", 2025-08-14T21:38:49.4188899Z ... } 2025-08-14T21:38:49.4189113Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-08-14T21:38:49.4189217Z {'credit card': '5555-1234-1234-1234', 2025-08-14T21:38:49.4189300Z 'name': 'Alice', 2025-08-14T21:38:49.4189468Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-08-14T21:38:49.4189547Z 2025-08-14T21:38:49.4189672Z >>> # updating a value when k0 is not in d 2025-08-14T21:38:49.4189797Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-08-14T21:38:49.4189965Z {1: {2: {3: 'bar'}}} 2025-08-14T21:38:49.4190109Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-08-14T21:38:49.4190199Z {1: 'foo', 2: {3: {4: 1}}} 2025-08-14T21:38:49.4190273Z 2025-08-14T21:38:49.4190526Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4190607Z 2025-08-14T21:38:49.4190698Z warnings.warn(msg) 2025-08-14T21:38:49.4190786Z 2025-08-14T21:38:49.4190971Z --- Parse Warning: 100 / 146 --- 2025-08-14T21:38:49.4191961Z /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. 2025-08-14T21:38:49.4192215Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4192371Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-08-14T21:38:49.4192458Z 2025-08-14T21:38:49.4192636Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-08-14T21:38:49.4193006Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-08-14T21:38:49.4193094Z 2025-08-14T21:38:49.4193294Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-08-14T21:38:49.4193491Z structures such as dictionaries and lists. 2025-08-14T21:38:49.4193567Z 2025-08-14T21:38:49.4193657Z >>> transaction = { 2025-08-14T21:38:49.4193758Z ... "name": "Alice", 2025-08-14T21:38:49.4193940Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-08-14T21:38:49.4194048Z ... "credit card": "5555-1234-1234-1234", 2025-08-14T21:38:49.4194139Z ... } 2025-08-14T21:38:49.4194269Z >>> get_in(["purchase", "items", 0], transaction) 2025-08-14T21:38:49.4194349Z 'Apple' 2025-08-14T21:38:49.4194462Z >>> get_in(["name"], transaction) 2025-08-14T21:38:49.4194545Z 'Alice' 2025-08-14T21:38:49.4194681Z >>> get_in(["purchase", "total"], transaction) 2025-08-14T21:38:49.4194828Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-08-14T21:38:49.4194954Z >>> get_in(["purchase", "items", 10], transaction) 2025-08-14T21:38:49.4195095Z >>> get_in(["purchase", "total"], transaction, 0) 2025-08-14T21:38:49.4195174Z 0 2025-08-14T21:38:49.4195277Z >>> get_in(["y"], {}, no_default=True) 2025-08-14T21:38:49.4195399Z Traceback (most recent call last): 2025-08-14T21:38:49.4195475Z ... 2025-08-14T21:38:49.4195559Z KeyError: 'y' 2025-08-14T21:38:49.4195645Z 2025-08-14T21:38:49.4195722Z See Also: 2025-08-14T21:38:49.4195812Z itertoolz.get 2025-08-14T21:38:49.4195914Z operator.getitem 2025-08-14T21:38:49.4195993Z 2025-08-14T21:38:49.4196250Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4196327Z 2025-08-14T21:38:49.4196420Z warnings.warn(msg) 2025-08-14T21:38:49.4196510Z 2025-08-14T21:38:49.4196694Z --- Parse Warning: 101 / 146 --- 2025-08-14T21:38:49.4197695Z /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. 2025-08-14T21:38:49.4197963Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4198072Z Group a collection by a key function 2025-08-14T21:38:49.4198159Z 2025-08-14T21:38:49.4198317Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-08-14T21:38:49.4198431Z >>> groupby(len, names) # doctest: +SKIP 2025-08-14T21:38:49.4198596Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-08-14T21:38:49.4198671Z 2025-08-14T21:38:49.4198858Z >>> iseven = lambda x: x % 2 == 0 2025-08-14T21:38:49.4199068Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-08-14T21:38:49.4199174Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-08-14T21:38:49.4199252Z 2025-08-14T21:38:49.4199404Z Non-callable keys imply grouping on a member. 2025-08-14T21:38:49.4199482Z 2025-08-14T21:38:49.4199565Z >>> groupby( 2025-08-14T21:38:49.4199662Z ... "gender", 2025-08-14T21:38:49.4199740Z ... [ 2025-08-14T21:38:49.4199866Z ... {"name": "Alice", "gender": "F"}, 2025-08-14T21:38:49.4199974Z ... {"name": "Bob", "gender": "M"}, 2025-08-14T21:38:49.4200091Z ... {"name": "Charlie", "gender": "M"}, 2025-08-14T21:38:49.4200179Z ... ], 2025-08-14T21:38:49.4200271Z ... ) # doctest:+SKIP 2025-08-14T21:38:49.4200381Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-08-14T21:38:49.4200500Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-08-14T21:38:49.4200610Z {'gender': 'M', 'name': 'Charlie'}]} 2025-08-14T21:38:49.4200685Z 2025-08-14T21:38:49.4200829Z Not to be confused with ``itertools.groupby`` 2025-08-14T21:38:49.4200907Z 2025-08-14T21:38:49.4201020Z See Also: 2025-08-14T21:38:49.4201102Z countby 2025-08-14T21:38:49.4201179Z 2025-08-14T21:38:49.4201430Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4201505Z 2025-08-14T21:38:49.4201599Z warnings.warn(msg) 2025-08-14T21:38:49.4201685Z 2025-08-14T21:38:49.4201864Z --- Parse Warning: 102 / 146 --- 2025-08-14T21:38:49.4202692Z /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. 2025-08-14T21:38:49.4202956Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4203178Z Return the recommended gain value for the given nonlinearity function. 2025-08-14T21:38:49.4203266Z 2025-08-14T21:38:49.4203364Z The values are as follows: 2025-08-14T21:38:49.4203441Z 2025-08-14T21:38:49.4203567Z ================= ==================================================== 2025-08-14T21:38:49.4203662Z nonlinearity gain 2025-08-14T21:38:49.4203769Z ================= ==================================================== 2025-08-14T21:38:49.4203879Z Linear / Identity :math:`1` 2025-08-14T21:38:49.4203970Z Conv{1,2,3}D :math:`1` 2025-08-14T21:38:49.4204061Z Sigmoid :math:`1` 2025-08-14T21:38:49.4204170Z Tanh :math:`\frac{5}{3}` 2025-08-14T21:38:49.4204267Z ReLU :math:`\sqrt{2}` 2025-08-14T21:38:49.4204461Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-08-14T21:38:49.4204564Z SELU :math:`\frac{3}{4}` 2025-08-14T21:38:49.4204675Z ================= ==================================================== 2025-08-14T21:38:49.4204761Z 2025-08-14T21:38:49.4204850Z .. warning:: 2025-08-14T21:38:49.4205034Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-08-14T21:38:49.4205279Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-08-14T21:38:49.4205440Z This gives the initial weights a variance of ``1 / N``, 2025-08-14T21:38:49.4205656Z which is necessary to induce a stable fixed point in the forward pass. 2025-08-14T21:38:49.4205877Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-08-14T21:38:49.4206053Z effect for more stable gradient flow in rectangular layers. 2025-08-14T21:38:49.4206136Z 2025-08-14T21:38:49.4206215Z Args: 2025-08-14T21:38:49.4206454Z nonlinearity: the non-linear function (`nn.functional` name) 2025-08-14T21:38:49.4206648Z param: optional parameter for the non-linear function 2025-08-14T21:38:49.4206724Z 2025-08-14T21:38:49.4206805Z Examples: 2025-08-14T21:38:49.4206922Z >>> gain = nn.init.calculate_gain( 2025-08-14T21:38:49.4207021Z ... "leaky_relu", 0.2 2025-08-14T21:38:49.4207146Z ... ) # leaky_relu with negative_slope=0.2 2025-08-14T21:38:49.4207231Z 2025-08-14T21:38:49.4207700Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-08-14T21:38:49.4207787Z 2025-08-14T21:38:49.4208030Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4208105Z 2025-08-14T21:38:49.4208209Z warnings.warn(msg) 2025-08-14T21:38:49.4208285Z 2025-08-14T21:38:49.4208466Z --- Parse Warning: 103 / 146 --- 2025-08-14T21:38:49.4209365Z /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. 2025-08-14T21:38:49.4209619Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4209824Z Applies Batch Normalization over a N-Dimensional input. 2025-08-14T21:38:49.4209899Z 2025-08-14T21:38:49.4210235Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-08-14T21:38:49.4210465Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-08-14T21:38:49.4210668Z Internal Covariate Shift `__ . 2025-08-14T21:38:49.4210746Z 2025-08-14T21:38:49.4210832Z .. math:: 2025-08-14T21:38:49.4210906Z 2025-08-14T21:38:49.4211122Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-08-14T21:38:49.4211212Z 2025-08-14T21:38:49.4211431Z The mean and standard-deviation are calculated per-dimension over all 2025-08-14T21:38:49.4211665Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-08-14T21:38:49.4211902Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-08-14T21:38:49.4212077Z By default, the elements of :math:`\gamma` are sampled from 2025-08-14T21:38:49.4212279Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-08-14T21:38:49.4212529Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-08-14T21:38:49.4212639Z `torch.var(input, unbiased=False)`. 2025-08-14T21:38:49.4212724Z 2025-08-14T21:38:49.4212951Z Also by default, during training this layer keeps running estimates of its 2025-08-14T21:38:49.4213185Z computed mean and variance, which are then used for normalization during 2025-08-14T21:38:49.4213421Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-08-14T21:38:49.4213502Z of 0.1. 2025-08-14T21:38:49.4213583Z 2025-08-14T21:38:49.4213802Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-08-14T21:38:49.4214016Z keep running estimates, and batch statistics are instead used during 2025-08-14T21:38:49.4214124Z evaluation time as well. 2025-08-14T21:38:49.4214202Z 2025-08-14T21:38:49.4214292Z .. note:: 2025-08-14T21:38:49.4214505Z This :attr:`momentum` argument is different from one used in optimizer 2025-08-14T21:38:49.4214722Z classes and the conventional notion of momentum. Mathematically, the 2025-08-14T21:38:49.4214861Z update rule for running statistics here is 2025-08-14T21:38:49.4215118Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-08-14T21:38:49.4215365Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-08-14T21:38:49.4215490Z new observed value. 2025-08-14T21:38:49.4215567Z 2025-08-14T21:38:49.4215861Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-08-14T21:38:49.4216112Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-08-14T21:38:49.4216289Z Normalization or Spatio-temporal Batch Normalization. 2025-08-14T21:38:49.4216367Z 2025-08-14T21:38:49.4216506Z Currently :class:`SyncBatchNorm` only supports 2025-08-14T21:38:49.4216785Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-08-14T21:38:49.4216991Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-08-14T21:38:49.4217195Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-08-14T21:38:49.4217287Z Network with DDP. 2025-08-14T21:38:49.4217363Z 2025-08-14T21:38:49.4217451Z Args: 2025-08-14T21:38:49.4217614Z num_features: :math:`C` from an expected input of size 2025-08-14T21:38:49.4217706Z :math:`(N, C, +)` 2025-08-14T21:38:49.4217919Z eps: a value added to the denominator for numerical stability. 2025-08-14T21:38:49.4218012Z Default: ``1e-5`` 2025-08-14T21:38:49.4218198Z momentum: the value used for the running_mean and running_var 2025-08-14T21:38:49.4218408Z computation. Can be set to ``None`` for cumulative moving average 2025-08-14T21:38:49.4218528Z (i.e. simple average). Default: 0.1 2025-08-14T21:38:49.4218728Z affine: a boolean value that when set to ``True``, this module has 2025-08-14T21:38:49.4218875Z learnable affine parameters. Default: ``True`` 2025-08-14T21:38:49.4219084Z track_running_stats: a boolean value that when set to ``True``, this 2025-08-14T21:38:49.4219314Z module tracks the running mean and variance, and when set to ``False``, 2025-08-14T21:38:49.4219527Z this module does not track such statistics, and initializes statistics 2025-08-14T21:38:49.4219718Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-08-14T21:38:49.4219946Z When these buffers are ``None``, this module always uses batch statistics. 2025-08-14T21:38:49.4220088Z in both training and eval modes. Default: ``True`` 2025-08-14T21:38:49.4220330Z process_group: synchronization of stats happen within each process group 2025-08-14T21:38:49.4220544Z individually. Default behavior is synchronization across the whole 2025-08-14T21:38:49.4220625Z world 2025-08-14T21:38:49.4220709Z 2025-08-14T21:38:49.4220791Z Shape: 2025-08-14T21:38:49.4220890Z - Input: :math:`(N, C, +)` 2025-08-14T21:38:49.4221040Z - Output: :math:`(N, C, +)` (same shape as input) 2025-08-14T21:38:49.4221117Z 2025-08-14T21:38:49.4221208Z .. note:: 2025-08-14T21:38:49.4221443Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-08-14T21:38:49.4221638Z synchronization is disabled when ``model.eval()`` is set or if 2025-08-14T21:38:49.4221768Z ``self.training`` is otherwise ``False``. 2025-08-14T21:38:49.4221843Z 2025-08-14T21:38:49.4221928Z Examples:: 2025-08-14T21:38:49.4222009Z 2025-08-14T21:38:49.4222101Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4222209Z >>> # With Learnable Parameters 2025-08-14T21:38:49.4222314Z >>> m = nn.SyncBatchNorm(100) 2025-08-14T21:38:49.4222432Z >>> # creating process group (optional) 2025-08-14T21:38:49.4222572Z >>> # ranks is a list of int identifying rank ids. 2025-08-14T21:38:49.4222676Z >>> ranks = list(range(8)) 2025-08-14T21:38:49.4222827Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-08-14T21:38:49.4223005Z >>> # Note: every rank calls into new_group for every 2025-08-14T21:38:49.4223148Z >>> # process group created, even if that rank is not 2025-08-14T21:38:49.4223245Z >>> # part of the group. 2025-08-14T21:38:49.4223589Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-08-14T21:38:49.4223788Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-08-14T21:38:49.4223901Z >>> # Without Learnable Parameters 2025-08-14T21:38:49.4224109Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-08-14T21:38:49.4224230Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-08-14T21:38:49.4224333Z >>> output = m(input) 2025-08-14T21:38:49.4224410Z 2025-08-14T21:38:49.4224523Z >>> # network is nn.BatchNorm layer 2025-08-14T21:38:49.4224804Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-08-14T21:38:49.4224962Z >>> # only single gpu per process is currently supported 2025-08-14T21:38:49.4225171Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-08-14T21:38:49.4225327Z >>> sync_bn_network, 2025-08-14T21:38:49.4225449Z >>> device_ids=[args.local_rank], 2025-08-14T21:38:49.4225583Z >>> output_device=args.local_rank) 2025-08-14T21:38:49.4225658Z 2025-08-14T21:38:49.4225901Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4225982Z 2025-08-14T21:38:49.4226073Z warnings.warn(msg) 2025-08-14T21:38:49.4226149Z 2025-08-14T21:38:49.4226362Z --- Parse Warning: 104 / 146 --- 2025-08-14T21:38:49.4227363Z /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. 2025-08-14T21:38:49.4227629Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4227934Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-08-14T21:38:49.4228014Z 2025-08-14T21:38:49.4228105Z Args: 2025-08-14T21:38:49.4228340Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-08-14T21:38:49.4228553Z process_group (optional): process group to scope synchronization, 2025-08-14T21:38:49.4228672Z default is the whole world 2025-08-14T21:38:49.4228750Z 2025-08-14T21:38:49.4228835Z Returns: 2025-08-14T21:38:49.4229097Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-08-14T21:38:49.4229309Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-08-14T21:38:49.4229520Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-08-14T21:38:49.4229601Z instead. 2025-08-14T21:38:49.4229681Z 2025-08-14T21:38:49.4229778Z Example:: 2025-08-14T21:38:49.4229855Z 2025-08-14T21:38:49.4229976Z >>> # Network with nn.BatchNorm layer 2025-08-14T21:38:49.4230119Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-14T21:38:49.4230230Z >>> module = torch.nn.Sequential( 2025-08-14T21:38:49.4230344Z >>> torch.nn.Linear(20, 100), 2025-08-14T21:38:49.4230467Z >>> torch.nn.BatchNorm1d(100), 2025-08-14T21:38:49.4230556Z >>> ).cuda() 2025-08-14T21:38:49.4230685Z >>> # creating process group (optional) 2025-08-14T21:38:49.4230868Z >>> # ranks is a list of int identifying rank ids. 2025-08-14T21:38:49.4230991Z >>> ranks = list(range(8)) 2025-08-14T21:38:49.4231103Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-08-14T21:38:49.4231242Z >>> # Note: every rank calls into new_group for every 2025-08-14T21:38:49.4231392Z >>> # process group created, even if that rank is not 2025-08-14T21:38:49.4231499Z >>> # part of the group. 2025-08-14T21:38:49.4231615Z >>> # xdoctest: +SKIP("distributed") 2025-08-14T21:38:49.4231867Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-08-14T21:38:49.4232066Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-08-14T21:38:49.4232351Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-08-14T21:38:49.4232433Z 2025-08-14T21:38:49.4232508Z 2025-08-14T21:38:49.4232756Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4232845Z 2025-08-14T21:38:49.4232937Z warnings.warn(msg) 2025-08-14T21:38:49.4233016Z 2025-08-14T21:38:49.4233207Z --- Parse Warning: 105 / 146 --- 2025-08-14T21:38:49.4234093Z /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. 2025-08-14T21:38:49.4234350Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4234422Z 2025-08-14T21:38:49.4234724Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-08-14T21:38:49.4234806Z 2025-08-14T21:38:49.4235068Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-08-14T21:38:49.4235289Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-08-14T21:38:49.4235376Z 2025-08-14T21:38:49.4235681Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-08-14T21:38:49.4235931Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-08-14T21:38:49.4236092Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-08-14T21:38:49.4236169Z 2025-08-14T21:38:49.4236256Z Shape: 2025-08-14T21:38:49.4236466Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-08-14T21:38:49.4236703Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-08-14T21:38:49.4236918Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-08-14T21:38:49.4237050Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-08-14T21:38:49.4237126Z 2025-08-14T21:38:49.4237205Z Args: 2025-08-14T21:38:49.4237356Z dim (Union[int, str]): Dimension to be unflattened 2025-08-14T21:38:49.4237688Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-08-14T21:38:49.4237763Z 2025-08-14T21:38:49.4237854Z Examples: 2025-08-14T21:38:49.4237954Z >>> input = torch.randn(2, 50) 2025-08-14T21:38:49.4238048Z >>> # With tuple of ints 2025-08-14T21:38:49.4238147Z >>> m = nn.Sequential( 2025-08-14T21:38:49.4238234Z >>> nn.Linear(50, 50), 2025-08-14T21:38:49.4238335Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-08-14T21:38:49.4238416Z >>> ) 2025-08-14T21:38:49.4238505Z >>> output = m(input) 2025-08-14T21:38:49.4238597Z >>> output.size() 2025-08-14T21:38:49.4238687Z torch.Size([2, 2, 5, 5]) 2025-08-14T21:38:49.4238774Z >>> # With torch.Size 2025-08-14T21:38:49.4238872Z >>> m = nn.Sequential( 2025-08-14T21:38:49.4239010Z >>> nn.Linear(50, 50), 2025-08-14T21:38:49.4239152Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-08-14T21:38:49.4239233Z >>> ) 2025-08-14T21:38:49.4239321Z >>> output = m(input) 2025-08-14T21:38:49.4239409Z >>> output.size() 2025-08-14T21:38:49.4239504Z torch.Size([2, 2, 5, 5]) 2025-08-14T21:38:49.4239618Z >>> # With namedshape (tuple of tuples) 2025-08-14T21:38:49.4239760Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-08-14T21:38:49.4239958Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-08-14T21:38:49.4240059Z >>> output = unflatten(input) 2025-08-14T21:38:49.4240153Z >>> output.size() 2025-08-14T21:38:49.4240244Z torch.Size([2, 2, 5, 5]) 2025-08-14T21:38:49.4240317Z 2025-08-14T21:38:49.4240565Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4240638Z 2025-08-14T21:38:49.4240729Z warnings.warn(msg) 2025-08-14T21:38:49.4240810Z 2025-08-14T21:38:49.4240997Z --- Parse Warning: 106 / 146 --- 2025-08-14T21:38:49.4241957Z /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. 2025-08-14T21:38:49.4242244Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4242434Z Creates a criterion that measures the triplet loss given input 2025-08-14T21:38:49.4242627Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-08-14T21:38:49.4242833Z positive, and negative examples, respectively), and a nonnegative, 2025-08-14T21:38:49.4243076Z real-valued function ("distance function") used to compute the relationship 2025-08-14T21:38:49.4243298Z between the anchor and positive example ("positive distance") and the 2025-08-14T21:38:49.4243445Z anchor and negative example ("negative distance"). 2025-08-14T21:38:49.4243528Z 2025-08-14T21:38:49.4243727Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-08-14T21:38:49.4243823Z can be described as: 2025-08-14T21:38:49.4243904Z 2025-08-14T21:38:49.4243986Z .. math:: 2025-08-14T21:38:49.4244116Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-08-14T21:38:49.4244270Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-08-14T21:38:49.4244342Z 2025-08-14T21:38:49.4244575Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-08-14T21:38:49.4244866Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-08-14T21:38:49.4245097Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-08-14T21:38:49.4245337Z between the positive and negative distances that is required for the loss to 2025-08-14T21:38:49.4245556Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-08-14T21:38:49.4245668Z that the distance function can handle. 2025-08-14T21:38:49.4245748Z 2025-08-14T21:38:49.4245856Z If :attr:`reduction` is not ``'none'`` 2025-08-14T21:38:49.4245952Z (default ``'mean'``), then: 2025-08-14T21:38:49.4246032Z 2025-08-14T21:38:49.4246111Z .. math:: 2025-08-14T21:38:49.4246194Z \ell(x, y) = 2025-08-14T21:38:49.4246284Z \begin{cases} 2025-08-14T21:38:49.4246477Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-08-14T21:38:49.4246663Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-08-14T21:38:49.4246741Z \end{cases} 2025-08-14T21:38:49.4246815Z 2025-08-14T21:38:49.4247088Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-08-14T21:38:49.4247346Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-08-14T21:38:49.4247420Z 2025-08-14T21:38:49.4247504Z Args: 2025-08-14T21:38:49.4247760Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-08-14T21:38:49.4247940Z quantifies the closeness of two tensors. If not specified, 2025-08-14T21:38:49.4248104Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-08-14T21:38:49.4248357Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-08-14T21:38:49.4248619Z between the positive and negative distances required for the loss to be 0. Larger 2025-08-14T21:38:49.4248884Z margins penalize cases where the negative examples are not distant enough from the 2025-08-14T21:38:49.4249051Z anchors, relative to the positives. Default: :math:`1`. 2025-08-14T21:38:49.4249296Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-08-14T21:38:49.4249544Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-08-14T21:38:49.4249779Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-08-14T21:38:49.4250063Z negative example than the anchor is, swaps the positive example and the anchor in 2025-08-14T21:38:49.4250190Z the loss computation. Default: ``False``. 2025-08-14T21:38:49.4250462Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-08-14T21:38:49.4250637Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-08-14T21:38:49.4250810Z ``'mean'``: the sum of the output will be divided by the number of 2025-08-14T21:38:49.4251046Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-08-14T21:38:49.4251119Z 2025-08-14T21:38:49.4251198Z 2025-08-14T21:38:49.4251276Z Shape: 2025-08-14T21:38:49.4251505Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-08-14T21:38:49.4251636Z as supported by the distance function. 2025-08-14T21:38:49.4251875Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-08-14T21:38:49.4251960Z otherwise. 2025-08-14T21:38:49.4252039Z 2025-08-14T21:38:49.4252120Z Examples: 2025-08-14T21:38:49.4252195Z 2025-08-14T21:38:49.4252297Z >>> # Initialize embeddings 2025-08-14T21:38:49.4252408Z >>> embedding = nn.Embedding(1000, 128) 2025-08-14T21:38:49.4252534Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-08-14T21:38:49.4252657Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-08-14T21:38:49.4252781Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-08-14T21:38:49.4252894Z >>> anchor = embedding(anchor_ids) 2025-08-14T21:38:49.4253006Z >>> positive = embedding(positive_ids) 2025-08-14T21:38:49.4253112Z >>> negative = embedding(negative_ids) 2025-08-14T21:38:49.4253195Z >>> 2025-08-14T21:38:49.4253298Z >>> # Built-in Distance Function 2025-08-14T21:38:49.4253392Z >>> triplet_loss = \ 2025-08-14T21:38:49.4253669Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-08-14T21:38:49.4253815Z >>> output = triplet_loss(anchor, positive, negative) 2025-08-14T21:38:49.4253919Z >>> output.backward() 2025-08-14T21:38:49.4253994Z >>> 2025-08-14T21:38:49.4254092Z >>> # Custom Distance Function 2025-08-14T21:38:49.4254191Z >>> def l_infinity(x1, x2): 2025-08-14T21:38:49.4254336Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-08-14T21:38:49.4254410Z >>> 2025-08-14T21:38:49.4254646Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-08-14T21:38:49.4254771Z >>> triplet_loss = ( 2025-08-14T21:38:49.4255042Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-08-14T21:38:49.4255197Z >>> output = triplet_loss(anchor, positive, negative) 2025-08-14T21:38:49.4255290Z >>> output.backward() 2025-08-14T21:38:49.4255372Z >>> 2025-08-14T21:38:49.4255485Z >>> # Custom Distance Function (Lambda) 2025-08-14T21:38:49.4255571Z >>> triplet_loss = ( 2025-08-14T21:38:49.4255702Z >>> nn.TripletMarginWithDistanceLoss( 2025-08-14T21:38:49.4255910Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-08-14T21:38:49.4256053Z >>> output = triplet_loss(anchor, positive, negative) 2025-08-14T21:38:49.4256156Z >>> output.backward() 2025-08-14T21:38:49.4256231Z 2025-08-14T21:38:49.4256312Z Reference: 2025-08-14T21:38:49.4256619Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-08-14T21:38:49.4256835Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-08-14T21:38:49.4256918Z 2025-08-14T21:38:49.4257187Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-08-14T21:38:49.4257258Z 2025-08-14T21:38:49.4257357Z warnings.warn(msg) 2025-08-14T21:38:49.4257431Z 2025-08-14T21:38:49.4257621Z --- Parse Warning: 107 / 146 --- 2025-08-14T21:38:49.4258461Z /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. 2025-08-14T21:38:49.4258713Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4258860Z The Connectionist Temporal Classification loss. 2025-08-14T21:38:49.4258939Z 2025-08-14T21:38:49.4259311Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-08-14T21:38:49.4259661Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-08-14T21:38:49.4259989Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-08-14T21:38:49.4260273Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-08-14T21:38:49.4260359Z 2025-08-14T21:38:49.4260435Z Args: 2025-08-14T21:38:49.4260601Z blank (int, optional): blank label. Default :math:`0`. 2025-08-14T21:38:49.4260830Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-08-14T21:38:49.4261006Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-08-14T21:38:49.4261215Z ``'mean'``: the output losses will be divided by the target lengths and 2025-08-14T21:38:49.4261451Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-08-14T21:38:49.4261544Z Default: ``'mean'`` 2025-08-14T21:38:49.4261661Z zero_infinity (bool, optional): 2025-08-14T21:38:49.4261851Z Whether to zero infinite losses and the associated gradients. 2025-08-14T21:38:49.4261947Z Default: ``False`` 2025-08-14T21:38:49.4262123Z Infinite losses mainly occur when the inputs are too short 2025-08-14T21:38:49.4262230Z to be aligned to the targets. 2025-08-14T21:38:49.4262309Z 2025-08-14T21:38:49.4262384Z Shape: 2025-08-14T21:38:49.4262560Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-08-14T21:38:49.4262682Z where :math:`T = \text{input length}`, 2025-08-14T21:38:49.4262845Z :math:`N = \text{batch size}`, and 2025-08-14T21:38:49.4263021Z :math:`C = \text{number of classes (including blank)}`. 2025-08-14T21:38:49.4263241Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-08-14T21:38:49.4263367Z :func:`torch.nn.functional.log_softmax`). 2025-08-14T21:38:49.4263581Z - Targets: Tensor of size :math:`(N, S)` or 2025-08-14T21:38:49.4263734Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-08-14T21:38:49.4263851Z where :math:`N = \text{batch size}` and 2025-08-14T21:38:49.4264019Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-08-14T21:38:49.4264210Z It represents the target sequences. Each element in the target 2025-08-14T21:38:49.4264439Z sequence is a class index. And the target index cannot be blank (default=0). 2025-08-14T21:38:49.4264592Z In the :math:`(N, S)` form, targets are padded to the 2025-08-14T21:38:49.4264726Z length of the longest sequence, and stacked. 2025-08-14T21:38:49.4264916Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-08-14T21:38:49.4265043Z the targets are assumed to be un-padded and 2025-08-14T21:38:49.4265183Z concatenated within 1 dimension. 2025-08-14T21:38:49.4265388Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-08-14T21:38:49.4265578Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-08-14T21:38:49.4265770Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-08-14T21:38:49.4265993Z for each sequence to achieve masking under the assumption that sequences 2025-08-14T21:38:49.4266104Z are padded to equal lengths. 2025-08-14T21:38:49.4266320Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-08-14T21:38:49.4266530Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-08-14T21:38:49.4266746Z Lengths are specified for each sequence to achieve masking under the 2025-08-14T21:38:49.4266999Z assumption that sequences are padded to equal lengths. If target shape is 2025-08-14T21:38:49.4267167Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-08-14T21:38:49.4267426Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-08-14T21:38:49.4267600Z each target in a batch. Lengths must each be :math:`\leq S` 2025-08-14T21:38:49.4267837Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-08-14T21:38:49.4268081Z targets, the target_lengths must add up to the total length of the tensor. 2025-08-14T21:38:49.4268268Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-08-14T21:38:49.4268505Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-08-14T21:38:49.4268716Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-08-14T21:38:49.4268799Z 2025-08-14T21:38:49.4268943Z Examples: 2025-08-14T21:38:49.4269023Z 2025-08-14T21:38:49.4269130Z >>> # Target are to be padded 2025-08-14T21:38:49.4269254Z >>> T = 50 # Input sequence length 2025-08-14T21:38:49.4269387Z >>> C = 20 # Number of classes (including blank) 2025-08-14T21:38:49.4269483Z >>> N = 16 # Batch size 2025-08-14T21:38:49.4269719Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-08-14T21:38:49.4269901Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-08-14T21:38:49.4269983Z >>> 2025-08-14T21:38:49.4270193Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-08-14T21:38:49.4270447Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-08-14T21:38:49.4270563Z >>> 2025-08-14T21:38:49.4270753Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-08-14T21:38:49.4270963Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-08-14T21:38:49.4271059Z >>> 2025-08-14T21:38:49.4271270Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-08-14T21:38:49.4271382Z >>> target_lengths = torch.randint( 2025-08-14T21:38:49.4271475Z ... low=S_min, 2025-08-14T21:38:49.4271556Z ... high=S, 2025-08-14T21:38:49.4271641Z ... size=(N,), 2025-08-14T21:38:49.4271750Z ... dtype=torch.long, 2025-08-14T21:38:49.4271831Z ... ) 2025-08-14T21:38:49.4271943Z >>> ctc_loss = nn.CTCLoss() 2025-08-14T21:38:49.4272126Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-08-14T21:38:49.4272227Z >>> loss.backward() 2025-08-14T21:38:49.4272321Z >>> 2025-08-14T21:38:49.4272401Z >>> 2025-08-14T21:38:49.4272510Z >>> # Target are to be un-padded 2025-08-14T21:38:49.4272657Z >>> T = 50 # Input sequence length 2025-08-14T21:38:49.4272787Z >>> C = 20 # Number of classes (including blank) 2025-08-14T21:38:49.4272882Z >>> N = 16 # Batch size 2025-08-14T21:38:49.4272978Z >>> 2025-08-14T21:38:49.4273173Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-08-14T21:38:49.4273384Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-08-14T21:38:49.4273595Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-08-14T21:38:49.4273674Z >>> 2025-08-14T21:38:49.4273878Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-08-14T21:38:49.4274112Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-08-14T21:38:49.4274218Z >>> target = torch.randint( 2025-08-14T21:38:49.4274318Z ... low=1, 2025-08-14T21:38:49.4274405Z ... high=C, 2025-08-14T21:38:49.4274526Z ... size=(sum(target_lengths),), 2025-08-14T21:38:49.4274627Z ... dtype=torch.long, 2025-08-14T21:38:49.4274708Z ... ) 2025-08-14T21:38:49.4274823Z >>> ctc_loss = nn.CTCLoss() 2025-08-14T21:38:49.4275000Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-08-14T21:38:49.4275095Z >>> loss.backward() 2025-08-14T21:38:49.4275185Z >>> 2025-08-14T21:38:49.4275265Z >>> 2025-08-14T21:38:49.4275444Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-08-14T21:38:49.4275567Z >>> T = 50 # Input sequence length 2025-08-14T21:38:49.4275700Z >>> C = 20 # Number of classes (including blank) 2025-08-14T21:38:49.4275782Z >>> 2025-08-14T21:38:49.4275979Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-08-14T21:38:49.4276112Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-08-14T21:38:49.4276322Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-08-14T21:38:49.4276471Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-08-14T21:38:49.4276554Z >>> 2025-08-14T21:38:49.4276757Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-08-14T21:38:49.4276981Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-08-14T21:38:49.4277083Z >>> target = torch.randint( 2025-08-14T21:38:49.4277178Z ... low=1, 2025-08-14T21:38:49.4277265Z ... high=C, 2025-08-14T21:38:49.4277432Z ... size=(target_lengths,), 2025-08-14T21:38:49.4277554Z ... dtype=torch.long, 2025-08-14T21:38:49.4277636Z ... ) 2025-08-14T21:38:49.4277746Z >>> ctc_loss = nn.CTCLoss() 2025-08-14T21:38:49.4277922Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-08-14T21:38:49.4278018Z >>> loss.backward() 2025-08-14T21:38:49.4278109Z 2025-08-14T21:38:49.4278191Z Reference: 2025-08-14T21:38:49.4278361Z A. Graves et al.: Connectionist Temporal Classification: 2025-08-14T21:38:49.4278593Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-08-14T21:38:49.4278750Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-08-14T21:38:49.4278838Z 2025-08-14T21:38:49.4278918Z Note: 2025-08-14T21:38:49.4279156Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-08-14T21:38:49.4279418Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-08-14T21:38:49.4279634Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-08-14T21:38:49.4279739Z dtype :attr:`torch.int32`. 2025-08-14T21:38:49.4279832Z 2025-08-14T21:38:49.4280124Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-08-14T21:38:49.4280204Z 2025-08-14T21:38:49.4280293Z 2025-08-14T21:38:49.4280373Z Note: 2025-08-14T21:38:49.4280621Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-08-14T21:38:49.4280869Z may select a nondeterministic algorithm to increase performance. If this is 2025-08-14T21:38:49.4281105Z undesirable, you can try to make the operation deterministic (potentially at 2025-08-14T21:38:49.4281347Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-08-14T21:38:49.4281429Z True``. 2025-08-14T21:38:49.4281625Z Please see the notes on :doc:`/notes/randomness` for background. 2025-08-14T21:38:49.4281718Z 2025-08-14T21:38:49.4281963Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4282043Z 2025-08-14T21:38:49.4282147Z warnings.warn(msg) 2025-08-14T21:38:49.4282226Z 2025-08-14T21:38:49.4282448Z --- Parse Warning: 108 / 146 --- 2025-08-14T21:38:49.4283323Z /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. 2025-08-14T21:38:49.4283581Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4283744Z Computes a partial inverse of :class:`MaxPool2d`. 2025-08-14T21:38:49.4283824Z 2025-08-14T21:38:49.4284085Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-08-14T21:38:49.4284181Z 2025-08-14T21:38:49.4284401Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-08-14T21:38:49.4284650Z including the indices of the maximal values and computes a partial inverse 2025-08-14T21:38:49.4284794Z in which all non-maximal values are set to zero. 2025-08-14T21:38:49.4284873Z 2025-08-14T21:38:49.4284976Z Note: 2025-08-14T21:38:49.4285283Z This operation may behave nondeterministically when the input indices has repeat values. 2025-08-14T21:38:49.4285653Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-08-14T21:38:49.4285743Z 2025-08-14T21:38:49.4285967Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-08-14T21:38:49.4286149Z sizes. Hence, the inversion process can get ambiguous. 2025-08-14T21:38:49.4286396Z To accommodate this, you can provide the needed output size 2025-08-14T21:38:49.4286620Z as an additional argument :attr:`output_size` in the forward call. 2025-08-14T21:38:49.4286749Z See the Inputs and Example below. 2025-08-14T21:38:49.4286828Z 2025-08-14T21:38:49.4286909Z Args: 2025-08-14T21:38:49.4287099Z kernel_size (int or tuple): Size of the max pooling window. 2025-08-14T21:38:49.4287264Z stride (int or tuple): Stride of the max pooling window. 2025-08-14T21:38:49.4287395Z It is set to :attr:`kernel_size` by default. 2025-08-14T21:38:49.4287586Z padding (int or tuple): Padding that was added to the input 2025-08-14T21:38:49.4287666Z 2025-08-14T21:38:49.4287762Z Inputs: 2025-08-14T21:38:49.4287878Z - `input`: the input Tensor to invert 2025-08-14T21:38:49.4288076Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-08-14T21:38:49.4288246Z - `output_size` (optional): the targeted output size 2025-08-14T21:38:49.4288324Z 2025-08-14T21:38:49.4288406Z Shape: 2025-08-14T21:38:49.4288595Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-08-14T21:38:49.4288822Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-08-14T21:38:49.4288910Z 2025-08-14T21:38:49.4288993Z .. math:: 2025-08-14T21:38:49.4289255Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-08-14T21:38:49.4289339Z 2025-08-14T21:38:49.4289421Z .. math:: 2025-08-14T21:38:49.4289668Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-08-14T21:38:49.4289755Z 2025-08-14T21:38:49.4289913Z or as given by :attr:`output_size` in the call operator 2025-08-14T21:38:49.4289992Z 2025-08-14T21:38:49.4290094Z Example:: 2025-08-14T21:38:49.4290175Z 2025-08-14T21:38:49.4290332Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-08-14T21:38:49.4290466Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-08-14T21:38:49.4290594Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-08-14T21:38:49.4290713Z [ 5., 6., 7., 8.], 2025-08-14T21:38:49.4290815Z [ 9., 10., 11., 12.], 2025-08-14T21:38:49.4290920Z [13., 14., 15., 16.]]]]) 2025-08-14T21:38:49.4291046Z >>> output, indices = pool(input) 2025-08-14T21:38:49.4291149Z >>> unpool(output, indices) 2025-08-14T21:38:49.4291252Z tensor([[[[ 0., 0., 0., 0.], 2025-08-14T21:38:49.4291362Z [ 0., 6., 0., 8.], 2025-08-14T21:38:49.4291456Z [ 0., 0., 0., 0.], 2025-08-14T21:38:49.4291570Z [ 0., 14., 0., 16.]]]]) 2025-08-14T21:38:49.4291779Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-08-14T21:38:49.4291919Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-08-14T21:38:49.4292043Z [ 6., 7., 8., 9., 10.], 2025-08-14T21:38:49.4292149Z [11., 12., 13., 14., 15.], 2025-08-14T21:38:49.4292263Z [16., 17., 18., 19., 20.]]]]) 2025-08-14T21:38:49.4292387Z >>> output, indices = pool(input) 2025-08-14T21:38:49.4292553Z >>> # This call will not work without specifying output_size 2025-08-14T21:38:49.4292714Z >>> unpool(output, indices, output_size=input.size()) 2025-08-14T21:38:49.4292980Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-08-14T21:38:49.4293123Z [ 0., 7., 0., 9., 0.], 2025-08-14T21:38:49.4293330Z [ 0., 0., 0., 0., 0.], 2025-08-14T21:38:49.4293464Z [ 0., 17., 0., 19., 0.]]]]) 2025-08-14T21:38:49.4293543Z 2025-08-14T21:38:49.4293632Z 2025-08-14T21:38:49.4293712Z 2025-08-14T21:38:49.4293963Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4294056Z 2025-08-14T21:38:49.4294150Z warnings.warn(msg) 2025-08-14T21:38:49.4294231Z 2025-08-14T21:38:49.4294456Z --- Parse Warning: 109 / 146 --- 2025-08-14T21:38:49.4295334Z /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. 2025-08-14T21:38:49.4295600Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4295907Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-08-14T21:38:49.4295986Z 2025-08-14T21:38:49.4296313Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-08-14T21:38:49.4296416Z and with 2D inputs, this class 2025-08-14T21:38:49.4296501Z 2025-08-14T21:38:49.4296833Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-08-14T21:38:49.4297133Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-08-14T21:38:49.4297430Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-08-14T21:38:49.4297502Z 2025-08-14T21:38:49.4297844Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-08-14T21:38:49.4297940Z operations. 2025-08-14T21:38:49.4298017Z 2025-08-14T21:38:49.4298270Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-08-14T21:38:49.4298513Z pass. This scales the output of the Embedding before performing a weighted 2025-08-14T21:38:49.4298753Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-08-14T21:38:49.4298992Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-08-14T21:38:49.4299093Z :attr:`per_sample_weights`. 2025-08-14T21:38:49.4299167Z 2025-08-14T21:38:49.4299253Z Args: 2025-08-14T21:38:49.4299425Z num_embeddings (int): size of the dictionary of embeddings 2025-08-14T21:38:49.4299590Z embedding_dim (int): the size of each embedding vector 2025-08-14T21:38:49.4299906Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-08-14T21:38:49.4300052Z is renormalized to have norm :attr:`max_norm`. 2025-08-14T21:38:49.4300396Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-08-14T21:38:49.4300716Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-08-14T21:38:49.4300872Z the words in the mini-batch. Default ``False``. 2025-08-14T21:38:49.4301063Z Note: this option is not supported when ``mode="max"``. 2025-08-14T21:38:49.4301305Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-08-14T21:38:49.4301530Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-08-14T21:38:49.4301740Z into consideration. ``"mean"`` computes the average of the values 2025-08-14T21:38:49.4301958Z in the bag, ``"max"`` computes the max value over each bag. 2025-08-14T21:38:49.4302105Z Default: ``"mean"`` 2025-08-14T21:38:49.4302417Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-08-14T21:38:49.4302680Z Notes for more details regarding sparse gradients. Note: this option is not 2025-08-14T21:38:49.4302804Z supported when ``mode="max"``. 2025-08-14T21:38:49.4303163Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-08-14T21:38:49.4303481Z is equivalent to the size of `indices`. This matches the CSR format. 2025-08-14T21:38:49.4303807Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-08-14T21:38:49.4304087Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-08-14T21:38:49.4304329Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-08-14T21:38:49.4304591Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-08-14T21:38:49.4304867Z zeros, but can be updated to another value to be used as the padding vector. 2025-08-14T21:38:49.4305106Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-08-14T21:38:49.4305210Z reduction. 2025-08-14T21:38:49.4305295Z 2025-08-14T21:38:49.4305381Z Attributes: 2025-08-14T21:38:49.4305700Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-08-14T21:38:49.4305842Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-08-14T21:38:49.4305921Z 2025-08-14T21:38:49.4306018Z Examples:: 2025-08-14T21:38:49.4306095Z 2025-08-14T21:38:49.4306254Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-08-14T21:38:49.4306413Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-08-14T21:38:49.4306535Z >>> # a batch of 2 samples of 4 indices each 2025-08-14T21:38:49.4306714Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-08-14T21:38:49.4306866Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-08-14T21:38:49.4307005Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-14T21:38:49.4307118Z >>> embedding_sum(input, offsets) 2025-08-14T21:38:49.4307222Z tensor([[-0.8861, -5.4350, -0.0523], 2025-08-14T21:38:49.4307317Z [ 1.1306, -2.5798, -1.0044]]) 2025-08-14T21:38:49.4307403Z 2025-08-14T21:38:49.4307512Z >>> # Example with padding_idx 2025-08-14T21:38:49.4307709Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-08-14T21:38:49.4307896Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-08-14T21:38:49.4308041Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-08-14T21:38:49.4308158Z >>> embedding_sum(input, offsets) 2025-08-14T21:38:49.4308259Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-08-14T21:38:49.4308354Z [-0.7082, 3.2145, -2.6251]]) 2025-08-14T21:38:49.4308438Z 2025-08-14T21:38:49.4308607Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-08-14T21:38:49.4308744Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-08-14T21:38:49.4308900Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-08-14T21:38:49.4309000Z embedding.weight, 2025-08-14T21:38:49.4309172Z padding_idx=embedding.padding_idx, 2025-08-14T21:38:49.4309291Z mode='sum') 2025-08-14T21:38:49.4309366Z 2025-08-14T21:38:49.4309615Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4309690Z 2025-08-14T21:38:49.4309780Z warnings.warn(msg) 2025-08-14T21:38:49.4309860Z 2025-08-14T21:38:49.4310061Z --- Parse Warning: 110 / 146 --- 2025-08-14T21:38:49.4310989Z /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. 2025-08-14T21:38:49.4311250Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4311390Z Take in and process masked source/target sequences. 2025-08-14T21:38:49.4311469Z 2025-08-14T21:38:49.4311553Z .. note:: 2025-08-14T21:38:49.4311632Z 2025-08-14T21:38:49.4312018Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-08-14T21:38:49.4312158Z not allowed to participate in the attention, 2025-08-14T21:38:49.4312374Z which is the opposite of the definition for :attr:`attn_mask` 2025-08-14T21:38:49.4312566Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-08-14T21:38:49.4312644Z 2025-08-14T21:38:49.4312723Z Args: 2025-08-14T21:38:49.4312869Z src: the sequence to the encoder (required). 2025-08-14T21:38:49.4312999Z tgt: the sequence to the decoder (required). 2025-08-14T21:38:49.4313181Z src_mask: the additive mask for the src sequence (optional). 2025-08-14T21:38:49.4313351Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-08-14T21:38:49.4313552Z memory_mask: the additive mask for the encoder output (optional). 2025-08-14T21:38:49.4313783Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-08-14T21:38:49.4314000Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-08-14T21:38:49.4314244Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-08-14T21:38:49.4314452Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-08-14T21:38:49.4314596Z Default: ``None``; try to detect a causal mask. 2025-08-14T21:38:49.4314693Z Warning: 2025-08-14T21:38:49.4314846Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-08-14T21:38:49.4315012Z the causal mask. Providing incorrect hints can result in 2025-08-14T21:38:49.4315179Z incorrect execution, including forward and backward 2025-08-14T21:38:49.4315278Z compatibility. 2025-08-14T21:38:49.4315478Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-08-14T21:38:49.4315626Z Default: ``None``; try to detect a causal mask. 2025-08-14T21:38:49.4315711Z Warning: 2025-08-14T21:38:49.4315867Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-08-14T21:38:49.4316038Z the causal mask. Providing incorrect hints can result in 2025-08-14T21:38:49.4316193Z incorrect execution, including forward and backward 2025-08-14T21:38:49.4316298Z compatibility. 2025-08-14T21:38:49.4316465Z memory_is_causal: If specified, applies a causal mask as 2025-08-14T21:38:49.4316551Z ``memory_mask``. 2025-08-14T21:38:49.4316657Z Default: ``False``. 2025-08-14T21:38:49.4316741Z Warning: 2025-08-14T21:38:49.4316961Z ``memory_is_causal`` provides a hint that 2025-08-14T21:38:49.4317150Z ``memory_mask`` is the causal mask. Providing incorrect 2025-08-14T21:38:49.4317301Z hints can result in incorrect execution, including 2025-08-14T21:38:49.4317433Z forward and backward compatibility. 2025-08-14T21:38:49.4317510Z 2025-08-14T21:38:49.4317587Z Shape: 2025-08-14T21:38:49.4317840Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-08-14T21:38:49.4317953Z `(N, S, E)` if `batch_first=True`. 2025-08-14T21:38:49.4318179Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-08-14T21:38:49.4318304Z `(N, T, E)` if `batch_first=True`. 2025-08-14T21:38:49.4318483Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-08-14T21:38:49.4318674Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-08-14T21:38:49.4318785Z - memory_mask: :math:`(T, S)`. 2025-08-14T21:38:49.4319015Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-08-14T21:38:49.4319255Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-08-14T21:38:49.4319525Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-08-14T21:38:49.4319612Z 2025-08-14T21:38:49.4319915Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-08-14T21:38:49.4320114Z positions. If a BoolTensor is provided, positions with ``True`` 2025-08-14T21:38:49.4320374Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-08-14T21:38:49.4320534Z is provided, it will be added to the attention weight. 2025-08-14T21:38:49.4320827Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-08-14T21:38:49.4321047Z the attention. If a BoolTensor is provided, the positions with the 2025-08-14T21:38:49.4321346Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-08-14T21:38:49.4321429Z 2025-08-14T21:38:49.4321676Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-08-14T21:38:49.4321785Z `(N, T, E)` if `batch_first=True`. 2025-08-14T21:38:49.4321867Z 2025-08-14T21:38:49.4322104Z Note: Due to the multi-head attention architecture in the transformer model, 2025-08-14T21:38:49.4322329Z the output sequence length of a transformer is same as the input sequence 2025-08-14T21:38:49.4322452Z (i.e. target) length of the decoder. 2025-08-14T21:38:49.4322526Z 2025-08-14T21:38:49.4322854Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-08-14T21:38:49.4322985Z batch size, :math:`E` is the feature number 2025-08-14T21:38:49.4323057Z 2025-08-14T21:38:49.4323153Z Examples: 2025-08-14T21:38:49.4323251Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4323358Z >>> output = transformer_model( 2025-08-14T21:38:49.4323507Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-08-14T21:38:49.4323588Z ... ) 2025-08-14T21:38:49.4323664Z 2025-08-14T21:38:49.4323918Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4323991Z 2025-08-14T21:38:49.4324090Z warnings.warn(msg) 2025-08-14T21:38:49.4324167Z 2025-08-14T21:38:49.4324357Z --- Parse Warning: 111 / 146 --- 2025-08-14T21:38:49.4325410Z /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. 2025-08-14T21:38:49.4325685Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4325764Z 2025-08-14T21:38:49.4326001Z Context manager for training with uneven inputs across processes in DDP. 2025-08-14T21:38:49.4326075Z 2025-08-14T21:38:49.4326289Z This context manager will keep track of already-joined DDP processes, 2025-08-14T21:38:49.4326501Z and "shadow" the forward and backward passes by inserting collective 2025-08-14T21:38:49.4326724Z communication operations to match with the ones created by non-joined 2025-08-14T21:38:49.4326959Z DDP processes. This will ensure each collective call has a corresponding 2025-08-14T21:38:49.4327169Z call by already-joined DDP processes, preventing hangs or errors that 2025-08-14T21:38:49.4327367Z would otherwise happen when training with uneven inputs across 2025-08-14T21:38:49.4327600Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-08-14T21:38:49.4327799Z specified to be ``True``, all trainers will throw an error once one rank 2025-08-14T21:38:49.4328021Z runs out of inputs, allowing these errors to be caught and handled 2025-08-14T21:38:49.4328128Z according to application logic. 2025-08-14T21:38:49.4328199Z 2025-08-14T21:38:49.4328417Z Once all DDP processes have joined, the context manager will broadcast 2025-08-14T21:38:49.4328635Z the model corresponding to the last joined process to all processes to 2025-08-14T21:38:49.4328776Z ensure the model is the same across all processes 2025-08-14T21:38:49.4328888Z (which is guaranteed by DDP). 2025-08-14T21:38:49.4328962Z 2025-08-14T21:38:49.4329156Z To use this to enable training with uneven inputs across processes, 2025-08-14T21:38:49.4329380Z simply wrap this context manager around your training loop. No further 2025-08-14T21:38:49.4329548Z modifications to the model or data loading is required. 2025-08-14T21:38:49.4329635Z 2025-08-14T21:38:49.4329722Z .. warning:: 2025-08-14T21:38:49.4329924Z If the model or training loop this context manager is wrapped around 2025-08-14T21:38:49.4330110Z has additional distributed collective operations, such as 2025-08-14T21:38:49.4330289Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-08-14T21:38:49.4330484Z ``throw_on_early_termination`` must be enabled. This is because this 2025-08-14T21:38:49.4330697Z context manager is not aware of non-DDP collective communication. 2025-08-14T21:38:49.4330857Z This flag will cause all ranks to throw when any one rank 2025-08-14T21:38:49.4331065Z exhausts inputs, allowing these errors to be caught and recovered 2025-08-14T21:38:49.4331162Z from across all ranks. 2025-08-14T21:38:49.4331285Z 2025-08-14T21:38:49.4331372Z Args: 2025-08-14T21:38:49.4331545Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-08-14T21:38:49.4331743Z gradients by the initial ``world_size`` DDP training was launched 2025-08-14T21:38:49.4331914Z with. If ``False``, will compute the effective world size 2025-08-14T21:38:49.4332090Z (number of ranks that have not depleted their inputs yet) and 2025-08-14T21:38:49.4332229Z divide gradients by that during allreduce. Set 2025-08-14T21:38:49.4332414Z ``divide_by_initial_world_size=True`` to ensure every input 2025-08-14T21:38:49.4332613Z sample including the uneven inputs have equal weight in terms of 2025-08-14T21:38:49.4332784Z how much they contribute to the global gradient. This is 2025-08-14T21:38:49.4332954Z achieved by always dividing the gradient by the initial 2025-08-14T21:38:49.4333178Z ``world_size`` even when we encounter uneven inputs. If you set 2025-08-14T21:38:49.4333371Z this to ``False``, we divide the gradient by the remaining 2025-08-14T21:38:49.4333554Z number of nodes. This ensures parity with training on a smaller 2025-08-14T21:38:49.4333727Z ``world_size`` although it also means the uneven inputs would 2025-08-14T21:38:49.4333925Z contribute more towards the global gradient. Typically, you 2025-08-14T21:38:49.4334103Z would want to set this to ``True`` for cases where the last few 2025-08-14T21:38:49.4334297Z inputs of your training job are uneven. In extreme cases, where 2025-08-14T21:38:49.4334472Z there is a large discrepancy in the number of inputs, setting 2025-08-14T21:38:49.4334605Z this to ``False`` might provide better results. 2025-08-14T21:38:49.4334812Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-08-14T21:38:49.4334980Z in ``enable=False`` to disable in cases where you know that 2025-08-14T21:38:49.4335161Z inputs are even across participating processes. Default is 2025-08-14T21:38:49.4335250Z ``True``. 2025-08-14T21:38:49.4335425Z throw_on_early_termination (bool): Whether to throw an error 2025-08-14T21:38:49.4335628Z or continue training when at least one rank has exhausted 2025-08-14T21:38:49.4335802Z inputs. If ``True``, will throw upon the first rank reaching end 2025-08-14T21:38:49.4335964Z of data. If ``False``, will continue training with a smaller 2025-08-14T21:38:49.4336155Z effective world size until all ranks are joined. Note that if 2025-08-14T21:38:49.4336273Z this flag is specified, then the flag 2025-08-14T21:38:49.4336436Z ``divide_by_initial_world_size`` would be ignored. Default 2025-08-14T21:38:49.4336532Z is ``False``. 2025-08-14T21:38:49.4336608Z 2025-08-14T21:38:49.4336688Z 2025-08-14T21:38:49.4336775Z Example:: 2025-08-14T21:38:49.4336848Z 2025-08-14T21:38:49.4336966Z >>> # xdoctest: +SKIP("Distributed") 2025-08-14T21:38:49.4337053Z >>> import torch 2025-08-14T21:38:49.4337162Z >>> import torch.distributed as dist 2025-08-14T21:38:49.4337261Z >>> import os 2025-08-14T21:38:49.4337379Z >>> import torch.multiprocessing as mp 2025-08-14T21:38:49.4337472Z >>> import torch.nn as nn 2025-08-14T21:38:49.4337581Z >>> # On each spawned worker 2025-08-14T21:38:49.4337668Z >>> def worker(rank): 2025-08-14T21:38:49.4337839Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-08-14T21:38:49.4337954Z >>> torch.cuda.set_device(rank) 2025-08-14T21:38:49.4338082Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-08-14T21:38:49.4338260Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-08-14T21:38:49.4338398Z >>> model, device_ids=[rank], output_device=rank 2025-08-14T21:38:49.4338477Z >>> ) 2025-08-14T21:38:49.4338614Z >>> # Rank 1 gets one more input than rank 0. 2025-08-14T21:38:49.4338792Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-08-14T21:38:49.4338884Z >>> with model.join(): 2025-08-14T21:38:49.4338990Z >>> for _ in range(5): 2025-08-14T21:38:49.4339087Z >>> for inp in inputs: 2025-08-14T21:38:49.4339192Z >>> loss = model(inp).sum() 2025-08-14T21:38:49.4339299Z >>> loss.backward() 2025-08-14T21:38:49.4339481Z >>> # Without the join() API, the below synchronization will hang 2025-08-14T21:38:49.4339622Z >>> # blocking for rank 1's allreduce to complete. 2025-08-14T21:38:49.4339740Z >>> torch.cuda.synchronize(device=rank) 2025-08-14T21:38:49.4339810Z 2025-08-14T21:38:49.4340061Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4340191Z 2025-08-14T21:38:49.4340302Z warnings.warn(msg) 2025-08-14T21:38:49.4340388Z 2025-08-14T21:38:49.4340578Z --- Parse Warning: 112 / 146 --- 2025-08-14T21:38:49.4341643Z /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. 2025-08-14T21:38:49.4341901Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4341973Z 2025-08-14T21:38:49.4342276Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-08-14T21:38:49.4342351Z 2025-08-14T21:38:49.4342550Z Registers an optimizer with DDP such that the optimization for a 2025-08-14T21:38:49.4342764Z parameter will run immediately when that parameter's gradient is 2025-08-14T21:38:49.4342964Z finished with reduction, instead of waiting for all parameters' 2025-08-14T21:38:49.4343184Z gradients to finish reduction. This can result in a training speedup 2025-08-14T21:38:49.4343478Z depending on your workload since the optimizer can run while gradient 2025-08-14T21:38:49.4343748Z reduction for other parameters are still ongoing. In addition, this has 2025-08-14T21:38:49.4343978Z the potential to reduce peak memory consumption during training, as it 2025-08-14T21:38:49.4344174Z only needs to load the per-parameter optimizer states of a single 2025-08-14T21:38:49.4344377Z parameter at a time, instead of loading all per-parameter optimizer 2025-08-14T21:38:49.4344475Z states at once. 2025-08-14T21:38:49.4344553Z 2025-08-14T21:38:49.4344630Z Args: 2025-08-14T21:38:49.4344830Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-08-14T21:38:49.4344924Z as a fused optimizer. 2025-08-14T21:38:49.4345099Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-08-14T21:38:49.4345308Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-08-14T21:38:49.4345518Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-08-14T21:38:49.4345725Z Optimizers. If this is omitted, all DDP model parameters will be 2025-08-14T21:38:49.4345807Z optimized. 2025-08-14T21:38:49.4345990Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-08-14T21:38:49.4346074Z 2025-08-14T21:38:49.4346157Z .. warning :: 2025-08-14T21:38:49.4346357Z _register_fused_optim should only be called once on a DDP instance, 2025-08-14T21:38:49.4346567Z and registering multiple fused optimizers for the same DDP model 2025-08-14T21:38:49.4346688Z is not currently supported. Please ping 2025-08-14T21:38:49.4346924Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-08-14T21:38:49.4347018Z for your use case. 2025-08-14T21:38:49.4347092Z 2025-08-14T21:38:49.4347186Z .. warning :: 2025-08-14T21:38:49.4347369Z _register_fused_optim and register_comm_hook currently do not 2025-08-14T21:38:49.4347574Z compose together, meaning that custom DDP communication hooks are 2025-08-14T21:38:49.4347749Z not supported with overlapped optimizers. Please ping 2025-08-14T21:38:49.4347965Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-08-14T21:38:49.4348067Z for your use case. 2025-08-14T21:38:49.4348141Z 2025-08-14T21:38:49.4348221Z .. warning :: 2025-08-14T21:38:49.4348448Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-08-14T21:38:49.4348567Z with overlapped optimizer. Please ping 2025-08-14T21:38:49.4348781Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-08-14T21:38:49.4348879Z for your use case. 2025-08-14T21:38:49.4348952Z 2025-08-14T21:38:49.4349083Z Example:: 2025-08-14T21:38:49.4349192Z 2025-08-14T21:38:49.4349314Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-08-14T21:38:49.4349611Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-08-14T21:38:49.4349802Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-08-14T21:38:49.4349885Z >>> lr = 1e-2 2025-08-14T21:38:49.4349986Z >>> betas = (0.9, 0.99) 2025-08-14T21:38:49.4350069Z >>> eps = 1e-6 2025-08-14T21:38:49.4350285Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-08-14T21:38:49.4350408Z >>> # Example with subset of parameters 2025-08-14T21:38:49.4350534Z >>> params_to_opt = [list(net.parameters())[0]] 2025-08-14T21:38:49.4350634Z >>> net._register_fused_optim( 2025-08-14T21:38:49.4350862Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-08-14T21:38:49.4350935Z ... ) 2025-08-14T21:38:49.4351052Z 2025-08-14T21:38:49.4351295Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4351366Z 2025-08-14T21:38:49.4351466Z warnings.warn(msg) 2025-08-14T21:38:49.4351538Z 2025-08-14T21:38:49.4351758Z --- Parse Warning: 113 / 146 --- 2025-08-14T21:38:49.4352745Z /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. 2025-08-14T21:38:49.4352994Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4353213Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-08-14T21:38:49.4353287Z 2025-08-14T21:38:49.4353553Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-08-14T21:38:49.4353833Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-08-14T21:38:49.4354078Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-08-14T21:38:49.4354372Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-08-14T21:38:49.4354461Z 2025-08-14T21:38:49.4354539Z .. note:: 2025-08-14T21:38:49.4354764Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-08-14T21:38:49.4354988Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-08-14T21:38:49.4355188Z layer with 4d weight will be affected by ``model.to``, which does not 2025-08-14T21:38:49.4355420Z necessarily benefit from conversion to specified ``memory_format``. 2025-08-14T21:38:49.4355634Z One place we are confident in is that NHWC(channels_last) conversion for 2025-08-14T21:38:49.4355848Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-08-14T21:38:49.4356053Z even in cases where we have to apply permutation to input tensors. 2025-08-14T21:38:49.4356127Z 2025-08-14T21:38:49.4356350Z Hence our strategy here is to convert only the weight of convolution to 2025-08-14T21:38:49.4356463Z channels_last. This ensures that; 2025-08-14T21:38:49.4356668Z 1. Fast convolution kernels will be used, the benefit of which could 2025-08-14T21:38:49.4356900Z outweigh overhead of permutation (if input is not in the same format). 2025-08-14T21:38:49.4357124Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-08-14T21:38:49.4357231Z from memory_format conversion. 2025-08-14T21:38:49.4357313Z 2025-08-14T21:38:49.4357528Z The optimal case is that, layers between convolution layers are channels 2025-08-14T21:38:49.4357803Z last compatible. Input tensor would be permuted to channels last when it 2025-08-14T21:38:49.4358058Z encounters the first convolution layer and stay in that memory format. 2025-08-14T21:38:49.4358286Z Hence following convolutions will not need to permute its input tensor. 2025-08-14T21:38:49.4358375Z 2025-08-14T21:38:49.4358588Z In case where a channels last incompatible layer is between convolution 2025-08-14T21:38:49.4358789Z layers, we need to permute the input tensor back to contiguous format 2025-08-14T21:38:49.4359007Z for that layer. The input tensor will go through the remaining layers in 2025-08-14T21:38:49.4359222Z contiguous format and be permuted to channels last when it encounters 2025-08-14T21:38:49.4359433Z another convolution layer. There's no point in propagating that 2025-08-14T21:38:49.4359643Z permutation to an earlier layer, as most layers are quite agnostic to 2025-08-14T21:38:49.4359739Z ``memory_format``. 2025-08-14T21:38:49.4359826Z 2025-08-14T21:38:49.4360049Z This claim might change when PyTorch supports fusion of permutation, as 2025-08-14T21:38:49.4360260Z there might have been a better spot to fuse the permutation other than 2025-08-14T21:38:49.4360411Z immediately before a convolution. 2025-08-14T21:38:49.4360488Z 2025-08-14T21:38:49.4360566Z Args: 2025-08-14T21:38:49.4360786Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-08-14T21:38:49.4360890Z ``nn.Module`` 2025-08-14T21:38:49.4361043Z memory_format: user specified ``memory_format``, 2025-08-14T21:38:49.4361217Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-08-14T21:38:49.4361294Z 2025-08-14T21:38:49.4361383Z Returns: 2025-08-14T21:38:49.4361523Z The original module with updated ``nn.Conv2d`` 2025-08-14T21:38:49.4361597Z 2025-08-14T21:38:49.4361689Z Example: 2025-08-14T21:38:49.4361825Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-14T21:38:49.4361971Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-08-14T21:38:49.4362085Z >>> input = torch.randint( 2025-08-14T21:38:49.4362231Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-08-14T21:38:49.4362320Z ... ) 2025-08-14T21:38:49.4362420Z >>> model = nn.Sequential( 2025-08-14T21:38:49.4362531Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-08-14T21:38:49.4362636Z >>> # This is identical to: 2025-08-14T21:38:49.4362872Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-08-14T21:38:49.4363035Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-08-14T21:38:49.4363153Z ... model, torch.channels_last 2025-08-14T21:38:49.4363234Z ... ) 2025-08-14T21:38:49.4363329Z >>> out = model(input) 2025-08-14T21:38:49.4363417Z 2025-08-14T21:38:49.4363665Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4363748Z 2025-08-14T21:38:49.4363841Z warnings.warn(msg) 2025-08-14T21:38:49.4363921Z 2025-08-14T21:38:49.4364121Z --- Parse Warning: 114 / 146 --- 2025-08-14T21:38:49.4365095Z /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. 2025-08-14T21:38:49.4365349Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4365567Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-08-14T21:38:49.4365835Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-08-14T21:38:49.4366158Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-08-14T21:38:49.4366426Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-08-14T21:38:49.4366725Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-08-14T21:38:49.4366813Z 2025-08-14T21:38:49.4366897Z .. note:: 2025-08-14T21:38:49.4367133Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-08-14T21:38:49.4367351Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-08-14T21:38:49.4367552Z layer with 4d weight will be affected by ``model.to``, which does not 2025-08-14T21:38:49.4367776Z necessarily benefit from conversion to specified ``memory_format``. 2025-08-14T21:38:49.4368004Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-08-14T21:38:49.4368223Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-08-14T21:38:49.4368426Z even in cases where we have to apply permutation to input tensors. 2025-08-14T21:38:49.4368500Z 2025-08-14T21:38:49.4368717Z Hence our strategy here is to convert only the weight of convolution to 2025-08-14T21:38:49.4368863Z channels_last_3d. This ensures that; 2025-08-14T21:38:49.4369072Z 1. Fast convolution kernels will be used, the benefit of which could 2025-08-14T21:38:49.4369305Z outweigh overhead of permutation (if input is not in the same format). 2025-08-14T21:38:49.4369530Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-08-14T21:38:49.4369637Z from memory_format conversion. 2025-08-14T21:38:49.4369723Z 2025-08-14T21:38:49.4369942Z The optimal case is that, layers between convolution layers are channels 2025-08-14T21:38:49.4370176Z last compatible. Input tensor would be permuted to channels last when it 2025-08-14T21:38:49.4370401Z encounters the first convolution layer and stay in that memory format. 2025-08-14T21:38:49.4370632Z Hence following convolutions will not need to permute its input tensor. 2025-08-14T21:38:49.4370712Z 2025-08-14T21:38:49.4370926Z In case where a channels last incompatible layer is between convolution 2025-08-14T21:38:49.4371130Z layers, we need to permute the input tensor back to contiguous format 2025-08-14T21:38:49.4371351Z for that layer. The input tensor will go through the remaining layers in 2025-08-14T21:38:49.4371567Z contiguous format and be permuted to channels last when it encounters 2025-08-14T21:38:49.4371770Z another convolution layer. There's no point in propagating that 2025-08-14T21:38:49.4371980Z permutation to an earlier layer, as most layers are quite agnostic to 2025-08-14T21:38:49.4372076Z ``memory_format``. 2025-08-14T21:38:49.4372165Z 2025-08-14T21:38:49.4372388Z This claim might change when PyTorch supports fusion of permutation, as 2025-08-14T21:38:49.4372600Z there might have been a better spot to fuse the permutation other than 2025-08-14T21:38:49.4372729Z immediately before a convolution. 2025-08-14T21:38:49.4372805Z 2025-08-14T21:38:49.4372886Z Args: 2025-08-14T21:38:49.4373096Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-08-14T21:38:49.4373194Z ``nn.Module`` 2025-08-14T21:38:49.4373342Z memory_format: user specified ``memory_format``, 2025-08-14T21:38:49.4373525Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-08-14T21:38:49.4373603Z 2025-08-14T21:38:49.4373694Z Returns: 2025-08-14T21:38:49.4373827Z The original module with updated ``nn.Conv3d`` 2025-08-14T21:38:49.4373951Z 2025-08-14T21:38:49.4374077Z Example: 2025-08-14T21:38:49.4374208Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-14T21:38:49.4374356Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-08-14T21:38:49.4374462Z >>> input = torch.randint( 2025-08-14T21:38:49.4374608Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-08-14T21:38:49.4374690Z ... ) 2025-08-14T21:38:49.4374796Z >>> model = nn.Sequential( 2025-08-14T21:38:49.4374905Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-08-14T21:38:49.4375013Z >>> # This is identical to: 2025-08-14T21:38:49.4375261Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-08-14T21:38:49.4375424Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-08-14T21:38:49.4375549Z ... model, torch.channels_last_3d 2025-08-14T21:38:49.4375632Z ... ) 2025-08-14T21:38:49.4375730Z >>> out = model(input) 2025-08-14T21:38:49.4375816Z 2025-08-14T21:38:49.4376063Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4376141Z 2025-08-14T21:38:49.4376263Z warnings.warn(msg) 2025-08-14T21:38:49.4376339Z 2025-08-14T21:38:49.4376536Z --- Parse Warning: 115 / 146 --- 2025-08-14T21:38:49.4377481Z /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. 2025-08-14T21:38:49.4377736Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4377894Z Register a parametrization to a tensor in a module. 2025-08-14T21:38:49.4377971Z 2025-08-14T21:38:49.4378245Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-08-14T21:38:49.4378524Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-08-14T21:38:49.4378779Z If the original tensor requires a gradient, the backward pass will differentiate 2025-08-14T21:38:49.4379076Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-08-14T21:38:49.4379152Z 2025-08-14T21:38:49.4379455Z The first time that a module registers a parametrization, this function will add an attribute 2025-08-14T21:38:49.4379701Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-08-14T21:38:49.4379777Z 2025-08-14T21:38:49.4380020Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-08-14T21:38:49.4380153Z ``module.parametrizations.weight``. 2025-08-14T21:38:49.4380228Z 2025-08-14T21:38:49.4380370Z The original tensor will be accessible under 2025-08-14T21:38:49.4380516Z ``module.parametrizations.weight.original``. 2025-08-14T21:38:49.4380591Z 2025-08-14T21:38:49.4380853Z Parametrizations may be concatenated by registering several parametrizations 2025-08-14T21:38:49.4380948Z on the same attribute. 2025-08-14T21:38:49.4381027Z 2025-08-14T21:38:49.4381268Z The training mode of a registered parametrization is updated on registration 2025-08-14T21:38:49.4381396Z to match the training mode of the host module 2025-08-14T21:38:49.4381472Z 2025-08-14T21:38:49.4381775Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-08-14T21:38:49.4381897Z using the context manager :func:`cached`. 2025-08-14T21:38:49.4381982Z 2025-08-14T21:38:49.4382214Z A :attr:`parametrization` may optionally implement a method with signature 2025-08-14T21:38:49.4382290Z 2025-08-14T21:38:49.4382400Z .. code-block:: python 2025-08-14T21:38:49.4382476Z 2025-08-14T21:38:49.4382733Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-08-14T21:38:49.4382845Z 2025-08-14T21:38:49.4383101Z This method is called on the unparametrized tensor when the first parametrization 2025-08-14T21:38:49.4383307Z is registered to compute the initial value of the original tensor. 2025-08-14T21:38:49.4383702Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-08-14T21:38:49.4383778Z 2025-08-14T21:38:49.4384091Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-08-14T21:38:49.4384372Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-08-14T21:38:49.4384448Z 2025-08-14T21:38:49.4384676Z It is possible for the first parametrization to depend on several inputs. 2025-08-14T21:38:49.4384914Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-08-14T21:38:49.4385145Z (see the example implementation of a ``RankOne`` parametrization below). 2025-08-14T21:38:49.4385229Z 2025-08-14T21:38:49.4385556Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-08-14T21:38:49.4385725Z with names ``original0``, ``original1``,... 2025-08-14T21:38:49.4385801Z 2025-08-14T21:38:49.4385886Z .. note:: 2025-08-14T21:38:49.4385971Z 2025-08-14T21:38:49.4386229Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-08-14T21:38:49.4386370Z once to perform a number of consistency checks. 2025-08-14T21:38:49.4386640Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-08-14T21:38:49.4386756Z and nothing will be called otherwise. 2025-08-14T21:38:49.4386836Z 2025-08-14T21:38:49.4386921Z .. note:: 2025-08-14T21:38:49.4386996Z 2025-08-14T21:38:49.4387212Z In most situations, ``right_inverse`` will be a function such that 2025-08-14T21:38:49.4387326Z ``forward(right_inverse(X)) == X`` (see 2025-08-14T21:38:49.4387605Z `right inverse `_). 2025-08-14T21:38:49.4387858Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-08-14T21:38:49.4387946Z to relax this. 2025-08-14T21:38:49.4388022Z 2025-08-14T21:38:49.4388114Z .. warning:: 2025-08-14T21:38:49.4388191Z 2025-08-14T21:38:49.4388457Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-08-14T21:38:49.4388718Z will register a number of new parameters. If such parametrization is registered 2025-08-14T21:38:49.4388982Z after the optimizer is created, these new parameters will need to be added manually 2025-08-14T21:38:49.4389184Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-08-14T21:38:49.4389262Z 2025-08-14T21:38:49.4389343Z Args: 2025-08-14T21:38:49.4389559Z module (nn.Module): module on which to register the parametrization 2025-08-14T21:38:49.4389765Z tensor_name (str): name of the parameter or buffer on which to register 2025-08-14T21:38:49.4389867Z the parametrization 2025-08-14T21:38:49.4390075Z parametrization (nn.Module): the parametrization to register 2025-08-14T21:38:49.4390160Z Keyword args: 2025-08-14T21:38:49.4390379Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-08-14T21:38:49.4390566Z may change the dtype and shape of the tensor. Default: `False` 2025-08-14T21:38:49.4390826Z Warning: the parametrization is not checked for consistency upon registration. 2025-08-14T21:38:49.4390953Z Enable this flag at your own risk. 2025-08-14T21:38:49.4391029Z 2025-08-14T21:38:49.4391158Z Raises: 2025-08-14T21:38:49.4391468Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-08-14T21:38:49.4391544Z 2025-08-14T21:38:49.4391626Z Examples: 2025-08-14T21:38:49.4391779Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-08-14T21:38:49.4391870Z >>> import torch 2025-08-14T21:38:49.4391982Z >>> import torch.nn as nn 2025-08-14T21:38:49.4392113Z >>> import torch.nn.utils.parametrize as P 2025-08-14T21:38:49.4392192Z >>> 2025-08-14T21:38:49.4392306Z >>> class Symmetric(nn.Module): 2025-08-14T21:38:49.4392405Z >>> def forward(self, X): 2025-08-14T21:38:49.4392579Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-08-14T21:38:49.4392666Z >>> 2025-08-14T21:38:49.4392779Z >>> def right_inverse(self, A): 2025-08-14T21:38:49.4393022Z >>> return A.triu() 2025-08-14T21:38:49.4393113Z >>> 2025-08-14T21:38:49.4393206Z >>> m = nn.Linear(5, 5) 2025-08-14T21:38:49.4393379Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-08-14T21:38:49.4393614Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-08-14T21:38:49.4393761Z True 2025-08-14T21:38:49.4393868Z >>> A = torch.rand(5, 5) 2025-08-14T21:38:49.4393978Z >>> A = A + A.T # A is now symmetric 2025-08-14T21:38:49.4394172Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-08-14T21:38:49.4394302Z >>> print(torch.allclose(m.weight, A)) 2025-08-14T21:38:49.4394383Z True 2025-08-14T21:38:49.4394455Z 2025-08-14T21:38:49.4394568Z >>> class RankOne(nn.Module): 2025-08-14T21:38:49.4394676Z >>> def forward(self, x, y): 2025-08-14T21:38:49.4394825Z >>> # Form a rank 1 matrix multiplying two vectors 2025-08-14T21:38:49.4394956Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-08-14T21:38:49.4395036Z >>> 2025-08-14T21:38:49.4395156Z >>> def right_inverse(self, Z): 2025-08-14T21:38:49.4395275Z >>> # Project Z onto the rank 1 matrices 2025-08-14T21:38:49.4395425Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-08-14T21:38:49.4395549Z >>> # Return rescaled singular vectors 2025-08-14T21:38:49.4395665Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-08-14T21:38:49.4395805Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-08-14T21:38:49.4395896Z >>> 2025-08-14T21:38:49.4396039Z >>> linear_rank_one = P.register_parametrization( 2025-08-14T21:38:49.4396161Z ... nn.Linear(4, 4), "weight", RankOne() 2025-08-14T21:38:49.4396239Z ... ) 2025-08-14T21:38:49.4396436Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-08-14T21:38:49.4396521Z 1 2025-08-14T21:38:49.4396599Z 2025-08-14T21:38:49.4396675Z 2025-08-14T21:38:49.4396936Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4397018Z 2025-08-14T21:38:49.4397106Z warnings.warn(msg) 2025-08-14T21:38:49.4397197Z 2025-08-14T21:38:49.4397406Z --- Parse Warning: 116 / 146 --- 2025-08-14T21:38:49.4398271Z /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. 2025-08-14T21:38:49.4398528Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4398828Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-08-14T21:38:49.4398915Z 2025-08-14T21:38:49.4399214Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-08-14T21:38:49.4399453Z by removing the specified ``amount`` of (currently unpruned) channels 2025-08-14T21:38:49.4399634Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-08-14T21:38:49.4399823Z Modifies module in place (and also return the modified module) 2025-08-14T21:38:49.4399910Z by: 2025-08-14T21:38:49.4399987Z 2025-08-14T21:38:49.4400186Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-08-14T21:38:49.4400406Z binary mask applied to the parameter ``name`` by the pruning method. 2025-08-14T21:38:49.4400608Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-08-14T21:38:49.4400804Z original (unpruned) parameter is stored in a new parameter named 2025-08-14T21:38:49.4400904Z ``name+'_orig'``. 2025-08-14T21:38:49.4400981Z 2025-08-14T21:38:49.4401061Z Args: 2025-08-14T21:38:49.4401242Z module (nn.Module): module containing the tensor to prune 2025-08-14T21:38:49.4401422Z name (str): parameter name within ``module`` on which pruning 2025-08-14T21:38:49.4401517Z will act. 2025-08-14T21:38:49.4401708Z amount (int or float): quantity of parameters to prune. 2025-08-14T21:38:49.4401879Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-08-14T21:38:49.4402084Z fraction of parameters to prune. If ``int``, it represents the 2025-08-14T21:38:49.4402212Z absolute number of parameters to prune. 2025-08-14T21:38:49.4402393Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-08-14T21:38:49.4402553Z entries for argument ``p`` in :func:`torch.norm`. 2025-08-14T21:38:49.4402744Z dim (int): index of the dim along which we define channels to prune. 2025-08-14T21:38:49.4402983Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-08-14T21:38:49.4403166Z shape as module parameter) used to compute mask for pruning. 2025-08-14T21:38:49.4403382Z The values in this tensor indicate the importance of the corresponding 2025-08-14T21:38:49.4403516Z elements in the parameter being pruned. 2025-08-14T21:38:49.4403734Z If unspecified or None, the module parameter will be used in its place. 2025-08-14T21:38:49.4403812Z 2025-08-14T21:38:49.4403901Z Returns: 2025-08-14T21:38:49.4404111Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-08-14T21:38:49.4404193Z 2025-08-14T21:38:49.4404272Z Examples: 2025-08-14T21:38:49.4404384Z >>> from torch.nn.utils import prune 2025-08-14T21:38:49.4404489Z >>> m = prune.ln_structured( 2025-08-14T21:38:49.4404669Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-08-14T21:38:49.4404749Z ... ) 2025-08-14T21:38:49.4404834Z 2025-08-14T21:38:49.4405078Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4405152Z 2025-08-14T21:38:49.4405252Z warnings.warn(msg) 2025-08-14T21:38:49.4405330Z 2025-08-14T21:38:49.4405523Z --- Parse Warning: 117 / 146 --- 2025-08-14T21:38:49.4406419Z /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. 2025-08-14T21:38:49.4406671Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4406754Z 2025-08-14T21:38:49.4407173Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-08-14T21:38:49.4407249Z 2025-08-14T21:38:49.4407419Z Modifies modules in place by: 2025-08-14T21:38:49.4407520Z 2025-08-14T21:38:49.4407722Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-08-14T21:38:49.4407938Z binary mask applied to the parameter ``name`` by the pruning method. 2025-08-14T21:38:49.4408142Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-08-14T21:38:49.4408356Z original (unpruned) parameter is stored in a new parameter named 2025-08-14T21:38:49.4408443Z ``name+'_orig'``. 2025-08-14T21:38:49.4408521Z 2025-08-14T21:38:49.4408610Z Args: 2025-08-14T21:38:49.4408804Z parameters (Iterable of (module, name) tuples): parameters of 2025-08-14T21:38:49.4408986Z the model to prune in a global fashion, i.e. by aggregating all 2025-08-14T21:38:49.4409192Z weights prior to deciding which ones to prune. module must be of 2025-08-14T21:38:49.4409337Z type :class:`nn.Module`, and name must be a string. 2025-08-14T21:38:49.4409568Z pruning_method (function): a valid pruning function from this module, 2025-08-14T21:38:49.4409739Z or a custom one implemented by the user that satisfies the 2025-08-14T21:38:49.4409962Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-08-14T21:38:49.4410217Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-08-14T21:38:49.4410431Z the corresponding parameter's importance scores tensor. The tensor 2025-08-14T21:38:49.4410635Z should be the same shape as the parameter, and is used for computing 2025-08-14T21:38:49.4410739Z mask for pruning. 2025-08-14T21:38:49.4410938Z If unspecified or None, the parameter will be used in place of its 2025-08-14T21:38:49.4411043Z importance scores. 2025-08-14T21:38:49.4411159Z kwargs: other keyword arguments such as: 2025-08-14T21:38:49.4411348Z amount (int or float): quantity of parameters to prune across the 2025-08-14T21:38:49.4411456Z specified parameters. 2025-08-14T21:38:49.4411619Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-08-14T21:38:49.4411810Z fraction of parameters to prune. If ``int``, it represents the 2025-08-14T21:38:49.4411947Z absolute number of parameters to prune. 2025-08-14T21:38:49.4412024Z 2025-08-14T21:38:49.4412104Z Raises: 2025-08-14T21:38:49.4412259Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-08-14T21:38:49.4412336Z 2025-08-14T21:38:49.4412424Z Note: 2025-08-14T21:38:49.4412630Z Since global structured pruning doesn't make much sense unless the 2025-08-14T21:38:49.4412821Z norm is normalized by the size of the parameter, we now limit the 2025-08-14T21:38:49.4412974Z scope of global pruning to unstructured methods. 2025-08-14T21:38:49.4413052Z 2025-08-14T21:38:49.4413134Z Examples: 2025-08-14T21:38:49.4413262Z >>> from torch.nn.utils import prune 2025-08-14T21:38:49.4413378Z >>> from collections import OrderedDict 2025-08-14T21:38:49.4413472Z >>> net = nn.Sequential( 2025-08-14T21:38:49.4413571Z ... OrderedDict( 2025-08-14T21:38:49.4413649Z ... [ 2025-08-14T21:38:49.4413765Z ... ("first", nn.Linear(10, 4)), 2025-08-14T21:38:49.4413886Z ... ("second", nn.Linear(4, 1)), 2025-08-14T21:38:49.4413968Z ... ] 2025-08-14T21:38:49.4414056Z ... ) 2025-08-14T21:38:49.4414129Z ... ) 2025-08-14T21:38:49.4414229Z >>> parameters_to_prune = ( 2025-08-14T21:38:49.4414338Z ... (net.first, "weight"), 2025-08-14T21:38:49.4414434Z ... (net.second, "weight"), 2025-08-14T21:38:49.4414512Z ... ) 2025-08-14T21:38:49.4414627Z >>> prune.global_unstructured( 2025-08-14T21:38:49.4414723Z ... parameters_to_prune, 2025-08-14T21:38:49.4414895Z ... pruning_method=prune.L1Unstructured, 2025-08-14T21:38:49.4415012Z ... amount=10, 2025-08-14T21:38:49.4415089Z ... ) 2025-08-14T21:38:49.4415316Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-08-14T21:38:49.4415396Z tensor(10) 2025-08-14T21:38:49.4415473Z 2025-08-14T21:38:49.4415554Z 2025-08-14T21:38:49.4415795Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4415870Z 2025-08-14T21:38:49.4415972Z warnings.warn(msg) 2025-08-14T21:38:49.4416045Z 2025-08-14T21:38:49.4416234Z --- Parse Warning: 118 / 146 --- 2025-08-14T21:38:49.4417104Z /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. 2025-08-14T21:38:49.4417357Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4417755Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-08-14T21:38:49.4417832Z 2025-08-14T21:38:49.4418037Z Modifies module in place (and also return the modified module) by: 2025-08-14T21:38:49.4418148Z 2025-08-14T21:38:49.4418348Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-08-14T21:38:49.4418562Z binary mask applied to the parameter ``name`` by the pruning method. 2025-08-14T21:38:49.4418779Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-08-14T21:38:49.4418978Z original (unpruned) parameter is stored in a new parameter named 2025-08-14T21:38:49.4419076Z ``name+'_orig'``. 2025-08-14T21:38:49.4419152Z 2025-08-14T21:38:49.4419228Z Args: 2025-08-14T21:38:49.4419412Z module (nn.Module): module containing the tensor to prune 2025-08-14T21:38:49.4419589Z name (str): parameter name within ``module`` on which pruning 2025-08-14T21:38:49.4419674Z will act. 2025-08-14T21:38:49.4419849Z mask (Tensor): binary mask to be applied to the parameter. 2025-08-14T21:38:49.4419922Z 2025-08-14T21:38:49.4420003Z Returns: 2025-08-14T21:38:49.4420221Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-08-14T21:38:49.4420294Z 2025-08-14T21:38:49.4420376Z Examples: 2025-08-14T21:38:49.4420499Z >>> from torch.nn.utils import prune 2025-08-14T21:38:49.4420602Z >>> m = prune.custom_from_mask( 2025-08-14T21:38:49.4420783Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-08-14T21:38:49.4420858Z ... ) 2025-08-14T21:38:49.4420951Z >>> print(m.bias_mask) 2025-08-14T21:38:49.4421057Z tensor([0., 1., 0.]) 2025-08-14T21:38:49.4421136Z 2025-08-14T21:38:49.4421217Z 2025-08-14T21:38:49.4421477Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4421557Z 2025-08-14T21:38:49.4421651Z warnings.warn(msg) 2025-08-14T21:38:49.4421740Z 2025-08-14T21:38:49.4421923Z --- Parse Warning: 119 / 146 --- 2025-08-14T21:38:49.4422804Z /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. 2025-08-14T21:38:49.4423060Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4423203Z Pad a packed batch of variable length sequences. 2025-08-14T21:38:49.4423293Z 2025-08-14T21:38:49.4423543Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-08-14T21:38:49.4423621Z 2025-08-14T21:38:49.4423903Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-08-14T21:38:49.4424223Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-08-14T21:38:49.4424350Z sequence and ``B`` is the batch size. 2025-08-14T21:38:49.4424429Z 2025-08-14T21:38:49.4424513Z Example: 2025-08-14T21:38:49.4424763Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-08-14T21:38:49.4424908Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-08-14T21:38:49.4425004Z >>> lens = [2, 1, 3] 2025-08-14T21:38:49.4425131Z >>> packed = pack_padded_sequence( 2025-08-14T21:38:49.4425280Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-08-14T21:38:49.4425360Z ... ) 2025-08-14T21:38:49.4425455Z >>> packed 2025-08-14T21:38:49.4425690Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-08-14T21:38:49.4425913Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-08-14T21:38:49.4426156Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-08-14T21:38:49.4426248Z >>> seq_unpacked 2025-08-14T21:38:49.4426374Z tensor([[1, 2, 0], 2025-08-14T21:38:49.4426461Z [3, 0, 0], 2025-08-14T21:38:49.4426544Z [4, 5, 6]]) 2025-08-14T21:38:49.4426646Z >>> lens_unpacked 2025-08-14T21:38:49.4426736Z tensor([2, 1, 3]) 2025-08-14T21:38:49.4426815Z 2025-08-14T21:38:49.4426910Z .. note:: 2025-08-14T21:38:49.4427055Z :attr:`total_length` is useful to implement the 2025-08-14T21:38:49.4427283Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-08-14T21:38:49.4427494Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-08-14T21:38:49.4427724Z See :ref:`this FAQ section ` for 2025-08-14T21:38:49.4427816Z details. 2025-08-14T21:38:49.4427895Z 2025-08-14T21:38:49.4427973Z Args: 2025-08-14T21:38:49.4428107Z sequence (PackedSequence): batch to pad 2025-08-14T21:38:49.4428340Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-08-14T21:38:49.4428452Z format, ``T x B x *`` otherwise. 2025-08-14T21:38:49.4428648Z padding_value (float, optional): values for padded elements. 2025-08-14T21:38:49.4428869Z total_length (int, optional): if not ``None``, the output will be padded to 2025-08-14T21:38:49.4429110Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-08-14T21:38:49.4429289Z if :attr:`total_length` is less than the max sequence length in 2025-08-14T21:38:49.4429384Z :attr:`sequence`. 2025-08-14T21:38:49.4429471Z 2025-08-14T21:38:49.4429551Z Returns: 2025-08-14T21:38:49.4429737Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-08-14T21:38:49.4429936Z containing the list of lengths of each sequence in the batch. 2025-08-14T21:38:49.4430154Z Batch elements will be re-ordered as they were ordered originally when 2025-08-14T21:38:49.4430378Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-08-14T21:38:49.4430454Z 2025-08-14T21:38:49.4430696Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4430784Z 2025-08-14T21:38:49.4430879Z warnings.warn(msg) 2025-08-14T21:38:49.4430957Z 2025-08-14T21:38:49.4431159Z --- Parse Warning: 120 / 146 --- 2025-08-14T21:38:49.4432079Z /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=808. 2025-08-14T21:38:49.4432376Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4432693Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-08-14T21:38:49.4432773Z 2025-08-14T21:38:49.4433144Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-08-14T21:38:49.4433354Z intervals by which each scheduler should be called at a given epoch. 2025-08-14T21:38:49.4433430Z 2025-08-14T21:38:49.4433526Z Args: 2025-08-14T21:38:49.4433657Z optimizer (Optimizer): Wrapped optimizer. 2025-08-14T21:38:49.4433798Z schedulers (list): List of chained schedulers. 2025-08-14T21:38:49.4434013Z milestones (list): List of integers that reflects milestone points. 2025-08-14T21:38:49.4434170Z last_epoch (int): The index of last epoch. Default: -1. 2025-08-14T21:38:49.4434262Z 2025-08-14T21:38:49.4434348Z Example: 2025-08-14T21:38:49.4434444Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4434603Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-08-14T21:38:49.4434705Z >>> # lr = 0.005 if epoch == 0 2025-08-14T21:38:49.4434835Z >>> # lr = 0.005 if epoch == 1 2025-08-14T21:38:49.4434939Z >>> # lr = 0.005 if epoch == 2 2025-08-14T21:38:49.4435018Z >>> # ... 2025-08-14T21:38:49.4435120Z >>> # lr = 0.05 if epoch == 20 2025-08-14T21:38:49.4435230Z >>> # lr = 0.045 if epoch == 21 2025-08-14T21:38:49.4435328Z >>> # lr = 0.0405 if epoch == 22 2025-08-14T21:38:49.4435534Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-08-14T21:38:49.4435686Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-08-14T21:38:49.4435795Z >>> scheduler = SequentialLR( 2025-08-14T21:38:49.4435901Z ... optimizer, 2025-08-14T21:38:49.4436030Z ... schedulers=[scheduler1, scheduler2], 2025-08-14T21:38:49.4436125Z ... milestones=[20], 2025-08-14T21:38:49.4436216Z ... ) 2025-08-14T21:38:49.4436317Z >>> for epoch in range(100): 2025-08-14T21:38:49.4436410Z >>> train(...) 2025-08-14T21:38:49.4436511Z >>> validate(...) 2025-08-14T21:38:49.4436608Z >>> scheduler.step() 2025-08-14T21:38:49.4436695Z 2025-08-14T21:38:49.4436866Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-08-14T21:38:49.4436947Z 2025-08-14T21:38:49.4437198Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4437275Z 2025-08-14T21:38:49.4437366Z warnings.warn(msg) 2025-08-14T21:38:49.4437450Z 2025-08-14T21:38:49.4437635Z --- Parse Warning: 121 / 146 --- 2025-08-14T21:38:49.4438545Z /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=1233. 2025-08-14T21:38:49.4438795Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-14T21:38:49.4438971Z Reduce learning rate when a metric has stopped improving. 2025-08-14T21:38:49.4439056Z 2025-08-14T21:38:49.4439251Z Models often benefit from reducing the learning rate by a factor 2025-08-14T21:38:49.4439433Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-08-14T21:38:49.4439627Z quantity and if no improvement is seen for a 'patience' number 2025-08-14T21:38:49.4439744Z of epochs, the learning rate is reduced. 2025-08-14T21:38:49.4439822Z 2025-08-14T21:38:49.4439911Z Args: 2025-08-14T21:38:49.4440042Z optimizer (Optimizer): Wrapped optimizer. 2025-08-14T21:38:49.4440264Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-08-14T21:38:49.4440439Z be reduced when the quantity monitored has stopped 2025-08-14T21:38:49.4440595Z decreasing; in `max` mode it will be reduced when the 2025-08-14T21:38:49.4440793Z quantity monitored has stopped increasing. Default: 'min'. 2025-08-14T21:38:49.4440956Z factor (float): Factor by which the learning rate will be 2025-08-14T21:38:49.4441091Z reduced. new_lr = lr * factor. Default: 0.1. 2025-08-14T21:38:49.4441313Z patience (int): The number of allowed epochs with no improvement after 2025-08-14T21:38:49.4441437Z which the learning rate will be reduced. 2025-08-14T21:38:49.4441673Z For example, consider the case of having no patience (`patience = 0`). 2025-08-14T21:38:49.4442027Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-08-14T21:38:49.4442234Z In the second epoch, if the performance is worse than the baseline, 2025-08-14T21:38:49.4442393Z we have what is considered an intolerable epoch. 2025-08-14T21:38:49.4442652Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-08-14T21:38:49.4442878Z the learning rate is reduced at the end of this epoch. 2025-08-14T21:38:49.4443199Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-08-14T21:38:49.4443507Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-08-14T21:38:49.4443633Z the learning rate is not adjusted. 2025-08-14T21:38:49.4443724Z Default: 10. 2025-08-14T21:38:49.4443905Z threshold (float): Threshold for measuring the new optimum, 2025-08-14T21:38:49.4444072Z to only focus on significant changes. Default: 1e-4. 2025-08-14T21:38:49.4444237Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-08-14T21:38:49.4444401Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-08-14T21:38:49.4444538Z mode or best * ( 1 - threshold ) in `min` mode. 2025-08-14T21:38:49.4444691Z In `abs` mode, dynamic_threshold = best + threshold in 2025-08-14T21:38:49.4444871Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-08-14T21:38:49.4445033Z cooldown (int): Number of epochs to wait before resuming 2025-08-14T21:38:49.4445200Z normal operation after lr has been reduced. Default: 0. 2025-08-14T21:38:49.4445362Z min_lr (float or list): A scalar or a list of scalars. A 2025-08-14T21:38:49.4445517Z lower bound on the learning rate of all param groups 2025-08-14T21:38:49.4445652Z or each group respectively. Default: 0. 2025-08-14T21:38:49.4445823Z eps (float): Minimal decay applied to lr. If the difference 2025-08-14T21:38:49.4445994Z between new and old lr is smaller than eps, the update is 2025-08-14T21:38:49.4446108Z ignored. Default: 1e-8. 2025-08-14T21:38:49.4446184Z 2025-08-14T21:38:49.4446268Z Example: 2025-08-14T21:38:49.4446373Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4446598Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-08-14T21:38:49.4446749Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-08-14T21:38:49.4446854Z >>> for epoch in range(10): 2025-08-14T21:38:49.4446945Z >>> train(...) 2025-08-14T21:38:49.4447065Z >>> val_loss = validate(...) 2025-08-14T21:38:49.4447212Z >>> # Note that step should be called after validate() 2025-08-14T21:38:49.4447323Z >>> scheduler.step(val_loss) 2025-08-14T21:38:49.4447413Z 2025-08-14T21:38:49.4447648Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-08-14T21:38:49.4447754Z 2025-08-14T21:38:49.4448165Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-08-14T21:38:49.4448243Z 2025-08-14T21:38:49.4448348Z scheduler.step(val_loss) 2025-08-14T21:38:49.4448438Z ^ 2025-08-14T21:38:49.4448532Z warnings.warn(msg) 2025-08-14T21:38:49.4448610Z 2025-08-14T21:38:49.4448807Z --- Parse Warning: 122 / 146 --- 2025-08-14T21:38:49.4449667Z /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=1430. 2025-08-14T21:38:49.4449933Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4450250Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-08-14T21:38:49.4450329Z 2025-08-14T21:38:49.4450614Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-08-14T21:38:49.4450863Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-08-14T21:38:49.4451128Z The distance between the two boundaries can be scaled on a per-iteration 2025-08-14T21:38:49.4451222Z or per-cycle basis. 2025-08-14T21:38:49.4451299Z 2025-08-14T21:38:49.4451547Z Cyclical learning rate policy changes the learning rate after every batch. 2025-08-14T21:38:49.4456885Z `step` should be called after a batch has been used for training. 2025-08-14T21:38:49.4457002Z 2025-08-14T21:38:49.4457227Z This class has three built-in policies, as put forth in the paper: 2025-08-14T21:38:49.4457304Z 2025-08-14T21:38:49.4457511Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-08-14T21:38:49.4457813Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-08-14T21:38:49.4458130Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-08-14T21:38:49.4458244Z at each cycle iteration. 2025-08-14T21:38:49.4458320Z 2025-08-14T21:38:49.4458555Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-08-14T21:38:49.4458638Z 2025-08-14T21:38:49.4458720Z Args: 2025-08-14T21:38:49.4458864Z optimizer (Optimizer): Wrapped optimizer. 2025-08-14T21:38:49.4459037Z base_lr (float or list): Initial learning rate which is the 2025-08-14T21:38:49.4459202Z lower boundary in the cycle for each parameter group. 2025-08-14T21:38:49.4459402Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-08-14T21:38:49.4459530Z for each parameter group. Functionally, 2025-08-14T21:38:49.4459688Z it defines the cycle amplitude (max_lr - base_lr). 2025-08-14T21:38:49.4459819Z The lr at any cycle is the sum of base_lr 2025-08-14T21:38:49.4459954Z and some scaling of the amplitude; therefore 2025-08-14T21:38:49.4460105Z max_lr may not actually be reached depending on 2025-08-14T21:38:49.4460198Z scaling function. 2025-08-14T21:38:49.4460361Z step_size_up (int): Number of training iterations in the 2025-08-14T21:38:49.4460498Z increasing half of a cycle. Default: 2000 2025-08-14T21:38:49.4460668Z step_size_down (int): Number of training iterations in the 2025-08-14T21:38:49.4460831Z decreasing half of a cycle. If step_size_down is None, 2025-08-14T21:38:49.4460961Z it is set to step_size_up. Default: None 2025-08-14T21:38:49.4461121Z mode (str): One of {triangular, triangular2, exp_range}. 2025-08-14T21:38:49.4461381Z Values correspond to policies detailed above. 2025-08-14T21:38:49.4461561Z If scale_fn is not None, this argument is ignored. 2025-08-14T21:38:49.4461667Z Default: 'triangular' 2025-08-14T21:38:49.4461838Z gamma (float): Constant in 'exp_range' scaling function: 2025-08-14T21:38:49.4461945Z gamma**(cycle iterations) 2025-08-14T21:38:49.4462035Z Default: 1.0 2025-08-14T21:38:49.4462230Z scale_fn (function): Custom scaling policy defined by a single 2025-08-14T21:38:49.4462345Z argument lambda function, where 2025-08-14T21:38:49.4462459Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-08-14T21:38:49.4462584Z If specified, then 'mode' is ignored. 2025-08-14T21:38:49.4462677Z Default: None 2025-08-14T21:38:49.4462810Z scale_mode (str): {'cycle', 'iterations'}. 2025-08-14T21:38:49.4462938Z Defines whether scale_fn is evaluated on 2025-08-14T21:38:49.4463072Z cycle number or cycle iterations (training 2025-08-14T21:38:49.4463196Z iterations since start of cycle). 2025-08-14T21:38:49.4463290Z Default: 'cycle' 2025-08-14T21:38:49.4463585Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-08-14T21:38:49.4463811Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-08-14T21:38:49.4463901Z Default: True 2025-08-14T21:38:49.4464111Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-08-14T21:38:49.4464320Z for each parameter group. Note that momentum is cycled inversely 2025-08-14T21:38:49.4464477Z to learning rate; at the peak of a cycle, momentum is 2025-08-14T21:38:49.4464626Z 'base_momentum' and learning rate is 'max_lr'. 2025-08-14T21:38:49.4464716Z Default: 0.8 2025-08-14T21:38:49.4464927Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-08-14T21:38:49.4465065Z for each parameter group. Functionally, 2025-08-14T21:38:49.4465255Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-08-14T21:38:49.4465434Z The momentum at any cycle is the difference of max_momentum 2025-08-14T21:38:49.4465578Z and some scaling of the amplitude; therefore 2025-08-14T21:38:49.4465745Z base_momentum may not actually be reached depending on 2025-08-14T21:38:49.4465933Z scaling function. Note that momentum is cycled inversely 2025-08-14T21:38:49.4466137Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-08-14T21:38:49.4466247Z and learning rate is 'base_lr' 2025-08-14T21:38:49.4466345Z Default: 0.9 2025-08-14T21:38:49.4466560Z last_epoch (int): The index of the last batch. This parameter is used when 2025-08-14T21:38:49.4466774Z resuming a training job. Since `step()` should be invoked after each 2025-08-14T21:38:49.4466994Z batch instead of after each epoch, this number represents the total 2025-08-14T21:38:49.4467207Z number of *batches* computed, not the total number of epochs computed. 2025-08-14T21:38:49.4467407Z When last_epoch=-1, the schedule is started from the beginning. 2025-08-14T21:38:49.4467497Z Default: -1 2025-08-14T21:38:49.4467572Z 2025-08-14T21:38:49.4467662Z Example: 2025-08-14T21:38:49.4467758Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4467979Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-08-14T21:38:49.4468137Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-08-14T21:38:49.4468228Z ... optimizer, 2025-08-14T21:38:49.4468330Z ... base_lr=0.01, 2025-08-14T21:38:49.4468465Z ... max_lr=0.1, 2025-08-14T21:38:49.4468588Z ... step_size_up=10, 2025-08-14T21:38:49.4468675Z ... ) 2025-08-14T21:38:49.4468821Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-08-14T21:38:49.4468919Z >>> for epoch in range(10): 2025-08-14T21:38:49.4469040Z >>> for batch in data_loader: 2025-08-14T21:38:49.4469134Z >>> train_batch(...) 2025-08-14T21:38:49.4469232Z >>> scheduler.step() 2025-08-14T21:38:49.4469316Z 2025-08-14T21:38:49.4469473Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-08-14T21:38:49.4469548Z 2025-08-14T21:38:49.4469852Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-08-14T21:38:49.4470011Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-08-14T21:38:49.4470098Z 2025-08-14T21:38:49.4470340Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4470420Z 2025-08-14T21:38:49.4470525Z warnings.warn(msg) 2025-08-14T21:38:49.4470599Z 2025-08-14T21:38:49.4470834Z --- Parse Warning: 123 / 146 --- 2025-08-14T21:38:49.4471816Z /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=1722. 2025-08-14T21:38:49.4472113Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4472371Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-08-14T21:38:49.4472444Z 2025-08-14T21:38:49.4472617Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-08-14T21:38:49.4472857Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-08-14T21:38:49.4472991Z of epochs between two warm restarts in SGDR: 2025-08-14T21:38:49.4473069Z 2025-08-14T21:38:49.4473165Z .. math:: 2025-08-14T21:38:49.4473339Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-08-14T21:38:49.4473487Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-08-14T21:38:49.4473564Z 2025-08-14T21:38:49.4473723Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-08-14T21:38:49.4473916Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-08-14T21:38:49.4473991Z 2025-08-14T21:38:49.4474089Z It has been proposed in 2025-08-14T21:38:49.4474261Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-08-14T21:38:49.4474335Z 2025-08-14T21:38:49.4474414Z Args: 2025-08-14T21:38:49.4474556Z optimizer (Optimizer): Wrapped optimizer. 2025-08-14T21:38:49.4474710Z T_0 (int): Number of iterations until the first restart. 2025-08-14T21:38:49.4475002Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-08-14T21:38:49.4475181Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-08-14T21:38:49.4475383Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-08-14T21:38:49.4475470Z 2025-08-14T21:38:49.4475632Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-08-14T21:38:49.4475746Z https://arxiv.org/abs/1608.03983 2025-08-14T21:38:49.4475831Z 2025-08-14T21:38:49.4475912Z Example: 2025-08-14T21:38:49.4476006Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4476195Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-08-14T21:38:49.4476413Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-08-14T21:38:49.4476519Z ... optimizer, T_0=20 2025-08-14T21:38:49.4476596Z ... ) 2025-08-14T21:38:49.4476745Z >>> for epoch in range(100): 2025-08-14T21:38:49.4476870Z >>> train(...) 2025-08-14T21:38:49.4476958Z >>> validate(...) 2025-08-14T21:38:49.4477054Z >>> scheduler.step() 2025-08-14T21:38:49.4477140Z 2025-08-14T21:38:49.4477371Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-08-14T21:38:49.4477452Z 2025-08-14T21:38:49.4477705Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4477779Z 2025-08-14T21:38:49.4477883Z warnings.warn(msg) 2025-08-14T21:38:49.4477958Z 2025-08-14T21:38:49.4478146Z --- Parse Warning: 124 / 146 --- 2025-08-14T21:38:49.4479033Z /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=1872. 2025-08-14T21:38:49.4479289Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4479584Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-08-14T21:38:49.4479673Z 2025-08-14T21:38:49.4479957Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-08-14T21:38:49.4480279Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-08-14T21:38:49.4480394Z lower than the initial learning rate. 2025-08-14T21:38:49.4480607Z This policy was initially described in the paper `Super-Convergence: 2025-08-14T21:38:49.4480822Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-08-14T21:38:49.4480896Z 2025-08-14T21:38:49.4481131Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-08-14T21:38:49.4481328Z `step` should be called after a batch has been used for training. 2025-08-14T21:38:49.4481406Z 2025-08-14T21:38:49.4481513Z This scheduler is not chainable. 2025-08-14T21:38:49.4481600Z 2025-08-14T21:38:49.4481827Z Note also that the total number of steps in the cycle can be determined in one 2025-08-14T21:38:49.4481973Z of two ways (listed in order of precedence): 2025-08-14T21:38:49.4482051Z 2025-08-14T21:38:49.4482192Z #. A value for total_steps is explicitly provided. 2025-08-14T21:38:49.4482381Z #. A number of epochs (epochs) and a number of steps per epoch 2025-08-14T21:38:49.4482489Z (steps_per_epoch) are provided. 2025-08-14T21:38:49.4482643Z In this case, the number of total steps is inferred by 2025-08-14T21:38:49.4482778Z total_steps = epochs * steps_per_epoch 2025-08-14T21:38:49.4482852Z 2025-08-14T21:38:49.4483087Z You must either provide a value for total_steps or provide a value for both 2025-08-14T21:38:49.4483208Z epochs and steps_per_epoch. 2025-08-14T21:38:49.4483284Z 2025-08-14T21:38:49.4483595Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-08-14T21:38:49.4483876Z claims that "unpublished work has shown even better results by using only two phases". To 2025-08-14T21:38:49.4484113Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-08-14T21:38:49.4484200Z 2025-08-14T21:38:49.4484282Z Args: 2025-08-14T21:38:49.4484413Z optimizer (Optimizer): Wrapped optimizer. 2025-08-14T21:38:49.4484617Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-08-14T21:38:49.4484718Z for each parameter group. 2025-08-14T21:38:49.4484928Z total_steps (int): The total number of steps in the cycle. Note that 2025-08-14T21:38:49.4485131Z if a value is not provided here, then it must be inferred by providing 2025-08-14T21:38:49.4485252Z a value for epochs and steps_per_epoch. 2025-08-14T21:38:49.4485404Z Default: None 2025-08-14T21:38:49.4485617Z epochs (int): The number of epochs to train for. This is used along 2025-08-14T21:38:49.4485851Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-08-14T21:38:49.4485997Z if a value for total_steps is not provided. 2025-08-14T21:38:49.4486083Z Default: None 2025-08-14T21:38:49.4486307Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-08-14T21:38:49.4486529Z used along with epochs in order to infer the total number of steps in the 2025-08-14T21:38:49.4486671Z cycle if a value for total_steps is not provided. 2025-08-14T21:38:49.4486770Z Default: None 2025-08-14T21:38:49.4486987Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-08-14T21:38:49.4487094Z increasing the learning rate. 2025-08-14T21:38:49.4487198Z Default: 0.3 2025-08-14T21:38:49.4487317Z anneal_strategy (str): {'cos', 'linear'} 2025-08-14T21:38:49.4487566Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-08-14T21:38:49.4487692Z linear annealing. 2025-08-14T21:38:49.4487780Z Default: 'cos' 2025-08-14T21:38:49.4487982Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-08-14T21:38:49.4488161Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-08-14T21:38:49.4488248Z Default: True 2025-08-14T21:38:49.4488466Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-08-14T21:38:49.4488663Z for each parameter group. Note that momentum is cycled inversely 2025-08-14T21:38:49.4488819Z to learning rate; at the peak of a cycle, momentum is 2025-08-14T21:38:49.4488973Z 'base_momentum' and learning rate is 'max_lr'. 2025-08-14T21:38:49.4489063Z Default: 0.85 2025-08-14T21:38:49.4489280Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-08-14T21:38:49.4489407Z for each parameter group. Functionally, 2025-08-14T21:38:49.4489595Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-08-14T21:38:49.4489727Z Note that momentum is cycled inversely 2025-08-14T21:38:49.4489927Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-08-14T21:38:49.4490033Z and learning rate is 'base_lr' 2025-08-14T21:38:49.4490135Z Default: 0.95 2025-08-14T21:38:49.4490310Z div_factor (float): Determines the initial learning rate via 2025-08-14T21:38:49.4490425Z initial_lr = max_lr/div_factor 2025-08-14T21:38:49.4490512Z Default: 25 2025-08-14T21:38:49.4490713Z final_div_factor (float): Determines the minimum learning rate via 2025-08-14T21:38:49.4490845Z min_lr = initial_lr/final_div_factor 2025-08-14T21:38:49.4490933Z Default: 1e4 2025-08-14T21:38:49.4491168Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-08-14T21:38:49.4491424Z learning rate according to 'final_div_factor' instead of modifying the second 2025-08-14T21:38:49.4491653Z phase (the first two phases will be symmetrical about the step indicated by 2025-08-14T21:38:49.4491753Z 'pct_start'). 2025-08-14T21:38:49.4491966Z last_epoch (int): The index of the last batch. This parameter is used when 2025-08-14T21:38:49.4492172Z resuming a training job. Since `step()` should be invoked after each 2025-08-14T21:38:49.4492391Z batch instead of after each epoch, this number represents the total 2025-08-14T21:38:49.4492655Z number of *batches* computed, not the total number of epochs computed. 2025-08-14T21:38:49.4493059Z When last_epoch=-1, the schedule is started from the beginning. 2025-08-14T21:38:49.4493165Z Default: -1 2025-08-14T21:38:49.4493242Z 2025-08-14T21:38:49.4493338Z Example: 2025-08-14T21:38:49.4493437Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4493584Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-08-14T21:38:49.4493820Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-08-14T21:38:49.4493975Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-08-14T21:38:49.4494176Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-08-14T21:38:49.4494269Z ... ) 2025-08-14T21:38:49.4494368Z >>> for epoch in range(10): 2025-08-14T21:38:49.4494473Z >>> for batch in data_loader: 2025-08-14T21:38:49.4494587Z >>> train_batch(...) 2025-08-14T21:38:49.4494687Z >>> optimizer.step() 2025-08-14T21:38:49.4494788Z >>> scheduler.step() 2025-08-14T21:38:49.4494866Z 2025-08-14T21:38:49.4495040Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-08-14T21:38:49.4495172Z 2025-08-14T21:38:49.4495451Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-08-14T21:38:49.4495582Z https://arxiv.org/abs/1708.07120 2025-08-14T21:38:49.4495657Z 2025-08-14T21:38:49.4495912Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4495988Z 2025-08-14T21:38:49.4496080Z warnings.warn(msg) 2025-08-14T21:38:49.4496171Z 2025-08-14T21:38:49.4496376Z --- Parse Warning: 125 / 146 --- 2025-08-14T21:38:49.4497288Z /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=867. 2025-08-14T21:38:49.4497552Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4497649Z Load the optimizer state. 2025-08-14T21:38:49.4497738Z 2025-08-14T21:38:49.4497819Z Args: 2025-08-14T21:38:49.4498009Z state_dict (dict): optimizer state. Should be an object returned 2025-08-14T21:38:49.4498140Z from a call to :meth:`state_dict`. 2025-08-14T21:38:49.4498214Z 2025-08-14T21:38:49.4498298Z .. warning:: 2025-08-14T21:38:49.4498643Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-08-14T21:38:49.4498847Z as calling it beforehand will overwrite the loaded learning rates. 2025-08-14T21:38:49.4498919Z 2025-08-14T21:38:49.4499008Z .. note:: 2025-08-14T21:38:49.4499290Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-08-14T21:38:49.4499469Z in :meth:`state_dict`) will not affect the loading process. 2025-08-14T21:38:49.4499782Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-08-14T21:38:49.4499929Z differ from those initialized in the optimizer), 2025-08-14T21:38:49.4500235Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-08-14T21:38:49.4500323Z accordingly. 2025-08-14T21:38:49.4500602Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-08-14T21:38:49.4500922Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-08-14T21:38:49.4501077Z the optimizer ``param_names`` will remain unchanged. 2025-08-14T21:38:49.4501229Z 2025-08-14T21:38:49.4501357Z Example: 2025-08-14T21:38:49.4501454Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4501577Z >>> model = torch.nn.Linear(10, 10) 2025-08-14T21:38:49.4501736Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-08-14T21:38:49.4501887Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-08-14T21:38:49.4501986Z ... optim, 2025-08-14T21:38:49.4502080Z ... start_factor=0.1, 2025-08-14T21:38:49.4502182Z ... end_factor=1, 2025-08-14T21:38:49.4502276Z ... total_iters=20, 2025-08-14T21:38:49.4502351Z ... ) 2025-08-14T21:38:49.4502543Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-08-14T21:38:49.4502631Z ... optim, 2025-08-14T21:38:49.4502715Z ... T_max=80, 2025-08-14T21:38:49.4502821Z ... eta_min=3e-5, 2025-08-14T21:38:49.4502903Z ... ) 2025-08-14T21:38:49.4503041Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-08-14T21:38:49.4503136Z ... optim, 2025-08-14T21:38:49.4503262Z ... schedulers=[scheduler1, scheduler2], 2025-08-14T21:38:49.4503443Z ... milestones=[20], 2025-08-14T21:38:49.4503539Z ... ) 2025-08-14T21:38:49.4503679Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-08-14T21:38:49.4503886Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-08-14T21:38:49.4504048Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-08-14T21:38:49.4504122Z 2025-08-14T21:38:49.4504206Z 2025-08-14T21:38:49.4504446Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4504520Z 2025-08-14T21:38:49.4504624Z warnings.warn(msg) 2025-08-14T21:38:49.4504701Z 2025-08-14T21:38:49.4504899Z --- Parse Warning: 126 / 146 --- 2025-08-14T21:38:49.4505775Z /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. 2025-08-14T21:38:49.4506026Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4506390Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-08-14T21:38:49.4506465Z 2025-08-14T21:38:49.4506698Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-08-14T21:38:49.4506918Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-08-14T21:38:49.4507125Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-08-14T21:38:49.4507221Z (UAI 2018). 2025-08-14T21:38:49.4507304Z 2025-08-14T21:38:49.4507513Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-08-14T21:38:49.4507761Z but using exponential weights instead of equal weights across iterations. 2025-08-14T21:38:49.4507836Z 2025-08-14T21:38:49.4508067Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-08-14T21:38:49.4508295Z on the device :attr:`device` and allows to compute running averages of the 2025-08-14T21:38:49.4508400Z parameters of the :attr:`model`. 2025-08-14T21:38:49.4508476Z 2025-08-14T21:38:49.4508567Z Args: 2025-08-14T21:38:49.4508712Z model (torch.nn.Module): model to use with SWA/EMA 2025-08-14T21:38:49.4508950Z device (torch.device, optional): if provided, the averaged model will be 2025-08-14T21:38:49.4509055Z stored on the :attr:`device` 2025-08-14T21:38:49.4509307Z avg_fn (function, optional): the averaging function used to update 2025-08-14T21:38:49.4509537Z parameters; the function must take in the current value of the 2025-08-14T21:38:49.4509751Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-08-14T21:38:49.4509938Z parameter, and the number of models already averaged; if None, 2025-08-14T21:38:49.4510103Z an equally weighted average is used (default: None) 2025-08-14T21:38:49.4510317Z multi_avg_fn (function, optional): the averaging function used to update 2025-08-14T21:38:49.4510557Z parameters inplace; the function must take in the current values of the 2025-08-14T21:38:49.4510816Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-08-14T21:38:49.4511034Z parameters as a list, and the number of models already averaged; if None, 2025-08-14T21:38:49.4511196Z an equally weighted average is used (default: None) 2025-08-14T21:38:49.4511398Z use_buffers (bool): if ``True``, it will compute running averages for 2025-08-14T21:38:49.4511612Z both the parameters and the buffers of the model. (default: ``False``) 2025-08-14T21:38:49.4511696Z 2025-08-14T21:38:49.4511774Z Example: 2025-08-14T21:38:49.4511937Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:49.4512059Z >>> loader, optimizer, model, loss_fn = ... 2025-08-14T21:38:49.4512222Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-08-14T21:38:49.4512447Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-08-14T21:38:49.4512552Z >>> T_max=300) 2025-08-14T21:38:49.4512640Z >>> swa_start = 160 2025-08-14T21:38:49.4512784Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-08-14T21:38:49.4512879Z >>> for i in range(300): 2025-08-14T21:38:49.4513001Z >>> for input, target in loader: 2025-08-14T21:38:49.4513111Z >>> optimizer.zero_grad() 2025-08-14T21:38:49.4513236Z >>> loss_fn(model(input), target).backward() 2025-08-14T21:38:49.4513343Z >>> optimizer.step() 2025-08-14T21:38:49.4513436Z >>> if i > swa_start: 2025-08-14T21:38:49.4513555Z >>> swa_model.update_parameters(model) 2025-08-14T21:38:49.4513672Z >>> swa_scheduler.step() 2025-08-14T21:38:49.4513755Z >>> else: 2025-08-14T21:38:49.4513850Z >>> scheduler.step() 2025-08-14T21:38:49.4513938Z >>> 2025-08-14T21:38:49.4514084Z >>> # Update bn statistics for the swa_model at the end 2025-08-14T21:38:49.4514251Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-08-14T21:38:49.4514325Z 2025-08-14T21:38:49.4514618Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-08-14T21:38:49.4514822Z If no averaging function is provided, the default is to compute 2025-08-14T21:38:49.4514965Z equally-weighted average of the weights (SWA). 2025-08-14T21:38:49.4515036Z 2025-08-14T21:38:49.4515129Z Example: 2025-08-14T21:38:49.4515254Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-14T21:38:49.4515462Z >>> # Compute exponential moving averages of the weights and buffers 2025-08-14T21:38:49.4515624Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-08-14T21:38:49.4515833Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-08-14T21:38:49.4515909Z 2025-08-14T21:38:49.4515992Z .. note:: 2025-08-14T21:38:49.4516211Z When using SWA/EMA with models containing Batch Normalization you may 2025-08-14T21:38:49.4516408Z need to update the activation statistics for Batch Normalization. 2025-08-14T21:38:49.4516678Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-08-14T21:38:49.4516924Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-08-14T21:38:49.4517157Z statistics in a post-training step by passing data through the model. The 2025-08-14T21:38:49.4517391Z second does it during the parameter update phase by averaging all buffers. 2025-08-14T21:38:49.4517626Z Empirical evidence has shown that updating the statistics in normalization 2025-08-14T21:38:49.4517841Z layers increases accuracy, but you may wish to empirically test which 2025-08-14T21:38:49.4517995Z approach yields the best results in your problem. 2025-08-14T21:38:49.4518070Z 2025-08-14T21:38:49.4518147Z .. note:: 2025-08-14T21:38:49.4518398Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-08-14T21:38:49.4518470Z 2025-08-14T21:38:49.4518552Z .. note:: 2025-08-14T21:38:49.4518747Z When :meth:`update_parameters` is called for the first time (i.e. 2025-08-14T21:38:49.4518920Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-08-14T21:38:49.4519122Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-08-14T21:38:49.4519322Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-08-14T21:38:49.4519425Z to update the parameters. 2025-08-14T21:38:49.4519504Z 2025-08-14T21:38:49.4519714Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-08-14T21:38:49.4519832Z https://arxiv.org/abs/1803.05407 2025-08-14T21:38:49.4520069Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-08-14T21:38:49.4520148Z Average: 2025-08-14T21:38:49.4520265Z https://arxiv.org/abs/1806.05594 2025-08-14T21:38:49.4520461Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-08-14T21:38:49.4520573Z https://arxiv.org/abs/1904.11943 2025-08-14T21:38:49.4520790Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-08-14T21:38:49.4520883Z Generalizes Well: 2025-08-14T21:38:49.4520994Z https://arxiv.org/abs/2001.02312 2025-08-14T21:38:49.4521093Z .. _Polyak averaging: 2025-08-14T21:38:49.4521258Z https://paperswithcode.com/method/polyak-averaging 2025-08-14T21:38:49.4521332Z 2025-08-14T21:38:49.4521580Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4521653Z 2025-08-14T21:38:49.4521751Z warnings.warn(msg) 2025-08-14T21:38:49.4521826Z 2025-08-14T21:38:49.4522015Z --- Parse Warning: 127 / 146 --- 2025-08-14T21:38:49.4522852Z /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. 2025-08-14T21:38:49.4523106Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4523311Z Anneals the learning rate in each parameter group to a fixed value. 2025-08-14T21:38:49.4523391Z 2025-08-14T21:38:49.4523610Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-08-14T21:38:49.4523821Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-08-14T21:38:49.4523892Z 2025-08-14T21:38:49.4523969Z Args: 2025-08-14T21:38:49.4524135Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-08-14T21:38:49.4524330Z swa_lrs (float or list): the learning rate value for all param groups 2025-08-14T21:38:49.4524451Z together or separately for each group. 2025-08-14T21:38:49.4524649Z annealing_epochs (int): number of epochs in the annealing phase 2025-08-14T21:38:49.4524788Z (default: 10) 2025-08-14T21:38:49.4525016Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-08-14T21:38:49.4525221Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-08-14T21:38:49.4525314Z (default: "cos") 2025-08-14T21:38:49.4525486Z last_epoch (int): the index of the last epoch (default: -1) 2025-08-14T21:38:49.4525557Z 2025-08-14T21:38:49.4525725Z The :class:`SWALR` scheduler can be used together with other 2025-08-14T21:38:49.4525938Z schedulers to switch to a constant learning rate late in the training 2025-08-14T21:38:49.4526030Z as in the example below. 2025-08-14T21:38:49.4526105Z 2025-08-14T21:38:49.4526189Z Example: 2025-08-14T21:38:49.4526312Z >>> # xdoctest: +SKIP("Undefined variables") 2025-08-14T21:38:49.4526419Z >>> loader, optimizer, model = ... 2025-08-14T21:38:49.4526535Z >>> lr_lambda = lambda epoch: 0.9 2025-08-14T21:38:49.4526748Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-08-14T21:38:49.4526850Z >>> lr_lambda=lr_lambda) 2025-08-14T21:38:49.4527011Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-08-14T21:38:49.4527205Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-08-14T21:38:49.4527301Z >>> swa_start = 160 2025-08-14T21:38:49.4527390Z >>> for i in range(300): 2025-08-14T21:38:49.4527497Z >>> for input, target in loader: 2025-08-14T21:38:49.4527606Z >>> optimizer.zero_grad() 2025-08-14T21:38:49.4527736Z >>> loss_fn(model(input), target).backward() 2025-08-14T21:38:49.4527839Z >>> optimizer.step() 2025-08-14T21:38:49.4527928Z >>> if i > swa_start: 2025-08-14T21:38:49.4528030Z >>> swa_scheduler.step() 2025-08-14T21:38:49.4528117Z >>> else: 2025-08-14T21:38:49.4528216Z >>> scheduler.step() 2025-08-14T21:38:49.4528289Z 2025-08-14T21:38:49.4528508Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-08-14T21:38:49.4528623Z https://arxiv.org/abs/1803.05407 2025-08-14T21:38:49.4528696Z 2025-08-14T21:38:49.4528940Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4529012Z 2025-08-14T21:38:49.4529099Z warnings.warn(msg) 2025-08-14T21:38:49.4529177Z 2025-08-14T21:38:49.4529363Z --- Parse Warning: 128 / 146 --- 2025-08-14T21:38:49.4530247Z /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. 2025-08-14T21:38:49.4530502Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4530643Z Asserts that ``actual`` and ``expected`` are close. 2025-08-14T21:38:49.4530723Z 2025-08-14T21:38:49.4531077Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-08-14T21:38:49.4531152Z 2025-08-14T21:38:49.4531240Z .. math:: 2025-08-14T21:38:49.4531314Z 2025-08-14T21:38:49.4531668Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-08-14T21:38:49.4531742Z 2025-08-14T21:38:49.4532076Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-08-14T21:38:49.4532279Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-08-14T21:38:49.4532351Z 2025-08-14T21:38:49.4532539Z In addition, they are only considered close if they have the same 2025-08-14T21:38:49.4532679Z 2025-08-14T21:38:49.4532883Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-08-14T21:38:49.4533005Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-08-14T21:38:49.4533147Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-08-14T21:38:49.4533268Z - stride (if ``check_stride`` is ``True``). 2025-08-14T21:38:49.4533346Z 2025-08-14T21:38:49.4533635Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-08-14T21:38:49.4533707Z 2025-08-14T21:38:49.4534057Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-08-14T21:38:49.4534416Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-08-14T21:38:49.4534632Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-08-14T21:38:49.4535022Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-08-14T21:38:49.4535098Z 2025-08-14T21:38:49.4535377Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-08-14T21:38:49.4535745Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-08-14T21:38:49.4535833Z definition above. 2025-08-14T21:38:49.4535913Z 2025-08-14T21:38:49.4536203Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-08-14T21:38:49.4536572Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-08-14T21:38:49.4536925Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-08-14T21:38:49.4537292Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-08-14T21:38:49.4537523Z their elements are considered close according to the above definition. 2025-08-14T21:38:49.4537597Z 2025-08-14T21:38:49.4537676Z .. note:: 2025-08-14T21:38:49.4537760Z 2025-08-14T21:38:49.4538079Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-08-14T21:38:49.4538392Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-08-14T21:38:49.4538663Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-08-14T21:38:49.4538734Z 2025-08-14T21:38:49.4538819Z Args: 2025-08-14T21:38:49.4538919Z actual (Any): Actual input. 2025-08-14T21:38:49.4539028Z expected (Any): Expected input. 2025-08-14T21:38:49.4539383Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-08-14T21:38:49.4539536Z are allowed. Otherwise type equality is required. 2025-08-14T21:38:49.4539897Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-08-14T21:38:49.4540152Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-08-14T21:38:49.4540500Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-08-14T21:38:49.4540754Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-08-14T21:38:49.4540988Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-08-14T21:38:49.4541262Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-08-14T21:38:49.4541555Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-08-14T21:38:49.4541801Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-08-14T21:38:49.4542144Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-08-14T21:38:49.4542480Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-08-14T21:38:49.4542633Z :func:`torch.promote_types`) before being compared. 2025-08-14T21:38:49.4542982Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-08-14T21:38:49.4543302Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-08-14T21:38:49.4543476Z compared. 2025-08-14T21:38:49.4543829Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-08-14T21:38:49.4544170Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-08-14T21:38:49.4544523Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-08-14T21:38:49.4544663Z should return the new message. 2025-08-14T21:38:49.4544746Z 2025-08-14T21:38:49.4544823Z Raises: 2025-08-14T21:38:49.4545045Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-08-14T21:38:49.4545210Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-08-14T21:38:49.4545528Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-08-14T21:38:49.4545883Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-08-14T21:38:49.4545991Z different types. 2025-08-14T21:38:49.4546338Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-08-14T21:38:49.4546701Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-08-14T21:38:49.4547005Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-08-14T21:38:49.4547291Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-08-14T21:38:49.4547410Z :attr:`~torch.Tensor.layout`. 2025-08-14T21:38:49.4547620Z AssertionError: If only one of corresponding tensors is quantized. 2025-08-14T21:38:49.4548005Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-08-14T21:38:49.4548289Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-08-14T21:38:49.4548398Z :attr:`~torch.Tensor.device`. 2025-08-14T21:38:49.4548725Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-08-14T21:38:49.4549070Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-08-14T21:38:49.4549417Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-08-14T21:38:49.4549497Z 2025-08-14T21:38:49.4549846Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-08-14T21:38:49.4549992Z ``dtype``'s, the maximum of both tolerances is used. 2025-08-14T21:38:49.4550065Z 2025-08-14T21:38:49.4550188Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4550365Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-08-14T21:38:49.4550490Z +===========================+============+==========+ 2025-08-14T21:38:49.4550619Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-08-14T21:38:49.4550753Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4550888Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-08-14T21:38:49.4551022Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4551151Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:49.4551268Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4551410Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-08-14T21:38:49.4551528Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4551661Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-08-14T21:38:49.4551797Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4551932Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:49.4552064Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4552198Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-08-14T21:38:49.4552341Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4552483Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:49.4552603Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4552735Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:49.4552868Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4553001Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:49.4553130Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4553265Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:49.4553387Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4553527Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-08-14T21:38:49.4553645Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4553762Z | other | ``0.0`` | ``0.0`` | 2025-08-14T21:38:49.4553895Z +---------------------------+------------+----------+ 2025-08-14T21:38:49.4553974Z 2025-08-14T21:38:49.4554059Z .. note:: 2025-08-14T21:38:49.4554155Z 2025-08-14T21:38:49.4554530Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-08-14T21:38:49.4554888Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-08-14T21:38:49.4555143Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-08-14T21:38:49.4555226Z 2025-08-14T21:38:49.4555338Z >>> import functools 2025-08-14T21:38:49.4555590Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-08-14T21:38:49.4555695Z >>> assert_equal(1e-9, 1e-10) 2025-08-14T21:38:49.4555826Z Traceback (most recent call last): 2025-08-14T21:38:49.4555907Z ... 2025-08-14T21:38:49.4556042Z AssertionError: Scalars are not equal! 2025-08-14T21:38:49.4556127Z 2025-08-14T21:38:49.4556235Z Expected 1e-10 but got 1e-09. 2025-08-14T21:38:49.4556373Z Absolute difference: 9.000000000000001e-10 2025-08-14T21:38:49.4556474Z Relative difference: 9.0 2025-08-14T21:38:49.4556554Z 2025-08-14T21:38:49.4556648Z Examples: 2025-08-14T21:38:49.4556757Z >>> # tensor to tensor comparison 2025-08-14T21:38:49.4556884Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-08-14T21:38:49.4557071Z >>> actual = torch.acos(torch.cos(expected)) 2025-08-14T21:38:49.4557235Z >>> torch.testing.assert_close(actual, expected) 2025-08-14T21:38:49.4557314Z 2025-08-14T21:38:49.4557431Z >>> # scalar to scalar comparison 2025-08-14T21:38:49.4557525Z >>> import math 2025-08-14T21:38:49.4557637Z >>> expected = math.sqrt(2.0) 2025-08-14T21:38:49.4557739Z >>> actual = 2.0 / math.sqrt(2.0) 2025-08-14T21:38:49.4557878Z >>> torch.testing.assert_close(actual, expected) 2025-08-14T21:38:49.4557969Z 2025-08-14T21:38:49.4558089Z >>> # numpy array to numpy array comparison 2025-08-14T21:38:49.4558186Z >>> import numpy as np 2025-08-14T21:38:49.4558316Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-08-14T21:38:49.4558437Z >>> actual = np.arccos(np.cos(expected)) 2025-08-14T21:38:49.4558575Z >>> torch.testing.assert_close(actual, expected) 2025-08-14T21:38:49.4558667Z 2025-08-14T21:38:49.4558785Z >>> # sequence to sequence comparison 2025-08-14T21:38:49.4558895Z >>> import numpy as np 2025-08-14T21:38:49.4559142Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-08-14T21:38:49.4559296Z >>> # length and their elements have to match. 2025-08-14T21:38:49.4559460Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-08-14T21:38:49.4559563Z >>> actual = tuple(expected) 2025-08-14T21:38:49.4559704Z >>> torch.testing.assert_close(actual, expected) 2025-08-14T21:38:49.4559792Z 2025-08-14T21:38:49.4559908Z >>> # mapping to mapping comparison 2025-08-14T21:38:49.4560027Z >>> from collections import OrderedDict 2025-08-14T21:38:49.4560137Z >>> import numpy as np 2025-08-14T21:38:49.4560237Z >>> foo = torch.tensor(1.0) 2025-08-14T21:38:49.4560337Z >>> bar = 2.0 2025-08-14T21:38:49.4560437Z >>> baz = np.array(3.0) 2025-08-14T21:38:49.4560687Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-08-14T21:38:49.4560898Z >>> # have to have the same set of keys and their elements have to match. 2025-08-14T21:38:49.4561096Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-08-14T21:38:49.4561230Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-08-14T21:38:49.4561381Z >>> torch.testing.assert_close(actual, expected) 2025-08-14T21:38:49.4561459Z 2025-08-14T21:38:49.4561588Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-08-14T21:38:49.4561694Z >>> actual = expected.clone() 2025-08-14T21:38:49.4561856Z >>> # By default, directly related instances can be compared 2025-08-14T21:38:49.4562078Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-08-14T21:38:49.4562269Z >>> # This check can be made more strict with allow_subclasses=False 2025-08-14T21:38:49.4562382Z >>> torch.testing.assert_close( 2025-08-14T21:38:49.4562588Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-08-14T21:38:49.4562668Z ... ) 2025-08-14T21:38:49.4562781Z Traceback (most recent call last): 2025-08-14T21:38:49.4562874Z ... 2025-08-14T21:38:49.4563075Z TypeError: No comparison pair was able to handle inputs of type 2025-08-14T21:38:49.4563295Z and . 2025-08-14T21:38:49.4563514Z >>> # If the inputs are not directly related, they are never considered close 2025-08-14T21:38:49.4563682Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-08-14T21:38:49.4563810Z Traceback (most recent call last): 2025-08-14T21:38:49.4563892Z ... 2025-08-14T21:38:49.4564236Z TypeError: No comparison pair was able to handle inputs of type 2025-08-14T21:38:49.4564378Z and . 2025-08-14T21:38:49.4564636Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-08-14T21:38:49.4564769Z >>> # their type if check_dtype=False. 2025-08-14T21:38:49.4564935Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-08-14T21:38:49.4565015Z 2025-08-14T21:38:49.4565127Z >>> # NaN != NaN by default. 2025-08-14T21:38:49.4565251Z >>> expected = torch.tensor(float("Nan")) 2025-08-14T21:38:49.4565357Z >>> actual = expected.clone() 2025-08-14T21:38:49.4565513Z >>> torch.testing.assert_close(actual, expected) 2025-08-14T21:38:49.4565628Z Traceback (most recent call last): 2025-08-14T21:38:49.4565706Z ... 2025-08-14T21:38:49.4565843Z AssertionError: Scalars are not close! 2025-08-14T21:38:49.4565939Z 2025-08-14T21:38:49.4566058Z Expected nan but got nan. 2025-08-14T21:38:49.4566195Z Absolute difference: nan (up to 1e-05 allowed) 2025-08-14T21:38:49.4566337Z Relative difference: nan (up to 1.3e-06 allowed) 2025-08-14T21:38:49.4566569Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-08-14T21:38:49.4566646Z 2025-08-14T21:38:49.4566767Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-08-14T21:38:49.4566891Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-08-14T21:38:49.4567031Z >>> # The default error message can be overwritten. 2025-08-14T21:38:49.4567142Z >>> torch.testing.assert_close( 2025-08-14T21:38:49.4567330Z ... actual, expected, msg="Argh, the tensors are not close!" 2025-08-14T21:38:49.4567410Z ... ) 2025-08-14T21:38:49.4567533Z Traceback (most recent call last): 2025-08-14T21:38:49.4567611Z ... 2025-08-14T21:38:49.4567763Z AssertionError: Argh, the tensors are not close! 2025-08-14T21:38:49.4567991Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-08-14T21:38:49.4568086Z >>> # extra information 2025-08-14T21:38:49.4568198Z >>> torch.testing.assert_close( 2025-08-14T21:38:49.4568399Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-08-14T21:38:49.4568482Z ... ) 2025-08-14T21:38:49.4568608Z Traceback (most recent call last): 2025-08-14T21:38:49.4568689Z ... 2025-08-14T21:38:49.4568791Z AssertionError: Header 2025-08-14T21:38:49.4568887Z 2025-08-14T21:38:49.4568990Z Tensor-likes are not close! 2025-08-14T21:38:49.4569075Z 2025-08-14T21:38:49.4569197Z Mismatched elements: 2 / 3 (66.7%) 2025-08-14T21:38:49.4569419Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-08-14T21:38:49.4569642Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-08-14T21:38:49.4569737Z 2025-08-14T21:38:49.4569817Z Footer 2025-08-14T21:38:49.4569897Z 2025-08-14T21:38:49.4570153Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4570230Z 2025-08-14T21:38:49.4570335Z warnings.warn(msg) 2025-08-14T21:38:49.4570412Z 2025-08-14T21:38:49.4570627Z --- Parse Warning: 129 / 146 --- 2025-08-14T21:38:49.4571536Z /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. 2025-08-14T21:38:49.4571792Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4571986Z Register a container-like type as pytree node. 2025-08-14T21:38:49.4572129Z 2025-08-14T21:38:49.4572210Z Args: 2025-08-14T21:38:49.4572405Z cls (type): A Python type to treat as an internal pytree node. 2025-08-14T21:38:49.4572671Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-08-14T21:38:49.4572919Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-08-14T21:38:49.4573216Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-08-14T21:38:49.4573331Z passed to the ``unflatten_fn``. 2025-08-14T21:38:49.4573601Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-08-14T21:38:49.4573866Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-08-14T21:38:49.4574024Z The function should return an instance of ``cls``. 2025-08-14T21:38:49.4574300Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-08-14T21:38:49.4574462Z qualified name used when serializing the tree spec. 2025-08-14T21:38:49.4574764Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-08-14T21:38:49.4575070Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-08-14T21:38:49.4575336Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-08-14T21:38:49.4575647Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-08-14T21:38:49.4575902Z how to convert the custom json dumpable representation of the context back to the 2025-08-14T21:38:49.4576153Z original context. This is used for json deserialization, which is being used in 2025-08-14T21:38:49.4576286Z :mod:`torch.export` right now. 2025-08-14T21:38:49.4576364Z 2025-08-14T21:38:49.4576452Z Example:: 2025-08-14T21:38:49.4576541Z 2025-08-14T21:38:49.4576640Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4576780Z >>> # Registry a Python type with lambda functions 2025-08-14T21:38:49.4576894Z >>> register_pytree_node( 2025-08-14T21:38:49.4576977Z ... set, 2025-08-14T21:38:49.4577110Z ... lambda s: (sorted(s), None, None), 2025-08-14T21:38:49.4577233Z ... lambda children, _: set(children), 2025-08-14T21:38:49.4577313Z ... ) 2025-08-14T21:38:49.4577404Z 2025-08-14T21:38:49.4577651Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4577727Z 2025-08-14T21:38:49.4577831Z warnings.warn(msg) 2025-08-14T21:38:49.4577909Z 2025-08-14T21:38:49.4578096Z --- Parse Warning: 130 / 146 --- 2025-08-14T21:38:49.4579062Z /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=1218. 2025-08-14T21:38:49.4579323Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4579410Z 2025-08-14T21:38:49.4579618Z Context passed to policy function during selective checkpointing. 2025-08-14T21:38:49.4579698Z 2025-08-14T21:38:49.4579923Z This class is used to pass relevant metadata to the policy function during 2025-08-14T21:38:49.4580178Z selective checkpointing. The metadata includes whether the current invocation 2025-08-14T21:38:49.4580337Z of the policy function is during recomputation or not. 2025-08-14T21:38:49.4580420Z 2025-08-14T21:38:49.4580499Z Example: 2025-08-14T21:38:49.4580597Z >>> # xdoctest: +SKIP(stub) 2025-08-14T21:38:49.4580739Z >>> 2025-08-14T21:38:49.4580886Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-08-14T21:38:49.4580995Z >>> print(ctx.is_recompute) 2025-08-14T21:38:49.4581072Z >>> 2025-08-14T21:38:49.4581338Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-08-14T21:38:49.4581426Z >>> 2025-08-14T21:38:49.4581561Z >>> out = torch.utils.checkpoint.checkpoint( 2025-08-14T21:38:49.4581645Z >>> fn, x, y, 2025-08-14T21:38:49.4581750Z >>> use_reentrant=False, 2025-08-14T21:38:49.4581848Z >>> context_fn=context_fn, 2025-08-14T21:38:49.4581923Z >>> ) 2025-08-14T21:38:49.4582006Z 2025-08-14T21:38:49.4582250Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4582334Z 2025-08-14T21:38:49.4582423Z warnings.warn(msg) 2025-08-14T21:38:49.4582496Z 2025-08-14T21:38:49.4582685Z --- Parse Warning: 131 / 146 --- 2025-08-14T21:38:49.4583752Z /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=1358. 2025-08-14T21:38:49.4584043Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4584127Z 2025-08-14T21:38:49.4584358Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-08-14T21:38:49.4584432Z 2025-08-14T21:38:49.4584647Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-08-14T21:38:49.4584803Z operations are recomputed during the backward pass. 2025-08-14T21:38:49.4584886Z 2025-08-14T21:38:49.4584962Z Args: 2025-08-14T21:38:49.4585075Z policy_fn_or_list (Callable or List): 2025-08-14T21:38:49.4585241Z - If a policy function is provided, it should accept a 2025-08-14T21:38:49.4585479Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-08-14T21:38:49.4585682Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-08-14T21:38:49.4585919Z indicating whether the execution of the op should be recomputed or not. 2025-08-14T21:38:49.4586115Z - If a list of operations is provided, it is equivalent to a policy 2025-08-14T21:38:49.4586296Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-08-14T21:38:49.4586503Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-08-14T21:38:49.4586590Z operations. 2025-08-14T21:38:49.4586803Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-08-14T21:38:49.4587004Z raised if any tensors cached by selective activation checkpoint are 2025-08-14T21:38:49.4587204Z mutated in order to ensure correctness. If set to `True`, this check 2025-08-14T21:38:49.4587305Z is disabled. 2025-08-14T21:38:49.4587384Z Returns: 2025-08-14T21:38:49.4587487Z A tuple of two context managers. 2025-08-14T21:38:49.4587567Z 2025-08-14T21:38:49.4587647Z Example: 2025-08-14T21:38:49.4587755Z >>> # xdoctest: +REQUIRES(LINUX) 2025-08-14T21:38:49.4587846Z >>> import functools 2025-08-14T21:38:49.4587926Z >>> 2025-08-14T21:38:49.4588052Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-08-14T21:38:49.4588170Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-08-14T21:38:49.4588248Z >>> 2025-08-14T21:38:49.4588341Z >>> ops_to_save = [ 2025-08-14T21:38:49.4588449Z >>> torch.ops.aten.mm.default, 2025-08-14T21:38:49.4588527Z >>> ] 2025-08-14T21:38:49.4588608Z >>> 2025-08-14T21:38:49.4588730Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-08-14T21:38:49.4588824Z >>> if op in ops_to_save: 2025-08-14T21:38:49.4588955Z >>> return CheckpointPolicy.MUST_SAVE 2025-08-14T21:38:49.4589083Z >>> else: 2025-08-14T21:38:49.4589258Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-08-14T21:38:49.4589335Z >>> 2025-08-14T21:38:49.4589597Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-08-14T21:38:49.4589684Z >>> 2025-08-14T21:38:49.4589778Z >>> # or equivalently 2025-08-14T21:38:49.4590044Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-08-14T21:38:49.4590129Z >>> 2025-08-14T21:38:49.4590218Z >>> def fn(x, y): 2025-08-14T21:38:49.4590407Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-08-14T21:38:49.4590489Z >>> 2025-08-14T21:38:49.4590623Z >>> out = torch.utils.checkpoint.checkpoint( 2025-08-14T21:38:49.4590712Z >>> fn, x, y, 2025-08-14T21:38:49.4590807Z >>> use_reentrant=False, 2025-08-14T21:38:49.4590910Z >>> context_fn=context_fn, 2025-08-14T21:38:49.4590991Z >>> ) 2025-08-14T21:38:49.4591066Z 2025-08-14T21:38:49.4591312Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4591391Z 2025-08-14T21:38:49.4591480Z warnings.warn(msg) 2025-08-14T21:38:49.4591589Z 2025-08-14T21:38:49.4591785Z --- Parse Warning: 132 / 146 --- 2025-08-14T21:38:49.4592678Z /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=1147. 2025-08-14T21:38:49.4593145Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4593221Z 2025-08-14T21:38:49.4593365Z Create a :class:`setuptools.Extension` for C++. 2025-08-14T21:38:49.4593449Z 2025-08-14T21:38:49.4593688Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-08-14T21:38:49.4593905Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-08-14T21:38:49.4593993Z 2025-08-14T21:38:49.4594195Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-08-14T21:38:49.4594339Z constructor. Full list arguments can be found at 2025-08-14T21:38:49.4594668Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-08-14T21:38:49.4594744Z 2025-08-14T21:38:49.4594835Z .. warning:: 2025-08-14T21:38:49.4595057Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-08-14T21:38:49.4595258Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-08-14T21:38:49.4595454Z the user's responsibility in their library to not use APIs from 2025-08-14T21:38:49.4595677Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-08-14T21:38:49.4595890Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-08-14T21:38:49.4596102Z example, to give access to custom ops from python, the library should 2025-08-14T21:38:49.4596225Z register the ops through the dispatcher. 2025-08-14T21:38:49.4596303Z 2025-08-14T21:38:49.4596517Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-08-14T21:38:49.4596714Z as a compile flag when py_limited_api is specified as an option for 2025-08-14T21:38:49.4596920Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-08-14T21:38:49.4597126Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-08-14T21:38:49.4597327Z safety, and sanity in order to encourage best practices. To target a 2025-08-14T21:38:49.4597540Z different version, set min_supported_cpython to the hexcode of the 2025-08-14T21:38:49.4597642Z CPython version of choice. 2025-08-14T21:38:49.4597725Z 2025-08-14T21:38:49.4597900Z Example: 2025-08-14T21:38:49.4598037Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4598187Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-14T21:38:49.4598292Z >>> from setuptools import setup 2025-08-14T21:38:49.4598501Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-08-14T21:38:49.4598592Z >>> setup( 2025-08-14T21:38:49.4598687Z ... name='extension', 2025-08-14T21:38:49.4598773Z ... ext_modules=[ 2025-08-14T21:38:49.4598876Z ... CppExtension( 2025-08-14T21:38:49.4598975Z ... name='extension', 2025-08-14T21:38:49.4599087Z ... sources=['extension.cpp'], 2025-08-14T21:38:49.4599210Z ... extra_compile_args=['-g'], 2025-08-14T21:38:49.4599356Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-08-14T21:38:49.4599442Z ... ], 2025-08-14T21:38:49.4599528Z ... cmdclass={ 2025-08-14T21:38:49.4599640Z ... 'build_ext': BuildExtension 2025-08-14T21:38:49.4599730Z ... }) 2025-08-14T21:38:49.4599803Z 2025-08-14T21:38:49.4600047Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4600131Z 2025-08-14T21:38:49.4600255Z warnings.warn(msg) 2025-08-14T21:38:49.4600327Z 2025-08-14T21:38:49.4600531Z --- Parse Warning: 133 / 146 --- 2025-08-14T21:38:49.4601428Z /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=1217. 2025-08-14T21:38:49.4601688Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4601760Z 2025-08-14T21:38:49.4601914Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-08-14T21:38:49.4602000Z 2025-08-14T21:38:49.4602239Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-08-14T21:38:49.4602432Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-08-14T21:38:49.4602666Z extension. This includes the CUDA include path, library path and runtime 2025-08-14T21:38:49.4602747Z library. 2025-08-14T21:38:49.4602834Z 2025-08-14T21:38:49.4603032Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-08-14T21:38:49.4603174Z constructor. Full list arguments can be found at 2025-08-14T21:38:49.4603497Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-08-14T21:38:49.4603574Z 2025-08-14T21:38:49.4603656Z .. warning:: 2025-08-14T21:38:49.4603887Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-08-14T21:38:49.4604091Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-08-14T21:38:49.4604284Z the user's responsibility in their library to not use APIs from 2025-08-14T21:38:49.4604518Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-08-14T21:38:49.4604725Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-08-14T21:38:49.4604935Z example, to give access to custom ops from python, the library should 2025-08-14T21:38:49.4605059Z register the ops through the dispatcher. 2025-08-14T21:38:49.4605139Z 2025-08-14T21:38:49.4605359Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-08-14T21:38:49.4605552Z as a compile flag when py_limited_api is specified as an option for 2025-08-14T21:38:49.4605749Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-08-14T21:38:49.4605963Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-08-14T21:38:49.4606167Z safety, and sanity in order to encourage best practices. To target a 2025-08-14T21:38:49.4606426Z different version, set min_supported_cpython to the hexcode of the 2025-08-14T21:38:49.4606554Z CPython version of choice. 2025-08-14T21:38:49.4606629Z 2025-08-14T21:38:49.4606718Z Example: 2025-08-14T21:38:49.4606810Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4606956Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-14T21:38:49.4607070Z >>> from setuptools import setup 2025-08-14T21:38:49.4607285Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-08-14T21:38:49.4607361Z >>> setup( 2025-08-14T21:38:49.4607467Z ... name='cuda_extension', 2025-08-14T21:38:49.4607554Z ... ext_modules=[ 2025-08-14T21:38:49.4607649Z ... CUDAExtension( 2025-08-14T21:38:49.4607759Z ... name='cuda_extension', 2025-08-14T21:38:49.4607915Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-08-14T21:38:49.4608053Z ... extra_compile_args={'cxx': ['-g'], 2025-08-14T21:38:49.4608167Z ... 'nvcc': ['-O2']}, 2025-08-14T21:38:49.4608313Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-08-14T21:38:49.4608399Z ... ], 2025-08-14T21:38:49.4608509Z ... cmdclass={ 2025-08-14T21:38:49.4608618Z ... 'build_ext': BuildExtension 2025-08-14T21:38:49.4608705Z ... }) 2025-08-14T21:38:49.4608780Z 2025-08-14T21:38:49.4608883Z Compute capabilities: 2025-08-14T21:38:49.4608959Z 2025-08-14T21:38:49.4609253Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-08-14T21:38:49.4609543Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-08-14T21:38:49.4609828Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-08-14T21:38:49.4610122Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-08-14T21:38:49.4610407Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-08-14T21:38:49.4610526Z support (see below for details on PTX). 2025-08-14T21:38:49.4610607Z 2025-08-14T21:38:49.4610909Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-08-14T21:38:49.4611019Z CCs you want the extension to support: 2025-08-14T21:38:49.4611100Z 2025-08-14T21:38:49.4611284Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-08-14T21:38:49.4611511Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-08-14T21:38:49.4611599Z 2025-08-14T21:38:49.4611909Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-08-14T21:38:49.4612217Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-08-14T21:38:49.4612527Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-08-14T21:38:49.4612813Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-08-14T21:38:49.4613133Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-08-14T21:38:49.4613394Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-08-14T21:38:49.4613707Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-08-14T21:38:49.4614018Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-08-14T21:38:49.4614112Z "8.0 8.6" would be better. 2025-08-14T21:38:49.4614197Z 2025-08-14T21:38:49.4614485Z Note that while it's possible to include all supported archs, the more archs get included the 2025-08-14T21:38:49.4614813Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-08-14T21:38:49.4614918Z 2025-08-14T21:38:49.4615239Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-08-14T21:38:49.4615449Z To workaround the issue, move python binding logic to pure C++ file. 2025-08-14T21:38:49.4615534Z 2025-08-14T21:38:49.4615618Z Example use: 2025-08-14T21:38:49.4615711Z #include 2025-08-14T21:38:49.4615862Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-08-14T21:38:49.4615934Z 2025-08-14T21:38:49.4616023Z Instead of: 2025-08-14T21:38:49.4616124Z #include 2025-08-14T21:38:49.4616272Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-08-14T21:38:49.4616356Z 2025-08-14T21:38:49.4616620Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-08-14T21:38:49.4617122Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-08-14T21:38:49.4617211Z 2025-08-14T21:38:49.4617313Z Relocatable device code linking: 2025-08-14T21:38:49.4617387Z 2025-08-14T21:38:49.4617662Z If you want to reference device symbols across compilation units (across object files), 2025-08-14T21:38:49.4617930Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-08-14T21:38:49.4618289Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-08-14T21:38:49.4618604Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-08-14T21:38:49.4618912Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-08-14T21:38:49.4619090Z helps reduce the protentional perf degradation of `-rdc`. 2025-08-14T21:38:49.4619253Z Note that it needs to be used at both steps to be useful. 2025-08-14T21:38:49.4619338Z 2025-08-14T21:38:49.4619695Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-08-14T21:38:49.4619860Z There is also a case where `-dlink` is used without `-rdc`: 2025-08-14T21:38:49.4620116Z when an extension is linked against a static lib containing rdc-compiled objects 2025-08-14T21:38:49.4620318Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-08-14T21:38:49.4620394Z 2025-08-14T21:38:49.4620591Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-08-14T21:38:49.4620663Z 2025-08-14T21:38:49.4620741Z Example: 2025-08-14T21:38:49.4620840Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4620987Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-14T21:38:49.4621085Z >>> CUDAExtension( 2025-08-14T21:38:49.4621185Z ... name='cuda_extension', 2025-08-14T21:38:49.4621340Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-08-14T21:38:49.4621439Z ... dlink=True, 2025-08-14T21:38:49.4621553Z ... dlink_libraries=["dlink_lib"], 2025-08-14T21:38:49.4621669Z ... extra_compile_args={'cxx': ['-g'], 2025-08-14T21:38:49.4621796Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-08-14T21:38:49.4621873Z 2025-08-14T21:38:49.4622118Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4622198Z 2025-08-14T21:38:49.4622289Z warnings.warn(msg) 2025-08-14T21:38:49.4622371Z 2025-08-14T21:38:49.4622566Z --- Parse Warning: 134 / 146 --- 2025-08-14T21:38:49.4623551Z /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=1408. 2025-08-14T21:38:49.4623872Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4623983Z 2025-08-14T21:38:49.4624138Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-08-14T21:38:49.4624217Z 2025-08-14T21:38:49.4624448Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-08-14T21:38:49.4624644Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-08-14T21:38:49.4624723Z extension. 2025-08-14T21:38:49.4624797Z 2025-08-14T21:38:49.4625002Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-08-14T21:38:49.4625087Z constructor. 2025-08-14T21:38:49.4625162Z 2025-08-14T21:38:49.4625254Z .. warning:: 2025-08-14T21:38:49.4625476Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-08-14T21:38:49.4625677Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-08-14T21:38:49.4625876Z the user's responsibility in their library to not use APIs from 2025-08-14T21:38:49.4626097Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-08-14T21:38:49.4626311Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-08-14T21:38:49.4626538Z example, to give access to custom ops from python, the library should 2025-08-14T21:38:49.4626658Z register the ops through the dispatcher. 2025-08-14T21:38:49.4626738Z 2025-08-14T21:38:49.4626948Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-08-14T21:38:49.4627144Z as a compile flag when py_limited_api is specified as an option for 2025-08-14T21:38:49.4627350Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-08-14T21:38:49.4627555Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-08-14T21:38:49.4627773Z safety, and sanity in order to encourage best practices. To target a 2025-08-14T21:38:49.4627981Z different version, set min_supported_cpython to the hexcode of the 2025-08-14T21:38:49.4628083Z CPython version of choice. 2025-08-14T21:38:49.4628171Z 2025-08-14T21:38:49.4628254Z Example: 2025-08-14T21:38:49.4628348Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4628498Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-14T21:38:49.4628713Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-08-14T21:38:49.4628795Z >>> setup( 2025-08-14T21:38:49.4628901Z ... name='xpu_extension', 2025-08-14T21:38:49.4628989Z ... ext_modules=[ 2025-08-14T21:38:49.4629083Z ... SyclExtension( 2025-08-14T21:38:49.4629200Z ... name='xpu_extension', 2025-08-14T21:38:49.4629359Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-08-14T21:38:49.4629541Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-08-14T21:38:49.4629619Z ... ], 2025-08-14T21:38:49.4629705Z ... cmdclass={ 2025-08-14T21:38:49.4629818Z ... 'build_ext': BuildExtension 2025-08-14T21:38:49.4629894Z ... }) 2025-08-14T21:38:49.4629969Z 2025-08-14T21:38:49.4630265Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-08-14T21:38:49.4630511Z building process of the extension. If down the road a new card is installed the 2025-08-14T21:38:49.4630762Z extension may need to be recompiled. You can override the default behavior using 2025-08-14T21:38:49.4631060Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-08-14T21:38:49.4631144Z to support: 2025-08-14T21:38:49.4631224Z 2025-08-14T21:38:49.4631415Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-08-14T21:38:49.4631490Z 2025-08-14T21:38:49.4631830Z Note that while it's possible to include all supported archs, the more archs get included the 2025-08-14T21:38:49.4632133Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-08-14T21:38:49.4632205Z 2025-08-14T21:38:49.4632344Z Note: Ninja is required to build SyclExtension. 2025-08-14T21:38:49.4632423Z 2025-08-14T21:38:49.4632674Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4632750Z 2025-08-14T21:38:49.4632840Z warnings.warn(msg) 2025-08-14T21:38:49.4632920Z 2025-08-14T21:38:49.4633116Z --- Parse Warning: 135 / 146 --- 2025-08-14T21:38:49.4633956Z /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=1585. 2025-08-14T21:38:49.4634216Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4634292Z 2025-08-14T21:38:49.4634441Z Load a PyTorch C++ extension just-in-time (JIT). 2025-08-14T21:38:49.4634513Z 2025-08-14T21:38:49.4634711Z To load an extension, a Ninja build file is emitted, which is used to 2025-08-14T21:38:49.4634944Z compile the given sources into a dynamic library. This library is 2025-08-14T21:38:49.4635157Z subsequently loaded into the current Python process as a module and 2025-08-14T21:38:49.4635275Z returned from this function, ready for use. 2025-08-14T21:38:49.4635358Z 2025-08-14T21:38:49.4635555Z By default, the directory to which the build file is emitted and the 2025-08-14T21:38:49.4635777Z resulting library compiled to is ``/torch_extensions/``, where 2025-08-14T21:38:49.4635980Z ```` is the temporary folder on the current platform and ```` 2025-08-14T21:38:49.4636185Z the name of the extension. This location can be overridden in two ways. 2025-08-14T21:38:49.4636394Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-08-14T21:38:49.4636605Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-08-14T21:38:49.4636813Z into subfolders of this directory. Second, if the ``build_directory`` 2025-08-14T21:38:49.4637041Z argument to this function is supplied, it overrides the entire path, i.e. 2025-08-14T21:38:49.4637196Z the library will be compiled into that folder directly. 2025-08-14T21:38:49.4637272Z 2025-08-14T21:38:49.4637479Z To compile the sources, the default system compiler (``c++``) is used, 2025-08-14T21:38:49.4637705Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-08-14T21:38:49.4637929Z additional arguments to the compilation process, ``extra_cflags`` or 2025-08-14T21:38:49.4638139Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-08-14T21:38:49.4638345Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-08-14T21:38:49.4638500Z ``extra_cflags`` to pass further include directories. 2025-08-14T21:38:49.4638574Z 2025-08-14T21:38:49.4638799Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-08-14T21:38:49.4638984Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-08-14T21:38:49.4639216Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-08-14T21:38:49.4639432Z passing the CUDA lib64 directory as a library directory, and linking 2025-08-14T21:38:49.4639577Z ``cudart``. You can pass additional flags to nvcc via 2025-08-14T21:38:49.4639772Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-08-14T21:38:49.4640003Z heuristics for finding the CUDA install directory are used, which usually 2025-08-14T21:38:49.4640203Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-08-14T21:38:49.4640287Z safest option. 2025-08-14T21:38:49.4640418Z 2025-08-14T21:38:49.4640669Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-08-14T21:38:49.4640870Z files (``.sycl``) along with other sources. Such files will be detected 2025-08-14T21:38:49.4641074Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-08-14T21:38:49.4641272Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-08-14T21:38:49.4641462Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-08-14T21:38:49.4641658Z SYCL compiler is expected to be found via system PATH environment 2025-08-14T21:38:49.4641737Z variable. 2025-08-14T21:38:49.4641817Z 2025-08-14T21:38:49.4641893Z Args: 2025-08-14T21:38:49.4642092Z name: The name of the extension to build. This MUST be the same as the 2025-08-14T21:38:49.4642212Z name of the pybind11 module! 2025-08-14T21:38:49.4642413Z sources: A list of relative or absolute paths to C++ source files. 2025-08-14T21:38:49.4642642Z extra_cflags: optional list of compiler flags to forward to the build. 2025-08-14T21:38:49.4642850Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-08-14T21:38:49.4642951Z when building CUDA sources. 2025-08-14T21:38:49.4643193Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-08-14T21:38:49.4643307Z compiler when building SYCL sources. 2025-08-14T21:38:49.4643518Z extra_ldflags: optional list of linker flags to forward to the build. 2025-08-14T21:38:49.4643739Z extra_include_paths: optional list of include directories to forward 2025-08-14T21:38:49.4643826Z to the build. 2025-08-14T21:38:49.4644012Z build_directory: optional path to use as build workspace. 2025-08-14T21:38:49.4644185Z verbose: If ``True``, turns on verbose logging of load steps. 2025-08-14T21:38:49.4644401Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-08-14T21:38:49.4644565Z the build. If set to ``None`` (default), this value is 2025-08-14T21:38:49.4644766Z automatically determined based on the existence of ``.cu`` or 2025-08-14T21:38:49.4644930Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-08-14T21:38:49.4645051Z and libraries to be included. 2025-08-14T21:38:49.4645262Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-08-14T21:38:49.4645427Z the build. If set to ``None`` (default), this value is 2025-08-14T21:38:49.4645628Z automatically determined based on the existence of ``.sycl`` in 2025-08-14T21:38:49.4645782Z ``sources``. Set it to `True`` to force SYCL headers and 2025-08-14T21:38:49.4645895Z libraries to be included. 2025-08-14T21:38:49.4646093Z is_python_module: If ``True`` (default), imports the produced shared 2025-08-14T21:38:49.4646277Z library as a Python module. If ``False``, behavior depends on 2025-08-14T21:38:49.4646382Z ``is_standalone``. 2025-08-14T21:38:49.4646580Z is_standalone: If ``False`` (default) loads the constructed extension 2025-08-14T21:38:49.4646775Z into the process as a plain dynamic library. If ``True``, build a 2025-08-14T21:38:49.4646877Z standalone executable. 2025-08-14T21:38:49.4646953Z 2025-08-14T21:38:49.4647047Z Returns: 2025-08-14T21:38:49.4647157Z If ``is_python_module`` is ``True``: 2025-08-14T21:38:49.4647326Z Returns the loaded PyTorch extension as a Python module. 2025-08-14T21:38:49.4647406Z 2025-08-14T21:38:49.4647601Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-08-14T21:38:49.4647805Z Returns nothing. (The shared library is loaded into the process as 2025-08-14T21:38:49.4647902Z a side effect.) 2025-08-14T21:38:49.4647979Z 2025-08-14T21:38:49.4648131Z If ``is_standalone`` is ``True``. 2025-08-14T21:38:49.4648360Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-08-14T21:38:49.4648527Z added to the PATH environment variable as a side effect.) 2025-08-14T21:38:49.4648609Z 2025-08-14T21:38:49.4648691Z Example: 2025-08-14T21:38:49.4648783Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4648924Z >>> from torch.utils.cpp_extension import load 2025-08-14T21:38:49.4649014Z >>> module = load( 2025-08-14T21:38:49.4649104Z ... name='extension', 2025-08-14T21:38:49.4649270Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-08-14T21:38:49.4649369Z ... extra_cflags=['-O2'], 2025-08-14T21:38:49.4649455Z ... verbose=True) 2025-08-14T21:38:49.4649541Z 2025-08-14T21:38:49.4649787Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4649878Z 2025-08-14T21:38:49.4649968Z warnings.warn(msg) 2025-08-14T21:38:49.4650043Z 2025-08-14T21:38:49.4650249Z --- Parse Warning: 136 / 146 --- 2025-08-14T21:38:49.4651122Z /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=1890. 2025-08-14T21:38:49.4651404Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4651492Z 2025-08-14T21:38:49.4651694Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-08-14T21:38:49.4651778Z 2025-08-14T21:38:49.4652002Z This function behaves exactly like :func:`load`, but takes its sources as 2025-08-14T21:38:49.4652224Z strings rather than filenames. These strings are stored to files in the 2025-08-14T21:38:49.4652440Z build directory, after which the behavior of :func:`load_inline` is 2025-08-14T21:38:49.4652543Z identical to :func:`load`. 2025-08-14T21:38:49.4652617Z 2025-08-14T21:38:49.4652709Z See `the 2025-08-14T21:38:49.4653030Z tests `_ 2025-08-14T21:38:49.4653147Z for good examples of using this function. 2025-08-14T21:38:49.4653235Z 2025-08-14T21:38:49.4653460Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-08-14T21:38:49.4653702Z the necessary header includes, as well as the (pybind11) binding code. More 2025-08-14T21:38:49.4653930Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-08-14T21:38:49.4654109Z single ``.cpp`` file. This file is then prepended with ``#include 2025-08-14T21:38:49.4654211Z `` 2025-08-14T21:38:49.4654288Z 2025-08-14T21:38:49.4654501Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-08-14T21:38:49.4654740Z automatically generated for each function specified. ``functions`` can 2025-08-14T21:38:49.4654951Z either be a list of function names, or a dictionary mapping from function 2025-08-14T21:38:49.4655172Z names to docstrings. If a list is given, the name of each function is used 2025-08-14T21:38:49.4655261Z as its docstring. 2025-08-14T21:38:49.4655339Z 2025-08-14T21:38:49.4655555Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-08-14T21:38:49.4655723Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-08-14T21:38:49.4655925Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-08-14T21:38:49.4656147Z separately, but ultimately linked into a single library. Note that no 2025-08-14T21:38:49.4656368Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-08-14T21:38:49.4656575Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-08-14T21:38:49.4656846Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-08-14T21:38:49.4656980Z include its name in ``functions``). 2025-08-14T21:38:49.4657061Z 2025-08-14T21:38:49.4657271Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-08-14T21:38:49.4657479Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-08-14T21:38:49.4657681Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-08-14T21:38:49.4657889Z linked into a single library. Note that no bindings are generated for 2025-08-14T21:38:49.4658090Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-08-14T21:38:49.4658304Z create a C++ function that calls it, and either declare or define this 2025-08-14T21:38:49.4658485Z C++ function in one of the ``cpp_sources`` (and include its name 2025-08-14T21:38:49.4658581Z in ``functions``). 2025-08-14T21:38:49.4658656Z 2025-08-14T21:38:49.4658734Z 2025-08-14T21:38:49.4658820Z 2025-08-14T21:38:49.4659000Z See :func:`load` for a description of arguments omitted below. 2025-08-14T21:38:49.4659077Z 2025-08-14T21:38:49.4659170Z Args: 2025-08-14T21:38:49.4659379Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-08-14T21:38:49.4659618Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-08-14T21:38:49.4659841Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-08-14T21:38:49.4660036Z functions: A list of function names for which to generate function 2025-08-14T21:38:49.4660245Z bindings. If a dictionary is given, it should map function names to 2025-08-14T21:38:49.4660420Z docstrings (which are otherwise just the function names). 2025-08-14T21:38:49.4660630Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-08-14T21:38:49.4660797Z the build. If set to ``None`` (default), this value is 2025-08-14T21:38:49.4660998Z automatically determined based on whether ``cuda_sources`` is 2025-08-14T21:38:49.4661143Z provided. Set it to ``True`` to force CUDA headers 2025-08-14T21:38:49.4661259Z and libraries to be included. 2025-08-14T21:38:49.4661471Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-08-14T21:38:49.4661631Z the build. If set to ``None`` (default), this value is 2025-08-14T21:38:49.4661827Z automatically determined based on whether ``sycl_sources`` is 2025-08-14T21:38:49.4661970Z provided. Set it to ``True`` to force SYCL headers 2025-08-14T21:38:49.4662085Z and libraries to be included. 2025-08-14T21:38:49.4662286Z with_pytorch_error_handling: Determines whether pytorch error and 2025-08-14T21:38:49.4662475Z warning macros are handled by pytorch instead of pybind. To do 2025-08-14T21:38:49.4662694Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-08-14T21:38:49.4662889Z function. This redirection might cause issues in obscure cases 2025-08-14T21:38:49.4663071Z of cpp. This flag should be set to ``False`` when this redirect 2025-08-14T21:38:49.4663158Z causes issues. 2025-08-14T21:38:49.4663480Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-08-14T21:38:49.4663712Z ``#include `` and ``#include `` lines. 2025-08-14T21:38:49.4663867Z Use this option to improve cold start times when you 2025-08-14T21:38:49.4664115Z already include the necessary headers in your source code. Default: ``False``. 2025-08-14T21:38:49.4664207Z 2025-08-14T21:38:49.4664287Z Example: 2025-08-14T21:38:49.4664436Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-14T21:38:49.4664587Z >>> from torch.utils.cpp_extension import load_inline 2025-08-14T21:38:49.4664671Z >>> source = """ 2025-08-14T21:38:49.4664878Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-08-14T21:38:49.4664999Z return x.sin() + y.sin(); 2025-08-14T21:38:49.4665075Z } 2025-08-14T21:38:49.4665167Z """ 2025-08-14T21:38:49.4665299Z >>> module = load_inline(name='inline_extension', 2025-08-14T21:38:49.4665414Z ... cpp_sources=[source], 2025-08-14T21:38:49.4665538Z ... functions=['sin_add']) 2025-08-14T21:38:49.4665610Z 2025-08-14T21:38:49.4665689Z .. note:: 2025-08-14T21:38:49.4665930Z Since load_inline will just-in-time compile the source code, please ensure 2025-08-14T21:38:49.4666147Z that you have the right toolchains installed in the runtime. For example, 2025-08-14T21:38:49.4666368Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-08-14T21:38:49.4666602Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-08-14T21:38:49.4666842Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-08-14T21:38:49.4667083Z are not included when you install torch and must be additionally installed. 2025-08-14T21:38:49.4667158Z 2025-08-14T21:38:49.4667437Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-08-14T21:38:49.4667650Z the extension. This may use up too many resources on some systems. One 2025-08-14T21:38:49.4667862Z can control the number of workers by setting the `MAX_JOBS` environment 2025-08-14T21:38:49.4667985Z variable to a non-negative number. 2025-08-14T21:38:49.4668064Z 2025-08-14T21:38:49.4668306Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4668390Z 2025-08-14T21:38:49.4668478Z warnings.warn(msg) 2025-08-14T21:38:49.4668552Z 2025-08-14T21:38:49.4668770Z --- Parse Warning: 137 / 146 --- 2025-08-14T21:38:49.4669725Z /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. 2025-08-14T21:38:49.4669990Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4670066Z 2025-08-14T21:38:49.4670346Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-08-14T21:38:49.4670433Z 2025-08-14T21:38:49.4670718Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-08-14T21:38:49.4670953Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-08-14T21:38:49.4671187Z server like load. It can emulate multiple calling threads to a single module 2025-08-14T21:38:49.4671419Z provided. In the future we plan to enhance this component to support inter and 2025-08-14T21:38:49.4671668Z intra-op parallelism as well as multiple models running in a single process. 2025-08-14T21:38:49.4671748Z 2025-08-14T21:38:49.4671988Z Please note that even though nn.Module is supported, it might incur an overhead 2025-08-14T21:38:49.4672211Z from the need to hold GIL every time we execute Python code or pass around 2025-08-14T21:38:49.4672446Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-08-14T21:38:49.4672682Z model for inference deployment it is better to switch to using it in this 2025-08-14T21:38:49.4672763Z benchmark. 2025-08-14T21:38:49.4672837Z 2025-08-14T21:38:49.4672928Z Example:: 2025-08-14T21:38:49.4673002Z 2025-08-14T21:38:49.4673116Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-14T21:38:49.4673263Z >>> from torch.utils import ThroughputBenchmark 2025-08-14T21:38:49.4673386Z >>> bench = ThroughputBenchmark(my_module) 2025-08-14T21:38:49.4673623Z >>> # Pre-populate benchmark's data set with the inputs 2025-08-14T21:38:49.4673757Z >>> for input in inputs: 2025-08-14T21:38:49.4673971Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-08-14T21:38:49.4674094Z ... bench.add_input(input[0], x2=input[1]) 2025-08-14T21:38:49.4674295Z >>> # Inputs supplied above are randomly used during the execution 2025-08-14T21:38:49.4674392Z >>> stats = bench.benchmark( 2025-08-14T21:38:49.4674501Z ... num_calling_threads=4, 2025-08-14T21:38:49.4674598Z ... num_warmup_iters = 100, 2025-08-14T21:38:49.4674688Z ... num_iters = 1000, 2025-08-14T21:38:49.4674777Z ... ) 2025-08-14T21:38:49.4674953Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-08-14T21:38:49.4675120Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-08-14T21:38:49.4675207Z 2025-08-14T21:38:49.4675452Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4675527Z 2025-08-14T21:38:49.4675628Z warnings.warn(msg) 2025-08-14T21:38:49.4675702Z 2025-08-14T21:38:49.4675896Z --- Parse Warning: 138 / 146 --- 2025-08-14T21:38:49.4676817Z /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. 2025-08-14T21:38:49.4677098Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4677298Z Sampler that restricts data loading to a subset of the dataset. 2025-08-14T21:38:49.4677375Z 2025-08-14T21:38:49.4677501Z It is especially useful in conjunction with 2025-08-14T21:38:49.4677752Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-08-14T21:38:49.4678007Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-08-14T21:38:49.4678243Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-08-14T21:38:49.4678369Z original dataset that is exclusive to it. 2025-08-14T21:38:49.4678440Z 2025-08-14T21:38:49.4678534Z .. note:: 2025-08-14T21:38:49.4678765Z Dataset is assumed to be of constant size and that any instance of it always 2025-08-14T21:38:49.4678899Z returns the same elements in the same order. 2025-08-14T21:38:49.4678986Z 2025-08-14T21:38:49.4679065Z Args: 2025-08-14T21:38:49.4679177Z dataset: Dataset used for sampling. 2025-08-14T21:38:49.4679396Z num_replicas (int, optional): Number of processes participating in 2025-08-14T21:38:49.4679635Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-08-14T21:38:49.4679751Z current distributed group. 2025-08-14T21:38:49.4679982Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-08-14T21:38:49.4680175Z By default, :attr:`rank` is retrieved from the current distributed 2025-08-14T21:38:49.4680272Z group. 2025-08-14T21:38:49.4680485Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-08-14T21:38:49.4680568Z indices. 2025-08-14T21:38:49.4680762Z seed (int, optional): random seed used to shuffle the sampler if 2025-08-14T21:38:49.4680952Z :attr:`shuffle=True`. This number should be identical across all 2025-08-14T21:38:49.4681122Z processes in the distributed group. Default: ``0``. 2025-08-14T21:38:49.4681324Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-08-14T21:38:49.4681511Z tail of the data to make it evenly divisible across the number of 2025-08-14T21:38:49.4681712Z replicas. If ``False``, the sampler will add extra indices to make 2025-08-14T21:38:49.4681957Z the data evenly divisible across the replicas. Default: ``False``. 2025-08-14T21:38:49.4682056Z 2025-08-14T21:38:49.4682150Z .. warning:: 2025-08-14T21:38:49.4682332Z In distributed mode, calling the :meth:`set_epoch` method at 2025-08-14T21:38:49.4682584Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-08-14T21:38:49.4682834Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-08-14T21:38:49.4682948Z the same ordering will be always used. 2025-08-14T21:38:49.4683037Z 2025-08-14T21:38:49.4683122Z Example:: 2025-08-14T21:38:49.4683196Z 2025-08-14T21:38:49.4683299Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4683512Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-08-14T21:38:49.4683688Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-08-14T21:38:49.4683808Z ... sampler=sampler) 2025-08-14T21:38:49.4683937Z >>> for epoch in range(start_epoch, n_epochs): 2025-08-14T21:38:49.4684049Z ... if is_distributed: 2025-08-14T21:38:49.4684158Z ... sampler.set_epoch(epoch) 2025-08-14T21:38:49.4684272Z ... train(loader) 2025-08-14T21:38:49.4684364Z 2025-08-14T21:38:49.4684614Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4684691Z 2025-08-14T21:38:49.4684793Z warnings.warn(msg) 2025-08-14T21:38:49.4684867Z 2025-08-14T21:38:49.4685047Z --- Parse Warning: 139 / 146 --- 2025-08-14T21:38:49.4685990Z /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. 2025-08-14T21:38:49.4686245Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4686503Z Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights). 2025-08-14T21:38:49.4686579Z 2025-08-14T21:38:49.4686655Z Args: 2025-08-14T21:38:49.4686897Z weights (sequence) : a sequence of weights, not necessary summing up to one 2025-08-14T21:38:49.4687030Z num_samples (int): number of samples to draw 2025-08-14T21:38:49.4687243Z replacement (bool): if ``True``, samples are drawn with replacement. 2025-08-14T21:38:49.4687442Z If not, they are drawn without replacement, which means that when a 2025-08-14T21:38:49.4687652Z sample index is drawn for a row, it cannot be drawn again for that row. 2025-08-14T21:38:49.4687816Z generator (Generator): Generator used in sampling. 2025-08-14T21:38:49.4687895Z 2025-08-14T21:38:49.4687973Z Example: 2025-08-14T21:38:49.4688122Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-14T21:38:49.4688203Z >>> list( 2025-08-14T21:38:49.4688309Z ... WeightedRandomSampler( 2025-08-14T21:38:49.4688455Z ... [0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True 2025-08-14T21:38:49.4688534Z ... ) 2025-08-14T21:38:49.4688622Z ... ) 2025-08-14T21:38:49.4688705Z [4, 4, 1, 4, 5] 2025-08-14T21:38:49.4688782Z >>> list( 2025-08-14T21:38:49.4688898Z ... WeightedRandomSampler( 2025-08-14T21:38:49.4689036Z ... [0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False 2025-08-14T21:38:49.4689111Z ... ) 2025-08-14T21:38:49.4689201Z ... ) 2025-08-14T21:38:49.4689282Z [0, 1, 4, 3, 2] 2025-08-14T21:38:49.4689359Z 2025-08-14T21:38:49.4689613Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4689687Z 2025-08-14T21:38:49.4689840Z warnings.warn(msg) 2025-08-14T21:38:49.4689954Z 2025-08-14T21:38:49.4690134Z --- Parse Warning: 140 / 146 --- 2025-08-14T21:38:49.4691023Z /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. 2025-08-14T21:38:49.4691280Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4691443Z Wraps another sampler to yield a mini-batch of indices. 2025-08-14T21:38:49.4691530Z 2025-08-14T21:38:49.4691607Z Args: 2025-08-14T21:38:49.4691828Z sampler (Sampler or Iterable): Base sampler. Can be any iterable object 2025-08-14T21:38:49.4691954Z batch_size (int): Size of mini-batch. 2025-08-14T21:38:49.4692151Z drop_last (bool): If ``True``, the sampler will drop the last batch if 2025-08-14T21:38:49.4692297Z its size would be less than ``batch_size`` 2025-08-14T21:38:49.4692374Z 2025-08-14T21:38:49.4692455Z Example: 2025-08-14T21:38:49.4692547Z >>> list( 2025-08-14T21:38:49.4692641Z ... BatchSampler( 2025-08-14T21:38:49.4693014Z ... SequentialSampler(range(10)), batch_size=3, drop_last=False 2025-08-14T21:38:49.4693165Z ... ) 2025-08-14T21:38:49.4693243Z ... ) 2025-08-14T21:38:49.4693341Z [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] 2025-08-14T21:38:49.4693439Z >>> list( 2025-08-14T21:38:49.4693692Z ... BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True) 2025-08-14T21:38:49.4693783Z ... ) 2025-08-14T21:38:49.4693878Z [[0, 1, 2], [3, 4, 5], [6, 7, 8]] 2025-08-14T21:38:49.4693957Z 2025-08-14T21:38:49.4694212Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4694287Z 2025-08-14T21:38:49.4694378Z warnings.warn(msg) 2025-08-14T21:38:49.4694469Z 2025-08-14T21:38:49.4694656Z --- Parse Warning: 141 / 146 --- 2025-08-14T21:38:49.4695587Z /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. 2025-08-14T21:38:49.4695853Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4695927Z 2025-08-14T21:38:49.4696037Z Iterable-style DataPipe. 2025-08-14T21:38:49.4696118Z 2025-08-14T21:38:49.4696366Z All DataPipes that represent an iterable of data samples should subclass this. 2025-08-14T21:38:49.4696627Z This style of DataPipes is particularly useful when data come from a stream, or 2025-08-14T21:38:49.4696980Z when the number of samples is too large to fit them all in memory. ``IterDataPipe`` is lazily initialized and its 2025-08-14T21:38:49.4697266Z elements are computed only when ``next()`` is called on the iterator of an ``IterDataPipe``. 2025-08-14T21:38:49.4697356Z 2025-08-14T21:38:49.4697571Z All subclasses should overwrite :meth:`__iter__`, which would return an 2025-08-14T21:38:49.4697920Z iterator of samples in this DataPipe. Calling ``__iter__`` of an ``IterDataPipe`` automatically invokes its 2025-08-14T21:38:49.4698275Z method ``reset()``, which by default performs no operation. When writing a custom ``IterDataPipe``, users should 2025-08-14T21:38:49.4698542Z override ``reset()`` if necessary. The common usages include resetting buffers, pointers, 2025-08-14T21:38:49.4698745Z and various state variables within the custom ``IterDataPipe``. 2025-08-14T21:38:49.4698823Z 2025-08-14T21:38:49.4698896Z Note: 2025-08-14T21:38:49.4699107Z Only `one` iterator can be valid for each ``IterDataPipe`` at a time, 2025-08-14T21:38:49.4699512Z and the creation a second iterator will invalidate the first one. This constraint is necessary because 2025-08-14T21:38:49.4699914Z some ``IterDataPipe`` have internal buffers, whose states can become invalid if there are multiple iterators. 2025-08-14T21:38:49.4700175Z The code example below presents details on how this constraint looks in practice. 2025-08-14T21:38:49.4700529Z If you have any feedback related to this constraint, please see `GitHub IterDataPipe Single Iterator Issue`_. 2025-08-14T21:38:49.4700615Z 2025-08-14T21:38:49.4700892Z These DataPipes can be invoked in two ways, using the class constructor or applying their 2025-08-14T21:38:49.4701241Z functional form onto an existing ``IterDataPipe`` (recommended, available to most but not all DataPipes). 2025-08-14T21:38:49.4701554Z You can chain multiple `IterDataPipe` together to form a pipeline that will perform multiple 2025-08-14T21:38:49.4701652Z operations in succession. 2025-08-14T21:38:49.4701736Z 2025-08-14T21:38:49.4701871Z .. _GitHub IterDataPipe Single Iterator Issue: 2025-08-14T21:38:49.4702005Z https://github.com/pytorch/data/issues/45 2025-08-14T21:38:49.4702094Z 2025-08-14T21:38:49.4702174Z Note: 2025-08-14T21:38:49.4702394Z When a subclass is used with :class:`~torch.utils.data.DataLoader`, each 2025-08-14T21:38:49.4702684Z item in the DataPipe will be yielded from the :class:`~torch.utils.data.DataLoader` 2025-08-14T21:38:49.4702893Z iterator. When :attr:`num_workers > 0`, each worker process will have a 2025-08-14T21:38:49.4703133Z different copy of the DataPipe object, so it is often desired to configure 2025-08-14T21:38:49.4703360Z each copy independently to avoid having duplicate data returned from the 2025-08-14T21:38:49.4703670Z workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker 2025-08-14T21:38:49.4703926Z process, returns information about the worker. It can be used in either the 2025-08-14T21:38:49.4704170Z dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's 2025-08-14T21:38:49.4704347Z :attr:`worker_init_fn` option to modify each copy's behavior. 2025-08-14T21:38:49.4704435Z 2025-08-14T21:38:49.4704517Z Examples: 2025-08-14T21:38:49.4704614Z General Usage: 2025-08-14T21:38:49.4704710Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4704909Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-08-14T21:38:49.4705038Z >>> dp = IterableWrapper(range(10)) 2025-08-14T21:38:49.4705218Z >>> map_dp_1 = Mapper(dp, lambda x: x + 1) # Using class constructor 2025-08-14T21:38:49.4705312Z >>> map_dp_2 = dp.map( 2025-08-14T21:38:49.4705419Z ... lambda x: x + 1 2025-08-14T21:38:49.4705544Z ... ) # Using functional form (recommended) 2025-08-14T21:38:49.4705633Z >>> list(map_dp_1) 2025-08-14T21:38:49.4705736Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-08-14T21:38:49.4705827Z >>> list(map_dp_2) 2025-08-14T21:38:49.4705928Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-08-14T21:38:49.4706072Z >>> filter_dp = map_dp_1.filter(lambda x: x % 2 == 0) 2025-08-14T21:38:49.4706162Z >>> list(filter_dp) 2025-08-14T21:38:49.4706260Z [2, 4, 6, 8, 10] 2025-08-14T21:38:49.4706379Z Single Iterator Constraint Example: 2025-08-14T21:38:49.4706578Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-08-14T21:38:49.4706713Z >>> source_dp = IterableWrapper(range(10)) 2025-08-14T21:38:49.4706813Z >>> it1 = iter(source_dp) 2025-08-14T21:38:49.4706896Z >>> list(it1) 2025-08-14T21:38:49.4706999Z [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 2025-08-14T21:38:49.4707099Z >>> it1 = iter(source_dp) 2025-08-14T21:38:49.4707197Z >>> it2 = iter( 2025-08-14T21:38:49.4707288Z ... source_dp 2025-08-14T21:38:49.4707530Z ... ) # The creation of a new iterator invalidates `it1` 2025-08-14T21:38:49.4707650Z >>> next(it2) 2025-08-14T21:38:49.4707731Z 0 2025-08-14T21:38:49.4707908Z >>> next(it1) # Further usage of `it1` will raise a `RunTimeError` 2025-08-14T21:38:49.4707996Z 2025-08-14T21:38:49.4708240Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4708318Z 2025-08-14T21:38:49.4708427Z warnings.warn(msg) 2025-08-14T21:38:49.4708505Z 2025-08-14T21:38:49.4708701Z --- Parse Warning: 142 / 146 --- 2025-08-14T21:38:49.4712270Z /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. 2025-08-14T21:38:49.4712546Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4712630Z 2025-08-14T21:38:49.4713093Z Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``). 2025-08-14T21:38:49.4713172Z 2025-08-14T21:38:49.4713370Z A list of the child DataPipes is returned from this operation. 2025-08-14T21:38:49.4713495Z 2025-08-14T21:38:49.4713576Z Args: 2025-08-14T21:38:49.4713720Z datapipe: Iterable DataPipe being filtered 2025-08-14T21:38:49.4713905Z num_instances: number of instances of the DataPipe to create 2025-08-14T21:38:49.4714269Z classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None`` 2025-08-14T21:38:49.4714595Z drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None`` 2025-08-14T21:38:49.4714943Z buffer_size: this defines the maximum number of inputs that the buffer can hold across all child 2025-08-14T21:38:49.4715119Z DataPipes while waiting for their values to be yielded. 2025-08-14T21:38:49.4715303Z Defaults to ``1000``. Use ``-1`` for the unlimited buffer. 2025-08-14T21:38:49.4715379Z 2025-08-14T21:38:49.4715463Z Examples: 2025-08-14T21:38:49.4715596Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-08-14T21:38:49.4715766Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-14T21:38:49.4715860Z >>> def odd_or_even(n): 2025-08-14T21:38:49.4715962Z ... return n % 2 2025-08-14T21:38:49.4716083Z >>> source_dp = IterableWrapper(range(5)) 2025-08-14T21:38:49.4716299Z >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even) 2025-08-14T21:38:49.4716384Z >>> list(dp1) 2025-08-14T21:38:49.4716462Z [0, 2, 4] 2025-08-14T21:38:49.4716557Z >>> list(dp2) 2025-08-14T21:38:49.4716643Z [1, 3] 2025-08-14T21:38:49.4716874Z >>> # It can also filter out any element that gets `None` from the `classifier_fn` 2025-08-14T21:38:49.4716989Z >>> def odd_or_even_no_zero(n): 2025-08-14T21:38:49.4717101Z ... return n % 2 if n != 0 else None 2025-08-14T21:38:49.4717201Z >>> dp1, dp2 = source_dp.demux( 2025-08-14T21:38:49.4717423Z ... num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True 2025-08-14T21:38:49.4717504Z ... ) 2025-08-14T21:38:49.4717603Z >>> list(dp1) 2025-08-14T21:38:49.4717683Z [2, 4] 2025-08-14T21:38:49.4717765Z >>> list(dp2) 2025-08-14T21:38:49.4717855Z [1, 3] 2025-08-14T21:38:49.4717932Z 2025-08-14T21:38:49.4718177Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4718266Z 2025-08-14T21:38:49.4718358Z warnings.warn(msg) 2025-08-14T21:38:49.4718436Z 2025-08-14T21:38:49.4718674Z --- Parse Warning: 143 / 146 --- 2025-08-14T21:38:49.4719738Z /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. 2025-08-14T21:38:49.4720031Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4720111Z 2025-08-14T21:38:49.4720416Z Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). 2025-08-14T21:38:49.4720502Z 2025-08-14T21:38:49.4720839Z As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, 2025-08-14T21:38:49.4721017Z and so on. It ends when the shortest input DataPipe is exhausted. 2025-08-14T21:38:49.4721105Z 2025-08-14T21:38:49.4721186Z Args: 2025-08-14T21:38:49.4721669Z datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted 2025-08-14T21:38:49.4721758Z 2025-08-14T21:38:49.4721840Z Example: 2025-08-14T21:38:49.4721969Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-08-14T21:38:49.4722140Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-14T21:38:49.4722232Z >>> dp1, dp2, dp3 = ( 2025-08-14T21:38:49.4722355Z ... IterableWrapper(range(3)), 2025-08-14T21:38:49.4722498Z ... IterableWrapper(range(10, 15)), 2025-08-14T21:38:49.4722612Z ... IterableWrapper(range(20, 25)), 2025-08-14T21:38:49.4722708Z ... ) 2025-08-14T21:38:49.4722804Z >>> list(dp1.mux(dp2, dp3)) 2025-08-14T21:38:49.4722894Z [0, 10, 20, 1, 11, 21, 2, 12, 22] 2025-08-14T21:38:49.4722985Z 2025-08-14T21:38:49.4723229Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4723316Z 2025-08-14T21:38:49.4723407Z warnings.warn(msg) 2025-08-14T21:38:49.4723484Z 2025-08-14T21:38:49.4723683Z --- Parse Warning: 144 / 146 --- 2025-08-14T21:38:49.4724684Z /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. 2025-08-14T21:38:49.4724941Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4725028Z 2025-08-14T21:38:49.4725328Z Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). 2025-08-14T21:38:49.4725401Z 2025-08-14T21:38:49.4725638Z The output is stopped as soon as the shortest input DataPipe is exhausted. 2025-08-14T21:38:49.4725713Z 2025-08-14T21:38:49.4725809Z Args: 2025-08-14T21:38:49.4725952Z *datapipes: Iterable DataPipes being aggregated 2025-08-14T21:38:49.4726029Z 2025-08-14T21:38:49.4726125Z Example: 2025-08-14T21:38:49.4726241Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-08-14T21:38:49.4726414Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-14T21:38:49.4726523Z >>> dp1, dp2, dp3 = ( 2025-08-14T21:38:49.4726629Z ... IterableWrapper(range(5)), 2025-08-14T21:38:49.4726742Z ... IterableWrapper(range(10, 15)), 2025-08-14T21:38:49.4726868Z ... IterableWrapper(range(20, 25)), 2025-08-14T21:38:49.4726947Z ... ) 2025-08-14T21:38:49.4727055Z >>> list(dp1.zip(dp2, dp3)) 2025-08-14T21:38:49.4727183Z [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)] 2025-08-14T21:38:49.4727258Z 2025-08-14T21:38:49.4727517Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4727595Z 2025-08-14T21:38:49.4727686Z warnings.warn(msg) 2025-08-14T21:38:49.4727776Z 2025-08-14T21:38:49.4727954Z --- Parse Warning: 145 / 146 --- 2025-08-14T21:38:49.4729005Z /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. 2025-08-14T21:38:49.4729295Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4729372Z 2025-08-14T21:38:49.4729751Z Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``). 2025-08-14T21:38:49.4729826Z 2025-08-14T21:38:49.4729904Z Args: 2025-08-14T21:38:49.4730072Z datapipe: Iterable datapipe that provides pathnames 2025-08-14T21:38:49.4730242Z mode: An optional string that specifies the mode in which 2025-08-14T21:38:49.4730445Z the file is opened by ``open()``. It defaults to ``r``, other options are 2025-08-14T21:38:49.4730653Z ``b`` for reading in binary mode and ``t`` for text mode. 2025-08-14T21:38:49.4730849Z encoding: An optional string that specifies the encoding of the 2025-08-14T21:38:49.4731114Z underlying file. It defaults to ``None`` to match the default encoding of ``open``. 2025-08-14T21:38:49.4731231Z length: Nominal length of the datapipe 2025-08-14T21:38:49.4731308Z 2025-08-14T21:38:49.4731402Z Note: 2025-08-14T21:38:49.4731692Z The opened file handles will be closed by Python's GC periodically. Users can choose 2025-08-14T21:38:49.4731791Z to close them explicitly. 2025-08-14T21:38:49.4731884Z 2025-08-14T21:38:49.4731964Z Example: 2025-08-14T21:38:49.4732061Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4732203Z >>> from torchdata.datapipes.iter import ( 2025-08-14T21:38:49.4732291Z ... FileLister, 2025-08-14T21:38:49.4732391Z ... FileOpener, 2025-08-14T21:38:49.4732482Z ... StreamReader, 2025-08-14T21:38:49.4732561Z ... ) 2025-08-14T21:38:49.4732783Z >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith(".txt")) 2025-08-14T21:38:49.4732879Z >>> dp = FileOpener(dp) 2025-08-14T21:38:49.4732977Z >>> dp = StreamReader(dp) 2025-08-14T21:38:49.4733073Z >>> list(dp) 2025-08-14T21:38:49.4733163Z [('./abc.txt', 'abc')] 2025-08-14T21:38:49.4733242Z 2025-08-14T21:38:49.4733499Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4733577Z 2025-08-14T21:38:49.4733670Z warnings.warn(msg) 2025-08-14T21:38:49.4733756Z 2025-08-14T21:38:49.4733940Z --- Parse Warning: 146 / 146 --- 2025-08-14T21:38:49.4734953Z /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. 2025-08-14T21:38:49.4735208Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-14T21:38:49.4735286Z 2025-08-14T21:38:49.4735696Z Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``. 2025-08-14T21:38:49.4735775Z 2025-08-14T21:38:49.4735876Z (functional name: ``groupby``). 2025-08-14T21:38:49.4735963Z 2025-08-14T21:38:49.4736340Z The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group 2025-08-14T21:38:49.4736640Z will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full, 2025-08-14T21:38:49.4736931Z the DataPipe will yield the largest batch with the same key, provided that its size is larger 2025-08-14T21:38:49.4737241Z than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``. 2025-08-14T21:38:49.4737330Z 2025-08-14T21:38:49.4737698Z After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity 2025-08-14T21:38:49.4738034Z will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``. 2025-08-14T21:38:49.4738146Z 2025-08-14T21:38:49.4738224Z Args: 2025-08-14T21:38:49.4738364Z datapipe: Iterable datapipe to be grouped 2025-08-14T21:38:49.4738630Z group_key_fn: Function used to generate group key from the data of the source datapipe 2025-08-14T21:38:49.4738857Z keep_key: Option to yield the matching key along with the items in a tuple, 2025-08-14T21:38:49.4739041Z resulting in `(key, [items])` otherwise returning [items] 2025-08-14T21:38:49.4739182Z buffer_size: The size of buffer for ungrouped data 2025-08-14T21:38:49.4739449Z group_size: The max size of each group, a batch is yielded as soon as it reaches this size 2025-08-14T21:38:49.4739813Z guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full 2025-08-14T21:38:49.4740155Z drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer 2025-08-14T21:38:49.4740269Z when the buffer is full 2025-08-14T21:38:49.4740344Z 2025-08-14T21:38:49.4740423Z Example: 2025-08-14T21:38:49.4740519Z >>> import os 2025-08-14T21:38:49.4740611Z >>> # xdoctest: +SKIP 2025-08-14T21:38:49.4740803Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-14T21:38:49.4740908Z >>> def group_fn(file): 2025-08-14T21:38:49.4741043Z ... return os.path.basename(file).split(".")[0] 2025-08-14T21:38:49.4746853Z >>> source_dp = IterableWrapper( 2025-08-14T21:38:49.4747039Z ... ["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"] 2025-08-14T21:38:49.4747130Z ... ) 2025-08-14T21:38:49.4747267Z >>> dp0 = source_dp.groupby(group_key_fn=group_fn) 2025-08-14T21:38:49.4747356Z >>> list(dp0) 2025-08-14T21:38:49.4747531Z [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']] 2025-08-14T21:38:49.4747716Z >>> # A group is yielded as soon as its size equals to `group_size` 2025-08-14T21:38:49.4747897Z >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2) 2025-08-14T21:38:49.4747991Z >>> list(dp1) 2025-08-14T21:38:49.4748144Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-08-14T21:38:49.4748490Z >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size` 2025-08-14T21:38:49.4748604Z >>> dp2 = source_dp.groupby( 2025-08-14T21:38:49.4748701Z ... group_key_fn=group_fn, 2025-08-14T21:38:49.4748804Z ... buffer_size=3, 2025-08-14T21:38:49.4748895Z ... group_size=3, 2025-08-14T21:38:49.4748997Z ... guaranteed_group_size=2, 2025-08-14T21:38:49.4749085Z ... ) 2025-08-14T21:38:49.4749168Z >>> list(dp2) 2025-08-14T21:38:49.4749328Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-08-14T21:38:49.4749409Z 2025-08-14T21:38:49.4749653Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-14T21:38:49.4749734Z 2025-08-14T21:38:49.4749836Z warnings.warn(msg) 2025-08-14T21:38:49.4749915Z 2025-08-14T21:38:49.4750061Z  2025-08-14T21:38:49.4750244Z === Found 8 run-time warnings === 2025-08-14T21:38:49.4750421Z --- Runtime Warning: 1 / 8 --- 2025-08-14T21:38:49.4750689Z example = 2025-08-14T21:38:49.4752006Z /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:1978.) 2025-08-14T21:38:49.4752115Z return super().refine_names(names) 2025-08-14T21:38:49.4752202Z 2025-08-14T21:38:49.4752455Z --- Runtime Warning: 2 / 8 --- 2025-08-14T21:38:49.4752800Z example = 2025-08-14T21:38:49.4753408Z /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. 2025-08-14T21:38:49.4753710Z Overriding a previously registered kernel for the same operator and the same dispatch key 2025-08-14T21:38:49.4753924Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2025-08-14T21:38:49.4754210Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2025-08-14T21:38:49.4754317Z dispatch key: CPU 2025-08-14T21:38:49.4754774Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2025-08-14T21:38:49.4755317Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:225.) 2025-08-14T21:38:49.4755484Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2025-08-14T21:38:49.4755559Z 2025-08-14T21:38:49.4755735Z --- Runtime Warning: 3 / 8 --- 2025-08-14T21:38:49.4756007Z example = 2025-08-14T21:38:49.4757804Z /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.) 2025-08-14T21:38:49.4758053Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2025-08-14T21:38:49.4758131Z 2025-08-14T21:38:49.4758304Z --- Runtime Warning: 4 / 8 --- 2025-08-14T21:38:49.4758613Z example = 2025-08-14T21:38:49.4760081Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/const_fold.py:271: 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 2025-08-14T21:38:49.4760252Z new_node = root_const_gm.graph.get_attr(in_node.target) 2025-08-14T21:38:49.4760328Z 2025-08-14T21:38:49.4760495Z --- Runtime Warning: 5 / 8 --- 2025-08-14T21:38:49.4760787Z example = 2025-08-14T21:38:49.4761843Z /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) 2025-08-14T21:38:49.4761944Z warnings.warn( 2025-08-14T21:38:49.4762025Z 2025-08-14T21:38:49.4762193Z --- Runtime Warning: 6 / 8 --- 2025-08-14T21:38:49.4762528Z example = 2025-08-14T21:38:49.4763574Z /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) 2025-08-14T21:38:49.4763672Z warnings.warn( 2025-08-14T21:38:49.4763747Z 2025-08-14T21:38:49.4763916Z --- Runtime Warning: 7 / 8 --- 2025-08-14T21:38:49.4764221Z example = 2025-08-14T21:38:49.4765033Z /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`. 2025-08-14T21:38:49.4765151Z WeightNorm.apply(module, name, dim) 2025-08-14T21:38:49.4765239Z 2025-08-14T21:38:49.4765408Z --- Runtime Warning: 8 / 8 --- 2025-08-14T21:38:49.4765710Z example = 2025-08-14T21:38:49.4766488Z /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`. 2025-08-14T21:38:49.4766631Z WeightNorm.apply(module, name, dim) 2025-08-14T21:38:49.4766722Z 2025-08-14T21:38:49.4767017Z === 338 passed, 394 skipped, 154 warnings in 13.81 seconds === 2025-08-14T21:38:49.4767192Z Running test_cpp_api_parity 1/1 ... [2025-08-14 21:38:49.241466] 2025-08-14T21:38:49.4767304Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:38:49.4768123Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_api_parity.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:38:49.241836] 2025-08-14T21:44:25.0517107Z 2025-08-14T21:44:25.0517966Z test_cpp_api_parity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_api_parity_1.1_e50eec0818fc7cd4_.log 2025-08-14T21:44:25.0708243Z Running 488 items in this shard: test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_circular_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_circular_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1size1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1size1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2size1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2size1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_valid, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_valid_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_reflect_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_reflect_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_replicate_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_replicate_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_stride, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_stride_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zero_batch, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zero_batch_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zeros_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zeros_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_circular_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_circular_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_padded, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_padded_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_strided, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_strided_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_with_multiplier, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_with_multiplier_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups, 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test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_tuple_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_tuple_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_launch_configs, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_launch_configs_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special_cuda 2025-08-14T21:44:25.0892264Z 2025-08-14T21:44:25.0892478Z Running dynamo/test_repros 1/1 ... [2025-08-14 21:44:25.052805] 2025-08-14T21:44:25.0893010Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:44:25.0894089Z 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'] ... [2025-08-14 21:44:25.053136] 2025-08-14T21:44:28.1702799Z 2025-08-14T21:44:28.1704179Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_f421e2f5d99bad14_.log 2025-08-14T21:44:28.1704912Z 2025-08-14T21:44:28.1706930Z Running dynamo/test_misc 1/1 ... [2025-08-14 21:44:28.170505] 2025-08-14T21:44:28.1707565Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:44:28.1710883Z 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'] ... [2025-08-14 21:44:28.170831] 2025-08-14T21:44:32.3805565Z 2025-08-14T21:44:32.3806648Z dynamo/test_misc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_misc_1.1_746d1f94fbb68c8c_.log 2025-08-14T21:44:32.3807287Z 2025-08-14T21:44:32.3809840Z Running dynamo/test_error_messages 1/1 ... [2025-08-14 21:44:32.380768] 2025-08-14T21:44:32.3810322Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:44:32.3813442Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_error_messages.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'] ... [2025-08-14 21:44:32.381088] 2025-08-14T21:44:35.4271498Z 2025-08-14T21:44:35.4272719Z dynamo/test_error_messages 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_error_messages_1.1_0c044c3b5b877765_.log 2025-08-14T21:44:35.4273444Z 2025-08-14T21:44:35.4275709Z Running dynamo/test_precompile_context 1/1 ... [2025-08-14 21:44:35.427374] 2025-08-14T21:44:35.4276353Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:44:35.4279498Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_precompile_context.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:44:35.427683] 2025-08-14T21:44:41.7889821Z 2025-08-14T21:44:41.7890759Z dynamo/test_precompile_context 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_precompile_context_1.1_034bf231c9e5a237_.log 2025-08-14T21:44:41.7891526Z 2025-08-14T21:44:41.7893816Z Running dynamo/test_unspec 1/1 ... [2025-08-14 21:44:41.789196] 2025-08-14T21:44:41.7894240Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:44:41.7897823Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_unspec.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:44:41.789568] 2025-08-14T21:44:44.9073720Z 2025-08-14T21:44:44.9074580Z dynamo/test_unspec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unspec_1.1_f285d1f8a56bd217_.log 2025-08-14T21:44:44.9075236Z 2025-08-14T21:44:44.9077970Z Running dynamo/test_after_aot 1/1 ... [2025-08-14 21:44:44.907636] 2025-08-14T21:44:44.9078402Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:44:44.9081911Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_after_aot.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'] ... [2025-08-14 21:44:44.907983] 2025-08-14T21:44:48.0128793Z 2025-08-14T21:44:48.0129642Z dynamo/test_after_aot 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_after_aot_1.1_e3ac2d4f7c6eaad6_.log 2025-08-14T21:44:48.0130325Z 2025-08-14T21:44:48.0133028Z Running dynamo/test_aot_autograd 1/1 ... [2025-08-14 21:44:48.013148] 2025-08-14T21:44:48.0133466Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:44:48.0137200Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:44:48.013493] 2025-08-14T21:44:51.1118060Z 2025-08-14T21:44:51.1119030Z dynamo/test_aot_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_1.1_33b3fb521a2a0a19_.log 2025-08-14T21:44:51.1119762Z 2025-08-14T21:44:51.1122646Z Running dynamo/test_deque_reconstruct 1/1 ... [2025-08-14 21:44:51.112046] 2025-08-14T21:44:51.1123135Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:44:51.1126071Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_deque_reconstruct.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:44:51.112371] 2025-08-14T21:44:54.1339154Z 2025-08-14T21:44:54.1340094Z dynamo/test_deque_reconstruct 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deque_reconstruct_1.1_ca2738da3edb9ea6_.log 2025-08-14T21:44:54.1340848Z 2025-08-14T21:44:54.1342651Z Running dynamo/test_interop 1/1 ... [2025-08-14 21:44:54.134095] 2025-08-14T21:44:54.1343136Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:44:54.1346634Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_interop.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:44:54.134433] 2025-08-14T21:44:57.2527979Z 2025-08-14T21:44:57.2528861Z dynamo/test_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_interop_1.1_b3ef946c3898f95e_.log 2025-08-14T21:44:57.2529519Z 2025-08-14T21:44:57.2531497Z Running dynamo/test_base_output 1/1 ... [2025-08-14 21:44:57.252996] 2025-08-14T21:44:57.2531985Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:44:57.2535453Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_output.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:44:57.253320] 2025-08-14T21:45:00.3569258Z 2025-08-14T21:45:00.3570156Z dynamo/test_base_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_output_1.1_45d7526e9ab12a6e_.log 2025-08-14T21:45:00.3570840Z 2025-08-14T21:45:00.3573048Z Running dynamo/test_nops 1/1 ... [2025-08-14 21:45:00.357134] 2025-08-14T21:45:00.3573530Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:00.3576716Z 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'] ... [2025-08-14 21:45:00.357458] 2025-08-14T21:45:03.5469411Z 2025-08-14T21:45:03.5470252Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_64f04364dd35541b_.log 2025-08-14T21:45:03.5470873Z 2025-08-14T21:45:03.5473201Z Running dynamo/test_pre_dispatch 1/1 ... [2025-08-14 21:45:03.547157] 2025-08-14T21:45:03.5473725Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:03.5477995Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_pre_dispatch.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:45:03.547521] 2025-08-14T21:45:06.3743732Z 2025-08-14T21:45:06.3744714Z dynamo/test_pre_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_pre_dispatch_1.1_e537cf39dae33e2b_.log 2025-08-14T21:45:06.3745425Z 2025-08-14T21:45:06.3747814Z Running dynamo/test_metrics_context 1/1 ... [2025-08-14 21:45:06.374595] 2025-08-14T21:45:06.3748334Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:06.3751182Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_metrics_context.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:45:06.374910] 2025-08-14T21:45:09.2195960Z 2025-08-14T21:45:09.2196883Z dynamo/test_metrics_context 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_metrics_context_1.1_2bfaa72143ee9b4c_.log 2025-08-14T21:45:09.2197611Z 2025-08-14T21:45:09.2199569Z Running dynamo/test_subgraphs 1/1 ... [2025-08-14 21:45:09.219798] 2025-08-14T21:45:09.2199997Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:09.2203503Z 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'] ... [2025-08-14 21:45:09.220109] 2025-08-14T21:45:12.2499500Z 2025-08-14T21:45:12.2500359Z dynamo/test_subgraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subgraphs_1.1_1fc23e8cda753e13_.log 2025-08-14T21:45:12.2501268Z 2025-08-14T21:45:12.2503280Z Running dynamo/test_install_free_tensors 1/1 ... [2025-08-14 21:45:12.250151] 2025-08-14T21:45:12.2503804Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:12.2507341Z 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'] ... [2025-08-14 21:45:12.250500] 2025-08-14T21:45:15.2832066Z 2025-08-14T21:45:15.2833050Z 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_344b39e256553b8d_.log 2025-08-14T21:45:15.2833814Z 2025-08-14T21:45:15.2836095Z Running dynamo/test_config 1/1 ... [2025-08-14 21:45:15.283462] 2025-08-14T21:45:15.2836497Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:15.2840307Z 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'] ... [2025-08-14 21:45:15.283813] 2025-08-14T21:45:18.3857654Z 2025-08-14T21:45:18.3859049Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_53b8c1980e25cbbd_.log 2025-08-14T21:45:18.3860250Z 2025-08-14T21:45:18.3860847Z Running dynamo/test_export_mutations 1/1 ... [2025-08-14 21:45:18.385861] 2025-08-14T21:45:18.3861346Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:18.3864951Z 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'] ... [2025-08-14 21:45:18.386259] 2025-08-14T21:45:21.4203289Z 2025-08-14T21:45:21.4204889Z 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_1b5c428ad3a76d99_.log 2025-08-14T21:45:21.4206225Z 2025-08-14T21:45:21.4208903Z Running dynamo/test_sources 1/1 ... [2025-08-14 21:45:21.420687] 2025-08-14T21:45:21.4209576Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:21.4213743Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:45:21.421083] 2025-08-14T21:45:24.2970441Z 2025-08-14T21:45:24.2971769Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_badd6b3ca5af9ec3_.log 2025-08-14T21:45:24.2972985Z 2025-08-14T21:45:24.2976224Z Running dynamo/test_guard_serialization 1/1 ... [2025-08-14 21:45:24.297394] 2025-08-14T21:45:24.2976725Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:24.2979667Z 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'] ... [2025-08-14 21:45:24.297721] 2025-08-14T21:45:30.4685925Z 2025-08-14T21:45:30.4687462Z 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_3d3416f18f29deda_.log 2025-08-14T21:45:30.4688242Z 2025-08-14T21:45:30.4690373Z Running dynamo/test_einops 1/1 ... [2025-08-14 21:45:30.468847] 2025-08-14T21:45:30.4691064Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:30.4694011Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_einops.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'] ... [2025-08-14 21:45:30.469177] 2025-08-14T21:45:33.2741017Z 2025-08-14T21:45:33.2742177Z dynamo/test_einops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_einops_1.1_f5aa26bd90b3a278_.log 2025-08-14T21:45:33.2742822Z 2025-08-14T21:45:33.2744830Z Running dynamo/test_export 1/1 ... [2025-08-14 21:45:33.274295] 2025-08-14T21:45:33.2745492Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:33.2748598Z 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'] ... [2025-08-14 21:45:33.274623] 2025-08-14T21:45:36.3508461Z 2025-08-14T21:45:36.3509595Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_bbe8530c3d9ce64f_.log 2025-08-14T21:45:36.3510276Z 2025-08-14T21:45:36.3512515Z Running dynamo/test_base_hop 1/1 ... [2025-08-14 21:45:36.351062] 2025-08-14T21:45:36.3513163Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:36.3516416Z 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'] ... [2025-08-14 21:45:36.351385] 2025-08-14T21:45:39.3940079Z 2025-08-14T21:45:39.3941239Z 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_8c248f97e1be7f91_.log 2025-08-14T21:45:39.3941923Z 2025-08-14T21:45:39.3944326Z Running dynamo/test_optimizers 1/1 ... [2025-08-14 21:45:39.394246] 2025-08-14T21:45:39.3945009Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:39.3948352Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_optimizers.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:45:39.394581] 2025-08-14T21:45:42.4504641Z 2025-08-14T21:45:42.4505819Z dynamo/test_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_optimizers_1.1_88bbd530c223ce6d_.log 2025-08-14T21:45:42.4506504Z 2025-08-14T21:45:42.4509011Z Running dynamo/test_compiler_bisector 1/1 ... [2025-08-14 21:45:42.450694] 2025-08-14T21:45:42.4509625Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:42.4513000Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_compiler_bisector.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:45:42.451013] 2025-08-14T21:45:48.6155110Z 2025-08-14T21:45:48.6156427Z dynamo/test_compiler_bisector 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compiler_bisector_1.1_a6be726fc3e86168_.log 2025-08-14T21:45:48.6157550Z 2025-08-14T21:45:48.6157848Z Running dynamo/test_bytecode_utils 1/1 ... [2025-08-14 21:45:48.615579] 2025-08-14T21:45:48.6158359Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:48.6161826Z 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'] ... [2025-08-14 21:45:48.615944] 2025-08-14T21:45:51.6396340Z 2025-08-14T21:45:51.6398028Z 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_6b5559f170ed823a_.log 2025-08-14T21:45:51.6399567Z 2025-08-14T21:45:51.6400070Z Running dynamo/test_cudagraphs_expandable_segments 1/1 ... [2025-08-14 21:45:51.639704] 2025-08-14T21:45:51.6400975Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:51.6403943Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_cudagraphs_expandable_segments.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:45:51.640123] 2025-08-14T21:45:54.7017053Z 2025-08-14T21:45:54.7018178Z dynamo/test_cudagraphs_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_expandable_segments_1.1_099392400173bad2_.log 2025-08-14T21:45:54.7019031Z 2025-08-14T21:45:54.7020678Z Running dynamo/test_minifier 1/1 ... [2025-08-14 21:45:54.701890] 2025-08-14T21:45:54.7021102Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:54.7024525Z 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'] ... [2025-08-14 21:45:54.702207] 2025-08-14T21:45:57.5385219Z 2025-08-14T21:45:57.5386132Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_bbd4c7b5b1932ab1_.log 2025-08-14T21:45:57.5386818Z 2025-08-14T21:45:57.5389475Z Running dynamo/test_reorder_logs 1/1 ... [2025-08-14 21:45:57.538748] 2025-08-14T21:45:57.5389948Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:45:57.5393189Z 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'] ... [2025-08-14 21:45:57.539073] 2025-08-14T21:46:00.5885947Z 2025-08-14T21:46:00.5886906Z 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_23b945a319e75984_.log 2025-08-14T21:46:00.5887594Z 2025-08-14T21:46:00.5889732Z Running dynamo/test_python_autograd 1/1 ... [2025-08-14 21:46:00.588801] 2025-08-14T21:46:00.5890258Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:00.5893546Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_python_autograd.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:46:00.589127] 2025-08-14T21:46:03.6235270Z 2025-08-14T21:46:03.6236292Z dynamo/test_python_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_python_autograd_1.1_1e78279c8d108be0_.log 2025-08-14T21:46:03.6237003Z 2025-08-14T21:46:03.6238603Z Running test_quantization 2/9 ... [2025-08-14 21:46:03.623680] 2025-08-14T21:46:03.6239099Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:03.6242501Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=2', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:46:03.624007] 2025-08-14T21:46:08.3443495Z 2025-08-14T21:46:08.3444615Z test_quantization 2/9 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_2.9_be8f940e9e565c4b_.log 2025-08-14T21:46:08.3445439Z Running 0 items in this shard: 2025-08-14T21:46:08.3445628Z 2025-08-14T21:46:08.3447644Z Running test_quantization 6/9 ... [2025-08-14 21:46:08.344574] 2025-08-14T21:46:08.3448073Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:08.3451271Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=6', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:46:08.344895] 2025-08-14T21:46:12.5645632Z 2025-08-14T21:46:12.5646632Z test_quantization 6/9 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_6.9_26d657d338b4e1a0_.log 2025-08-14T21:46:12.5647379Z Running 0 items in this shard: 2025-08-14T21:46:12.5647569Z 2025-08-14T21:46:12.5649657Z Running test_quantization 7/9 ... [2025-08-14 21:46:12.564810] 2025-08-14T21:46:12.5650095Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:12.5653366Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=7', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:46:12.565125] 2025-08-14T21:46:16.8347816Z 2025-08-14T21:46:16.8348687Z test_quantization 7/9 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_7.9_045fc3a2c797d1ee_.log 2025-08-14T21:46:16.8349467Z Running 0 items in this shard: 2025-08-14T21:46:16.8349656Z 2025-08-14T21:46:16.8351782Z Running test_quantization 8/9 ... [2025-08-14 21:46:16.835018] 2025-08-14T21:46:16.8352256Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:16.8355365Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=8', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:46:16.835326] 2025-08-14T21:46:21.0550381Z 2025-08-14T21:46:21.0551310Z test_quantization 8/9 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_8.9_1ade5ec5a9d6ff41_.log 2025-08-14T21:46:21.0552154Z Running 0 items in this shard: 2025-08-14T21:46:21.0552348Z 2025-08-14T21:46:21.0554415Z Running test_transformers 2/7 ... [2025-08-14 21:46:21.055286] 2025-08-14T21:46:21.0554842Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:21.0558127Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_transformers.py', '-m', 'serial', '--shard-id=2', '--num-shards=7', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:46:21.055612] 2025-08-14T21:46:25.6758516Z 2025-08-14T21:46:25.6759812Z test_transformers 2/7 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_2.7_2477fe4bf4273cb0_.log 2025-08-14T21:46:25.6761191Z Running 0 items in this shard: 2025-08-14T21:46:25.6761531Z 2025-08-14T21:46:25.6764524Z Running test_transformers 3/7 ... [2025-08-14 21:46:25.676213] 2025-08-14T21:46:25.6765256Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:25.6768769Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_transformers.py', '-m', 'serial', '--shard-id=3', '--num-shards=7', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:46:25.676569] 2025-08-14T21:46:30.2969876Z 2025-08-14T21:46:30.2970776Z test_transformers 3/7 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_3.7_f2d8458018dd5bc0_.log 2025-08-14T21:46:30.2971529Z Running 0 items in this shard: 2025-08-14T21:46:30.2971731Z 2025-08-14T21:46:30.2974055Z Running test_transformers 4/7 ... [2025-08-14 21:46:30.297241] 2025-08-14T21:46:30.2974677Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:30.2978287Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_transformers.py', '-m', 'serial', '--shard-id=4', '--num-shards=7', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:46:30.297575] 2025-08-14T21:46:35.0180638Z 2025-08-14T21:46:35.0182138Z test_transformers 4/7 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_4.7_30e52a6229185733_.log 2025-08-14T21:46:35.0183761Z Running 0 items in this shard: 2025-08-14T21:46:35.0184028Z 2025-08-14T21:46:35.0186090Z Running test_ao_sparsity 1/1 ... [2025-08-14 21:46:35.018395] 2025-08-14T21:46:35.0186719Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:35.0191212Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ao_sparsity.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'] ... [2025-08-14 21:46:35.018805] 2025-08-14T21:46:38.4377108Z 2025-08-14T21:46:38.4377986Z test_ao_sparsity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ao_sparsity_1.1_40553ea25f96fa76_.log 2025-08-14T21:46:38.4378724Z Running 0 items in this shard: 2025-08-14T21:46:38.4378911Z 2025-08-14T21:46:38.4381218Z Running test_license 1/1 ... [2025-08-14 21:46:38.437950] 2025-08-14T21:46:38.4381619Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:38.4384969Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_license.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'] ... [2025-08-14 21:46:38.438270] 2025-08-14T21:46:41.4565714Z 2025-08-14T21:46:41.4566562Z test_license 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_license_1.1_d08d6fe09e41ecdf_.log 2025-08-14T21:46:41.4567345Z Running 0 items in this shard: 2025-08-14T21:46:41.4567594Z 2025-08-14T21:46:41.4570074Z Running torch_np/test_binary_ufuncs 1/1 ... [2025-08-14 21:46:41.456831] 2025-08-14T21:46:41.4570541Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:41.4573845Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_binary_ufuncs.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:46:41.457157] 2025-08-14T21:46:44.3751646Z 2025-08-14T21:46:44.3752637Z torch_np/test_binary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_binary_ufuncs_1.1_3aef576d0d30f8ba_.log 2025-08-14T21:46:44.3753530Z Running 0 items in this shard: 2025-08-14T21:46:44.3753739Z 2025-08-14T21:46:44.3755289Z Running xpu/test_fusion 1/1 ... [2025-08-14 21:46:44.375375] 2025-08-14T21:46:44.3755758Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:44.3759327Z 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'] ... [2025-08-14 21:46:44.375687] 2025-08-14T21:46:47.3034784Z 2025-08-14T21:46:47.3035856Z xpu/test_fusion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_fusion_1.1_21ba8dcd1ceda2c1_.log 2025-08-14T21:46:47.3036583Z Running 0 items in this shard: 2025-08-14T21:46:47.3036771Z 2025-08-14T21:46:47.3038693Z Running torch_np/test_unary_ufuncs 1/1 ... [2025-08-14 21:46:47.303716] 2025-08-14T21:46:47.3039136Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:47.3042615Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_unary_ufuncs.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:46:47.304028] 2025-08-14T21:46:50.2720283Z 2025-08-14T21:46:50.2721291Z torch_np/test_unary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_unary_ufuncs_1.1_b63042003ee9e0d0_.log 2025-08-14T21:46:50.2722099Z Running 0 items in this shard: 2025-08-14T21:46:50.2722495Z 2025-08-14T21:46:50.2724282Z Running test_foreach 1/1 ... [2025-08-14 21:46:50.272268] 2025-08-14T21:46:50.2724669Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:50.2727989Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_foreach.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'] ... [2025-08-14 21:46:50.272593] 2025-08-14T21:46:58.2505821Z 2025-08-14T21:46:58.2506683Z test_foreach 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_foreach_1.1_c11cb1699f4c76ab_.log 2025-08-14T21:46:58.2507416Z Running 0 items in this shard: 2025-08-14T21:46:58.2507709Z 2025-08-14T21:46:58.2508604Z Running test_expanded_weights 1/1 ... [2025-08-14 21:46:58.250686] 2025-08-14T21:46:58.2509025Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:46:58.2512395Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_expanded_weights.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:46:58.251012] 2025-08-14T21:47:02.2704893Z 2025-08-14T21:47:02.2705787Z test_expanded_weights 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_expanded_weights_1.1_740f47009b80c797_.log 2025-08-14T21:47:02.2706555Z Running 0 items in this shard: 2025-08-14T21:47:02.2706758Z 2025-08-14T21:47:02.2708901Z Running typing/test_python_operators 1/1 ... [2025-08-14 21:47:02.270730] 2025-08-14T21:47:02.2709400Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:02.2713013Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'typing/test_python_operators.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'] ... [2025-08-14 21:47:02.271070] 2025-08-14T21:47:05.2392430Z 2025-08-14T21:47:05.2393743Z typing/test_python_operators 1/1 was successful, full logs can be found in artifacts with path test/test-reports/typing.test_python_operators_1.1_5e89ab75ea422d0b_.log 2025-08-14T21:47:05.2394602Z Running 0 items in this shard: 2025-08-14T21:47:05.2394790Z 2025-08-14T21:47:05.2397123Z Running test_fx_experimental 1/1 ... [2025-08-14 21:47:05.239528] 2025-08-14T21:47:05.2397547Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:05.2401394Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fx_experimental.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'] ... [2025-08-14 21:47:05.239895] 2025-08-14T21:47:09.5098005Z 2025-08-14T21:47:09.5099319Z test_fx_experimental 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_experimental_1.1_38b20493de8cd5c8_.log 2025-08-14T21:47:09.5100754Z Running 0 items in this shard: 2025-08-14T21:47:09.5101094Z 2025-08-14T21:47:09.5104037Z Running test_file_check 1/1 ... [2025-08-14 21:47:09.510192] 2025-08-14T21:47:09.5104663Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:09.5109262Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_file_check.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:47:09.510618] 2025-08-14T21:47:12.4286989Z 2025-08-14T21:47:12.4288055Z test_file_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_file_check_1.1_b893776715fe4e95_.log 2025-08-14T21:47:12.4288777Z Running 0 items in this shard: 2025-08-14T21:47:12.4288988Z 2025-08-14T21:47:12.4291246Z Running torch_np/test_dtype 1/1 ... [2025-08-14 21:47:12.428966] 2025-08-14T21:47:12.4291669Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:12.4295665Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_dtype.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'] ... [2025-08-14 21:47:12.429344] 2025-08-14T21:47:15.3474072Z 2025-08-14T21:47:15.3475401Z torch_np/test_dtype 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_dtype_1.1_ad4f1551cf83262b_.log 2025-08-14T21:47:15.3476767Z Running 0 items in this shard: 2025-08-14T21:47:15.3477122Z 2025-08-14T21:47:15.3480017Z Running higher_order_ops/test_with_effects 1/1 ... [2025-08-14 21:47:15.347804] 2025-08-14T21:47:15.3480806Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:15.3485270Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'higher_order_ops/test_with_effects.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'] ... [2025-08-14 21:47:15.348206] 2025-08-14T21:47:18.8670685Z 2025-08-14T21:47:18.8671964Z higher_order_ops/test_with_effects 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_with_effects_1.1_3529426e8a087253_.log 2025-08-14T21:47:18.8672849Z Running 0 items in this shard: 2025-08-14T21:47:18.8673035Z 2025-08-14T21:47:18.8674936Z Running test_native_functions 1/1 ... [2025-08-14 21:47:18.867310] 2025-08-14T21:47:18.8675623Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:18.8678977Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_native_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'] ... [2025-08-14 21:47:18.867634] 2025-08-14T21:47:21.8357871Z 2025-08-14T21:47:21.8359225Z test_native_functions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_functions_1.1_fed876a100c85c47_.log 2025-08-14T21:47:21.8360663Z Running 0 items in this shard: 2025-08-14T21:47:21.8361002Z 2025-08-14T21:47:21.8363780Z Running functorch/test_minifier 1/1 ... [2025-08-14 21:47:21.836161] 2025-08-14T21:47:21.8364507Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:21.8368859Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/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'] ... [2025-08-14 21:47:21.836564] 2025-08-14T21:47:25.0048677Z 2025-08-14T21:47:25.0050293Z functorch/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_minifier_1.1_86c487f0c9fba9c0_.log 2025-08-14T21:47:25.0051858Z Running 0 items in this shard: 2025-08-14T21:47:25.0052184Z 2025-08-14T21:47:25.0054982Z Running test_flop_counter 1/1 ... [2025-08-14 21:47:25.005285] 2025-08-14T21:47:25.0055634Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:25.0060044Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_flop_counter.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'] ... [2025-08-14 21:47:25.005690] 2025-08-14T21:47:28.0738706Z 2025-08-14T21:47:28.0739987Z test_flop_counter 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_flop_counter_1.1_13d5afe0ab3b20af_.log 2025-08-14T21:47:28.0741550Z Running 0 items in this shard: 2025-08-14T21:47:28.0741884Z 2025-08-14T21:47:28.0744780Z Running torch_np/test_nep50_examples 1/1 ... [2025-08-14 21:47:28.074274] 2025-08-14T21:47:28.0745545Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:28.0749871Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_nep50_examples.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'] ... [2025-08-14 21:47:28.074672] 2025-08-14T21:47:31.1429805Z 2025-08-14T21:47:31.1430805Z torch_np/test_nep50_examples 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_nep50_examples_1.1_a3bdd1bd09549f1c_.log 2025-08-14T21:47:31.1431654Z Running 0 items in this shard: 2025-08-14T21:47:31.1431842Z 2025-08-14T21:47:31.1434141Z Running higher_order_ops/test_invoke_quant 1/1 ... [2025-08-14 21:47:31.143246] 2025-08-14T21:47:31.1434682Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:31.1438268Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'higher_order_ops/test_invoke_quant.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'] ... [2025-08-14 21:47:31.143595] 2025-08-14T21:47:37.5161233Z 2025-08-14T21:47:37.5162788Z higher_order_ops/test_invoke_quant 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_invoke_quant_1.1_c643d72526ee876f_.log 2025-08-14T21:47:37.5164396Z Running 0 items in this shard: 2025-08-14T21:47:37.5164720Z 2025-08-14T21:47:37.5167303Z Running test_per_overload_api 1/1 ... [2025-08-14 21:47:37.516515] 2025-08-14T21:47:37.5167759Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:37.5170768Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_per_overload_api.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'] ... [2025-08-14 21:47:37.516864] 2025-08-14T21:47:40.4348438Z 2025-08-14T21:47:40.4349310Z test_per_overload_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_per_overload_api_1.1_1cae1eb1059310c8_.log 2025-08-14T21:47:40.4350118Z Running 0 items in this shard: 2025-08-14T21:47:40.4350306Z 2025-08-14T21:47:40.4352759Z Running test_tensorexpr_pybind 1/1 ... [2025-08-14 21:47:40.435080] 2025-08-14T21:47:40.4353254Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:40.4356403Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensorexpr_pybind.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'] ... [2025-08-14 21:47:40.435401] 2025-08-14T21:47:43.4035561Z 2025-08-14T21:47:43.4036652Z test_tensorexpr_pybind 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_pybind_1.1_d7c4685389a6564f_.log 2025-08-14T21:47:43.4037525Z Running 0 items in this shard: 2025-08-14T21:47:43.4037713Z 2025-08-14T21:47:43.4040037Z Running test_pytree 1/1 ... [2025-08-14 21:47:43.403828] 2025-08-14T21:47:43.4040404Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:43.4044052Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_pytree.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:47:43.404203] 2025-08-14T21:47:46.3222240Z 2025-08-14T21:47:46.3223548Z test_pytree 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_pytree_1.1_0a340910f19067c9_.log 2025-08-14T21:47:46.3224767Z Running 0 items in this shard: 2025-08-14T21:47:46.3225122Z 2025-08-14T21:47:46.3228440Z Running test_fx_passes 1/1 ... [2025-08-14 21:47:46.322638] 2025-08-14T21:47:46.3229068Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:46.3233553Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fx_passes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:47:46.323045] 2025-08-14T21:47:49.2911940Z 2025-08-14T21:47:49.2912870Z test_fx_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_passes_1.1_a6992de134efb899_.log 2025-08-14T21:47:49.2913592Z Running 0 items in this shard: 2025-08-14T21:47:49.2913786Z 2025-08-14T21:47:49.2916544Z Running test_module_tracker 1/1 ... [2025-08-14 21:47:49.291466] 2025-08-14T21:47:49.2919464Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:49.2920648Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_module_tracker.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:47:49.291801] 2025-08-14T21:47:52.2099573Z 2025-08-14T21:47:52.2100802Z test_module_tracker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_module_tracker_1.1_63f4e0e0a73e75ad_.log 2025-08-14T21:47:52.2102022Z Running 0 items in this shard: 2025-08-14T21:47:52.2102342Z 2025-08-14T21:47:52.2105972Z Running test_typing 1/1 ... [2025-08-14 21:47:52.210389] 2025-08-14T21:47:52.2106587Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:52.2110990Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_typing.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'] ... [2025-08-14 21:47:52.210809] 2025-08-14T21:47:55.2290725Z 2025-08-14T21:47:55.2291846Z test_typing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_typing_1.1_05917c7fa1cfaa65_.log 2025-08-14T21:47:55.2292535Z Running 0 items in this shard: 2025-08-14T21:47:55.2292747Z 2025-08-14T21:47:55.2294807Z Running test_openmp 1/1 ... [2025-08-14 21:47:55.229302] 2025-08-14T21:47:55.2295202Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:55.2298846Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_openmp.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'] ... [2025-08-14 21:47:55.229647] 2025-08-14T21:47:58.1476414Z 2025-08-14T21:47:58.1477533Z test_openmp 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_openmp_1.1_7a1a5f7bbebc2650_.log 2025-08-14T21:47:58.1478239Z Running 0 items in this shard: 2025-08-14T21:47:58.1478428Z 2025-08-14T21:47:58.1480774Z Running torch_np/numpy_tests/core/test_scalarinherit 1/1 ... [2025-08-14 21:47:58.147869] 2025-08-14T21:47:58.1481351Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:47:58.1484894Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalarinherit.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:47:58.148190] 2025-08-14T21:48:01.0661846Z 2025-08-14T21:48:01.0663334Z torch_np/numpy_tests/core/test_scalarinherit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalarinherit_1.1_2e5dce3235a7cef3_.log 2025-08-14T21:48:01.0664325Z Running 0 items in this shard: 2025-08-14T21:48:01.0664530Z 2025-08-14T21:48:01.0666190Z Running functorch/test_ac_knapsack 1/1 ... [2025-08-14 21:48:01.066436] 2025-08-14T21:48:01.0666844Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:01.0670383Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ac_knapsack.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'] ... [2025-08-14 21:48:01.066758] 2025-08-14T21:48:04.0848847Z 2025-08-14T21:48:04.0849820Z functorch/test_ac_knapsack 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_knapsack_1.1_c49dc0ad9aa31f5b_.log 2025-08-14T21:48:04.0850873Z Running 0 items in this shard: 2025-08-14T21:48:04.0851074Z 2025-08-14T21:48:04.0853111Z Running backends/xeon/test_launch 1/1 ... [2025-08-14 21:48:04.085126] 2025-08-14T21:48:04.0853556Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:04.0857102Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'backends/xeon/test_launch.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'] ... [2025-08-14 21:48:04.085458] 2025-08-14T21:48:07.0535863Z 2025-08-14T21:48:07.0536838Z backends/xeon/test_launch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/backends.xeon.test_launch_1.1_e6b2e074b7d76ad0_.log 2025-08-14T21:48:07.0537681Z Running 0 items in this shard: 2025-08-14T21:48:07.0537885Z 2025-08-14T21:48:07.0539901Z Running test_nestedtensor 1/1 ... [2025-08-14 21:48:07.053833] 2025-08-14T21:48:07.0540336Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:07.0543714Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nestedtensor.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'] ... [2025-08-14 21:48:07.054159] 2025-08-14T21:48:12.1749976Z 2025-08-14T21:48:12.1750839Z test_nestedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_nestedtensor_1.1_3b0fcf47f86a45cc_.log 2025-08-14T21:48:12.1752017Z Running 1 items in this shard: test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_linear_backward_memory_usage_cpu_float32 2025-08-14T21:48:12.1752638Z 2025-08-14T21:48:12.1753758Z Running test_namedtensor 1/1 ... [2025-08-14 21:48:12.175220] 2025-08-14T21:48:12.1754189Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:12.1757587Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_namedtensor.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:48:12.175538] 2025-08-14T21:48:15.1437360Z 2025-08-14T21:48:15.1438612Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_d517d53056ffcc20_.log 2025-08-14T21:48:15.1439937Z Running 0 items in this shard: 2025-08-14T21:48:15.1440271Z 2025-08-14T21:48:15.1442931Z Running torch_np/test_ufuncs_basic 1/1 ... [2025-08-14 21:48:15.144107] 2025-08-14T21:48:15.1443626Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:15.1448614Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_ufuncs_basic.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:48:15.144510] 2025-08-14T21:48:18.1126173Z 2025-08-14T21:48:18.1127559Z torch_np/test_ufuncs_basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_ufuncs_basic_1.1_5feac9a45b8bafa6_.log 2025-08-14T21:48:18.1129053Z Running 0 items in this shard: 2025-08-14T21:48:18.1129391Z 2025-08-14T21:48:18.1131827Z Running test_meta 1/1 ... [2025-08-14 21:48:18.113008] 2025-08-14T21:48:18.1132398Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:18.1137325Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_meta.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'] ... [2025-08-14 21:48:18.113402] 2025-08-14T21:48:28.6471626Z 2025-08-14T21:48:28.6472393Z test_meta 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_meta_1.1_395f675c7a228b3f_.log 2025-08-14T21:48:28.6473060Z Running 0 items in this shard: 2025-08-14T21:48:28.6473470Z 2025-08-14T21:48:28.6475621Z Running test_utils_filelock 1/1 ... [2025-08-14 21:48:28.647381] 2025-08-14T21:48:28.6476036Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:28.6479563Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_utils_filelock.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'] ... [2025-08-14 21:48:28.647700] 2025-08-14T21:48:31.6175166Z 2025-08-14T21:48:31.6176195Z test_utils_filelock 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_filelock_1.1_da0bff96de0a4037_.log 2025-08-14T21:48:31.6177595Z Running 0 items in this shard: 2025-08-14T21:48:31.6177804Z 2025-08-14T21:48:31.6179566Z Running torch_np/test_random 1/1 ... [2025-08-14 21:48:31.617767] 2025-08-14T21:48:31.6180223Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:31.6183222Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_random.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'] ... [2025-08-14 21:48:31.618075] 2025-08-14T21:48:34.5876728Z 2025-08-14T21:48:34.5877886Z torch_np/test_random 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_random_1.1_4cb61c485e4f57d7_.log 2025-08-14T21:48:34.5878667Z Running 0 items in this shard: 2025-08-14T21:48:34.5878856Z 2025-08-14T21:48:34.5881089Z Running profiler/test_kineto 1/1 ... [2025-08-14 21:48:34.587911] 2025-08-14T21:48:34.5881813Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:34.5884903Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_kineto.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'] ... [2025-08-14 21:48:34.588235] 2025-08-14T21:48:37.5063237Z 2025-08-14T21:48:37.5064410Z profiler/test_kineto 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_kineto_1.1_b9788f0b52326706_.log 2025-08-14T21:48:37.5065184Z Running 0 items in this shard: 2025-08-14T21:48:37.5065386Z 2025-08-14T21:48:37.5067522Z Running test_compile_benchmark_util 1/1 ... [2025-08-14 21:48:37.506570] 2025-08-14T21:48:37.5068186Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:37.5071526Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_compile_benchmark_util.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'] ... [2025-08-14 21:48:37.506891] 2025-08-14T21:48:40.6752954Z 2025-08-14T21:48:40.6754177Z test_compile_benchmark_util 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_compile_benchmark_util_1.1_3ec9923bee194a8a_.log 2025-08-14T21:48:40.6755028Z Running 0 items in this shard: 2025-08-14T21:48:40.6755216Z 2025-08-14T21:48:40.6756740Z Running test_optim 1/3 ... [2025-08-14 21:48:40.675512] 2025-08-14T21:48:40.6757388Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:40.6760727Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_optim.py', '-m', 'serial', '--shard-id=1', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:48:40.675836] 2025-08-14T21:48:44.7454802Z 2025-08-14T21:48:44.7456155Z test_optim 1/3 was successful, full logs can be found in artifacts with path test/test-reports/test_optim_1.3_64bbddefa5b2ad11_.log 2025-08-14T21:48:44.7456843Z Running 0 items in this shard: 2025-08-14T21:48:44.7457042Z 2025-08-14T21:48:44.7458673Z Running test_legacy_vmap 2/2 ... [2025-08-14 21:48:44.745689] 2025-08-14T21:48:44.7459319Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:44.7462622Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_legacy_vmap.py', '-m', 'serial', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:48:44.746001] 2025-08-14T21:48:47.7642341Z 2025-08-14T21:48:47.7643440Z test_legacy_vmap 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_2.2_177963ef3bce6a76_.log 2025-08-14T21:48:47.7644162Z Running 0 items in this shard: 2025-08-14T21:48:47.7644365Z 2025-08-14T21:48:47.7646291Z Running test_modules 1/1 ... [2025-08-14 21:48:47.764471] 2025-08-14T21:48:47.7646932Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:47.7650251Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_modules.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:48:47.764784] 2025-08-14T21:48:52.1347424Z 2025-08-14T21:48:52.1348721Z test_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_modules_1.1_faad6db46be2f11c_.log 2025-08-14T21:48:52.1349423Z Running 0 items in this shard: 2025-08-14T21:48:52.1349628Z 2025-08-14T21:48:52.1351693Z Running test_matmul_cuda 1/1 ... [2025-08-14 21:48:52.134997] 2025-08-14T21:48:52.1352156Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:52.1355540Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:48:52.135332] 2025-08-14T21:48:55.1089811Z 2025-08-14T21:48:55.1091043Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_c18ab7374bbc4e0c_.log 2025-08-14T21:48:55.1091800Z Running 0 items in this shard: 2025-08-14T21:48:55.1091988Z 2025-08-14T21:48:55.1094586Z Running test_weak 1/1 ... [2025-08-14 21:48:55.109267] 2025-08-14T21:48:55.1095295Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:55.1099151Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_weak.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'] ... [2025-08-14 21:48:55.109664] 2025-08-14T21:48:58.0277723Z 2025-08-14T21:48:58.0278630Z test_weak 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_weak_1.1_6646934e02a91ceb_.log 2025-08-14T21:48:58.0279524Z Running 0 items in this shard: 2025-08-14T21:48:58.0279815Z 2025-08-14T21:48:58.0282030Z Running functorch/test_logging 1/1 ... [2025-08-14 21:48:58.028029] 2025-08-14T21:48:58.0282461Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:48:58.0285789Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_logging.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:48:58.028355] 2025-08-14T21:49:01.1466925Z 2025-08-14T21:49:01.1467961Z functorch/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_logging_1.1_b1744fd0511e6543_.log 2025-08-14T21:49:01.1468776Z Running 0 items in this shard: 2025-08-14T21:49:01.1468969Z 2025-08-14T21:49:01.1471785Z Running test_logging 1/1 ... [2025-08-14 21:49:01.146988] 2025-08-14T21:49:01.1472177Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:01.1475764Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_logging.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:49:01.147357] 2025-08-14T21:49:04.0655353Z 2025-08-14T21:49:04.0656158Z test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_logging_1.1_f37e256dd0ccb00d_.log 2025-08-14T21:49:04.0656849Z Running 0 items in this shard: 2025-08-14T21:49:04.0657057Z 2025-08-14T21:49:04.0659874Z Running test_out_dtype_op 1/1 ... [2025-08-14 21:49:04.065819] 2025-08-14T21:49:04.0660340Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:04.0663774Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_out_dtype_op.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:49:04.066151] 2025-08-14T21:49:07.3347511Z 2025-08-14T21:49:07.3348630Z test_out_dtype_op 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_out_dtype_op_1.1_b53427f23313554b_.log 2025-08-14T21:49:07.3349941Z Running 0 items in this shard: 2025-08-14T21:49:07.3350306Z 2025-08-14T21:49:07.3353381Z Running test_complex 1/1 ... [2025-08-14 21:49:07.335145] 2025-08-14T21:49:07.3353986Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:07.3358360Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_complex.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'] ... [2025-08-14 21:49:07.335528] 2025-08-14T21:49:10.3037516Z 2025-08-14T21:49:10.3038398Z test_complex 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_complex_1.1_4df35ccfe09fdbed_.log 2025-08-14T21:49:10.3039094Z Running 0 items in this shard: 2025-08-14T21:49:10.3039301Z 2025-08-14T21:49:10.3041727Z Running torch_np/numpy_tests/core/test_einsum 1/1 ... [2025-08-14 21:49:10.303999] 2025-08-14T21:49:10.3042277Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:10.3045616Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_einsum.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:49:10.304340] 2025-08-14T21:49:13.2724569Z 2025-08-14T21:49:13.2725554Z torch_np/numpy_tests/core/test_einsum 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_einsum_1.1_2175ffa5fac43db9_.log 2025-08-14T21:49:13.2726473Z Running 0 items in this shard: 2025-08-14T21:49:13.2726661Z 2025-08-14T21:49:13.2728618Z Running test_ops_jit 1/1 ... [2025-08-14 21:49:13.272690] 2025-08-14T21:49:13.2729272Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:13.2732497Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_jit.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:49:13.273016] 2025-08-14T21:49:17.4425978Z 2025-08-14T21:49:17.4426637Z test_ops_jit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_jit_1.1_9aedd86b5cf37563_.log 2025-08-14T21:49:17.4427341Z Running 0 items in this shard: 2025-08-14T21:49:17.4427531Z 2025-08-14T21:49:17.4429982Z Running lazy/test_step_closures 1/1 ... [2025-08-14 21:49:17.442838] 2025-08-14T21:49:17.4430404Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:17.4433947Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_step_closures.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'] ... [2025-08-14 21:49:17.443160] 2025-08-14T21:49:20.4112628Z 2025-08-14T21:49:20.4113566Z lazy/test_step_closures 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_step_closures_1.1_1888a9986f3bf46f_.log 2025-08-14T21:49:20.4114623Z Running 0 items in this shard: 2025-08-14T21:49:20.4114812Z 2025-08-14T21:49:20.4116621Z Running test_ops_gradients 1/1 ... [2025-08-14 21:49:20.411475] 2025-08-14T21:49:20.4117125Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:20.4120346Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_gradients.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:49:20.411800] 2025-08-14T21:49:25.3324591Z 2025-08-14T21:49:25.3325886Z test_ops_gradients 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_gradients_1.1_d1ceae5bd0129110_.log 2025-08-14T21:49:25.3327264Z Running 0 items in this shard: 2025-08-14T21:49:25.3327597Z 2025-08-14T21:49:25.3330582Z Running test_fx_reinplace_pass 1/1 ... [2025-08-14 21:49:25.332834] 2025-08-14T21:49:25.3331299Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:25.3335571Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fx_reinplace_pass.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'] ... [2025-08-14 21:49:25.333239] 2025-08-14T21:49:28.2514207Z 2025-08-14T21:49:28.2515450Z test_fx_reinplace_pass 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_reinplace_pass_1.1_21d6e9f7adf11a97_.log 2025-08-14T21:49:28.2516355Z Running 0 items in this shard: 2025-08-14T21:49:28.2516583Z 2025-08-14T21:49:28.2517201Z Running nn/test_multihead_attention 1/1 ... [2025-08-14 21:49:28.251531] 2025-08-14T21:49:28.2517777Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:28.2521288Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_multihead_attention.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:49:28.251849] 2025-08-14T21:49:31.2198754Z 2025-08-14T21:49:31.2199779Z nn/test_multihead_attention 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_multihead_attention_1.1_d86c921046d7337f_.log 2025-08-14T21:49:31.2200611Z Running 0 items in this shard: 2025-08-14T21:49:31.2200814Z 2025-08-14T21:49:31.2202945Z Running functorch/test_aot_joint_with_descriptors 1/1 ... [2025-08-14 21:49:31.220121] 2025-08-14T21:49:31.2203458Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:31.2206812Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aot_joint_with_descriptors.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'] ... [2025-08-14 21:49:31.220440] 2025-08-14T21:49:34.3386476Z 2025-08-14T21:49:34.3387816Z functorch/test_aot_joint_with_descriptors 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aot_joint_with_descriptors_1.1_94bb1b0f3d5b33b9_.log 2025-08-14T21:49:34.3388802Z Running 0 items in this shard: 2025-08-14T21:49:34.3388992Z 2025-08-14T21:49:34.3390369Z Running test_pruning_op 1/1 ... [2025-08-14 21:49:34.338885] 2025-08-14T21:49:34.3391059Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:34.3394606Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_pruning_op.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:49:34.339186] 2025-08-14T21:49:37.2571409Z 2025-08-14T21:49:37.2572520Z test_pruning_op 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_pruning_op_1.1_82bf5a27891b44f4_.log 2025-08-14T21:49:37.2573254Z Running 0 items in this shard: 2025-08-14T21:49:37.2573652Z 2025-08-14T21:49:37.2575669Z Running lazy/test_functionalization 1/1 ... [2025-08-14 21:49:37.257380] 2025-08-14T21:49:37.2576305Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:37.2579249Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_functionalization.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:49:37.257681] 2025-08-14T21:49:40.2258566Z 2025-08-14T21:49:40.2259581Z lazy/test_functionalization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_functionalization_1.1_14c03a4fae487642_.log 2025-08-14T21:49:40.2260437Z Running 0 items in this shard: 2025-08-14T21:49:40.2260630Z 2025-08-14T21:49:40.2262023Z Running functorch/test_ac 1/1 ... [2025-08-14 21:49:40.226044] 2025-08-14T21:49:40.2262450Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:40.2265884Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ac.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:49:40.226373] 2025-08-14T21:49:46.4221588Z 2025-08-14T21:49:46.4222524Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_996b0ed1ce1e4001_.log 2025-08-14T21:49:46.4223182Z 2025-08-14T21:49:46.4225587Z Running test_ops_fwd_gradients 1/1 ... [2025-08-14 21:49:46.422386] 2025-08-14T21:49:46.4226121Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:46.4229607Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_fwd_gradients.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:49:46.422705] 2025-08-14T21:49:50.7425300Z 2025-08-14T21:49:50.7426325Z test_ops_fwd_gradients 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_fwd_gradients_1.1_927af6f7691c96ac_.log 2025-08-14T21:49:50.7427117Z Running 0 items in this shard: 2025-08-14T21:49:50.7427306Z 2025-08-14T21:49:50.7429063Z Running test_hub 1/1 ... [2025-08-14 21:49:50.742752] 2025-08-14T21:49:50.7429440Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:50.7432696Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_hub.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:49:50.743068] 2025-08-14T21:49:53.7113018Z 2025-08-14T21:49:53.7113900Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_9940dd3527bcf5bf_.log 2025-08-14T21:49:53.7114569Z Running 0 items in this shard: 2025-08-14T21:49:53.7114756Z 2025-08-14T21:49:53.7117381Z Running test_set_default_mobile_cpu_allocator 1/1 ... [2025-08-14 21:49:53.711525] 2025-08-14T21:49:53.7117908Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:53.7121064Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_set_default_mobile_cpu_allocator.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'] ... [2025-08-14 21:49:53.711862] 2025-08-14T21:49:56.6298920Z 2025-08-14T21:49:56.6300227Z test_set_default_mobile_cpu_allocator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_set_default_mobile_cpu_allocator_1.1_62cc1a4c31a84171_.log 2025-08-14T21:49:56.6301769Z Running 0 items in this shard: 2025-08-14T21:49:56.6302014Z 2025-08-14T21:49:56.6303553Z Running test_stateless 1/1 ... [2025-08-14 21:49:56.630171] 2025-08-14T21:49:56.6303984Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:56.6307632Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_stateless.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:49:56.630539] 2025-08-14T21:49:59.5485360Z 2025-08-14T21:49:59.5486485Z test_stateless 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_stateless_1.1_2c85133a38e56e97_.log 2025-08-14T21:49:59.5487198Z Running 0 items in this shard: 2025-08-14T21:49:59.5487386Z 2025-08-14T21:49:59.5489625Z Running distributions/test_constraints 1/1 ... [2025-08-14 21:49:59.548768] 2025-08-14T21:49:59.5490303Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:49:59.5493786Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_constraints.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:49:59.549094] 2025-08-14T21:50:02.5672953Z 2025-08-14T21:50:02.5673941Z distributions/test_constraints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_constraints_1.1_1352ba6cd9142c44_.log 2025-08-14T21:50:02.5674821Z Running 0 items in this shard: 2025-08-14T21:50:02.5675008Z 2025-08-14T21:50:02.5677242Z Running profiler/test_record_function 1/1 ... [2025-08-14 21:50:02.567542] 2025-08-14T21:50:02.5677768Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:02.5680920Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_record_function.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:50:02.567855] 2025-08-14T21:50:05.5361097Z 2025-08-14T21:50:05.5362074Z profiler/test_record_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_record_function_1.1_cea0508918cb69c1_.log 2025-08-14T21:50:05.5371098Z Running 0 items in this shard: 2025-08-14T21:50:05.5371450Z 2025-08-14T21:50:05.5371633Z Running nn/test_lazy_modules 1/1 ... [2025-08-14 21:50:05.536344] 2025-08-14T21:50:05.5372053Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:05.5373153Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_lazy_modules.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:50:05.536665] 2025-08-14T21:50:08.4547111Z 2025-08-14T21:50:08.4548393Z nn/test_lazy_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_lazy_modules_1.1_03a6bbd6183f5504_.log 2025-08-14T21:50:08.4549243Z Running 0 items in this shard: 2025-08-14T21:50:08.4549484Z 2025-08-14T21:50:08.4550658Z Running test_jit_fuser_te 1/2 ... [2025-08-14 21:50:08.454908] 2025-08-14T21:50:08.4551340Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:08.4554736Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit_fuser_te.py', '-m', 'serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:50:08.455231] 2025-08-14T21:50:14.2772110Z 2025-08-14T21:50:14.2772968Z test_jit_fuser_te 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_fuser_te_1.2_20ff14a2a2725a0e_.log 2025-08-14T21:50:14.2773912Z Running 0 items in this shard: 2025-08-14T21:50:14.2774102Z 2025-08-14T21:50:14.2776033Z Running optim/test_lrscheduler 1/1 ... [2025-08-14 21:50:14.277413] 2025-08-14T21:50:14.2776469Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:14.2780663Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_lrscheduler.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:50:14.277734] 2025-08-14T21:50:17.1437217Z 2025-08-14T21:50:17.1438267Z optim/test_lrscheduler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_lrscheduler_1.1_598e91eafea94f7f_.log 2025-08-14T21:50:17.1438956Z 2025-08-14T21:50:17.1441332Z Running test_jit 2/2 ... [2025-08-14 21:50:17.143979] 2025-08-14T21:50:17.1441719Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:17.1445503Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit.py', '-m', 'serial', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:50:17.144335] 2025-08-14T21:50:21.9148640Z 2025-08-14T21:50:21.9149392Z test_jit 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_2.2_e7a864559e75ef85_.log 2025-08-14T21:50:21.9150093Z Running 0 items in this shard: 2025-08-14T21:50:21.9150284Z 2025-08-14T21:50:21.9153020Z Running nn/test_packed_sequence 1/1 ... [2025-08-14 21:50:21.915116] 2025-08-14T21:50:21.9153442Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:21.9156513Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_packed_sequence.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:50:21.915435] 2025-08-14T21:50:24.8334267Z 2025-08-14T21:50:24.8335175Z nn/test_packed_sequence 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_packed_sequence_1.1_ca8e8f7134674191_.log 2025-08-14T21:50:24.8335975Z Running 0 items in this shard: 2025-08-14T21:50:24.8336162Z 2025-08-14T21:50:24.8338537Z Running cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic 1/1 ... [2025-08-14 21:50:24.833671] 2025-08-14T21:50:24.8339194Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:24.8342161Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.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'] ... [2025-08-14 21:50:24.833994] 2025-08-14T21:50:27.8021142Z 2025-08-14T21:50:27.8022495Z cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp_extensions.libtorch_agnostic_extension.test.test_libtorch_agnostic_1.1_3b0f2e486b43cb31_.log 2025-08-14T21:50:27.8024041Z Running 0 items in this shard: 2025-08-14T21:50:27.8024349Z 2025-08-14T21:50:27.8025098Z Running functorch/test_ops 1/1 ... [2025-08-14 21:50:27.802324] 2025-08-14T21:50:27.8025523Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:27.8028732Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_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'] ... [2025-08-14 21:50:27.802662] 2025-08-14T21:50:33.7747201Z 2025-08-14T21:50:33.7748043Z functorch/test_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ops_1.1_7f43f825db4f1e87_.log 2025-08-14T21:50:33.7748805Z Running 0 items in this shard: 2025-08-14T21:50:33.7748993Z 2025-08-14T21:50:33.7751170Z Running test_schema_check 1/1 ... [2025-08-14 21:50:33.774956] 2025-08-14T21:50:33.7751571Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:33.7754877Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_schema_check.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:50:33.775275] 2025-08-14T21:50:38.6459155Z 2025-08-14T21:50:38.6460522Z test_schema_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_schema_check_1.1_e8342b865ede5e4e_.log 2025-08-14T21:50:38.6461842Z Running 0 items in this shard: 2025-08-14T21:50:38.6462196Z 2025-08-14T21:50:38.6464983Z Running test_datapipe 1/1 ... [2025-08-14 21:50:38.646300] 2025-08-14T21:50:38.6465616Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:38.6470058Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_datapipe.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:50:38.646692] 2025-08-14T21:50:41.6149050Z 2025-08-14T21:50:41.6149928Z test_datapipe 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_datapipe_1.1_fb41dd76c5a8965c_.log 2025-08-14T21:50:41.6150727Z Running 0 items in this shard: 2025-08-14T21:50:41.6150918Z 2025-08-14T21:50:41.6154097Z Running test_bundled_inputs 1/1 ... [2025-08-14 21:50:41.615196] 2025-08-14T21:50:41.6154535Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:41.6157580Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_bundled_inputs.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'] ... [2025-08-14 21:50:41.615550] 2025-08-14T21:50:44.5336480Z 2025-08-14T21:50:44.5337532Z test_bundled_inputs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_bundled_inputs_1.1_c0c696a4ca4297f1_.log 2025-08-14T21:50:44.5338423Z Running 0 items in this shard: 2025-08-14T21:50:44.5338697Z 2025-08-14T21:50:44.5341683Z Running profiler/test_execution_trace 1/1 ... [2025-08-14 21:50:44.533929] 2025-08-14T21:50:44.5342169Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:44.5345275Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_execution_trace.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:50:44.534298] 2025-08-14T21:50:47.5025492Z 2025-08-14T21:50:47.5027125Z profiler/test_execution_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_execution_trace_1.1_d030c93745eba37e_.log 2025-08-14T21:50:47.5028680Z Running 0 items in this shard: 2025-08-14T21:50:47.5029024Z 2025-08-14T21:50:47.5031381Z Running lazy/test_generator 1/1 ... [2025-08-14 21:50:47.502935] 2025-08-14T21:50:47.5032310Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:47.5036692Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/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'] ... [2025-08-14 21:50:47.503352] 2025-08-14T21:50:50.4214546Z 2025-08-14T21:50:50.4215717Z lazy/test_generator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_generator_1.1_c4c39fc62169c3e3_.log 2025-08-14T21:50:50.4216493Z Running 0 items in this shard: 2025-08-14T21:50:50.4216682Z 2025-08-14T21:50:50.4218309Z Running torch_np/numpy_tests/core/test_indexing 1/1 ... [2025-08-14 21:50:50.421663] 2025-08-14T21:50:50.4218794Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:50.4222394Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_indexing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:50:50.421982] 2025-08-14T21:50:53.4403738Z 2025-08-14T21:50:53.4404844Z torch_np/numpy_tests/core/test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_indexing_1.1_a3c6096d9072e1d7_.log 2025-08-14T21:50:53.4406108Z Running 0 items in this shard: 2025-08-14T21:50:53.4406312Z 2025-08-14T21:50:53.4408297Z Running test_maskedtensor 1/1 ... [2025-08-14 21:50:53.440607] 2025-08-14T21:50:53.4408715Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:53.4411705Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_maskedtensor.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'] ... [2025-08-14 21:50:53.440951] 2025-08-14T21:50:57.4606511Z 2025-08-14T21:50:57.4607341Z test_maskedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_maskedtensor_1.1_51d56ed53902f68a_.log 2025-08-14T21:50:57.4608093Z Running 0 items in this shard: 2025-08-14T21:50:57.4608283Z 2025-08-14T21:50:57.4610486Z Running test_tensorboard 1/1 ... [2025-08-14 21:50:57.460880] 2025-08-14T21:50:57.4610971Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:50:57.4614619Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensorboard.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'] ... [2025-08-14 21:50:57.461196] 2025-08-14T21:51:00.7798593Z 2025-08-14T21:51:00.7799530Z test_tensorboard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorboard_1.1_cf58776dc18b4b1d_.log 2025-08-14T21:51:00.7800261Z Running 0 items in this shard: 2025-08-14T21:51:00.7800467Z 2025-08-14T21:51:00.7803070Z Running torch_np/numpy_tests/lib/test_type_check 1/1 ... [2025-08-14 21:51:00.780134] 2025-08-14T21:51:00.7803583Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:00.7807060Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_type_check.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:51:00.780491] 2025-08-14T21:51:03.7486648Z 2025-08-14T21:51:03.7487721Z torch_np/numpy_tests/lib/test_type_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_type_check_1.1_6a49d2dee606cec3_.log 2025-08-14T21:51:03.7488664Z Running 0 items in this shard: 2025-08-14T21:51:03.7488869Z 2025-08-14T21:51:03.7491561Z Running lazy/test_ts_opinfo 1/1 ... [2025-08-14 21:51:03.748932] 2025-08-14T21:51:03.7492272Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:03.7496556Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_ts_opinfo.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:51:03.749316] 2025-08-14T21:51:07.7189125Z 2025-08-14T21:51:07.7190089Z lazy/test_ts_opinfo 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_ts_opinfo_1.1_fd8a3e552e22efb8_.log 2025-08-14T21:51:07.7190863Z Running 0 items in this shard: 2025-08-14T21:51:07.7191059Z 2025-08-14T21:51:07.7192762Z Running test_mkldnn_fusion 1/1 ... [2025-08-14 21:51:07.719105] 2025-08-14T21:51:07.7193356Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:07.7197763Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_mkldnn_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'] ... [2025-08-14 21:51:07.719478] 2025-08-14T21:51:10.6875682Z 2025-08-14T21:51:10.6876936Z test_mkldnn_fusion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_fusion_1.1_9f6ccdee98aa4aa6_.log 2025-08-14T21:51:10.6877932Z Running 0 items in this shard: 2025-08-14T21:51:10.6878120Z 2025-08-14T21:51:10.6880153Z Running benchmark_utils/test_benchmark_utils 1/1 ... [2025-08-14 21:51:10.687804] 2025-08-14T21:51:10.6880726Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:10.6884115Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'benchmark_utils/test_benchmark_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'] ... [2025-08-14 21:51:10.688135] 2025-08-14T21:51:13.6562107Z 2025-08-14T21:51:13.6563458Z benchmark_utils/test_benchmark_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/benchmark_utils.test_benchmark_utils_1.1_272ccf2738c28bdc_.log 2025-08-14T21:51:13.6564373Z Running 0 items in this shard: 2025-08-14T21:51:13.6564561Z 2025-08-14T21:51:13.6566177Z Running test_linalg 1/6 ... [2025-08-14 21:51:13.656439] 2025-08-14T21:51:13.6566821Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:13.6570214Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_linalg.py', '-m', 'serial', '--shard-id=1', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:51:13.656767] 2025-08-14T21:51:19.1782528Z 2025-08-14T21:51:19.1783627Z test_linalg 1/6 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_1.6_99e106bbf259fc9a_.log 2025-08-14T21:51:19.1784604Z Running 1 items in this shard: test/test_linalg.py::TestLinalgCPU::test_svd_cpu_float32 2025-08-14T21:51:19.1785001Z 2025-08-14T21:51:19.1786802Z Running test_linalg 5/6 ... [2025-08-14 21:51:19.178514] 2025-08-14T21:51:19.1787196Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:19.1790680Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_linalg.py', '-m', 'serial', '--shard-id=5', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:51:19.178847] 2025-08-14T21:51:22.6977010Z 2025-08-14T21:51:22.6977875Z test_linalg 5/6 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_5.6_d46d2c28869c1cd0_.log 2025-08-14T21:51:22.6978627Z Running 0 items in this shard: 2025-08-14T21:51:22.6978863Z 2025-08-14T21:51:22.6980956Z Running test_linalg 6/6 ... [2025-08-14 21:51:22.697933] 2025-08-14T21:51:22.6981403Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:22.6985111Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_linalg.py', '-m', 'serial', '--shard-id=6', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:51:22.698261] 2025-08-14T21:51:26.2170815Z 2025-08-14T21:51:26.2171633Z test_linalg 6/6 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_6.6_05689e2d81c45f49_.log 2025-08-14T21:51:26.2172345Z Running 0 items in this shard: 2025-08-14T21:51:26.2172552Z 2025-08-14T21:51:26.2174306Z Running test_futures 1/1 ... [2025-08-14 21:51:26.217272] 2025-08-14T21:51:26.2174737Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:26.2178730Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_futures.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'] ... [2025-08-14 21:51:26.217603] 2025-08-14T21:51:29.1356996Z 2025-08-14T21:51:29.1357828Z test_futures 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_futures_1.1_8b53d8dc558b8072_.log 2025-08-14T21:51:29.1358527Z Running 0 items in this shard: 2025-08-14T21:51:29.1358732Z 2025-08-14T21:51:29.1360963Z Running torch_np/numpy_tests/lib/test_histograms 1/1 ... [2025-08-14 21:51:29.135937] 2025-08-14T21:51:29.1361571Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:29.1365188Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_histograms.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:51:29.136257] 2025-08-14T21:51:32.1060081Z 2025-08-14T21:51:32.1061757Z torch_np/numpy_tests/lib/test_histograms 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_histograms_1.1_1cf4c7bf1f9999f1_.log 2025-08-14T21:51:32.1063528Z Running 0 items in this shard: 2025-08-14T21:51:32.1063842Z 2025-08-14T21:51:32.1066430Z Running nn/test_embedding 1/1 ... [2025-08-14 21:51:32.106417] 2025-08-14T21:51:32.1066889Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:32.1069760Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_embedding.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'] ... [2025-08-14 21:51:32.106765] 2025-08-14T21:51:35.0748458Z 2025-08-14T21:51:35.0749326Z nn/test_embedding 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_embedding_1.1_fd6ebf9da02ad78f_.log 2025-08-14T21:51:35.0750069Z Running 0 items in this shard: 2025-08-14T21:51:35.0750257Z 2025-08-14T21:51:35.0752568Z Running functorch/test_vmap 4/6 ... [2025-08-14 21:51:35.075105] 2025-08-14T21:51:35.0753084Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:35.0756441Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_vmap.py', '-m', 'serial', '--shard-id=4', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:51:35.075431] 2025-08-14T21:51:39.4953307Z 2025-08-14T21:51:39.4954420Z functorch/test_vmap 4/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_vmap_4.6_208c88853052e963_.log 2025-08-14T21:51:39.4955812Z Running 0 items in this shard: 2025-08-14T21:51:39.4956006Z 2025-08-14T21:51:39.4957723Z Running functorch/test_vmap 5/6 ... [2025-08-14 21:51:39.495592] 2025-08-14T21:51:39.4958148Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:39.4961507Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_vmap.py', '-m', 'serial', '--shard-id=5', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:51:39.495914] 2025-08-14T21:51:43.9158729Z 2025-08-14T21:51:43.9159933Z functorch/test_vmap 5/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_vmap_5.6_a4808efaa892b2df_.log 2025-08-14T21:51:43.9160811Z Running 0 items in this shard: 2025-08-14T21:51:43.9161003Z 2025-08-14T21:51:43.9162768Z Running functorch/test_vmap 6/6 ... [2025-08-14 21:51:43.916106] 2025-08-14T21:51:43.9163264Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:43.9166894Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_vmap.py', '-m', 'serial', '--shard-id=6', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:51:43.916445] 2025-08-14T21:51:48.3363952Z 2025-08-14T21:51:48.3365046Z functorch/test_vmap 6/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_vmap_6.6_beb943eb9c75a77f_.log 2025-08-14T21:51:48.3365823Z Running 0 items in this shard: 2025-08-14T21:51:48.3366012Z 2025-08-14T21:51:48.3369804Z Running test_sparse_csr 2/2 ... [2025-08-14 21:51:48.336637] 2025-08-14T21:51:48.3370248Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:48.3371865Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_sparse_csr.py', '-m', 'serial', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:51:48.336977] 2025-08-14T21:51:53.3078658Z 2025-08-14T21:51:53.3079551Z test_sparse_csr 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_sparse_csr_2.2_ab40e03c5be0b2e7_.log 2025-08-14T21:51:53.3080347Z Running 0 items in this shard: 2025-08-14T21:51:53.3080554Z 2025-08-14T21:51:53.3082289Z Running test_view_ops 1/1 ... [2025-08-14 21:51:53.308059] 2025-08-14T21:51:53.3082702Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:53.3085998Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_view_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:51:53.308390] 2025-08-14T21:51:56.3265540Z 2025-08-14T21:51:56.3266419Z test_view_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_view_ops_1.1_b619cfb31d9096b1_.log 2025-08-14T21:51:56.3267216Z Running 0 items in this shard: 2025-08-14T21:51:56.3267403Z 2025-08-14T21:51:56.3271395Z Running test_accelerator 1/1 ... [2025-08-14 21:51:56.326956] 2025-08-14T21:51:56.3271808Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:56.3276385Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_accelerator.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'] ... [2025-08-14 21:51:56.327367] 2025-08-14T21:51:59.1288469Z 2025-08-14T21:51:59.1289585Z test_accelerator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_accelerator_1.1_4e487a85e62af087_.log 2025-08-14T21:51:59.1290226Z 2025-08-14T21:51:59.1292569Z Running profiler/test_memory_profiler 1/1 ... [2025-08-14 21:51:59.129059] 2025-08-14T21:51:59.1293402Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:51:59.1296598Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_memory_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'] ... [2025-08-14 21:51:59.129405] 2025-08-14T21:52:02.0975396Z 2025-08-14T21:52:02.0976402Z profiler/test_memory_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_memory_profiler_1.1_738c531253c98ed2_.log 2025-08-14T21:52:02.0977266Z Running 0 items in this shard: 2025-08-14T21:52:02.0977453Z 2025-08-14T21:52:02.0980460Z Running torch_np/numpy_tests/fft/test_pocketfft 1/1 ... [2025-08-14 21:52:02.097813] 2025-08-14T21:52:02.0981072Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:02.0984195Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_pocketfft.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:52:02.098176] 2025-08-14T21:52:05.0664155Z 2025-08-14T21:52:05.0665222Z torch_np/numpy_tests/fft/test_pocketfft 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_pocketfft_1.1_40468ca940ab59b3_.log 2025-08-14T21:52:05.0666140Z Running 0 items in this shard: 2025-08-14T21:52:05.0666344Z 2025-08-14T21:52:05.0669498Z Running test_function_schema 1/1 ... [2025-08-14 21:52:05.066683] 2025-08-14T21:52:05.0670009Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:05.0672306Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_function_schema.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:52:05.067014] 2025-08-14T21:52:07.9850561Z 2025-08-14T21:52:07.9851613Z test_function_schema 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_function_schema_1.1_6a8111b0dbf9702c_.log 2025-08-14T21:52:07.9852526Z Running 0 items in this shard: 2025-08-14T21:52:07.9852799Z 2025-08-14T21:52:07.9855064Z Running torch_np/numpy_tests/lib/test_index_tricks 1/1 ... [2025-08-14 21:52:07.985294] 2025-08-14T21:52:07.9855586Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:07.9858828Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_index_tricks.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:52:07.985636] 2025-08-14T21:52:10.9538687Z 2025-08-14T21:52:10.9539809Z torch_np/numpy_tests/lib/test_index_tricks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_index_tricks_1.1_e3c8c0aa4fea4082_.log 2025-08-14T21:52:10.9540788Z Running 0 items in this shard: 2025-08-14T21:52:10.9540976Z 2025-08-14T21:52:10.9543066Z Running test_scatter_gather_ops 1/1 ... [2025-08-14 21:52:10.954120] 2025-08-14T21:52:10.9543637Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:10.9546836Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_scatter_gather_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'] ... [2025-08-14 21:52:10.954459] 2025-08-14T21:52:13.9225827Z 2025-08-14T21:52:13.9226870Z test_scatter_gather_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_scatter_gather_ops_1.1_d06298e96f2ac441_.log 2025-08-14T21:52:13.9227667Z Running 0 items in this shard: 2025-08-14T21:52:13.9227858Z 2025-08-14T21:52:13.9229864Z Running test_unary_ufuncs 1/3 ... [2025-08-14 21:52:13.922809] 2025-08-14T21:52:13.9230283Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:13.9233627Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_unary_ufuncs.py', '-m', 'serial', '--shard-id=1', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:52:13.923142] 2025-08-14T21:52:21.4971785Z 2025-08-14T21:52:21.4972694Z test_unary_ufuncs 1/3 was successful, full logs can be found in artifacts with path test/test-reports/test_unary_ufuncs_1.3_4d596b11b693d68f_.log 2025-08-14T21:52:21.4973510Z Running 0 items in this shard: 2025-08-14T21:52:21.4973759Z 2025-08-14T21:52:21.4976013Z Running torch_np/numpy_tests/core/test_scalar_methods 1/1 ... [2025-08-14 21:52:21.497416] 2025-08-14T21:52:21.4976621Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:21.4979640Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalar_methods.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:52:21.497741] 2025-08-14T21:52:24.4657683Z 2025-08-14T21:52:24.4658833Z torch_np/numpy_tests/core/test_scalar_methods 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalar_methods_1.1_fdb83b64dcf59537_.log 2025-08-14T21:52:24.4659906Z Running 0 items in this shard: 2025-08-14T21:52:24.4660110Z 2025-08-14T21:52:24.4661906Z Running nn/test_init 1/1 ... [2025-08-14 21:52:24.466005] 2025-08-14T21:52:24.4662348Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:24.4665379Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_init.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:52:24.466309] 2025-08-14T21:52:27.7348022Z 2025-08-14T21:52:27.7348784Z nn/test_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_init_1.1_7730f757dbcb0ffe_.log 2025-08-14T21:52:27.7349480Z Running 0 items in this shard: 2025-08-14T21:52:27.7349669Z 2025-08-14T21:52:27.7351872Z Running torch_np/numpy_tests/core/test_numerictypes 1/1 ... [2025-08-14 21:52:27.735025] 2025-08-14T21:52:27.7352376Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:27.7355614Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numerictypes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:52:27.735343] 2025-08-14T21:52:30.7034507Z 2025-08-14T21:52:30.7035710Z torch_np/numpy_tests/core/test_numerictypes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numerictypes_1.1_9a7dd96711ba2d67_.log 2025-08-14T21:52:30.7036715Z Running 0 items in this shard: 2025-08-14T21:52:30.7036903Z 2025-08-14T21:52:30.7038682Z Running torch_np/numpy_tests/lib/test_arraypad 1/1 ... [2025-08-14 21:52:30.703701] 2025-08-14T21:52:30.7039162Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:30.7042412Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraypad.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'] ... [2025-08-14 21:52:30.704028] 2025-08-14T21:52:33.6220642Z 2025-08-14T21:52:33.6221735Z torch_np/numpy_tests/lib/test_arraypad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraypad_1.1_932b437efc9d843f_.log 2025-08-14T21:52:33.6222653Z Running 0 items in this shard: 2025-08-14T21:52:33.6222853Z 2025-08-14T21:52:33.6224828Z Running torch_np/numpy_tests/lib/test_arraysetops 1/1 ... [2025-08-14 21:52:33.622316] 2025-08-14T21:52:33.6225349Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:33.6229183Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraysetops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:52:33.622698] 2025-08-14T21:52:36.5907590Z 2025-08-14T21:52:36.5909277Z torch_np/numpy_tests/lib/test_arraysetops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraysetops_1.1_f1f4e4a6d1fc52f6_.log 2025-08-14T21:52:36.5911252Z Running 0 items in this shard: 2025-08-14T21:52:36.5911622Z 2025-08-14T21:52:36.5913646Z Running test_sparse 1/2 ... [2025-08-14 21:52:36.591173] 2025-08-14T21:52:36.5914237Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:36.5919193Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_sparse.py', '-m', 'serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:52:36.591594] 2025-08-14T21:52:41.0117086Z 2025-08-14T21:52:41.0117967Z test_sparse 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_sparse_1.2_14b78e7f9a702e78_.log 2025-08-14T21:52:41.0118665Z Running 0 items in this shard: 2025-08-14T21:52:41.0118862Z 2025-08-14T21:52:43.4485799Z /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. 2025-08-14T21:52:43.4488792Z import pkg_resources 2025-08-14T21:52:43.4607126Z /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. 2025-08-14T21:52:43.4609062Z import pkg_resources 2025-08-14T21:52:43.4610884Z /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. 2025-08-14T21:52:43.4612339Z import pkg_resources 2025-08-14T21:52:43.5672869Z Running dynamo/test_repros 1/1 ... [2025-08-14 21:52:43.566965] 2025-08-14T21:52:43.5673320Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:43.5676269Z 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'] ... [2025-08-14 21:52:43.567357] 2025-08-14T21:52:43.5791878Z Running dynamo/test_misc 1/1 ... [2025-08-14 21:52:43.578932] 2025-08-14T21:52:43.5792321Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:43.5796309Z 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'] ... [2025-08-14 21:52:43.579267] 2025-08-14T21:52:43.5798366Z Running dynamo/test_error_messages 1/1 ... [2025-08-14 21:52:43.579631] 2025-08-14T21:52:43.5798815Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:43.5802136Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_error_messages.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'] ... [2025-08-14 21:52:43.579976] 2025-08-14T21:52:46.9733279Z 2025-08-14T21:52:46.9734526Z dynamo/test_error_messages 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_error_messages_1.1_b429871f516b7a9c_.log 2025-08-14T21:52:46.9735622Z 2025-08-14T21:52:47.0404666Z 2025-08-14T21:52:47.0406047Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_5a3ee427a069ee69_.log 2025-08-14T21:52:47.0407294Z 2025-08-14T21:52:48.2556353Z 2025-08-14T21:52:48.2557879Z dynamo/test_misc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_misc_1.1_7b384a17d5290ad7_.log 2025-08-14T21:52:48.2559214Z 2025-08-14T21:52:50.1646423Z /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. 2025-08-14T21:52:50.1648052Z import pkg_resources 2025-08-14T21:52:50.1998565Z /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. 2025-08-14T21:52:50.2000423Z import pkg_resources 2025-08-14T21:52:50.2838660Z Running dynamo/test_precompile_context 1/1 ... [2025-08-14 21:52:50.283554] 2025-08-14T21:52:50.2839162Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:50.2843592Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_precompile_context.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:52:50.283946] 2025-08-14T21:52:50.3195278Z Running dynamo/test_unspec 1/1 ... [2025-08-14 21:52:50.319153] 2025-08-14T21:52:50.3196070Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:50.3198907Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_unspec.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:52:50.319528] 2025-08-14T21:52:51.4226381Z /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. 2025-08-14T21:52:51.4229259Z import pkg_resources 2025-08-14T21:52:51.5418930Z Running dynamo/test_after_aot 1/1 ... [2025-08-14 21:52:51.541498] 2025-08-14T21:52:51.5419735Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:51.5421437Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_after_aot.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'] ... [2025-08-14 21:52:51.541849] 2025-08-14T21:52:53.8070119Z 2025-08-14T21:52:53.8071490Z dynamo/test_unspec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unspec_1.1_be0c8bd11e6add4d_.log 2025-08-14T21:52:53.8072744Z 2025-08-14T21:52:54.9737610Z 2025-08-14T21:52:54.9739093Z dynamo/test_after_aot 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_after_aot_1.1_bcac9ce983793088_.log 2025-08-14T21:52:54.9740392Z 2025-08-14T21:52:57.1092225Z /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. 2025-08-14T21:52:57.1095206Z import pkg_resources 2025-08-14T21:52:57.2310695Z Running dynamo/test_aot_autograd 1/1 ... [2025-08-14 21:52:57.230672] 2025-08-14T21:52:57.2311459Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:57.2314827Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:52:57.231102] 2025-08-14T21:52:57.2342757Z 2025-08-14T21:52:57.2344094Z dynamo/test_precompile_context 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_precompile_context_1.1_cc94acf267945c59_.log 2025-08-14T21:52:57.2344925Z 2025-08-14T21:52:58.1879059Z /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. 2025-08-14T21:52:58.1880580Z import pkg_resources 2025-08-14T21:52:58.3092000Z Running dynamo/test_deque_reconstruct 1/1 ... [2025-08-14 21:52:58.308780] 2025-08-14T21:52:58.3093441Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:52:58.3095530Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_deque_reconstruct.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:52:58.309206] 2025-08-14T21:53:00.4343010Z /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. 2025-08-14T21:53:00.4346085Z import pkg_resources 2025-08-14T21:53:00.5554227Z Running dynamo/test_interop 1/1 ... [2025-08-14 21:53:00.555010] 2025-08-14T21:53:00.5554966Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:00.5557335Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_interop.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:53:00.555411] 2025-08-14T21:53:00.6933543Z 2025-08-14T21:53:00.6935028Z dynamo/test_aot_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_1.1_aa3423c5a5b4264a_.log 2025-08-14T21:53:00.6935929Z 2025-08-14T21:53:01.4852274Z 2025-08-14T21:53:01.4854107Z dynamo/test_deque_reconstruct 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deque_reconstruct_1.1_ab2d10d76da55484_.log 2025-08-14T21:53:01.4855597Z 2025-08-14T21:53:04.0234561Z /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. 2025-08-14T21:53:04.0237373Z import pkg_resources 2025-08-14T21:53:04.0418263Z 2025-08-14T21:53:04.0419395Z dynamo/test_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_interop_1.1_bc505cf45f3c40e6_.log 2025-08-14T21:53:04.0420155Z 2025-08-14T21:53:04.1459480Z Running dynamo/test_base_output 1/1 ... [2025-08-14 21:53:04.145585] 2025-08-14T21:53:04.1460145Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:04.1462008Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_output.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:53:04.145932] 2025-08-14T21:53:04.7431131Z /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. 2025-08-14T21:53:04.7432857Z import pkg_resources 2025-08-14T21:53:04.8646608Z Running dynamo/test_nops 1/1 ... [2025-08-14 21:53:04.864208] 2025-08-14T21:53:04.8647145Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:04.8649019Z 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'] ... [2025-08-14 21:53:04.864621] 2025-08-14T21:53:07.2400374Z /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. 2025-08-14T21:53:07.2402049Z import pkg_resources 2025-08-14T21:53:07.3600556Z Running dynamo/test_pre_dispatch 1/1 ... [2025-08-14 21:53:07.359679] 2025-08-14T21:53:07.3601141Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:07.3603572Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_pre_dispatch.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:53:07.360051] 2025-08-14T21:53:07.5359092Z 2025-08-14T21:53:07.5360432Z dynamo/test_base_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_output_1.1_37c001a692240b32_.log 2025-08-14T21:53:07.5361437Z 2025-08-14T21:53:08.2361359Z 2025-08-14T21:53:08.2362541Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_5ddb53cf273e1032_.log 2025-08-14T21:53:08.2363575Z 2025-08-14T21:53:10.6111164Z 2025-08-14T21:53:10.6112575Z dynamo/test_pre_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_pre_dispatch_1.1_4c17e2fa36a9ee55_.log 2025-08-14T21:53:10.6113453Z 2025-08-14T21:53:10.6708526Z /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. 2025-08-14T21:53:10.6710065Z import pkg_resources 2025-08-14T21:53:10.7913752Z Running dynamo/test_metrics_context 1/1 ... [2025-08-14 21:53:10.790952] 2025-08-14T21:53:10.7914278Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:10.7916941Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_metrics_context.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:53:10.791389] 2025-08-14T21:53:11.3886871Z /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. 2025-08-14T21:53:11.3888404Z import pkg_resources 2025-08-14T21:53:11.5089070Z Running dynamo/test_subgraphs 1/1 ... [2025-08-14 21:53:11.508518] 2025-08-14T21:53:11.5089828Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:11.5092100Z 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'] ... [2025-08-14 21:53:11.508889] 2025-08-14T21:53:13.7764077Z /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. 2025-08-14T21:53:13.7767019Z import pkg_resources 2025-08-14T21:53:13.8968857Z Running dynamo/test_install_free_tensors 1/1 ... [2025-08-14 21:53:13.896490] 2025-08-14T21:53:13.8969635Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:13.8973012Z 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'] ... [2025-08-14 21:53:13.896932] 2025-08-14T21:53:14.0498219Z 2025-08-14T21:53:14.0500009Z dynamo/test_metrics_context 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_metrics_context_1.1_591eace922cb816d_.log 2025-08-14T21:53:14.0501474Z 2025-08-14T21:53:14.9672427Z 2025-08-14T21:53:14.9673681Z dynamo/test_subgraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subgraphs_1.1_17d99b4cb7045ea9_.log 2025-08-14T21:53:14.9674605Z 2025-08-14T21:53:17.2341856Z /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. 2025-08-14T21:53:17.2344581Z import pkg_resources 2025-08-14T21:53:17.2393253Z 2025-08-14T21:53:17.2394719Z 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_9d522ec4421e9e16_.log 2025-08-14T21:53:17.2396051Z 2025-08-14T21:53:17.3552672Z Running dynamo/test_config 1/1 ... [2025-08-14 21:53:17.354909] 2025-08-14T21:53:17.3553375Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:17.3556992Z 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'] ... [2025-08-14 21:53:17.355330] 2025-08-14T21:53:18.1557364Z /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. 2025-08-14T21:53:18.1560165Z import pkg_resources 2025-08-14T21:53:18.2877911Z Running dynamo/test_export_mutations 1/1 ... [2025-08-14 21:53:18.287404] 2025-08-14T21:53:18.2878827Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:18.2884324Z 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'] ... [2025-08-14 21:53:18.288078] 2025-08-14T21:53:20.3582514Z /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. 2025-08-14T21:53:20.3584953Z import pkg_resources 2025-08-14T21:53:20.4790798Z Running dynamo/test_sources 1/1 ... [2025-08-14 21:53:20.478667] 2025-08-14T21:53:20.4791634Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:20.4794345Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:53:20.479047] 2025-08-14T21:53:20.7115432Z 2025-08-14T21:53:20.7116759Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_b6808e346442b67a_.log 2025-08-14T21:53:20.7117943Z 2025-08-14T21:53:21.6436974Z 2025-08-14T21:53:21.6438339Z 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_6d6172e7b0ea6060_.log 2025-08-14T21:53:21.6439459Z 2025-08-14T21:53:23.6282056Z 2025-08-14T21:53:23.6283453Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_bcb0cae5f26ccdf3_.log 2025-08-14T21:53:23.6284743Z 2025-08-14T21:53:23.8984566Z /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. 2025-08-14T21:53:23.8986076Z import pkg_resources 2025-08-14T21:53:24.0190668Z Running dynamo/test_guard_serialization 1/1 ... [2025-08-14 21:53:24.018700] 2025-08-14T21:53:24.0191259Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:24.0194354Z 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'] ... [2025-08-14 21:53:24.019088] 2025-08-14T21:53:24.8793893Z /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. 2025-08-14T21:53:24.8796716Z import pkg_resources 2025-08-14T21:53:25.0081390Z Running dynamo/test_einops 1/1 ... [2025-08-14 21:53:25.007783] 2025-08-14T21:53:25.0082134Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:25.0085479Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_einops.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'] ... [2025-08-14 21:53:25.008178] 2025-08-14T21:53:26.7275391Z /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. 2025-08-14T21:53:26.7278227Z import pkg_resources 2025-08-14T21:53:26.8464152Z Running dynamo/test_export 1/1 ... [2025-08-14 21:53:26.846031] 2025-08-14T21:53:26.8464884Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:26.8467578Z 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'] ... [2025-08-14 21:53:26.846414] 2025-08-14T21:53:28.0764125Z 2025-08-14T21:53:28.0765476Z dynamo/test_einops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_einops_1.1_072921af624fcc84_.log 2025-08-14T21:53:28.0766682Z 2025-08-14T21:53:30.3472738Z 2025-08-14T21:53:30.3474221Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_1f830ccb3799415f_.log 2025-08-14T21:53:30.3475483Z 2025-08-14T21:53:30.9282686Z 2025-08-14T21:53:30.9284231Z 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_a9a918b58c9e8d04_.log 2025-08-14T21:53:30.9285586Z 2025-08-14T21:53:31.2622588Z /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. 2025-08-14T21:53:31.2624273Z import pkg_resources 2025-08-14T21:53:31.3831129Z Running dynamo/test_base_hop 1/1 ... [2025-08-14 21:53:31.382711] 2025-08-14T21:53:31.3831685Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:31.3833298Z 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'] ... [2025-08-14 21:53:31.383065] 2025-08-14T21:53:33.5206464Z /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. 2025-08-14T21:53:33.5208239Z import pkg_resources 2025-08-14T21:53:33.6402364Z Running dynamo/test_optimizers 1/1 ... [2025-08-14 21:53:33.639843] 2025-08-14T21:53:33.6402995Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:33.6404695Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_optimizers.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:53:33.640188] 2025-08-14T21:53:34.0984825Z /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. 2025-08-14T21:53:34.0987867Z import pkg_resources 2025-08-14T21:53:34.2188452Z Running dynamo/test_compiler_bisector 1/1 ... [2025-08-14 21:53:34.218474] 2025-08-14T21:53:34.2189219Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:34.2192237Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_compiler_bisector.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:53:34.218903] 2025-08-14T21:53:34.7728321Z 2025-08-14T21:53:34.7729695Z 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_3545280c297d9ba5_.log 2025-08-14T21:53:34.7730920Z 2025-08-14T21:53:37.0258044Z 2025-08-14T21:53:37.0259723Z dynamo/test_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_optimizers_1.1_6fb274bd334b397f_.log 2025-08-14T21:53:37.0260632Z 2025-08-14T21:53:37.9428143Z /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. 2025-08-14T21:53:37.9430946Z import pkg_resources 2025-08-14T21:53:38.0634516Z Running dynamo/test_bytecode_utils 1/1 ... [2025-08-14 21:53:38.063111] 2025-08-14T21:53:38.0635245Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:38.0638434Z 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'] ... [2025-08-14 21:53:38.063536] 2025-08-14T21:53:40.2645224Z /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. 2025-08-14T21:53:40.2647458Z import pkg_resources 2025-08-14T21:53:40.3867102Z Running dynamo/test_cudagraphs_expandable_segments 1/1 ... [2025-08-14 21:53:40.386239] 2025-08-14T21:53:40.3868160Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:40.3870800Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_cudagraphs_expandable_segments.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:53:40.386605] 2025-08-14T21:53:41.2285171Z 2025-08-14T21:53:41.2286766Z dynamo/test_compiler_bisector 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compiler_bisector_1.1_f3deceaec4046934_.log 2025-08-14T21:53:41.2288474Z 2025-08-14T21:53:41.5322918Z 2025-08-14T21:53:41.5324356Z 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_18cc7657eb3fc719_.log 2025-08-14T21:53:41.5325552Z 2025-08-14T21:53:43.8562854Z 2025-08-14T21:53:43.8564670Z dynamo/test_cudagraphs_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_expandable_segments_1.1_c08e2d14e8c12f30_.log 2025-08-14T21:53:43.8565683Z 2025-08-14T21:53:44.4281006Z /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. 2025-08-14T21:53:44.4282546Z import pkg_resources 2025-08-14T21:53:44.5491069Z Running dynamo/test_minifier 1/1 ... [2025-08-14 21:53:44.548719] 2025-08-14T21:53:44.5491627Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:44.5493812Z 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'] ... [2025-08-14 21:53:44.549090] 2025-08-14T21:53:44.7348663Z /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. 2025-08-14T21:53:44.7351586Z import pkg_resources 2025-08-14T21:53:44.8570280Z Running dynamo/test_reorder_logs 1/1 ... [2025-08-14 21:53:44.856650] 2025-08-14T21:53:44.8570855Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:44.8572971Z 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'] ... [2025-08-14 21:53:44.857001] 2025-08-14T21:53:47.0453478Z /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. 2025-08-14T21:53:47.0456612Z import pkg_resources 2025-08-14T21:53:47.1653875Z Running dynamo/test_python_autograd 1/1 ... [2025-08-14 21:53:47.164964] 2025-08-14T21:53:47.1654765Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:47.1656929Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_python_autograd.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:53:47.165320] 2025-08-14T21:53:47.7726786Z 2025-08-14T21:53:47.7727989Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_aafdf46ec1be8fef_.log 2025-08-14T21:53:47.7729101Z 2025-08-14T21:53:48.2532416Z 2025-08-14T21:53:48.2534207Z 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_21572a8210b00f37_.log 2025-08-14T21:53:48.2535258Z 2025-08-14T21:53:50.5759855Z 2025-08-14T21:53:50.5761194Z dynamo/test_python_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_python_autograd_1.1_914bc9b6c9fb9b44_.log 2025-08-14T21:53:50.5762414Z 2025-08-14T21:53:50.9093592Z /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. 2025-08-14T21:53:50.9096456Z import pkg_resources 2025-08-14T21:53:51.0299784Z Running test_quantization 2/9 ... [2025-08-14 21:53:51.029606] 2025-08-14T21:53:51.0300483Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:51.0303239Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=2', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:53:51.030008] 2025-08-14T21:53:51.3803418Z /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. 2025-08-14T21:53:51.3806089Z import pkg_resources 2025-08-14T21:53:51.5259908Z Running test_quantization 6/9 ... [2025-08-14 21:53:51.525643] 2025-08-14T21:53:51.5260369Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:51.5263167Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=6', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:53:51.526012] 2025-08-14T21:53:53.7259085Z /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. 2025-08-14T21:53:53.7261939Z import pkg_resources 2025-08-14T21:53:53.8470049Z Running test_quantization 7/9 ... [2025-08-14 21:53:53.846619] 2025-08-14T21:53:53.8470787Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T21:53:53.8473360Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=7', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 21:53:53.846985] 2025-08-14T22:01:36.7266181Z 2025-08-14T22:01:36.7267738Z test_quantization 7/9 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_7.9_e0cab9237c03edb3_.log 2025-08-14T22:01:36.7358840Z Running 144 items in this shard: test/test_quantization.py::TestQuantizedOps::test_adaptive_avg_pool3d_ndhwc, test/test_quantization.py::TestQuantizedOps::test_custom_module_multi_head_attention, test/test_quantization.py::TestQuantizedOps::test_hardswish, test/test_quantization.py::TestQuantizedOps::test_int8_mul_onednn, test/test_quantization.py::TestQuantizedOps::test_max_pool2d_nhwc, test/test_quantization.py::TestQuantizedOps::test_mul_scalar_relu, test/test_quantization.py::TestQuantizedOps::test_qadd_relu_same_qparams, test/test_quantization.py::TestQuantizedOps::test_qprelu, test/test_quantization.py::TestQuantizedOps::test_qrelu6, test/test_quantization.py::TestQuantizedOps::test_sigmoid_non_observed, test/test_quantization.py::TestQuantizedLinear::test_qlinear_qnnpack_free_memory_and_unpack, test/test_quantization.py::TestQuantizedLinear::test_qlinear_relu_fp8, test/test_quantization.py::TestQuantizedLinear::test_qlinear_sum_relu_pt2e, test/test_quantization.py::TestQuantizedConv::test_conv_transpose_reorder_issue_onednn, test/test_quantization.py::TestQuantizedConv::test_qconv1d_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv2d_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv3d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_conv3d, test/test_quantization.py::TestPadding::test_reflection_pad2d, test/test_quantization.py::TestQuantizedFunctionalOps::test_conv1d_api, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_channel_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_channel, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_channel_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_channel_half_precision_numerics, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_decomposed_dequantize_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_dequantize_fp16_cpu, test/test_quantization.py::TestQuantizedTensor::test_per_tensor_qtensor_to_memory_format, test/test_quantization.py::TestQuantizedTensor::test_qtensor_dtypes, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_qtensor_masked_fill_cpu, test/test_quantization.py::TestQuantizedTensor::test_qtensor_per_channel_load_save, test/test_quantization.py::TestFakeQuantize::test_fq_serializable_per_channel, test/test_quantization.py::TestStaticQuantizedModule::test_batch_norm3d, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_extreme_inputs, test/test_quantization.py::TestDistributed::test_qat_convbn_fused_syncbn_replacement, test/test_quantization.py::TestFusedObsFakeQuantModule::test_embedding_qat_config, test/test_quantization.py::TestFusedObsFakeQuantModule::test_fused_mod_per_channel, test/test_quantization.py::TestBackendConfig::test_backend_config_from_dict, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_extra_inputs_getter, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_fuser_method, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_observation_type, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_root_module, test/test_quantization.py::TestUtils::test_get_fqn_to_example_inputs_default_kwargs, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_convtranspose_per_channel_qconfig_none, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_save_load_state_dict, test/test_quantization.py::TestQuantizeEagerOps::test_conv_transpose_3d, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_relu, test/test_quantization.py::TestModelNumericsEager::test_fake_quant_true_quant_compare, test/test_quantization.py::TestModelNumericsEager::test_float_quant_compare_per_tensor, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_outputs_conv_static, test/test_quantization.py::TestEqualizeEager::test_cross_layer_equalization, test/test_quantization.py::TestEqualizeEager::test_equalize, test/test_quantization.py::TestFuseFx::test_fuse_conv_bn_relu, test/test_quantization.py::TestQuantizeFx::test__convert_to_reference_decomposed_fx_dynamic_quant, test/test_quantization.py::TestQuantizeFx::test__convert_to_reference_decomposed_fx_per_channel_quant, test/test_quantization.py::TestQuantizeFx::test_backend_config_quantization_range, test/test_quantization.py::TestQuantizeFx::test_backend_config_scale_min, test/test_quantization.py::TestQuantizeFx::test_deepcopy_preserve_attributes, test/test_quantization.py::TestQuantizeFx::test_dynamic_with_fusion, test/test_quantization.py::TestQuantizeFx::test_fp32_input_fp32_output, test/test_quantization.py::TestQuantizeFx::test_masked_fill_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_non_traceable_module, test/test_quantization.py::TestQuantizeFx::test_output_lists_and_dicts, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_standalone_module_name, test/test_quantization.py::TestQuantizeFx::test_prepare_mode, test/test_quantization.py::TestQuantizeFx::test_prepared_model_deepcopy, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_split_list_args, test/test_quantization.py::TestQuantizeFx::test_qat_prepare_device_affinity, test/test_quantization.py::TestQuantizeFx::test_reroute_tuple_getitem_patterns, test/test_quantization.py::TestQuantizeFx::test_return_none, test/test_quantization.py::TestQuantizeFx::test_sub_scalar, test/test_quantization.py::TestQuantizeFxOps::test_cat, test/test_quantization.py::TestQuantizeFxOps::test_elu, test/test_quantization.py::TestQuantizeFxOps::test_instance_norm, test/test_quantization.py::TestQuantizeFxOps::test_int8_input_no_unnecessary_fq, test/test_quantization.py::TestQuantizeFxOps::test_leaky_relu, test/test_quantization.py::TestQuantizeFxOps::test_linear_dynamic_fp16, test/test_quantization.py::TestQuantizeFxOps::test_linear_module, test/test_quantization.py::TestQuantizeFxOps::test_norm_weight_bias, test/test_quantization.py::TestQuantizeFxOps::test_pixel_unshuffle_module, test/test_quantization.py::TestQuantizeFxOps::test_qmatmul, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_internal_pattern_nodes_cannot_have_users_that_are_not_matched, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_replaces_referenced_submodules, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_single_pattern_match, test/test_quantization.py::TestGraphUtils::test_conv_bn_conv_relu, test/test_quantization.py::TestMetaDataPorting::test_no_metadata_porting, test/test_quantization.py::TestNumericDebugger::test_control_flow, test/test_quantization.py::TestNumericDebugger::test_extract_results_from_loggers, test/test_quantization.py::TestQuantizePT2E::test_max_pool2d_quantizer, test/test_quantization.py::TestXNNPACKQuantizer::test_propagate_annotation, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_adaptive_avg_pool2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_filter_linear_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_dynamic, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_dynamic_fp16, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_per_channel_weight_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_transpose_bn, test/test_quantization.py::TestQuantizePT2EQATModels::test_qat_resnet18, test/test_quantization.py::TestFXGraphMatcher::test_matching_failure_node_count, test/test_quantization.py::TestFXGraphMatcher::test_simple_mod, test/test_quantization.py::TestFXGraphMatcher::test_simple_mod_multi, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_loggers_cuda, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_cuda, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_ops_with_same_fp32_and_int8_signature, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_fp32_fp32, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_quant_quant, test/test_quantization.py::TestFXNumericSuiteNShadows::test_partial_qconfig_mapping, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_deduplication, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_weights_linear, test/test_quantization.py::TestFxModelReportClass::test_equalization_mapping_generation, test/test_quantization.py::TestFxModelReportVisualizer::test_generate_tables_match_with_report, test/test_quantization.py::TestSerialization::test_conv2d, test/test_quantization.py::TestSerialization::test_conv2d_nobias, test/test_quantization.py::TestSerialization::test_conv2d_relu, test/test_quantization.py::TestSerialization::test_conv3d_relu, test/test_quantization.py::TestSerialization::test_linear, test/test_quantization.py::TestSerialization::test_per_channel_observer, test/test_quantization.py::TestQuantizeJitPasses::test_finalize_debug, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_trivial, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_shared_class_type, test/test_quantization.py::TestQuantizeJitPasses::test_insert_quant_dequant, test/test_quantization.py::TestQuantizeJitPasses::test_interface_with_fork, test/test_quantization.py::TestQuantizeJitOps::test_dequantize_tuple, test/test_quantization.py::TestQuantizeJitOps::test_instance_norm, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_multi_op, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_weight_observer, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_prepare_dynamic, test/test_quantization.py::TestDeprecatedJitQuantized::test_erase_class_tensor_shapes, test/test_quantization.py::TestDeprecatedJitQuantized::test_quantization_modules, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_normalization, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_import_nn_intrinsic, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_quantized_linear_relu, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_no_import_nn_intrinsic_quantized_dynamic, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_fuse, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_match_utils, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_empty_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_special_numbers_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_special_numbers_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_special_numbers_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_type_promotion_fails_cpu_float8_e5m2fnuz 2025-08-14T22:01:36.7448246Z 2025-08-14T22:01:37.1945823Z Uploading artifacts took 0.47 seconds 2025-08-14T22:01:39.7280153Z /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. 2025-08-14T22:01:39.7283041Z import pkg_resources 2025-08-14T22:01:39.8466840Z Running test_quantization 8/9 ... [2025-08-14 22:01:39.846333] 2025-08-14T22:01:39.8467320Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:01:39.8469989Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=8', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:01:39.846703] 2025-08-14T22:06:05.2198154Z 2025-08-14T22:06:05.2199327Z test_quantization 6/9 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_6.9_4d3f09b3e955439a_.log 2025-08-14T22:06:05.2252275Z Running 142 items in this shard: test/test_quantization.py::TestQuantizedOps::test_interpolate3d, test/test_quantization.py::TestQuantizedOps::test_linear_bias_unpack, test/test_quantization.py::TestQuantizedOps::test_qadd_broadcast, test/test_quantization.py::TestQuantizedOps::test_qclamp, test/test_quantization.py::TestQuantizedOps::test_qsoftmax, test/test_quantization.py::TestQNNPackOps::test_adaptive_avg_pool2d, test/test_quantization.py::TestQuantizedLinear::test_qlinear_add_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv1d, test/test_quantization.py::TestQuantizedConv::test_qconv2d_relu_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_relu_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv2d_sum_fp8, test/test_quantization.py::TestQuantizedConv::test_qconv2d_unpack, test/test_quantization.py::TestQuantizedConv::test_qconv_transpose1d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_conv2d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_convtranspose2d, test/test_quantization.py::TestDynamicQuantizedOps::test_qlinear_dynamic_fp16, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_bag_4bit, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_bag_byte, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_tensor_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_tensor_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_tensor_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_forward_per_tensor_cuda, test/test_quantization.py::TestQuantizedTensor::test_bfp16_quantize, test/test_quantization.py::TestQuantizedTensor::test_cuda_quantization_does_not_pin_memory, test/test_quantization.py::TestQuantizedTensor::test_decomposed_dynamic_quant_pattern, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_tensor_bfloat16_input, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_channel, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_select_cuda, test/test_quantization.py::TestQuantizedTensor::test_quant_pin_memory, test/test_quantization.py::TestQuantizedTensor::test_repeat, test/test_quantization.py::TestQuantizedTensor::test_torch_qtensor_deepcopy, test/test_quantization.py::TestFakeQuantize::test_quant_min_max_override, test/test_quantization.py::TestObserver::test_histogram_observer_consistent_buffer_shape, test/test_quantization.py::TestObserver::test_state_dict_respects_device_affinity, test/test_quantization.py::TestStaticQuantizedModule::test_embedding_api, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_conv3d, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_against_reference, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_correct_numel, test/test_quantization.py::TestBackendConfig::test_backend_config_set_backend_pattern_config, test/test_quantization.py::TestBackendConfig::test_backend_op_config_from_dict, test/test_quantization.py::TestUtils::test_get_fqn_to_example_inputs_complex_args, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_dequant_stub, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_forward_hooks_preserved, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_nested2, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_two_layers, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_linear_relu_fusion, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_per_channel_linear_quantize, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_two_layers, test/test_quantization.py::TestQuantizeEagerOps::test_conv_1d, test/test_quantization.py::TestQuantizeEagerOps::test_leaky_relu, test/test_quantization.py::TestQuantizeEagerOps::test_relu, test/test_quantization.py::TestQuantizeEagerQAT::test_dynamic_qat_linear, test/test_quantization.py::TestQuantizeEagerQAT::test_manual, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_conv_bn_relu, test/test_quantization.py::TestFuseEager::test_fuse_module_eval, test/test_quantization.py::TestFuseEager::test_fusion_sequential_model_train, test/test_quantization.py::TestNumericSuiteEager::test_compare_weights_linear_dynamic, test/test_quantization.py::TestFuseFx::test_fuse_addtional_fuser_method, test/test_quantization.py::TestFuseFx::test_fuse_linear_bn_eval, test/test_quantization.py::TestFuseFx::test_fuse_linear_tanh_for_onednn_backend, test/test_quantization.py::TestFuseFx::test_problematic_fuse_example, test/test_quantization.py::TestQuantizeFx::test_convert_custom_config_to_dict, test/test_quantization.py::TestQuantizeFx::test_custom_module_class_input_has_duplicate_nodes, test/test_quantization.py::TestQuantizeFx::test_get_executorch_backend_config, test/test_quantization.py::TestQuantizeFx::test_linear_shape_view, test/test_quantization.py::TestQuantizeFx::test_lowering_functional_conv_with_kwargs, test/test_quantization.py::TestQuantizeFx::test_match_pattern_with_multiple_args, test/test_quantization.py::TestQuantizeFx::test_packed_weight_fused_op, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_output_quantized_indexes, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_to_dict, test/test_quantization.py::TestQuantizeFx::test_preserve_tuple, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_list_args, test/test_quantization.py::TestQuantizeFx::test_qat_skip_untraced, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_global, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_to_dict, test/test_quantization.py::TestQuantizeFx::test_size_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_standalone_module_float_interface, test/test_quantization.py::TestQuantizeFx::test_torch_unsqueeze_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_unsqueeze__nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFxOps::test_bmm, test/test_quantization.py::TestQuantizeFxOps::test_conv_transpose_2d, test/test_quantization.py::TestQuantizeFxOps::test_narrow, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_multiple_pattern_match, test/test_quantization.py::TestNumericDebugger::test_added_node_gets_unique_id, test/test_quantization.py::TestQuantizePT2E::test_composable_quantizer_throw, test/test_quantization.py::TestQuantizePT2E::test_conv_padding_bn_relu, test/test_quantization.py::TestQuantizePT2E::test_conv_transpose_bn_relu, test/test_quantization.py::TestQuantizePT2E::test_embedding_conv_linear_quantization, test/test_quantization.py::TestQuantizePT2E::test_embedding_quantizer, test/test_quantization.py::TestQuantizePT2E::test_fixed_qparams_qspec_qat, test/test_quantization.py::TestQuantizePT2E::test_fold_quantize, test/test_quantization.py::TestQuantizePT2E::test_save_load, test/test_quantization.py::TestQuantizePT2E::test_shared_qspec, test/test_quantization.py::TestQuantizePT2EAffineQuantization::test_dynamic_affine_act_per_channel_weights, test/test_quantization.py::TestPT2ERepresentation::test_conv2d, test/test_quantization.py::TestXNNPACKQuantizer::test_dynamic_linear_int4_weight, test/test_quantization.py::TestXNNPACKQuantizer::test_linear_gru, test/test_quantization.py::TestXNNPACKQuantizer::test_mul_and_inplace_mul, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_cat_recipe_same_inputs, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_filter_conv2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_unary_qat, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_preserve_source_fn_stack, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_shadow_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_vs_linear_fp16_shadow_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_shadow_activations_fqn, test/test_quantization.py::TestFXNumericSuiteNShadows::test_linear_mod, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_activations_conv, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_mobilenet_v2, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_resnet18, test/test_quantization.py::TestFxModelReportDetector::test_multi_linear_model_without_per_channel, test/test_quantization.py::TestFxDetectInputWeightEqualization::test_input_weight_equalization_report_gen_empty, test/test_quantization.py::TestFxDetectOutliers::test_multiple_run_consistent_spike_outlier_report_gen, test/test_quantization.py::TestFxModelReportVisualizer::test_generate_tables_no_match, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_results, test/test_quantization.py::TestSerialization::test_linear_relu_package_quantization_transforms, test/test_quantization.py::TestQuantizeJit::test_conv_bn, test/test_quantization.py::TestQuantizeJit::test_single_linear, test/test_quantization.py::TestQuantizeJit::test_single_linear_dynamic, test/test_quantization.py::TestQuantizeJitPasses::test_dedup_module_uses, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_complex_cases, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_skip_values, test/test_quantization.py::TestQuantizeJitPasses::test_insert_quant_dequant_shared_class_type, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_dequant_same_value, test/test_quantization.py::TestQuantizeJitOps::test_clamp, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul_scalar, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_qconfig, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quantize_jit, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_qat_embedding_ops, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_qat_linear_relu, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_graph_module, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_soak_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_cat_cpu_float8_e8m0fnu, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_finfo_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_finfo_cpu_float8_e8m0fnu, test/test_quantization.py::TestFloat8DtypeCPU::test_save_load_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_to_string_cpu_float8_e5m2fnuz 2025-08-14T22:06:05.2334228Z 2025-08-14T22:06:08.5120328Z /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. 2025-08-14T22:06:08.5123616Z import pkg_resources 2025-08-14T22:06:08.6333232Z Running test_transformers 2/7 ... [2025-08-14 22:06:08.632964] 2025-08-14T22:06:08.6333737Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:06:08.6336685Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_transformers.py', '-m', 'not serial', '--shard-id=2', '--num-shards=7', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:06:08.633373] 2025-08-14T22:07:10.7225807Z 2025-08-14T22:07:10.7227060Z test_quantization 2/9 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_2.9_d4740c886a2a71cb_.log 2025-08-14T22:07:10.7282154Z Running 156 items in this shard: test/test_quantization.py::TestQuantizedOps::test_adaptive_avg_pool2d_nhwc, test/test_quantization.py::TestQuantizedOps::test_empty_batch, test/test_quantization.py::TestQuantizedOps::test_leaky_relu_observed_output, test/test_quantization.py::TestQuantizedOps::test_max_pool1d, test/test_quantization.py::TestQuantizedOps::test_max_pool2d, test/test_quantization.py::TestQuantizedOps::test_qsoftmax_qnnpack, test/test_quantization.py::TestQuantizedOps::test_qthreshold, test/test_quantization.py::TestQuantizedLinear::test_qlinear_cudnn, test/test_quantization.py::TestQuantizedLinear::test_qlinear_sum_fp8, test/test_quantization.py::TestQuantizedLinear::test_qlinear_tanh, test/test_quantization.py::TestQuantizedLinear::test_qlinear_unpack, test/test_quantization.py::TestQuantizedLinear::test_wrapped_quantized_linear, test/test_quantization.py::TestQuantizedConv::test_qconv1d_relu_fp8, test/test_quantization.py::TestQuantizedConv::test_qconv2d_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_hardtanh_fp8, test/test_quantization.py::TestQuantizedConv::test_qconv3d_fp8, test/test_quantization.py::TestQuantizedConv::test_qconv3d_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv3d_relu, test/test_quantization.py::TestQuantizedConv::test_qconv3d_unpack, test/test_quantization.py::TestDynamicQuantizedOps::test_qlinear, test/test_quantization.py::TestDynamicQuantizedOps::test_unpacked_qlinear_dynamic_fp16, test/test_quantization.py::TestQuantizedFunctionalOps::test_conv2d_api, test/test_quantization.py::TestQuantizedFunctionalOps::test_relu_api, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_channel_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_backward_per_tensor_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_numerical_consistency_per_channel, test/test_quantization.py::TestQuantizedTensor::test_compare_per_channel_device_numerics, test/test_quantization.py::TestQuantizedTensor::test_dequantize_fp16_cuda, test/test_quantization.py::TestQuantizedTensor::test_per_channel_qtensor_creation_cpu, test/test_quantization.py::TestQuantizedTensor::test_per_channel_qtensor_creation_cuda, test/test_quantization.py::TestQuantizedTensor::test_qtensor_cpu, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_tensor_nhwc, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_select_cpu, test/test_quantization.py::TestQuantizedTensor::test_qtensor_quantize_per_channel, test/test_quantization.py::TestQuantizedTensor::test_quantize_per_channel_float_qparams, test/test_quantization.py::TestObserver::test_dynamic_quant_observer_matching_choose_qparams, test/test_quantization.py::TestObserver::test_per_channel_observers, test/test_quantization.py::TestObserver::test_per_tensor_observers, test/test_quantization.py::TestStaticQuantizedModule::test_linear_leaky_relu, test/test_quantization.py::TestDistributed::test_device_affinity, test/test_quantization.py::TestDistributed::test_qat_data_parallel, test/test_quantization.py::TestDistributed::test_syncbn_preserves_qconfig, test/test_quantization.py::TestFusedObsFakeQuantModule::test_fused_mod_reduce_range, test/test_quantization.py::TestFusedObsFakeQuantModule::test_fused_obs_fq_module, test/test_quantization.py::TestFusedObsFakeQuantModule::test_fused_obs_fq_moving_avg_module, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_input_type_to_index, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_num_tensor_args_to_observation_type, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_reference_quantized_module, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_custom_module_class, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_single_layer, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_forward_hooks_preserved, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_nested1, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_nested3, test/test_quantization.py::TestQuantizeEagerOps::test_int16_reference_module, test/test_quantization.py::TestQuantizeEagerQAT::test_embedding_bag_linear, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_conv_bn_folded_vs_unfolded, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_linear_dynamic, test/test_quantization.py::TestNumericSuiteEager::test_mobilenet_v3, test/test_quantization.py::TestNumericSuiteEager::test_shadow_logger, test/test_quantization.py::TestBiasCorrectionEager::test_linear_chain, test/test_quantization.py::TestFuseFx::test_fuse_conv_bn_add_relu_onednn, test/test_quantization.py::TestFuseFx::test_fuse_custom_pattern, test/test_quantization.py::TestQuantizeFx::test_conv_linear_reference, test/test_quantization.py::TestQuantizeFx::test_copy_node_has_shared_actpp_instance, test/test_quantization.py::TestQuantizeFx::test_custom_module_class_input_has_multiple_users, test/test_quantization.py::TestQuantizeFx::test_fusion_pattern_unquantized, test/test_quantization.py::TestQuantizeFx::test_get_default_qconfig_valid_backend, test/test_quantization.py::TestQuantizeFx::test_getattr_with_nontensor_result, test/test_quantization.py::TestQuantizeFx::test_keep_original_weights, test/test_quantization.py::TestQuantizeFx::test_linear_size_view, test/test_quantization.py::TestQuantizeFx::test_mixed_dtypes, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_split_tuple_args, test/test_quantization.py::TestQuantizeFx::test_qconfig_dict_with_fused_modules, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_module_name, test/test_quantization.py::TestQuantizeFx::test_qconfig_module_name_object_type_order, test/test_quantization.py::TestQuantizeFx::test_qconfig_qat_module_type, test/test_quantization.py::TestQuantizeFx::test_remove_qconfig, test/test_quantization.py::TestQuantizeFx::test_reuse_input_qconfig, test/test_quantization.py::TestQuantizeFx::test_sequential, test/test_quantization.py::TestQuantizeFx::test_torch_transpose_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_transpose_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFxOps::test_add, test/test_quantization.py::TestQuantizeFxOps::test_layer_norm, test/test_quantization.py::TestQuantizeFxOps::test_multiple_qconfigs_for_single_value, test/test_quantization.py::TestQuantizeFxOps::test_quantized_add_qat, test/test_quantization.py::TestQuantizeFxOps::test_rnn_cell, test/test_quantization.py::TestQuantizeFxOps::test_sub, test/test_quantization.py::TestQuantizeFxModels::test_qat_embeddingbag_linear, test/test_quantization.py::TestQuantizeFxModels::test_resnet18_ddp, test/test_quantization.py::TestQuantizeFxModels::test_static_gpu_convert_basic, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_is_entire_graph, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_writer_replace_consecutive_submodules, test/test_quantization.py::TestDuplicateDQPass::test_no_need_for_duplicate_dq, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_with_no_quant_inbetween, test/test_quantization.py::TestNumericDebugger::test_deepcopy_preserve_handle, test/test_quantization.py::TestQuantizePT2E::test_derived_qspec, test/test_quantization.py::TestQuantizePT2E::test_disallow_eval_train, test/test_quantization.py::TestQuantizePT2E::test_prepare_obs_or_fq_callback, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_bfloat16_float8_e4m3fn, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_float32_float8_e5m2, test/test_quantization.py::TestQuantizePT2E::test_shared_qspec_transitivity, test/test_quantization.py::TestPT2ERepresentation::test_dynamic_linear, test/test_quantization.py::TestXNNPACKQuantizer::test_dynamic_linear, test/test_quantization.py::TestXNNPACKQuantizer::test_dynamic_linear_with_conv, test/test_quantization.py::TestXNNPACKQuantizer::test_gru, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_attention_block, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d_binary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d_binary2, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_dynamic_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_case1, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_cuda, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_update_shared_qspec, test/test_quantization.py::TestFXGraphMatcher::test_simple_tensor_ops, test/test_quantization.py::TestFXGraphMatcherModels::test_mobilenet_v2_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_meth_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_mod_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_dynamic, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_fp16_shadows_fp32, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_int8_shadows_fp32_simple, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_mul_add_cat_stack_skips_shadowing, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_fp32_quant, test/test_quantization.py::TestFXNumericSuiteNShadows::test_conv_bn_relu_mod, test/test_quantization.py::TestFXNumericSuiteNShadows::test_custom_functions_and_tracer, test/test_quantization.py::TestFXNumericSuiteNShadows::test_functions, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_sparsenn_shadow, test/test_quantization.py::TestFxModelReportObserver::test_zero_tensor_errors, test/test_quantization.py::TestFxDetectOutliers::test_outlier_detection_determine_points, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_activation_values, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_branching, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_graphs, test/test_quantization.py::TestSerialization::test_conv2d_nobias_graph, test/test_quantization.py::TestQuantizeJit::test_observer_with_ignored_function, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_nested_if, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_interface_unshare_type, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_propagate_observed_for_function, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_weight_dtype, test/test_quantization.py::TestQuantizeJitOps::test_conv_with_benchmark_flag, test/test_quantization.py::TestQuantizeJitOps::test_general_shape_ops, test/test_quantization.py::TestQuantizeJitOps::test_general_value_ops, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_alpha, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_scalar_relu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul_relu, test/test_quantization.py::TestQuantizeDynamicJitOps::test_linear, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_observer, test/test_quantization.py::TestAOMigrationNNQuantized::test_functional_import, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_qat_conv, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_qat_linear, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_quantizable_rnn, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_qat_conv_fused, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_quantization_patterns, test/test_quantization.py::TestFloat8DtypeCPU::test_to_string_cpu_float8_e4m3fn 2025-08-14T22:07:10.7334253Z 2025-08-14T22:07:15.2395565Z /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. 2025-08-14T22:07:15.2398431Z import pkg_resources 2025-08-14T22:07:15.4366918Z Running test_transformers 3/7 ... [2025-08-14 22:07:15.435724] 2025-08-14T22:07:15.4367682Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:07:15.4369971Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_transformers.py', '-m', 'not serial', '--shard-id=3', '--num-shards=7', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:07:15.436179] 2025-08-14T22:10:48.0613121Z 2025-08-14T22:10:48.0614460Z test_quantization 8/9 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_8.9_49374969d9463049_.log 2025-08-14T22:10:48.0738075Z Running 163 items in this shard: test/test_quantization.py::TestQuantizedOps::test_add_scalar_relu, test/test_quantization.py::TestQuantizedOps::test_avg_pool3d, test/test_quantization.py::TestQuantizedOps::test_avg_pool3d_nhwc, test/test_quantization.py::TestQuantizedOps::test_channel_shuffle, test/test_quantization.py::TestQuantizedOps::test_leaky_relu, test/test_quantization.py::TestQuantizedOps::test_mean, test/test_quantization.py::TestQuantizedOps::test_qmatmul, test/test_quantization.py::TestQuantizedOps::test_qmul_relu_same_qparams, test/test_quantization.py::TestQuantizedOps::test_qrelu, test/test_quantization.py::TestQuantizedOps::test_sigmoid, test/test_quantization.py::TestQNNPackOps::test_avg_pool2d, test/test_quantization.py::TestQNNPackOps::test_mean, test/test_quantization.py::TestQuantizedLinear::test_qlinear_add_relu_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_fp8, test/test_quantization.py::TestQuantizedLinear::test_qlinear_gelu_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_with_input_q_dq_qweight_dq_output_fp32, test/test_quantization.py::TestQuantizedConv::test_qconv1d_relu, test/test_quantization.py::TestQuantizedConv::test_qconv2d_add, test/test_quantization.py::TestQuantizedConv::test_qconv2d_hardswish_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv2d_hardtanh_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv2d_relu, test/test_quantization.py::TestDynamicQuantizedOps::test_wrapped_fbgemm_pack_gemm_matrix_fp16_pt2_compliant, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_bag_2d_indices, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_channel, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_tensor, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_tensor_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_fake_quantize_per_tensor_affine_inf, test/test_quantization.py::TestFakeQuantizeOps::test_fixed_qparams_fq_module, test/test_quantization.py::TestFakeQuantizeOps::test_fq_serializable_per_tensor, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_forward_per_channel_cuda, test/test_quantization.py::TestFusedObsFakeQuant::test_fused_backward_op_fake_quant_off, test/test_quantization.py::TestQuantizedTensor::test_decomposed_choose_qparams_per_token_asymmetric_backward, test/test_quantization.py::TestQuantizedTensor::test_decomposed_dequantize_per_channel, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_channel, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_per_channel_to_device, test/test_quantization.py::TestQuantizedTensor::test_pickle_checkpoint_qtensor, test/test_quantization.py::TestQuantizedTensor::test_qtensor_equal, test/test_quantization.py::TestQuantizedTensor::test_qtensor_float_assignment, test/test_quantization.py::TestQuantizedTensor::test_qtensor_int_repr, test/test_quantization.py::TestQuantizedTensor::test_qtensor_legacy_new_failure, test/test_quantization.py::TestQuantizedTensor::test_qtensor_reshape, test/test_quantization.py::TestObserver::test_dynamic_quant_observer, test/test_quantization.py::TestObserver::test_histogram_observer_handle_close_to_infinity, test/test_quantization.py::TestStaticQuantizedModule::test_channel_shuffle, test/test_quantization.py::TestStaticQuantizedModule::test_conv1d_api, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_add_relu, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_api, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_relu_api, test/test_quantization.py::TestStaticQuantizedModule::test_dropout_serialization, test/test_quantization.py::TestStaticQuantizedModule::test_elu, test/test_quantization.py::TestStaticQuantizedModule::test_instance_norm, test/test_quantization.py::TestStaticQuantizedModule::test_layer_norm, test/test_quantization.py::TestStaticQuantizedModule::test_quant_dequant_api, test/test_quantization.py::TestDynamicQuantizedModule::test_gru_api, test/test_quantization.py::TestReferenceQuantizedModule::test_sparse, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_single_inputs, test/test_quantization.py::TestBackendConfig::test_dtype_config_from_dict, test/test_quantization.py::TestQuantizeEagerOps::test_conv_2d, test/test_quantization.py::TestQuantizeEagerOps::test_conv_transpose_1d, test/test_quantization.py::TestQuantizeEagerQAT::test_conv_linear, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_linear_bn_numerics, test/test_quantization.py::TestModelNumericsEager::test_weight_only_activation_only_fakequant, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_outputs_functional_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_outputs_lstm_dynamic, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_partial, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_submodule_static, test/test_quantization.py::TestFuseFx::test_fuse_conv_bn_add_relu_by_default, test/test_quantization.py::TestFuseFx::test_fuse_linear_bn_leaky_relu_onednn, test/test_quantization.py::TestFuseFx::test_linear_bn_leaky_relu_not_fused_by_default, test/test_quantization.py::TestFuseFx::test_linear_tanh_not_fused_by_default, test/test_quantization.py::TestQuantizeFx::test_attention, test/test_quantization.py::TestQuantizeFx::test_conv_transpose_not_reference, test/test_quantization.py::TestQuantizeFx::test_fp32_input_quantized_output, test/test_quantization.py::TestQuantizeFx::test_fp32_sum, test/test_quantization.py::TestQuantizeFx::test_fuse_custom_config_set_preserved_attributes, test/test_quantization.py::TestQuantizeFx::test_fused_module_qat_swap, test/test_quantization.py::TestQuantizeFx::test_pattern_match, test/test_quantization.py::TestQuantizeFx::test_pattern_match_constant, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_preserved_attributes, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_standalone_module_class, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_dict_args, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_dict_split_tuple_args, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_from_dict, test/test_quantization.py::TestQuantizeFx::test_qconfig_module_type, test/test_quantization.py::TestQuantizeFx::test_quantized_input_quantized_output, test/test_quantization.py::TestQuantizeFx::test_static_lstm, test/test_quantization.py::TestQuantizeFxOps::test_ave_pool_with_custom_cfg, test/test_quantization.py::TestQuantizeFxOps::test_bmm_int_reference, test/test_quantization.py::TestQuantizeFxOps::test_boolean_tensor, test/test_quantization.py::TestQuantizeFxOps::test_float_functional, test/test_quantization.py::TestQuantizeFxOps::test_mish_reference, test/test_quantization.py::TestQuantizeFxOps::test_mul_relu, test/test_quantization.py::TestQuantizeFxOps::test_quantized_conv_relu, test/test_quantization.py::TestQuantizeFxModels::test_prepare_serialize_switch_device_convert, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_annotations_int, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_correct_output_replacement, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_with_oneliner_pattern, test/test_quantization.py::TestNumericDebugger::test_copy_preserve_handle, test/test_quantization.py::TestNumericDebugger::test_extract_results_from_loggers_list_output, test/test_quantization.py::TestQuantizePT2E::test_composable_quantizer_linear_conv, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_float32_float8_e4m3fn, test/test_quantization.py::TestQuantizePT2EAffineQuantization::test_dynamic_per_tok_act_per_group_weights, test/test_quantization.py::TestPT2ERepresentation::test_qdq_per_channel, test/test_quantization.py::TestXNNPACKQuantizer::test_conv2d, test/test_quantization.py::TestXNNPACKQuantizer::test_qat_dynamic_linear, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d_binary_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_dynamic_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_serials, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_case2, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_no_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_transpose_bn_relu, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_bias_derived_qspec, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_cuda, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_no_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_inplace_add_relu, test/test_quantization.py::TestFXGraphMatcher::test_nodes_before_cat, test/test_quantization.py::TestFXGraphMatcherModels::test_mobilenet_v2, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_linear_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_linear_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_mod_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_mod_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_loggers_preserve_qat_numerics, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_mobilenet_v2, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_insert_padding, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_retroactive_padding, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_weights_conv, test/test_quantization.py::TestFxModelReportDetector::test_fusion_layer_in_sequential, test/test_quantization.py::TestFxModelReportDetector::test_qat_aware_model_example, test/test_quantization.py::TestFxModelReportDetector::test_sequential_model_format, test/test_quantization.py::TestFxModelReportObserver::test_observer_after_relu, test/test_quantization.py::TestFxModelReportClass::test_generate_visualizer, test/test_quantization.py::TestFxDetectInputWeightEqualization::test_input_weight_equalization_determine_points, test/test_quantization.py::TestSerialization::test_conv2d_nobias_graph_v3, test/test_quantization.py::TestSerialization::test_default_qat_qconfig, test/test_quantization.py::TestQuantizeJitPasses::test_convtranspose_trace, test/test_quantization.py::TestQuantizeJitPasses::test_fuse_linear, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_interface, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_propagate_observed_in_submodule, test/test_quantization.py::TestQuantizeJitPasses::test_quantize_fork_wait, test/test_quantization.py::TestQuantizeJitOps::test_elu, test/test_quantization.py::TestQuantizeJitOps::test_qbatch_norm, test/test_quantization.py::TestQuantizeJitOps::test_qbatch_norm_relu_BNFuncRelu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_relu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul_scalar_relu, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_quant_multi_uses, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_with_if, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_insert_quant_dequant_linear_dynamic, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_prepare_dynamic_child_qconfig, test/test_quantization.py::TestFusionPasses::test_quantized_add_relu_fusion, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quantization_mappings, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_qat_dynamic_linear, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_quantizable_activation, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_dropout, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_import, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_utils, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_quantize_fx, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_rte_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_soak_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_empty_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_finfo_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_type_promotion_fails_cpu_float8_e4m3fnuz 2025-08-14T22:10:48.0850352Z 2025-08-14T22:10:52.3545586Z /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. 2025-08-14T22:10:52.3548023Z import pkg_resources 2025-08-14T22:10:52.5458287Z Running test_transformers 4/7 ... [2025-08-14 22:10:52.545403] 2025-08-14T22:10:52.5459419Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:10:52.5544632Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_transformers.py', '-m', 'not serial', '--shard-id=4', '--num-shards=7', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:10:52.545858] 2025-08-14T22:15:16.6241075Z 2025-08-14T22:15:16.6242539Z test_transformers 2/7 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_2.7_6d4e7f6e93fdb925_.log 2025-08-14T22:15:16.7166854Z Running 451 items in this shard: test/test_transformers.py::TestTransformersCPU::test_math_backend_high_precision_cpu, test/test_transformers.py::TestTransformersCPU::test_multiheadattention_fastpath_attn_mask_attn_mask_dim_2_key_padding_mask_dim_2_float32_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_3D_input_dim_2D_causal_attn_mask_dropout_p_0_0_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_3D_input_dim_2D_causal_attn_mask_dropout_p_0_5_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_2D_attn_mask_dropout_p_0_0_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_4D_causal_attn_mask_dropout_p_0_2_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_no_attn_mask_dropout_p_0_2_cpu, test/test_transformers.py::TestTransformersCPU::test_train_with_pad_and_catch_error_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_batch_first_True_training_False_enable_nested_tensor_True_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_square_input_with_no_grad_False_training_False_enable_nested_tensor_False_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_square_input_with_no_grad_True_training_False_enable_nested_tensor_False_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoderlayer_src_mask_nhead_4_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_inputs_different_devices_kernel0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_mask_invalid_last_dim_stride_kernel0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_mem_eff_attention_fail_with_batch_size_geq_65536_error_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_mem_efficient_fail_bfloat16_less_than_sm80_cpu, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_128_head_dim_16_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_32_head_dim_32_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_64_head_dim_16_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend1_seq_len_64_head_dim_32_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_nested_dropout_0_0_bfloat16_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_non_masked_rows_nan_props_float32_fill_val_inf_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_gqa_vs_math_cpu_fused_kernel0_float32_n_heads2_train_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_gqa_vs_math_cpu_fused_kernel0_float64_n_heads0_train_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_causal_variant_1_shape3_cpu, test/test_transformers.py::TestAttnBiasCPU::test_is_causal_equals_upper_left_shape2_cpu 2025-08-14T22:15:16.8089552Z 2025-08-14T22:15:21.2588104Z /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. 2025-08-14T22:15:21.2592636Z import pkg_resources 2025-08-14T22:15:21.3914954Z Running test_ao_sparsity 1/1 ... [2025-08-14 22:15:21.391006] 2025-08-14T22:15:21.3915869Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:15:21.3920054Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ao_sparsity.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'] ... [2025-08-14 22:15:21.391566] 2025-08-14T22:16:41.5218919Z 2025-08-14T22:16:41.5220661Z test_transformers 3/7 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_3.7_c1a84fba1563265d_.log 2025-08-14T22:16:41.5681843Z Running 471 items in this shard: test/test_transformers.py::TestTransformersCPU::test_mask_check_fastpath_cpu, test/test_transformers.py::TestTransformersCPU::test_mha_native_args_nb_heads_1_bias_False_cpu, test/test_transformers.py::TestTransformersCPU::test_mha_native_args_nb_heads_1_bias_True_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_3D_input_dim_no_attn_mask_dropout_p_0_2_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_2D_causal_attn_mask_dropout_p_0_0_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_4D_attn_mask_dropout_p_0_5_cpu, test/test_transformers.py::TestTransformersCPU::test_script_mha_in_proj_weight_none_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_batch_first_True_training_False_enable_nested_tensor_False_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_batch_first_True_training_True_enable_nested_tensor_True_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_fused_inputs_attn_mask_present_kernel0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_fused_inputs_dim_3_kernel0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_inputs_different_datatypes_kernel1_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_sdpa_kernel_grouped_query_attention_cuda_fused_kernel0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_unaligned_tensors_cpu, test/test_transformers.py::TestSDPACpuOnlyCPU::test_cpu_flash_attn_nan_propagation_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_128_head_dim_32_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_32_head_dim_32_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend1_seq_len_32_head_dim_32_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend1_seq_len_32_head_dim_32_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend1_seq_len_64_head_dim_16_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_dense_dropout_0_0_bfloat16_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_dense_dropout_0_7_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_dense_dropout_0_7_float64_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_nested_dropout_0_7_bfloat16_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_non_masked_rows_nan_props_float16_fill_val_inf_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_attention_math_with_negative_scale_kernel0_cpu, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_gqa_vs_math_cpu_fused_kernel0_bfloat16_n_heads2_train_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_gqa_vs_math_cpu_fused_kernel0_float16_n_heads0_train_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_gqa_vs_math_cpu_fused_kernel0_float32_n_heads0_train_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_gqa_vs_math_cpu_fused_kernel0_float64_n_heads2_train_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_gqa_vs_math_cpu_fused_kernel0_float64_n_heads3_train_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_causal_variant_1_shape1_cpu, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_compile_causal_variant_1_shape1_cpu, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_compile_causal_variant_2_shape3_cpu, test/test_transformers.py::TestAttnBiasCPU::test_is_causal_equals_upper_left_shape0_cpu 2025-08-14T22:16:41.6134112Z 2025-08-14T22:16:44.7290580Z /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. 2025-08-14T22:16:44.7295010Z import pkg_resources 2025-08-14T22:16:44.8514868Z Running test_license 1/1 ... [2025-08-14 22:16:44.850993] 2025-08-14T22:16:44.8515868Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:16:44.8520474Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_license.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'] ... [2025-08-14 22:16:44.851412] 2025-08-14T22:16:50.8104293Z 2025-08-14T22:16:50.8105929Z test_license 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_license_1.1_b7a2e65b8b1015c0_.log 2025-08-14T22:16:50.8108222Z Running 2 items in this shard: test/test_license.py::TestLicense::test_distinfo_license, test/test_license.py::TestLicense::test_license_for_wheel 2025-08-14T22:16:50.8109436Z 2025-08-14T22:16:54.7269211Z /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. 2025-08-14T22:16:54.7272642Z import pkg_resources 2025-08-14T22:16:54.8942216Z Running torch_np/test_binary_ufuncs 1/1 ... [2025-08-14 22:16:54.893846] 2025-08-14T22:16:54.8942759Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:16:54.8945729Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_binary_ufuncs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:16:54.894243] 2025-08-14T22:17:08.9044667Z 2025-08-14T22:17:08.9046414Z torch_np/test_binary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_binary_ufuncs_1.1_c618d27d2cf6e82d_.log 2025-08-14T22:17:08.9061928Z Running 38 items in this shard: test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_add, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_arctan2, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_bitwise_and, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_bitwise_or, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_bitwise_xor, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_copysign, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_divide, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_equal, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_float_power, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_floor_divide, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_fmax, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_fmin, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_fmod, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_gcd, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_greater, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_greater_equal, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_heaviside, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_hypot, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_lcm, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_ldexp, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_left_shift, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_less, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_less_equal, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logaddexp, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logaddexp2, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logical_and, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logical_or, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logical_xor, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_matmul, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_maximum, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_minimum, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_multiply, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_nextafter, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_not_equal, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_power, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_remainder, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_right_shift, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_subtract 2025-08-14T22:17:08.9075008Z 2025-08-14T22:17:13.0457756Z /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. 2025-08-14T22:17:13.0460564Z import pkg_resources 2025-08-14T22:17:13.2241407Z Running xpu/test_fusion 1/1 ... [2025-08-14 22:17:13.223644] 2025-08-14T22:17:13.2242153Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:17:13.2244220Z 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'] ... [2025-08-14 22:17:13.224093] 2025-08-14T22:17:17.4329349Z 2025-08-14T22:17:17.4330455Z xpu/test_fusion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_fusion_1.1_874cd2ed9e92f48a_.log 2025-08-14T22:17:17.4331613Z Running 0 items in this shard: 2025-08-14T22:17:17.4331919Z 2025-08-14T22:17:20.5914418Z /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. 2025-08-14T22:17:20.5917577Z import pkg_resources 2025-08-14T22:17:20.7102117Z Running torch_np/test_unary_ufuncs 1/1 ... [2025-08-14 22:17:20.709830] 2025-08-14T22:17:20.7102896Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:17:20.7105168Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_unary_ufuncs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:17:20.710217] 2025-08-14T22:17:40.0240198Z 2025-08-14T22:17:40.0242450Z torch_np/test_unary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_unary_ufuncs_1.1_77df7e9309b3fe95_.log 2025-08-14T22:17:40.0265547Z Running 42 items in this shard: test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_absolute, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arccos, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arccosh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arcsin, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arcsinh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arctan, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arctanh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_cbrt, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_ceil, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_conjugate, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_cos, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_cosh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_deg2rad, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_degrees, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_exp, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_exp2, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_expm1, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_fabs, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_floor, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_isfinite, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_isinf, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_isnan, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_log, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_log10, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_log1p, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_log2, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_logical_not, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_negative, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_positive, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_rad2deg, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_radians, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_reciprocal, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_rint, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_sign, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_signbit, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_sin, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_sinh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_sqrt, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_square, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_tan, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_tanh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_trunc 2025-08-14T22:17:40.0292427Z 2025-08-14T22:17:43.8001544Z /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. 2025-08-14T22:17:43.8004617Z import pkg_resources 2025-08-14T22:17:43.9220222Z Running test_foreach 1/1 ... [2025-08-14 22:17:43.920880] 2025-08-14T22:17:43.9221207Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:17:43.9223240Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_foreach.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'] ... [2025-08-14 22:17:43.921324] 2025-08-14T22:19:27.6275020Z 2025-08-14T22:19:27.6276602Z test_transformers 4/7 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_4.7_e7457f9b5d1395a5_.log 2025-08-14T22:19:27.6740953Z Running 445 items in this shard: test/test_transformers.py::TestTransformersCPU::test_multiheadattention_fastpath_attn_mask_attn_mask_dim2_key_padding_mask_dim1_float32_cpu, test/test_transformers.py::TestTransformersCPU::test_multiheadattention_fastpath_attn_mask_attn_mask_dim2_key_padding_mask_dim_2_float32_cpu, test/test_transformers.py::TestTransformersCPU::test_multiheadattention_fastpath_attn_mask_attn_mask_dim_2_key_padding_mask_dim1_float32_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_4D_attn_mask_dropout_p_0_0_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_4D_causal_attn_mask_dropout_p_0_5_cpu, test/test_transformers.py::TestTransformersCPU::test_transformer_bias_is_none_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_batch_first_False_training_False_enable_nested_tensor_False_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_fastpath_use_torchscript_False_enable_nested_tensor_False_use_autocast_False_d_model_256_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_fastpath_use_torchscript_False_enable_nested_tensor_False_use_autocast_True_d_model_12_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_fastpath_use_torchscript_False_enable_nested_tensor_True_use_autocast_False_d_model_12_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_fastpath_use_torchscript_False_enable_nested_tensor_True_use_autocast_False_d_model_256_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_fastpath_use_torchscript_False_enable_nested_tensor_True_use_autocast_True_d_model_12_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoderlayer_subclass_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_flash_backward_failure_sm86plus_head_dim_193_dropout_p_0_2_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_flash_backward_failure_sm86plus_head_dim_256_dropout_p_0_0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_inputs_different_datatypes_kernel0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_inputs_different_devices_kernel2_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_mem_eff_attention_fail_with_batch_size_geq_65536_cpu, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_64_head_dim_16_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_64_head_dim_32_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend1_seq_len_32_head_dim_16_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_nested_dropout_0_0_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_gqa_vs_math_cpu_fused_kernel0_bfloat16_n_heads0_train_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_gqa_vs_math_cpu_fused_kernel0_bfloat16_n_heads0_train_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_gqa_vs_math_cpu_fused_kernel0_bfloat16_n_heads3_train_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_gqa_vs_math_cpu_fused_kernel0_float16_n_heads2_train_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_sdpa_with_inf_cpu, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_causal_variant_2_shape0_cpu, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_causal_variant_2_shape2_cpu, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_causal_variant_2_shape3_cpu 2025-08-14T22:19:27.7168981Z 2025-08-14T22:19:30.8232057Z /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. 2025-08-14T22:19:30.8235100Z import pkg_resources 2025-08-14T22:19:30.9429598Z Running test_expanded_weights 1/1 ... [2025-08-14 22:19:30.942557] 2025-08-14T22:19:30.9430457Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:19:30.9432716Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_expanded_weights.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:19:30.942936] 2025-08-14T22:20:24.2175805Z 2025-08-14T22:20:24.2177119Z test_expanded_weights 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_expanded_weights_1.1_57af5e5b1c26748f_.log 2025-08-14T22:20:24.2271840Z Running 189 items in this shard: test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_forward_helper_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_forward_helper_failure_args_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_set_grad_sample_if_exists_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_set_grad_sample_if_exists_failure_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_sum_over_all_but_batch_and_last_n_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_unpack_expanded_weight_or_tensor_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_unpack_expanded_weight_or_tensor_failure_cpu, test/test_expanded_weights.py::TestExpandedWeightHelperFunctionCPU::test_unpack_expanded_weight_or_tensor_with_custom_function_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_cnn_model_mean_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_cnn_model_sum_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_embedding_model_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_error_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv1d_cpu_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv1d_cpu_complex128, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv1d_cpu_complex64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv1d_cpu_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv1d_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv1d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv1d_cpu_int64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv2d_cpu_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv2d_cpu_complex128, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv2d_cpu_complex64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv2d_cpu_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv2d_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv2d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv2d_cpu_int64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv3d_cpu_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv3d_cpu_complex128, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv3d_cpu_complex64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv3d_cpu_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv3d_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv3d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_conv3d_cpu_int64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_embedding_cpu_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_embedding_cpu_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_embedding_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_embedding_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_group_norm_cpu_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_group_norm_cpu_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_group_norm_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_group_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_instance_norm_cpu_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_instance_norm_cpu_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_instance_norm_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_instance_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_layer_norm_cpu_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_layer_norm_cpu_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_layer_norm_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_layer_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_linear_cpu_bfloat16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_linear_cpu_complex128, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_linear_cpu_complex64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_linear_cpu_float16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_linear_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_linear_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_linear_cpu_int16, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_linear_cpu_int32, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_linear_cpu_int64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_linear_cpu_int8, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_forward_nn_functional_linear_cpu_uint8, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_mean_nn_functional_conv1d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_mean_nn_functional_conv2d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_mean_nn_functional_conv3d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_mean_nn_functional_embedding_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_mean_nn_functional_group_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_mean_nn_functional_instance_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_mean_nn_functional_layer_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_mean_nn_functional_linear_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_sum_nn_functional_conv1d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_sum_nn_functional_conv2d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_sum_nn_functional_conv3d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_sum_nn_functional_embedding_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_sum_nn_functional_group_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_sum_nn_functional_instance_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_sum_nn_functional_layer_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weight_per_sample_grad_sum_nn_functional_linear_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weights_per_sample_grad_input_no_grad_nn_functional_conv1d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weights_per_sample_grad_input_no_grad_nn_functional_conv2d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weights_per_sample_grad_input_no_grad_nn_functional_conv3d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weights_per_sample_grad_input_no_grad_nn_functional_embedding_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weights_per_sample_grad_input_no_grad_nn_functional_group_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weights_per_sample_grad_input_no_grad_nn_functional_instance_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weights_per_sample_grad_input_no_grad_nn_functional_layer_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_expanded_weights_per_sample_grad_input_no_grad_nn_functional_linear_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_group_norm_error_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_group_norm_model_num_dim_1_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_group_norm_model_num_dim_2_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_group_norm_model_num_dim_3_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_instance_norm_model_num_dim_1_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_instance_norm_model_num_dim_2_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_instance_norm_model_num_dim_3_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_layer_norm_model_num_dim_1_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_layer_norm_model_num_dim_2_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_layer_norm_model_num_dim_3_cpu, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_unsupported_expand_weights_nn_functional_conv1d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_unsupported_expand_weights_nn_functional_conv2d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_unsupported_expand_weights_nn_functional_conv3d_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_unsupported_expand_weights_nn_functional_embedding_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_unsupported_expand_weights_nn_functional_group_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_unsupported_expand_weights_nn_functional_instance_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_unsupported_expand_weights_nn_functional_layer_norm_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightFunctionalCPU::test_unsupported_expand_weights_nn_functional_linear_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_circular_stride2_pad2_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_circular_stride2_pad2_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_pad1_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_pad1_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_pad1size1_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_pad1size1_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_pad2_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_pad2_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_pad2size1_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_pad2size1_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_reflect_stride2_pad2_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_reflect_stride2_pad2_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_replicate_stride2_pad2_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_replicate_stride2_pad2_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_stride_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_stride_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_zero_batch_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_zero_batch_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_zeros_stride2_pad2_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv1d_zeros_stride2_pad2_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_circular_stride2_pad2_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_circular_stride2_pad2_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_dilated_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_dilated_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_no_bias_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_no_bias_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_padding_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_padding_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_reflect_stride2_pad2_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_reflect_stride2_pad2_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_replicate_stride2_pad2_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_replicate_stride2_pad2_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_strided_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_strided_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_zero_batch_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_zero_batch_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_zeros_stride2_pad2_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv2d_zeros_stride2_pad2_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_1x1x1_no_bias_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_1x1x1_no_bias_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_circular_stride2_pad2_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_circular_stride2_pad2_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_no_bias_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_no_bias_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_replicate_stride2_pad2_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_replicate_stride2_pad2_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_stride_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_stride_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_stride_padding_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_stride_padding_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_zero_batch_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_zero_batch_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_zeros_stride2_pad2_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Conv3d_zeros_stride2_pad2_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Embedding_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Embedding_discontiguous_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Embedding_discontiguous_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Embedding_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_LayerNorm_3d_no_affine_large_feature_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_LayerNorm_3d_no_affine_large_feature_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Linear_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Linear_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Linear_no_batch_dim_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Linear_no_batch_dim_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Linear_no_bias_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_Linear_no_bias_multiple_inputs_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_module_nn_GRU_eval_mode_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_module_nn_GRU_eval_mode_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_module_nn_GRU_train_mode_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_module_nn_GRU_train_mode_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_module_nn_LSTM_eval_mode_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_module_nn_LSTM_eval_mode_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_module_nn_LSTM_train_mode_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_module_nn_LSTM_train_mode_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_module_nn_RNN_eval_mode_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_module_nn_RNN_eval_mode_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_module_nn_RNN_train_mode_cpu_float32, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_module_nn_RNN_train_mode_cpu_float64, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_per_sample_api_compute_batch_size_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_per_sample_api_compute_batch_size_not_pytreeable_cpu, test/test_expanded_weights.py::TestExpandedWeightModuleCPU::test_per_sample_api_failing_cpu 2025-08-14T22:20:24.2362250Z 2025-08-14T22:20:27.4304329Z /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. 2025-08-14T22:20:27.4306085Z import pkg_resources 2025-08-14T22:20:27.5507914Z Running typing/test_python_operators 1/1 ... [2025-08-14 22:20:27.550392] 2025-08-14T22:20:27.5508587Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:20:27.5510070Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'typing/test_python_operators.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'] ... [2025-08-14 22:20:27.550742] 2025-08-14T22:20:45.0679609Z 2025-08-14T22:20:45.0680666Z PRINTING LOG FILE of test_ao_sparsity 1/1 (test/test-reports/test_ao_sparsity_1.1_e9554bf9fc0b9beb_.log) 2025-08-14T22:20:45.0683962Z /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. 2025-08-14T22:20:45.0686568Z import pkg_resources 2025-08-14T22:20:45.0687585Z Test results will be stored in test-reports/python-pytest/test_ao_sparsity/test_ao_sparsity-238ea0524b608aa6.xml 2025-08-14T22:20:45.0688864Z ============================= test session starts ============================== 2025-08-14T22:20:45.0689839Z platform linux -- Python 3.13.5, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.13/bin/python 2025-08-14T22:20:45.0690769Z cachedir: .pytest_cache 2025-08-14T22:20:45.0691845Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-08-14T22:20:45.0693170Z rootdir: /var/lib/jenkins/workspace 2025-08-14T22:20:45.0693741Z configfile: pytest.ini 2025-08-14T22:20:45.0694843Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-08-14T22:20:45.0696048Z collecting ... collected 88 items 2025-08-14T22:20:45.0696654Z stepcurrent: Cannot find last run test, not skipping 2025-08-14T22:20:45.0740792Z Running 88 items in this shard: test/test_ao_sparsity.py::TestQuantizedSparseKernels::test_sparse_qlinear, test/test_ao_sparsity.py::TestQuantizedSparseLayers::test_sparse_qlinear, test/test_ao_sparsity.py::TestQuantizedSparseLayers::test_sparse_qlinear_serdes, test/test_ao_sparsity.py::TestFakeSparsity::test_jit_trace, test/test_ao_sparsity.py::TestFakeSparsity::test_masking_logic, test/test_ao_sparsity.py::TestFakeSparsity::test_state_dict_preserved, test/test_ao_sparsity.py::TestFakeSparsity::test_weights_parametrized, test/test_ao_sparsity.py::TestCubicScheduler::test_constructor, test/test_ao_sparsity.py::TestCubicScheduler::test_step, test/test_ao_sparsity.py::TestScheduler::test_constructor, test/test_ao_sparsity.py::TestScheduler::test_lambda_scheduler, test/test_ao_sparsity.py::TestScheduler::test_order_of_steps, test/test_ao_sparsity.py::TestScheduler::test_step, test/test_ao_sparsity.py::TestBaseSparsifier::test_constructor, test/test_ao_sparsity.py::TestBaseSparsifier::test_convert, test/test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash, test/test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash_with_params1, test/test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash_with_params2, test/test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash_with_params3, test/test_ao_sparsity.py::TestBaseSparsifier::test_prepare_config, test/test_ao_sparsity.py::TestBaseSparsifier::test_state_dict, test/test_ao_sparsity.py::TestBaseSparsifier::test_step, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_constructor, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_mask_squash, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_prepare, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_sparsity_levels, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_step, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_constructor, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_mask_squash, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_prepare, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_sparsity_levels, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_step, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_step_2_of_4, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_complex_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_constructor, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prepare_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prepare_linear, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_activation_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_bias_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_padding_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_pool_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_activation_linear, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_bias_linear, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_linear, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_layernorm_linear_multiple_layer, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_layernorm_linear_single_layer, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_linear_multiple_layer, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_linear_single_layer, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_step_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_step_linear, test/test_ao_sparsity.py::TestFPGMPruner::test_compute_distance, test/test_ao_sparsity.py::TestFPGMPruner::test_update_mask, test/test_ao_sparsity.py::TestSaliencyPruner::test_lstm_saliency_pruner_update_mask, test/test_ao_sparsity.py::TestSaliencyPruner::test_saliency_pruner_update_mask, test/test_ao_sparsity.py::TestComposability::test_convert_without_squash_mask, test/test_ao_sparsity.py::TestComposability::test_fusion_before_s_prep, test/test_ao_sparsity.py::TestComposability::test_q_prep_before_s_prep, test/test_ao_sparsity.py::TestComposability::test_qat_prep_before_s_prep, test/test_ao_sparsity.py::TestComposability::test_s_prep_before_fusion, test/test_ao_sparsity.py::TestComposability::test_s_prep_before_q_prep, test/test_ao_sparsity.py::TestComposability::test_s_prep_before_qat_prep, test/test_ao_sparsity.py::TestFxComposability::test_q_prep_fx_before_s_prep, test/test_ao_sparsity.py::TestFxComposability::test_q_prep_fx_s_prep_ref_conv, test/test_ao_sparsity.py::TestFxComposability::test_s_prep_before_q_prep_fx, test/test_ao_sparsity.py::TestFxComposability::test_s_prep_before_qat_prep_fx, test/test_ao_sparsity.py::TestFxComposability::test_s_prep_q_prep_fx_ref, test/test_ao_sparsity.py::TestActivationSparsifier::test_activation_sparsifier, test/test_ao_sparsity.py::TestBaseDataScheduler::test_constructor, test/test_ao_sparsity.py::TestBaseDataScheduler::test_order_of_steps, test/test_ao_sparsity.py::TestBaseDataScheduler::test_state_dict, test/test_ao_sparsity.py::TestBaseDataScheduler::test_step, test/test_ao_sparsity.py::TestBaseDataSparsifier::test_nn_embeddings, test/test_ao_sparsity.py::TestBaseDataSparsifier::test_nn_parameters, test/test_ao_sparsity.py::TestBaseDataSparsifier::test_tensors, test/test_ao_sparsity.py::TestNormDataSparsifiers::test_nn_embeddings, test/test_ao_sparsity.py::TestNormDataSparsifiers::test_nn_parameters, test/test_ao_sparsity.py::TestNormDataSparsifiers::test_tensors, test/test_ao_sparsity.py::TestQuantizationUtils::test_ptq_quantize_first, test/test_ao_sparsity.py::TestQuantizationUtils::test_ptq_sparsify_first, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_fqn_to_module, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_fqn_to_module_fail, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_fqn_to_module_for_tensors, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_get_arg_info_from_tensor_fqn, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_get_arg_info_from_tensor_fqn_fail, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_module_to_fqn, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_module_to_fqn_fail, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_module_to_fqn_root 2025-08-14T22:20:45.0790456Z 2025-08-14T22:20:45.0792478Z test_ao_sparsity.py::TestQuantizedSparseKernels::test_sparse_qlinear <- ../../../../opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_quantized.py SKIPPED [1.3472s] (TorchDynamo fails here for unknown reasons) [ 1%] 2025-08-14T22:20:45.0797580Z test_ao_sparsity.py::TestQuantizedSparseLayers::test_sparse_qlinear <- ../../../../opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_quantized.py [W814 22:15:28.855549156 qlinear_dynamic.cpp:251] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function operator()) 2025-08-14T22:20:45.0801474Z W0814 22:15:29.135000 19274 site-packages/torch/_dynamo/utils.py:2858] [10/3] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.0803600Z W0814 22:15:29.141000 19274 site-packages/torch/_dynamo/utils.py:2858] [10/3_1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.0805031Z PASSED [2.3481s] [ 2%] 2025-08-14T22:20:45.0807895Z test_ao_sparsity.py::TestQuantizedSparseLayers::test_sparse_qlinear_serdes <- ../../../../opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_quantized.py W0814 22:15:32.077000 19274 site-packages/torch/_dynamo/utils.py:2858] [10/2] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.0811767Z W0814 22:15:32.083000 19274 site-packages/torch/_dynamo/utils.py:2858] [10/2_1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.0813292Z PASSED [2.9619s] [ 3%] 2025-08-14T22:20:45.0814353Z test_ao_sparsity.py::TestFakeSparsity::test_jit_trace <- test/ao/sparsity/test_parametrization.py PASSED [1.6837s] [ 4%] 2025-08-14T22:20:45.0816203Z test_ao_sparsity.py::TestFakeSparsity::test_masking_logic <- test/ao/sparsity/test_parametrization.py PASSED [0.8837s] [ 5%] 2025-08-14T22:20:45.0818064Z test_ao_sparsity.py::TestFakeSparsity::test_state_dict_preserved <- test/ao/sparsity/test_parametrization.py PASSED [3.8702s] [ 6%] 2025-08-14T22:20:45.0820139Z test_ao_sparsity.py::TestFakeSparsity::test_weights_parametrized <- test/ao/sparsity/test_parametrization.py PASSED [1.2036s] [ 7%] 2025-08-14T22:20:45.0822096Z test_ao_sparsity.py::TestCubicScheduler::test_constructor <- test/ao/sparsity/test_scheduler.py PASSED [0.3577s] [ 9%] 2025-08-14T22:20:45.0824015Z test_ao_sparsity.py::TestCubicScheduler::test_step <- test/ao/sparsity/test_scheduler.py PASSED [0.3751s] [ 10%] 2025-08-14T22:20:45.0825753Z test_ao_sparsity.py::TestScheduler::test_constructor <- test/ao/sparsity/test_scheduler.py PASSED [0.3712s] [ 11%] 2025-08-14T22:20:45.0827552Z test_ao_sparsity.py::TestScheduler::test_lambda_scheduler <- test/ao/sparsity/test_scheduler.py PASSED [0.3677s] [ 12%] 2025-08-14T22:20:45.0829308Z test_ao_sparsity.py::TestScheduler::test_order_of_steps <- test/ao/sparsity/test_scheduler.py PASSED [0.4237s] [ 13%] 2025-08-14T22:20:45.0830880Z test_ao_sparsity.py::TestScheduler::test_step <- test/ao/sparsity/test_scheduler.py PASSED [0.5468s] [ 14%] 2025-08-14T22:20:45.0832634Z test_ao_sparsity.py::TestBaseSparsifier::test_constructor <- test/ao/sparsity/test_sparsifier.py PASSED [0.0916s] [ 15%] 2025-08-14T22:20:45.0834568Z test_ao_sparsity.py::TestBaseSparsifier::test_convert <- test/ao/sparsity/test_sparsifier.py PASSED [0.5269s] [ 17%] 2025-08-14T22:20:45.0836479Z test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash <- test/ao/sparsity/test_sparsifier.py PASSED [0.0539s] [ 18%] 2025-08-14T22:20:45.0838338Z test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash_with_params1 <- test/ao/sparsity/test_sparsifier.py PASSED [0.0809s] [ 19%] 2025-08-14T22:20:45.0840285Z test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash_with_params2 <- test/ao/sparsity/test_sparsifier.py PASSED [0.1018s] [ 20%] 2025-08-14T22:20:45.0842287Z test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash_with_params3 <- test/ao/sparsity/test_sparsifier.py PASSED [0.1130s] [ 21%] 2025-08-14T22:20:45.0844361Z test_ao_sparsity.py::TestBaseSparsifier::test_prepare_config <- test/ao/sparsity/test_sparsifier.py PASSED [0.0769s] [ 22%] 2025-08-14T22:20:45.0846235Z test_ao_sparsity.py::TestBaseSparsifier::test_state_dict <- test/ao/sparsity/test_sparsifier.py PASSED [0.6743s] [ 23%] 2025-08-14T22:20:45.0847933Z test_ao_sparsity.py::TestBaseSparsifier::test_step <- test/ao/sparsity/test_sparsifier.py PASSED [0.4448s] [ 25%] 2025-08-14T22:20:45.0849817Z test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_constructor <- test/ao/sparsity/test_sparsifier.py PASSED [0.1989s] [ 26%] 2025-08-14T22:20:45.0851922Z test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_mask_squash <- test/ao/sparsity/test_sparsifier.py PASSED [0.4113s] [ 27%] 2025-08-14T22:20:45.0853930Z test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_prepare <- test/ao/sparsity/test_sparsifier.py PASSED [0.0756s] [ 28%] 2025-08-14T22:20:45.0856003Z test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_sparsity_levels <- test/ao/sparsity/test_sparsifier.py PASSED [6.9353s] [ 29%] 2025-08-14T22:20:45.0858041Z test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_step <- test/ao/sparsity/test_sparsifier.py PASSED [0.4132s] [ 30%] 2025-08-14T22:20:45.0859943Z test_ao_sparsity.py::TestWeightNormSparsifier::test_constructor <- test/ao/sparsity/test_sparsifier.py PASSED [0.2344s] [ 31%] 2025-08-14T22:20:45.0861860Z test_ao_sparsity.py::TestWeightNormSparsifier::test_mask_squash <- test/ao/sparsity/test_sparsifier.py PASSED [0.0531s] [ 32%] 2025-08-14T22:20:45.0863839Z test_ao_sparsity.py::TestWeightNormSparsifier::test_prepare <- test/ao/sparsity/test_sparsifier.py PASSED [0.0536s] [ 34%] 2025-08-14T22:20:45.0865657Z test_ao_sparsity.py::TestWeightNormSparsifier::test_sparsity_levels <- test/ao/sparsity/test_sparsifier.py PASSED [0.1285s] [ 35%] 2025-08-14T22:20:45.0867556Z test_ao_sparsity.py::TestWeightNormSparsifier::test_step <- test/ao/sparsity/test_sparsifier.py PASSED [0.5410s] [ 36%] 2025-08-14T22:20:45.0870306Z test_ao_sparsity.py::TestWeightNormSparsifier::test_step_2_of_4 <- test/ao/sparsity/test_sparsifier.py W0814 22:15:53.843000 19274 site-packages/torch/_dynamo/variables/tensor.py:1048] [5/0] Graph break from `Tensor.item()`, consider setting: 2025-08-14T22:20:45.0873019Z W0814 22:15:53.843000 19274 site-packages/torch/_dynamo/variables/tensor.py:1048] [5/0] torch._dynamo.config.capture_scalar_outputs = True 2025-08-14T22:20:45.0874551Z W0814 22:15:53.843000 19274 site-packages/torch/_dynamo/variables/tensor.py:1048] [5/0] or: 2025-08-14T22:20:45.0876037Z W0814 22:15:53.843000 19274 site-packages/torch/_dynamo/variables/tensor.py:1048] [5/0] env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1 2025-08-14T22:20:45.0877996Z W0814 22:15:53.843000 19274 site-packages/torch/_dynamo/variables/tensor.py:1048] [5/0] to include these operations in the captured graph. 2025-08-14T22:20:45.0879608Z W0814 22:15:53.843000 19274 site-packages/torch/_dynamo/variables/tensor.py:1048] [5/0] 2025-08-14T22:20:45.0881006Z W0814 22:15:53.843000 19274 site-packages/torch/_dynamo/variables/tensor.py:1048] [5/0] Graph break: from user code at: 2025-08-14T22:20:45.0883687Z W0814 22:15:53.843000 19274 site-packages/torch/_dynamo/variables/tensor.py:1048] [5/0] File "/var/lib/jenkins/workspace/test/ao/sparsity/test_sparsifier.py", line 273, in torch_dynamo_resume_in_test_step_2_of_4_at_268 2025-08-14T22:20:45.0886556Z W0814 22:15:53.843000 19274 site-packages/torch/_dynamo/variables/tensor.py:1048] [5/0] self.assertAlmostEqual(mask.mean().item(), 0.5, places=2) 2025-08-14T22:20:45.0888084Z W0814 22:15:53.843000 19274 site-packages/torch/_dynamo/variables/tensor.py:1048] [5/0] 2025-08-14T22:20:45.0889257Z W0814 22:15:53.843000 19274 site-packages/torch/_dynamo/variables/tensor.py:1048] [5/0] 2025-08-14T22:20:45.0890163Z PASSED [0.6334s] [ 37%] 2025-08-14T22:20:45.0891877Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_complex_conv2d <- test/ao/sparsity/test_structured_sparsifier.py SKIPPED [0.0623s] (TorchDynamo fails with unknown reason) [ 38%] 2025-08-14T22:20:45.0894666Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_constructor <- test/ao/sparsity/test_structured_sparsifier.py PASSED [0.9131s] [ 39%] 2025-08-14T22:20:45.0896927Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prepare_conv2d <- test/ao/sparsity/test_structured_sparsifier.py PASSED [1.7599s] [ 40%] 2025-08-14T22:20:45.0899206Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prepare_linear <- test/ao/sparsity/test_structured_sparsifier.py PASSED [1.6542s] [ 42%] 2025-08-14T22:20:45.0902605Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_activation_conv2d <- test/ao/sparsity/test_structured_sparsifier.py W0814 22:16:03.883000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] torch._dynamo hit config.recompile_limit (8) 2025-08-14T22:20:45.0906024Z W0814 22:16:03.883000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] function: 'update_mask' (/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:50) 2025-08-14T22:20:45.0909609Z W0814 22:16:03.883000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] last reason: 6/7: ___check_type_id(module, 94630938984272) # num_rows = len(module.parametrizations[tensor_name][0].mask) # ar/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:52 in update_mask 2025-08-14T22:20:45.0912919Z W0814 22:16:03.883000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2025-08-14T22:20:45.0915271Z W0814 22:16:03.883000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html 2025-08-14T22:20:45.0916985Z PASSED [6.2924s] [ 43%] 2025-08-14T22:20:45.0919242Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_bias_conv2d <- test/ao/sparsity/test_structured_sparsifier.py W0814 22:16:08.304000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] torch._dynamo hit config.recompile_limit (8) 2025-08-14T22:20:45.0922626Z W0814 22:16:08.304000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] function: 'update_mask' (/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:50) 2025-08-14T22:20:45.0925697Z W0814 22:16:08.304000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] last reason: 6/7: ___check_type_id(module, 94630938886624) # num_rows = len(module.parametrizations[tensor_name][0].mask) # ar/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:52 in update_mask 2025-08-14T22:20:45.0928791Z W0814 22:16:08.304000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2025-08-14T22:20:45.0931254Z W0814 22:16:08.304000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html 2025-08-14T22:20:45.0932881Z PASSED [4.2874s] [ 44%] 2025-08-14T22:20:45.0934278Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_conv2d <- test/ao/sparsity/test_structured_sparsifier.py PASSED [4.1165s] [ 45%] 2025-08-14T22:20:45.0937325Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_padding_conv2d <- test/ao/sparsity/test_structured_sparsifier.py W0814 22:16:21.389000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] torch._dynamo hit config.recompile_limit (8) 2025-08-14T22:20:45.0940493Z W0814 22:16:21.389000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] function: 'update_mask' (/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:50) 2025-08-14T22:20:45.0943727Z W0814 22:16:21.389000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] last reason: 6/7: ___check_type_id(module, 94630958595456) # num_rows = len(module.parametrizations[tensor_name][0].mask) # ar/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:52 in update_mask 2025-08-14T22:20:45.0947026Z W0814 22:16:21.389000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2025-08-14T22:20:45.0949218Z W0814 22:16:21.389000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html 2025-08-14T22:20:45.0951360Z W0814 22:16:21.480000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [15/8] torch._dynamo hit config.recompile_limit (8) 2025-08-14T22:20:45.0953656Z W0814 22:16:21.480000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [15/8] function: 'torch_dynamo_resume_in__test_conv2d_on_device_at_485' (/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:485) 2025-08-14T22:20:45.0957218Z W0814 22:16:21.480000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [15/8] last reason: 15/7: ___check_type_id(___stack0, 94630951586496) # y_pruned = pruned(x) # ar/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:486 in torch_dynamo_resume_in__test_conv2d_on_device_at_485 2025-08-14T22:20:45.0960076Z W0814 22:16:21.480000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [15/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2025-08-14T22:20:45.0962249Z W0814 22:16:21.480000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [15/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html 2025-08-14T22:20:45.0963854Z PASSED [8.4224s] [ 46%] 2025-08-14T22:20:45.0965217Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_pool_conv2d <- test/ao/sparsity/test_structured_sparsifier.py PASSED [5.8531s] [ 47%] 2025-08-14T22:20:45.0968282Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_activation_linear <- test/ao/sparsity/test_structured_sparsifier.py W0814 22:16:32.290000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] torch._dynamo hit config.recompile_limit (8) 2025-08-14T22:20:45.0971753Z W0814 22:16:32.290000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] function: 'update_mask' (/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:50) 2025-08-14T22:20:45.0975225Z W0814 22:16:32.290000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] last reason: 6/7: ___check_type_id(module, 94630951003248) # num_rows = len(module.parametrizations[tensor_name][0].mask) # ar/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:52 in update_mask 2025-08-14T22:20:45.0978451Z W0814 22:16:32.290000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2025-08-14T22:20:45.0980868Z W0814 22:16:32.290000 19274 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html 2025-08-14T22:20:45.0982530Z PASSED [8.3990s] [ 48%] 2025-08-14T22:20:45.0984501Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_bias_linear <- test/ao/sparsity/test_structured_sparsifier.py terminate called after throwing an instance of 'std::bad_alloc' 2025-08-14T22:20:45.0986430Z what(): std::bad_alloc 2025-08-14T22:20:45.0986934Z Fatal Python error: Aborted 2025-08-14T22:20:45.0987268Z 2025-08-14T22:20:45.0987588Z Current thread 0x00007f027e50c740 (most recent call first): 2025-08-14T22:20:45.0989226Z File "/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py", line 348 in torch_dynamo_resume_in__test_linear_on_device_at_345 2025-08-14T22:20:45.0991633Z File "/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py", line 345 in torch_dynamo_resume_in__test_linear_on_device_at_342 2025-08-14T22:20:45.0994238Z File "/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py", line 342 in torch_dynamo_resume_in__test_linear_on_device_at_340 2025-08-14T22:20:45.0996569Z File "/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py", line 340 in torch_dynamo_resume_in__test_linear_on_device_at_338 2025-08-14T22:20:45.0998794Z File "/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py", line 338 in torch_dynamo_resume_in__test_linear_on_device_at_337 2025-08-14T22:20:45.1001137Z File "/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py", line 337 in torch_dynamo_resume_in__test_linear_on_device_at_332 2025-08-14T22:20:45.1003530Z File "/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py", line 332 in _test_linear_on_device 2025-08-14T22:20:45.1005608Z File "/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py", line 431 in test_prune_linear_bias_linear 2025-08-14T22:20:45.1007674Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py", line 3224 in wrapper 2025-08-14T22:20:45.1009343Z File "/opt/conda/envs/py_3.13/lib/python3.13/unittest/case.py", line 606 in _callTestMethod 2025-08-14T22:20:45.1010632Z File "/opt/conda/envs/py_3.13/lib/python3.13/unittest/case.py", line 651 in run 2025-08-14T22:20:45.1012071Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/external_utils.py", line 68 in inner 2025-08-14T22:20:45.1013695Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/eval_frame.py", line 804 in compile_wrapper 2025-08-14T22:20:45.1015315Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py", line 3376 in _run_custom 2025-08-14T22:20:45.1017134Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py", line 3406 in run 2025-08-14T22:20:45.1018612Z File "/opt/conda/envs/py_3.13/lib/python3.13/unittest/case.py", line 707 in __call__ 2025-08-14T22:20:45.1019936Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/_pytest/unittest.py", line 333 in runtest 2025-08-14T22:20:45.1021509Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/_pytest/runner.py", line 169 in pytest_runtest_call 2025-08-14T22:20:45.1023102Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pluggy/_callers.py", line 121 in _multicall 2025-08-14T22:20:45.1024724Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pluggy/_manager.py", line 120 in _hookexec 2025-08-14T22:20:45.1026171Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pluggy/_hooks.py", line 512 in __call__ 2025-08-14T22:20:45.1027616Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/_pytest/runner.py", line 262 in 2025-08-14T22:20:45.1028961Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/_pytest/runner.py", line 341 in from_call 2025-08-14T22:20:45.1030511Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/_pytest/runner.py", line 261 in call_runtest_hook 2025-08-14T22:20:45.1032102Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/_pytest/runner.py", line 222 in call_and_report 2025-08-14T22:20:45.1033848Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/_pytest/runner.py", line 133 in runtestprotocol 2025-08-14T22:20:45.1035662Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytest_rerunfailures.py", line 549 in pytest_runtest_protocol 2025-08-14T22:20:45.1037363Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pluggy/_callers.py", line 121 in _multicall 2025-08-14T22:20:45.1038823Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pluggy/_manager.py", line 120 in _hookexec 2025-08-14T22:20:45.1040311Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pluggy/_hooks.py", line 512 in __call__ 2025-08-14T22:20:45.1041782Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/_pytest/main.py", line 348 in pytest_runtestloop 2025-08-14T22:20:45.1043235Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pluggy/_callers.py", line 121 in _multicall 2025-08-14T22:20:45.1044654Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pluggy/_manager.py", line 120 in _hookexec 2025-08-14T22:20:45.1046058Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pluggy/_hooks.py", line 512 in __call__ 2025-08-14T22:20:45.1047384Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/_pytest/main.py", line 323 in _main 2025-08-14T22:20:45.1048867Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/_pytest/main.py", line 269 in wrap_session 2025-08-14T22:20:45.1050447Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/_pytest/main.py", line 316 in pytest_cmdline_main 2025-08-14T22:20:45.1051865Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pluggy/_callers.py", line 121 in _multicall 2025-08-14T22:20:45.1053208Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pluggy/_manager.py", line 120 in _hookexec 2025-08-14T22:20:45.1054569Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pluggy/_hooks.py", line 512 in __call__ 2025-08-14T22:20:45.1056056Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/_pytest/config/__init__.py", line 166 in main 2025-08-14T22:20:45.1057837Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py", line 1299 in run_tests 2025-08-14T22:20:45.1059448Z File "/var/lib/jenkins/workspace/test/test_ao_sparsity.py", line 65 in 2025-08-14T22:20:45.1060153Z 2025-08-14T22:20:45.1068191Z Extension modules: numpy._core._multiarray_umath, numpy.linalg._umath_linalg, torch._C, torch._C._dynamo.autograd_compiler, torch._C._dynamo.eval_frame, torch._C._dynamo.guards, torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, multidict._multidict, yarl._quoting_c, propcache._helpers_c, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket.mask, aiohttp._websocket.reader_c, frozenlist._frozenlist, charset_normalizer.md, requests.packages.charset_normalizer.md, requests.packages.chardet.md, thriftpy2.transport.cybase, thriftpy2.transport.buffered.cybuffered, thriftpy2.transport.framed.cyframed, thriftpy2.transport.memory.cymemory, thriftpy2.transport.sasl.cysasl, thriftpy2.protocol.cybin.cybin, PIL._imaging, PIL._imagingft, markupsafe._speedups, psutil._psutil_linux, psutil._psutil_posix (total: 44) 2025-08-14T22:20:45.1077013Z Got exit code -6 (SIGIOT) 2025-08-14T22:20:45.1077482Z Retrying single test... 2025-08-14T22:20:45.1079893Z /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. 2025-08-14T22:20:45.1082489Z import pkg_resources 2025-08-14T22:20:45.1083537Z Test results will be stored in test-reports/python-pytest/test_ao_sparsity/test_ao_sparsity-a386806776e117c5.xml 2025-08-14T22:20:45.1084763Z ============================= test session starts ============================== 2025-08-14T22:20:45.1085715Z platform linux -- Python 3.13.5, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.13/bin/python 2025-08-14T22:20:45.1086553Z cachedir: .pytest_cache 2025-08-14T22:20:45.1087555Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-08-14T22:20:45.1088717Z rootdir: /var/lib/jenkins/workspace 2025-08-14T22:20:45.1089214Z configfile: pytest.ini 2025-08-14T22:20:45.1090351Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-08-14T22:20:45.1091780Z collecting ... collected 88 items / 87 deselected / 1 selected 2025-08-14T22:20:45.1093473Z stepcurrent: skipping 43 already run items. Running only test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_bias_linear 2025-08-14T22:20:45.1094832Z Running 1 items in this shard 2025-08-14T22:20:45.1095192Z 2025-08-14T22:20:45.1097097Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_bias_linear <- test/ao/sparsity/test_structured_sparsifier.py W0814 22:19:34.465000 19667 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] torch._dynamo hit config.recompile_limit (8) 2025-08-14T22:20:45.1100578Z W0814 22:19:34.465000 19667 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] function: 'update_mask' (/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:50) 2025-08-14T22:20:45.1104241Z W0814 22:19:34.465000 19667 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] last reason: 6/7: ___check_type_id(module, 94005241283488) # num_rows = len(module.parametrizations[tensor_name][0].mask) # ar/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:52 in update_mask 2025-08-14T22:20:45.1107478Z W0814 22:19:34.465000 19667 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2025-08-14T22:20:45.1109897Z W0814 22:19:34.465000 19667 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html 2025-08-14T22:20:45.1111565Z PASSED [3.7592s] [100%] 2025-08-14T22:20:45.1111872Z 2025-08-14T22:20:45.1112899Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ao_sparsity/test_ao_sparsity-a386806776e117c5.xml - 2025-08-14T22:20:45.1114449Z ======================= 1 passed, 87 deselected in 3.80s ======================= 2025-08-14T22:20:45.1115117Z Got exit code 0 2025-08-14T22:20:45.1115782Z Test succeeeded in new process, continuing with the rest of the tests 2025-08-14T22:20:45.1118845Z /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. 2025-08-14T22:20:45.1121533Z import pkg_resources 2025-08-14T22:20:45.1122555Z Test results will be stored in test-reports/python-pytest/test_ao_sparsity/test_ao_sparsity-23660823092453fa.xml 2025-08-14T22:20:45.1123657Z ============================= test session starts ============================== 2025-08-14T22:20:45.1124627Z platform linux -- Python 3.13.5, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.13/bin/python 2025-08-14T22:20:45.1125476Z cachedir: .pytest_cache 2025-08-14T22:20:45.1126611Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-08-14T22:20:45.1127783Z rootdir: /var/lib/jenkins/workspace 2025-08-14T22:20:45.1128342Z configfile: pytest.ini 2025-08-14T22:20:45.1129613Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2025-08-14T22:20:45.1131166Z collecting ... collected 88 items / 44 deselected / 44 selected 2025-08-14T22:20:45.1131894Z stepcurrent: skipping 44 already run items. 2025-08-14T22:20:45.1132514Z Running 44 items in this shard 2025-08-14T22:20:45.1132841Z 2025-08-14T22:20:45.1134704Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_linear <- test/ao/sparsity/test_structured_sparsifier.py W0814 22:19:41.711000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] torch._dynamo hit config.recompile_limit (8) 2025-08-14T22:20:45.1138047Z W0814 22:19:41.711000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] function: 'update_mask' (/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:50) 2025-08-14T22:20:45.1141651Z W0814 22:19:41.711000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] last reason: 6/7: ___check_type_id(module, 94665487518192) # num_rows = len(module.parametrizations[tensor_name][0].mask) # ar/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:52 in update_mask 2025-08-14T22:20:45.1144808Z W0814 22:19:41.711000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2025-08-14T22:20:45.1146889Z W0814 22:19:41.711000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html 2025-08-14T22:20:45.1148321Z PASSED [3.6137s] [ 2%] 2025-08-14T22:20:45.1149747Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_layernorm_linear_multiple_layer <- test/ao/sparsity/test_structured_sparsifier.py PASSED [0.7875s] [ 4%] 2025-08-14T22:20:45.1152188Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_layernorm_linear_single_layer <- test/ao/sparsity/test_structured_sparsifier.py PASSED [0.7250s] [ 6%] 2025-08-14T22:20:45.1154544Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_linear_multiple_layer <- test/ao/sparsity/test_structured_sparsifier.py PASSED [0.7492s] [ 9%] 2025-08-14T22:20:45.1156472Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_linear_single_layer <- test/ao/sparsity/test_structured_sparsifier.py PASSED [0.7153s] [ 11%] 2025-08-14T22:20:45.1157891Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_step_conv2d <- test/ao/sparsity/test_structured_sparsifier.py SKIPPED [0.0471s] (TorchDynamo fails with unknown reason) [ 13%] 2025-08-14T22:20:45.1159625Z test_ao_sparsity.py::TestBaseStructuredSparsifier::test_step_linear <- test/ao/sparsity/test_structured_sparsifier.py W0814 22:19:46.732000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [12/8] torch._dynamo hit config.recompile_limit (8) 2025-08-14T22:20:45.1161360Z W0814 22:19:46.732000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [12/8] function: 'update_mask' (/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:45) 2025-08-14T22:20:45.1163252Z W0814 22:19:46.732000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [12/8] last reason: 12/7: ___check_type_id(module, 94665491034928) # getattr(module.parametrizations, tensor_name)[0].mask[1] = False # ar/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py:46 in update_mask 2025-08-14T22:20:45.1164948Z W0814 22:19:46.732000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [12/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2025-08-14T22:20:45.1166220Z W0814 22:19:46.732000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [12/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html 2025-08-14T22:20:45.1167253Z PASSED [1.4562s] [ 15%] 2025-08-14T22:20:45.1168319Z test_ao_sparsity.py::TestFPGMPruner::test_compute_distance <- test/ao/sparsity/test_structured_sparsifier.py PASSED [0.3122s] [ 18%] 2025-08-14T22:20:45.1170908Z test_ao_sparsity.py::TestFPGMPruner::test_update_mask <- test/ao/sparsity/test_structured_sparsifier.py W0814 22:19:48.079000 19700 site-packages/torch/_dynamo/variables/tensor.py:1048] [8/0] Graph break from `Tensor.item()`, consider setting: 2025-08-14T22:20:45.1173428Z W0814 22:19:48.079000 19700 site-packages/torch/_dynamo/variables/tensor.py:1048] [8/0] torch._dynamo.config.capture_scalar_outputs = True 2025-08-14T22:20:45.1174868Z W0814 22:19:48.079000 19700 site-packages/torch/_dynamo/variables/tensor.py:1048] [8/0] or: 2025-08-14T22:20:45.1176195Z W0814 22:19:48.079000 19700 site-packages/torch/_dynamo/variables/tensor.py:1048] [8/0] env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1 2025-08-14T22:20:45.1178059Z W0814 22:19:48.079000 19700 site-packages/torch/_dynamo/variables/tensor.py:1048] [8/0] to include these operations in the captured graph. 2025-08-14T22:20:45.1179489Z W0814 22:19:48.079000 19700 site-packages/torch/_dynamo/variables/tensor.py:1048] [8/0] 2025-08-14T22:20:45.1180879Z W0814 22:19:48.079000 19700 site-packages/torch/_dynamo/variables/tensor.py:1048] [8/0] Graph break: from user code at: 2025-08-14T22:20:45.1183464Z W0814 22:19:48.079000 19700 site-packages/torch/_dynamo/variables/tensor.py:1048] [8/0] File "/var/lib/jenkins/workspace/test/ao/sparsity/test_structured_sparsifier.py", line 1017, in torch_dynamo_resume_in__test_update_mask_on_single_layer_at_1015 2025-08-14T22:20:45.1186558Z W0814 22:19:48.079000 19700 site-packages/torch/_dynamo/variables/tensor.py:1048] [8/0] pruner.groups[0]["module"].parametrizations.weight[0].mask[-1].item() 2025-08-14T22:20:45.1188334Z W0814 22:19:48.079000 19700 site-packages/torch/_dynamo/variables/tensor.py:1048] [8/0] 2025-08-14T22:20:45.1189530Z W0814 22:19:48.079000 19700 site-packages/torch/_dynamo/variables/tensor.py:1048] [8/0] 2025-08-14T22:20:45.1190431Z PASSED [1.5152s] [ 20%] 2025-08-14T22:20:45.1191780Z test_ao_sparsity.py::TestSaliencyPruner::test_lstm_saliency_pruner_update_mask <- test/ao/sparsity/test_structured_sparsifier.py PASSED [0.3428s] [ 22%] 2025-08-14T22:20:45.1194061Z test_ao_sparsity.py::TestSaliencyPruner::test_saliency_pruner_update_mask <- test/ao/sparsity/test_structured_sparsifier.py PASSED [0.5959s] [ 25%] 2025-08-14T22:20:45.1196960Z test_ao_sparsity.py::TestComposability::test_convert_without_squash_mask <- test/ao/sparsity/test_composability.py W0814 22:19:51.656000 19700 site-packages/torch/_dynamo/utils.py:2858] [19/1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1199342Z PASSED [1.7480s] [ 27%] 2025-08-14T22:20:45.1201456Z test_ao_sparsity.py::TestComposability::test_fusion_before_s_prep <- test/ao/sparsity/test_composability.py W0814 22:19:53.603000 19700 site-packages/torch/_dynamo/utils.py:2858] [21/1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1203514Z PASSED [1.9448s] [ 29%] 2025-08-14T22:20:45.1205449Z test_ao_sparsity.py::TestComposability::test_q_prep_before_s_prep <- test/ao/sparsity/test_composability.py W0814 22:19:55.948000 19700 site-packages/torch/_dynamo/utils.py:2858] [25/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1207568Z PASSED [2.3500s] [ 31%] 2025-08-14T22:20:45.1209670Z test_ao_sparsity.py::TestComposability::test_qat_prep_before_s_prep <- test/ao/sparsity/test_composability.py W0814 22:19:57.674000 19700 site-packages/torch/_dynamo/utils.py:2858] [26/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1212512Z W0814 22:19:57.695000 19700 site-packages/torch/_dynamo/utils.py:2858] [27/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1213921Z PASSED [1.7393s] [ 34%] 2025-08-14T22:20:45.1216137Z test_ao_sparsity.py::TestComposability::test_s_prep_before_fusion <- test/ao/sparsity/test_composability.py W0814 22:19:59.627000 19700 site-packages/torch/_dynamo/utils.py:2858] [27/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1218539Z PASSED [1.9369s] [ 36%] 2025-08-14T22:20:45.1220728Z test_ao_sparsity.py::TestComposability::test_s_prep_before_q_prep <- test/ao/sparsity/test_composability.py W0814 22:20:01.447000 19700 site-packages/torch/_dynamo/utils.py:2858] [25/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1223075Z PASSED [1.8175s] [ 38%] 2025-08-14T22:20:45.1225284Z test_ao_sparsity.py::TestComposability::test_s_prep_before_qat_prep <- test/ao/sparsity/test_composability.py W0814 22:20:03.514000 19700 site-packages/torch/_dynamo/utils.py:2858] [26/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1228328Z W0814 22:20:03.538000 19700 site-packages/torch/_dynamo/utils.py:2858] [27/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1229664Z PASSED [2.0834s] [ 40%] 2025-08-14T22:20:45.1231746Z test_ao_sparsity.py::TestFxComposability::test_q_prep_fx_before_s_prep <- test/ao/sparsity/test_composability.py W0814 22:20:06.156000 19700 site-packages/torch/_dynamo/utils.py:2858] [41/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1235153Z W0814 22:20:06.162000 19700 site-packages/torch/_dynamo/utils.py:2858] [41/0_1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1237288Z W0814 22:20:06.547000 19700 site-packages/torch/_dynamo/utils.py:2858] [44/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1239399Z W0814 22:20:06.551000 19700 site-packages/torch/_dynamo/utils.py:2858] [44/0_1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1241560Z W0814 22:20:06.559000 19700 site-packages/torch/_dynamo/utils.py:2858] [28/1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1242908Z PASSED [3.0155s] [ 43%] 2025-08-14T22:20:45.1245025Z test_ao_sparsity.py::TestFxComposability::test_q_prep_fx_s_prep_ref_conv <- test/ao/sparsity/test_composability.py W0814 22:20:09.081000 19700 site-packages/torch/_dynamo/utils.py:2858] [41/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1247111Z PASSED [3.2972s] [ 45%] 2025-08-14T22:20:45.1249306Z test_ao_sparsity.py::TestFxComposability::test_s_prep_before_q_prep_fx <- test/ao/sparsity/test_composability.py W0814 22:20:12.546000 19700 site-packages/torch/_dynamo/utils.py:2858] [41/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1252359Z W0814 22:20:12.552000 19700 site-packages/torch/_dynamo/utils.py:2858] [41/0_1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1254638Z W0814 22:20:12.919000 19700 site-packages/torch/_dynamo/utils.py:2858] [44/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1256808Z W0814 22:20:12.922000 19700 site-packages/torch/_dynamo/utils.py:2858] [44/0_1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1259045Z W0814 22:20:12.931000 19700 site-packages/torch/_dynamo/utils.py:2858] [28/1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1260376Z PASSED [3.0718s] [ 47%] 2025-08-14T22:20:45.1262404Z test_ao_sparsity.py::TestFxComposability::test_s_prep_before_qat_prep_fx <- test/ao/sparsity/test_composability.py W0814 22:20:15.460000 19700 site-packages/torch/_dynamo/utils.py:2858] [41/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1265784Z W0814 22:20:15.466000 19700 site-packages/torch/_dynamo/utils.py:2858] [41/0_1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1268094Z W0814 22:20:15.846000 19700 site-packages/torch/_dynamo/utils.py:2858] [44/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1270305Z W0814 22:20:15.849000 19700 site-packages/torch/_dynamo/utils.py:2858] [44/0_1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1272609Z W0814 22:20:15.858000 19700 site-packages/torch/_dynamo/utils.py:2858] [30/1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1274082Z PASSED [2.9257s] [ 50%] 2025-08-14T22:20:45.1276124Z test_ao_sparsity.py::TestFxComposability::test_s_prep_q_prep_fx_ref <- test/ao/sparsity/test_composability.py W0814 22:20:18.674000 19700 site-packages/torch/_dynamo/utils.py:2858] [41/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1278185Z PASSED [3.3111s] [ 52%] 2025-08-14T22:20:45.1279703Z test_ao_sparsity.py::TestActivationSparsifier::test_activation_sparsifier <- test/ao/sparsity/test_activation_sparsifier.py SKIPPED [0.0638s] (TorchDynamo fails with unknown reason) [ 54%] 2025-08-14T22:20:45.1282034Z test_ao_sparsity.py::TestBaseDataScheduler::test_constructor <- test/ao/sparsity/test_data_scheduler.py PASSED [0.3401s] [ 56%] 2025-08-14T22:20:45.1283818Z test_ao_sparsity.py::TestBaseDataScheduler::test_order_of_steps <- test/ao/sparsity/test_data_scheduler.py PASSED [0.3335s] [ 59%] 2025-08-14T22:20:45.1285590Z test_ao_sparsity.py::TestBaseDataScheduler::test_state_dict <- test/ao/sparsity/test_data_scheduler.py PASSED [0.4266s] [ 61%] 2025-08-14T22:20:45.1287313Z test_ao_sparsity.py::TestBaseDataScheduler::test_step <- test/ao/sparsity/test_data_scheduler.py PASSED [0.3420s] [ 63%] 2025-08-14T22:20:45.1289805Z test_ao_sparsity.py::TestBaseDataSparsifier::test_nn_embeddings <- test/ao/sparsity/test_data_sparsifier.py W0814 22:20:25.140000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [19/8] torch._dynamo hit config.recompile_limit (8) 2025-08-14T22:20:45.1293021Z W0814 22:20:25.140000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [19/8] function: 'torch_dynamo_resume_in_update_mask_at_25' (/var/lib/jenkins/workspace/test/ao/sparsity/test_data_sparsifier.py:25) 2025-08-14T22:20:45.1296210Z W0814 22:20:25.140000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [19/8] last reason: 19/7: name == 'emb2' # linear_state = self.state[name] # ar/lib/jenkins/workspace/test/ao/sparsity/test_data_sparsifier.py:27 in torch_dynamo_resume_in_update_mask_at_25 2025-08-14T22:20:45.1298941Z W0814 22:20:25.140000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [19/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2025-08-14T22:20:45.1301156Z W0814 22:20:25.140000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [19/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html 2025-08-14T22:20:45.1302688Z PASSED [5.1557s] [ 65%] 2025-08-14T22:20:45.1304571Z test_ao_sparsity.py::TestBaseDataSparsifier::test_nn_parameters <- test/ao/sparsity/test_data_sparsifier.py W0814 22:20:27.138000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [18/8] torch._dynamo hit config.recompile_limit (8) 2025-08-14T22:20:45.1307582Z W0814 22:20:27.138000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [18/8] function: 'torch_dynamo_resume_in_update_mask_at_25' (/var/lib/jenkins/workspace/test/ao/sparsity/test_data_sparsifier.py:25) 2025-08-14T22:20:45.1310909Z W0814 22:20:27.138000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [18/8] last reason: 18/7: name == 'param4' # linear_state = self.state[name] # ar/lib/jenkins/workspace/test/ao/sparsity/test_data_sparsifier.py:27 in torch_dynamo_resume_in_update_mask_at_25 2025-08-14T22:20:45.1313744Z W0814 22:20:27.138000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [18/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2025-08-14T22:20:45.1315923Z W0814 22:20:27.138000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [18/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html 2025-08-14T22:20:45.1317450Z PASSED [2.3550s] [ 68%] 2025-08-14T22:20:45.1319217Z test_ao_sparsity.py::TestBaseDataSparsifier::test_tensors <- test/ao/sparsity/test_data_sparsifier.py W0814 22:20:29.234000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [13/8] torch._dynamo hit config.recompile_limit (8) 2025-08-14T22:20:45.1322254Z W0814 22:20:29.234000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [13/8] function: 'torch_dynamo_resume_in_update_mask_at_25' (/var/lib/jenkins/workspace/test/ao/sparsity/test_data_sparsifier.py:25) 2025-08-14T22:20:45.1325469Z W0814 22:20:29.234000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [13/8] last reason: 13/7: name == 'tensor4' # linear_state = self.state[name] # ar/lib/jenkins/workspace/test/ao/sparsity/test_data_sparsifier.py:27 in torch_dynamo_resume_in_update_mask_at_25 2025-08-14T22:20:45.1328295Z W0814 22:20:29.234000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [13/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2025-08-14T22:20:45.1330486Z W0814 22:20:29.234000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [13/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html 2025-08-14T22:20:45.1331996Z PASSED [1.7716s] [ 70%] 2025-08-14T22:20:45.1333124Z test_ao_sparsity.py::TestNormDataSparsifiers::test_nn_embeddings <- test/ao/sparsity/test_data_sparsifier.py PASSED [3.8278s] [ 72%] 2025-08-14T22:20:45.1335012Z test_ao_sparsity.py::TestNormDataSparsifiers::test_nn_parameters <- test/ao/sparsity/test_data_sparsifier.py PASSED [4.0901s] [ 75%] 2025-08-14T22:20:45.1336818Z test_ao_sparsity.py::TestNormDataSparsifiers::test_tensors <- test/ao/sparsity/test_data_sparsifier.py PASSED [3.5422s] [ 77%] 2025-08-14T22:20:45.1339593Z test_ao_sparsity.py::TestQuantizationUtils::test_ptq_quantize_first <- test/ao/sparsity/test_data_sparsifier.py W0814 22:20:41.923000 19700 site-packages/torch/_dynamo/utils.py:2858] [7/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1342523Z W0814 22:20:41.928000 19700 site-packages/torch/_dynamo/utils.py:2858] [7/0_1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1343945Z PASSED [0.4940s] [ 79%] 2025-08-14T22:20:45.1346030Z test_ao_sparsity.py::TestQuantizationUtils::test_ptq_sparsify_first <- test/ao/sparsity/test_data_sparsifier.py W0814 22:20:42.480000 19700 site-packages/torch/_dynamo/utils.py:2858] [7/0] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1348965Z W0814 22:20:42.484000 19700 site-packages/torch/_dynamo/utils.py:2858] [7/0_1] Encountered exception (quantized nyi in meta tensors) during fake tensor propagation. 2025-08-14T22:20:45.1350293Z PASSED [0.5540s] [ 81%] 2025-08-14T22:20:45.1352091Z test_ao_sparsity.py::TestSparsityUtilFunctions::test_fqn_to_module <- test/ao/sparsity/test_sparsity_utils.py W0814 22:20:42.819000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [1/8] torch._dynamo hit config.recompile_limit (8) 2025-08-14T22:20:45.1354615Z W0814 22:20:42.819000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [1/8] function: '__subclasshook__' (:345) 2025-08-14T22:20:45.1356774Z W0814 22:20:42.819000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [1/8] last reason: 1/7: ___check_obj_id(cls, 94665340492944) # # :347 in __subclasshook__ 2025-08-14T22:20:45.1358979Z W0814 22:20:42.819000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [1/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2025-08-14T22:20:45.1361211Z W0814 22:20:42.819000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [1/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html 2025-08-14T22:20:45.1363284Z W0814 22:20:43.048000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [2/8] torch._dynamo hit config.recompile_limit (8) 2025-08-14T22:20:45.1365063Z W0814 22:20:43.048000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [2/8] function: '__subclasshook__' (:439) 2025-08-14T22:20:45.1367268Z W0814 22:20:43.048000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [2/8] last reason: 2/7: ___check_obj_id(cls, 94665340715584) # # :441 in __subclasshook__ 2025-08-14T22:20:45.1369402Z W0814 22:20:43.048000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2025-08-14T22:20:45.1371576Z W0814 22:20:43.048000 19700 site-packages/torch/_dynamo/convert_frame.py:1102] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html 2025-08-14T22:20:45.1373154Z PASSED [0.5761s] [ 84%] 2025-08-14T22:20:45.1374351Z test_ao_sparsity.py::TestSparsityUtilFunctions::test_fqn_to_module_fail <- test/ao/sparsity/test_sparsity_utils.py PASSED [0.0546s] [ 86%] 2025-08-14T22:20:45.1376341Z test_ao_sparsity.py::TestSparsityUtilFunctions::test_fqn_to_module_for_tensors <- test/ao/sparsity/test_sparsity_utils.py PASSED [0.0624s] [ 88%] 2025-08-14T22:20:45.1378349Z test_ao_sparsity.py::TestSparsityUtilFunctions::test_get_arg_info_from_tensor_fqn <- test/ao/sparsity/test_sparsity_utils.py PASSED [0.0617s] [ 90%] 2025-08-14T22:20:45.1380488Z test_ao_sparsity.py::TestSparsityUtilFunctions::test_get_arg_info_from_tensor_fqn_fail <- test/ao/sparsity/test_sparsity_utils.py PASSED [0.0598s] [ 93%] 2025-08-14T22:20:45.1382516Z test_ao_sparsity.py::TestSparsityUtilFunctions::test_module_to_fqn <- test/ao/sparsity/test_sparsity_utils.py PASSED [0.0591s] [ 95%] 2025-08-14T22:20:45.1384484Z test_ao_sparsity.py::TestSparsityUtilFunctions::test_module_to_fqn_fail <- test/ao/sparsity/test_sparsity_utils.py PASSED [0.1019s] [ 97%] 2025-08-14T22:20:45.1386408Z test_ao_sparsity.py::TestSparsityUtilFunctions::test_module_to_fqn_root <- test/ao/sparsity/test_sparsity_utils.py PASSED [0.0584s] [100%] 2025-08-14T22:20:45.1387507Z 2025-08-14T22:20:45.1388437Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_ao_sparsity/test_ao_sparsity-23660823092453fa.xml - 2025-08-14T22:20:45.1389957Z =========== 42 passed, 2 skipped, 44 deselected in 64.52s (0:01:04) ============ 2025-08-14T22:20:45.1391539Z The following tests failed and then succeeded when run in a new process['test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_bias_linear'] 2025-08-14T22:20:45.1392735Z 2025-08-14T22:20:45.1393569Z FINISHED PRINTING LOG FILE of test_ao_sparsity 1/1 (test/test-reports/test_ao_sparsity_1.1_e9554bf9fc0b9beb_.log) 2025-08-14T22:20:45.1394446Z 2025-08-14T22:20:48.2218337Z /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. 2025-08-14T22:20:48.2219872Z import pkg_resources 2025-08-14T22:20:48.3435314Z Running test_fx_experimental 1/1 ... [2025-08-14 22:20:48.343156] 2025-08-14T22:20:48.3435768Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:20:48.3439774Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fx_experimental.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'] ... [2025-08-14 22:20:48.343541] 2025-08-14T22:20:52.7553133Z 2025-08-14T22:20:52.7554891Z typing/test_python_operators 1/1 was successful, full logs can be found in artifacts with path test/test-reports/typing.test_python_operators_1.1_2852b691928f87dd_.log 2025-08-14T22:20:52.7766491Z Running 318 items in this shard: test/typing/test_python_operators.py::TestPythonOperators::test_binary_a100_op_%_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a101_op_%_b101, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a102_op_%_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a103_op_%_b103, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a104_op_*_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a105_op_*_b105, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a106_op_*_b_3, 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test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_1_5_op_|_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_!=_b153, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_!=_b155, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_!=_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_!=_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_%_b177, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_%_b179, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_%_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_%_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_&_b205, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_&_b207, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_&_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_&_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_**_b185, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_**_b187, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_**_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_**_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_*_b181, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_*_b183, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_*_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_*_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_+_b189, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_+_b191, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_+_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_+_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_-_b193, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_-_b195, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_-_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_-_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_//_b201, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_//_b203, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_//_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_//_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_/_b197, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_/_b199, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_/_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_/_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<<_b209, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<<_b211, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<<_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<<_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<=_b161, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<=_b163, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<=_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<=_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<_b157, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<_b159, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_==_b165, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_==_b167, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_==_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_==_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>=_b173, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>=_b175, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>=_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>=_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>>_b213, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>>_b215, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>>_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>>_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>_b169, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>_b171, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_@_b225, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_@_b227, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_@_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_@_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_^_b217, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_^_b219, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_^_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_^_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_|_b221, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_|_b223, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_|_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_|_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_operators_are_correct_and_complete, test/typing/test_python_operators.py::TestPythonOperators::test_type_tests_are_complete, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_+_a1, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_+_a3, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_+_a_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_+_a_3, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_-_a5, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_-_a7, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_-_a_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_-_a_3, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_~_a11, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_~_a9, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_~_a_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_~_a_3 2025-08-14T22:20:52.7975157Z 2025-08-14T22:20:56.0338513Z /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. 2025-08-14T22:20:56.0340024Z import pkg_resources 2025-08-14T22:20:56.1534883Z Running test_file_check 1/1 ... [2025-08-14 22:20:56.153078] 2025-08-14T22:20:56.1535518Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:20:56.1538343Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_file_check.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:20:56.153474] 2025-08-14T22:21:02.1264750Z 2025-08-14T22:21:02.1265916Z test_file_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_file_check_1.1_8948e76861183030_.log 2025-08-14T22:21:02.1267154Z Running 2 items in this shard: test/test_file_check.py::TestFileCheck::test_all_python_api, test/test_file_check.py::TestFileCheck::test_not_run 2025-08-14T22:21:02.1267838Z 2025-08-14T22:21:05.2058969Z /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. 2025-08-14T22:21:05.2061818Z import pkg_resources 2025-08-14T22:21:05.3259339Z Running torch_np/test_dtype 1/1 ... [2025-08-14 22:21:05.325537] 2025-08-14T22:21:05.3260038Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:21:05.3262850Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_dtype.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'] ... [2025-08-14 22:21:05.325947] 2025-08-14T22:21:09.8138535Z 2025-08-14T22:21:09.8139941Z test_foreach 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_foreach_1.1_4586faa3ff7724cc_.log 2025-08-14T22:21:09.9808645Z Running 3577 items in this shard: test/test_foreach.py::TestForeachCPU::test_0dim_tensor_overload_cpu_ok_cpu, test/test_foreach.py::TestForeachCPU::test_0dim_tensor_overload_exception_cpu, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_bfloat16, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_complex128, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_complex64, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_float16, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_float32, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_float64, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_int16, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_int32, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_int64, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_int8, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_uint8, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_abs_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_acos_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_add_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_addcdiv_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_addcmul_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_asin_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_atan_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_ceil_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_clamp_max_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_clamp_min_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_copy_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_cos_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_cosh_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_div_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_erf_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_erfc_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_exp_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_expm1_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_floor_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_frac_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_lerp_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_lgamma_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_log10_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_log1p_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_log2_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_log_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_max_cpu_float32, 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test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_rsqrt_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_sigmoid_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_sign_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_sin_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_sinh_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_sqrt_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_sub_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_tan_cpu_float32, 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test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_float32, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_float64, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_int16, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_int32, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_int64, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_int8, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_uint8 2025-08-14T22:21:10.2357772Z 2025-08-14T22:21:13.2004627Z /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. 2025-08-14T22:21:13.2007455Z import pkg_resources 2025-08-14T22:21:13.3588516Z Running higher_order_ops/test_with_effects 1/1 ... [2025-08-14 22:21:13.358229] 2025-08-14T22:21:13.3592340Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:21:13.3595628Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'higher_order_ops/test_with_effects.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'] ... [2025-08-14 22:21:13.358874] 2025-08-14T22:21:13.8197526Z 2025-08-14T22:21:13.8203089Z torch_np/test_dtype 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_dtype_1.1_bb076b6ea2b4a32e_.log 2025-08-14T22:21:13.8242676Z Running 44 items in this shard: test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'bool_', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'complex128', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'complex64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'float16', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'float32', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'float64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'int16', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'int32', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'int64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'int8', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'uint16', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'uint32', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'uint64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'uint8', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_bool, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'bool_', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'complex128', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'complex64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'float16', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'float32', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'float64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'int16', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'int32', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'int64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'int8', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'uint16', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'uint32', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'uint64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'uint8', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.bool_, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.complex128, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.complex64, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.dtype('bool'), test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.float16, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.float32, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.float64, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.int16, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.int32, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.int64, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.int8, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.uint16, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.uint32, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.uint64, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.uint8 2025-08-14T22:21:13.8278719Z 2025-08-14T22:21:17.0361533Z /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. 2025-08-14T22:21:17.0364358Z import pkg_resources 2025-08-14T22:21:17.1577147Z Running test_native_functions 1/1 ... [2025-08-14 22:21:17.157245] 2025-08-14T22:21:17.1588795Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:21:17.1591295Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_native_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'] ... [2025-08-14 22:21:17.157788] 2025-08-14T22:21:25.3859594Z 2025-08-14T22:21:25.3861210Z test_native_functions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_functions_1.1_0dfc7e98540e5873_.log 2025-08-14T22:21:25.3872578Z Running 11 items in this shard: test/test_native_functions.py::TestNativeFunctions::test_intlist_error_with_overload, test/test_native_functions.py::TestNativeFunctions::test_optional_filled_intlist, test/test_native_functions.py::TestNativeFunctions::test_optional_floatlist, test/test_native_functions.py::TestNativeFunctions::test_optional_floatlist_invalid, test/test_native_functions.py::TestNativeFunctions::test_optional_intlist, test/test_native_functions.py::TestNativeFunctions::test_optional_intlist_invalid, test/test_native_functions.py::TestNativeFunctions::test_string_defaults, test/test_native_functions.py::TestNativeFunctions::test_symintlist_error, test/test_native_functions.py::TestNativeFunctions::test_symintlist_error_with_overload, test/test_native_functions.py::TestNativeFunctions::test_symintlist_error_with_overload_but_is_unique, test/test_native_functions.py::TestNativeFunctions::test_vararg_symintlist_error 2025-08-14T22:21:25.3882299Z 2025-08-14T22:21:29.6808716Z /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. 2025-08-14T22:21:29.6813392Z import pkg_resources 2025-08-14T22:21:29.8588145Z Running functorch/test_minifier 1/1 ... [2025-08-14 22:21:29.857678] 2025-08-14T22:21:29.8588940Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:21:29.8590761Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/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'] ... [2025-08-14 22:21:29.858297] 2025-08-14T22:21:36.8480792Z 2025-08-14T22:21:36.8482281Z functorch/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_minifier_1.1_85159090fa556c6c_.log 2025-08-14T22:21:36.8486372Z Running 5 items in this shard: test/functorch/test_minifier.py::TestMinifier::test_has_add_mul, test/functorch/test_minifier.py::TestMinifier::test_has_mul_minifier, test/functorch/test_minifier.py::TestMinifier::test_input_returned, test/functorch/test_minifier.py::TestMinifier::test_module, test/functorch/test_minifier.py::TestMinifier::test_tup_use 2025-08-14T22:21:36.8489316Z 2025-08-14T22:21:40.0044588Z /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. 2025-08-14T22:21:40.0046140Z import pkg_resources 2025-08-14T22:21:40.1265668Z Running test_flop_counter 1/1 ... [2025-08-14 22:21:40.126133] 2025-08-14T22:21:40.1266340Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:21:40.1268073Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_flop_counter.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'] ... [2025-08-14 22:21:40.126529] 2025-08-14T22:21:42.8567796Z 2025-08-14T22:21:42.8569858Z higher_order_ops/test_with_effects 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_with_effects_1.1_b64b304139ee58b1_.log 2025-08-14T22:21:42.8584845Z Running 18 items in this shard: test/higher_order_ops/test_with_effects.py::TestWithEffects::test_alias_op, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_aot_eager, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_aot_eager_requires_grad, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_inductor, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_inductor_external_op_return_none, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effectful_custom_op_with_subclasses, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effectful_op_in_backward, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_aliased_outputs, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_input_mutation_is_output, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_input_mutation_return, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_input_output_view_simple, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_print, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_print_with_buffer_mutations, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_print_with_input_mutations, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_register_effectful_custom_op, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_regular_effectful_op_in_forward_and_backward, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_regular_effectful_op_only_in_backward, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_torchbind_custom_op 2025-08-14T22:21:42.8598748Z 2025-08-14T22:21:43.4051178Z Uploading artifacts took 0.55 seconds 2025-08-14T22:21:43.6473379Z 2025-08-14T22:21:43.6474693Z test_flop_counter 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_flop_counter_1.1_a7e61bd7930de83e_.log 2025-08-14T22:21:43.6482451Z Running 22 items in this shard: test/test_flop_counter.py::TestFlopCounter::test_addmm_out, test/test_flop_counter.py::TestFlopCounter::test_autograd_op, test/test_flop_counter.py::TestFlopCounter::test_backward, test/test_flop_counter.py::TestFlopCounter::test_backward_reset, test/test_flop_counter.py::TestFlopCounter::test_conv_backwards_as_decomposition, test/test_flop_counter.py::TestFlopCounter::test_conv_transpose_loop, test/test_flop_counter.py::TestFlopCounter::test_convs, test/test_flop_counter.py::TestFlopCounter::test_custom, test/test_flop_counter.py::TestFlopCounter::test_custom_op, test/test_flop_counter.py::TestFlopCounter::test_flop_counter_variety, test/test_flop_counter.py::TestFlopCounter::test_hook_registration, test/test_flop_counter.py::TestFlopCounter::test_inference_mode, test/test_flop_counter.py::TestFlopCounter::test_module, test/test_flop_counter.py::TestFlopCounter::test_nested_attention_fake_tensors, test/test_flop_counter.py::TestFlopCounter::test_noop, test/test_flop_counter.py::TestFlopCounter::test_op, test/test_flop_counter.py::TestFlopCounter::test_pytrees, test/test_flop_counter.py::TestFlopCounter::test_scaled_mm, test/test_flop_counter.py::TestFlopCounter::test_sdpa, test/test_flop_counter.py::TestFlopCounter::test_sdpa_nested_tensor, test/test_flop_counter.py::TestFlopCounter::test_torchscript, test/test_flop_counter.py::TestFlopCounter::test_warning 2025-08-14T22:21:43.6489150Z 2025-08-14T22:21:46.1569035Z /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. 2025-08-14T22:21:46.1570561Z import pkg_resources 2025-08-14T22:21:46.2789337Z Running torch_np/test_nep50_examples 1/1 ... [2025-08-14 22:21:46.278582] 2025-08-14T22:21:46.2789853Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:21:46.2793388Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_nep50_examples.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'] ... [2025-08-14 22:21:46.278982] 2025-08-14T22:21:46.8812240Z /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. 2025-08-14T22:21:46.8815116Z import pkg_resources 2025-08-14T22:21:47.0016244Z Running higher_order_ops/test_invoke_quant 1/1 ... [2025-08-14 22:21:47.001125] 2025-08-14T22:21:47.0017436Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:21:47.0019548Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'higher_order_ops/test_invoke_quant.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'] ... [2025-08-14 22:21:47.001524] 2025-08-14T22:21:51.5017092Z 2025-08-14T22:21:51.5018043Z torch_np/test_nep50_examples 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_nep50_examples_1.1_e83fd9b997d5045e_.log 2025-08-14T22:21:51.5741419Z Running 1573 items in this shard: test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_3j + array(3, complex64), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_True + uint8(2), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array(1_0, float32) + 1e-14 == 1_0, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([0_1], float32) == float64(0_1), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([100], uint8) + 200, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + 1, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + 200, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + 300, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + array(1, int64), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + int64(1), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_0], float32) + 1e-14 == 1_0, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + 3, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + array(1_, float64), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + float64(1_), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + int64(3), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_bool_(True) + 1, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_float32(1) + 1j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_float32(1) + 3e100, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_float32(5) + 5j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_int16(2) + 2, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_int16(4) + 4j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_int32(1) + 5j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_uint8(1) + 2, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_uint8(1) + 300, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_uint8(100) + 200, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar27_array27, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar28_array28, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar29_array29, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar30_array30, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar31_array31, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar32_array32, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar33_array33, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar34_array34, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar35_array35, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array10, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array11, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array12, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array13, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array14, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array15, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array16, 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test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_1_array16, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_1_array17, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_1_array9, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_2_0_array18, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_2_0_array19, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_2_0_array20, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_2_0_array21, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_2_0_array22, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_2_0_array23, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_2_0_array24, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_2_0_array25, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_2_0_array26, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_True_array0, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_True_array1, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_True_array2, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_True_array3, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_True_array4, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_True_array5, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_True_array6, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_True_array7, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_true_divide_scalar_True_array8, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar27_array27_dtype27, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar28_array28_dtype28, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar29_array29_dtype29, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar30_array30_dtype30, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar31_array31_dtype31, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar32_array32_dtype32, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar33_array33_dtype33, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar34_array34_dtype34, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar35_array35_dtype35, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array10_dtype10, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array11_dtype11, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array12_dtype12, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array13_dtype13, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array14_dtype14, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array15_dtype15, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array16_dtype16, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array17_dtype17, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array9_dtype9, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array18_dtype18, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array19_dtype19, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array20_dtype20, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array21_dtype21, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array22_dtype22, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array23_dtype23, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array24_dtype24, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array25_dtype25, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array26_dtype26, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array0_dtype0, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array1_dtype1, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array2_dtype2, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array3_dtype3, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array4_dtype4, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array5_dtype5, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array6_dtype6, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array7_dtype7, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array8_dtype8 2025-08-14T22:21:51.6440310Z 2025-08-14T22:21:54.2773481Z 2025-08-14T22:21:54.2774858Z higher_order_ops/test_invoke_quant 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_invoke_quant_1.1_b073a1a14522b3be_.log 2025-08-14T22:21:54.2784712Z Running 14 items in this shard: test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantEager::test_construct_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantEager::test_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantEager::test_multiple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantEager::test_simple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantAotEager::test_construct_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantAotEager::test_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantAotEager::test_multiple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantAotEager::test_simple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_construct_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_multiple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_pattern_matching, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_prologue, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_simple 2025-08-14T22:21:54.2794801Z 2025-08-14T22:21:54.7876329Z /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. 2025-08-14T22:21:54.7878946Z import pkg_resources 2025-08-14T22:21:54.9193852Z Running test_per_overload_api 1/1 ... [2025-08-14 22:21:54.918972] 2025-08-14T22:21:54.9194640Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:21:54.9197330Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_per_overload_api.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'] ... [2025-08-14 22:21:54.919407] 2025-08-14T22:21:57.5049483Z /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. 2025-08-14T22:21:57.5052302Z import pkg_resources 2025-08-14T22:21:57.6299657Z Running test_tensorexpr_pybind 1/1 ... [2025-08-14 22:21:57.629566] 2025-08-14T22:21:57.6300396Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:21:57.6303040Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensorexpr_pybind.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'] ... [2025-08-14 22:21:57.629983] 2025-08-14T22:22:01.6933113Z 2025-08-14T22:22:01.6934172Z test_per_overload_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_per_overload_api_1.1_10356a126ccc356a_.log 2025-08-14T22:22:01.6936013Z Running 3 items in this shard: test/test_per_overload_api.py::TestPerOverloadAPI::test_basics_opoverload, test/test_per_overload_api.py::TestPerOverloadAPI::test_basics_opoverloadpacket, test/test_per_overload_api.py::TestPerOverloadAPI::test_decompose 2025-08-14T22:22:01.6937175Z 2025-08-14T22:22:02.5905540Z 2025-08-14T22:22:02.5906456Z test_fx_experimental 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_experimental_1.1_babf22e2881be452_.log 2025-08-14T22:22:02.6278026Z Running 723 items in this shard: test/test_fx_experimental.py::TestFXExperimental::test_annotate_getitem_node, test/test_fx_experimental.py::TestFXExperimental::test_annotate_returns_with_schema, test/test_fx_experimental.py::TestFXExperimental::test_aot_based_partition, test/test_fx_experimental.py::TestFXExperimental::test_call_to_assert_no_msg, test/test_fx_experimental.py::TestFXExperimental::test_call_to_assert_with_empty_msg, test/test_fx_experimental.py::TestFXExperimental::test_call_to_assert_with_msg, test/test_fx_experimental.py::TestFXExperimental::test_call_to_assert_with_multiline_message, test/test_fx_experimental.py::TestFXExperimental::test_conv_bn_fusion, test/test_fx_experimental.py::TestFXExperimental::test_conv_bn_fusion_mixed_dtype, test/test_fx_experimental.py::TestFXExperimental::test_conv_bn_fusion_not_running_state, test/test_fx_experimental.py::TestFXExperimental::test_cost_aware_partition, test/test_fx_experimental.py::TestFXExperimental::test_fetch, test/test_fx_experimental.py::TestFXExperimental::test_find_single_partition, test/test_fx_experimental.py::TestFXExperimental::test_lack_of_devices, test/test_fx_experimental.py::TestFXExperimental::test_large_node_error, test/test_fx_experimental.py::TestFXExperimental::test_merge_matmuls, test/test_fx_experimental.py::TestFXExperimental::test_meta_tracer, test/test_fx_experimental.py::TestFXExperimental::test_normalize_args, test/test_fx_experimental.py::TestFXExperimental::test_normalize_args_perserve_type, test/test_fx_experimental.py::TestFXExperimental::test_normalize_args_preserve_meta, test/test_fx_experimental.py::TestFXExperimental::test_normalize_binary_operators, test/test_fx_experimental.py::TestFXExperimental::test_normalize_modules_exhaustive, test/test_fx_experimental.py::TestFXExperimental::test_optimize_for_inference_cpu, test/test_fx_experimental.py::TestFXExperimental::test_optimize_for_inference_cpu_torchvision, test/test_fx_experimental.py::TestFXExperimental::test_partition_device_mapping, test/test_fx_experimental.py::TestFXExperimental::test_partition_latency, test/test_fx_experimental.py::TestFXExperimental::test_partition_node_manipulation, test/test_fx_experimental.py::TestFXExperimental::test_replace_target_nodes_with, test/test_fx_experimental.py::TestFXExperimental::test_saturate_host, test/test_fx_experimental.py::TestFXExperimental::test_size_based_partition, test/test_fx_experimental.py::TestFXExperimental::test_sparse_nn_partition, test/test_fx_experimental.py::TestFXExperimental::test_split_module_dead_code, test/test_fx_experimental.py::TestFXExperimental::test_split_module_default_arg, test/test_fx_experimental.py::TestFXExperimental::test_split_module_input_names, test/test_fx_experimental.py::TestFXExperimental::test_split_module_keep_original_order_and_noop_graph, test/test_fx_experimental.py::TestFXExperimental::test_split_module_kwargs_expansion, test/test_fx_experimental.py::TestFXExperimental::test_split_module_return_node, test/test_fx_experimental.py::TestFXExperimental::test_split_module_symint_dependency_handling, test/test_fx_experimental.py::TestFXExperimental::test_split_qualname_mapping, test/test_fx_experimental.py::TestFXExperimental::test_subgraph_creation, test/test_fx_experimental.py::TestFXExperimental::test_subgraph_trivial_resnet, test/test_fx_experimental.py::TestFXExperimental::test_subgraph_uniquename, test/test_fx_experimental.py::TestFXExperimental::test_to_folder, test/test_fx_experimental.py::TestFXExperimental::test_traceable_function_with_nonstandard_name, test/test_fx_experimental.py::TestFXExperimental::test_type_matches, test/test_fx_experimental.py::TestTranslationValidation::test_sat, test/test_fx_experimental.py::TestTranslationValidation::test_sat_bitwise, test/test_fx_experimental.py::TestTranslationValidation::test_sympy_to_z3, test/test_fx_experimental.py::TestTranslationValidation::test_unsat, test/test_fx_experimental.py::TestTranslationValidation::test_z3str, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_args_op_overload_cpu, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_H_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_T_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___getitem___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___radd___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___rdiv___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___rmatmul___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___rmod___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___rmul___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___rpow___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___rsub___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__batch_norm_with_update_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__chunk_cat_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__native_batch_norm_legit_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__segment_reduce_lengths_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__segment_reduce_offsets_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__softmax_backward_data_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__unsafe_masked_index_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__unsafe_masked_index_put_accumulate_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__upsample_bilinear2d_aa_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_abs_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_acos_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_acosh_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_add_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addbmm_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addcdiv_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addcmul_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addmm_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addmm_decomposed_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addmv_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addr_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_alias_copy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_all_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_allclose_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_amax_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_amin_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_aminmax_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_angle_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_any_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_arange_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_argmax_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_argmin_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_argsort_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_argwhere_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_as_strided_copy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_as_strided_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_as_strided_partial_views_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_as_strided_scatter_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_asin_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_asinh_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_atan2_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_atan_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_atanh_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_atleast_1d_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_atleast_2d_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_atleast_3d_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_baddbmm_cpu_float32, 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test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_sqrt_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_square_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_squeeze_copy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_squeeze_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_squeeze_multiple_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_stack_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_std_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_std_mean_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_std_mean_unbiased_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_std_unbiased_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_stft_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_sub_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_sum_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_sum_to_size_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_svd_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_svd_lowrank_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_t_copy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_t_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_take_along_dim_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_take_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_tan_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_tanh_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_tensor_split_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_tensordot_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_tile_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_to_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_to_sparse_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_topk_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_trace_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_transpose_copy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_transpose_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_trapezoid_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_trapz_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_triangular_solve_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_tril_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_triu_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_true_divide_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_trunc_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unbind_copy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unbind_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unflatten_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unfold_copy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unfold_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_uniform_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unique_consecutive_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unique_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unsafe_chunk_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unsafe_split_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unsqueeze_copy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unsqueeze_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_var_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_var_mean_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_var_mean_unbiased_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_var_unbiased_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_vdot_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_view_as_complex_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_view_as_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_view_copy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_view_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_vsplit_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_vstack_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_where_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_xlogy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_zero__cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_zeros_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_zeros_like_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_quantized_eb_cpu 2025-08-14T22:22:02.6639434Z 2025-08-14T22:22:05.0218517Z /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. 2025-08-14T22:22:05.0221730Z import pkg_resources 2025-08-14T22:22:05.1419212Z Running test_pytree 1/1 ... [2025-08-14 22:22:05.141553] 2025-08-14T22:22:05.1419981Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:22:05.1422572Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_pytree.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:22:05.141929] 2025-08-14T22:22:05.7347367Z /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. 2025-08-14T22:22:05.7348890Z import pkg_resources 2025-08-14T22:22:05.8542808Z Running test_fx_passes 1/1 ... [2025-08-14 22:22:05.853937] 2025-08-14T22:22:05.8543318Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:22:05.8546962Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fx_passes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:22:05.854296] 2025-08-14T22:22:13.7723300Z 2025-08-14T22:22:13.7724551Z test_tensorexpr_pybind 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_pybind_1.1_d85d33cee76ca668_.log 2025-08-14T22:22:13.7731223Z Running 17 items in this shard: test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_alloc_in_loop, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_call_raw, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_dtype_error, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_dynamic_shape, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_dynamic_shape_2d, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_external_calls, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_kernel_shape_prop, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_kernel_shape_prop_module, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_kernel_with_custom_lowering, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_kernel_with_expand, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_kernel_with_permute, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_kernel_with_scalar_inputs, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_kernel_with_t, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_kernel_with_tensor_inputs, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_kernel_with_transpose, test/test_tensorexpr_pybind.py::TestTensorExprPyBind::test_simple_sum, test/test_tensorexpr_pybind.py::TestExprHandlePyBind::test_unary_ops 2025-08-14T22:22:13.7737056Z 2025-08-14T22:22:16.9175273Z /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. 2025-08-14T22:22:16.9178209Z import pkg_resources 2025-08-14T22:22:17.0384703Z Running test_module_tracker 1/1 ... [2025-08-14 22:22:17.038069] 2025-08-14T22:22:17.0385554Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:22:17.0387838Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_module_tracker.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:22:17.038449] 2025-08-14T22:22:24.2988442Z 2025-08-14T22:22:24.2989651Z test_fx_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_passes_1.1_6ae03fa004067b67_.log 2025-08-14T22:22:24.3010685Z Running 53 items in this shard: test/test_fx_passes.py::TestFXGraphPasses::test_fuser_pass_deep_model, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition0, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition1, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition10, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition11, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition2, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition3, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition4, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition5, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition6, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition7, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition8, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition9, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_xfail_partition0, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_xfail_partition1, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_xfail_partition2, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_xfail_partition3, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn0_expected_partition0_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn10_expected_partition10_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn11_expected_partition11_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn12_expected_partition12_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn13_expected_partition13_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn14_expected_partition14_bookend_non_compute_pass_True, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn15_expected_partition15_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn16_expected_partition16_bookend_non_compute_pass_True, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn17_expected_partition17_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn18_expected_partition18_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn1_expected_partition1_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn2_expected_partition2_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn3_expected_partition3_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn4_expected_partition4_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn5_expected_partition5_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn6_expected_partition6_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn7_expected_partition7_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn8_expected_partition8_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn9_expected_partition9_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_independent_output_fn0_expected_partition0, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model0, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model1, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model10, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model11, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model12, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model13, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model14, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model15, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model2, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model3, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model4, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model5, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model6, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model7, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model8, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model9 2025-08-14T22:22:24.3030479Z 2025-08-14T22:22:27.4227814Z /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. 2025-08-14T22:22:27.4230454Z import pkg_resources 2025-08-14T22:22:27.5424379Z Running test_typing 1/1 ... [2025-08-14 22:22:27.542044] 2025-08-14T22:22:27.5425110Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:22:27.5428416Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_typing.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'] ... [2025-08-14 22:22:27.542479] 2025-08-14T22:22:32.4287096Z 2025-08-14T22:22:32.4288300Z test_module_tracker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_module_tracker_1.1_cb0547368e7295a8_.log 2025-08-14T22:22:32.4289870Z Running 3 items in this shard: test/test_module_tracker.py::TestModuleTracker::test_bw_detection, test/test_module_tracker.py::TestModuleTracker::test_confused_hierarchy, test/test_module_tracker.py::TestModuleTracker::test_module_hierarchy 2025-08-14T22:22:32.4290932Z 2025-08-14T22:22:33.8043015Z 2025-08-14T22:22:33.8044231Z test_pytree 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_pytree_1.1_d6d01abd5a8d6409_.log 2025-08-14T22:22:33.8099857Z Running 98 items in this shard: test/test_pytree.py::TestGenericPytree::test_aligned_public_apis, test/test_pytree.py::TestGenericPytree::test_broadcast_to_and_flatten_cxx, test/test_pytree.py::TestGenericPytree::test_broadcast_to_and_flatten_py, test/test_pytree.py::TestGenericPytree::test_enum_treespec_roundtrip_cxx, test/test_pytree.py::TestGenericPytree::test_enum_treespec_roundtrip_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_defaultdict_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_defaultdict_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_deque_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_deque_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_dict_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_dict_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_leaf_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_leaf_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_list_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_list_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_namedtuple_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_namedtuple_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_nested_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_nested_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_ordereddict_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_ordereddict_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_return_types_max_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_return_types_max_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_return_types_min_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_return_types_min_py, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_tuple_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_unflatten_tuple_py, test/test_pytree.py::TestGenericPytree::test_flatten_with_is_leaf_cxx, test/test_pytree.py::TestGenericPytree::test_flatten_with_is_leaf_py, test/test_pytree.py::TestGenericPytree::test_is_namedtuple_cxx, test/test_pytree.py::TestGenericPytree::test_is_namedtuple_py, test/test_pytree.py::TestGenericPytree::test_is_structseq_cxx, test/test_pytree.py::TestGenericPytree::test_is_structseq_py, test/test_pytree.py::TestGenericPytree::test_pytree_serialize_bad_input_cxx, test/test_pytree.py::TestGenericPytree::test_pytree_serialize_bad_input_py, test/test_pytree.py::TestGenericPytree::test_register_pytree_node_cxx, test/test_pytree.py::TestGenericPytree::test_register_pytree_node_py, test/test_pytree.py::TestGenericPytree::test_tree_all_any_cxx, test/test_pytree.py::TestGenericPytree::test_tree_all_any_py, test/test_pytree.py::TestGenericPytree::test_tree_map_cxx, test/test_pytree.py::TestGenericPytree::test_tree_map_multi_inputs_cxx, test/test_pytree.py::TestGenericPytree::test_tree_map_multi_inputs_py, test/test_pytree.py::TestGenericPytree::test_tree_map_only_cxx, test/test_pytree.py::TestGenericPytree::test_tree_map_only_predicate_fn_cxx, test/test_pytree.py::TestGenericPytree::test_tree_map_only_predicate_fn_py, test/test_pytree.py::TestGenericPytree::test_tree_map_only_py, test/test_pytree.py::TestGenericPytree::test_tree_map_py, test/test_pytree.py::TestPythonPytree::test_constant, test/test_pytree.py::TestPythonPytree::test_constant_default_eq_error, test/test_pytree.py::TestPythonPytree::test_constant_default_hash_error, test/test_pytree.py::TestPythonPytree::test_dataclass, test/test_pytree.py::TestPythonPytree::test_deprecated_register_pytree_node, test/test_pytree.py::TestPythonPytree::test_flatten_flatten_with_key_consistency, test/test_pytree.py::TestPythonPytree::test_import_pytree_doesnt_import_optree, test/test_pytree.py::TestPythonPytree::test_key_access, test/test_pytree.py::TestPythonPytree::test_key_str, test/test_pytree.py::TestPythonPytree::test_pytree_context_serialize_bad, test/test_pytree.py::TestPythonPytree::test_pytree_custom_type_serialize, test/test_pytree.py::TestPythonPytree::test_pytree_custom_type_serialize_bad, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_bad_protocol, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_defaultdict_enum, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_enum, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_namedtuple, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_namedtuple_bad, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_register_bad, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec0, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec1, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec2, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec3, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec4, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec5, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec6, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec7, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec8, test/test_pytree.py::TestPythonPytree::test_pytree_serialize_spec9, test/test_pytree.py::TestPythonPytree::test_register_dataclass_class, test/test_pytree.py::TestPythonPytree::test_saved_serialized, test/test_pytree.py::TestPythonPytree::test_tree_flatten_with_path_is_leaf, test/test_pytree.py::TestPythonPytree::test_tree_flatten_with_path_roundtrip, test/test_pytree.py::TestPythonPytree::test_tree_leaves_with_path, test/test_pytree.py::TestPythonPytree::test_tree_map_with_path, test/test_pytree.py::TestPythonPytree::test_tree_map_with_path_multiple_trees, test/test_pytree.py::TestPythonPytree::test_treespec_equality, test/test_pytree.py::TestPythonPytree::test_treespec_repr, test/test_pytree.py::TestCxxPytree::test_pytree_custom_type_serialize, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_namedtuple, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec0, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec1, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec2, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec3, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec4, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec5, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec6, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec7, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec8, test/test_pytree.py::TestCxxPytree::test_pytree_serialize_spec9, test/test_pytree.py::TestCxxPytree::test_treespec_equality, test/test_pytree.py::TestCxxPytree::test_treespec_repr 2025-08-14T22:22:33.8152576Z 2025-08-14T22:22:35.6170770Z /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. 2025-08-14T22:22:35.6173742Z import pkg_resources 2025-08-14T22:22:35.7364377Z Running test_openmp 1/1 ... [2025-08-14 22:22:35.736061] 2025-08-14T22:22:35.7365134Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:22:35.7367714Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_openmp.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'] ... [2025-08-14 22:22:35.736420] 2025-08-14T22:22:36.9642324Z /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. 2025-08-14T22:22:36.9645173Z import pkg_resources 2025-08-14T22:22:37.0849353Z Running torch_np/numpy_tests/core/test_scalarinherit 1/1 ... [2025-08-14 22:22:37.084543] 2025-08-14T22:22:37.0850081Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:22:37.0852191Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalarinherit.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:22:37.084917] 2025-08-14T22:22:40.4548485Z 2025-08-14T22:22:40.4550549Z torch_np/numpy_tests/core/test_scalarinherit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalarinherit_1.1_f009980b2b5500a8_.log 2025-08-14T22:22:40.4554480Z Running 3 items in this shard: test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_gh_15395, test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_init, test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_init2 2025-08-14T22:22:40.4556864Z 2025-08-14T22:22:43.8369593Z /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. 2025-08-14T22:22:43.8372622Z import pkg_resources 2025-08-14T22:22:43.9554174Z Running functorch/test_ac_knapsack 1/1 ... [2025-08-14 22:22:43.955034] 2025-08-14T22:22:43.9555334Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:22:43.9557557Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ac_knapsack.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'] ... [2025-08-14 22:22:43.955380] 2025-08-14T22:22:46.6681590Z 2025-08-14T22:22:46.6683026Z test_openmp 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_openmp_1.1_fa9224b87db48c2c_.log 2025-08-14T22:22:46.6685336Z Running 2 items in this shard: test/test_openmp.py::TestOpenMP_ParallelFor::test_n_threads, test/test_openmp.py::TestOpenMP_ParallelFor::test_one_thread 2025-08-14T22:22:46.6686616Z 2025-08-14T22:22:49.1267921Z 2025-08-14T22:22:49.1269403Z functorch/test_ac_knapsack 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_knapsack_1.1_80281a00c699d5de_.log 2025-08-14T22:22:49.1277256Z Running 15 items in this shard: test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_full_joint_nx_graph, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_get_knapsack_memory_input, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_get_knapsack_runtime_input, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_get_non_ac_peak_memory, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_get_theoretical_max_runtime, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_inialize_from_graph, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_recomputable_node_only_graph, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_recomputable_node_only_graph_with_larger_graph_context, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_simplified_fx_joint_graph, test/functorch/test_ac_knapsack.py::TestKnapsackEvaluator::test_evaluate_distribution_of_results_for_knapsack_algo, test/functorch/test_ac_knapsack.py::TestKnapsackEvaluator::test_evaluate_knapsack_output_accounting_for_backward_pass, test/functorch/test_ac_knapsack.py::TestKnapsackEvaluator::test_evaluate_knapsack_output_not_accounting_for_backward_pass, test/functorch/test_ac_knapsack.py::TestKnapsackEvaluator::test_evaluate_knapsack_output_with_wrong_sized_values, test/functorch/test_ac_knapsack.py::TestKnapsackEvaluator::test_get_backward_memory_from_topologically_sorted_graph, test/functorch/test_ac_knapsack.py::TestKnapsackEvaluator::test_get_knee_point_memory_budget 2025-08-14T22:22:49.1284790Z 2025-08-14T22:22:49.9123610Z /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. 2025-08-14T22:22:49.9126523Z import pkg_resources 2025-08-14T22:22:50.0334144Z Running backends/xeon/test_launch 1/1 ... [2025-08-14 22:22:50.032980] 2025-08-14T22:22:50.0334984Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:22:50.0336977Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'backends/xeon/test_launch.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'] ... [2025-08-14 22:22:50.033336] 2025-08-14T22:22:52.2722045Z /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. 2025-08-14T22:22:52.2724864Z import pkg_resources 2025-08-14T22:22:52.3951751Z Running test_nestedtensor 1/1 ... [2025-08-14 22:22:52.394796] 2025-08-14T22:22:52.3952418Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:22:52.3954273Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nestedtensor.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'] ... [2025-08-14 22:22:52.395136] 2025-08-14T22:22:56.2086588Z 2025-08-14T22:22:56.2088159Z backends/xeon/test_launch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/backends.xeon.test_launch_1.1_47b1d196a3bd50b0_.log 2025-08-14T22:22:56.2090685Z Running 2 items in this shard: test/backends/xeon/test_launch.py::TestTorchrun::test_cpu_info, test/backends/xeon/test_launch.py::TestTorchrun::test_multi_threads 2025-08-14T22:22:56.2091400Z 2025-08-14T22:23:00.2285481Z /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. 2025-08-14T22:23:00.2288874Z import pkg_resources 2025-08-14T22:23:00.3490026Z Running test_namedtensor 1/1 ... [2025-08-14 22:23:00.348617] 2025-08-14T22:23:00.3491704Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:23:00.3494088Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_namedtensor.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:23:00.349060] 2025-08-14T22:23:56.9614438Z 2025-08-14T22:23:56.9616454Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_7ad05771520ca164_.log 2025-08-14T22:23:56.9670334Z Running 88 items in this shard: test/test_namedtensor.py::TestNamedTensor::test_aaa_must_run_first_check_experimental_warning, test/test_namedtensor.py::TestNamedTensor::test_addcmul_addcdiv, test/test_namedtensor.py::TestNamedTensor::test_addmm, test/test_namedtensor.py::TestNamedTensor::test_addmv, test/test_namedtensor.py::TestNamedTensor::test_align_as, test/test_namedtensor.py::TestNamedTensor::test_align_tensors, test/test_namedtensor.py::TestNamedTensor::test_align_tensors_two_inputs, test/test_namedtensor.py::TestNamedTensor::test_align_to, test/test_namedtensor.py::TestNamedTensor::test_align_to_ellipsis, test/test_namedtensor.py::TestNamedTensor::test_any_all, test/test_namedtensor.py::TestNamedTensor::test_as_strided, test/test_namedtensor.py::TestNamedTensor::test_as_strided_cuda, test/test_namedtensor.py::TestNamedTensor::test_autograd_ignores_names, test/test_namedtensor.py::TestNamedTensor::test_autograd_smoke, test/test_namedtensor.py::TestNamedTensor::test_autograd_warns_named_grad, test/test_namedtensor.py::TestNamedTensor::test_bernoulli, test/test_namedtensor.py::TestNamedTensor::test_big_tensor_repr_has_names, test/test_namedtensor.py::TestNamedTensor::test_binary_ops, test/test_namedtensor.py::TestNamedTensor::test_bitwise_not, test/test_namedtensor.py::TestNamedTensor::test_bmm, test/test_namedtensor.py::TestNamedTensor::test_cat, test/test_namedtensor.py::TestNamedTensor::test_cdist, test/test_namedtensor.py::TestNamedTensor::test_comparison_ops, test/test_namedtensor.py::TestNamedTensor::test_copy_transpose, test/test_namedtensor.py::TestNamedTensor::test_cummax_cummin, test/test_namedtensor.py::TestNamedTensor::test_detach, test/test_namedtensor.py::TestNamedTensor::test_diagonal, test/test_namedtensor.py::TestNamedTensor::test_dot, test/test_namedtensor.py::TestNamedTensor::test_equal, test/test_namedtensor.py::TestNamedTensor::test_expand, test/test_namedtensor.py::TestNamedTensor::test_factory_coverage, test/test_namedtensor.py::TestNamedTensor::test_factory_edge_cases, test/test_namedtensor.py::TestNamedTensor::test_flatten, test/test_namedtensor.py::TestNamedTensor::test_flatten_index_error, test/test_namedtensor.py::TestNamedTensor::test_flatten_nodims, test/test_namedtensor.py::TestNamedTensor::test_has_names, test/test_namedtensor.py::TestNamedTensor::test_index_fill, test/test_namedtensor.py::TestNamedTensor::test_info_smoke, test/test_namedtensor.py::TestNamedTensor::test_logcumsumexp, test/test_namedtensor.py::TestNamedTensor::test_logical_not, test/test_namedtensor.py::TestNamedTensor::test_logical_ops, test/test_namedtensor.py::TestNamedTensor::test_masked_fill, test/test_namedtensor.py::TestNamedTensor::test_masked_select, test/test_namedtensor.py::TestNamedTensor::test_matmul, test/test_namedtensor.py::TestNamedTensor::test_max_pooling, test/test_namedtensor.py::TestNamedTensor::test_max_pooling_without_names_does_not_warn, test/test_namedtensor.py::TestNamedTensor::test_mm, test/test_namedtensor.py::TestNamedTensor::test_mv, test/test_namedtensor.py::TestNamedTensor::test_no_jit_script_support, test/test_namedtensor.py::TestNamedTensor::test_no_jit_tracer_support, test/test_namedtensor.py::TestNamedTensor::test_no_multiprocessing_support, test/test_namedtensor.py::TestNamedTensor::test_no_pickle_support, test/test_namedtensor.py::TestNamedTensor::test_no_save_support, test/test_namedtensor.py::TestNamedTensor::test_noncontig_contiguous, test/test_namedtensor.py::TestNamedTensor::test_none_names_refcount, test/test_namedtensor.py::TestNamedTensor::test_nyi_dimname_overload_msg, test/test_namedtensor.py::TestNamedTensor::test_out_fn_semantics, test/test_namedtensor.py::TestNamedTensor::test_pow_special, test/test_namedtensor.py::TestNamedTensor::test_py3_ellipsis, test/test_namedtensor.py::TestNamedTensor::test_reduction_fns, test/test_namedtensor.py::TestNamedTensor::test_refine_names, test/test_namedtensor.py::TestNamedTensor::test_rename, test/test_namedtensor.py::TestNamedTensor::test_rename_, test/test_namedtensor.py::TestNamedTensor::test_rename_globber, test/test_namedtensor.py::TestNamedTensor::test_rename_rename_map, test/test_namedtensor.py::TestNamedTensor::test_repr, test/test_namedtensor.py::TestNamedTensor::test_resize, test/test_namedtensor.py::TestNamedTensor::test_select, test/test_namedtensor.py::TestNamedTensor::test_select_cuda, test/test_namedtensor.py::TestNamedTensor::test_set_names_property, test/test_namedtensor.py::TestNamedTensor::test_size, test/test_namedtensor.py::TestNamedTensor::test_split_fns_propagates_names, test/test_namedtensor.py::TestNamedTensor::test_squeeze, test/test_namedtensor.py::TestNamedTensor::test_stride, test/test_namedtensor.py::TestNamedTensor::test_support_device_named_grad, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_lists, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_named_tensor, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_numpy, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_tensor, test/test_namedtensor.py::TestNamedTensor::test_tensor_grad_is_unnamed, test/test_namedtensor.py::TestNamedTensor::test_transpose_variants, test/test_namedtensor.py::TestNamedTensor::test_trivial, test/test_namedtensor.py::TestNamedTensor::test_unary_propagate_names_fns, test/test_namedtensor.py::TestNamedTensor::test_unflatten, test/test_namedtensor.py::TestNamedTensor::test_unsupported_op_error_msg, test/test_namedtensor.py::TestNamedTensor::test_using_seen_interned_string_doesnt_bump_refcount, test/test_namedtensor.py::TestNamedTensor::test_using_unseen_interned_string_bumps_refcount_permanently, test/test_namedtensor.py::TestNamedTensor::test_using_unseen_uninterned_string_refcounts 2025-08-14T22:23:56.9718518Z 2025-08-14T22:24:00.5848945Z /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. 2025-08-14T22:24:00.5851841Z import pkg_resources 2025-08-14T22:24:00.7055561Z Running torch_np/test_ufuncs_basic 1/1 ... [2025-08-14 22:24:00.704815] 2025-08-14T22:24:00.7056511Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:24:00.7058762Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_ufuncs_basic.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:24:00.705140] 2025-08-14T22:24:08.4820846Z 2025-08-14T22:24:08.4821684Z test_typing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_typing_1.1_e8c7255bc6a4e489_.log 2025-08-14T22:24:08.4826582Z Running 18 items in this shard: test/test_typing.py::TestTyping::test_fail_arithmetic_ops.py, test/test_typing.py::TestTyping::test_fail_creation_ops.py, test/test_typing.py::TestTyping::test_fail_random.py, test/test_typing.py::TestTyping::test_fail_torch_size.py, test/test_typing.py::TestTyping::test_reveal_module_list.py, test/test_typing.py::TestTyping::test_reveal_namedtuple.py, test/test_typing.py::TestTyping::test_reveal_opt_size.py, test/test_typing.py::TestTyping::test_reveal_size.py, test/test_typing.py::TestTyping::test_reveal_tensor_constructors.py, test/test_typing.py::TestTyping::test_reveal_tensor_copy.py, test/test_typing.py::TestTyping::test_reveal_tensor_sampling.py, test/test_typing.py::TestTyping::test_reveal_torch_optim.py, test/test_typing.py::TestTyping::test_success_arithmetic_ops.py, test/test_typing.py::TestTyping::test_success_creation_ops.py, test/test_typing.py::TestTyping::test_success_cuda_steam.py, test/test_typing.py::TestTyping::test_success_distributions.py, test/test_typing.py::TestTyping::test_success_math_ops.py, test/test_typing.py::TestTyping::test_success_torch_size.py 2025-08-14T22:24:08.4831476Z 2025-08-14T22:24:11.6443658Z /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. 2025-08-14T22:24:11.6446454Z import pkg_resources 2025-08-14T22:24:11.7657090Z Running test_meta 1/1 ... [2025-08-14 22:24:11.765329] 2025-08-14T22:24:11.7657798Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:24:11.7660885Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_meta.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'] ... [2025-08-14 22:24:11.765729] 2025-08-14T22:24:56.8108527Z 2025-08-14T22:24:56.8110024Z torch_np/test_ufuncs_basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_ufuncs_basic_1.1_ba752a36a0da323a_.log 2025-08-14T22:24:56.8280929Z Running 371 items in this shard: test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_scalar_ufunc0, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_equiv_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_equiv_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_equiv_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_no_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_no_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_no_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_safe_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_safe_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_safe_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_same_kind_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_same_kind_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_same_kind_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_unsafe_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_unsafe_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_unsafe_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_ufunc0, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_broadcast_ufunc0, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_equiv_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_equiv_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_equiv_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_no_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_no_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_no_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_safe_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_safe_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_safe_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_same_kind_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_same_kind_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_same_kind_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_unsafe_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_unsafe_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_unsafe_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc0, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc1, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc10, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc11, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc12, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc13, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc14, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc15, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc16, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc2, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc3, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc4, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc5, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc6, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc7, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc8, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc9, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc0, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc1, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc10, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc11, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc12, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc13, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc14, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc15, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc16, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc2, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc3, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc4, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc5, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc6, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc7, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc8, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc9, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc0, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc1, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc10, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc11, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc12, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc13, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc14, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc15, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc16, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc2, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc3, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc4, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc5, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc6, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc7, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc8, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc9, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc10_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc10_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc10_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc11_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc11_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc11_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc12_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc12_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc12_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc13_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc13_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc13_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc14_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc14_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc14_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc15_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc15_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc15_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc16_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc16_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc16_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc1_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc1_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc1_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc2_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc2_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc2_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc3_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc3_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc3_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc4_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc4_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc4_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc5_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc5_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc5_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc6_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc6_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc6_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc7_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc7_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc7_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc8_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc8_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc8_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc9_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc9_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc9_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_no_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_no_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_no_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_no_ufunc10_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_no_ufunc10_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_no_ufunc10_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_no_ufunc11_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_no_ufunc11_out_dtype_float32, 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test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc4_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc4_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc5_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc5_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc5_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc6_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc6_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc6_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc7_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc7_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc7_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc8_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc8_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc8_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc9_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc9_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc9_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc10_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc10_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc10_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc11_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc11_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc11_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc12_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc12_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc12_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc13_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc13_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc13_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc14_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc14_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc14_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc15_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc15_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc15_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc16_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc16_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc16_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc1_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc1_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc1_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc2_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc2_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc2_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc3_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc3_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc3_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc4_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc4_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc4_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc5_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc5_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc5_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc6_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc6_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc6_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc7_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc7_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc7_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc8_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc8_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc8_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc9_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc9_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc9_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_basic_ufunc0_op0_iop0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_basic_ufunc1_op1_iop1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_basic_ufunc2_op2_iop2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_bcast_ufunc0_op0_iop0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_bcast_ufunc1_op1_iop1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_bcast_ufunc2_op2_iop2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc0_op0_iop0_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc0_op0_iop0_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc0_op0_iop0_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc0_op0_iop0_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc1_op1_iop1_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc1_op1_iop1_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc1_op1_iop1_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc1_op1_iop1_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc2_op2_iop2_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc2_op2_iop2_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc2_op2_iop2_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc2_op2_iop2_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc0_op0_iop0_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc0_op0_iop0_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc0_op0_iop0_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc0_op0_iop0_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc1_op1_iop1_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc1_op1_iop1_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc1_op1_iop1_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc1_op1_iop1_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc2_op2_iop2_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc2_op2_iop2_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc2_op2_iop2_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc2_op2_iop2_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestUfuncDtypeKwd::test_binary_ufunc_dtype, test/torch_np/test_ufuncs_basic.py::TestUfuncDtypeKwd::test_binary_ufunc_dtype_and_out 2025-08-14T22:24:56.8444440Z 2025-08-14T22:24:59.9349491Z /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. 2025-08-14T22:25:00.0556554Z import pkg_resources 2025-08-14T22:25:00.0557161Z Running test_utils_filelock 1/1 ... [2025-08-14 22:25:00.055297] 2025-08-14T22:25:00.0557745Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:25:00.0559776Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_utils_filelock.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'] ... [2025-08-14 22:25:00.055676] 2025-08-14T22:25:04.6261001Z 2025-08-14T22:25:04.6262387Z test_utils_filelock 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_filelock_1.1_839e76ce7c1a0cfd_.log 2025-08-14T22:25:04.6264337Z Running 2 items in this shard: test/test_utils_filelock.py::TestFileLock::test_no_crash, test/test_utils_filelock.py::TestFileLock::test_sequencing 2025-08-14T22:25:04.6265315Z 2025-08-14T22:25:07.7891992Z /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. 2025-08-14T22:25:07.7893928Z import pkg_resources 2025-08-14T22:25:07.9075290Z Running torch_np/test_random 1/1 ... [2025-08-14 22:25:07.907147] 2025-08-14T22:25:07.9076105Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:25:07.9078417Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_random.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'] ... [2025-08-14 22:25:07.907509] 2025-08-14T22:25:15.8828862Z 2025-08-14T22:25:15.8830838Z torch_np/test_random 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_random_1.1_8eb88e24dbebdd54_.log 2025-08-14T22:25:15.8857752Z Running 41 items in this shard: test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_random_sample, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_random_sample, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_random_sample, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_random_sample, test/torch_np/test_random.py::TestShuffle::test_1d_use_numpy_False, test/torch_np/test_random.py::TestShuffle::test_1d_use_numpy_True, test/torch_np/test_random.py::TestShuffle::test_2d_use_numpy_False, test/torch_np/test_random.py::TestShuffle::test_2d_use_numpy_True, test/torch_np/test_random.py::TestShuffle::test_shuffle_list_use_numpy_False, test/torch_np/test_random.py::TestShuffle::test_shuffle_list_use_numpy_True, test/torch_np/test_random.py::TestChoice::test_choice_use_numpy_False, test/torch_np/test_random.py::TestChoice::test_choice_use_numpy_True, test/torch_np/test_random.py::TestNumpyGlobal::test_numpy_global 2025-08-14T22:25:15.8883019Z 2025-08-14T22:25:19.0309915Z /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. 2025-08-14T22:25:19.0312799Z import pkg_resources 2025-08-14T22:25:19.1534860Z Running profiler/test_kineto 1/1 ... [2025-08-14 22:25:19.153079] 2025-08-14T22:25:19.1535682Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:25:19.1537471Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_kineto.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'] ... [2025-08-14 22:25:19.153455] 2025-08-14T22:25:23.5244467Z 2025-08-14T22:25:23.5245535Z profiler/test_kineto 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_kineto_1.1_372b67a046986d0a_.log 2025-08-14T22:25:23.5247658Z Running 1 items in this shard: test/profiler/test_kineto.py::SimpleKinetoInitializationTest::test_kineto_profiler_with_environment_variable 2025-08-14T22:25:23.5248369Z 2025-08-14T22:25:26.7191693Z /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. 2025-08-14T22:25:26.7194857Z import pkg_resources 2025-08-14T22:25:26.8392113Z Running test_compile_benchmark_util 1/1 ... [2025-08-14 22:25:26.838809] 2025-08-14T22:25:26.8393315Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:25:26.8395598Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_compile_benchmark_util.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'] ... [2025-08-14 22:25:26.839166] 2025-08-14T22:25:30.1597226Z 2025-08-14T22:25:30.1598632Z test_compile_benchmark_util 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_compile_benchmark_util_1.1_ee82a2fe2a629148_.log 2025-08-14T22:25:30.1599805Z Running 1 items in this shard: test/test_compile_benchmark_util.py::TestCompileBenchmarkUtil::test_training_and_inference 2025-08-14T22:25:30.1600525Z 2025-08-14T22:25:33.6167197Z /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. 2025-08-14T22:25:33.6169984Z import pkg_resources 2025-08-14T22:25:33.7372102Z Running test_optim 1/3 ... [2025-08-14 22:25:33.736836] 2025-08-14T22:25:33.7372559Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:25:33.7374683Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_optim.py', '-m', 'not serial', '--shard-id=1', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:25:33.737189] 2025-08-14T22:27:13.4077853Z 2025-08-14T22:27:13.4078981Z test_meta 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_meta_1.1_151f49656ed9cc78_.log 2025-08-14T22:27:15.5844886Z Running 40877 items in this shard: test/test_meta.py::TestMetaConverter::test_channels_last, test/test_meta.py::TestMetaConverter::test_channels_last_leaf, test/test_meta.py::TestMetaConverter::test_channels_last_non_leaf, test/test_meta.py::TestMetaConverter::test_complex_noncontiguous_bug, test/test_meta.py::TestMetaConverter::test_empty_strided_non_dense_leaf, test/test_meta.py::TestMetaConverter::test_imag, test/test_meta.py::TestMetaConverter::test_inplace_set_storage, test/test_meta.py::TestMetaConverter::test_leaf, test/test_meta.py::TestMetaConverter::test_non_leaf, test/test_meta.py::TestMetaConverter::test_non_leaf_torture, test/test_meta.py::TestMetaConverter::test_requires_grad_false, test/test_meta.py::TestMetaConverter::test_tensor_outlives_converter, test/test_meta.py::TestMetaConverter::test_view_as_complex, test/test_meta.py::TestMetaConverter::test_view_as_real, test/test_meta.py::TestMetaConverter::test_view_dtype, test/test_meta.py::TestMetaConverter::test_view_mutate, test/test_meta.py::TestMetaConverter::test_view_of_leaf, test/test_meta.py::TestMetaConverter::test_view_of_non_leaf, test/test_meta.py::TestMetaConverter::test_view_of_view_of_leaf, test/test_meta.py::TestMetaConverter::test_weakref, test/test_meta.py::TestMetaCPU::test_batch_norm_backward_output_mask0_cpu, test/test_meta.py::TestMetaCPU::test_batch_norm_backward_output_mask1_cpu, test/test_meta.py::TestMetaCPU::test_batch_norm_backward_output_mask2_cpu, test/test_meta.py::TestMetaCPU::test_batch_norm_backward_output_mask3_cpu, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype___radd___cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype___rdiv___cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype___rmod___cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype___rmul___cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype___rpow___cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype___rsub___cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs__conversions_complex_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs__conversions_polar_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_add_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_atan2_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_clamp_max_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_clamp_min_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_copysign_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_div_floor_rounding_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_div_no_rounding_mode_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_div_trunc_rounding_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_eq_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_float_power_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_floor_divide_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_fmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_fmin_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_fmod_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_ge_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_gt_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_heaviside_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_hypot_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_igamma_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_igammac_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_isclose_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_le_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_logaddexp_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_logical_and_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_logical_or_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_logical_xor_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_lt_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_maximum_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_minimum_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_mul_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_ne_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_nextafter_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_pow_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_remainder_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_rsub_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_special_xlog1py_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_special_zeta_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_sub_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_true_divide_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype__refs_xlogy_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_add_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_atan2_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_clamp_max_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_clamp_min_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_complex_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_copysign_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_div_floor_rounding_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_div_no_rounding_mode_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_div_trunc_rounding_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_eq_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_float_power_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_floor_divide_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_fmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_fmin_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_fmod_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_ge_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_gt_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_heaviside_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_hypot_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_igamma_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_igammac_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_isclose_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_jiterator_binary_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_jiterator_binary_return_by_ref_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_ldexp_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_le_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_logaddexp_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_logical_and_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_logical_or_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_logical_xor_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_lt_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_max_binary_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_maximum_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_min_binary_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_minimum_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_mul_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_ne_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_nextafter_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_polar_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_pow_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_remainder_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_rsub_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_chebyshev_polynomial_t_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_chebyshev_polynomial_u_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_chebyshev_polynomial_v_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_chebyshev_polynomial_w_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_hermite_polynomial_h_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_hermite_polynomial_he_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_laguerre_polynomial_l_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_legendre_polynomial_p_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_xlog1py_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_special_zeta_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_sub_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_true_divide_cpu_float32, test/test_meta.py::TestMetaCPU::test_binary_ufuncs_mixed_dtype_xlogy_cpu_float32, test/test_meta.py::TestMetaCPU::test_cdist_forward_cpu, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_H_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_T_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___getitem___cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace___radd___cpu_int64, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_tensor_overload_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_tensor_overload_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_tensor_overload_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_tensor_overload_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_tensor_overload_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_tensor_overload_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_tensor_overload_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_tensor_overload_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_tensor_overload_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_tensor_overload_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logspace_tensor_overload_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logsumexp_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logsumexp_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logsumexp_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logsumexp_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logsumexp_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logsumexp_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logsumexp_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logsumexp_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logsumexp_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logsumexp_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logsumexp_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_logsumexp_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_long_cpu_bfloat16, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trapz_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trapz_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trapz_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trapz_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trapz_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trapz_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trapz_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trapz_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trapz_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trapz_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trapz_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_triangular_solve_cpu_complex128, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trunc_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trunc_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trunc_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trunc_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trunc_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_trunc_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unbind_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unbind_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unbind_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unbind_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unbind_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unbind_copy_cpu_float16, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unflatten_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unflatten_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unflatten_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unflatten_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unflatten_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unflatten_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unflatten_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unflatten_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unflatten_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_copy_cpu_complex128, 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test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_cpu_complex32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_cpu_int16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_cpu_int32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_cpu_int64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_cpu_int8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unfold_cpu_uint8, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_uniform_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_uniform_cpu_complex128, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_uniform_cpu_complex64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_uniform_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_uniform_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_uniform_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unique_consecutive_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unique_consecutive_cpu_bool, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unique_consecutive_cpu_float16, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unique_consecutive_cpu_float32, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unique_consecutive_cpu_float64, test/test_meta.py::TestMetaCPU::test_dispatch_meta_inplace_unique_consecutive_cpu_int16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sign_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sign_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sign_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sign_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sinh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_sqrt_cpu_int8, 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test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tan_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_tanh_cpu_bfloat16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__foreach_zero_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__native_batch_norm_legit_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__native_batch_norm_legit_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__native_batch_norm_legit_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__native_batch_norm_legit_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__segment_reduce_lengths_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__segment_reduce_lengths_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__segment_reduce_lengths_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__segment_reduce_lengths_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__segment_reduce_offsets_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__segment_reduce_offsets_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__segment_reduce_offsets_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__segment_reduce_offsets_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__softmax_backward_data_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__softmax_backward_data_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__softmax_backward_data_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__softmax_backward_data_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_cpu_int16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_put_accumulate_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_put_accumulate_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_put_accumulate_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_put_accumulate_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_put_accumulate_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace__unsafe_masked_index_put_accumulate_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace__upsample_bilinear2d_aa_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace__upsample_bilinear2d_aa_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace__upsample_bilinear2d_aa_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_abs_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_acos_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_acosh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_add_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_addbmm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addbmm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_addbmm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addbmm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addbmm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_addbmm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addbmm_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addbmm_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_addbmm_cpu_int64, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_addmm_decomposed_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_addmv_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_addr_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_alias_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_all_cpu_float16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_amin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_amin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_amin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_amin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_amin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_aminmax_cpu_int8, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_argwhere_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_argwhere_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_argwhere_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_argwhere_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_copy_cpu_int16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_partial_views_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_as_strided_scatter_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_asin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_asinh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_asinh_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_asinh_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_asinh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_asinh_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_asinh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_asinh_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_asinh_cpu_int16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bfloat16_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bincount_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bincount_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bincount_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bincount_cpu_int8, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_right_shift_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_right_shift_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_right_shift_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_xor_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_xor_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_xor_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_xor_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_xor_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bitwise_xor_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_block_diag_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bmm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bmm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_bmm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bmm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bmm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bmm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bmm_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bmm_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bmm_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bmm_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bmm_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_bool_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_shapes_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_tensors_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_broadcast_to_cpu_float64, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_bucketize_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_byte_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cartesian_prod_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cat_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cauchy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cauchy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cauchy_cpu_float32, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_cdouble_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cdouble_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_ceil_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cfloat_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_chalf_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_chalf_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_chalf_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_chalf_cpu_complex32, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_contiguous_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_copysign_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_corrcoef_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cos_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_cosh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_cosh_cpu_bool, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_diag_embed_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagflat_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagflat_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagflat_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_diagflat_cpu_complex64, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfft_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_hfftn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft2_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft2_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft2_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft2_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft2_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft2_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft2_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft2_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft2_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifft_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ifftshift_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft2_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft2_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft2_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft2_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft2_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft2_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft2_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfft_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfftn_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfftn_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfftn_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfftn_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfftn_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfftn_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfftn_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_ihfftn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft2_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft2_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft2_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft2_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft2_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft2_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft2_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft2_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft2_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfft_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfftn_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfftn_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfftn_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfftn_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfftn_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfftn_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfftn_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfftn_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfftn_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_irfftn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft2_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft2_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft2_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft2_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft2_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft2_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft2_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfft_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfftn_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfftn_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfftn_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfftn_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfftn_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfftn_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfftn_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fft_rfftn_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fill_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_flatten_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_flip_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_fliplr_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_flipud_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_power_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_float_power_cpu_bool, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_divide_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_divide_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_divide_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_divide_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_divide_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_divide_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_floor_divide_cpu_int64, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_hstack_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_hypot_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_hypot_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_hypot_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_hypot_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_i0_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_i0_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_i0_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_i0_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_i0_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_i0_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_i0_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_i0_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_i0_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_i0_cpu_uint8, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_svd_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_svd_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_svd_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_svdvals_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_svdvals_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_svdvals_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_svdvals_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_tensorinv_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_tensorinv_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_tensorinv_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_tensorinv_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_tensorsolve_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_tensorsolve_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_tensorsolve_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_tensorsolve_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vander_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vander_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vander_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vander_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vander_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vander_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vander_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vander_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_linalg_vander_cpu_uint8, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_log_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_normal_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_normal_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_normal_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_normal_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_with_dtype_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_with_dtype_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_log_softmax_with_dtype_cpu_complex128, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_long_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_long_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_long_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_long_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_long_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_long_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_lt_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_lt_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_lt_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_lt_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_lt_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_lt_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_lt_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_lt_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_lt_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_lt_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_solve_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_solve_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_solve_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_solve_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_unpack_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_unpack_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_unpack_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_lu_unpack_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_mH_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_mT_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_amax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_amax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_amax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_amax_cpu_float64, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_amin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_argmin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumprod_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_cumsum_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_fill_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_log_softmax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_log_softmax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_log_softmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_log_softmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logaddexp_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logaddexp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logaddexp_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logaddexp_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_logsumexp_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_mean_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_mean_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_mean_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_mean_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_mean_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_mean_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_median_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_median_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_median_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_median_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_norm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_norm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_norm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_normalize_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_normalize_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_normalize_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_normalize_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_normalize_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_normalize_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_prod_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_scatter_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_select_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_softmax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_softmax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_softmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_softmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_softmin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_softmin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_softmin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_softmin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_std_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_std_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_masked_std_cpu_complex64, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nan_to_num_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmean_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmean_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmean_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmean_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanmedian_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanquantile_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nanquantile_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nansum_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nansum_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_nansum_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nansum_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nansum_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nansum_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nansum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nansum_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nansum_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nansum_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_narrow_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_batch_norm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_batch_norm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_batch_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_batch_norm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_dropout_backward_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_dropout_backward_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_dropout_backward_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_dropout_backward_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_dropout_backward_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_dropout_backward_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_dropout_backward_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_dropout_backward_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_dropout_backward_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_dropout_backward_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_layer_norm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_layer_norm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_layer_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_native_layer_norm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_ne_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_neg_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_empty_strided_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_full_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_ones_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_new_zeros_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nextafter_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nextafter_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nextafter_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nextafter_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool1d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool1d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool1d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool2d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool2d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool3d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool3d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool1d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool1d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool2d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool2d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool3d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool3d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_adaptive_max_pool3d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_alpha_dropout_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_alpha_dropout_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_alpha_dropout_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_alpha_dropout_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool1d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool1d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool1d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool1d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool1d_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool2d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool2d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool2d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool2d_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool3d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool3d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_avg_pool3d_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_batch_norm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_batch_norm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_batch_norm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_batch_norm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_bilinear_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_bilinear_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_bilinear_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_bilinear_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_bilinear_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_bilinear_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_bilinear_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_bilinear_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_bilinear_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_binary_cross_entropy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_binary_cross_entropy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_binary_cross_entropy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_binary_cross_entropy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_binary_cross_entropy_with_logits_cpu_bfloat16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_channel_shuffle_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_conv1d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_nn_functional_conv1d_cpu_complex128, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_ormqr_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ormqr_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_ormqr_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_outer_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_pca_lowrank_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_pca_lowrank_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_pca_lowrank_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_pca_lowrank_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_permute_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_pinverse_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_pinverse_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_pinverse_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_pinverse_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_polar_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_polar_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_0_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_0_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_0_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_0_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_0_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_0_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_0_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_0_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_0_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_0_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_1_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_1_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_1_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_1_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_1_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_1_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_1_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_1_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_1_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_2_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_2_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_2_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_2_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_2_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_2_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_2_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_2_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_2_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_3_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_3_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_3_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_3_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_3_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_3_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_3_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_3_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_3_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_4_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_4_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_4_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_4_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_4_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_4_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_4_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_4_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_polygamma_polygamma_n_4_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_positive_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_positive_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_positive_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_positive_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_positive_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_positive_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_positive_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_positive_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_positive_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_positive_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_positive_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_positive_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_pow_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_pow_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_pow_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_pow_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_pow_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_pow_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_pow_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_pow_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_pow_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_pow_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_pow_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_prod_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_prod_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_prod_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_prod_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_prod_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_prod_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_prod_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_prod_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_prod_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_prod_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_prod_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_prod_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_put_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_put_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_put_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_put_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_put_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_put_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_put_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_put_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_put_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_put_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_put_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_put_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_qr_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_qr_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_qr_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_qr_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_quantile_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_quantile_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_rad2deg_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rad2deg_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_rad2deg_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rad2deg_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_rad2deg_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_rad2deg_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rad2deg_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_rad2deg_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_rad2deg_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_rad2deg_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_rand_like_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rand_like_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_rand_like_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_rand_like_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_rand_like_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rand_like_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_rand_like_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_like_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_like_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_like_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_like_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_like_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_like_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_like_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_like_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_randint_like_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_like_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_like_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_like_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_like_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_like_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_like_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_randn_like_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ravel_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ravel_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_ravel_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_ravel_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_ravel_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ravel_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ravel_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_ravel_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ravel_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_ravel_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_ravel_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_ravel_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_ravel_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_real_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_real_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_real_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_real_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_real_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_real_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_real_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_real_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_real_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_real_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_real_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_real_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_real_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_reciprocal_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_reciprocal_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_reciprocal_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_reciprocal_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_reciprocal_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_reciprocal_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_reciprocal_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_reciprocal_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_reciprocal_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_reciprocal_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_reciprocal_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_reciprocal_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_remainder_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_remainder_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_remainder_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_remainder_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_remainder_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_remainder_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_remainder_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_remainder_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_remainder_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_renorm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_renorm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_renorm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_renorm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_renorm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_renorm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_interleave_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_interleave_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_interleave_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_interleave_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_interleave_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_interleave_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_interleave_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_repeat_interleave_cpu_float64, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_roll_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_roll_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_roll_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_roll_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_roll_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_roll_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_roll_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_roll_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_roll_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_roll_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_roll_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_roll_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_roll_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_rot90_cpu_bfloat16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_round_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_decimals_0_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_decimals_0_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_decimals_0_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_decimals_0_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_decimals_3_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_decimals_3_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_decimals_3_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_decimals_neg_3_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_decimals_neg_3_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_round_decimals_neg_3_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsqrt_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_rsub_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_scalar_tensor_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_add_cpu_bfloat16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_reduce_amax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_reduce_amax_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_scatter_reduce_amax_cpu_float16, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_take_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_take_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_take_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_take_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_take_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_take_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_tan_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tanh_cpu_int8, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_tile_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_tile_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tile_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tile_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tile_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tile_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tile_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tile_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tile_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_tile_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_sparse_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_sparse_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_sparse_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_sparse_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_sparse_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_to_sparse_cpu_float32, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_topk_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_torch__scaled_mm_cpu_float8_e4m3fn, test/test_meta.py::TestMetaCPU::test_meta_inplace_torch__scaled_mm_cpu_float8_e4m3fnuz, test/test_meta.py::TestMetaCPU::test_meta_inplace_torch__scaled_mm_cpu_float8_e5m2, test/test_meta.py::TestMetaCPU::test_meta_inplace_torch__scaled_mm_cpu_float8_e5m2fnuz, test/test_meta.py::TestMetaCPU::test_meta_inplace_torch_ops_aten__safe_softmax_default_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_torch_ops_aten__safe_softmax_default_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_torch_ops_aten__safe_softmax_default_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_torch_ops_aten__safe_softmax_default_cpu_float64, 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test/test_meta.py::TestMetaCPU::test_meta_inplace_trace_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_trace_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_transpose_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapezoid_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapz_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapz_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapz_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapz_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapz_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapz_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapz_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapz_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapz_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapz_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_trapz_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_triangular_solve_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_triangular_solve_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_triangular_solve_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_triangular_solve_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_indices_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_tril_indices_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_indices_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_triu_indices_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_true_divide_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_trunc_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unbind_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unflatten_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unfold_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_uniform_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_uniform_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_uniform_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_uniform_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_uniform_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_uniform_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_consecutive_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_consecutive_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_consecutive_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_consecutive_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_consecutive_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_consecutive_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_consecutive_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_consecutive_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_consecutive_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_consecutive_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_uint16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_uint32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_uint64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unique_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unravel_index_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unravel_index_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unravel_index_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unravel_index_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unravel_index_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_chunk_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsafe_split_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_unsqueeze_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_unbiased_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_unbiased_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_unbiased_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_unbiased_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_unbiased_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_mean_unbiased_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_unbiased_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_unbiased_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_unbiased_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_unbiased_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_unbiased_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_var_unbiased_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_vdot_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_complex_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_complex_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_complex_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_real_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_as_real_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_inplace_view_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_inplace_vsplit_cpu_int8, 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test/test_meta.py::TestMetaCPU::test_meta_outplace__unsafe_masked_index_put_accumulate_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace__unsafe_masked_index_put_accumulate_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__unsafe_masked_index_put_accumulate_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace__unsafe_masked_index_put_accumulate_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace__unsafe_masked_index_put_accumulate_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace__unsafe_masked_index_put_accumulate_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace__unsafe_masked_index_put_accumulate_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace__unsafe_masked_index_put_accumulate_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace__upsample_bilinear2d_aa_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace__upsample_bilinear2d_aa_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace__upsample_bilinear2d_aa_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_abs_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_abs_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_abs_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_abs_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_abs_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_abs_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_abs_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_abs_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_abs_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_abs_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_abs_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_abs_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_acos_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_acosh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_add_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addbmm_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcdiv_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcdiv_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcdiv_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcdiv_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcdiv_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcdiv_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addcmul_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_decomposed_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_decomposed_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_decomposed_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_decomposed_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_decomposed_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_decomposed_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_decomposed_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_decomposed_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_decomposed_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_decomposed_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmm_decomposed_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmv_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmv_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmv_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmv_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmv_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmv_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmv_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmv_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmv_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmv_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addmv_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_addr_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_alias_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_all_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_allclose_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_allclose_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_allclose_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_allclose_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_allclose_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_allclose_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_amax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_amax_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_amax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_amax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_amax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_amax_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_amax_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_amax_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_amax_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_amax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_amin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_aminmax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_aminmax_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_aminmax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_aminmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_aminmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_aminmax_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_aminmax_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_aminmax_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_aminmax_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_aminmax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_angle_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_any_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_arange_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_arange_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_arange_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_arange_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_arange_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_arange_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_arange_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_arange_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_arange_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmax_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmax_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmax_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmax_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_argmin_cpu_int8, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_argwhere_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_argwhere_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_argwhere_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_argwhere_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_argwhere_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_argwhere_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_argwhere_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_argwhere_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_argwhere_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_partial_views_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_as_strided_scatter_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_asin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_asin_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_asin_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_asin_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_asin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_asin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_asin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_asin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_asin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_asin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_asin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_asin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_asinh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_atan2_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atan2_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_atan2_cpu_float16, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_atanh_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atanh_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_atanh_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_1d_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_2d_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_atleast_3d_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_baddbmm_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bernoulli_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bernoulli_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bernoulli_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bernoulli_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bfloat16_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bincount_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bincount_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bincount_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bincount_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bincount_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_and_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_and_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_and_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_and_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_and_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_and_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_left_shift_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_left_shift_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_left_shift_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_left_shift_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_left_shift_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_not_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_not_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_not_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_not_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_not_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_not_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_or_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_or_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_or_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_or_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_or_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_or_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_right_shift_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_right_shift_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_right_shift_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_right_shift_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_right_shift_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_xor_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_xor_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_xor_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_xor_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_xor_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_bitwise_xor_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_block_diag_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_block_diag_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_block_diag_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_block_diag_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_block_diag_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_block_diag_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_block_diag_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_block_diag_cpu_float64, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_div_floor_rounding_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_floor_rounding_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_floor_rounding_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_floor_rounding_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_floor_rounding_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_floor_rounding_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_floor_rounding_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_floor_rounding_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_floor_rounding_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_no_rounding_mode_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_no_rounding_mode_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_div_no_rounding_mode_cpu_complex128, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_eq_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_equal_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_equal_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_equal_cpu_complex128, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_logcumsumexp_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logcumsumexp_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logdet_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_logdet_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logdet_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logdet_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_and_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_and_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_and_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_and_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_and_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_and_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_and_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_and_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_and_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_and_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_and_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_and_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_not_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_or_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logical_xor_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logit_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logit_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_logit_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logit_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logit_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logit_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logit_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logit_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logit_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logit_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logspace_tensor_overload_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_logsumexp_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_long_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_lt_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_lt_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_lt_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_lt_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_lt_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_lt_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_lt_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_lt_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_lt_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_lt_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_lu_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_lu_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_lu_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_lu_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_lu_solve_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_lu_solve_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_lu_solve_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_lu_solve_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_lu_unpack_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_lu_unpack_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_lu_unpack_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_lu_unpack_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_mH_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_mT_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amax_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amax_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amax_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amax_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_amin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmax_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmax_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmax_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmax_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmax_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmax_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmax_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmax_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmax_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmin_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmin_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmin_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmin_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmin_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmin_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmin_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmin_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_argmin_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumprod_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumprod_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumprod_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumprod_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumprod_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumprod_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumprod_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumprod_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumprod_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumprod_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumprod_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_cumsum_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_fill_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_fill_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_fill_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_fill_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_fill_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_fill_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_fill_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_masked_fill_cpu_float64, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_qr_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_quantile_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_quantile_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_rad2deg_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_rad2deg_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_rad2deg_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_rad2deg_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_rad2deg_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_rad2deg_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_rad2deg_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_rad2deg_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_rad2deg_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_rad2deg_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_rand_like_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_rand_like_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_rand_like_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_rand_like_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_rand_like_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_rand_like_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_rand_like_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_randint_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_randint_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_randint_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_randint_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_randint_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_randint_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_randint_cpu_int64, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_reciprocal_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_reciprocal_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_reciprocal_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_reciprocal_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_reciprocal_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_reciprocal_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_reciprocal_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_reciprocal_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_reciprocal_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_remainder_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_remainder_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_remainder_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_remainder_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_remainder_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_remainder_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_remainder_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_remainder_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_remainder_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_renorm_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_renorm_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_renorm_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_renorm_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_renorm_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_renorm_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_repeat_interleave_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_as_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_reshape_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize__cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize__cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize__cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize__cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize__cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize__cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize__cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize__cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize__cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize__cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize__cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize__cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize_as__cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize_as__cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize_as__cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize_as__cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize_as__cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize_as__cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize_as__cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize_as__cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize_as__cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize_as__cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize_as__cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_resize_as__cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_conj_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_conj_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_conj_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_conj_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_conj_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_conj_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_conj_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_conj_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_conj_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_conj_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_conj_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_conj_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_neg_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_neg_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_neg_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_neg_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_resolve_neg_cpu_complex64, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_transpose_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_transpose_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_transpose_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_transpose_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_transpose_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_transpose_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_transpose_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_trapezoid_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_trapezoid_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_trapezoid_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_trapezoid_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_trapezoid_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_trapezoid_cpu_float64, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_trunc_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_trunc_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_trunc_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_trunc_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unbind_copy_cpu_int16, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_unflatten_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unflatten_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unflatten_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unflatten_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unfold_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_uniform_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_uniform_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_uniform_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_uniform_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_uniform_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_uniform_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_consecutive_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_uint16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_uint32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_uint64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unique_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unravel_index_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unravel_index_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unravel_index_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unravel_index_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unravel_index_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_chunk_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsafe_split_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_copy_cpu_bfloat16, 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test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_unsqueeze_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_unbiased_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_unbiased_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_unbiased_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_unbiased_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_unbiased_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_mean_unbiased_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_unbiased_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_unbiased_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_unbiased_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_unbiased_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_unbiased_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_var_unbiased_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_vdot_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_complex_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_complex_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_complex_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_real_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_as_real_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_copy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_view_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_vsplit_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_vstack_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_where_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_xlogy_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_zero__cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_cpu_uint8, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_bfloat16, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_bool, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_complex128, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_complex32, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_complex64, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_float16, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_float32, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_float64, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_int16, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_int32, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_int64, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_int8, test/test_meta.py::TestMetaCPU::test_meta_outplace_zeros_like_cpu_uint8, test/test_meta.py::TestMetaCPU::test_nan_to_num_cpu, test/test_meta.py::TestMetaCPU::test_nonzero_cpu, test/test_meta.py::TestMetaCPU::test_quantized_embedding_bag_cpu, test/test_meta.py::TestMetaCPU::test_segment_reduce_backward_cpu, test/test_meta.py::TestMetaCPU::test_stride_for_index_Tensor_cpu, test/test_meta.py::TestMetaCPU::test_triangular_solve_out_cpu 2025-08-14T22:27:17.5562355Z 2025-08-14T22:27:17.6049508Z /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. 2025-08-14T22:27:17.6051000Z import pkg_resources 2025-08-14T22:27:17.7526380Z Running test_legacy_vmap 2/2 ... [2025-08-14 22:27:17.752071] 2025-08-14T22:27:17.7527288Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:27:17.7530536Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_legacy_vmap.py', '-m', 'not serial', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:27:17.752523] 2025-08-14T22:27:20.0285320Z 2025-08-14T22:27:20.0286986Z test_nestedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_nestedtensor_1.1_513f0ddf4713c704_.log 2025-08-14T22:27:20.1878442Z Running 1584 items in this shard: test/test_nestedtensor.py::TestNestedTensor::test_2d_nested_tensor_batch_size_2_max_seq_len_3_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_2d_nested_tensor_batch_size_2_max_seq_len_3_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_2d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_2d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_2d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_2d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_2d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_2d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_batch_size_2_max_seq_len_3_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_batch_size_2_max_seq_len_3_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_batch_size_2_max_seq_len_5_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_batch_size_4_max_seq_len_3_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_batch_size_4_max_seq_len_5_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_float_batch_size_2_max_seq_len_3_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_float_batch_size_2_max_seq_len_3_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_float_batch_size_2_max_seq_len_5_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_float_batch_size_2_max_seq_len_5_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_float_batch_size_4_max_seq_len_3_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_float_batch_size_4_max_seq_len_3_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_float_batch_size_4_max_seq_len_5_vocab_size_10, test/test_nestedtensor.py::TestNestedTensor::test_3d_nested_tensor_float_batch_size_4_max_seq_len_5_vocab_size_20, test/test_nestedtensor.py::TestNestedTensor::test_cat, test/test_nestedtensor.py::TestNestedTensor::test_copy_, test/test_nestedtensor.py::TestNestedTensor::test_default_nested_tensor, test/test_nestedtensor.py::TestNestedTensor::test_dim, test/test_nestedtensor.py::TestNestedTensor::test_fill_, test/test_nestedtensor.py::TestNestedTensor::test_is_contiguous, test/test_nestedtensor.py::TestNestedTensor::test_like_functions_ones_like, test/test_nestedtensor.py::TestNestedTensor::test_like_functions_randn_like, test/test_nestedtensor.py::TestNestedTensor::test_like_functions_zeros_like, test/test_nestedtensor.py::TestNestedTensor::test_nested_namespace, test/test_nestedtensor.py::TestNestedTensor::test_nested_tensor, test/test_nestedtensor.py::TestNestedTensor::test_nested_tensor_matching_dim, test/test_nestedtensor.py::TestNestedTensor::test_nested_view_from_buffer_overflow_errors, test/test_nestedtensor.py::TestNestedTensor::test_numel, test/test_nestedtensor.py::TestNestedTensor::test_repr_string, test/test_nestedtensor.py::TestNestedTensor::test_size, test/test_nestedtensor.py::TestNestedTensor::test_size_dim, test/test_nestedtensor.py::TestNestedTensor::test_stride, test/test_nestedtensor.py::TestNestedTensor::test_to, test/test_nestedtensor.py::TestNestedTensor::test_to_padded_tensor_on_empty_tensor, test/test_nestedtensor.py::TestNestedTensor::test_unbind_0, test/test_nestedtensor.py::TestNestedTensor::test_unbind_1, test/test_nestedtensor.py::TestNestedTensor::test_unbind_3, test/test_nestedtensor.py::TestNestedTensor::test_unbind_4, test/test_nestedtensor.py::TestNestedTensor::test_unbind_dim, test/test_nestedtensor.py::TestNestedTensor::test_zero_, test/test_nestedtensor.py::TestNestedInt::test_comparisons, test/test_nestedtensor.py::TestNestedInt::test_with_factor, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_binary_ops_with_scalar_eq_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_binary_ops_with_scalar_ge_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_bmm_cpu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_bmm_cpu_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_bmm_cuda_cpu_bfloat16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_bmm_cuda_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_bmm_cuda_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_bmm_cuda_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_bmm_noncontiguous_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_bmm_noncontiguous_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_clone_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_clone_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_contiguous_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_contiguous_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_detach_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_detach_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_detach_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_device_checks_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_dropout_jagged_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_dropout_jagged_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_dropout_noncontiguous_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_dropout_noncontiguous_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_dropout_strided_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_dropout_strided_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_embedding_jagged_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_embedding_strided_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_empty_like_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_empty_like_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_empty_like_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_layer_norm_breaking_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_layer_norm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_linear_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_linear_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_linear_noncontiguous_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_linear_noncontiguous_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_masked_fill_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_masked_fill_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_masked_fill_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_matmul_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_matmul_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_matmul_noncontiguous_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_matmul_noncontiguous_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_matmul_nt_with_broadcasted_t_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_matmul_nt_with_broadcasted_t_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_matmul_with_bmm_path_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_matmul_with_bmm_path_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_narrow_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_narrow_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_narrow_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_masked_select_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_add_in_place_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_add_in_place_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_add_transpose_False_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_add_transpose_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_add_transpose_True_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_add_transpose_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_chunk_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_chunk_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_chunk_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_dense_elementwise_embedding_dim_128_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_dense_elementwise_embedding_dim_128_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_dense_elementwise_embedding_dim_256_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_dense_elementwise_embedding_dim_256_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_dense_elementwise_embedding_dim_384_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_dense_elementwise_embedding_dim_384_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_dense_elementwise_embedding_dim_8_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_dense_elementwise_embedding_dim_8_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_div_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_div_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_indexing_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_indexing_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_indexing_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_indexing_noncontiguous_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_indexing_noncontiguous_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_indexing_noncontiguous_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_mul_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_mul_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_mul_in_place_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_mul_in_place_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_split_with_sizes_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_split_with_sizes_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_split_with_sizes_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_sub_transpose_False_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_sub_transpose_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_sub_transpose_True_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_sub_transpose_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_nested_tensor_sum_dim_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_reshape_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_reshape_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_reshape_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_scaled_dot_product_attention_input_dim_3_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_scaled_dot_product_attention_input_dim_4_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_serialization_requires_grad_False_weights_only_False_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_serialization_requires_grad_False_weights_only_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_serialization_requires_grad_False_weights_only_False_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_serialization_requires_grad_False_weights_only_True_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_serialization_requires_grad_False_weights_only_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_serialization_requires_grad_False_weights_only_True_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_serialization_requires_grad_True_weights_only_False_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_serialization_requires_grad_True_weights_only_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_serialization_requires_grad_True_weights_only_False_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_serialization_requires_grad_True_weights_only_True_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_serialization_requires_grad_True_weights_only_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_serialization_requires_grad_True_weights_only_True_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_softmax_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_softmax_noncontiguous_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_softmax_noncontiguous_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_squeeze_unsqueeze_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_squeeze_unsqueeze_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_squeeze_unsqueeze_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_dim2_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_dim2_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_dim2_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_dim3_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_dim3_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_dim3_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_dim4_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_dim4_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_dim4_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_noncontiguous_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_noncontiguous_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_noncontiguous_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_output_size_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_output_size_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_simple_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_simple_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_zero_numel_errors_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_zero_numel_errors_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_padded_tensor_zero_numel_errors_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_to_then_from_padded_tensor_no_transform0213_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_transpose_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_transpose_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_transpose_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_transpose_inference_mode_interaction_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_transpose_inference_mode_interaction_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_transpose_inference_mode_interaction_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_abs__cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_abs_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_cos_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_gelu__cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_gelu_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_isinf_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_isnan_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_isneginf_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_isposinf_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_logical_not_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_neg_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_relu__cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_relu_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_sgn_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_silu__cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_silu_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_sin_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_sqrt_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_tanh__cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unary_funcs_tanh_cpu, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unbind_noncontiguous_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unbind_noncontiguous_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_unbind_noncontiguous_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_view_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_view_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_view_cpu_float64, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_view_inference_mode_interaction_cpu_float16, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_view_inference_mode_interaction_cpu_float32, test/test_nestedtensor.py::TestNestedTensorDeviceTypeCPU::test_view_inference_mode_interaction_cpu_float64, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_abs_backward_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_accumulate_grad_different_strides_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_as_nested_tensor_propagates_gradients_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_backward_add_strided_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_backward_for_add_op_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_backward_for_sub_op_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_backward_sub_strided_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_dropout_backward_jagged_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_dropout_backward_strided_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_gelu_backward_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_indexing_backward_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_5d_size_128_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_5d_size_2_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_5d_size_32_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_5d_size_4_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_edge_case_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_size_1023_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_size_1024_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_size_128_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_size_256_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_size_2_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_size_32_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_size_4_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_size_512_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_layer_norm_backward_size_513_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_masked_fill_backward_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_bmm_backward_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_bmm_gradcheck_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_from_list_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_from_mask_and_to_padded_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_from_padded_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_from_padded_fused_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_generates_leaf_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_linear_backward_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_linear_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_linear_plus_transpose_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_matmul_backward_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_matmul_gradcheck_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_reshape_backward_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_reshape_gradcheck_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_softmax_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_squeeze_backward_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_squeeze_gradcheck_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_to_padded_tensor_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_transpose_backward_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_transpose_gradcheck_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_unsqueeze_backward_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_nested_tensor_unsqueeze_gradcheck_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_relu_backward_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_selu_backward_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_set_requires_grad_from_list_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_set_requires_grad_from_mask_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_split_with_sizes_flow_through_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_to_buffer_series_ops_grad_with_broadcast_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_unbind_flow_through_cpu, test/test_nestedtensor.py::TestNestedTensorAutogradCPU::test_values_grad_with_broadcast_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_apply__cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_jagged_requires_grad_False_contiguous_False_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_jagged_requires_grad_False_contiguous_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_jagged_requires_grad_False_contiguous_False_cpu_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_jagged_requires_grad_False_contiguous_True_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_jagged_requires_grad_False_contiguous_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_jagged_requires_grad_False_contiguous_True_cpu_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_jagged_requires_grad_True_contiguous_False_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_jagged_requires_grad_True_contiguous_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_jagged_requires_grad_True_contiguous_False_cpu_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_jagged_requires_grad_True_contiguous_True_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_jagged_requires_grad_True_contiguous_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_jagged_requires_grad_True_contiguous_True_cpu_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_strided_requires_grad_False_contiguous_False_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_strided_requires_grad_False_contiguous_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_strided_requires_grad_False_contiguous_False_cpu_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_strided_requires_grad_False_contiguous_True_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_strided_requires_grad_False_contiguous_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_as_nested_tensor_from_tensor_dim_0_layout_strided_requires_grad_False_contiguous_True_cpu_float64, 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test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_reduce_ragged_idx_greater_than_1_different_output_shape_sum_transpose_offset_2_keepdim_False_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_reduce_ragged_idx_greater_than_1_different_output_shape_sum_transpose_offset_2_keepdim_False_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_reduce_ragged_idx_greater_than_1_different_output_shape_sum_transpose_offset_2_keepdim_False_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_reduce_ragged_idx_greater_than_1_different_output_shape_sum_transpose_offset_2_keepdim_True_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_reduce_ragged_idx_greater_than_1_different_output_shape_sum_transpose_offset_2_keepdim_True_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_reduce_ragged_idx_greater_than_1_different_output_shape_sum_transpose_offset_2_keepdim_True_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_reduce_ragged_idx_greater_than_1_different_output_shape_sum_transpose_offset_2_keepdim_True_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_mean_keepdim_False_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_mean_keepdim_False_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_mean_keepdim_False_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_mean_keepdim_False_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_mean_keepdim_True_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_mean_keepdim_True_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_mean_keepdim_True_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_mean_keepdim_True_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_sum_keepdim_False_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_sum_keepdim_False_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_sum_keepdim_False_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_sum_keepdim_False_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_sum_keepdim_True_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_sum_keepdim_True_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_sum_keepdim_True_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_transpose_non_ragged_dim_different_output_shape_sum_keepdim_True_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_mean_keepdim_False_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_mean_keepdim_False_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_mean_keepdim_False_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_mean_keepdim_False_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_mean_keepdim_True_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_mean_keepdim_True_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_mean_keepdim_True_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_mean_keepdim_True_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_sum_keepdim_False_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_sum_keepdim_False_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_sum_keepdim_False_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_sum_keepdim_False_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_sum_keepdim_True_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_sum_keepdim_True_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_sum_keepdim_True_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_op_dim_with_lengths_different_output_shape_sum_keepdim_True_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_permute_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_pin_memory_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_profiler_sequence_nr_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_record_stream_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_reshape_decomp_requires_grad_False_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_reshape_decomp_requires_grad_True_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sdpa_autocast_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sdpa_backwards_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sdpa_backwards_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sdpa_compile_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sdpa_compile_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sdpa_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sdpa_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sdpa_flop_counter_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sdpa_with_constant_sequence_length_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sdpa_with_constant_sequence_length_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sdpa_with_constant_sequence_length_cpu_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sdpa_with_packed_in_proj_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sdpa_with_packed_in_proj_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_serialization_contig_weights_only_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_serialization_contig_weights_only_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_serialization_noncontig_transposed_weights_only_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_serialization_noncontig_transposed_weights_only_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_serialization_noncontig_with_holes_weights_only_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_serialization_noncontig_with_holes_weights_only_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_reduce_ragged_idx_1_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_reduce_ragged_idx_1_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_reduce_ragged_idx_1_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_reduce_ragged_idx_1_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_reduce_ragged_idx_greater_than_1_same_output_shape_transpose_offset_1_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_reduce_ragged_idx_greater_than_1_same_output_shape_transpose_offset_1_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_reduce_ragged_idx_greater_than_1_same_output_shape_transpose_offset_1_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_reduce_ragged_idx_greater_than_1_same_output_shape_transpose_offset_1_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_reduce_ragged_idx_greater_than_1_same_output_shape_transpose_offset_2_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_reduce_ragged_idx_greater_than_1_same_output_shape_transpose_offset_2_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_reduce_ragged_idx_greater_than_1_same_output_shape_transpose_offset_2_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_reduce_ragged_idx_greater_than_1_same_output_shape_transpose_offset_2_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_requires_grad_False_components_require_grad_False_log_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_requires_grad_False_components_require_grad_False_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_requires_grad_False_components_require_grad_True_log_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_requires_grad_False_components_require_grad_True_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_requires_grad_True_components_require_grad_False_log_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_requires_grad_True_components_require_grad_False_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_requires_grad_True_components_require_grad_True_log_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_requires_grad_True_components_require_grad_True_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_transpose_non_ragged_dim_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_transpose_non_ragged_dim_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_transpose_non_ragged_dim_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_transpose_non_ragged_dim_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_with_lengths_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_with_lengths_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_with_lengths_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_dim_with_lengths_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_reduce_batch_dim_requires_grad_False_components_require_grad_False_log_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_reduce_batch_dim_requires_grad_False_components_require_grad_False_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_reduce_batch_dim_requires_grad_False_components_require_grad_True_log_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_reduce_batch_dim_requires_grad_False_components_require_grad_True_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_reduce_batch_dim_requires_grad_True_components_require_grad_False_log_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_reduce_batch_dim_requires_grad_True_components_require_grad_False_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_reduce_batch_dim_requires_grad_True_components_require_grad_True_log_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_softmax_reduce_batch_dim_requires_grad_True_components_require_grad_True_softmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_specialize_dynamic_shape_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_specialize_dynamic_shape_recompile_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_split_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_split_with_sizes_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_squeeze_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_batch_and_non_batch_keepdim_False_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_batch_and_non_batch_keepdim_False_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_batch_and_non_batch_keepdim_False_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_batch_and_non_batch_keepdim_False_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_batch_and_non_batch_keepdim_True_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_batch_and_non_batch_keepdim_True_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_batch_and_non_batch_keepdim_True_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_batch_and_non_batch_keepdim_True_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_ragged_and_non_batch_keepdim_False_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_ragged_and_non_batch_keepdim_False_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_ragged_and_non_batch_keepdim_False_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_ragged_and_non_batch_keepdim_False_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_ragged_and_non_batch_keepdim_True_requires_grad_False_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_ragged_and_non_batch_keepdim_True_requires_grad_False_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_ragged_and_non_batch_keepdim_True_requires_grad_True_components_require_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_sum_dim_reduce_ragged_and_non_batch_keepdim_True_requires_grad_True_components_require_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_tensor_attributes_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_threshold_backward_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_copy_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_dtype_cpu, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_compile_nt_dim_2_requires_grad_False_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_compile_nt_dim_2_requires_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_compile_nt_dim_2_requires_grad_False_cpu_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_compile_nt_dim_2_requires_grad_True_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_compile_nt_dim_2_requires_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_compile_nt_dim_2_requires_grad_True_cpu_float64, 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test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_compile_nt_dim_4_requires_grad_False_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_compile_nt_dim_4_requires_grad_False_cpu_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_compile_nt_dim_4_requires_grad_True_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_compile_nt_dim_4_requires_grad_True_cpu_float32, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_compile_nt_dim_4_requires_grad_True_cpu_float64, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_nt_dim_2_requires_grad_False_cpu_bool, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_nt_dim_2_requires_grad_False_cpu_float16, test/test_nestedtensor.py::TestNestedTensorSubclassCPU::test_to_padded_tensor_nt_dim_2_requires_grad_False_cpu_float32, 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test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_linear_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_logsigmoid_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_mish_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_prelu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_relu6_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_relu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_rms_norm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_rrelu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_selu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_silu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_softplus_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_softshrink_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_softsign_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_tanhshrink_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_nn_functional_threshold_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_polar_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_polygamma_polygamma_n_0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_backward_polygamma_polygamma_n_1_cpu_float32, 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test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_add_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_amax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_amin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_angle_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_asin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_asinh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_atan2_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_atan_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_atanh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_bfloat16_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_bmm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_cdouble_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_ceil_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_cfloat_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_chalf_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_chunk_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_clamp_max_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_clamp_min_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_clone_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_complex_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_conj_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_conj_physical_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_copysign_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_cos_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_cosh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_deg2rad_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_digamma_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_div_floor_rounding_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_div_no_rounding_mode_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_div_trunc_rounding_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_double_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_erf_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_erfc_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_erfinv_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_exp2_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_exp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_expm1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_fill_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_float_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_float_power_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_floor_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_fmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_fmin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_fmod_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_frac_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_frexp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_half_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_hypot_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_i0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_index_put_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_ldexp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_lgamma_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_linalg_vector_norm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_log10_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_log1p_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_log2_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_log_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_logaddexp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_logit_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_masked_amax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_masked_amin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_masked_logsumexp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_masked_mean_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_masked_norm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_masked_prod_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_masked_select_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_masked_std_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_masked_sum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_masked_var_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_matmul_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_max_binary_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_max_reduction_with_dim_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_maximum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_mean_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_min_binary_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_min_reduction_with_dim_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_minimum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_mul_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nan_to_num_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nanmean_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nansum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_narrow_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_neg_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_celu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_elu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_embedding_bag_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_embedding_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_hardshrink_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_hardsigmoid_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_hardtanh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_linear_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_logsigmoid_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_mish_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_prelu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_relu6_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_relu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_rms_norm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_rrelu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_selu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_silu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_softplus_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_softshrink_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_softsign_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_tanhshrink_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_nn_functional_threshold_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_polar_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_polygamma_polygamma_n_0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_polygamma_polygamma_n_1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_polygamma_polygamma_n_2_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_polygamma_polygamma_n_3_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_polygamma_polygamma_n_4_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_positive_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_pow_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_prod_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_rad2deg_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_real_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_reciprocal_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_remainder_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_round_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_round_decimals_0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_round_decimals_3_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_round_decimals_neg_3_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_rsqrt_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_rsub_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_select_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_sgn_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_sigmoid_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_sign_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_sin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_sinc_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_sinh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_special_entr_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_special_erfcx_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_special_i0e_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_special_i1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_special_i1e_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_special_log_ndtr_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_special_ndtr_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_special_ndtri_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_special_xlog1py_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_split_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_split_with_sizes_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_sqrt_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_square_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_squeeze_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_std_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_std_unbiased_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_sub_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_sum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_tan_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_tanh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_to_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_true_divide_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_trunc_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_unflatten_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_unsqueeze_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_var_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_var_unbiased_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_where_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_backward_xlogy_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward___radd___cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward___rdiv___cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward___rmod___cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward___rmul___cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward___rpow___cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward___rsub___cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_abs_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_acos_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_acosh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_add_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_all_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_amax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_amin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_angle_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_any_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_argmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_argmin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_asin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_asinh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_atan2_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_atan_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_atanh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_bfloat16_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_bmm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_bool_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_byte_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_cdouble_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_ceil_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_cfloat_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_chalf_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_char_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_chunk_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_clamp_max_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_clamp_min_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_clone_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_complex_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_conj_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_conj_physical_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_copysign_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_cos_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_cosh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_count_nonzero_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_deg2rad_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_digamma_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_div_floor_rounding_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_div_no_rounding_mode_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_div_trunc_rounding_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_double_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_eq_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_erf_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_erfc_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_erfinv_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_exp2_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_exp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_expm1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_fill_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_float_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_float_power_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_floor_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_floor_divide_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_fmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_fmin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_fmod_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_frac_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_frexp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_ge_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_gt_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_half_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_hash_tensor_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_heaviside_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_hypot_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_i0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_igamma_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_igammac_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_index_put_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_int_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_isclose_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_isfinite_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_isinf_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_isnan_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_isneginf_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_isposinf_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_isreal_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_jiterator_binary_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_jiterator_binary_return_by_ref_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_jiterator_unary_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_ldexp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_le_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_lgamma_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_linalg_vector_norm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_log10_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_log1p_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_log2_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_log_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_logaddexp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_logical_and_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_logical_not_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_logical_or_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_logical_xor_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_logit_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_long_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_lt_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_masked_amax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_masked_amin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_masked_argmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_masked_argmin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_masked_logsumexp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_masked_mean_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_masked_norm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_masked_prod_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_masked_select_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_masked_std_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_masked_sum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_masked_var_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_matmul_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_max_binary_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_max_reduction_with_dim_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_maximum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_mean_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_min_binary_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_min_reduction_with_dim_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_minimum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_mul_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nan_to_num_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nanmean_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nansum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_narrow_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_ne_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_neg_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nextafter_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_celu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_elu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_embedding_bag_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_embedding_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_hardshrink_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_hardsigmoid_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_hardtanh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_linear_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_logsigmoid_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_mish_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_prelu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_relu6_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_relu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_rms_norm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_rrelu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_selu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_silu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_softplus_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_softshrink_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_softsign_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_tanhshrink_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_nn_functional_threshold_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_polar_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_polygamma_polygamma_n_0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_polygamma_polygamma_n_1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_polygamma_polygamma_n_2_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_polygamma_polygamma_n_3_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_polygamma_polygamma_n_4_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_positive_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_pow_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_prod_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_rad2deg_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_real_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_reciprocal_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_remainder_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_round_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_round_decimals_0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_round_decimals_3_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_round_decimals_neg_3_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_rsqrt_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_rsub_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_select_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_sgn_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_short_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_sigmoid_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_sign_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_signbit_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_sin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_sinc_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_sinh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_airy_ai_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_bessel_j0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_bessel_j1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_bessel_y0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_bessel_y1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_chebyshev_polynomial_t_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_chebyshev_polynomial_u_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_chebyshev_polynomial_v_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_chebyshev_polynomial_w_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_entr_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_erfcx_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_hermite_polynomial_h_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_hermite_polynomial_he_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_i0e_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_i1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_i1e_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_laguerre_polynomial_l_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_legendre_polynomial_p_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_log_ndtr_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_modified_bessel_i0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_modified_bessel_i1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_modified_bessel_k0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_modified_bessel_k1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_ndtr_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_ndtri_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_scaled_modified_bessel_k0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_scaled_modified_bessel_k1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_spherical_bessel_j0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_xlog1py_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_special_zeta_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_split_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_split_with_sizes_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_sqrt_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_square_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_squeeze_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_std_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_std_unbiased_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_sub_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_sum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_tan_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_tanh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_to_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_true_divide_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_trunc_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_unflatten_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_unsqueeze_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_var_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_var_unbiased_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_where_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_compile_forward_xlogy_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward___radd___cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward___rdiv___cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward___rmod___cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward___rmul___cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward___rpow___cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward___rsub___cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_abs_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_acos_cpu_float32, 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test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_atan2_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_atan_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_atanh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_bfloat16_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_bmm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_bool_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_byte_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_cdouble_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_ceil_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_cfloat_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_chalf_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_char_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_chunk_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_clamp_max_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_clamp_min_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_clone_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_complex_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_conj_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_conj_physical_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_copysign_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_cos_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_cosh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_count_nonzero_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_deg2rad_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_digamma_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_div_floor_rounding_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_div_no_rounding_mode_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_div_trunc_rounding_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_double_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_eq_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_erf_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_erfc_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_erfinv_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_exp2_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_exp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_expm1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_fill_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_float_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_float_power_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_floor_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_floor_divide_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_fmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_fmin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_fmod_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_frac_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_frexp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_ge_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_gt_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_half_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_hash_tensor_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_heaviside_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_hypot_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_i0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_igamma_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_igammac_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_index_put_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_int_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_isclose_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_isfinite_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_isinf_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_isnan_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_isneginf_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_isposinf_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_isreal_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_jiterator_binary_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_jiterator_binary_return_by_ref_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_jiterator_unary_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_ldexp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_le_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_lgamma_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_linalg_vector_norm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_log10_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_log1p_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_log2_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_log_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_logaddexp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_logical_and_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_logical_not_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_logical_or_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_logical_xor_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_logit_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_long_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_lt_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_masked_amax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_masked_amin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_masked_argmax_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_masked_argmin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_masked_logsumexp_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_masked_mean_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_masked_norm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_masked_prod_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_masked_select_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_masked_std_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_masked_sum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_masked_var_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_matmul_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_max_binary_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_max_reduction_with_dim_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_maximum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_mean_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_min_binary_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_min_reduction_with_dim_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_minimum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_mul_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nan_to_num_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nanmean_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nansum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_narrow_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_ne_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_neg_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nextafter_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_celu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_elu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_embedding_bag_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_embedding_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_hardshrink_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_hardsigmoid_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_hardtanh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_linear_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_logsigmoid_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_mish_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_prelu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_relu6_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_relu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_rms_norm_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_rrelu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_selu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_silu_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_softplus_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_softshrink_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_softsign_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_tanhshrink_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_nn_functional_threshold_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_polar_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_polygamma_polygamma_n_0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_polygamma_polygamma_n_1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_polygamma_polygamma_n_2_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_polygamma_polygamma_n_3_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_polygamma_polygamma_n_4_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_positive_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_pow_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_prod_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_rad2deg_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_real_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_reciprocal_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_remainder_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_round_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_round_decimals_0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_round_decimals_3_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_round_decimals_neg_3_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_rsqrt_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_rsub_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_select_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_sgn_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_short_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_sigmoid_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_sign_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_signbit_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_sin_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_sinc_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_sinh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_airy_ai_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_bessel_j0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_bessel_j1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_bessel_y0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_bessel_y1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_chebyshev_polynomial_t_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_chebyshev_polynomial_u_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_chebyshev_polynomial_v_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_chebyshev_polynomial_w_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_entr_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_erfcx_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_hermite_polynomial_h_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_hermite_polynomial_he_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_i0e_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_i1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_i1e_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_laguerre_polynomial_l_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_legendre_polynomial_p_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_log_ndtr_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_modified_bessel_i0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_modified_bessel_i1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_modified_bessel_k0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_modified_bessel_k1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_ndtr_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_ndtri_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_scaled_modified_bessel_k0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_scaled_modified_bessel_k1_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_spherical_bessel_j0_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_xlog1py_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_special_zeta_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_split_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_split_with_sizes_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_sqrt_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_square_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_squeeze_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_std_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_std_unbiased_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_sub_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_sum_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_tan_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_tanh_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_to_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_true_divide_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_trunc_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_unflatten_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_unsqueeze_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_var_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_var_unbiased_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_where_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_forward_xlogy_cpu_float32, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_nested_tensor_input_mutation_backward_cpu, test/test_nestedtensor.py::TestNestedTensorOpInfoCPU::test_nested_tensor_non_contiguous_mutation_cpu 2025-08-14T22:27:20.3093566Z 2025-08-14T22:27:23.5456224Z /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. 2025-08-14T22:27:23.5459921Z import pkg_resources 2025-08-14T22:27:23.7231506Z Running test_modules 1/1 ... [2025-08-14 22:27:23.722736] 2025-08-14T22:27:23.7232083Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:27:23.7235137Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_modules.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:27:23.723097] 2025-08-14T22:30:18.5256954Z 2025-08-14T22:30:18.5258204Z test_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_modules_1.1_ebe3b1fcb2db0294_.log 2025-08-14T22:30:18.6563988Z Running 3605 items in this shard: test/test_modules.py::TestModuleCPU::test_check_inplace_nn_CELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_CELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_ELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_ELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_Hardswish_cpu_float32, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_Hardswish_cpu_float64, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_Hardtanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_Hardtanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_LeakyReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_LeakyReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_Mish_cpu_float32, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_Mish_cpu_float64, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_ReLU6_cpu_float32, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_ReLU6_cpu_float64, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_ReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_ReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_SELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_SELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_SiLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_SiLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_check_inplace_nn_Threshold_cpu_float32, 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test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_RNNCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_RNN_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_RNN_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_RNN_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_RNN_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReLU6_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReLU6_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReflectionPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReflectionPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReflectionPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReflectionPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReflectionPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReflectionPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReplicationPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReplicationPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReplicationPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReplicationPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReplicationPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ReplicationPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_SELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_SELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_SiLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_SiLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Sigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Sigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_SmoothL1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_SmoothL1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_SoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_SoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Softmax2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Softmax2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Softmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Softmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Softmin_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Softmin_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Softplus_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Softplus_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Softshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Softshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Softsign_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Softsign_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Tanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Tanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Tanhshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Tanhshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Threshold_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Threshold_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_TransformerDecoderLayer_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_TransformerDecoderLayer_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_TransformerEncoderLayer_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_TransformerEncoderLayer_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_TransformerEncoderLayer_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_TransformerEncoderLayer_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_TransformerEncoder_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_TransformerEncoder_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_TransformerEncoder_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_TransformerEncoder_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Transformer_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_Transformer_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ZeroPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ZeroPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ZeroPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ZeroPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ZeroPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_cpu_gpu_parity_nn_ZeroPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AdaptiveAvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AdaptiveAvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AdaptiveAvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AdaptiveAvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AdaptiveAvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AdaptiveAvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AdaptiveMaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AdaptiveMaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AdaptiveMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AdaptiveMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AdaptiveMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AdaptiveMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_AvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BCELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BCELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BCEWithLogitsLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BCEWithLogitsLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BatchNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BatchNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BatchNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BatchNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BatchNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BatchNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BatchNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BatchNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BatchNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BatchNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BatchNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_BatchNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Bilinear_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Bilinear_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CTCLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CTCLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CircularPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CircularPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CircularPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CircularPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CircularPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CircularPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConstantPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConstantPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConstantPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConstantPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConstantPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConstantPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Conv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Conv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Conv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Conv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Conv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Conv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose1d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose1d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose1d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose2d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose2d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose2d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose3d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose3d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose3d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CosineEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CosineEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CrossEntropyLoss_cpu_float16, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CrossEntropyLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_CrossEntropyLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Embedding_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Embedding_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_FractionalMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_FractionalMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_FractionalMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_FractionalMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GRUCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GRUCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GRU_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GRU_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GRU_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GRU_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GaussianNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GaussianNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GroupNorm_cpu_bfloat16, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GroupNorm_cpu_float16, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GroupNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_GroupNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Hardshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Hardshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Hardswish_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Hardswish_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Hardtanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Hardtanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_HingeEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_HingeEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_HuberLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_HuberLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_InstanceNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_InstanceNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_InstanceNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_InstanceNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_InstanceNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_InstanceNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_InstanceNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_InstanceNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_InstanceNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_InstanceNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_InstanceNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_InstanceNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_KLDivLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_KLDivLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_L1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_L1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LPPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LPPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LPPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LPPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LPPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LPPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LSTMCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LSTMCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LSTM_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LSTM_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LSTM_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LSTM_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LayerNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LayerNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LazyConv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LazyConv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LazyConv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LazyConv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LazyConv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LazyConv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LazyConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LazyConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LazyConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LazyConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LazyConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LazyConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LeakyReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LeakyReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Linear_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Linear_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LocalResponseNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LocalResponseNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LogSigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LogSigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LogSoftmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_LogSoftmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MSELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MSELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MarginRankingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MarginRankingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Mish_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Mish_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MultiLabelMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MultiLabelMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MultiLabelSoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MultiLabelSoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MultiMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MultiMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MultiheadAttention_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MultiheadAttention_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MultiheadAttention_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_MultiheadAttention_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_NLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_NLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_PReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_PReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_PoissonNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_PoissonNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_RMSNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_RMSNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_RNNCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_RNNCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_RNN_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_RNN_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_RNN_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_RNN_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReLU6_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReLU6_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReflectionPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReflectionPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReflectionPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReflectionPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReflectionPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReflectionPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReplicationPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReplicationPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReplicationPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReplicationPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReplicationPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ReplicationPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_SELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_SELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_SiLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_SiLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Sigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Sigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_SmoothL1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_SmoothL1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_SoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_SoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Softmax2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Softmax2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Softmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Softmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Softmin_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Softmin_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Softplus_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Softplus_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Softshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Softshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Softsign_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Softsign_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Tanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Tanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Tanhshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Tanhshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Threshold_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Threshold_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_TransformerDecoderLayer_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_TransformerDecoderLayer_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_TransformerEncoderLayer_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_TransformerEncoderLayer_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_TransformerEncoderLayer_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_TransformerEncoderLayer_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_TransformerEncoder_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_TransformerEncoder_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_TransformerEncoder_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_TransformerEncoder_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Transformer_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_Transformer_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ZeroPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ZeroPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ZeroPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ZeroPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ZeroPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_device_ctx_init_nn_ZeroPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_errors_nn_CircularPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_errors_nn_CircularPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_errors_nn_CircularPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_errors_nn_CircularPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_errors_nn_CircularPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_errors_nn_CircularPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_errors_nn_GRUCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_errors_nn_GRUCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_errors_nn_GRU_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_errors_nn_GRU_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_errors_nn_GRU_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_errors_nn_GRU_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_errors_nn_LSTMCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_errors_nn_LSTMCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_errors_nn_LSTM_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_errors_nn_LSTM_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_errors_nn_LSTM_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_errors_nn_LSTM_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_errors_nn_RNNCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_errors_nn_RNNCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_errors_nn_RNN_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_errors_nn_RNN_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_errors_nn_RNN_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_errors_nn_RNN_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AdaptiveAvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AdaptiveAvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AdaptiveAvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AdaptiveAvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AdaptiveAvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AdaptiveAvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AdaptiveMaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AdaptiveMaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AdaptiveMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AdaptiveMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AdaptiveMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AdaptiveMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_AvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BCELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BCELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BCEWithLogitsLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BCEWithLogitsLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BatchNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BatchNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BatchNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BatchNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BatchNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BatchNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BatchNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BatchNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BatchNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BatchNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BatchNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_BatchNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Bilinear_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Bilinear_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CTCLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CTCLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CircularPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CircularPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CircularPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CircularPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CircularPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CircularPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConstantPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConstantPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConstantPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConstantPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConstantPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConstantPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Conv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Conv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Conv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Conv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Conv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Conv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose1d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose1d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose1d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose2d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose2d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose2d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose3d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose3d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose3d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CosineEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CosineEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CrossEntropyLoss_cpu_float16, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CrossEntropyLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_CrossEntropyLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Embedding_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Embedding_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_FractionalMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_FractionalMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_FractionalMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_FractionalMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GRUCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GRUCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GRU_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GRU_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GRU_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GRU_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GaussianNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GaussianNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GroupNorm_cpu_bfloat16, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GroupNorm_cpu_float16, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GroupNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_GroupNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Hardshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Hardshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Hardswish_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Hardswish_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Hardtanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Hardtanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_HingeEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_HingeEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_HuberLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_HuberLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_InstanceNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_InstanceNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_InstanceNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_InstanceNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_InstanceNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_InstanceNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_InstanceNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_InstanceNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_InstanceNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_InstanceNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_InstanceNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_InstanceNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_KLDivLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_KLDivLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_L1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_L1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LPPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LPPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LPPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LPPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LPPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LPPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LSTMCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LSTMCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LSTM_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LSTM_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LSTM_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LSTM_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LayerNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LayerNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LazyConv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LazyConv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LazyConv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LazyConv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LazyConv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LazyConv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LazyConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LazyConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LazyConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LazyConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LazyConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LazyConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LeakyReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LeakyReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Linear_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Linear_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LocalResponseNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LocalResponseNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LogSigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LogSigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LogSoftmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_LogSoftmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MSELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MSELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MarginRankingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MarginRankingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Mish_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Mish_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MultiLabelMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MultiLabelMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MultiLabelSoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MultiLabelSoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MultiMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MultiMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MultiheadAttention_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MultiheadAttention_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MultiheadAttention_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_MultiheadAttention_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_NLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_NLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_PReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_PReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_PoissonNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_PoissonNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_RMSNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_RMSNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_RNNCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_RNNCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_RNN_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_RNN_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_RNN_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_RNN_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReLU6_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReLU6_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReflectionPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReflectionPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReflectionPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReflectionPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReflectionPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReflectionPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReplicationPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReplicationPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReplicationPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReplicationPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReplicationPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ReplicationPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_SELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_SELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_SiLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_SiLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Sigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Sigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_SmoothL1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_SmoothL1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_SoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_SoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Softmax2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Softmax2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Softmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Softmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Softmin_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Softmin_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Softplus_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Softplus_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Softshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Softshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Softsign_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Softsign_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Tanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Tanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Tanhshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Tanhshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Threshold_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Threshold_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_TransformerDecoderLayer_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_TransformerDecoderLayer_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_TransformerEncoderLayer_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_TransformerEncoderLayer_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_TransformerEncoderLayer_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_TransformerEncoderLayer_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_TransformerEncoder_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_TransformerEncoder_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_TransformerEncoder_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_TransformerEncoder_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Transformer_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_Transformer_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ZeroPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ZeroPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ZeroPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ZeroPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ZeroPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_factory_kwargs_nn_ZeroPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_AdaptiveAvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_AdaptiveAvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_AdaptiveAvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_AdaptiveAvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_AdaptiveAvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_AdaptiveAvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_AdaptiveMaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_AdaptiveMaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_AdaptiveMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_AdaptiveMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_AdaptiveMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_AdaptiveMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_AvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_AvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_AvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_AvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_AvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_AvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_BCELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_BCELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_BCEWithLogitsLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_BCEWithLogitsLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_BatchNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_BatchNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_BatchNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_BatchNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_BatchNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_BatchNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_BatchNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_BatchNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_BatchNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_BatchNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_BatchNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_BatchNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Bilinear_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Bilinear_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_CELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_CELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_CTCLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_CTCLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_CircularPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_CircularPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_CircularPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_CircularPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_CircularPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_CircularPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ConstantPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ConstantPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ConstantPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ConstantPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ConstantPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ConstantPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Conv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Conv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Conv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Conv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Conv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Conv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose1d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose1d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose1d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose2d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose2d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose2d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose3d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose3d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose3d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_CosineEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_CosineEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_CrossEntropyLoss_cpu_float16, test/test_modules.py::TestModuleCPU::test_forward_nn_CrossEntropyLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_CrossEntropyLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Embedding_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Embedding_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_FractionalMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_FractionalMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_FractionalMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_FractionalMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_GELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_GELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_GLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_GLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_GRUCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_GRUCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_GRU_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_GRU_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_GRU_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_GRU_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_GaussianNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_GaussianNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_GroupNorm_cpu_bfloat16, test/test_modules.py::TestModuleCPU::test_forward_nn_GroupNorm_cpu_float16, test/test_modules.py::TestModuleCPU::test_forward_nn_GroupNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_GroupNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Hardshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Hardshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Hardswish_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Hardswish_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Hardtanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Hardtanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_HingeEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_HingeEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_HuberLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_HuberLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_InstanceNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_InstanceNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_InstanceNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_InstanceNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_InstanceNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_InstanceNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_InstanceNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_InstanceNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_InstanceNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_InstanceNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_InstanceNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_InstanceNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_KLDivLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_KLDivLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_L1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_L1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LPPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LPPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LPPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LPPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LPPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LPPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LSTMCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LSTMCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LSTM_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LSTM_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LSTM_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LSTM_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LayerNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LayerNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LazyConv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LazyConv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LazyConv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LazyConv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LazyConv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LazyConv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LazyConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LazyConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LazyConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LazyConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LazyConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LazyConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LeakyReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LeakyReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Linear_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Linear_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LocalResponseNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LocalResponseNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LogSigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LogSigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_LogSoftmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_LogSoftmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_MSELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_MSELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_MarginRankingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_MarginRankingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_MaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_MaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_MaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_MaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_MaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_MaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Mish_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Mish_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_MultiLabelMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_MultiLabelMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_MultiLabelSoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_MultiLabelSoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_MultiMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_MultiMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_MultiheadAttention_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_MultiheadAttention_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_MultiheadAttention_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_MultiheadAttention_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_NLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_NLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_PReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_PReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_PoissonNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_PoissonNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_RMSNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_RMSNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_RNNCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_RNNCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_RNN_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_RNN_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_RNN_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_RNN_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ReLU6_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ReLU6_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ReflectionPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ReflectionPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ReflectionPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ReflectionPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ReflectionPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ReflectionPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ReplicationPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ReplicationPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ReplicationPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ReplicationPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ReplicationPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ReplicationPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_SELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_SELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_SiLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_SiLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Sigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Sigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_SmoothL1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_SmoothL1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_SoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_SoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Softmax2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Softmax2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Softmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Softmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Softmin_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Softmin_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Softplus_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Softplus_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Softshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Softshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Softsign_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Softsign_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Tanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Tanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Tanhshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Tanhshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Threshold_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Threshold_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_TransformerDecoderLayer_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_TransformerDecoderLayer_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_TransformerEncoderLayer_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_TransformerEncoderLayer_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_TransformerEncoderLayer_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_TransformerEncoderLayer_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_TransformerEncoder_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_TransformerEncoder_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_TransformerEncoder_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_TransformerEncoder_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_Transformer_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_Transformer_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ZeroPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ZeroPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ZeroPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ZeroPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_forward_nn_ZeroPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_forward_nn_ZeroPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_AdaptiveAvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_AdaptiveAvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_AdaptiveAvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_AdaptiveMaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_AdaptiveMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_AdaptiveMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_AvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_AvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_AvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_BCELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_BCEWithLogitsLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_BatchNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_BatchNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_BatchNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_BatchNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_BatchNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_BatchNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Bilinear_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_CELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_CTCLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_CircularPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_CircularPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_CircularPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ConstantPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ConstantPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ConstantPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Conv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Conv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Conv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_CosineEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_CrossEntropyLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Embedding_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_FractionalMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_FractionalMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_GELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_GLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_GRUCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_GRU_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_GRU_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_GaussianNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_GroupNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Hardshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Hardswish_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Hardtanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_HingeEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_HuberLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_InstanceNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_InstanceNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_InstanceNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_InstanceNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_InstanceNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_InstanceNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_KLDivLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_L1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LPPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LPPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LPPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LSTMCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LSTM_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LSTM_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LayerNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LazyConv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LazyConv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LazyConv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LazyConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LazyConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LazyConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LeakyReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Linear_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LocalResponseNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LogSigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_LogSoftmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_MSELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_MarginRankingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_MaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_MaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_MaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Mish_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_MultiLabelMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_MultiLabelSoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_MultiMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_MultiheadAttention_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_MultiheadAttention_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_NLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_PReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_PoissonNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_RMSNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_RNNCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_RNN_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_RNN_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ReLU6_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ReflectionPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ReflectionPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ReflectionPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ReplicationPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ReplicationPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ReplicationPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_SELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_SiLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Sigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_SmoothL1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_SoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Softmax2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Softmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Softmin_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Softplus_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Softshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Softsign_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Tanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Tanhshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Threshold_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_TransformerDecoderLayer_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_TransformerEncoderLayer_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_TransformerEncoderLayer_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_TransformerEncoder_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_TransformerEncoder_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_Transformer_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ZeroPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ZeroPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_grad_nn_ZeroPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_AdaptiveAvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_AdaptiveAvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_AdaptiveAvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_AdaptiveMaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_AdaptiveMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_AdaptiveMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_AvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_AvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_AvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_BCELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_BCEWithLogitsLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_BatchNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_BatchNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_BatchNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_BatchNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_BatchNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_BatchNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Bilinear_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_CELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_CTCLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_CircularPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_CircularPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_CircularPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ConstantPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ConstantPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ConstantPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Conv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Conv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Conv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_CosineEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_CrossEntropyLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Embedding_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_FractionalMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_FractionalMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_GELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_GLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_GRUCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_GRU_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_GRU_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_GaussianNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_GroupNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Hardshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Hardtanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_HingeEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_HuberLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_InstanceNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_InstanceNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_InstanceNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_InstanceNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_InstanceNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_InstanceNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_KLDivLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_L1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LPPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LPPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LPPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LSTMCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LSTM_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LSTM_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LayerNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LazyConv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LazyConv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LazyConv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LazyConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LazyConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LazyConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LeakyReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Linear_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LocalResponseNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LogSigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_LogSoftmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_MSELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_MarginRankingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_MaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_MaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_MaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Mish_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_MultiLabelMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_MultiLabelSoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_MultiMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_MultiheadAttention_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_MultiheadAttention_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_NLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_PReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_PoissonNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_RMSNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_RNNCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_RNN_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_RNN_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ReLU6_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ReflectionPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ReflectionPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ReflectionPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ReplicationPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ReplicationPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ReplicationPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_SELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_SiLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Sigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_SmoothL1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_SoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Softmax2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Softmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Softmin_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Softplus_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Softshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Softsign_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Tanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Tanhshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Threshold_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_TransformerDecoderLayer_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_TransformerEncoderLayer_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_TransformerEncoderLayer_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_TransformerEncoder_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_TransformerEncoder_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_Transformer_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ZeroPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ZeroPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_gradgrad_nn_ZeroPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AdaptiveAvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AdaptiveAvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AdaptiveAvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AdaptiveAvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AdaptiveAvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AdaptiveAvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AdaptiveMaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AdaptiveMaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AdaptiveMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AdaptiveMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AdaptiveMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AdaptiveMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_AvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_BCELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_BCELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_BCEWithLogitsLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_BCEWithLogitsLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_BatchNorm1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_BatchNorm1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_BatchNorm2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_BatchNorm2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_BatchNorm3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_BatchNorm3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Bilinear_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Bilinear_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CTCLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CTCLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CircularPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CircularPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CircularPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CircularPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CircularPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CircularPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConstantPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConstantPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConstantPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConstantPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConstantPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConstantPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Conv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Conv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Conv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Conv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Conv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Conv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose1d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose1d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose1d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose2d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose2d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose2d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose3d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose3d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose3d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CosineEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CosineEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CrossEntropyLoss_cpu_float16, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CrossEntropyLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_CrossEntropyLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Embedding_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Embedding_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_FractionalMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_FractionalMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_FractionalMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_FractionalMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GRUCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GRUCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GRU_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GRU_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GaussianNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GaussianNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GroupNorm_cpu_bfloat16, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GroupNorm_cpu_float16, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GroupNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_GroupNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Hardshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Hardshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Hardswish_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Hardswish_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Hardtanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Hardtanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_HingeEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_HingeEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_HuberLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_HuberLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_InstanceNorm1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_InstanceNorm1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_InstanceNorm2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_InstanceNorm2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_InstanceNorm3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_InstanceNorm3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_KLDivLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_KLDivLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_L1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_L1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LPPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LPPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LPPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LPPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LPPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LPPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LSTMCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LSTMCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LSTM_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LSTM_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LayerNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LayerNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LazyConv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LazyConv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LazyConv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LazyConv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LazyConv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LazyConv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LazyConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LazyConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LazyConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LazyConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LazyConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LazyConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LeakyReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LeakyReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Linear_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Linear_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LocalResponseNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LocalResponseNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LogSigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LogSigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LogSoftmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_LogSoftmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MSELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MSELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MarginRankingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MarginRankingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Mish_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Mish_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MultiLabelMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MultiLabelMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MultiLabelSoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MultiLabelSoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MultiMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MultiMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MultiheadAttention_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_MultiheadAttention_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_NLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_NLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_PReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_PReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_PoissonNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_PoissonNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_RMSNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_RMSNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_RNNCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_RNNCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_RNN_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_RNN_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReLU6_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReLU6_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReflectionPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReflectionPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReflectionPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReflectionPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReflectionPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReflectionPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReplicationPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReplicationPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReplicationPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReplicationPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReplicationPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ReplicationPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_SELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_SELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_SiLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_SiLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Sigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Sigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_SmoothL1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_SmoothL1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_SoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_SoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Softmax2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Softmax2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Softmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Softmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Softmin_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Softmin_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Softplus_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Softplus_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Softshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Softshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Softsign_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Softsign_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Tanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Tanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Tanhshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Tanhshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Threshold_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Threshold_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_TransformerDecoderLayer_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_TransformerDecoderLayer_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_TransformerEncoderLayer_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_TransformerEncoderLayer_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_TransformerEncoder_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_TransformerEncoder_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Transformer_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_Transformer_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ZeroPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ZeroPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ZeroPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ZeroPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ZeroPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_if_train_and_eval_modes_differ_nn_ZeroPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AdaptiveAvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AdaptiveAvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AdaptiveAvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AdaptiveAvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AdaptiveAvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AdaptiveAvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AdaptiveMaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AdaptiveMaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AdaptiveMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AdaptiveMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AdaptiveMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AdaptiveMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_AvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BCELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BCELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BCEWithLogitsLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BCEWithLogitsLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BatchNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BatchNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BatchNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BatchNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BatchNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BatchNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BatchNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BatchNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BatchNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BatchNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BatchNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_BatchNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Bilinear_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Bilinear_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CTCLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CTCLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CircularPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CircularPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CircularPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CircularPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CircularPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CircularPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConstantPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConstantPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConstantPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConstantPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConstantPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConstantPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Conv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Conv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Conv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Conv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Conv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Conv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose1d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose1d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose1d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose2d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose2d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose2d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose3d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose3d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose3d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CosineEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CosineEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CrossEntropyLoss_cpu_float16, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CrossEntropyLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_CrossEntropyLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Embedding_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Embedding_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_FractionalMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_FractionalMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_FractionalMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_FractionalMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GRUCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GRUCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GRU_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GRU_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GRU_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GRU_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GaussianNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GaussianNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GroupNorm_cpu_bfloat16, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GroupNorm_cpu_float16, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GroupNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_GroupNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Hardshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Hardshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Hardswish_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Hardswish_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Hardtanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Hardtanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_HingeEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_HingeEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_HuberLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_HuberLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_InstanceNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_InstanceNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_InstanceNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_InstanceNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_InstanceNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_InstanceNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_InstanceNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_InstanceNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_InstanceNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_InstanceNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_InstanceNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_InstanceNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_KLDivLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_KLDivLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_L1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_L1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LPPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LPPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LPPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LPPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LPPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LPPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LSTMCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LSTMCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LSTM_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LSTM_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LSTM_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LSTM_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LayerNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LayerNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LazyConv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LazyConv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LazyConv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LazyConv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LazyConv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LazyConv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LazyConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LazyConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LazyConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LazyConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LazyConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LazyConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LeakyReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LeakyReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Linear_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Linear_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LocalResponseNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LocalResponseNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LogSigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LogSigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LogSoftmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_LogSoftmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MSELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MSELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MarginRankingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MarginRankingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Mish_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Mish_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MultiLabelMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MultiLabelMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MultiLabelSoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MultiLabelSoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MultiMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MultiMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MultiheadAttention_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MultiheadAttention_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MultiheadAttention_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_MultiheadAttention_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_NLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_NLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_PReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_PReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_PoissonNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_PoissonNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_RMSNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_RMSNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_RNNCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_RNNCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_RNN_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_RNN_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_RNN_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_RNN_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReLU6_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReLU6_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReflectionPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReflectionPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReflectionPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReflectionPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReflectionPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReflectionPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReplicationPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReplicationPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReplicationPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReplicationPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReplicationPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ReplicationPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_SELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_SELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_SiLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_SiLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Sigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Sigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_SmoothL1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_SmoothL1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_SoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_SoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Softmax2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Softmax2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Softmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Softmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Softmin_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Softmin_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Softplus_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Softplus_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Softshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Softshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Softsign_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Softsign_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Tanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Tanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Tanhshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Tanhshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Threshold_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Threshold_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_TransformerDecoderLayer_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_TransformerDecoderLayer_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_TransformerEncoderLayer_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_TransformerEncoderLayer_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_TransformerEncoderLayer_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_TransformerEncoderLayer_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_TransformerEncoder_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_TransformerEncoder_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_TransformerEncoder_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_TransformerEncoder_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Transformer_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_Transformer_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ZeroPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ZeroPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ZeroPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ZeroPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ZeroPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_memory_format_nn_ZeroPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AdaptiveAvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AdaptiveAvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AdaptiveAvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AdaptiveAvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AdaptiveAvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AdaptiveAvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AdaptiveMaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AdaptiveMaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AdaptiveMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AdaptiveMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AdaptiveMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AdaptiveMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_AvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BCELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BCELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BCEWithLogitsLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BCEWithLogitsLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BatchNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BatchNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BatchNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BatchNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BatchNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BatchNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BatchNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BatchNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BatchNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BatchNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BatchNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_BatchNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Bilinear_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Bilinear_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CTCLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CTCLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CircularPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CircularPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CircularPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CircularPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CircularPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CircularPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConstantPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConstantPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConstantPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConstantPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConstantPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConstantPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Conv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Conv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Conv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Conv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Conv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Conv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose1d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose1d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose1d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose2d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose2d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose2d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose3d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose3d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose3d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CosineEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CosineEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CrossEntropyLoss_cpu_float16, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CrossEntropyLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_CrossEntropyLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Embedding_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Embedding_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_FractionalMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_FractionalMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_FractionalMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_FractionalMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GRUCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GRUCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GRU_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GRU_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GRU_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GRU_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GaussianNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GaussianNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GroupNorm_cpu_bfloat16, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GroupNorm_cpu_float16, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GroupNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_GroupNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Hardshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Hardshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Hardswish_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Hardswish_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Hardtanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Hardtanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_HingeEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_HingeEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_HuberLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_HuberLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_InstanceNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_InstanceNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_InstanceNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_InstanceNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_InstanceNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_InstanceNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_InstanceNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_InstanceNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_InstanceNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_InstanceNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_InstanceNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_InstanceNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_KLDivLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_KLDivLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_L1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_L1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LPPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LPPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LPPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LPPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LPPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LPPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LSTMCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LSTMCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LSTM_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LSTM_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LSTM_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LSTM_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LayerNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LayerNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LazyConv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LazyConv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LazyConv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LazyConv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LazyConv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LazyConv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LazyConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LazyConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LazyConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LazyConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LazyConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LazyConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LeakyReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LeakyReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Linear_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Linear_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LocalResponseNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LocalResponseNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LogSigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LogSigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LogSoftmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_LogSoftmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MSELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MSELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MarginRankingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MarginRankingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Mish_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Mish_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MultiLabelMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MultiLabelMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MultiLabelSoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MultiLabelSoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MultiMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MultiMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MultiheadAttention_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MultiheadAttention_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MultiheadAttention_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_MultiheadAttention_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_NLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_NLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_PReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_PReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_PoissonNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_PoissonNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_RMSNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_RMSNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_RNNCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_RNNCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_RNN_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_RNN_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_RNN_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_RNN_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReLU6_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReLU6_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReflectionPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReflectionPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReflectionPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReflectionPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReflectionPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReflectionPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReplicationPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReplicationPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReplicationPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReplicationPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReplicationPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ReplicationPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_SELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_SELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_SiLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_SiLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Sigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Sigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_SmoothL1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_SmoothL1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_SoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_SoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Softmax2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Softmax2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Softmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Softmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Softmin_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Softmin_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Softplus_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Softplus_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Softshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Softshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Softsign_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Softsign_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Tanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Tanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Tanhshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Tanhshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Threshold_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Threshold_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_TransformerDecoderLayer_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_TransformerDecoderLayer_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_TransformerEncoderLayer_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_TransformerEncoderLayer_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_TransformerEncoderLayer_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_TransformerEncoderLayer_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_TransformerEncoder_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_TransformerEncoder_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_TransformerEncoder_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_TransformerEncoder_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Transformer_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_Transformer_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ZeroPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ZeroPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_multiple_device_transfer_nn_ZeroPad2d_cpu_float32, 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test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_BatchNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_BatchNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_BatchNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_BatchNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_BatchNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_BatchNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_BatchNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Bilinear_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Bilinear_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_CELU_cpu_float32, 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test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Conv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose1d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose1d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose1d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose2d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose2d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose2d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose3d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose3d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose3d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_CosineEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_CosineEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_CrossEntropyLoss_cpu_float16, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_CrossEntropyLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_CrossEntropyLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Embedding_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Embedding_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_FractionalMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_FractionalMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_FractionalMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_FractionalMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GRUCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GRUCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GRU_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GRU_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GRU_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GRU_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GaussianNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GaussianNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GroupNorm_cpu_bfloat16, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GroupNorm_cpu_float16, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GroupNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_GroupNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Hardshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Hardshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Hardswish_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Hardswish_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Hardtanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Hardtanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_HingeEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_HingeEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_HuberLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_HuberLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_InstanceNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_InstanceNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_InstanceNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_InstanceNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_InstanceNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_InstanceNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_InstanceNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_InstanceNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_InstanceNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_InstanceNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_InstanceNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_InstanceNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_KLDivLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_KLDivLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_L1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_L1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LPPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LPPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LPPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LPPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LPPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LPPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LSTMCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LSTMCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LSTM_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LSTM_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LSTM_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LSTM_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LayerNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LayerNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LazyConv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LazyConv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LazyConv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LazyConv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LazyConv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LazyConv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LazyConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LazyConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LazyConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LazyConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LazyConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LazyConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LeakyReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LeakyReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Linear_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Linear_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LocalResponseNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LocalResponseNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LogSigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LogSigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LogSoftmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_LogSoftmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MSELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MSELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MarginRankingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MarginRankingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Mish_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Mish_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MultiLabelMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MultiLabelMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MultiLabelSoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MultiLabelSoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MultiMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MultiMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MultiheadAttention_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MultiheadAttention_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MultiheadAttention_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MultiheadAttention_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_NLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_NLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_PReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_PReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_PoissonNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_PoissonNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_RMSNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_RMSNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_RNNCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_RNNCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_RNN_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_RNN_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_RNN_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_RNN_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReLU6_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReLU6_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReflectionPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReflectionPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReflectionPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReflectionPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReflectionPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReflectionPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReplicationPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReplicationPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReplicationPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReplicationPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReplicationPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ReplicationPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_SELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_SELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_SiLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_SiLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Sigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Sigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_SmoothL1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_SmoothL1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_SoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_SoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Softmax2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Softmax2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Softmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Softmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Softmin_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Softmin_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Softplus_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Softplus_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Softshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Softshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Softsign_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Softsign_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Tanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Tanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Tanhshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Tanhshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Threshold_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Threshold_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_TransformerDecoderLayer_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_TransformerDecoderLayer_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_TransformerEncoderLayer_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_TransformerEncoderLayer_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_TransformerEncoderLayer_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_TransformerEncoderLayer_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_TransformerEncoder_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_TransformerEncoder_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_TransformerEncoder_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_TransformerEncoder_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Transformer_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_Transformer_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ZeroPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ZeroPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ZeroPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ZeroPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ZeroPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_ZeroPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_AdaptiveAvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_AdaptiveAvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_AdaptiveAvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_AdaptiveAvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_AdaptiveAvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_AdaptiveAvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_AdaptiveMaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_AdaptiveMaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_AdaptiveMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_AdaptiveMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_AdaptiveMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_AdaptiveMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_AvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_AvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_AvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_AvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_AvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_AvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_BCELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_BCELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_BCEWithLogitsLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_BCEWithLogitsLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_BatchNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_BatchNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_BatchNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_BatchNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_BatchNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_BatchNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_BatchNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_BatchNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_BatchNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_BatchNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_BatchNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_BatchNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Bilinear_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Bilinear_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_CELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_CELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_CTCLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_CTCLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_CircularPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_CircularPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_CircularPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_CircularPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_CircularPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_CircularPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ConstantPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ConstantPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ConstantPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ConstantPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ConstantPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ConstantPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Conv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Conv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Conv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Conv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Conv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Conv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose1d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose1d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose1d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose2d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose2d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose2d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose3d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose3d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose3d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_CosineEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_CosineEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_CrossEntropyLoss_cpu_float16, test/test_modules.py::TestModuleCPU::test_repr_nn_CrossEntropyLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_CrossEntropyLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Embedding_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Embedding_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_FractionalMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_FractionalMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_FractionalMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_FractionalMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_GELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_GELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_GLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_GLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_GRUCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_GRUCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_GRU_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_GRU_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_GRU_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_GRU_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_GaussianNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_GaussianNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_GroupNorm_cpu_bfloat16, test/test_modules.py::TestModuleCPU::test_repr_nn_GroupNorm_cpu_float16, test/test_modules.py::TestModuleCPU::test_repr_nn_GroupNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_GroupNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Hardshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Hardshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Hardswish_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Hardswish_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Hardtanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Hardtanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_HingeEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_HingeEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_HuberLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_HuberLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_InstanceNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_InstanceNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_InstanceNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_InstanceNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_InstanceNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_InstanceNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_InstanceNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_InstanceNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_InstanceNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_InstanceNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_InstanceNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_InstanceNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_KLDivLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_KLDivLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_L1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_L1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LPPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LPPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LPPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LPPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LPPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LPPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LSTMCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LSTMCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LSTM_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LSTM_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LSTM_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LSTM_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LayerNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LayerNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LazyConv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LazyConv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LazyConv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LazyConv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LazyConv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LazyConv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LazyConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LazyConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LazyConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LazyConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LazyConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LazyConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LeakyReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LeakyReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Linear_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Linear_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LocalResponseNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LocalResponseNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LogSigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LogSigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_LogSoftmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_LogSoftmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_MSELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_MSELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_MarginRankingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_MarginRankingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_MaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_MaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_MaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_MaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_MaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_MaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Mish_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Mish_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_MultiLabelMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_MultiLabelMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_MultiLabelSoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_MultiLabelSoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_MultiMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_MultiMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_MultiheadAttention_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_MultiheadAttention_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_MultiheadAttention_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_MultiheadAttention_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_NLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_NLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_PReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_PReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_PoissonNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_PoissonNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_RMSNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_RMSNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_RNNCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_RNNCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_RNN_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_RNN_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_RNN_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_RNN_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ReLU6_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ReLU6_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ReflectionPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ReflectionPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ReflectionPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ReflectionPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ReflectionPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ReflectionPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ReplicationPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ReplicationPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ReplicationPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ReplicationPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ReplicationPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ReplicationPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_SELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_SELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_SiLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_SiLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Sigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Sigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_SmoothL1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_SmoothL1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_SoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_SoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Softmax2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Softmax2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Softmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Softmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Softmin_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Softmin_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Softplus_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Softplus_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Softshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Softshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Softsign_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Softsign_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Tanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Tanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Tanhshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Tanhshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Threshold_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Threshold_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_TransformerDecoderLayer_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_TransformerDecoderLayer_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_TransformerEncoderLayer_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_TransformerEncoderLayer_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_TransformerEncoderLayer_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_TransformerEncoderLayer_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_TransformerEncoder_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_TransformerEncoder_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_TransformerEncoder_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_TransformerEncoder_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_Transformer_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_Transformer_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ZeroPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ZeroPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ZeroPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ZeroPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_repr_nn_ZeroPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_repr_nn_ZeroPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_AdaptiveAvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_AdaptiveAvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_AdaptiveAvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_AdaptiveAvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_AdaptiveAvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_AdaptiveAvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_AdaptiveMaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_AdaptiveMaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_AdaptiveMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_AdaptiveMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_AdaptiveMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_AdaptiveMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_AvgPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_AvgPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_AvgPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_AvgPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_AvgPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_AvgPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_BCELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_BCELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_BCEWithLogitsLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_BCEWithLogitsLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_BatchNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_BatchNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_BatchNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_BatchNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_BatchNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_BatchNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_BatchNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_BatchNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_BatchNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_BatchNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_BatchNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_BatchNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Bilinear_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Bilinear_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_CELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_CELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_CTCLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_CTCLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_CircularPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_CircularPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_CircularPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_CircularPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_CircularPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_CircularPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConstantPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConstantPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConstantPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConstantPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConstantPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConstantPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Conv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Conv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Conv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Conv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Conv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Conv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose1d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose1d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose1d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose2d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose2d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose2d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose3d_cpu_complex128, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose3d_cpu_complex32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose3d_cpu_complex64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_CosineEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_CosineEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_CrossEntropyLoss_cpu_float16, test/test_modules.py::TestModuleCPU::test_save_load_nn_CrossEntropyLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_CrossEntropyLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Embedding_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Embedding_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_FractionalMaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_FractionalMaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_FractionalMaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_FractionalMaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_GELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_GELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_GLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_GLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_GRUCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_GRUCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_GRU_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_GRU_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_GRU_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_GRU_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_GaussianNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_GaussianNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_GroupNorm_cpu_bfloat16, test/test_modules.py::TestModuleCPU::test_save_load_nn_GroupNorm_cpu_float16, test/test_modules.py::TestModuleCPU::test_save_load_nn_GroupNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_GroupNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Hardshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Hardshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Hardswish_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Hardswish_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Hardtanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Hardtanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_HingeEmbeddingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_HingeEmbeddingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_HuberLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_HuberLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_InstanceNorm1d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_InstanceNorm1d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_InstanceNorm1d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_InstanceNorm1d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_InstanceNorm2d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_InstanceNorm2d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_InstanceNorm2d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_InstanceNorm2d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_InstanceNorm3d_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_InstanceNorm3d_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_InstanceNorm3d_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_InstanceNorm3d_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_KLDivLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_KLDivLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_L1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_L1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LPPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LPPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LPPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LPPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LPPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LPPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LSTMCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LSTMCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LSTM_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LSTM_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LSTM_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LSTM_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LayerNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LayerNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LazyConv1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LazyConv1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LazyConv2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LazyConv2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LazyConv3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LazyConv3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LazyConvTranspose1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LazyConvTranspose1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LazyConvTranspose2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LazyConvTranspose2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LazyConvTranspose3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LazyConvTranspose3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LeakyReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LeakyReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Linear_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Linear_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LocalResponseNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LocalResponseNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LogSigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LogSigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_LogSoftmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_LogSoftmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_MSELoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_MSELoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_MarginRankingLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_MarginRankingLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_MaxPool1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_MaxPool1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_MaxPool2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_MaxPool2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_MaxPool3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_MaxPool3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Mish_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Mish_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_MultiLabelMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_MultiLabelMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_MultiLabelSoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_MultiLabelSoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_MultiMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_MultiMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_MultiheadAttention_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_MultiheadAttention_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_MultiheadAttention_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_MultiheadAttention_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_NLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_NLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_PReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_PReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_PoissonNLLLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_PoissonNLLLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_RMSNorm_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_RMSNorm_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_RNNCell_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_RNNCell_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_RNN_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_RNN_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_RNN_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_RNN_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReLU6_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReLU6_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReflectionPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReflectionPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReflectionPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReflectionPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReflectionPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReflectionPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReplicationPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReplicationPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReplicationPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReplicationPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReplicationPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ReplicationPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_SELU_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_SELU_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_SiLU_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_SiLU_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Sigmoid_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Sigmoid_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_SmoothL1Loss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_SmoothL1Loss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_SoftMarginLoss_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_SoftMarginLoss_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Softmax2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Softmax2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Softmax_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Softmax_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Softmin_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Softmin_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Softplus_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Softplus_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Softshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Softshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Softsign_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Softsign_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Tanh_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Tanh_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Tanhshrink_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Tanhshrink_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Threshold_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Threshold_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_TransformerDecoderLayer_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_TransformerDecoderLayer_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_TransformerEncoderLayer_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_TransformerEncoderLayer_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_TransformerEncoderLayer_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_TransformerEncoderLayer_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_TransformerEncoder_eval_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_TransformerEncoder_eval_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_TransformerEncoder_train_mode_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_TransformerEncoder_train_mode_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_Transformer_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_Transformer_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ZeroPad1d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ZeroPad1d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ZeroPad2d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ZeroPad2d_cpu_float64, test/test_modules.py::TestModuleCPU::test_save_load_nn_ZeroPad3d_cpu_float32, test/test_modules.py::TestModuleCPU::test_save_load_nn_ZeroPad3d_cpu_float64, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AdaptiveAvgPool1d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AdaptiveAvgPool1d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AdaptiveAvgPool2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AdaptiveAvgPool2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AdaptiveAvgPool3d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AdaptiveAvgPool3d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AdaptiveMaxPool1d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AdaptiveMaxPool1d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AdaptiveMaxPool2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AdaptiveMaxPool2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AdaptiveMaxPool3d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AdaptiveMaxPool3d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AvgPool1d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AvgPool1d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AvgPool2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AvgPool2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AvgPool3d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_AvgPool3d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BCELoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BCELoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BCEWithLogitsLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BCEWithLogitsLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BatchNorm1d_eval_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BatchNorm1d_eval_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BatchNorm1d_train_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BatchNorm1d_train_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BatchNorm2d_eval_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BatchNorm2d_eval_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BatchNorm2d_train_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BatchNorm2d_train_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BatchNorm3d_eval_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BatchNorm3d_eval_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BatchNorm3d_train_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_BatchNorm3d_train_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Bilinear_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Bilinear_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CELU_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CELU_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CTCLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CTCLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CircularPad1d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CircularPad1d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CircularPad2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CircularPad2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CircularPad3d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CircularPad3d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ConstantPad1d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ConstantPad1d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ConstantPad2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ConstantPad2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ConstantPad3d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ConstantPad3d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Conv1d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Conv1d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Conv2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Conv2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Conv3d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Conv3d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ConvTranspose1d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ConvTranspose1d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ConvTranspose2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ConvTranspose2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ConvTranspose3d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ConvTranspose3d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CosineEmbeddingLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CosineEmbeddingLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CrossEntropyLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_CrossEntropyLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ELU_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ELU_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Embedding_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Embedding_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_FractionalMaxPool2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_FractionalMaxPool2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_FractionalMaxPool3d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_FractionalMaxPool3d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GELU_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GELU_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GLU_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GLU_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GRUCell_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GRUCell_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GRU_eval_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GRU_eval_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GRU_train_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GRU_train_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GaussianNLLLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GaussianNLLLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GroupNorm_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_GroupNorm_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Hardshrink_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Hardshrink_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Hardswish_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Hardswish_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Hardtanh_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Hardtanh_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_HingeEmbeddingLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_HingeEmbeddingLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_HuberLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_HuberLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_InstanceNorm1d_eval_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_InstanceNorm1d_eval_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_InstanceNorm1d_train_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_InstanceNorm1d_train_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_InstanceNorm2d_eval_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_InstanceNorm2d_eval_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_InstanceNorm2d_train_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_InstanceNorm2d_train_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_InstanceNorm3d_eval_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_InstanceNorm3d_eval_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_InstanceNorm3d_train_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_InstanceNorm3d_train_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_KLDivLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_KLDivLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_L1Loss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_L1Loss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LPPool1d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LPPool1d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LPPool2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LPPool2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LPPool3d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LPPool3d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LSTMCell_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LSTMCell_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LSTM_eval_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LSTM_eval_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LSTM_train_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LSTM_train_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LayerNorm_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LayerNorm_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LeakyReLU_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LeakyReLU_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Linear_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Linear_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LocalResponseNorm_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LocalResponseNorm_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LogSigmoid_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LogSigmoid_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LogSoftmax_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_LogSoftmax_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MSELoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MSELoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MarginRankingLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MarginRankingLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MaxPool1d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MaxPool1d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MaxPool2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MaxPool2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MaxPool3d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MaxPool3d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Mish_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Mish_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MultiLabelMarginLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MultiLabelMarginLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MultiLabelSoftMarginLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MultiLabelSoftMarginLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MultiMarginLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MultiMarginLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MultiheadAttention_eval_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MultiheadAttention_eval_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MultiheadAttention_train_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_MultiheadAttention_train_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_NLLLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_NLLLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_PReLU_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_PReLU_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_PoissonNLLLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_PoissonNLLLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_RMSNorm_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_RMSNorm_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_RNNCell_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_RNNCell_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_RNN_eval_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_RNN_eval_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_RNN_train_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_RNN_train_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReLU6_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReLU6_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReLU_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReLU_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReflectionPad1d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReflectionPad1d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReflectionPad2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReflectionPad2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReflectionPad3d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReflectionPad3d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReplicationPad1d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReplicationPad1d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReplicationPad2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReplicationPad2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReplicationPad3d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ReplicationPad3d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_SELU_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_SELU_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_SiLU_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_SiLU_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Sigmoid_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Sigmoid_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_SmoothL1Loss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_SmoothL1Loss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_SoftMarginLoss_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_SoftMarginLoss_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Softmax2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Softmax2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Softmax_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Softmax_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Softmin_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Softmin_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Softplus_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Softplus_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Softshrink_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Softshrink_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Softsign_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Softsign_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Tanh_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Tanh_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Tanhshrink_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Tanhshrink_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Threshold_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Threshold_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_TransformerDecoderLayer_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_TransformerDecoderLayer_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_TransformerEncoderLayer_eval_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_TransformerEncoderLayer_eval_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_TransformerEncoderLayer_train_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_TransformerEncoderLayer_train_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_TransformerEncoder_eval_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_TransformerEncoder_eval_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_TransformerEncoder_train_mode_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_TransformerEncoder_train_mode_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Transformer_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_Transformer_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ZeroPad1d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ZeroPad1d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ZeroPad2d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ZeroPad2d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ZeroPad3d_swap_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_empty_nn_ZeroPad3d_swap_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveAvgPool1d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveAvgPool1d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveAvgPool1d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveAvgPool1d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveAvgPool2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveAvgPool2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveAvgPool2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveAvgPool2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveAvgPool3d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveAvgPool3d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveAvgPool3d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveAvgPool3d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveMaxPool1d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveMaxPool1d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveMaxPool1d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveMaxPool1d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveMaxPool2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveMaxPool2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveMaxPool2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveMaxPool2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveMaxPool3d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveMaxPool3d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveMaxPool3d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AdaptiveMaxPool3d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AvgPool1d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AvgPool1d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AvgPool1d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AvgPool1d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AvgPool2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AvgPool2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AvgPool2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AvgPool2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AvgPool3d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AvgPool3d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AvgPool3d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_AvgPool3d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BCELoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BCELoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BCELoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BCELoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BCEWithLogitsLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BCEWithLogitsLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BCEWithLogitsLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BCEWithLogitsLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm1d_eval_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm1d_eval_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm1d_eval_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm1d_eval_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm1d_train_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm1d_train_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm1d_train_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm1d_train_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm2d_eval_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm2d_eval_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm2d_eval_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm2d_eval_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm2d_train_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm2d_train_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm2d_train_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm2d_train_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm3d_eval_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm3d_eval_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm3d_eval_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm3d_eval_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm3d_train_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm3d_train_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm3d_train_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_BatchNorm3d_train_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Bilinear_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Bilinear_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Bilinear_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Bilinear_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CELU_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CELU_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CELU_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CELU_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CTCLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CTCLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CTCLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CTCLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CircularPad1d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CircularPad1d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CircularPad1d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CircularPad1d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CircularPad2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CircularPad2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CircularPad2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CircularPad2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CircularPad3d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CircularPad3d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CircularPad3d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CircularPad3d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConstantPad1d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConstantPad1d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConstantPad1d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConstantPad1d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConstantPad2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConstantPad2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConstantPad2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConstantPad2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConstantPad3d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConstantPad3d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConstantPad3d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConstantPad3d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Conv1d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Conv1d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Conv1d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Conv1d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Conv2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Conv2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Conv2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Conv2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Conv3d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Conv3d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Conv3d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Conv3d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConvTranspose1d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConvTranspose1d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConvTranspose1d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConvTranspose1d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConvTranspose2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConvTranspose2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConvTranspose2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConvTranspose2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConvTranspose3d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConvTranspose3d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConvTranspose3d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ConvTranspose3d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CosineEmbeddingLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CosineEmbeddingLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CosineEmbeddingLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CosineEmbeddingLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CrossEntropyLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CrossEntropyLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CrossEntropyLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_CrossEntropyLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ELU_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ELU_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ELU_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ELU_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Embedding_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Embedding_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Embedding_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Embedding_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_FractionalMaxPool2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_FractionalMaxPool2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_FractionalMaxPool2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_FractionalMaxPool2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_FractionalMaxPool3d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_FractionalMaxPool3d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_FractionalMaxPool3d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_FractionalMaxPool3d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GELU_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GELU_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GELU_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GELU_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GLU_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GLU_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GLU_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GLU_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GRUCell_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GRUCell_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GRUCell_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GRUCell_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GRU_eval_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GRU_eval_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GRU_eval_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GRU_eval_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GRU_train_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GRU_train_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GRU_train_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GRU_train_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GaussianNLLLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GaussianNLLLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GaussianNLLLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GaussianNLLLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GroupNorm_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GroupNorm_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GroupNorm_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_GroupNorm_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Hardshrink_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Hardshrink_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Hardshrink_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Hardshrink_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Hardswish_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Hardswish_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Hardswish_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Hardswish_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Hardtanh_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Hardtanh_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Hardtanh_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Hardtanh_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_HingeEmbeddingLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_HingeEmbeddingLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_HingeEmbeddingLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_HingeEmbeddingLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_HuberLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_HuberLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_HuberLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_HuberLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm1d_eval_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm1d_eval_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm1d_eval_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm1d_eval_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm1d_train_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm1d_train_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm1d_train_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm1d_train_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm2d_eval_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm2d_eval_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm2d_eval_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm2d_eval_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm2d_train_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm2d_train_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm2d_train_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm2d_train_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm3d_eval_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm3d_eval_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm3d_eval_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm3d_eval_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm3d_train_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm3d_train_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm3d_train_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_InstanceNorm3d_train_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_KLDivLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_KLDivLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_KLDivLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_KLDivLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_L1Loss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_L1Loss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_L1Loss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_L1Loss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LPPool1d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LPPool1d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LPPool1d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LPPool1d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LPPool2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LPPool2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LPPool2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LPPool2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LPPool3d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LPPool3d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LPPool3d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LPPool3d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LSTMCell_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LSTMCell_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LSTMCell_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LSTMCell_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LSTM_eval_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LSTM_eval_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LSTM_eval_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LSTM_eval_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LSTM_train_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LSTM_train_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LSTM_train_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LSTM_train_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LayerNorm_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LayerNorm_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LayerNorm_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LayerNorm_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LeakyReLU_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LeakyReLU_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LeakyReLU_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LeakyReLU_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Linear_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Linear_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Linear_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Linear_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LocalResponseNorm_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LocalResponseNorm_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LocalResponseNorm_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LocalResponseNorm_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LogSigmoid_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LogSigmoid_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LogSigmoid_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LogSigmoid_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LogSoftmax_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LogSoftmax_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LogSoftmax_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_LogSoftmax_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MSELoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MSELoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MSELoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MSELoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MarginRankingLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MarginRankingLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MarginRankingLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MarginRankingLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MaxPool1d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MaxPool1d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MaxPool1d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MaxPool1d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MaxPool2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MaxPool2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MaxPool2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MaxPool2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MaxPool3d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MaxPool3d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MaxPool3d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MaxPool3d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Mish_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Mish_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Mish_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Mish_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiLabelMarginLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiLabelMarginLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiLabelMarginLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiLabelMarginLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiLabelSoftMarginLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiLabelSoftMarginLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiLabelSoftMarginLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiLabelSoftMarginLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiMarginLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiMarginLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiMarginLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiMarginLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiheadAttention_eval_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiheadAttention_eval_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiheadAttention_eval_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiheadAttention_eval_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiheadAttention_train_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiheadAttention_train_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiheadAttention_train_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_MultiheadAttention_train_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_NLLLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_NLLLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_NLLLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_NLLLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_PReLU_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_PReLU_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_PReLU_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_PReLU_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_PoissonNLLLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_PoissonNLLLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_PoissonNLLLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_PoissonNLLLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RMSNorm_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RMSNorm_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RMSNorm_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RMSNorm_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RNNCell_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RNNCell_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RNNCell_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RNNCell_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RNN_eval_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RNN_eval_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RNN_eval_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RNN_eval_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RNN_train_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RNN_train_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RNN_train_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_RNN_train_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReLU6_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReLU6_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReLU6_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReLU6_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReLU_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReLU_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReLU_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReLU_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReflectionPad1d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReflectionPad1d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReflectionPad1d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReflectionPad1d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReflectionPad2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReflectionPad2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReflectionPad2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReflectionPad2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReflectionPad3d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReflectionPad3d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReflectionPad3d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReflectionPad3d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReplicationPad1d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReplicationPad1d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReplicationPad1d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReplicationPad1d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReplicationPad2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReplicationPad2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReplicationPad2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReplicationPad2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReplicationPad3d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReplicationPad3d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReplicationPad3d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ReplicationPad3d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SELU_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SELU_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SELU_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SELU_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SiLU_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SiLU_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SiLU_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SiLU_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Sigmoid_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Sigmoid_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Sigmoid_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Sigmoid_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SmoothL1Loss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SmoothL1Loss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SmoothL1Loss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SmoothL1Loss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SoftMarginLoss_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SoftMarginLoss_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SoftMarginLoss_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_SoftMarginLoss_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softmax2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softmax2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softmax2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softmax2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softmax_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softmax_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softmax_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softmax_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softmin_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softmin_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softmin_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softmin_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softplus_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softplus_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softplus_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softplus_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softshrink_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softshrink_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softshrink_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softshrink_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softsign_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softsign_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softsign_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Softsign_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Tanh_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Tanh_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Tanh_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Tanh_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Tanhshrink_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Tanhshrink_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Tanhshrink_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Tanhshrink_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Threshold_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Threshold_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Threshold_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Threshold_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerDecoderLayer_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerDecoderLayer_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerDecoderLayer_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerDecoderLayer_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoderLayer_eval_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoderLayer_eval_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoderLayer_eval_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoderLayer_eval_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoderLayer_train_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoderLayer_train_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoderLayer_train_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoderLayer_train_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoder_eval_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoder_eval_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoder_eval_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoder_eval_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoder_train_mode_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoder_train_mode_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoder_train_mode_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_TransformerEncoder_train_mode_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Transformer_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Transformer_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Transformer_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_Transformer_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ZeroPad1d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ZeroPad1d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ZeroPad1d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ZeroPad1d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ZeroPad2d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ZeroPad2d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ZeroPad2d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ZeroPad2d_swap_True_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ZeroPad3d_swap_False_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ZeroPad3d_swap_False_set_grad_True_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ZeroPad3d_swap_True_set_grad_False_cpu_float32, test/test_modules.py::TestModuleCPU::test_to_nn_ZeroPad3d_swap_True_set_grad_True_cpu_float32 2025-08-14T22:30:18.7830944Z 2025-08-14T22:30:21.6869012Z /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. 2025-08-14T22:30:21.6870538Z import pkg_resources 2025-08-14T22:30:21.8070368Z Running test_matmul_cuda 1/1 ... [2025-08-14 22:30:21.806603] 2025-08-14T22:30:21.8071012Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:30:21.8072602Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:30:21.806973] 2025-08-14T22:30:25.1289683Z 2025-08-14T22:30:25.1291331Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_f4775ed8c3e28d56_.log 2025-08-14T22:30:25.1295199Z Running 0 items in this shard: 2025-08-14T22:30:25.1295446Z 2025-08-14T22:30:28.6689154Z /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. 2025-08-14T22:30:28.6692023Z import pkg_resources 2025-08-14T22:30:28.7980421Z Running test_weak 1/1 ... [2025-08-14 22:30:28.797642] 2025-08-14T22:30:28.7981177Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:30:28.7985125Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_weak.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'] ... [2025-08-14 22:30:28.798130] 2025-08-14T22:30:45.7911341Z 2025-08-14T22:30:45.7912507Z test_weak 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_weak_1.1_f44b4600a5ee662a_.log 2025-08-14T22:30:45.7931635Z Running 39 items in this shard: test/test_weak.py::WeakTest::test_make_weak_keyed_dict_from_dict, test/test_weak.py::WeakTest::test_make_weak_keyed_dict_from_weak_keyed_dict, test/test_weak.py::WeakTest::test_make_weak_keyed_dict_repr, test/test_weak.py::WeakTest::test_threaded_weak_key_dict_copy, test/test_weak.py::WeakTest::test_threaded_weak_key_dict_deepcopy, test/test_weak.py::WeakTest::test_weak_keyed_bad_delitem, test/test_weak.py::WeakTest::test_weak_keyed_delitem, test/test_weak.py::WeakTest::test_weak_keyed_dict_popitem, test/test_weak.py::WeakTest::test_weak_keyed_dict_setdefault, test/test_weak.py::WeakTest::test_weak_keyed_dict_update, test/test_weak.py::WeakTest::test_weak_keyed_union_operators, test/test_weak.py::WeakKeyDictionaryTestCase::test_bool, test/test_weak.py::WeakKeyDictionaryTestCase::test_constructor, test/test_weak.py::WeakKeyDictionaryTestCase::test_get, test/test_weak.py::WeakKeyDictionaryTestCase::test_getitem, test/test_weak.py::WeakKeyDictionaryTestCase::test_items, test/test_weak.py::WeakKeyDictionaryTestCase::test_keys, test/test_weak.py::WeakKeyDictionaryTestCase::test_len, test/test_weak.py::WeakKeyDictionaryTestCase::test_pop, test/test_weak.py::WeakKeyDictionaryTestCase::test_popitem, test/test_weak.py::WeakKeyDictionaryTestCase::test_read, test/test_weak.py::WeakKeyDictionaryTestCase::test_setdefault, test/test_weak.py::WeakKeyDictionaryTestCase::test_update, test/test_weak.py::WeakKeyDictionaryTestCase::test_values, test/test_weak.py::WeakKeyDictionaryTestCase::test_write, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_bool, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_constructor, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_get, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_getitem, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_items, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_keys, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_len, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_pop, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_popitem, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_read, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_setdefault, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_update, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_values, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_write 2025-08-14T22:30:45.7951979Z 2025-08-14T22:30:48.9941502Z /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. 2025-08-14T22:30:48.9943183Z import pkg_resources 2025-08-14T22:30:49.1142030Z Running functorch/test_logging 1/1 ... [2025-08-14 22:30:49.113813] 2025-08-14T22:30:49.1142978Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:30:49.1145079Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_logging.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:30:49.114156] 2025-08-14T22:30:54.3871676Z 2025-08-14T22:30:54.3872914Z functorch/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_logging_1.1_6267faa2c9855468_.log 2025-08-14T22:30:54.3874542Z Running 1 items in this shard: test/functorch/test_logging.py::TestAOTLogging::test_logging 2025-08-14T22:30:54.3875236Z 2025-08-14T22:30:57.5243425Z /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. 2025-08-14T22:30:57.5245154Z import pkg_resources 2025-08-14T22:30:57.6453719Z Running test_logging 1/1 ... [2025-08-14 22:30:57.645024] 2025-08-14T22:30:57.6454132Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:30:57.6457659Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_logging.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:30:57.645394] 2025-08-14T22:31:04.3209927Z 2025-08-14T22:31:04.3211077Z test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_logging_1.1_ca7b4a0b8b21be43_.log 2025-08-14T22:31:04.3212292Z Running 1 items in this shard: test/test_logging.py::LoggingTest::testApiUsage 2025-08-14T22:31:04.3212744Z 2025-08-14T22:31:07.5183860Z /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. 2025-08-14T22:31:07.5185397Z import pkg_resources 2025-08-14T22:31:07.6394456Z Running test_out_dtype_op 1/1 ... [2025-08-14 22:31:07.639055] 2025-08-14T22:31:07.6394881Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:31:07.6397603Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_out_dtype_op.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:31:07.639487] 2025-08-14T22:31:19.0231477Z 2025-08-14T22:31:19.0232954Z test_out_dtype_op 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_out_dtype_op_1.1_bc34bfe981345a02_.log 2025-08-14T22:31:19.0240973Z Running 12 items in this shard: test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_dynamo, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_inductor_decomp, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_inductor_decomp_trace, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_int_mm_default_trace, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_make_fx, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_mm_numerical, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_mul_scalar_numerical, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_no_autograd, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_non_functional, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_non_op_overload, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_op_functional, test/test_out_dtype_op.py::TestOutDtypeOp::test_out_dtype_wrong_output 2025-08-14T22:31:19.0247760Z 2025-08-14T22:31:22.2541012Z /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. 2025-08-14T22:31:22.2542530Z import pkg_resources 2025-08-14T22:31:22.3750193Z Running test_complex 1/1 ... [2025-08-14 22:31:22.374676] 2025-08-14T22:31:22.3750639Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:31:22.3753248Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_complex.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'] ... [2025-08-14 22:31:22.375041] 2025-08-14T22:31:28.9515707Z 2025-08-14T22:31:28.9517185Z test_complex 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_complex_1.1_0561d3ba3477891b_.log 2025-08-14T22:31:28.9526645Z Running 15 items in this shard: test/test_complex.py::TestComplexTensorCPU::test_all_cpu_complex128, test/test_complex.py::TestComplexTensorCPU::test_all_cpu_complex64, test/test_complex.py::TestComplexTensorCPU::test_any_cpu_complex128, test/test_complex.py::TestComplexTensorCPU::test_any_cpu_complex64, test/test_complex.py::TestComplexTensorCPU::test_conj_copy_cpu_complex128, test/test_complex.py::TestComplexTensorCPU::test_conj_copy_cpu_complex64, test/test_complex.py::TestComplexTensorCPU::test_dtype_inference_cpu_float16, test/test_complex.py::TestComplexTensorCPU::test_dtype_inference_cpu_float32, test/test_complex.py::TestComplexTensorCPU::test_dtype_inference_cpu_float64, test/test_complex.py::TestComplexTensorCPU::test_eq_cpu_complex128, test/test_complex.py::TestComplexTensorCPU::test_eq_cpu_complex64, test/test_complex.py::TestComplexTensorCPU::test_ne_cpu_complex128, test/test_complex.py::TestComplexTensorCPU::test_ne_cpu_complex64, test/test_complex.py::TestComplexTensorCPU::test_to_list_cpu_complex128, test/test_complex.py::TestComplexTensorCPU::test_to_list_cpu_complex64 2025-08-14T22:31:28.9535715Z 2025-08-14T22:31:32.0367294Z /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. 2025-08-14T22:31:32.0370332Z import pkg_resources 2025-08-14T22:31:32.1563947Z Running torch_np/numpy_tests/core/test_einsum 1/1 ... [2025-08-14 22:31:32.155963] 2025-08-14T22:31:32.1564912Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:31:32.1567126Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_einsum.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:31:32.156304] 2025-08-14T22:33:16.5485215Z 2025-08-14T22:33:16.5486338Z test_optim 1/3 was successful, full logs can be found in artifacts with path test/test-reports/test_optim_1.3_e54ce01753c91cce_.log 2025-08-14T22:33:16.5614861Z Running 314 items in this shard: test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_T_cur_reset, test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr1_T_mult_2, test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr1_T_mult_4, test/test_optim.py::TestLRScheduler::test_add_param_group_does_not_break_reduce_lr_on_plateau_min_lr_scalar, test/test_optim.py::TestLRScheduler::test_chained_lr2_get_last_lr_before_step, test/test_optim.py::TestLRScheduler::test_closed_form_poly_lr, test/test_optim.py::TestLRScheduler::test_closed_form_step_lr, test/test_optim.py::TestLRScheduler::test_compound_cosanneal_and_exp_lr, test/test_optim.py::TestLRScheduler::test_compound_exp_and_linearlr, test/test_optim.py::TestLRScheduler::test_compound_reduce_lr_on_plateau1, test/test_optim.py::TestLRScheduler::test_compound_reduce_lr_on_plateau5, test/test_optim.py::TestLRScheduler::test_compound_step_and_constantlr, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass3, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass4, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass6, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass7, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass9, test/test_optim.py::TestLRScheduler::test_constant_initial_params_cyclelr, test/test_optim.py::TestLRScheduler::test_constantlr_is_constant_for_constant_epoch, test/test_optim.py::TestLRScheduler::test_cos_anneal_lr_continue, test/test_optim.py::TestLRScheduler::test_cycle_lr_cycle_momentum_fail_with_momentumless_optimizer, test/test_optim.py::TestLRScheduler::test_cycle_lr_exp_range_mode_step_size_up_down, test/test_optim.py::TestLRScheduler::test_cycle_lr_invalid_mode, test/test_optim.py::TestLRScheduler::test_cycle_lr_scale_fn_restored_from_state_dict, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular2_mode, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular2_mode_step_size_up_down, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular_mode, test/test_optim.py::TestLRScheduler::test_cycle_lr_with_adam, test/test_optim.py::TestLRScheduler::test_cycle_lr_with_momentumless_optimizer, test/test_optim.py::TestLRScheduler::test_exp_lr, test/test_optim.py::TestLRScheduler::test_exp_step_lr_state_dict, test/test_optim.py::TestLRScheduler::test_exponential_lr_is_constant_for_constant_epoch, test/test_optim.py::TestLRScheduler::test_get_last_lr_multi_step_lr, test/test_optim.py::TestLRScheduler::test_get_last_lr_step_lr, test/test_optim.py::TestLRScheduler::test_linearlr_start_factor_limits2, test/test_optim.py::TestLRScheduler::test_linearlr_with_epoch, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass1, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass10, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass11, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass2, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass4, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_on_plateau, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass11_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass1_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass2_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass4_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass5_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass7_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass7_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass8_weights_only_False, test/test_optim.py::TestLRScheduler::test_multiplicative_lr_with_lr_lambda, test/test_optim.py::TestLRScheduler::test_no_cyclic_references, test/test_optim.py::TestLRScheduler::test_old_pattern_warning, test/test_optim.py::TestLRScheduler::test_onecycle_lr_cannot_calculate_total_steps, test/test_optim.py::TestLRScheduler::test_onecycle_lr_invalid_anneal_strategy, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau7, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau8, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau_get_last_lr_before_step, test/test_optim.py::TestLRScheduler::test_sequentiallr1, test/test_optim.py::TestLRScheduler::test_step_lr, test/test_optim.py::TestLRScheduler::test_step_lr_is_constant_for_constant_epoch, test/test_optim.py::TestLRScheduler::test_swa_lr_state_dict, test/test_optim.py::TestLRScheduler::test_swalr_cosine_anneal_after_multiplicative, test/test_optim.py::TestLRScheduler::test_swalr_linear_anneal_after_multiplicative, test/test_optim.py::TestLRScheduler::test_swalr_no_anneal, test/test_optim.py::TestDifferentiableOptimizer::test_adam, test/test_optim.py::TestDifferentiableOptimizer::test_adam_differentiable_betas, test/test_optim.py::TestDifferentiableOptimizer::test_adam_differentiable_weight_decay, test/test_optim.py::TestDifferentiableOptimizer::test_adamw, test/test_optim.py::TestDifferentiableOptimizer::test_adamw_differentiable_lr, test/test_optim.py::TestDifferentiableOptimizer::test_adamw_differentiable_weight_decay, test/test_optim.py::TestDifferentiableOptimizer::test_asgd, test/test_optim.py::TestDifferentiableOptimizer::test_differentiable_weight_decay, test/test_optim.py::TestDifferentiableOptimizer::test_rmsprop, test/test_optim.py::TestDifferentiableOptimizer::test_sgd, test/test_optim.py::TestSWAUtils::test_averaged_model_mixed_device_ema_False, test/test_optim.py::TestSWAUtils::test_averaged_model_mixed_device_ema_True, test/test_optim.py::TestSWAUtils::test_bn_update_eval_momentum, test/test_optim.py::TestSWAUtils::test_update_bn_cnn, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_Adadelta_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_Adam_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_RMSprop_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_AdamW_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_LBFGS_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_RMSprop_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_Rprop_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_SGD_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_capturable_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_capturable_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_fused_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_defaults_changed_to_foreach_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_defaults_changed_to_foreach_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_defaults_changed_to_foreach_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_defaults_changed_to_foreach_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_defaults_changed_to_foreach_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_errors_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_errors_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_errors_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_foreach_large_tensor_Adagrad_cpu_float16, test/test_optim.py::TestOptimRenewedCPU::test_foreach_large_tensor_Adamax_cpu_float16, 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test/test_optim.py::TestOptimRenewedCPU::test_forloop_goes_right_direction_multigpu_with_lrsched_False_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_forloop_goes_right_direction_multigpu_with_lrsched_False_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_forloop_goes_right_direction_multigpu_with_lrsched_False_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_forloop_goes_right_direction_multigpu_with_lrsched_False_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_forloop_goes_right_direction_multigpu_with_lrsched_True_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_forloop_goes_right_direction_multigpu_with_lrsched_True_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_forloop_goes_right_direction_multigpu_with_lrsched_True_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_forloop_goes_right_direction_multigpu_with_lrsched_True_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_forloop_goes_right_direction_multigpu_with_lrsched_True_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_forloop_goes_right_direction_multigpu_with_lrsched_True_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_fused_cpu_matches_cuda_AdamW_cpu_float16, test/test_optim.py::TestOptimRenewedCPU::test_fused_cpu_matches_cuda_SGD_cpu_bfloat16, test/test_optim.py::TestOptimRenewedCPU::test_fused_cpu_matches_cuda_SGD_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_fused_does_not_step_if_foundinf_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_fused_error_on_params_on_meta_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_fused_error_on_params_on_meta_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_fused_error_on_params_on_meta_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_fused_large_tensor_AdamW_cpu_float16, test/test_optim.py::TestOptimRenewedCPU::test_fused_large_tensor_SGD_cpu_float16, test/test_optim.py::TestOptimRenewedCPU::test_fused_matches_forloop_Adagrad_cpu_bfloat16, test/test_optim.py::TestOptimRenewedCPU::test_fused_matches_forloop_Adagrad_cpu_float16, test/test_optim.py::TestOptimRenewedCPU::test_fused_matches_forloop_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_fused_matches_forloop_AdamW_cpu_float16, test/test_optim.py::TestOptimRenewedCPU::test_fused_matches_forloop_Adam_cpu_float16, test/test_optim.py::TestOptimRenewedCPU::test_fused_matches_forloop_SGD_cpu_bfloat16, test/test_optim.py::TestOptimRenewedCPU::test_grads_are_never_inplaced_into_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_grads_are_never_inplaced_into_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_grads_are_never_inplaced_into_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_grads_are_never_inplaced_into_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_nontensor_step_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_nontensor_step_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_nontensor_step_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_nontensor_step_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_state_dict_post_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_state_dict_post_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_state_dict_post_hook_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_state_dict_pre_hook_and_prepend_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_state_dict_pre_hook_and_prepend_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_state_dict_pre_hook_and_prepend_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_state_dict_pre_hook_and_prepend_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_state_dict_pre_post_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_state_dict_pre_post_hook_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_state_dict_pre_post_hook_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_state_dict_pre_post_hook_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_state_dict_pre_post_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_load_state_dict_pre_post_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_mixed_device_dtype_impl_foreach_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_mixed_device_dtype_impl_foreach_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_mixed_device_dtype_impl_fused_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_mixed_device_dtype_impl_fused_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_mixed_device_dtype_impl_fused_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_non_empty_state_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_non_empty_state_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_non_empty_state_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_peak_memory_foreach_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_peak_memory_foreach_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_peak_memory_foreach_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_peak_memory_foreach_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_ASGD_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_Adam_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_Adamax_cpu_float32 2025-08-14T22:33:16.5735384Z 2025-08-14T22:33:19.5322022Z /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. 2025-08-14T22:33:19.5324912Z import pkg_resources 2025-08-14T22:33:19.6504541Z Running test_ops_jit 1/1 ... [2025-08-14 22:33:19.650086] 2025-08-14T22:33:19.6505208Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:33:19.6508359Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_jit.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:33:19.650476] 2025-08-14T22:33:28.3792485Z 2025-08-14T22:33:28.3794375Z test_ops_jit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_jit_1.1_43579a9c69eeb050_.log 2025-08-14T22:33:28.4529337Z Running 1137 items in this shard: test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_abs_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_acos_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_acosh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_asin_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_asinh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_atan2_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_atan_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_atanh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_cat_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_clamp_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_digamma_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_div_floor_rounding_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_div_no_rounding_mode_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_div_trunc_rounding_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_erf_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_erfc_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_erfinv_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_exp2_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_expm1_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_ge_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_gt_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_i0_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_igamma_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_igammac_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_le_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_lgamma_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_linalg_det_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_linalg_householder_product_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_linalg_inv_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_linalg_matrix_power_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_log1p_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_log_softmax_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_log_softmax_with_dtype_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_logit_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_logsumexp_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_lt_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_mH_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_matmul_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_matrix_exp_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_max_binary_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_min_binary_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_movedim_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_mul_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_ne_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_neg_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_nn_functional_conv1d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_nn_functional_conv2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_nn_functional_conv3d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_nn_functional_conv_transpose1d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_nn_functional_conv_transpose2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_nn_functional_conv_transpose3d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_nn_functional_group_norm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_nn_functional_layer_norm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_nn_functional_rms_norm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_outer_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_round_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_round_decimals_0_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_round_decimals_3_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_round_decimals_neg_3_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_sigmoid_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_sinc_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_softmax_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_softmax_with_dtype_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_sub_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_tanh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_transpose_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_trunc_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_vstack_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_xlogy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_H_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_H_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_T_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_T_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___getitem___cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___getitem___cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___radd___cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___radd___cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___rdiv___cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___rdiv___cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___rmatmul___cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___rmatmul___cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___rmod___cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___rmul___cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___rmul___cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___rpow___cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___rpow___cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___rsub___cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit___rsub___cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit__batch_norm_with_update_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit__chunk_cat_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit__chunk_cat_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit__native_batch_norm_legit_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit__segment_reduce_lengths_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit__segment_reduce_offsets_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit__softmax_backward_data_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit__unsafe_masked_index_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit__unsafe_masked_index_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit__unsafe_masked_index_put_accumulate_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit__unsafe_masked_index_put_accumulate_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit__upsample_bilinear2d_aa_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_abs_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_abs_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_acos_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_acos_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_acosh_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_acosh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_add_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_add_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addbmm_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addbmm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addcdiv_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addcdiv_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addcmul_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addcmul_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addmm_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addmm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addmm_decomposed_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addmm_decomposed_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addmv_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addmv_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addr_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_addr_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_alias_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_alias_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_all_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_all_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_allclose_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_allclose_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_amax_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_amin_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_aminmax_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_angle_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_angle_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_any_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_any_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_arange_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_argmax_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_argmin_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_argsort_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_argwhere_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_argwhere_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_as_strided_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_as_strided_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_as_strided_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_as_strided_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_as_strided_partial_views_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_as_strided_partial_views_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_as_strided_scatter_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_as_strided_scatter_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_asin_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_asin_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_asinh_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_asinh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_atan2_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_atan_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_atan_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_atanh_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_atanh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_atleast_1d_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_atleast_1d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_atleast_2d_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_atleast_2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_atleast_3d_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_atleast_3d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_baddbmm_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_baddbmm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_bernoulli_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_bfloat16_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_bfloat16_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_block_diag_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_block_diag_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_bmm_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_bmm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_bool_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_bool_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_broadcast_shapes_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_broadcast_tensors_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_broadcast_tensors_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_broadcast_to_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_broadcast_to_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_bucketize_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_byte_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_byte_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cartesian_prod_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cartesian_prod_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cat_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cat_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cauchy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cdist_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cdouble_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cdouble_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_ceil_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cfloat_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cfloat_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_chalf_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_chalf_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_char_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_char_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cholesky_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cholesky_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cholesky_inverse_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cholesky_inverse_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cholesky_solve_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cholesky_solve_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_chunk_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_chunk_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_clamp_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_clamp_max_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_clamp_min_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_clone_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_clone_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_column_stack_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_column_stack_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_combinations_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_combinations_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_complex_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_conj_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_conj_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_conj_physical_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_conj_physical_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_constant_pad_nd_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_constant_pad_nd_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_contiguous_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_contiguous_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_copysign_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_corrcoef_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_corrcoef_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cos_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cos_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cosh_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cosh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_count_nonzero_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_count_nonzero_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cov_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cov_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cross_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_cross_cpu_float32, 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test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_diag_embed_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_diag_embed_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_diagflat_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_diagflat_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_diagonal_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_diagonal_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_diagonal_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_diagonal_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_diagonal_scatter_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_diagonal_scatter_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_diff_cpu_complex64, 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test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_isin_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_isinf_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_isinf_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_isnan_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_isnan_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_isneginf_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_isposinf_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_isreal_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_isreal_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_istft_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_item_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_item_cpu_float32, 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test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_mul_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_mul_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_multinomial_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_mv_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_mv_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nan_to_num_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nanmean_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nanmedian_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nanquantile_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nansum_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_narrow_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_narrow_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_narrow_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_narrow_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_native_batch_norm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_native_dropout_backward_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_native_layer_norm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_ne_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_ne_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_neg_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_neg_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_new_empty_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_new_empty_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_new_empty_strided_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_new_empty_strided_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_new_full_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_new_full_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_new_ones_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_new_ones_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_new_zeros_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_new_zeros_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nextafter_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_alpha_dropout_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_avg_pool1d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_avg_pool2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_avg_pool3d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_batch_norm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_bilinear_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_binary_cross_entropy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_celu_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_channel_shuffle_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_channel_shuffle_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv1d_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv1d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv2d_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv3d_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv3d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv_transpose1d_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv_transpose1d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv_transpose2d_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv_transpose2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv_transpose3d_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv_transpose3d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_cosine_embedding_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_cosine_similarity_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_cross_entropy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_ctc_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_dropout2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_dropout3d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_dropout_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_elu_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_embedding_bag_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_embedding_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_feature_alpha_dropout_without_train_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_fractional_max_pool2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_fractional_max_pool3d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_gaussian_nll_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_gelu_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_glu_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_grid_sample_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_group_norm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_hardshrink_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_hardsigmoid_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_hardswish_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_hardtanh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_hinge_embedding_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_huber_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_instance_norm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_interpolate_area_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_interpolate_bicubic_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_interpolate_linear_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_interpolate_nearest-exact_cpu_float32, 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test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_local_response_norm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_logsigmoid_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_margin_ranking_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_max_pool1d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_max_pool2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_max_pool3d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_max_unpool1d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_max_unpool2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_max_unpool2d_grad_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_max_unpool3d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_max_unpool3d_grad_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_mish_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_mse_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_multi_head_attention_forward_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_multi_margin_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_nll_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_normalize_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_normalize_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pad_circular_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pad_circular_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pad_constant_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pad_constant_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pad_reflect_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pad_reflect_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pad_replicate_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pad_replicate_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pad_replicate_negative_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pad_replicate_negative_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pairwise_distance_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pairwise_distance_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pdist_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pixel_shuffle_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pixel_shuffle_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pixel_unshuffle_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_poisson_nll_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_prelu_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_relu6_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_relu_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_rms_norm_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_rms_norm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_rrelu_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_selu_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_silu_complex_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_silu_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_smooth_l1_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_soft_margin_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_softmin_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_softmin_with_dtype_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_softmin_with_dtype_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_softplus_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_softshrink_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_softsign_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_softsign_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_tanhshrink_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_tanhshrink_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_threshold_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_triplet_margin_loss_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_triplet_margin_loss_cpu_float32, 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test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_squeeze_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_squeeze_multiple_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_squeeze_multiple_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_stack_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_stack_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_std_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_std_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_std_mean_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_std_mean_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_std_mean_unbiased_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_std_mean_unbiased_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_std_unbiased_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_std_unbiased_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_stft_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_stft_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sub_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sub_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sum_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sum_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sum_to_size_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sum_to_size_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_svd_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_svd_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_svd_lowrank_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_svd_lowrank_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_t_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_t_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_t_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_t_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_take_along_dim_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_take_along_dim_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_take_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_take_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tan_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tan_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tanh_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tanh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tensor_split_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tensor_split_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tensordot_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tensordot_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tile_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tile_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_to_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_to_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_to_sparse_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_to_sparse_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_topk_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_trace_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_trace_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_transpose_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_transpose_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_transpose_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_transpose_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_trapezoid_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_trapezoid_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_trapz_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_trapz_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_triangular_solve_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_triangular_solve_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tril_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tril_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_triu_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_triu_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_true_divide_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_true_divide_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_trunc_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unbind_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unbind_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unbind_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unbind_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unflatten_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unflatten_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unfold_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unfold_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unfold_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unfold_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_uniform_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_uniform_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unique_consecutive_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unique_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsafe_chunk_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsafe_chunk_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsafe_split_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsafe_split_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsqueeze_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsqueeze_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsqueeze_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsqueeze_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_mean_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_mean_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_mean_unbiased_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_mean_unbiased_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_unbiased_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_unbiased_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_vdot_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_vdot_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_as_complex_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_as_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_as_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_as_real_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_vsplit_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_vsplit_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_vstack_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_vstack_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_where_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_where_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_xlogy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_zero__cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_zero__cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_zeros_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_zeros_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_zeros_like_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_zeros_like_cpu_float32 2025-08-14T22:33:28.4918500Z 2025-08-14T22:33:31.4158234Z /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. 2025-08-14T22:33:31.4160995Z import pkg_resources 2025-08-14T22:33:31.5341656Z Running lazy/test_step_closures 1/1 ... [2025-08-14 22:33:31.533805] 2025-08-14T22:33:31.5342252Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:33:31.5345819Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_step_closures.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'] ... [2025-08-14 22:33:31.534176] 2025-08-14T22:33:38.1592428Z 2025-08-14T22:33:38.1593868Z lazy/test_step_closures 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_step_closures_1.1_5e8c17dadde468a1_.log 2025-08-14T22:33:38.1596009Z Running 4 items in this shard: test/lazy/test_step_closures.py::ClosuresTest::test_asynchronous, test/lazy/test_step_closures.py::ClosuresTest::test_asynchronous_exception, test/lazy/test_step_closures.py::ClosuresTest::test_synchronous, test/lazy/test_step_closures.py::ClosuresTest::test_synchronous_exception 2025-08-14T22:33:38.1597342Z 2025-08-14T22:33:41.3242106Z /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. 2025-08-14T22:33:41.3245061Z import pkg_resources 2025-08-14T22:33:41.4441604Z Running test_ops_gradients 1/1 ... [2025-08-14 22:33:41.443745] 2025-08-14T22:33:41.4442391Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:33:41.4444542Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_gradients.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:33:41.444109] 2025-08-14T22:33:43.6875725Z 2025-08-14T22:33:43.6877312Z torch_np/numpy_tests/core/test_einsum 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_einsum_1.1_76d5dc576de8c98e_.log 2025-08-14T22:33:43.6910310Z Running 50 items in this shard: test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_broadcasting_dot_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_collapse, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_combined_views_mapping, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_complex, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_B, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_D, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_F, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_b, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_d, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_e, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_f, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_h, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_i, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_l, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_edge_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_all_contig_non_contig_output, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_broadcast, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_errors, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_failed_on_p9_and_s390x, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_fixed_collapsingbug, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_fixedstridebug, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_misc, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_cfloat128, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_cfloat64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float16, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float32, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int16, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int32, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int8, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_uint8, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_views, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_expand, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_hadamard_like_products, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_index_transformations, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_inner_product, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_out_is_res, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_output_order, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_random_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_small_boolean_arrays, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_subscript_range, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_edge_paths, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_long_paths, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_memory_contraints, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input_internal_trace, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input_invalid, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_spaces, test/torch_np/numpy_tests/core/test_einsum.py::TestMisc::test_overlap 2025-08-14T22:33:43.6941813Z 2025-08-14T22:33:46.8376986Z /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. 2025-08-14T22:33:46.8378504Z import pkg_resources 2025-08-14T22:33:46.9590353Z Running test_fx_reinplace_pass 1/1 ... [2025-08-14 22:33:46.958672] 2025-08-14T22:33:46.9590866Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:33:46.9593302Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fx_reinplace_pass.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'] ... [2025-08-14 22:33:46.959018] 2025-08-14T22:34:01.6947229Z 2025-08-14T22:34:01.6948457Z test_fx_reinplace_pass 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_reinplace_pass_1.1_6dc578fba2b3321a_.log 2025-08-14T22:34:01.6957000Z Running 12 items in this shard: test/test_fx_reinplace_pass.py::TestReinplacePass::test_out_node_updated, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_basic, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_different_metadata, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_index_mutation, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_overlapping_memory, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_scatter_op, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_scatter_twice, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_scatter_twice_with_different_view_op_invalid, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_scatter_twice_with_different_view_op_invalid2, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_scatter_twice_with_different_view_op_valid, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_sym_input, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_with_view 2025-08-14T22:34:01.6965184Z 2025-08-14T22:34:04.3991316Z 2025-08-14T22:34:04.3992422Z test_ops_gradients 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_gradients_1.1_90f159befb8f6bc7_.log 2025-08-14T22:34:04.6136736Z Running 5384 items in this shard: test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_H_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_H_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyCatCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyCubeCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyMulCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyMulScalarCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyNMSCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyNonzeroCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpySortCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpySplitCopyCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpySplitCopyWithIntCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyTakeCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyViewCopyCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_T_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_T_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___getitem___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___getitem___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___radd___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___radd___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rdiv___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rdiv___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rmatmul___cpu_complex128, 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test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__chunk_cat_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__native_batch_norm_legit_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__segment_reduce_lengths_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__segment_reduce_offsets_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__softmax_backward_data_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__unsafe_masked_index_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__unsafe_masked_index_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__unsafe_masked_index_put_accumulate_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__unsafe_masked_index_put_accumulate_cpu_float64, 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test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addbmm_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addcdiv_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addcdiv_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addcmul_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addcmul_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmm_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmm_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmm_decomposed_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmm_decomposed_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmv_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmv_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addr_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addr_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_alias_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_alias_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_all_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_all_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_allclose_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_allclose_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_amax_cpu_float64, 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test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_bessel_y0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_bessel_y1_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_chebyshev_polynomial_t_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_chebyshev_polynomial_u_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_chebyshev_polynomial_v_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_chebyshev_polynomial_w_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_entr_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_erfcx_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_hermite_polynomial_h_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_hermite_polynomial_he_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_i0e_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_i1_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_i1e_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_laguerre_polynomial_l_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_legendre_polynomial_p_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_log_ndtr_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_modified_bessel_i0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_modified_bessel_i1_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_modified_bessel_k0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_modified_bessel_k1_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_ndtr_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_ndtri_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_polygamma_special_polygamma_n_0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_scaled_modified_bessel_k0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_scaled_modified_bessel_k1_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_t_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_u_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_v_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_w_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_spherical_bessel_j0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_xlog1py_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_zeta_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_list_args_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_list_args_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_with_sizes_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_with_sizes_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_with_sizes_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_with_sizes_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sqrt_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sqrt_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_square_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_square_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_multiple_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_multiple_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_stack_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_stack_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_unbiased_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_unbiased_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_unbiased_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_unbiased_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_stft_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_stft_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sub_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sub_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sum_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sum_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sum_to_size_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sum_to_size_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_lowrank_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_lowrank_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_t_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_t_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_t_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_t_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_take_along_dim_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_take_along_dim_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_take_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_take_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tan_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tan_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tanh_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tanh_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tensor_split_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tensor_split_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tensordot_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tensordot_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tile_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tile_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_sparse_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_sparse_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_topk_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_torch_ops_aten__safe_softmax_default_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trace_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trace_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_transpose_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_transpose_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_transpose_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_transpose_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trapezoid_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trapezoid_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trapz_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trapz_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_triangular_solve_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_triangular_solve_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tril_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tril_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_triu_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_triu_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_true_divide_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_true_divide_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_trunc_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unbind_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unbind_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unbind_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unbind_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unflatten_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unflatten_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unfold_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unfold_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unfold_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unfold_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_uniform_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_uniform_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unique_consecutive_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unique_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsafe_chunk_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsafe_chunk_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsafe_split_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsafe_split_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsqueeze_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsqueeze_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsqueeze_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsqueeze_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_unbiased_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_unbiased_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_unbiased_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_unbiased_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vdot_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vdot_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_complex_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_real_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vsplit_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vsplit_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vstack_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vstack_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_where_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_where_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_xlogy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zero__cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zero__cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_like_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_like_cpu_float64 2025-08-14T22:34:04.8291336Z 2025-08-14T22:34:04.8425175Z /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. 2025-08-14T22:34:04.8426711Z import pkg_resources 2025-08-14T22:34:04.9633641Z Running nn/test_multihead_attention 1/1 ... [2025-08-14 22:34:04.962971] 2025-08-14T22:34:04.9634325Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:34:04.9636467Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_multihead_attention.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:34:04.963341] 2025-08-14T22:34:07.5070247Z /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. 2025-08-14T22:34:07.5071920Z import pkg_resources 2025-08-14T22:34:07.6258262Z Running functorch/test_aot_joint_with_descriptors 1/1 ... [2025-08-14 22:34:07.625476] 2025-08-14T22:34:07.6258819Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:34:07.6261347Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aot_joint_with_descriptors.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'] ... [2025-08-14 22:34:07.625859] 2025-08-14T22:34:18.2076135Z 2025-08-14T22:34:18.2077832Z functorch/test_aot_joint_with_descriptors 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aot_joint_with_descriptors_1.1_7a02ef2f5bff081f_.log 2025-08-14T22:34:18.2085538Z Running 10 items in this shard: test/functorch/test_aot_joint_with_descriptors.py::TestAOTJointWithDescriptors::test_conv_bn_module, test/functorch/test_aot_joint_with_descriptors.py::TestAOTJointWithDescriptors::test_export_and_compile, test/functorch/test_aot_joint_with_descriptors.py::TestAOTJointWithDescriptors::test_fx_utils_conv_bn_module, test/functorch/test_aot_joint_with_descriptors.py::TestAOTJointWithDescriptors::test_fx_utils_multiple_outputs, test/functorch/test_aot_joint_with_descriptors.py::TestAOTJointWithDescriptors::test_fx_utils_node_consistency, test/functorch/test_aot_joint_with_descriptors.py::TestAOTJointWithDescriptors::test_fx_utils_simple_linear, test/functorch/test_aot_joint_with_descriptors.py::TestAOTJointWithDescriptors::test_in_out_specs, test/functorch/test_aot_joint_with_descriptors.py::TestAOTJointWithDescriptors::test_module_with_kwargs, test/functorch/test_aot_joint_with_descriptors.py::TestAOTJointWithDescriptors::test_multiple_outputs_module, test/functorch/test_aot_joint_with_descriptors.py::TestAOTJointWithDescriptors::test_simple_linear_module 2025-08-14T22:34:18.2090519Z 2025-08-14T22:34:21.3315977Z /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. 2025-08-14T22:34:21.3318847Z import pkg_resources 2025-08-14T22:34:21.4529465Z Running test_pruning_op 1/1 ... [2025-08-14 22:34:21.452524] 2025-08-14T22:34:21.4530227Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:34:21.4532835Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_pruning_op.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:34:21.452897] 2025-08-14T22:34:25.9744913Z 2025-08-14T22:34:25.9746216Z test_pruning_op 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_pruning_op_1.1_dec15f55e50e5273_.log 2025-08-14T22:34:25.9748682Z Running 2 items in this shard: test/test_pruning_op.py::PruningOpTest::test_rowwise_prune_op_32bit_indices, test/test_pruning_op.py::PruningOpTest::test_rowwise_prune_op_64bit_indices 2025-08-14T22:34:25.9750115Z 2025-08-14T22:34:29.1098539Z /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. 2025-08-14T22:34:29.1100075Z import pkg_resources 2025-08-14T22:34:29.2301023Z Running lazy/test_functionalization 1/1 ... [2025-08-14 22:34:29.229705] 2025-08-14T22:34:29.2301657Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:34:29.2303638Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_functionalization.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:34:29.230064] 2025-08-14T22:34:34.4037297Z 2025-08-14T22:34:34.4038625Z lazy/test_functionalization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_functionalization_1.1_62e525e7e354f0f0_.log 2025-08-14T22:34:34.4040921Z Running 2 items in this shard: test/lazy/test_functionalization.py::LazyFuncionalizationTest::test_data_assign, test/lazy/test_functionalization.py::LazyFuncionalizationTest::test_lazy_init_with_view 2025-08-14T22:34:34.4042262Z 2025-08-14T22:34:37.5677133Z /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. 2025-08-14T22:34:37.5678674Z import pkg_resources 2025-08-14T22:34:37.6872200Z Running functorch/test_ac 1/1 ... [2025-08-14 22:34:37.686852] 2025-08-14T22:34:37.6872703Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:34:37.6875140Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ac.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:34:37.687224] 2025-08-14T22:34:44.6609997Z 2025-08-14T22:34:44.6611342Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_7ad7b6c1fb0fc2da_.log 2025-08-14T22:34:44.6612542Z 2025-08-14T22:34:48.1387615Z /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. 2025-08-14T22:34:48.1392015Z import pkg_resources 2025-08-14T22:34:48.2598690Z Running test_ops_fwd_gradients 1/1 ... [2025-08-14 22:34:48.259440] 2025-08-14T22:34:48.2600313Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:34:48.2603549Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_fwd_gradients.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:34:48.259879] 2025-08-14T22:34:50.8804101Z 2025-08-14T22:34:50.8805674Z test_legacy_vmap 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_2.2_e29d6e32e6177520_.log 2025-08-14T22:34:50.8844677Z Running 66 items in this shard: test/test_legacy_vmap.py::TestVmapAPILegacy::test_backward_unsupported_interaction, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_multiple_returns, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_warns_when_warnings_are_enabled, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_with_undefined_grad, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_zero_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_func_with_no_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_functools_partial, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_nary_different_levels, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_unary, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_with_different_map_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_zero_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_none_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_noop_in_inner_vmap, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dim_out_of_bounds_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_and_num_outputs_mismatch_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_edge_case, test/test_legacy_vmap.py::TestVmapAPILegacy::test_single_input, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_T_numpy, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_as_strided, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_binary_pointwise_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_clamp, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_clone, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_conj, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_diagonal, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_dot, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_expand_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_fill_and_zero_inplace, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_mm, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_movedim, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_mv, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_narrow, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_empty, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_real, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_reshape_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_select, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_stride, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_sum_dim, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_t, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_tensor_split, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_trace, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_transpose, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unbind, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as_real, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_vmap_fallback_check, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_vmap_fallback_check_ok, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_binary_cross_entropy_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_div_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_expand_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_index_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_inplace_manyview_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_log1p_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_log_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_logsumexp_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_max_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_min_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_mul_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_sigmoid_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_slice_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_stack_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_threshold_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_trace_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_unrelated_output_multiple_grad_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_vmap_fallback_check, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_vmap_fallback_check_ok 2025-08-14T22:34:50.8882368Z 2025-08-14T22:34:53.9509571Z /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. 2025-08-14T22:34:53.9511102Z import pkg_resources 2025-08-14T22:34:54.0692178Z Running test_hub 1/1 ... [2025-08-14 22:34:54.068841] 2025-08-14T22:34:54.0692633Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:34:54.0695469Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_hub.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:34:54.069209] 2025-08-14T22:35:03.8489314Z 2025-08-14T22:35:03.8498690Z test_ops_fwd_gradients 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_fwd_gradients_1.1_60deb54a71b1351a_.log 2025-08-14T22:35:04.0917778Z Running 3192 items in this shard: test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_H_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_H_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_T_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_T_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___getitem___cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___getitem___cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___radd___cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___radd___cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rdiv___cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rdiv___cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rmatmul___cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rmatmul___cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rmod___cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rmul___cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rmul___cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rpow___cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rpow___cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rsub___cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rsub___cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__batch_norm_with_update_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__chunk_cat_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__chunk_cat_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__native_batch_norm_legit_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__segment_reduce_lengths_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__segment_reduce_offsets_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__softmax_backward_data_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__unsafe_masked_index_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__unsafe_masked_index_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__unsafe_masked_index_put_accumulate_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__unsafe_masked_index_put_accumulate_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__upsample_bilinear2d_aa_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_abs_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_abs_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_acos_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_acos_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_acosh_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_acosh_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_add_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_add_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addbmm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addbmm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addcdiv_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addcdiv_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addcmul_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addcmul_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addmm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addmm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addmm_decomposed_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addmm_decomposed_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addmv_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addmv_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addr_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addr_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_alias_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_alias_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_all_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_all_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_allclose_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_allclose_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_amax_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_amin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_aminmax_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_angle_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_angle_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_any_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_any_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_arange_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_argmax_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_argmin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_argsort_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_argwhere_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_argwhere_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_as_strided_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_as_strided_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_as_strided_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_as_strided_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_as_strided_partial_views_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_as_strided_partial_views_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_as_strided_scatter_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_as_strided_scatter_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_asin_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_asin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_asinh_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_asinh_cpu_float64, 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test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_movedim_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_msort_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mul_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mul_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_multinomial_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mv_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mv_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nan_to_num_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nanmean_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nanmedian_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nanquantile_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nansum_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_narrow_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_narrow_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_narrow_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_narrow_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_native_batch_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_native_dropout_backward_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_native_layer_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ne_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ne_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_neg_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_neg_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_empty_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_empty_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_empty_strided_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_empty_strided_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_full_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_full_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_ones_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_ones_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_zeros_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_zeros_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nextafter_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_avg_pool1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_avg_pool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_max_pool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_max_pool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_alpha_dropout_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_avg_pool1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_avg_pool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_avg_pool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_batch_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_bilinear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_binary_cross_entropy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_celu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_channel_shuffle_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_channel_shuffle_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv1d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv2d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv3d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose1d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose2d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose3d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_cosine_embedding_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_cosine_similarity_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_cross_entropy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_ctc_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_dropout2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_dropout3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_dropout_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_elu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_embedding_bag_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_embedding_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_feature_alpha_dropout_with_train_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_feature_alpha_dropout_without_train_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_feature_alpha_dropout_without_train_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_fractional_max_pool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_fractional_max_pool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_gaussian_nll_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_gelu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_glu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_grid_sample_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_group_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_hardshrink_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_hardsigmoid_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_hardswish_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_hardtanh_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_hinge_embedding_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_huber_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_instance_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_area_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_bicubic_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_bilinear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_linear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_nearest-exact_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_nearest_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_trilinear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_kl_div_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_l1_loss_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_l1_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_layer_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_leaky_relu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_linear_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_linear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_local_response_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_logsigmoid_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_margin_ranking_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_pool1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_pool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_pool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool1d_grad_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool2d_grad_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool3d_grad_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_mish_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_mse_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_multi_head_attention_forward_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_multi_margin_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_multilabel_margin_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_multilabel_soft_margin_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_nll_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_normalize_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_normalize_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_circular_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_circular_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_constant_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_constant_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_reflect_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_reflect_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_replicate_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_replicate_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_replicate_negative_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_replicate_negative_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pairwise_distance_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pairwise_distance_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pdist_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pixel_shuffle_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pixel_shuffle_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pixel_unshuffle_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pixel_unshuffle_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_poisson_nll_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_prelu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_relu6_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_relu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_rms_norm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_rms_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_rrelu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_scaled_dot_product_attention_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_selu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_silu_complex_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_silu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_smooth_l1_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_soft_margin_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softmin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softmin_with_dtype_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softmin_with_dtype_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softplus_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softshrink_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softsign_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softsign_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_tanhshrink_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_tanhshrink_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_threshold_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_triplet_margin_loss_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_triplet_margin_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_triplet_margin_with_distance_loss_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_unfold_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_unfold_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_upsample_bilinear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_upsample_nearest_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nonzero_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nonzero_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nonzero_static_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nonzero_static_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_norm_cpu_complex128, 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test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_normal_in_place_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_normal_number_mean_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ones_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ones_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ones_like_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ones_like_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ormqr_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ormqr_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_outer_cpu_complex128, 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test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_entr_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_erfcx_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_hermite_polynomial_h_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_hermite_polynomial_he_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_i0e_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_i1_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_i1e_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_laguerre_polynomial_l_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_legendre_polynomial_p_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_log_ndtr_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_modified_bessel_i0_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_modified_bessel_i1_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_modified_bessel_k0_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_modified_bessel_k1_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_ndtr_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_ndtri_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_polygamma_special_polygamma_n_0_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_scaled_modified_bessel_k0_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_scaled_modified_bessel_k1_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_shifted_chebyshev_polynomial_t_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_shifted_chebyshev_polynomial_u_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_shifted_chebyshev_polynomial_v_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_shifted_chebyshev_polynomial_w_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_spherical_bessel_j0_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_xlog1py_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_zeta_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_split_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_split_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_split_list_args_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_split_list_args_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_split_with_sizes_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_split_with_sizes_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_split_with_sizes_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_split_with_sizes_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sqrt_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sqrt_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_square_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_square_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_squeeze_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_squeeze_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_squeeze_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_squeeze_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_squeeze_multiple_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_squeeze_multiple_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_stack_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_stack_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_std_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_std_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_std_mean_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_std_mean_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_std_mean_unbiased_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_std_mean_unbiased_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_std_unbiased_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_std_unbiased_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_stft_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_stft_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sub_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sub_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sum_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sum_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sum_to_size_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sum_to_size_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_svd_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_svd_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_svd_lowrank_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_svd_lowrank_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_t_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_t_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_t_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_t_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_take_along_dim_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_take_along_dim_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_take_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_take_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tan_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tan_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tanh_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tanh_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tensor_split_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tensor_split_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tensordot_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tensordot_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tile_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tile_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_to_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_to_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_to_sparse_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_to_sparse_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_topk_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_torch_ops_aten__safe_softmax_default_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trace_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trace_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_transpose_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_transpose_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_transpose_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_transpose_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trapezoid_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trapezoid_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trapz_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trapz_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_triangular_solve_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_triangular_solve_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tril_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tril_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_triu_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_triu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_true_divide_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_true_divide_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trunc_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unbind_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unbind_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unbind_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unbind_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unflatten_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unflatten_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unfold_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unfold_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unfold_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unfold_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_uniform_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_uniform_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unique_consecutive_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unique_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsafe_chunk_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsafe_chunk_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsafe_split_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsafe_split_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsqueeze_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsqueeze_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsqueeze_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsqueeze_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_mean_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_mean_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_mean_unbiased_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_mean_unbiased_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_unbiased_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_unbiased_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vdot_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vdot_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_as_complex_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_as_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_as_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_as_real_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vsplit_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vsplit_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vstack_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vstack_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_where_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_where_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_xlogy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_zero__cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_zero__cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_zeros_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_zeros_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_zeros_like_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_zeros_like_cpu_float64 2025-08-14T22:35:04.3649204Z 2025-08-14T22:35:07.2954136Z /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. 2025-08-14T22:35:07.2957051Z import pkg_resources 2025-08-14T22:35:07.4133192Z Running test_set_default_mobile_cpu_allocator 1/1 ... [2025-08-14 22:35:07.412946] 2025-08-14T22:35:07.4134167Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:35:07.4136823Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_set_default_mobile_cpu_allocator.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'] ... [2025-08-14 22:35:07.413349] 2025-08-14T22:35:13.1878190Z 2025-08-14T22:35:13.1879740Z test_set_default_mobile_cpu_allocator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_set_default_mobile_cpu_allocator_1.1_0aed78abc210c8b2_.log 2025-08-14T22:35:13.1882148Z Running 2 items in this shard: test/test_set_default_mobile_cpu_allocator.py::TestSetDefaultMobileCPUAllocator::test_exception, test/test_set_default_mobile_cpu_allocator.py::TestSetDefaultMobileCPUAllocator::test_no_exception 2025-08-14T22:35:13.1883158Z 2025-08-14T22:35:16.2727029Z /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. 2025-08-14T22:35:16.2732398Z import pkg_resources 2025-08-14T22:35:16.4497563Z Running test_stateless 1/1 ... [2025-08-14 22:35:16.449325] 2025-08-14T22:35:16.4501021Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:35:16.4507427Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_stateless.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:35:16.449776] 2025-08-14T22:35:16.9003931Z 2025-08-14T22:35:16.9005599Z nn/test_multihead_attention 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_multihead_attention_1.1_8dea6b666b16d01a_.log 2025-08-14T22:35:16.9025801Z Running 19 items in this shard: test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attention_average_attn_weights_False, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attention_average_attn_weights_True, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_3d_attn_mask, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_fast_path_invalid_shape, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_invalid_shape, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_nested_tensor_outside_fast_path, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_no_bias, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_batch_first_cpu_float16, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_batch_first_cpu_float32, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_batch_first_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_cpu_float16, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_cpu_float32, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attn_fast_path_query_and_bias_have_different_dtypes_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attn_fast_path_small_test_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attn_in_proj_bias_none_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attn_in_proj_weight_none_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_self_attn_two_masks_fast_path_cpu, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_self_attn_two_masks_fast_path_mock_cpu 2025-08-14T22:35:16.9045090Z 2025-08-14T22:35:20.1013866Z /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. 2025-08-14T22:35:20.1015391Z import pkg_resources 2025-08-14T22:35:20.2201590Z Running distributions/test_constraints 1/1 ... [2025-08-14 22:35:20.219792] 2025-08-14T22:35:20.2202161Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:35:20.2205359Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_constraints.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:35:20.220159] 2025-08-14T22:35:23.9928610Z 2025-08-14T22:35:23.9930337Z distributions/test_constraints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_constraints_1.1_f2f0ad39ca34ed7e_.log 2025-08-14T22:35:23.9981467Z Running 136 items in this shard: test/distributions/test_constraints.py::test_constraint[False-constraint_fn0-False-value0], test/distributions/test_constraints.py::test_constraint[False-constraint_fn1-False-value1], test/distributions/test_constraints.py::test_constraint[False-constraint_fn2-False-value2], test/distributions/test_constraints.py::test_constraint[False-constraint_fn3-True-value3], test/distributions/test_constraints.py::test_constraint[False-constraint_fn4-False-value4], test/distributions/test_constraints.py::test_constraint[False-constraint_fn5-False-value5], test/distributions/test_constraints.py::test_constraint[False-constraint_fn6-True-value6], test/distributions/test_constraints.py::test_constraint[False-constraint_fn7-True-value7], test/distributions/test_constraints.py::test_constraint[False-constraint_fn8-False-value8], test/distributions/test_constraints.py::test_constraint[False-constraint_fn9-True-value9], test/distributions/test_constraints.py::test_constraint[False-constraint_fn10-False-value10], test/distributions/test_constraints.py::test_constraint[False-constraint_fn11-False-value11], test/distributions/test_constraints.py::test_constraint[False-constraint_fn12-True-value12], test/distributions/test_constraints.py::test_constraint[False-constraint_fn13-True-value13], test/distributions/test_constraints.py::test_constraint[False-constraint_fn14-False-value14], test/distributions/test_constraints.py::test_constraint[False-constraint_fn15-True-value15], test/distributions/test_constraints.py::test_constraint[False-constraint_fn16-True-value16], test/distributions/test_constraints.py::test_constraint[False-constraint_fn17-True-value17], test/distributions/test_constraints.py::test_constraint[True-constraint_fn0-False-value0], test/distributions/test_constraints.py::test_constraint[True-constraint_fn1-False-value1], test/distributions/test_constraints.py::test_constraint[True-constraint_fn2-False-value2], test/distributions/test_constraints.py::test_constraint[True-constraint_fn3-True-value3], test/distributions/test_constraints.py::test_constraint[True-constraint_fn4-False-value4], test/distributions/test_constraints.py::test_constraint[True-constraint_fn5-False-value5], test/distributions/test_constraints.py::test_constraint[True-constraint_fn6-True-value6], test/distributions/test_constraints.py::test_constraint[True-constraint_fn7-True-value7], test/distributions/test_constraints.py::test_constraint[True-constraint_fn8-False-value8], test/distributions/test_constraints.py::test_constraint[True-constraint_fn9-True-value9], test/distributions/test_constraints.py::test_constraint[True-constraint_fn10-False-value10], test/distributions/test_constraints.py::test_constraint[True-constraint_fn11-False-value11], test/distributions/test_constraints.py::test_constraint[True-constraint_fn12-True-value12], test/distributions/test_constraints.py::test_constraint[True-constraint_fn13-True-value13], test/distributions/test_constraints.py::test_constraint[True-constraint_fn14-False-value14], test/distributions/test_constraints.py::test_constraint[True-constraint_fn15-True-value15], test/distributions/test_constraints.py::test_constraint[True-constraint_fn16-True-value16], test/distributions/test_constraints.py::test_constraint[True-constraint_fn17-True-value17], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn0-args0], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn1-args1], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn2-args2], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args3], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args4], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args5], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args6], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args10], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args11], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args12], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args13], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn14-args14], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args15], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args16], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args17], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn21-args21], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn22-args22], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn23-args23], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn24-args24], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn0-args0], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn1-args1], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn2-args2], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args3], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args4], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args5], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args6], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args10], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args11], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args12], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args13], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn14-args14], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args15], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args16], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args17], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn21-args21], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn22-args22], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn23-args23], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn24-args24], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn0-args0], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn1-args1], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn2-args2], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args3], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args4], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args5], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args6], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args10], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args11], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args12], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args13], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn14-args14], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args15], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args16], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args17], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn21-args21], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn22-args22], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn23-args23], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn24-args24], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn0-args0], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn1-args1], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn2-args2], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args3], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args4], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args5], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args6], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args10], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args11], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args12], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args13], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn14-args14], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args15], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args16], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args17], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn21-args21], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn22-args22], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn23-args23], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn24-args24] 2025-08-14T22:35:24.0031779Z 2025-08-14T22:35:27.2693361Z /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. 2025-08-14T22:35:27.2695039Z import pkg_resources 2025-08-14T22:35:27.3902408Z Running profiler/test_record_function 1/1 ... [2025-08-14 22:35:27.389878] 2025-08-14T22:35:27.3902925Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:35:27.3905431Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_record_function.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:35:27.390239] 2025-08-14T22:35:32.9123045Z 2025-08-14T22:35:32.9124461Z profiler/test_record_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_record_function_1.1_fa605b9e812cba88_.log 2025-08-14T22:35:32.9129363Z Running 4 items in this shard: test/profiler/test_record_function.py::TestRecordFunction::test_datapipe_delegation_with_profiler, test/profiler/test_record_function.py::TestRecordFunction::test_datapipe_with_record_function, test/profiler/test_record_function.py::TestRecordFunction::test_datapipe_with_record_function_fork, test/profiler/test_record_function.py::TestRecordFunction::test_record_function 2025-08-14T22:35:32.9132500Z 2025-08-14T22:35:35.9469588Z /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. 2025-08-14T22:35:35.9472481Z import pkg_resources 2025-08-14T22:35:36.0651696Z Running nn/test_lazy_modules 1/1 ... [2025-08-14 22:35:36.064781] 2025-08-14T22:35:36.0652353Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:35:36.0653945Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_lazy_modules.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:35:36.065132] 2025-08-14T22:35:51.3488832Z 2025-08-14T22:35:51.3490143Z test_stateless 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_stateless_1.1_80389c78e5da33ff_.log 2025-08-14T22:35:51.3533665Z Running 50 items in this shard: test/test_stateless.py::TestStatelessFunctionalAPI::test_circular_references_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_circular_references_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_batch_norm_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_batch_norm_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_member_reference_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_member_reference_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_multiple_dicts_error, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_tuple_dicts, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_data_parallel_error_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_data_parallel_error_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_data_parallel_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_data_parallel_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_gradient_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_gradient_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_jit_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_jit_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_kwargs_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_kwargs_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_in_place_operator_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_in_place_operator_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_module_fail_reset_to_original_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_module_fail_reset_to_original_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_some_weights_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_some_weights_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_special_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_special_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_strict_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_strict_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_some_weights_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_some_weights_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_weights_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_weights_strict_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_weights_strict_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_weights_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrized_module_change_parametrization_original_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrized_module_change_parametrization_original_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_setattr_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_setattr_strict_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_setattr_strict_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_setattr_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_errors_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_errors_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_no_error_without_flag, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_warns_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_warns_torch_func, test/test_stateless.py::TestStatelessDeprecation::test_private_stateless_warns, test/test_stateless.py::TestStatelessDeprecation::test_stateless_functional_call_warns, test/test_stateless.py::TestPythonOptimizeMode::test_runs_with_optimize_flag 2025-08-14T22:35:51.3575383Z 2025-08-14T22:35:53.7278353Z 2025-08-14T22:35:53.7279807Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_1c19e9a3b8fac24d_.log 2025-08-14T22:35:53.7373824Z Running 20 items in this shard: test/test_hub.py::TestHub::test_download_url_to_file, test/test_hub.py::TestHub::test_get_set_dir, test/test_hub.py::TestHub::test_hub_parse_repo_info, test/test_hub.py::TestHub::test_list_entrypoints, test/test_hub.py::TestHub::test_load_commit_from_forked_repo, test/test_hub.py::TestHub::test_load_from_branch, test/test_hub.py::TestHub::test_load_from_github, test/test_hub.py::TestHub::test_load_from_local_dir, test/test_hub.py::TestHub::test_load_legacy_zip_checkpoint, test/test_hub.py::TestHub::test_load_state_dict_from_url, test/test_hub.py::TestHub::test_load_zip_1_6_checkpoint, test/test_hub.py::TestHub::test_trust_repo_builtin_trusted_owners, test/test_hub.py::TestHub::test_trust_repo_check_no, test/test_hub.py::TestHub::test_trust_repo_check_yes, test/test_hub.py::TestHub::test_trust_repo_false_emptystring, test/test_hub.py::TestHub::test_trust_repo_false_no, test/test_hub.py::TestHub::test_trust_repo_legacy, test/test_hub.py::TestHub::test_trust_repo_none, test/test_hub.py::TestHub::test_trust_repo_true, test/test_hub.py::TestHub::test_trusted_repo_false_yes 2025-08-14T22:35:53.7382551Z 2025-08-14T22:35:54.7081972Z /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. 2025-08-14T22:35:54.7084817Z import pkg_resources 2025-08-14T22:35:54.8718661Z Running test_jit_fuser_te 1/2 ... [2025-08-14 22:35:54.871285] 2025-08-14T22:35:54.8720655Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:35:54.8721844Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit_fuser_te.py', '-m', 'not serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:35:54.871719] 2025-08-14T22:35:56.9602584Z /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. 2025-08-14T22:35:56.9605393Z import pkg_resources 2025-08-14T22:35:57.0797328Z Running optim/test_lrscheduler 1/1 ... [2025-08-14 22:35:57.079332] 2025-08-14T22:35:57.0798226Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:35:57.0800215Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_lrscheduler.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:35:57.079716] 2025-08-14T22:36:00.5346366Z 2025-08-14T22:36:00.5348085Z optim/test_lrscheduler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_lrscheduler_1.1_d2f11209310ecb8d_.log 2025-08-14T22:36:00.5349428Z 2025-08-14T22:36:03.8207189Z /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. 2025-08-14T22:36:03.8209802Z import pkg_resources 2025-08-14T22:36:03.9423909Z Running test_jit 2/2 ... [2025-08-14 22:36:03.941948] 2025-08-14T22:36:03.9424974Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:36:03.9429497Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit.py', '-m', 'not serial', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:36:03.942308] 2025-08-14T22:36:20.6034067Z 2025-08-14T22:36:20.6035480Z nn/test_lazy_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_lazy_modules_1.1_f598b5ac6e696f98_.log 2025-08-14T22:36:20.6054827Z Running 59 items in this shard: test/nn/test_lazy_modules.py::TestLazyModules::test_chained_initialization, test/nn/test_lazy_modules.py::TestLazyModules::test_invalid_functions, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm1d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm1d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm1d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm2d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm2d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm2d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm3d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm3d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm3d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_batchnorm_with_dict_input, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv1d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv1d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv1d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv2d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv2d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv2d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv3d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv3d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv3d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose1d_kwargs, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose1d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose1d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose2d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose2d_kwargs, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose2d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose2d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose3d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose3d_kwargs, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose3d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transpose3d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_conv_transposed1d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_forward_hook, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm1d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm1d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm1d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm2d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm2d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm2d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm3d, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm3d_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_instancenorm3d_state, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_linear_pickle, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_linear_state_and_forward, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_module_buffer, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_module_jit_buffer, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_module_jit_param, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_module_parameter, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_pre_forward_hook, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_share_memory_buffer, test/nn/test_lazy_modules.py::TestLazyModules::test_lazy_share_memory_param, test/nn/test_lazy_modules.py::TestLazyModules::test_linear, test/nn/test_lazy_modules.py::TestLazyModules::test_linear_state, test/nn/test_lazy_modules.py::TestLazyModules::test_materialize_device, test/nn/test_lazy_modules.py::TestLazyModules::test_materialize_dtype, test/nn/test_lazy_modules.py::TestLazyModules::test_optimizer_pass, test/nn/test_lazy_modules.py::TestLazyModules::test_spectral_norm, test/nn/test_lazy_modules.py::TestLazyModules::test_weight_norm 2025-08-14T22:36:20.6072773Z 2025-08-14T22:36:23.7606978Z /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. 2025-08-14T22:36:23.7609934Z import pkg_resources 2025-08-14T22:36:23.8816032Z Running nn/test_packed_sequence 1/1 ... [2025-08-14 22:36:23.881212] 2025-08-14T22:36:23.8816780Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:36:23.8819988Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_packed_sequence.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:36:23.881641] 2025-08-14T22:36:45.0787735Z 2025-08-14T22:36:45.0789194Z nn/test_packed_sequence 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_packed_sequence_1.1_60bcc35dff065da6_.log 2025-08-14T22:36:45.0794666Z Running 12 items in this shard: test/nn/test_packed_sequence.py::PackedSequenceTest::test_pack_padded_sequence, test/nn/test_packed_sequence.py::PackedSequenceTest::test_pack_sequence, test/nn/test_packed_sequence.py::PackedSequenceTest::test_pad_sequence, test/nn/test_packed_sequence.py::PackedSequenceTest::test_pad_sequence_with_non_iterable_sequences, test/nn/test_packed_sequence.py::PackedSequenceTest::test_pad_sequence_with_tensor_sequences, test/nn/test_packed_sequence.py::PackedSequenceTest::test_to, test/nn/test_packed_sequence.py::PackedSequenceTest::test_to_memory_format, test/nn/test_packed_sequence.py::PackedSequenceTest::test_total_length, test/nn/test_packed_sequence.py::PackedSequenceTest::test_type_casts, test/nn/test_packed_sequence.py::PackedSequenceTest::test_unpack_sequence, test/nn/test_packed_sequence.py::PackedSequenceTest::test_unpad_sequence, test/nn/test_packed_sequence.py::PackedSequenceTest::test_wrong_order 2025-08-14T22:36:45.0799068Z 2025-08-14T22:36:48.2366897Z /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. 2025-08-14T22:36:48.2368604Z import pkg_resources 2025-08-14T22:36:48.3560556Z Running cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic 1/1 ... [2025-08-14 22:36:48.355623] 2025-08-14T22:36:48.3561366Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:36:48.3562911Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.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'] ... [2025-08-14 22:36:48.355979] 2025-08-14T22:37:02.9931978Z 2025-08-14T22:37:02.9934272Z cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp_extensions.libtorch_agnostic_extension.test.test_libtorch_agnostic_1.1_28d31f86160b1d9a_.log 2025-08-14T22:37:02.9955115Z Running 19 items in this shard: test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_default_constructor_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_device_guard_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_device_guard_set_index_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_divide_neg_exp_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_exp_neg_is_leaf_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_fill_infinity_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_get_current_device_index_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_identity_does_not_hog_memory_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_is_contiguous_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_my_abs_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_my_empty_like_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_my_narrow_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_my_ones_like_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_my_pad_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_my_transpose_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_my_zero__cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_neg_exp_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_slow_sgd_cpu, test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py::TestLibtorchAgnosticCPU::test_stream_cpu 2025-08-14T22:37:02.9975324Z 2025-08-14T22:37:06.0036526Z /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. 2025-08-14T22:37:06.0039588Z import pkg_resources 2025-08-14T22:37:06.1225901Z Running functorch/test_ops 1/1 ... [2025-08-14 22:37:06.122245] 2025-08-14T22:37:06.1226715Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:37:06.1229631Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_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'] ... [2025-08-14 22:37:06.122622] 2025-08-14T22:38:32.0199282Z 2025-08-14T22:38:32.0200621Z functorch/test_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ops_1.1_0a30ce0f1bb02021_.log 2025-08-14T22:38:32.4293791Z Running 10208 items in this shard: test/functorch/test_ops.py::TestOperatorsCPU::test_data_write_errors_under_transform_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_extremal_numerics_binary_cross_entropy_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_extremal_numerics_cross_entropy_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_extremal_numerics_l1_loss_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_extremal_numerics_layer_norm_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_extremal_numerics_log_softmax_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_extremal_numerics_mse_loss_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_extremal_numerics_nll_loss_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_extremal_numerics_softmax_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_H_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_NumpySortAutogradFunction_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_det_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_eigh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_lstsq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_pinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linalg_vector_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_log10_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_log_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logical_xor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_masked_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_meshgrid_variadic_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_native_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_logsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_max_unpool2d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_max_unpool3d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_mish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_multi_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_pad_constant_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_pad_reflect_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_pdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_prelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_rms_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_rrelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_selu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_silu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_softplus_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_softshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_tanhshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_threshold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_triplet_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_upsample_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nn_functional_upsample_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_nonzero_static_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_norm_fro_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_norm_inf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_norm_nuc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_normal_number_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_ones_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_ormqr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_outer_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_pca_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_permute_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_permute_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_polar_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_polygamma_polygamma_n_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_polygamma_polygamma_n_4_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_pow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_quantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_rad2deg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_randint_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_randint_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_randn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_ravel_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_reciprocal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_remainder_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_renorm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_repeat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_repeat_interleave_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_reshape_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_reshape_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_resize__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_resize_as__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_resolve_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_resolve_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_roll_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_round_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_round_decimals_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_round_decimals_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_round_decimals_neg_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_rsub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_scatter_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_scatter_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_scatter_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_scatter_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_scatter_reduce_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sgn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_short_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_short_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_bartlett_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_gaussian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_general_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_general_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_slice_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_slice_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sparse_mm_reduce_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_bessel_j1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_bessel_y0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_bessel_y1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_erfcx_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_hermite_polynomial_h_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_hermite_polynomial_he_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_i0e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_i1e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_legendre_polynomial_p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_log_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_modified_bessel_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_modified_bessel_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_scaled_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_spherical_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_xlog1py_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_special_zeta_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_split_list_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_split_with_sizes_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_split_with_sizes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_squeeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_std_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_std_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_std_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_stft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_t_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_take_along_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_tan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_to_sparse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_transpose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_triangular_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_tril_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_triu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_true_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_trunc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unbind_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unflatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unfold_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unsqueeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_unsqueeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_var_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_view_as_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_xlogy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_grad_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_H_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_T_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp___getitem___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp___getitem___functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp___radd___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp___rdiv___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp___rmatmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp___rmod___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp___rmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp___rpow___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp___rsub___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp__batch_norm_with_update_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp__chunk_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp__segment_reduce_lengths_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp__segment_reduce_offsets_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp__softmax_backward_data_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp__unsafe_masked_index_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp__upsample_bilinear2d_aa_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_abs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_acos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_acosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_addbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_addcmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_addmm_decomposed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_addmv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_addr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_alias_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_all_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_allclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_aminmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_angle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_any_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_argsort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_argwhere_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_as_strided_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_as_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_as_strided_partial_views_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_as_strided_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_asin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_asinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_atan2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_atan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_atanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_atleast_1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_atleast_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_baddbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_bernoulli_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_bfloat16_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_bfloat16_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_block_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_bmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_bool_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_bool_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_broadcast_shapes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_bucketize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cartesian_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cauchy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cdouble_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ceil_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cfloat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_chalf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_char_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cholesky_inverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cholesky_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_clamp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_clamp_min_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_clone_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_column_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_combinations_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_conj_physical_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_constant_pad_nd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_contiguous_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_copysign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_corrcoef_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_count_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cov_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cummin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_cumulative_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_deg2rad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_diag_embed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_diagflat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_diagonal_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_diagonal_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_digamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_div_no_rounding_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_div_trunc_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_double_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_double_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_dstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_empty_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_empty_permuted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_eq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_equal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_erf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_erfc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_exp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_expand_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_expand_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_expand_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_expm1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_eye_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_fft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_fft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_fftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_hfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_hfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_hfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_ifft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_ifft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_ifftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_ifftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_ihfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_ihfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_ihfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_irfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_irfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_irfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_rfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_rfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fft_rfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_flatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_flip_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fliplr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_flipud_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_float_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_float_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_floor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_floor_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_fmod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_frac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_frexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_full_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ge_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_geometric_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_geqrf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_gradient_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_grid_sampler_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_gt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_hash_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_heaviside_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_hstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_index_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_index_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_index_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_int_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_isfinite_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_isin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_isneginf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_isreal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_item_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_jiterator_4inputs_with_extra_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_jiterator_binary_return_by_ref_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_kthvalue_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ldexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_le_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_lerp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_lgamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_cond_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_det_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_eigh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_lstsq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_pinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linalg_vector_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_log10_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_log_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_logical_xor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_logspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_logspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_masked_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_meshgrid_variadic_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_native_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_logsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_max_unpool2d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_max_unpool3d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_mish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_multi_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_pad_constant_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_pad_reflect_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_pdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_prelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_rms_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_rrelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_selu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_silu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_softplus_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_softshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_tanhshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_threshold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_triplet_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_upsample_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nn_functional_upsample_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_nonzero_static_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_norm_fro_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_norm_inf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_norm_nuc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_normal_number_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ones_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ormqr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_outer_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_pca_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_permute_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_permute_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_polar_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_polygamma_polygamma_n_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_polygamma_polygamma_n_4_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_pow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_quantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_rad2deg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_randint_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_randint_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_randn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_ravel_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_reciprocal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_remainder_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_renorm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_repeat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_repeat_interleave_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_reshape_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_reshape_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_resize__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_resize_as__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_resolve_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_resolve_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_roll_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_round_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_round_decimals_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_round_decimals_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_round_decimals_neg_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_rsub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_scatter_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_scatter_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_scatter_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_scatter_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_scatter_reduce_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sgn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_short_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_short_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_bartlett_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_gaussian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_general_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_general_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_slice_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_slice_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sparse_mm_reduce_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_bessel_j1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_bessel_y0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_bessel_y1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_erfcx_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_hermite_polynomial_h_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_hermite_polynomial_he_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_i0e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_i1e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_legendre_polynomial_p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_log_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_modified_bessel_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_modified_bessel_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_scaled_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_spherical_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_xlog1py_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_special_zeta_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_split_list_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_split_with_sizes_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_split_with_sizes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_squeeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_std_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_std_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_std_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_stft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_t_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_take_along_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_tan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_to_sparse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_transpose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_triangular_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_tril_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_triu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_true_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_trunc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_unbind_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_unflatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_unfold_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_unsqueeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_unsqueeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_var_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_view_as_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_xlogy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvp_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpjvpvmap_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_H_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_T_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp___getitem___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp___getitem___functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp___radd___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp___rdiv___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp___rmatmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp___rmod___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp___rmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp___rpow___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp___rsub___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp__batch_norm_with_update_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp__chunk_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp__segment_reduce_lengths_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp__segment_reduce_offsets_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp__softmax_backward_data_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp__unsafe_masked_index_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp__upsample_bilinear2d_aa_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_abs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_acos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_acosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_addbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_addcmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_addmm_decomposed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_addmv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_addr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_alias_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_all_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_allclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_aminmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_angle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_any_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_argsort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_argwhere_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_as_strided_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_as_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_as_strided_partial_views_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_as_strided_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_asin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_asinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_atan2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_atan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_atanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_atleast_1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_atleast_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_baddbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_bernoulli_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_bfloat16_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_bfloat16_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_block_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_bmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_bool_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_bool_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_broadcast_shapes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_bucketize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cartesian_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cauchy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cdouble_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ceil_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cfloat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_chalf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_char_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cholesky_inverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cholesky_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_clamp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_clamp_min_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_clone_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_column_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_combinations_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_conj_physical_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_constant_pad_nd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_contiguous_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_copysign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_corrcoef_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_count_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cov_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cummin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_cumulative_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_deg2rad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_diag_embed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_diagflat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_diagonal_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_diagonal_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_digamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_div_no_rounding_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_div_trunc_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_double_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_double_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_dstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_empty_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_empty_permuted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_eq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_equal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_erf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_erfc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_exp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_expand_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_expand_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_expand_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_expm1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_eye_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_fft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_fft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_fftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_hfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_hfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_hfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_ifft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_ifft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_ifftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_ifftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_ihfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_ihfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_ihfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_irfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_irfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_irfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_rfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_rfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fft_rfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_flatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_flip_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fliplr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_flipud_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_float_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_float_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_floor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_floor_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_fmod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_frac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_frexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_full_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ge_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_geometric_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_geqrf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_gradient_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_grid_sampler_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_gt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_hash_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_heaviside_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_hstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_index_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_index_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_index_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_int_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_isfinite_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_isin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_isneginf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_isreal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_item_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_jiterator_4inputs_with_extra_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_jiterator_binary_return_by_ref_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_kthvalue_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ldexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_le_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_lerp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_lgamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_cond_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_det_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_eigh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_lstsq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_pinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linalg_vector_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_log10_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_log_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logical_xor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_masked_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_meshgrid_variadic_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_native_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_logsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_max_unpool2d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_max_unpool3d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_mish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_multi_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_pad_constant_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_pad_reflect_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_pdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_prelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_rms_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_rrelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_selu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_silu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_softplus_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_softshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_tanhshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_threshold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_triplet_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_upsample_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nn_functional_upsample_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_nonzero_static_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_norm_fro_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_norm_inf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_norm_nuc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_normal_number_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ones_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ormqr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_outer_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_pca_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_permute_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_permute_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_polar_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_polygamma_polygamma_n_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_polygamma_polygamma_n_4_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_pow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_quantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_rad2deg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_randint_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_randint_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_randn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_ravel_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_reciprocal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_remainder_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_renorm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_repeat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_repeat_interleave_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_reshape_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_reshape_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_resize__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_resize_as__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_resolve_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_resolve_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_roll_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_round_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_round_decimals_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_round_decimals_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_round_decimals_neg_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_rsub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_scatter_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_scatter_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_scatter_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_scatter_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_scatter_reduce_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sgn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_short_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_short_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_bartlett_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_gaussian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_general_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_general_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_slice_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_slice_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sparse_mm_reduce_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_bessel_j1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_bessel_y0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_bessel_y1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_erfcx_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_hermite_polynomial_h_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_hermite_polynomial_he_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_i0e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_i1e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_legendre_polynomial_p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_log_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_modified_bessel_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_modified_bessel_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_scaled_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_spherical_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_xlog1py_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_special_zeta_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_split_list_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_split_with_sizes_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_split_with_sizes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_squeeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_std_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_std_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_std_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_stft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_t_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_take_along_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_tan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_to_sparse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_transpose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_triangular_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_tril_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_triu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_true_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_trunc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unbind_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unflatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unfold_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unsqueeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_unsqueeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_var_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_view_as_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_xlogy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjp_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvjpvmap_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmap_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_jvpvmapvmap_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_bool_raises_argmax_cpu_bool, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_bool_raises_argmin_cpu_bool, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_bool_raises_ceil_cpu_bool, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_bool_raises_floor_cpu_bool, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_bool_raises_topk_cpu_bool, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_amax_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_amax_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_amax_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_amin_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_amin_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_amin_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_argmax_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_argmax_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_argmax_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_argmin_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_argmin_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_argmin_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_ceil_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_ceil_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_ceil_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_clamp_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_clamp_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_clamp_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_floor_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_floor_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_floor_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_ge_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_ge_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_ge_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_gt_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_gt_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_gt_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_le_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_le_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_le_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_lt_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_lt_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_lt_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_maximum_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_maximum_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_maximum_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_minimum_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_minimum_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_minimum_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_sort_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_sort_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_sort_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_topk_cpu_complex128, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_topk_cpu_complex32, test/functorch/test_ops.py::TestOperatorsCPU::test_ordered_complex_raises_topk_cpu_complex64, test/functorch/test_ops.py::TestOperatorsCPU::test_tensor_with_scalar_list_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_T_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_T_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_broadcast_to_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_broadcast_to_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_conj_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_conj_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_contiguous_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_contiguous_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_diagonal_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_diagonal_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_expand_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_expand_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_flatten_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_flatten_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_dsplit_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_dsplit_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_hsplit_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_hsplit_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_split_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_split_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_split_list_args_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_split_list_args_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_unbind_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_unbind_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_vsplit_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_list_return_vsplit_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_mH_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_mH_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_mT_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_mT_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_movedim_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_movedim_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_narrow_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_narrow_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_permute_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_permute_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_positive_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_positive_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_real_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_real_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_reshape_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_reshape_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_resolve_conj_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_resolve_conj_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_resolve_neg_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_resolve_neg_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_select_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_select_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_special_grad_op_jvp_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_special_grad_op_vjp_cpu, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_squeeze_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_squeeze_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_squeeze_multiple_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_squeeze_multiple_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_transpose_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_transpose_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_unflatten_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_unflatten_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_unfold_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_unfold_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_unsqueeze_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_unsqueeze_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_view_as_complex_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_view_as_complex_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_view_as_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_view_as_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_view_grad_op_jvp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_view_then_inplace_view_grad_op_vjp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_H_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_T_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp___getitem___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp___getitem___functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp___radd___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp___rdiv___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp___rmatmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp___rmod___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp___rmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp___rpow___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp___rsub___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp__batch_norm_with_update_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp__chunk_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp__segment_reduce_lengths_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp__segment_reduce_offsets_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp__softmax_backward_data_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp__unsafe_masked_index_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp__upsample_bilinear2d_aa_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_abs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_acos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_acosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_addbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_addcmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_addmm_decomposed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_addmv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_addr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_alias_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_all_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_allclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_aminmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_angle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_any_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_argsort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_argwhere_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_as_strided_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_as_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_as_strided_partial_views_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_as_strided_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_asin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_asinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_atan2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_atan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_atanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_atleast_1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_atleast_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_baddbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_bernoulli_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_bfloat16_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_bfloat16_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_block_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_bmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_bool_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_bool_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_broadcast_shapes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_bucketize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cartesian_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cauchy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cdouble_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ceil_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cfloat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_chalf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_char_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cholesky_inverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cholesky_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_clamp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_clamp_min_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_clone_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_column_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_combinations_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_conj_physical_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_constant_pad_nd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_contiguous_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_copysign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_corrcoef_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_count_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cov_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cummin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_cumulative_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_deg2rad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_diag_embed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_diagflat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_diagonal_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_diagonal_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_digamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_div_no_rounding_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_div_trunc_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_double_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_double_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_dstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_empty_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_empty_permuted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_eq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_equal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_erf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_erfc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_exp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_expand_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_expand_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_expand_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_expm1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_eye_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_fft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_fft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_fftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_hfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_hfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_hfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_ifft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_ifft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_ifftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_ifftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_ihfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_ihfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_ihfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_irfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_irfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_irfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_rfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_rfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fft_rfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_flatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_flip_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fliplr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_flipud_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_float_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_float_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_floor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_floor_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_fmod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_frac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_frexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_full_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ge_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_geometric_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_geqrf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_gradient_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_grid_sampler_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_gt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_hash_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_heaviside_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_hstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_int_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_isfinite_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_isin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_isneginf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_isreal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_item_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_jiterator_4inputs_with_extra_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_jiterator_binary_return_by_ref_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_kthvalue_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ldexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_le_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_lerp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_lgamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_cond_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_det_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_eigh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_lstsq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_pinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linalg_vector_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_log10_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_log_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logical_xor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_masked_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_meshgrid_variadic_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_native_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_logsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_max_unpool2d_grad_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pad_constant_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pad_reflect_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_nn_functional_pixel_unshuffle_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_xlogy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjp_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_H_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_T_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp___getitem___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp___getitem___functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp___radd___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp___rdiv___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp___rmatmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp___rmod___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp___rmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp___rpow___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp___rsub___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp__batch_norm_with_update_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp__chunk_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp__segment_reduce_lengths_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp__segment_reduce_offsets_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp__softmax_backward_data_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp__unsafe_masked_index_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp__upsample_bilinear2d_aa_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_abs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_acos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_acosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_addbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_addcmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_addmm_decomposed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_addmv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_addr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_alias_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_all_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_allclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_aminmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_angle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_any_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_argsort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_argwhere_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_as_strided_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_as_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_as_strided_partial_views_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_as_strided_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_asin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_asinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_atan2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_atan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_atanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_atleast_1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_atleast_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_baddbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_bernoulli_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_bfloat16_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_bfloat16_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_block_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_bmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_bool_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_bool_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_broadcast_shapes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_bucketize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cartesian_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cauchy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cdouble_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_ceil_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cfloat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_chalf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_char_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cholesky_inverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cholesky_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_clamp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_clamp_min_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_clone_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_column_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_combinations_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_conj_physical_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_constant_pad_nd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_contiguous_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_copysign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_corrcoef_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_count_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cov_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cummin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_cumulative_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_deg2rad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_diag_embed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_diagflat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_diagonal_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_diagonal_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_digamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_div_no_rounding_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_div_trunc_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_double_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_double_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_dstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_empty_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_empty_permuted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_eq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_equal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_erf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_erfc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_exp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_expand_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_expand_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_expand_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_expm1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_eye_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_fft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_fft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_fftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_hfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_hfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_hfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_ifft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_ifft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fft_ifftn_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_flip_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fliplr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_flipud_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_float_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_float_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_floor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_floor_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_fmod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_frac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_frexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_full_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_ge_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_geometric_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_geqrf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_gradient_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_grid_sampler_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_gt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_hash_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_heaviside_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_hstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_int_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_isfinite_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_isin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_isneginf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_isreal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_item_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_jiterator_4inputs_with_extra_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_jiterator_binary_return_by_ref_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_kthvalue_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_ldexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_le_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_lerp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_lgamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_cond_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_det_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_eigh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_lstsq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_pinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linalg_vector_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_log10_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_log_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logical_xor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_masked_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_meshgrid_variadic_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_native_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_nn_functional_conv2d_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_xlogy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjp_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvjpvmap_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_CubeGenVmapAutogradFunction_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_bucketize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cartesian_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cauchy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cdouble_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_ceil_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cfloat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_chalf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_char_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cholesky_inverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cholesky_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_clamp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_clamp_min_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_clone_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_column_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_combinations_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_conj_physical_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_constant_pad_nd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_contiguous_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_copysign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_corrcoef_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_count_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cov_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cummin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_cumulative_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_deg2rad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_diag_embed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_diagflat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_diagonal_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_diagonal_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_digamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_div_no_rounding_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_div_trunc_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_double_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_double_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_dstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_empty_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_empty_permuted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_eq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_equal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_erf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_erfc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_exp2_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fft_rfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fft_rfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fft_rfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_flatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_flip_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fliplr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_flipud_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_float_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_float_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_floor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_floor_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_fmod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_frac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_frexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_full_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_ge_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_geometric_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_geqrf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_gradient_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_grid_sampler_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_gt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_hash_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_heaviside_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_hstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_int_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_isfinite_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_isin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_isneginf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_isreal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_item_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_jiterator_4inputs_with_extra_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_jiterator_binary_return_by_ref_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_kthvalue_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_ldexp_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_pinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linalg_vector_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_log10_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_log_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logical_xor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_masked_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_meshgrid_variadic_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_native_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_logsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_unpool2d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_max_unpool3d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_mish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_multi_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_pad_constant_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_nn_functional_pad_reflect_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_scatter_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_scatter_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_scatter_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_scatter_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_scatter_reduce_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sgn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_short_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_short_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_bartlett_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_gaussian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_general_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_general_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_slice_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_slice_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sparse_mm_reduce_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_bessel_j1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_bessel_y0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_bessel_y1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_special_chebyshev_polynomial_t_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_std_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_std_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_stft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_t_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_take_along_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_tan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_to_sparse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_transpose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_triangular_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_tril_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_triu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_true_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_trunc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_unbind_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_unflatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_unfold_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_unsqueeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_unsqueeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_var_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_view_as_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_xlogy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmap_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vjpvmapvmap_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_CubeGenVmapAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ForwardHasDefaultArgsAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_H_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_H_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_MulGenVmapAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyCubeAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyCubeNotComposableAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyExpMarkDirtyAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyMulAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpySortAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_NumpyTakeAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ScaleGradGenVmapAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_SelectAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_SelectGenVmapAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_SortGenVmapAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_T_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_T_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ZeroGradientsGenVmapAutogradFunction_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___getitem___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___getitem___cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___getitem___functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___getitem___functorch_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___radd___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___radd___cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rdiv___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rdiv___cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rmatmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rmatmul___cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rmod___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rmod___cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rmul___cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rpow___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rpow___cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rsub___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad___rsub___cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__batch_norm_with_update_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__batch_norm_with_update_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__chunk_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__chunk_cat_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__native_batch_norm_legit_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__segment_reduce_lengths_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__segment_reduce_lengths_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__segment_reduce_offsets_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__segment_reduce_offsets_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__softmax_backward_data_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__softmax_backward_data_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__unsafe_masked_index_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__unsafe_masked_index_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__unsafe_masked_index_put_accumulate_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__upsample_bilinear2d_aa_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad__upsample_bilinear2d_aa_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_abs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_abs_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_acos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_acos_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_acosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_acosh_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_add_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addbmm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addcdiv_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addcmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addcmul_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addmm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addmm_decomposed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addmm_decomposed_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addmv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addmv_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_addr_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_alias_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_alias_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_all_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_all_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_allclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_allclose_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_amax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_amin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_aminmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_aminmax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_angle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_angle_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_any_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_any_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_arange_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_argmax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_argmin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_argsort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_argsort_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_argwhere_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_argwhere_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_as_strided_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_as_strided_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_as_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_as_strided_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_as_strided_partial_views_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_as_strided_partial_views_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_as_strided_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_as_strided_scatter_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_asin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_asin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_asinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_asinh_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atan2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atan2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atan_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atanh_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atleast_1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atleast_1d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atleast_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atleast_2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_atleast_3d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_baddbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_baddbmm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bernoulli_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bernoulli_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bfloat16_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bfloat16_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bfloat16_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bfloat16_functorch_no_channels_last_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_block_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_block_diag_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bmm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bool_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bool_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bool_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bool_functorch_no_channels_last_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_broadcast_shapes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_broadcast_tensors_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_broadcast_to_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bucketize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_bucketize_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_byte_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_byte_functorch_no_channels_last_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cartesian_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cartesian_prod_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cat_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cauchy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cauchy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cdist_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cdouble_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cdouble_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ceil_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ceil_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cfloat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cfloat_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_chalf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_chalf_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_char_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_char_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_char_functorch_no_channels_last_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cholesky_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cholesky_inverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cholesky_inverse_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cholesky_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cholesky_solve_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_chunk_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_clamp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_clamp_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_clamp_max_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_clamp_min_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_clamp_min_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_clone_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_clone_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_column_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_column_stack_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_combinations_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_combinations_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_complex_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_conj_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_conj_physical_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_conj_physical_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_constant_pad_nd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_constant_pad_nd_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_contiguous_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_contiguous_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_copysign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_copysign_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_corrcoef_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_corrcoef_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cos_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cosh_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_count_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_count_nonzero_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cov_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cov_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cross_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cummax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cummin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cummin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cumprod_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cumsum_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cumulative_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_cumulative_trapezoid_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_deg2rad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_deg2rad_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diag_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diag_embed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diag_embed_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diagflat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diagflat_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diagonal_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diagonal_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diagonal_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diagonal_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diagonal_scatter_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_diff_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_digamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_digamma_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_dist_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_div_floor_rounding_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_div_no_rounding_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_div_no_rounding_mode_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_div_trunc_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_div_trunc_rounding_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_dot_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_double_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_double_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_double_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_double_functorch_no_channels_last_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_dsplit_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_dstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_dstack_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_einsum_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_empty_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_empty_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_empty_like_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_empty_permuted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_empty_permuted_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_empty_strided_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_eq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_eq_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_equal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_equal_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_erf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_erf_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_erfc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_erfc_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_erfinv_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_exp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_exp2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_exp_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_expand_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_expand_as_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_expand_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_expand_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_expand_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_expand_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_expm1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_expm1_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_exponential_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_eye_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_eye_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_fft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_fft2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_fft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_fft_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_fftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_fftn_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_fftshift_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_hfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_hfft2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_hfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_hfft_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_hfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_hfftn_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ifft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ifft2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ifft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ifft_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ifftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ifftn_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ifftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ifftshift_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ihfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ihfft2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ihfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ihfft_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ihfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_ihfftn_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_irfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_irfft2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_irfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_irfft_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_irfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_irfftn_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_rfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_rfft2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_rfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_rfft_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_rfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fft_rfftn_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fill_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_flatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_flatten_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_flip_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_flip_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fliplr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fliplr_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_flipud_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_flipud_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_float_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_float_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_float_functorch_no_channels_last_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_float_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_float_power_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_floor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_floor_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_floor_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_floor_divide_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fmax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fmin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fmod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_fmod_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_frac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_frac_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_frexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_frexp_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_full_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_full_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_full_like_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_gather_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ge_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ge_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_geometric_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_geometric_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_geqrf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_geqrf_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_gradient_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_gradient_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_grid_sampler_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_grid_sampler_2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_gt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_gt_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_half_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_half_functorch_no_channels_last_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_hash_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_hash_tensor_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_heaviside_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_heaviside_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_histc_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_histogram_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_histogramdd_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_hsplit_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_hstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_hstack_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_hypot_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_i0_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_igamma_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_igammac_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_add_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_fill_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_put_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_put_functorch_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_reduce_amax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_reduce_amin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_reduce_mean_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_reduce_prod_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_index_select_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_inner_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_int_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_int_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_int_functorch_no_channels_last_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isclose_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isfinite_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isfinite_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isinf_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isnan_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isneginf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isneginf_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isposinf_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isreal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_isreal_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_item_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_item_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_2inputs_2outputs_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_4inputs_with_extra_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_4inputs_with_extra_args_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_binary_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_binary_return_by_ref_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_binary_return_by_ref_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_jiterator_unary_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_kron_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_kthvalue_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_kthvalue_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ldexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ldexp_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_le_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_le_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lerp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lerp_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lgamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lgamma_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_cholesky_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_cholesky_ex_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_cond_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_cond_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_cross_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_det_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_det_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_diagonal_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_eig_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_eigh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_eigh_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_eigvals_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_eigvalsh_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_householder_product_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_inv_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_inv_ex_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_ldl_factor_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_ldl_factor_ex_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_ldl_solve_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lstsq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lstsq_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lstsq_grad_oriented_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lu_factor_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lu_factor_ex_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_lu_solve_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_matrix_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_matrix_power_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_matrix_rank_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_matrix_rank_hermitian_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_multi_dot_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_norm_subgradients_at_zero_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_pinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_pinv_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_pinv_hermitian_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_pinv_singular_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_qr_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_slogdet_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_solve_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_solve_ex_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_solve_triangular_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_svd_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_svdvals_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_tensorinv_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_tensorsolve_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_vander_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_vecdot_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_vector_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linalg_vector_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linspace_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_linspace_tensor_overload_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log10_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log10_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log1p_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log_normal_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log_softmax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_log_softmax_with_dtype_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logaddexp2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logaddexp_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logcumsumexp_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logdet_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logical_and_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logical_not_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logical_or_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logical_xor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logical_xor_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logit_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logspace_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logspace_tensor_overload_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_logsumexp_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_long_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_long_functorch_no_channels_last_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lt_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lu_solve_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_lu_unpack_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mH_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mT_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_amax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_amin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_argmax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_argmin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_cumprod_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_cumsum_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_fill_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_fill_functorch_Scalar_only_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_log_softmax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_logaddexp_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_logsumexp_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_mean_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_median_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_normalize_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_prod_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_scatter_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_select_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_softmax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_softmin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_std_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_sum_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_masked_var_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_matmul_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_matrix_exp_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_max_binary_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_max_pool2d_with_indices_backward_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_max_reduction_no_dim_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_max_reduction_with_dim_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_maximum_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mean_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_median_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_meshgrid_list_of_tensors_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_meshgrid_variadic_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_meshgrid_variadic_tensors_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_min_binary_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_min_reduction_no_dim_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_min_reduction_with_dim_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_minimum_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mode_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_movedim_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_msort_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mul_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_multinomial_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mv_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mvlgamma_mvlgamma_p_1_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mvlgamma_mvlgamma_p_3_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_mvlgamma_mvlgamma_p_5_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nan_to_num_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nanmean_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nanmedian_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nanquantile_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nansum_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_narrow_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_narrow_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_native_batch_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_native_dropout_backward_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_native_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_native_layer_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ne_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_neg_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_new_empty_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_new_empty_strided_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_new_full_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_new_ones_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_new_zeros_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nextafter_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_avg_pool1d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_avg_pool2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_avg_pool3d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_max_pool1d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_max_pool2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_adaptive_max_pool3d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_alpha_dropout_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_avg_pool1d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_avg_pool2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_avg_pool3d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_batch_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_bilinear_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_binary_cross_entropy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_celu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_channel_shuffle_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv1d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_no_bias_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_groups_with_bias_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_no_bias_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_padding_no_bias_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_padding_with_bias_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_stride_with_bias_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv2d_with_bias_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv3d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv_transpose1d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv_transpose2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_conv_transpose3d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_cosine_embedding_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_cosine_similarity_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_cross_entropy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_ctc_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_dropout2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_dropout3d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_dropout_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_elu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_embedding_bag_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_embedding_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_embedding_functorch_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_feature_alpha_dropout_with_train_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_feature_alpha_dropout_without_train_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_fractional_max_pool2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_fractional_max_pool3d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_gaussian_nll_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_gelu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_glu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_grid_sample_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_group_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_hardshrink_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_hardsigmoid_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_hardswish_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_hardtanh_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_hinge_embedding_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_huber_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_instance_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_area_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_bicubic_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_bilinear_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_linear_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_nearest-exact_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_nearest_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_interpolate_trilinear_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_kl_div_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_l1_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_layer_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_leaky_relu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_linear_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_local_response_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_logsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_logsigmoid_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_margin_ranking_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_pool1d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_pool2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_pool3d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool1d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool1d_grad_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool2d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool2d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool2d_grad_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool3d_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool3d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_max_unpool3d_grad_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_mish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_mish_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_mse_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_mse_loss_functorch_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_multi_head_attention_forward_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_multi_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_multi_margin_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_multilabel_margin_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_multilabel_soft_margin_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_nll_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_normalize_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pad_circular_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pad_constant_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pad_constant_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pad_reflect_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pad_reflect_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pad_replicate_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pad_replicate_negative_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pairwise_distance_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pdist_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pixel_shuffle_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_pixel_unshuffle_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_poisson_nll_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_prelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_prelu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_relu6_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_relu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_rms_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_rms_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_rrelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_rrelu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_scaled_dot_product_attention_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_selu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_selu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_silu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_silu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_smooth_l1_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_soft_margin_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_softmin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_softmin_with_dtype_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_softplus_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_softplus_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_softshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_softshrink_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_softsign_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_tanhshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_tanhshrink_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_threshold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_threshold_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_triplet_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_triplet_margin_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_unfold_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_upsample_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_upsample_bilinear_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_upsample_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nn_functional_upsample_nearest_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nonzero_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nonzero_static_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_nonzero_static_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_norm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_norm_fro_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_norm_fro_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_norm_inf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_norm_inf_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_norm_nuc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_norm_nuc_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_normal_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_normal_in_place_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_normal_number_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_normal_number_mean_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ones_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ones_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ones_like_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ops_aten_index_put_functorch_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ormqr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ormqr_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_outer_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_outer_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_pca_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_pca_lowrank_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_permute_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_permute_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_permute_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_permute_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_pinverse_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polar_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polar_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polygamma_polygamma_n_0_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polygamma_polygamma_n_1_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polygamma_polygamma_n_2_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polygamma_polygamma_n_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polygamma_polygamma_n_3_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polygamma_polygamma_n_4_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_polygamma_polygamma_n_4_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_positive_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_pow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_pow_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_prod_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_put_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_qr_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_quantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_quantile_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rad2deg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rad2deg_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rand_like_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randint_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randint_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randint_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randint_like_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randn_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_randn_like_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ravel_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_ravel_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_real_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_reciprocal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_reciprocal_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_remainder_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_remainder_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_renorm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_renorm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_repeat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_repeat_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_repeat_interleave_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_repeat_interleave_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_reshape_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_reshape_as_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_reshape_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_reshape_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_resize__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_resize__cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_resize_as__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_resize_as__cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_resolve_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_resolve_conj_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_resolve_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_resolve_neg_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_roll_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_roll_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rot90_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_round_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_round_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_round_decimals_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_round_decimals_0_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_round_decimals_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_round_decimals_3_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_round_decimals_neg_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_round_decimals_neg_3_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rsqrt_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rsub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_rsub_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scalar_tensor_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_add_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_reduce_amax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_reduce_amin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_reduce_mean_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_reduce_prod_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_reduce_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_scatter_reduce_sum_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_searchsorted_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_select_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_select_scatter_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sgn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sgn_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_short_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_short_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_short_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_short_functorch_no_channels_last_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sigmoid_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sign_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_bartlett_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_bartlett_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_blackman_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_cosine_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_exponential_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_gaussian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_gaussian_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_general_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_general_cosine_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_general_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_general_hamming_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_hamming_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_hann_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_kaiser_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signal_windows_nuttall_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_signbit_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sin_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sinc_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sinh_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_slice_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_slice_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_slice_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_slice_scatter_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_softmax_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_softmax_with_dtype_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sort_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sparse_mm_reduce_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sparse_mm_reduce_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sparse_sampled_addmm_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_airy_ai_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_bessel_j0_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_bessel_j1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_bessel_j1_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_bessel_y0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_bessel_y0_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_bessel_y1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_bessel_y1_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_chebyshev_polynomial_t_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_chebyshev_polynomial_u_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_chebyshev_polynomial_v_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_chebyshev_polynomial_w_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_entr_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_erfcx_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_erfcx_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_hermite_polynomial_h_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_hermite_polynomial_h_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_hermite_polynomial_he_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_hermite_polynomial_he_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_i0e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_i0e_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_i1_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_i1e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_i1e_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_laguerre_polynomial_l_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_legendre_polynomial_p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_legendre_polynomial_p_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_log_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_log_ndtr_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_modified_bessel_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_modified_bessel_i0_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_modified_bessel_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_modified_bessel_i1_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_modified_bessel_k0_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_modified_bessel_k1_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_ndtr_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_ndtri_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_polygamma_special_polygamma_n_0_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_scaled_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_scaled_modified_bessel_k0_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_scaled_modified_bessel_k1_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_shifted_chebyshev_polynomial_t_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_shifted_chebyshev_polynomial_u_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_shifted_chebyshev_polynomial_v_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_shifted_chebyshev_polynomial_w_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_spherical_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_spherical_bessel_j0_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_xlog1py_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_xlog1py_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_zeta_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_special_zeta_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_split_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_split_list_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_split_list_args_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_split_with_sizes_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_split_with_sizes_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_split_with_sizes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_split_with_sizes_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sqrt_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_square_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_squeeze_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_squeeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_squeeze_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_squeeze_multiple_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_stack_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_std_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_std_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_std_mean_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_std_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_std_mean_unbiased_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_std_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_std_unbiased_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_stft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_stft_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sub_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sum_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_sum_to_size_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_svd_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_svd_lowrank_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_t_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_t_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_t_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_take_along_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_take_along_dim_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_take_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tan_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tanh_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tensor_split_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tensordot_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tile_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_to_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_to_sparse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_to_sparse_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_topk_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_torch_ops_aten__safe_softmax_default_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_trace_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_transpose_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_transpose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_transpose_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_trapezoid_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_trapz_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_triangular_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_triangular_solve_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tril_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_tril_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_triu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_triu_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_true_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_true_divide_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_trunc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_trunc_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unbind_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unbind_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unbind_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unflatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unflatten_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unfold_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unfold_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unfold_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_uniform_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unique_consecutive_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unique_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unsafe_chunk_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unsafe_split_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unsqueeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unsqueeze_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unsqueeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_unsqueeze_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_var_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_var_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_var_mean_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_var_mean_unbiased_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_var_unbiased_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_vdot_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_view_as_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_view_as_complex_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_view_as_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_view_copy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_view_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_vsplit_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_vstack_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_where_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_xlogy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_xlogy_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_zero__cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_zeros_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmap_autograd_grad_zeros_like_cpu_float64, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_H_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_T_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall___getitem___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall___getitem___functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall___radd___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall___rdiv___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall___rmatmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall___rmod___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall___rmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall___rpow___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall___rsub___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall__batch_norm_with_update_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall__chunk_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall__segment_reduce_lengths_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall__segment_reduce_offsets_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall__softmax_backward_data_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall__unsafe_masked_index_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall__upsample_bilinear2d_aa_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_abs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_acos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_acosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_addbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_addcmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_addmm_decomposed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_addmv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_addr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_alias_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_all_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_allclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_aminmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_angle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_any_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_argsort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_argwhere_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_as_strided_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_as_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_as_strided_partial_views_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_as_strided_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_asin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_asinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_atan2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_atan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_atanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_atleast_1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_atleast_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_baddbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_bernoulli_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_bfloat16_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_bfloat16_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_block_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_bmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_bool_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_bool_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_broadcast_shapes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_bucketize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cartesian_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cauchy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cdouble_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ceil_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cfloat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_chalf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_char_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cholesky_inverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cholesky_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_clamp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_clamp_min_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_clone_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_column_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_combinations_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_conj_physical_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_constant_pad_nd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_contiguous_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_copysign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_corrcoef_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_count_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cov_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cummin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_cumulative_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_deg2rad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_diag_embed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_diagflat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_diagonal_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_diagonal_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_digamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_div_no_rounding_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_div_trunc_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_double_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_double_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_dstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_empty_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_empty_permuted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_eq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_equal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_erf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_erfc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_exp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_expand_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_expand_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_expand_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_expm1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_eye_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_fft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_fft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_fftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_hfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_hfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_hfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_ifft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_ifft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_ifftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_ifftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_ihfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_ihfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_ihfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_irfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_irfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_irfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_rfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_rfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fft_rfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_flatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_flip_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fliplr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_flipud_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_float_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_float_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_floor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_floor_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_fmod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_frac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_frexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_full_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ge_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_geometric_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_geqrf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_gradient_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_grid_sampler_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_gt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_H_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_NumpyExpMarkDirtyAutogradFunction_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_hash_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_heaviside_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_hstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_igamma_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linalg_vector_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_log10_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_log1p_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_masked_argmax_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_native_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_logsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_max_unpool2d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_max_unpool3d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_mish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_multi_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_pad_constant_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_pad_reflect_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_pdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_prelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_rms_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_rrelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_selu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_silu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_softplus_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_softshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_tanhshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_threshold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_triplet_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_upsample_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nn_functional_upsample_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_nonzero_static_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_norm_fro_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_norm_inf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_norm_nuc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_normal_number_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_ones_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_ormqr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_outer_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_pca_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_permute_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_permute_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_polar_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_polygamma_polygamma_n_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_polygamma_polygamma_n_4_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_pow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_put_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_unsqueeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_unsqueeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_var_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_view_as_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_xlogy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_has_batch_rule_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_hash_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_heaviside_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_hstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_index_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_index_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_index_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_int_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_isfinite_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_isin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_isneginf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_isreal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_item_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_jiterator_4inputs_with_extra_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_jiterator_binary_return_by_ref_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_kthvalue_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ldexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_le_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_lerp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_lgamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_cond_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_det_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_eigh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_lstsq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_pinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linalg_vector_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_log10_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_log_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logical_xor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_masked_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_meshgrid_variadic_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_native_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_logsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_max_unpool2d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_max_unpool3d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_mish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_multi_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_pad_constant_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_pad_reflect_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_pdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_prelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_rms_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_rrelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_selu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_silu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_softplus_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_softshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_tanhshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_threshold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_triplet_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_upsample_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nn_functional_upsample_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_nonzero_static_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_norm_fro_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_norm_inf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_norm_nuc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_normal_number_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ones_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ormqr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_outer_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_pca_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_permute_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_permute_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_polar_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_polygamma_polygamma_n_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_polygamma_polygamma_n_4_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_pow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_quantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_rad2deg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_randint_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_randint_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_randn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_ravel_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_reciprocal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_remainder_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_renorm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_repeat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_repeat_interleave_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_reshape_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_reshape_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_resize__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_resize_as__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_resolve_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_resolve_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_roll_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_round_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_round_decimals_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_round_decimals_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_round_decimals_neg_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_rsub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_scatter_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_scatter_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_scatter_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_scatter_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_scatter_reduce_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sgn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_short_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_short_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_bartlett_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_gaussian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_general_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_general_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_slice_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_slice_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sparse_mm_reduce_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_bessel_j1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_bessel_y0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_bessel_y1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_erfcx_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_hermite_polynomial_h_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_hermite_polynomial_he_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_i0e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_i1e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_legendre_polynomial_p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_log_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_modified_bessel_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_modified_bessel_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_scaled_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_spherical_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_xlog1py_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_special_zeta_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_split_list_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_split_with_sizes_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_split_with_sizes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_squeeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_std_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_std_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_std_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_stft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_t_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_take_along_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_tan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_to_sparse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_transpose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_triangular_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_tril_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_triu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_true_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_trunc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unbind_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unflatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unfold_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unsqueeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_unsqueeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_var_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_view_as_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_xlogy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpall_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_H_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_T_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp___getitem___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp___getitem___functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp___radd___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp___rdiv___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp___rmatmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp___rmod___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp___rmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp___rpow___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp___rsub___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp__batch_norm_with_update_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp__chunk_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp__segment_reduce_lengths_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp__segment_reduce_offsets_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp__softmax_backward_data_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp__unsafe_masked_index_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp__upsample_bilinear2d_aa_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_abs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_acos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_acosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_addbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_addcmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_addmm_decomposed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_addmv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_addr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_alias_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_all_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_allclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_aminmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_angle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_any_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_argsort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_argwhere_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_as_strided_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_as_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_as_strided_partial_views_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_as_strided_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_asin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_asinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_atan2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_atan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_atanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_atleast_1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_atleast_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_baddbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_bernoulli_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_bfloat16_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_bfloat16_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_block_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_bmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_bool_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_bool_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_broadcast_shapes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_bucketize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cartesian_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cauchy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cdouble_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ceil_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cfloat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_chalf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_char_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cholesky_inverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cholesky_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_clamp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_clamp_min_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_clone_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_column_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_combinations_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_conj_physical_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_constant_pad_nd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_contiguous_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_copysign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_corrcoef_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_count_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cov_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cummin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_cumulative_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_deg2rad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_diag_embed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_diagflat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_diagonal_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_diagonal_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_diff_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_digamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_dist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_div_floor_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_div_no_rounding_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_div_trunc_rounding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_double_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_double_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_dsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_dstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_einsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_empty_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_empty_permuted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_eq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_equal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_erf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_erfc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_erfinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_exp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_expand_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_expand_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_expand_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_expm1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_eye_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_fft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_fft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_fftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_fftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_hfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_hfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_hfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_ifft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_ifft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_ifftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_ifftshift_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_ihfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_ihfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_ihfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_irfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_irfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_irfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_rfft2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_rfft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fft_rfftn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_flatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_flip_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fliplr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_flipud_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_float_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_float_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_float_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_floor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_floor_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_fmod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_frac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_frexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_full_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_gather_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ge_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_geometric_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_geqrf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_gradient_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_grid_sampler_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_gt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_hash_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_heaviside_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_hstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_index_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_index_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_index_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_int_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isfinite_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isneginf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_isreal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_item_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_jiterator_4inputs_with_extra_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_jiterator_binary_return_by_ref_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_kthvalue_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ldexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_le_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_lerp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_lgamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_cond_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_det_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_eigh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_lstsq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_pinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linalg_vector_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_log10_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_log_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logical_xor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_masked_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_meshgrid_variadic_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_native_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_logsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_max_unpool2d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_max_unpool3d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_mish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_multi_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_pad_constant_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_pad_reflect_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_pdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_prelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_rms_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_rrelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_selu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_silu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_softplus_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_softshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_tanhshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_threshold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_triplet_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_upsample_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nn_functional_upsample_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_nonzero_static_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_norm_fro_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_norm_inf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_norm_nuc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_normal_number_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ones_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ormqr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_outer_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_pca_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_permute_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_permute_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_polar_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_polygamma_polygamma_n_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_polygamma_polygamma_n_4_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_pow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_quantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_rad2deg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_randint_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_randint_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_randn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_ravel_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_reciprocal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_remainder_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_renorm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_repeat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_repeat_interleave_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_reshape_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_reshape_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_resize__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_resize_as__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_resolve_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_resolve_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_roll_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_round_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_round_decimals_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_round_decimals_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_round_decimals_neg_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_rsub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_scatter_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_scatter_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_scatter_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_scatter_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_scatter_reduce_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sgn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_short_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_short_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_bartlett_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_gaussian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_general_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_general_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_slice_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_slice_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sparse_mm_reduce_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_bessel_j1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_bessel_y0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_bessel_y1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_erfcx_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_hermite_polynomial_h_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_hermite_polynomial_he_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_i0e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_i1e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_legendre_polynomial_p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_log_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_modified_bessel_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_modified_bessel_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_scaled_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_spherical_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_xlog1py_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_special_zeta_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_split_list_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_split_with_sizes_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_split_with_sizes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_squeeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_std_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_std_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_std_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_stft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_t_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_take_along_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_tan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_to_sparse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_transpose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_triangular_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_tril_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_triu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_true_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_trunc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_unbind_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_unflatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_unfold_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_unsqueeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_unsqueeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_var_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_view_as_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_xlogy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvjp_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapjvpvmap_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_H_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_T_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp___getitem___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp___getitem___functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp___radd___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp___rdiv___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp___rmatmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp___rmod___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp___rmul___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp___rpow___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp___rsub___cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp__batch_norm_with_update_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp__chunk_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp__native_batch_norm_legit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp__segment_reduce_lengths_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp__segment_reduce_offsets_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp__softmax_backward_data_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp__unsafe_masked_index_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp__upsample_bilinear2d_aa_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_abs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_acos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_acosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_addbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_addcdiv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_addcmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_addmm_decomposed_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_addmv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_addr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_alias_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_all_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_allclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_aminmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_angle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_any_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_arange_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_argsort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_argwhere_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_as_strided_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_as_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_as_strided_partial_views_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_as_strided_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_asin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_asinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_atan2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_atan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_atanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_atleast_1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_atleast_2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_atleast_3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_baddbmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_bernoulli_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_bfloat16_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_bfloat16_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_block_diag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_bmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_bool_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_bool_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_broadcast_shapes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_broadcast_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_broadcast_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_bucketize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_byte_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_byte_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cartesian_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cauchy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cdouble_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ceil_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cfloat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_chalf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_char_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_char_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cholesky_inverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cholesky_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_clamp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_clamp_max_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_clamp_min_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_clone_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_column_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_combinations_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_conj_physical_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_constant_pad_nd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_contiguous_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_copysign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_corrcoef_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cos_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cosh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_count_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cov_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cummax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cummin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_cumulative_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_deg2rad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_diag_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_logspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_log_softmax_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_masked_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_max_pool2d_with_indices_backward_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_native_batch_norm_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_nn_functional_local_response_norm_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_spherical_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_xlog1py_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_special_zeta_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_split_list_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_split_with_sizes_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_split_with_sizes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_sqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_squeeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_std_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_std_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_std_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_stft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_sub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_t_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_take_along_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_tan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_to_sparse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_transpose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_triangular_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_tril_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_triu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_true_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_trunc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_unbind_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_unflatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_unfold_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_unsqueeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_unsqueeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_var_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_view_as_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_xlogy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_has_batch_rule_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_hash_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_heaviside_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_hstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_index_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_index_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_index_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_int_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_isfinite_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_isin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_isneginf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_isreal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_item_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_jiterator_4inputs_with_extra_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_jiterator_binary_return_by_ref_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_kthvalue_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ldexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_le_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_lerp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_lgamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_cond_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_det_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_eigh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_lstsq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_pinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linalg_vector_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_log10_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_log_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logical_xor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_masked_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_meshgrid_variadic_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_native_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_logsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_max_unpool2d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_max_unpool3d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_mish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_multi_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_pad_constant_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_pad_reflect_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_pdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_prelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_rms_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_rrelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_selu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_silu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_softplus_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_softshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_tanhshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_threshold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_triplet_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_upsample_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nn_functional_upsample_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_nonzero_static_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_norm_fro_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_norm_inf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_norm_nuc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_normal_number_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ones_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ormqr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_outer_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_pca_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_permute_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_permute_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_polar_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_polygamma_polygamma_n_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_polygamma_polygamma_n_4_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_pow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_quantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_rad2deg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_randint_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_randint_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_randn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_ravel_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_reciprocal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_remainder_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_renorm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_repeat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_repeat_interleave_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_reshape_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_reshape_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_resize__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_resize_as__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_resolve_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_resolve_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_roll_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_round_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_round_decimals_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_round_decimals_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_round_decimals_neg_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_rsub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_scatter_reduce_amax_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_bartlett_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_gaussian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_general_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_general_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_sin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_slice_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_slice_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_sort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_sparse_mm_reduce_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_bessel_j1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_bessel_y0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_bessel_y1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_erfcx_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_hermite_polynomial_h_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_hermite_polynomial_he_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_i0e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_i1e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_legendre_polynomial_p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_log_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_modified_bessel_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_modified_bessel_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_scaled_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_spherical_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_xlog1py_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_special_zeta_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_split_list_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_split_with_sizes_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_split_with_sizes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_sqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_squeeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_std_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_std_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_std_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_stft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_sub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_t_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_take_along_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_tan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_to_sparse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_transpose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_triangular_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_tril_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_triu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_true_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_trunc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_unbind_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_unflatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_unfold_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_unsqueeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_unsqueeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_var_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_view_as_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_xlogy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjp_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_H_cpu_float32, 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test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_half_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_half_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_hash_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_heaviside_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_histc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_histogram_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_histogramdd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_hsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_hstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_hypot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_igamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_igammac_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_index_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_inner_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_int_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_int_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_isclose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_isfinite_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_isin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_isinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_isnan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_isneginf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_isposinf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_isreal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_item_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_jiterator_2inputs_2outputs_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_jiterator_4inputs_with_extra_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_jiterator_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_jiterator_binary_return_by_ref_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_jiterator_unary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_kron_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_kthvalue_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ldexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_le_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_lerp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_lgamma_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_cholesky_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_cholesky_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_cond_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_cross_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_det_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_diagonal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_eig_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_eigh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_eigvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_eigvalsh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_householder_product_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_inv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_inv_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_ldl_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_ldl_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_lstsq_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_lstsq_grad_oriented_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_lu_factor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_lu_factor_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_matrix_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_matrix_power_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_matrix_rank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_multi_dot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_pinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_pinv_hermitian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_pinv_singular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_slogdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_solve_ex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_solve_triangular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_svdvals_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_tensorinv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_tensorsolve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_vander_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_vecdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linalg_vector_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_linspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_log10_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_log1p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_log2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_log_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_log_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_log_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logaddexp2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logcumsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logdet_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logical_and_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logical_not_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logical_or_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logical_xor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logspace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logspace_tensor_overload_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_long_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_long_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_lt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_lu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_lu_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_lu_unpack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mH_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mT_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_argmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_argmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_cumprod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_cumsum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_fill_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_fill_functorch_Scalar_only_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_log_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_logaddexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_logsumexp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_masked_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_matmul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_matrix_exp_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_max_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_max_pool2d_with_indices_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_max_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_max_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_maximum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_median_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_meshgrid_list_of_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_meshgrid_variadic_tensors_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_min_binary_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_min_reduction_no_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_min_reduction_with_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_minimum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mode_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_movedim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_msort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mul_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_multinomial_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mv_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mvlgamma_mvlgamma_p_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mvlgamma_mvlgamma_p_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_mvlgamma_mvlgamma_p_5_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nan_to_num_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nanmean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nanmedian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nanquantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nansum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_narrow_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_narrow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_native_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_native_dropout_backward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_native_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ne_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_new_empty_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_new_empty_strided_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_new_full_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_new_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_new_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nextafter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_adaptive_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_adaptive_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_adaptive_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_adaptive_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_alpha_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_avg_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_batch_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_celu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_channel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_stride_groups_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_stride_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_stride_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_strided_padding_dilation_no_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv2d_with_bias_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_cosine_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_cross_entropy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_ctc_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_dropout2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_dropout3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_dropout_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_elu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_embedding_bag_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_embedding_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_embedding_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_fractional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_gelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_glu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_grid_sample_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_group_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_hardshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_hardsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_hardswish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_hardtanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_hinge_embedding_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_huber_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_instance_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_interpolate_area_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_interpolate_bicubic_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_interpolate_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_interpolate_nearest-exact_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_interpolate_trilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_kl_div_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_layer_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_leaky_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_linear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_local_response_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_logsigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_max_pool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_max_pool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_max_pool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_max_unpool2d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_max_unpool2d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_max_unpool3d_grad_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_mish_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_mse_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_multi_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_normalize_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_pad_circular_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_pad_constant_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_pad_reflect_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_pad_replicate_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_pairwise_distance_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_pdist_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_prelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_relu6_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_relu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_rms_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_rrelu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_selu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_silu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_soft_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_softmin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_softplus_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_softshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_softsign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_tanhshrink_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_threshold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_triplet_margin_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_upsample_bilinear_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nn_functional_upsample_nearest_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nonzero_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_nonzero_static_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_norm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_norm_fro_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_norm_inf_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_norm_nuc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_normal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_normal_in_place_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_normal_number_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ones_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ones_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ormqr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_outer_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_pca_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_permute_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_permute_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_pinverse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_polar_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_polygamma_polygamma_n_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_polygamma_polygamma_n_4_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_positive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_pow_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_put_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_qr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_quantile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_rad2deg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_rand_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_randint_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_randint_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_randn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_randn_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_ravel_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_real_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_reciprocal_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_remainder_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_renorm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_repeat_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_repeat_interleave_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_reshape_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_reshape_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_resize__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_resize_as__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_resolve_conj_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_resolve_neg_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_roll_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_rot90_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_round_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_round_decimals_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_round_decimals_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_round_decimals_neg_3_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_rsqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_rsub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_scalar_tensor_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_scatter_add_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_scatter_reduce_amax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_scatter_reduce_amin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_scatter_reduce_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_scatter_reduce_prod_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_scatter_reduce_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_searchsorted_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_select_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_select_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sgn_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_short_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_short_functorch_no_channels_last_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sigmoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sign_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_bartlett_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_blackman_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_exponential_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_gaussian_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_general_cosine_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_general_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_hamming_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_hann_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_kaiser_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signal_windows_nuttall_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_signbit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sin_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sinc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sinh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_slice_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_slice_scatter_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_softmax_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_softmax_with_dtype_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sort_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sparse_mm_reduce_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sparse_sampled_addmm_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_airy_ai_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_bessel_j1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_bessel_y0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_bessel_y1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_entr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_erfcx_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_hermite_polynomial_h_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_hermite_polynomial_he_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_i0e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_i1e_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_legendre_polynomial_p_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_log_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_modified_bessel_i0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_modified_bessel_i1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_ndtr_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_ndtri_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_scaled_modified_bessel_k0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_spherical_bessel_j0_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_xlog1py_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_special_zeta_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_split_list_args_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_split_with_sizes_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_split_with_sizes_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sqrt_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_square_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_squeeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_squeeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_squeeze_multiple_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_stack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_std_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_std_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_std_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_std_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_stft_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sub_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sum_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_sum_to_size_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_svd_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_svd_lowrank_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_t_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_t_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_take_along_dim_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_take_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_tan_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_tanh_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_tensor_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_tensordot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_tile_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_to_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_to_sparse_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_topk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_torch_ops_aten__safe_softmax_default_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_trace_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_transpose_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_transpose_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_trapezoid_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_trapz_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_triangular_solve_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_tril_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_triu_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_true_divide_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_trunc_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_unbind_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_unbind_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_unflatten_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_unfold_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_unfold_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_uniform_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_unique_consecutive_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_unique_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_unsafe_chunk_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_unsafe_split_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_unsqueeze_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_unsqueeze_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_var_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_var_mean_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_var_mean_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_var_unbiased_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_vdot_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_view_as_complex_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_view_as_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_view_copy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_view_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_vsplit_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_vstack_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_where_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_xlogy_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_zero__cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_zeros_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvjp_zeros_like_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_CubeGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_NumpyExpMarkDirtyAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_NumpySortAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_ScaleGradGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_SelectAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_SortGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvjpvmap_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_ops.py::TestOperatorsCPU::test_vmapvmapjvp_linalg_solve_cpu 2025-08-14T22:38:32.8146571Z 2025-08-14T22:38:35.2453362Z /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. 2025-08-14T22:38:35.2456024Z import pkg_resources 2025-08-14T22:38:35.3669376Z Running test_schema_check 1/1 ... [2025-08-14 22:38:35.366542] 2025-08-14T22:38:35.3670151Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:38:35.3672240Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_schema_check.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:38:35.366939] 2025-08-14T22:41:03.9492682Z 2025-08-14T22:41:03.9493984Z test_jit_fuser_te 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_fuser_te_1.2_0421c4a6e0f0dcc7_.log 2025-08-14T22:41:04.0739169Z Running 3384 items in this shard: test/test_jit_fuser_te.py::TestFuserCommon::test_autodiff_fallback, test/test_jit_fuser_te.py::TestTEFuserStatic::test_adaptive_avg_pool2d, test/test_jit_fuser_te.py::TestTEFuserStatic::test_autocast_down, test/test_jit_fuser_te.py::TestTEFuserStatic::test_binary_div_ops, test/test_jit_fuser_te.py::TestTEFuserStatic::test_binary_ops, test/test_jit_fuser_te.py::TestTEFuserStatic::test_binary_scalar_ops, test/test_jit_fuser_te.py::TestTEFuserStatic::test_binary_tensor_scalar_ops, test/test_jit_fuser_te.py::TestTEFuserStatic::test_bitwise_ops, test/test_jit_fuser_te.py::TestTEFuserStatic::test_broadcast, test/test_jit_fuser_te.py::TestTEFuserStatic::test_cat_2k_args, test/test_jit_fuser_te.py::TestTEFuserStatic::test_cat_graph_opt, test/test_jit_fuser_te.py::TestTEFuserStatic::test_checks_cat_inputs, test/test_jit_fuser_te.py::TestTEFuserStatic::test_chunk_correctness, test/test_jit_fuser_te.py::TestTEFuserStatic::test_comparison_eq_ne, test/test_jit_fuser_te.py::TestTEFuserStatic::test_comparison_ge_le, test/test_jit_fuser_te.py::TestTEFuserStatic::test_comparison_gt_lt, test/test_jit_fuser_te.py::TestTEFuserStatic::test_concat_invariant, test/test_jit_fuser_te.py::TestTEFuserStatic::test_constant_chunk_shapes, test/test_jit_fuser_te.py::TestTEFuserStatic::test_conv2d, test/test_jit_fuser_te.py::TestTEFuserStatic::test_cuda_half, test/test_jit_fuser_te.py::TestTEFuserStatic::test_dims, test/test_jit_fuser_te.py::TestTEFuserStatic::test_dynamic_cat, test/test_jit_fuser_te.py::TestTEFuserStatic::test_exp, test/test_jit_fuser_te.py::TestTEFuserStatic::test_gelu, test/test_jit_fuser_te.py::TestTEFuserStatic::test_isnan, test/test_jit_fuser_te.py::TestTEFuserStatic::test_lstm_gates_permutations, test/test_jit_fuser_te.py::TestTEFuserStatic::test_masked_fill, test/test_jit_fuser_te.py::TestTEFuserStatic::test_milstm, test/test_jit_fuser_te.py::TestTEFuserStatic::test_mul_bool, test/test_jit_fuser_te.py::TestTEFuserStatic::test_nonzero_device_cuda, test/test_jit_fuser_te.py::TestTEFuserStatic::test_nop, test/test_jit_fuser_te.py::TestTEFuserStatic::test_rand_cuda, test/test_jit_fuser_te.py::TestTEFuserStatic::test_rand_diamond, test/test_jit_fuser_te.py::TestTEFuserStatic::test_relu, test/test_jit_fuser_te.py::TestTEFuserStatic::test_relu_fwd_bwd, test/test_jit_fuser_te.py::TestTEFuserStatic::test_remove_output_used_only_in_size, test/test_jit_fuser_te.py::TestTEFuserStatic::test_scalar, test/test_jit_fuser_te.py::TestTEFuserStatic::test_scalar_arg, test/test_jit_fuser_te.py::TestTEFuserStatic::test_scalar_only_inputs, test/test_jit_fuser_te.py::TestTEFuserStatic::test_small_constant, 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test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_var_mean_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_var_unbiased_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_view_as_complex_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_view_copy_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_vsplit_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_xlogy_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_unsupported_zero__cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working___radd___cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_acos_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_addcmul_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_addmm_decomposed_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_byte_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_ceil_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_char_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_contiguous_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_cos_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_div_floor_rounding_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_div_trunc_rounding_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_eq_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_erf_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_exp_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_expand_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_expm1_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_floor_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_fmod_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_gt_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_lerp_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_lgamma_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_lt_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_masked_fill_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_max_binary_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_mul_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_hardshrink_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_hardsigmoid_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_hardtanh_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_relu_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_softsign_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_remainder_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sigmoid_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sin_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sinh_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sqrt_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sum_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_t_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_transpose_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_view_as_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_where_cpu_float32, test/test_jit_fuser_te.py::TestLoopnestRandomizationCPU::test_relu_cpu 2025-08-14T22:41:04.1929792Z 2025-08-14T22:41:07.0852706Z /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. 2025-08-14T22:41:07.0854227Z import pkg_resources 2025-08-14T22:41:07.2055020Z Running test_datapipe 1/1 ... [2025-08-14 22:41:07.205098] 2025-08-14T22:41:07.2055802Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:41:07.2057751Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_datapipe.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:41:07.205464] 2025-08-14T22:41:54.7980524Z 2025-08-14T22:41:54.7981645Z test_datapipe 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_datapipe_1.1_aeeba326376b0617_.log 2025-08-14T22:41:54.8035695Z Running 94 items in this shard: test/test_datapipe.py::TestDataChunk::test_as_string, test/test_datapipe.py::TestDataChunk::test_getitem, test/test_datapipe.py::TestDataChunk::test_iter, test/test_datapipe.py::TestDataChunk::test_len, test/test_datapipe.py::TestDataChunk::test_random_shuffle, test/test_datapipe.py::TestDataChunk::test_reverse, test/test_datapipe.py::TestDataChunk::test_sort, test/test_datapipe.py::TestStreamWrapper::test_api, test/test_datapipe.py::TestStreamWrapper::test_dir, test/test_datapipe.py::TestStreamWrapper::test_pickle, test/test_datapipe.py::TestStreamWrapper::test_repr, test/test_datapipe.py::TestIterableDataPipeBasic::test_demux_mux_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_groupby_iterable_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_listdirfiles_iterable_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_listdirfilesdeterministic_iterable_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_map_with_col_file_handle_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_openfilesfromdisk_iterable_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_routeddecoder_iterable_datapipe, test/test_datapipe.py::TestCaptureDataFrame::test_basic_capture, test/test_datapipe.py::TestDataFramesPipes::test_batch, test/test_datapipe.py::TestDataFramesPipes::test_capture, test/test_datapipe.py::TestDataFramesPipes::test_collate, test/test_datapipe.py::TestDataFramesPipes::test_filter, test/test_datapipe.py::TestDataFramesPipes::test_shuffle, test/test_datapipe.py::TestDataFramesPipes::test_unbatch, test/test_datapipe.py::TestFunctionalIterDataPipe::test_batch_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_collate_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_concat_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_demux_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_docstring, test/test_datapipe.py::TestFunctionalIterDataPipe::test_filter_datapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_fork_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_iterable_wrapper_datapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_map_dict_with_col_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_map_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_map_tuple_list_with_col_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_mux_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_sampler_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_serializable, test/test_datapipe.py::TestFunctionalIterDataPipe::test_serializable_with_dill, test/test_datapipe.py::TestFunctionalIterDataPipe::test_shuffler_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_stream_reader_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_unbatch_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_zip_iterdatapipe, test/test_datapipe.py::TestFunctionalMapDataPipe::test_batch_mapdatapipe, test/test_datapipe.py::TestFunctionalMapDataPipe::test_concat_mapdatapipe, test/test_datapipe.py::TestFunctionalMapDataPipe::test_docstring, test/test_datapipe.py::TestFunctionalMapDataPipe::test_map_mapdatapipe, test/test_datapipe.py::TestFunctionalMapDataPipe::test_sequence_wrapper_datapipe, test/test_datapipe.py::TestFunctionalMapDataPipe::test_serializable, test/test_datapipe.py::TestFunctionalMapDataPipe::test_serializable_with_dill, test/test_datapipe.py::TestFunctionalMapDataPipe::test_shuffler_mapdatapipe, test/test_datapipe.py::TestFunctionalMapDataPipe::test_zip_mapdatapipe, test/test_datapipe.py::TestTyping::test_compile_time, test/test_datapipe.py::TestTyping::test_construct_time, test/test_datapipe.py::TestTyping::test_isinstance, test/test_datapipe.py::TestTyping::test_issubinstance, test/test_datapipe.py::TestTyping::test_protocol, test/test_datapipe.py::TestTyping::test_reinforce, test/test_datapipe.py::TestTyping::test_runtime, test/test_datapipe.py::TestTyping::test_subtype, test/test_datapipe.py::TestGraph::test_simple_traverse, test/test_datapipe.py::TestGraph::test_traverse_circular_datapipe, test/test_datapipe.py::TestGraph::test_traverse_forked, test/test_datapipe.py::TestGraph::test_traverse_mapdatapipe, test/test_datapipe.py::TestGraph::test_traverse_mixdatapipe, test/test_datapipe.py::TestGraph::test_traverse_unhashable_datapipe, test/test_datapipe.py::TestSerialization::test_spawn_lambdas_iter, test/test_datapipe.py::TestSerialization::test_spawn_lambdas_map, test/test_datapipe.py::TestCircularSerialization::test_circular_serialization_with_dill, test/test_datapipe.py::TestCircularSerialization::test_circular_serialization_with_pickle, test/test_datapipe.py::TestSharding::test_legacy_custom_sharding, test/test_datapipe.py::TestSharding::test_legacy_custom_sharding_with_old_dataloader, test/test_datapipe.py::TestSharding::test_multi_sharding, test/test_datapipe.py::TestSharding::test_old_dataloader, test/test_datapipe.py::TestSharding::test_sharding_groups, test/test_datapipe.py::TestSharding::test_sharding_groups_in_legacy_grouping_package, test/test_datapipe.py::TestSharding::test_sharding_length, test/test_datapipe.py::TestSharding::test_simple_sharding, test/test_datapipe.py::TestIterDataPipeSingletonConstraint::test_iterdatapipe_singleton_buggy, test/test_datapipe.py::TestIterDataPipeSingletonConstraint::test_iterdatapipe_singleton_constraint_multiple_outputs, test/test_datapipe.py::TestIterDataPipeSingletonConstraint::test_iterdatapipe_singleton_generator, test/test_datapipe.py::TestIterDataPipeSingletonConstraint::test_iterdatapipe_singleton_new_object, test/test_datapipe.py::TestIterDataPipeSingletonConstraint::test_iterdatapipe_singleton_self_next, test/test_datapipe.py::TestIterDataPipeCountSampleYielded::test_iterdatapipe_sample_yielded_generator_function, test/test_datapipe.py::TestIterDataPipeCountSampleYielded::test_iterdatapipe_sample_yielded_generator_function_exception, test/test_datapipe.py::TestIterDataPipeCountSampleYielded::test_iterdatapipe_sample_yielded_next, test/test_datapipe.py::TestIterDataPipeCountSampleYielded::test_iterdatapipe_sample_yielded_next_exception, test/test_datapipe.py::TestIterDataPipeCountSampleYielded::test_iterdatapipe_sample_yielded_return_self, test/test_datapipe.py::TestIterDataPipeGraphFastForward::test_simple_snapshot_custom_non_generator, test/test_datapipe.py::TestIterDataPipeGraphFastForward::test_simple_snapshot_custom_self_next, test/test_datapipe.py::TestIterDataPipeGraphFastForward::test_simple_snapshot_graph, test/test_datapipe.py::TestIterDataPipeGraphFastForward::test_simple_snapshot_graph_repeated, test/test_datapipe.py::TestIterDataPipeGraphFastForward::test_simple_snapshot_graph_with_serialization 2025-08-14T22:41:54.8085241Z 2025-08-14T22:41:56.0365838Z Uploading artifacts took 1.24 seconds 2025-08-14T22:41:58.0492614Z /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. 2025-08-14T22:41:58.0495469Z import pkg_resources 2025-08-14T22:41:58.1717999Z Running test_bundled_inputs 1/1 ... [2025-08-14 22:41:58.171327] 2025-08-14T22:41:58.1718695Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:41:58.1720382Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_bundled_inputs.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'] ... [2025-08-14 22:41:58.171696] 2025-08-14T22:42:10.2649393Z 2025-08-14T22:42:10.2650778Z test_bundled_inputs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_bundled_inputs_1.1_4d0a8370543d33d4_.log 2025-08-14T22:42:10.2659491Z Running 12 items in this shard: test/test_bundled_inputs.py::TestBundledInputs::test_bad_inputs, test/test_bundled_inputs.py::TestBundledInputs::test_dict_args, test/test_bundled_inputs.py::TestBundledInputs::test_double_augment_fail, test/test_bundled_inputs.py::TestBundledInputs::test_double_augment_non_mutator, test/test_bundled_inputs.py::TestBundledInputs::test_double_augment_success, test/test_bundled_inputs.py::TestBundledInputs::test_large_tensor_with_inflation, test/test_bundled_inputs.py::TestBundledInputs::test_multiple_methods_with_inputs, test/test_bundled_inputs.py::TestBundledInputs::test_multiple_methods_with_inputs_both_defined_failure, test/test_bundled_inputs.py::TestBundledInputs::test_multiple_methods_with_inputs_neither_defined_failure, test/test_bundled_inputs.py::TestBundledInputs::test_non_tensors, test/test_bundled_inputs.py::TestBundledInputs::test_rejected_tensors, test/test_bundled_inputs.py::TestBundledInputs::test_single_tensors 2025-08-14T22:42:10.2666563Z 2025-08-14T22:42:13.4164794Z /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. 2025-08-14T22:42:13.4167758Z import pkg_resources 2025-08-14T22:42:13.5391231Z Running profiler/test_execution_trace 1/1 ... [2025-08-14 22:42:13.538684] 2025-08-14T22:42:13.5392109Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:42:13.5394336Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_execution_trace.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:42:13.539045] 2025-08-14T22:42:23.0180489Z 2025-08-14T22:42:23.0182659Z profiler/test_execution_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_execution_trace_1.1_61b119b12af06c7c_.log 2025-08-14T22:42:23.0196384Z Running 13 items in this shard: test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_alone_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_env_disabled_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_env_enabled_with_kineto_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_env_enabled_with_pt2_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_nested_tensor_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_no_capture_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_record_integral_tensor_data_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_record_integral_tensor_range_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_repeat_in_loop_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_start_stop_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_with_kineto_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_with_pt2_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_triton_fx_graph_with_et_cpu 2025-08-14T22:42:23.0208742Z 2025-08-14T22:42:26.1515487Z /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. 2025-08-14T22:42:26.1518135Z import pkg_resources 2025-08-14T22:42:26.2721749Z Running lazy/test_generator 1/1 ... [2025-08-14 22:42:26.271771] 2025-08-14T22:42:26.2722593Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:42:26.2724799Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/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'] ... [2025-08-14 22:42:26.272145] 2025-08-14T22:42:31.0433276Z 2025-08-14T22:42:31.0434630Z lazy/test_generator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_generator_1.1_3417e4138aae6ed3_.log 2025-08-14T22:42:31.0438289Z Running 2 items in this shard: test/lazy/test_generator.py::LazyGeneratorTest::test_generator, test/lazy/test_generator.py::LazyGeneratorTest::test_generator_causes_multiple_compiles 2025-08-14T22:42:31.0439883Z 2025-08-14T22:42:34.1258111Z /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. 2025-08-14T22:42:34.1261027Z import pkg_resources 2025-08-14T22:42:34.2463769Z Running torch_np/numpy_tests/core/test_indexing 1/1 ... [2025-08-14 22:42:34.245958] 2025-08-14T22:42:34.2464613Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:42:34.2466775Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_indexing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:42:34.246313] 2025-08-14T22:42:47.2802214Z 2025-08-14T22:42:47.2804445Z torch_np/numpy_tests/core/test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_indexing_1.1_5720d938e806ba32_.log 2025-08-14T22:42:47.2838418Z Running 67 items in this shard: test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_assignment_value_mismatch, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_indexing_list, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_indexing_onedim, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_indexing_twodim, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_shape_mismatch, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_broaderrors_indexing, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_broken_sequence_not_nd_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_ellipsis_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_ellipsis_index_2, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_empty_fancy_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_empty_tuple_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_everything_returns_views, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_index_no_array_to_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_index_no_floats, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_indexing_array_negative_strides, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_indexing_array_weird_strides, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_memory_order, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_none_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_nontuple_ndindex, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_reverse_strides_and_subspace_bufferinit, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_reversed_strides_result_allocation, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_same_kind_index_casting, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_scalar_array_bool, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_single_bool_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_single_int_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_slicing_no_floats, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_small_regressions, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_32_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_32_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_40_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_40_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_32_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_32_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_40_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_40_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_32_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_32_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_40_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_40_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_fancy_indices_special_case, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_trivial_fancy_not_possible, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_trivial_fancy_out_of_bounds, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_tuple_subclass, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_uncontiguous_subspace_assignment, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_error_reports_correct_shape_index0, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_error_reports_correct_shape_index1, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_error_reports_correct_shape_index2, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_subspace, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_index_is_larger, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_prepend_not_one, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_prepending_ones, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_simple_broadcasting_errors, test/torch_np/numpy_tests/core/test_indexing.py::TestFancyIndexingCast::test_boolean_index_cast_assign, test/torch_np/numpy_tests/core/test_indexing.py::TestMultiIndexingAutomated::test_1d, test/torch_np/numpy_tests/core/test_indexing.py::TestMultiIndexingAutomated::test_boolean, test/torch_np/numpy_tests/core/test_indexing.py::TestMultiIndexingAutomated::test_multidim, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_non_integer_argument_errors, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_non_integer_sequence_multiplication, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_reduce_axis_float_index, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_valid_indexing, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_valid_slicing, test/torch_np/numpy_tests/core/test_indexing.py::TestBooleanIndexing::test_bool_as_int_argument_errors, test/torch_np/numpy_tests/core/test_indexing.py::TestBooleanIndexing::test_boolean_indexing_fast_path, test/torch_np/numpy_tests/core/test_indexing.py::TestBooleanIndexing::test_boolean_indexing_weirdness, test/torch_np/numpy_tests/core/test_indexing.py::TestArrayToIndexDeprecation::test_array_to_index_error, test/torch_np/numpy_tests/core/test_indexing.py::TestNonIntegerArrayLike::test_basic, test/torch_np/numpy_tests/core/test_indexing.py::TestMultipleEllipsisError::test_basic 2025-08-14T22:42:47.2867288Z 2025-08-14T22:42:50.4268615Z /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. 2025-08-14T22:42:50.4271510Z import pkg_resources 2025-08-14T22:42:50.5473039Z Running test_maskedtensor 1/1 ... [2025-08-14 22:42:50.546902] 2025-08-14T22:42:50.5473731Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:42:50.5475744Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_maskedtensor.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'] ... [2025-08-14 22:42:50.547256] 2025-08-14T22:43:26.1884590Z 2025-08-14T22:43:26.1885918Z test_schema_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_schema_check_1.1_cf3da4485c9313f0_.log 2025-08-14T22:43:26.4980248Z Running 6023 items in this shard: test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_custom_ops_output_is_input, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_custom_ops_secretly_aliasing, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_custom_ops_secretly_mutating, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_multiple_operators, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_multiple_operators_centered, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_outputs_unexpectedly_aliasing, test/test_schema_check.py::TestSchemaCheck::test_alias_check_fail_simple, test/test_schema_check.py::TestSchemaCheck::test_is_alias_of_basic, test/test_schema_check.py::TestSchemaCheck::test_is_alias_of_empty_container, test/test_schema_check.py::TestSchemaCheck::test_mutation_check_fail, test/test_schema_check.py::TestSchemaCheck::test_mutation_check_fail_multiple_operators, test/test_schema_check.py::TestSchemaCheck::test_overlaps_basic, test/test_schema_check.py::TestSchemaCheck::test_overlaps_empty_container, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_empty_list_input, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_aliasing_inputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_default_replaced, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_device_input, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_kwarg_tensor, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_list_input, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_mutable_inputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_nested_training_op, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_training_op, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_wildcard_after, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_with_multiple_outputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_functionality_with_multiple_outputs_aliasing, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_aliasing_inputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_aliasing_outputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_as_strided, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_multiple_outputs, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_mutation, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_none, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_mutated_aliasing_resize_, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_operator_order, test/test_schema_check.py::TestSchemaCheck::test_schema_check_mode_operator_order_without_grad, test/test_schema_check.py::TestSchemaCheck::test_schema_info_bind_basic, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_complex32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_H_cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_complex32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_T_cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_complex32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___getitem___cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___radd___cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rand___cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rand___cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rand___cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rand___cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rand___cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rand___cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rdiv___cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness___rmatmul___cpu_int32, 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test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_vstack_cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_vstack_cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_vstack_cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_vstack_cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_vstack_cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_vstack_cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_where_cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_where_cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_where_cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_where_cpu_complex32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_where_cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_where_cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_where_cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_where_cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_where_cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_where_cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_where_cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_where_cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_where_cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_xlogy_cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_xlogy_cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_xlogy_cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_xlogy_cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_xlogy_cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_xlogy_cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_xlogy_cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_xlogy_cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_xlogy_cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_xlogy_cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zero__cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zero__cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zero__cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zero__cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zero__cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zero__cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zero__cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zero__cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zero__cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zero__cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zero__cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zero__cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_complex32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_cpu_uint8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_bfloat16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_bool, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_complex128, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_complex32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_complex64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_float16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_float32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_float64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_int16, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_int32, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_int64, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_int8, test/test_schema_check.py::TestSchemaCheckModeOpInfoCPU::test_schema_correctness_zeros_like_cpu_uint8 2025-08-14T22:43:26.7689937Z 2025-08-14T22:43:29.4083020Z /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. 2025-08-14T22:43:29.4085784Z import pkg_resources 2025-08-14T22:43:29.5294175Z Running test_tensorboard 1/1 ... [2025-08-14 22:43:29.529001] 2025-08-14T22:43:29.5294856Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:43:29.5298247Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensorboard.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'] ... [2025-08-14 22:43:29.529463] 2025-08-14T22:43:48.5728420Z 2025-08-14T22:43:48.5729767Z test_tensorboard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorboard_1.1_54c032cab06ef410_.log 2025-08-14T22:43:48.5760245Z Running 50 items in this shard: test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_autograd_np, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_histogram, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_histogram_raw, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_np, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_write, test/test_tensorboard.py::TestTensorBoardUtils::test_convert_to_HWC_dtype_remains_same, test/test_tensorboard.py::TestTensorBoardUtils::test_numpy_vid_uint8, test/test_tensorboard.py::TestTensorBoardUtils::test_prepare_video, test/test_tensorboard.py::TestTensorBoardUtils::test_to_HWC, test/test_tensorboard.py::TestTensorBoardWriter::test_writer, test/test_tensorboard.py::TestTensorBoardSummaryWriter::test_pathlib, test/test_tensorboard.py::TestTensorBoardSummaryWriter::test_summary_writer_close, test/test_tensorboard.py::TestTensorBoardSummaryWriter::test_summary_writer_ctx, test/test_tensorboard.py::TestTensorBoardEmbedding::test_embedding, test/test_tensorboard.py::TestTensorBoardEmbedding::test_embedding_64, test/test_tensorboard.py::TestTensorBoardSummary::test_audio, test/test_tensorboard.py::TestTensorBoardSummary::test_custom_scalars, test/test_tensorboard.py::TestTensorBoardSummary::test_empty_input, test/test_tensorboard.py::TestTensorBoardSummary::test_float32_image, test/test_tensorboard.py::TestTensorBoardSummary::test_histogram_auto, test/test_tensorboard.py::TestTensorBoardSummary::test_histogram_doane, test/test_tensorboard.py::TestTensorBoardSummary::test_histogram_fd, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_3_channel_batched, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_boxes, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_one_channel, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_one_channel_batched, test/test_tensorboard.py::TestTensorBoardSummary::test_image_without_channel, test/test_tensorboard.py::TestTensorBoardSummary::test_list_input, test/test_tensorboard.py::TestTensorBoardSummary::test_mesh, test/test_tensorboard.py::TestTensorBoardSummary::test_scalar_new_style, test/test_tensorboard.py::TestTensorBoardSummary::test_text, test/test_tensorboard.py::TestTensorBoardSummary::test_uint8_image, test/test_tensorboard.py::TestTensorBoardSummary::test_video, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_mlp_graph, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_nested_nn_squential, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_pytorch_graph, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_pytorch_graph_dict_input, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_torchvision_smoke, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_wrong_input_size, test/test_tensorboard.py::TestTensorBoardFigure::test_figure, test/test_tensorboard.py::TestTensorBoardFigure::test_figure_list, test/test_tensorboard.py::TestTensorBoardNumpy::test_pytorch_np_expect_fail, test/test_tensorboard.py::TestTensorBoardNumpy::test_scalar, test/test_tensorboard.py::TestTensorProtoSummary::test_complex_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_empty_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_float_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_half_tensor_proto_bfloat16_proto_type_14, test/test_tensorboard.py::TestTensorProtoSummary::test_half_tensor_proto_float16_proto_type_19, test/test_tensorboard.py::TestTensorProtoSummary::test_int_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_scalar_tensor_proto 2025-08-14T22:43:48.5791060Z 2025-08-14T22:43:51.6631852Z /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. 2025-08-14T22:43:51.6634437Z import pkg_resources 2025-08-14T22:43:51.7843285Z Running torch_np/numpy_tests/lib/test_type_check 1/1 ... [2025-08-14 22:43:51.783911] 2025-08-14T22:43:51.7844250Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:43:51.7846407Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_type_check.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:43:51.784254] 2025-08-14T22:44:09.0239971Z 2025-08-14T22:44:09.0241593Z torch_np/numpy_tests/lib/test_type_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_type_check_1.1_b2fdb987efed8a85_.log 2025-08-14T22:44:09.0272243Z Running 50 items in this shard: test/torch_np/numpy_tests/lib/test_type_check.py::TestCommonType::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestMintypecode::test_default_1, test/torch_np/numpy_tests/lib/test_type_check.py::TestMintypecode::test_default_2, test/torch_np/numpy_tests/lib/test_type_check.py::TestMintypecode::test_default_3, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsscalar::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestReal::test_cmplx, test/torch_np/numpy_tests/lib/test_type_check.py::TestReal::test_real, test/torch_np/numpy_tests/lib/test_type_check.py::TestImag::test_cmplx, test/torch_np/numpy_tests/lib/test_type_check.py::TestImag::test_real, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplex::test_fail, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplex::test_pass, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsreal::test_fail, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsreal::test_isreal_real, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsreal::test_pass, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplexobj::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplexobj::test_list, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplexobj::test_scalar, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsrealobj::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_complex, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_complex1, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_goodvalues, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_ind, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_integer, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_neginf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_posinf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_complex, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_complex1, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_goodvalues, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_ind, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_integer, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_neginf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_posinf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_goodvalues, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_ind, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_neginf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_neginf_scalar, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_posinf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_posinf_scalar, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsposinf::test_generic, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsneginf::test_generic, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_array, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_complex_bad, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_complex_bad2, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_complex_good, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_do_not_rewrite_previous_keyword, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_float, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_generic, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_integer, test/torch_np/numpy_tests/lib/test_type_check.py::TestRealIfClose::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestArrayConversion::test_asfarray 2025-08-14T22:44:09.0301300Z 2025-08-14T22:44:12.2276443Z /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. 2025-08-14T22:44:12.2279298Z import pkg_resources 2025-08-14T22:44:12.3490087Z Running lazy/test_ts_opinfo 1/1 ... [2025-08-14 22:44:12.348583] 2025-08-14T22:44:12.3490822Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:44:12.3493035Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_ts_opinfo.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:44:12.348948] 2025-08-14T22:44:32.4433259Z 2025-08-14T22:44:32.4434591Z lazy/test_ts_opinfo 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_ts_opinfo_1.1_76c6f72f8d51bdc9_.log 2025-08-14T22:44:32.4553535Z Running 292 items in this shard: test/lazy/test_ts_opinfo.py::TestLazyTensor::testConvolutionBackward, test/lazy/test_ts_opinfo.py::TestLazyTensor::test_tensor_ctr, test/lazy/test_ts_opinfo.py::TestLazyTensor::test_view_mark_step_preserved, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness__softmax_backward_data_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_abs_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_addcmul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_addmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_addmm_decomposed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_alias_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_arange_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_as_strided_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_as_strided_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_baddbmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_block_diag_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_bmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_cat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_clamp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_clamp_min_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_constant_pad_nd_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_cos_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_cumsum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_diag_embed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_diagonal_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_diagonal_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_div_floor_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_div_no_rounding_mode_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_div_trunc_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_empty_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_eq_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_exp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_expand_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_flip_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_floor_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_frac_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_gather_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_ge_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_grid_sampler_2d_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_gt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_le_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_log2_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_log_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_logsumexp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_lt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_masked_fill_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_pool2d_with_indices_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_maximum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mean_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_min_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_min_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_min_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_minimum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_narrow_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_native_batch_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_native_dropout_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_native_layer_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_ne_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_neg_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_new_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_fro_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_inf_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_nuc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_permute_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_reciprocal_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_remainder_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_repeat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_rsqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_scatter_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_select_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sgn_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sigmoid_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_slice_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sort_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_squeeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_stack_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_std_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_std_unbiased_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sub_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_t_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_tanh_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_topk_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_trace_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_transpose_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_tril_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_triu_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_trunc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_unfold_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_uniform_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_unsqueeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_view_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir__softmax_backward_data_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_abs_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_addcmul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_addmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_addmm_decomposed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_alias_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_arange_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_as_strided_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_as_strided_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_baddbmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_block_diag_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_bmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_cat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_clamp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_clamp_min_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_constant_pad_nd_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_cos_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_cumsum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_diag_embed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_diagonal_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_diagonal_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_div_floor_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_div_no_rounding_mode_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_div_trunc_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_empty_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_eq_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_exp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_expand_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_flip_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_floor_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_frac_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_gather_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_ge_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_grid_sampler_2d_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_gt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_le_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_log2_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_log_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_logsumexp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_lt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_masked_fill_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_pool2d_with_indices_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_maximum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mean_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_min_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_min_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_min_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_minimum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_narrow_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_native_batch_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_native_dropout_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_native_layer_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_ne_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_neg_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_new_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_fro_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_inf_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_nuc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_permute_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_reciprocal_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_remainder_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_repeat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_rsqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_scatter_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_select_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sgn_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sigmoid_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_slice_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sort_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_squeeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_stack_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_std_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_std_unbiased_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sub_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_t_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_tanh_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_topk_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_trace_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_transpose_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_tril_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_triu_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_trunc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_unfold_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_uniform_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_unsqueeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_view_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy__softmax_backward_data_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_abs_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addcdiv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addcmul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addmm_decomposed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_alias_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_arange_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_as_strided_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_as_strided_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_baddbmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_bernoulli_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_bmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_cat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_clamp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_clamp_min_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_constant_pad_nd_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_cos_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_cumsum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_diagonal_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_diagonal_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_div_floor_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_div_no_rounding_mode_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_div_trunc_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_empty_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_eq_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_exp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_expand_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_flip_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_floor_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_frac_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_gather_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_ge_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_grid_sampler_2d_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_gt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_le_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_log2_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_log_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_lt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_masked_fill_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_pool2d_with_indices_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_maximum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mean_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_min_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_min_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_minimum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_native_batch_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_native_dropout_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_native_layer_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_ne_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_neg_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_norm_fro_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_norm_inf_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_permute_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_pow_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_reciprocal_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_remainder_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_repeat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_rsqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_scatter_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_select_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sgn_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sigmoid_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_slice_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sort_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_squeeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_stack_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_std_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_std_unbiased_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sub_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_t_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_tanh_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_topk_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_trace_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_transpose_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_tril_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_triu_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_trunc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_unfold_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_uniform_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_unsqueeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_view_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyDynamicOps::test_adaptiveavgpool3d_dynamic, test/lazy/test_ts_opinfo.py::TestLazyDynamicOps::test_nonzero_dynamic 2025-08-14T22:44:32.4664119Z 2025-08-14T22:44:35.5534300Z /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. 2025-08-14T22:44:35.5537114Z import pkg_resources 2025-08-14T22:44:35.6749034Z Running test_mkldnn_fusion 1/1 ... [2025-08-14 22:44:35.674507] 2025-08-14T22:44:35.6749543Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:44:35.6751981Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_mkldnn_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'] ... [2025-08-14 22:44:35.674875] 2025-08-14T22:44:39.0459527Z 2025-08-14T22:44:39.0460716Z test_mkldnn_fusion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_fusion_1.1_4871e8ce96446fa1_.log 2025-08-14T22:44:39.0464234Z Running 8 items in this shard: test/test_mkldnn_fusion.py::TestMkldnnFusion::test_conv_binary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_conv_transpose_unary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_conv_unary_fusion_nnc, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_conv_unary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_linear_binary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_linear_unary_fusion_ops, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_single_conv, test/test_mkldnn_fusion.py::TestMkldnnFusion::test_unsupported_conv 2025-08-14T22:44:39.0466999Z 2025-08-14T22:44:42.4621591Z /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. 2025-08-14T22:44:42.4624635Z import pkg_resources 2025-08-14T22:44:42.5830699Z Running benchmark_utils/test_benchmark_utils 1/1 ... [2025-08-14 22:44:42.582709] 2025-08-14T22:44:42.5831556Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:44:42.5834337Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'benchmark_utils/test_benchmark_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'] ... [2025-08-14 22:44:42.583054] 2025-08-14T22:44:50.7399236Z 2025-08-14T22:44:50.7402721Z benchmark_utils/test_benchmark_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/benchmark_utils.test_benchmark_utils_1.1_1a900b0c60113736_.log 2025-08-14T22:44:50.7410014Z Running 9 items in this shard: test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_adaptive_timer, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_collect_callgrind, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_collect_cpp_callgrind, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_compare, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_cpp_timer, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_fuzzer, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_manipulate_callgrind_stats, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_timer, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_timer_tiny_fast_snippet 2025-08-14T22:44:50.7417043Z 2025-08-14T22:44:54.1556426Z /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. 2025-08-14T22:44:54.1559414Z import pkg_resources 2025-08-14T22:44:54.3150458Z Running test_linalg 1/6 ... [2025-08-14 22:44:54.314612] 2025-08-14T22:44:54.3151470Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:44:54.3155364Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_linalg.py', '-m', 'not serial', '--shard-id=1', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:44:54.315060] 2025-08-14T22:45:29.4537967Z 2025-08-14T22:45:29.4538875Z test_maskedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_maskedtensor_1.1_82b4727027d6a672_.log 2025-08-14T22:45:29.4863975Z Running 955 items in this shard: test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn0, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn1, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn10, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn11, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn12, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn13, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn14, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn15, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn16, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn17, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn18, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn19, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn2, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn20, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn21, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn22, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn23, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn24, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn25, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn26, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn27, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn28, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn29, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn3, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn30, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn31, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn32, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn33, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn34, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn35, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn36, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn37, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn38, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn39, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn4, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn40, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn41, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn42, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn43, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn44, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn45, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn46, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn47, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn48, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn49, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn5, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn50, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn51, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn52, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn53, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn54, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn55, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn56, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn57, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn6, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn7, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn8, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn9, test/test_maskedtensor.py::TestUnary::test_unary_fn0, test/test_maskedtensor.py::TestUnary::test_unary_fn1, test/test_maskedtensor.py::TestUnary::test_unary_fn10, test/test_maskedtensor.py::TestUnary::test_unary_fn11, test/test_maskedtensor.py::TestUnary::test_unary_fn12, test/test_maskedtensor.py::TestUnary::test_unary_fn13, test/test_maskedtensor.py::TestUnary::test_unary_fn14, test/test_maskedtensor.py::TestUnary::test_unary_fn15, test/test_maskedtensor.py::TestUnary::test_unary_fn16, test/test_maskedtensor.py::TestUnary::test_unary_fn17, test/test_maskedtensor.py::TestUnary::test_unary_fn18, test/test_maskedtensor.py::TestUnary::test_unary_fn19, test/test_maskedtensor.py::TestUnary::test_unary_fn2, test/test_maskedtensor.py::TestUnary::test_unary_fn20, test/test_maskedtensor.py::TestUnary::test_unary_fn21, test/test_maskedtensor.py::TestUnary::test_unary_fn22, test/test_maskedtensor.py::TestUnary::test_unary_fn23, test/test_maskedtensor.py::TestUnary::test_unary_fn24, test/test_maskedtensor.py::TestUnary::test_unary_fn25, test/test_maskedtensor.py::TestUnary::test_unary_fn26, test/test_maskedtensor.py::TestUnary::test_unary_fn27, test/test_maskedtensor.py::TestUnary::test_unary_fn28, test/test_maskedtensor.py::TestUnary::test_unary_fn29, test/test_maskedtensor.py::TestUnary::test_unary_fn3, test/test_maskedtensor.py::TestUnary::test_unary_fn30, test/test_maskedtensor.py::TestUnary::test_unary_fn31, test/test_maskedtensor.py::TestUnary::test_unary_fn32, test/test_maskedtensor.py::TestUnary::test_unary_fn33, test/test_maskedtensor.py::TestUnary::test_unary_fn34, test/test_maskedtensor.py::TestUnary::test_unary_fn35, test/test_maskedtensor.py::TestUnary::test_unary_fn36, test/test_maskedtensor.py::TestUnary::test_unary_fn37, test/test_maskedtensor.py::TestUnary::test_unary_fn38, test/test_maskedtensor.py::TestUnary::test_unary_fn39, test/test_maskedtensor.py::TestUnary::test_unary_fn4, test/test_maskedtensor.py::TestUnary::test_unary_fn40, test/test_maskedtensor.py::TestUnary::test_unary_fn41, test/test_maskedtensor.py::TestUnary::test_unary_fn42, test/test_maskedtensor.py::TestUnary::test_unary_fn43, test/test_maskedtensor.py::TestUnary::test_unary_fn44, test/test_maskedtensor.py::TestUnary::test_unary_fn45, test/test_maskedtensor.py::TestUnary::test_unary_fn46, test/test_maskedtensor.py::TestUnary::test_unary_fn47, test/test_maskedtensor.py::TestUnary::test_unary_fn48, test/test_maskedtensor.py::TestUnary::test_unary_fn49, test/test_maskedtensor.py::TestUnary::test_unary_fn5, test/test_maskedtensor.py::TestUnary::test_unary_fn50, test/test_maskedtensor.py::TestUnary::test_unary_fn51, test/test_maskedtensor.py::TestUnary::test_unary_fn52, test/test_maskedtensor.py::TestUnary::test_unary_fn53, test/test_maskedtensor.py::TestUnary::test_unary_fn54, test/test_maskedtensor.py::TestUnary::test_unary_fn55, 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test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_round_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_round_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_round_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_round_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_round_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_round_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_round_layout2_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_rsqrt_layout0_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_rsqrt_layout0_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_rsqrt_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_rsqrt_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_rsqrt_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_rsqrt_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_rsqrt_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_rsqrt_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_rsqrt_layout2_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sgn_layout0_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sgn_layout0_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sgn_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sgn_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sgn_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sgn_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sgn_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sgn_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sgn_layout2_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sigmoid_layout0_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sigmoid_layout0_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sigmoid_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sigmoid_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sigmoid_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sigmoid_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sigmoid_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sigmoid_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sigmoid_layout2_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sign_layout0_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sign_layout0_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sign_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sign_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sign_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sign_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sign_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sign_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sign_layout2_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_signbit_layout0_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_signbit_layout0_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_signbit_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_signbit_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_signbit_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_signbit_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_signbit_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_signbit_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_signbit_layout2_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sin_layout0_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sin_layout0_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sin_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sin_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sin_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sin_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sin_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sin_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sin_layout2_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinc_layout0_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinc_layout0_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinc_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinc_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinc_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinc_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinc_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinc_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinc_layout2_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinh_layout0_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinh_layout0_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinh_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinh_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinh_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinh_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinh_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinh_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sinh_layout2_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sqrt_layout0_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sqrt_layout0_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sqrt_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sqrt_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sqrt_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sqrt_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sqrt_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sqrt_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_sqrt_layout2_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_square_layout0_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_square_layout0_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_square_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_square_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_square_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_square_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_square_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_square_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_square_layout2_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tan_layout0_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tan_layout0_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tan_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tan_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tan_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tan_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tan_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tan_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tan_layout2_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tanh_layout0_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tanh_layout0_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tanh_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tanh_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tanh_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tanh_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tanh_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tanh_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_tanh_layout2_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_trunc_layout0_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_trunc_layout0_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_trunc_layout0_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_trunc_layout1_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_trunc_layout1_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_trunc_layout1_cpu_float64, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_trunc_layout2_cpu_float16, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_trunc_layout2_cpu_float32, test/test_maskedtensor.py::TestOperatorsCPU::test_unary_core_trunc_layout2_cpu_float64, test/test_maskedtensor.py::TestBasicsCPU::test_add_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_contiguous_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_diff_dim_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_diff_layouts_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_diff_sizes_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_grad_warning_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_invalid_sparse_coo_values_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_invalid_sparse_csr_values_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_invalid_sparse_layout_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_invalid_tensor_inputs_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_nn_unfold_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_softmax_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_stack_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_to_dense_and_sparse_coo_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_to_dense_and_sparse_csr_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_to_dense_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_to_device_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_to_dtype_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_to_sparse_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_unfold_cpu, test/test_maskedtensor.py::TestBasicsCPU::test_where_cpu 2025-08-14T22:45:29.5179660Z 2025-08-14T22:45:32.5494412Z /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. 2025-08-14T22:45:32.5495959Z import pkg_resources 2025-08-14T22:45:32.6750981Z Running test_linalg 5/6 ... [2025-08-14 22:45:32.674706] 2025-08-14T22:45:32.6751513Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:45:32.6753492Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_linalg.py', '-m', 'not serial', '--shard-id=5', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:45:32.675086] 2025-08-14T22:46:14.9060805Z 2025-08-14T22:46:14.9061866Z test_jit 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_2.2_ea43bb9af90b47c7_.log 2025-08-14T22:46:14.9587443Z Running 1267 items in this shard: test/test_jit.py::TestTracer::test_call_traced_module_from_traced_module, test/test_jit.py::TestTracer::test_canonicalize_tensor_iterator, test/test_jit.py::TestTracer::test_constant, test/test_jit.py::TestTracer::test_conv, test/test_jit.py::TestTracer::test_export_no_reorder, test/test_jit.py::TestTracer::test_force_outplace_check_fill, test/test_jit.py::TestTracer::test_ge, test/test_jit.py::TestTracer::test_index_put, test/test_jit.py::TestTracer::test_index_put_trace_without_view, test/test_jit.py::TestTracer::test_inplace_flags, test/test_jit.py::TestTracer::test_input_dict_empty, test/test_jit.py::TestTracer::test_input_dict_empty_list, test/test_jit.py::TestTracer::test_input_dict_of_dicts, test/test_jit.py::TestTracer::test_input_dict_remembers_keys, test/test_jit.py::TestTracer::test_input_flatten, test/test_jit.py::TestTracer::test_input_list_mixed_type, test/test_jit.py::TestTracer::test_input_list_of_tuples, test/test_jit.py::TestTracer::test_input_list_toplevel_flatten, test/test_jit.py::TestTracer::test_large_nbr_kernel_args, test/test_jit.py::TestTracer::test_lhs_index_trivial, test/test_jit.py::TestTracer::test_nested_inplace, test/test_jit.py::TestTracer::test_non_tensor_tracing, test/test_jit.py::TestTracer::test_python_function, test/test_jit.py::TestTracer::test_repeated_output, test/test_jit.py::TestTracer::test_shared_param, test/test_jit.py::TestTracer::test_tensor_with_grad_as_constant, test/test_jit.py::TestTracer::test_trace_annotation, test/test_jit.py::TestTracer::test_trace_arange, test/test_jit.py::TestTracer::test_trace_autograd_function, test/test_jit.py::TestTracer::test_trace_casts, test/test_jit.py::TestTracer::test_trace_checker_slice_lhs, test/test_jit.py::TestTracer::test_trace_checking_with_deprecated_name, test/test_jit.py::TestTracer::test_trace_detach, test/test_jit.py::TestTracer::test_trace_detach_inplace, test/test_jit.py::TestTracer::test_trace_detach_inplace_redispatch, test/test_jit.py::TestTracer::test_trace_dict_input, test/test_jit.py::TestTracer::test_trace_export_fns, test/test_jit.py::TestTracer::test_trace_indexed_assignment, test/test_jit.py::TestTracer::test_trace_legacy_ctor, test/test_jit.py::TestTracer::test_trace_module_argument_names_captured, test/test_jit.py::TestTracer::test_trace_multi_output_function, test/test_jit.py::TestTracer::test_trace_namedtuple, test/test_jit.py::TestTracer::test_trace_nested_fn, test/test_jit.py::TestTracer::test_trace_numel, test/test_jit.py::TestTracer::test_trace_optioanl_dtype, test/test_jit.py::TestTracer::test_trace_partial_func_argument_names_captured, test/test_jit.py::TestTracer::test_trace_records_names, test/test_jit.py::TestTracer::test_trace_single_tuple, test/test_jit.py::TestTracer::test_trace_slice, test/test_jit.py::TestTracer::test_trace_slice_setitem_dynamic_shape, test/test_jit.py::TestTracer::test_trace_tensor_factory, test/test_jit.py::TestTracer::test_trace_topk, test/test_jit.py::TestTracer::test_trace_tuple, test/test_jit.py::TestTracer::test_trace_variable_instantiation, test/test_jit.py::TestTracer::test_trace_with_conditional_property, test/test_jit.py::TestTracer::test_trace_with_nested_strided_tensor_output, test/test_jit.py::TestTracer::test_trace_with_tensor_list_output, test/test_jit.py::TestTracer::test_trace_with_tuple_tensor, test/test_jit.py::TestTracer::test_tracing_backward_hook_error, test/test_jit.py::TestTracer::test_tracing_multiple_methods, test/test_jit.py::TestTracer::test_type_same_device, test/test_jit.py::TestTracer::test_typeas_trace_check, test/test_jit.py::TestTracer::test_wrapped_number, test/test_jit.py::TestMixTracingScripting::test_call_script_module_from_traced_module, test/test_jit.py::TestMixTracingScripting::test_call_tracing_mod_from_script_module, test/test_jit.py::TestMixTracingScripting::test_script_inline_trace_multiple_args, test/test_jit.py::TestMixTracingScripting::test_trace_dict_mix_script, test/test_jit.py::TestMixTracingScripting::test_trace_linear, test/test_jit.py::TestMixTracingScripting::test_trace_mixed_by_script_with_dict_output, test/test_jit.py::TestMixTracingScripting::test_trace_of_script, test/test_jit.py::TestMixTracingScripting::test_trace_parameter, test/test_jit.py::TestMixTracingScripting::test_trace_script_returning_complex_dict, test/test_jit.py::TestMixTracingScripting::test_trace_with_size, test/test_jit.py::TestMixTracingScripting::test_tracing_slicing, test/test_jit.py::TestRecursiveScript::test_attributes, test/test_jit.py::TestRecursiveScript::test_class_compile, test/test_jit.py::TestRecursiveScript::test_constants_with_final, test/test_jit.py::TestRecursiveScript::test_dir, test/test_jit.py::TestRecursiveScript::test_error_stack, test/test_jit.py::TestRecursiveScript::test_failed_function_compilation, test/test_jit.py::TestRecursiveScript::test_function_attribute_in_submodule, test/test_jit.py::TestRecursiveScript::test_init_error, test/test_jit.py::TestRecursiveScript::test_module_basic, test/test_jit.py::TestRecursiveScript::test_module_name, test/test_jit.py::TestRecursiveScript::test_module_repr, test/test_jit.py::TestRecursiveScript::test_optional_module, test/test_jit.py::TestRecursiveScript::test_override_instance_method_ignore, test/test_jit.py::TestRecursiveScript::test_prepare_scriptable_basic, test/test_jit.py::TestRecursiveScript::test_prepare_scriptable_escape_hatch, test/test_jit.py::TestRecursiveScript::test_repeated_error_stack, test/test_jit.py::TestRecursiveScript::test_script_function_attribute, test/test_jit.py::TestRecursiveScript::test_thread_safe_error_stacks, test/test_jit.py::TestTypeSharing::test_assign_python_attr, test/test_jit.py::TestTypeSharing::test_builtin_function_same, test/test_jit.py::TestTypeSharing::test_constants, test/test_jit.py::TestTypeSharing::test_module_dict_same_type_different_name, test/test_jit.py::TestTypeSharing::test_mutate_attr_value, test/test_jit.py::TestTypeSharing::test_python_function_attribute_same, test/test_jit.py::TestTypeSharing::test_script_module_containing_traced_module, test/test_jit.py::TestTypeSharing::test_submodules, test/test_jit.py::TestTypeSharing::test_type_shared_ignored_attributes, test/test_jit.py::TestTypeSharing::test_type_sharing_disabled, test/test_jit.py::TestLogging::test_time_measurement_counter_script, test/test_jit.py::TestBackends::test_save_load, test/test_jit.py::TestBackendsWithCompiler::test_execution, test/test_jit.py::TestNnapiBackend::test_avg_pool2d, test/test_jit.py::TestNnapiBackend::test_dequantize, test/test_jit.py::TestNnapiBackend::test_flatten, test/test_jit.py::TestNnapiBackend::test_mean, test/test_jit.py::TestNnapiBackend::test_pointwise_binary_const, test/test_jit.py::TestNnapiBackend::test_reshape, test/test_jit.py::TestNnapiBackend::test_softmax, test/test_jit.py::TestNnapiBackend::test_unsqueeze, test/test_jit.py::TestNnapiBackend::test_upsample_nearest2d, test/test_jit.py::TestList::test_comprehension_out_type_not_in_type, test/test_jit.py::TestList::test_comprehensions_basic, test/test_jit.py::TestList::test_comprehensions_basic_float, test/test_jit.py::TestList::test_copy_list_mutable, test/test_jit.py::TestList::test_dict_keyword_is_correctly_typed, test/test_jit.py::TestList::test_dict_keyword_with_dict_comprehension_and_kwargs, test/test_jit.py::TestList::test_dict_keyword_with_empty_iterable, test/test_jit.py::TestList::test_dict_keyword_with_iterable, test/test_jit.py::TestList::test_dict_keyword_with_kwargs, test/test_jit.py::TestList::test_dict_keyword_with_kwargs_using_container_values, test/test_jit.py::TestList::test_dict_keyword_with_mapping_and_kwargs, test/test_jit.py::TestList::test_dict_keyword_with_previously_declared_variable, test/test_jit.py::TestList::test_list_count_not_existing, test/test_jit.py::TestList::test_list_len, test/test_jit.py::TestList::test_list_none, test/test_jit.py::TestList::test_list_ops, test/test_jit.py::TestList::test_list_slice, test/test_jit.py::TestList::test_list_sort, test/test_jit.py::TestList::test_list_variance, test/test_jit.py::TestList::test_min_bool_list, test/test_jit.py::TestList::test_min_max_single_list, test/test_jit.py::TestList::test_mutable_list_append_if, test/test_jit.py::TestList::test_mutable_list_append_loop, test/test_jit.py::TestList::test_mutable_list_append_loop_if, test/test_jit.py::TestList::test_mutable_list_clear, test/test_jit.py::TestList::test_mutable_list_clear_empty, test/test_jit.py::TestList::test_mutable_list_insert, test/test_jit.py::TestList::test_mutable_list_insert_neg_out_of_bounds, test/test_jit.py::TestList::test_mutable_list_insert_negative, test/test_jit.py::TestList::test_mutable_list_nested_loop, test/test_jit.py::TestList::test_mutable_list_pop2, test/test_jit.py::TestList::test_mutable_list_pop_at, test/test_jit.py::TestList::test_mutable_list_pop_at_negative, test/test_jit.py::TestList::test_mutable_list_pop_slice, test/test_jit.py::TestList::test_mutable_list_remove_tensor, test/test_jit.py::TestList::test_mutable_list_reverse_empty, test/test_jit.py::TestList::test_no_element_type_annotation, test/test_jit.py::TestList::test_tensor_list_count_not_existing, test/test_jit.py::TestList::test_tensor_list_index_not_existing, test/test_jit.py::TestList::test_to_list_gpu, test/test_jit.py::TestDict::test_copy, test/test_jit.py::TestDict::test_del, test/test_jit.py::TestDict::test_dict_preserves_order, test/test_jit.py::TestDict::test_dict_to_python, test/test_jit.py::TestDict::test_keys, test/test_jit.py::TestDict::test_len, test/test_jit.py::TestDict::test_loop, test/test_jit.py::TestDict::test_optional_dict_construct, test/test_jit.py::TestDict::test_pop, test/test_jit.py::TestDict::test_popitem, test/test_jit.py::TestDict::test_update_existing_key, test/test_jit.py::TestDict::test_values, test/test_jit.py::TestNamedTuple::test_namedtuple, test/test_jit.py::TestNamedTuple::test_namedtuple_as_attr, test/test_jit.py::TestNamedTuple::test_namedtuple_constant, test/test_jit.py::TestNamedTuple::test_namedtuple_kwarg_construct, test/test_jit.py::TestNamedTuple::test_namedtuple_lower, test/test_jit.py::TestNamedTuple::test_namedtuple_resolution, test/test_jit.py::TestNamedTuple::test_namedtuple_resolution_forwardref, test/test_jit.py::TestNamedTuple::test_namedtuple_slice_unpack, test/test_jit.py::TestNamedTuple::test_namedtuple_wrong_types, test/test_jit.py::TestScriptDict::test_items, test/test_jit.py::TestScriptDict::test_reference_semantics, test/test_jit.py::TestScriptDict::test_setitem, test/test_jit.py::TestScriptList::test_append, test/test_jit.py::TestScriptList::test_bool, test/test_jit.py::TestScriptList::test_contains, test/test_jit.py::TestScriptList::test_count, test/test_jit.py::TestScriptList::test_defaultdict, test/test_jit.py::TestScriptList::test_extend, test/test_jit.py::TestScriptList::test_getitem, test/test_jit.py::TestScriptList::test_insert, test/test_jit.py::TestScriptList::test_nested, test/test_jit.py::TestScriptList::test_reference_semantics, test/test_jit.py::TestScriptList::test_remove, test/test_jit.py::TestScriptList::test_repr, test/test_jit.py::TestAsync::test_async_future_type_python, test/test_jit.py::TestAsync::test_async_grad_guard_no_grad, test/test_jit.py::TestAsync::test_async_python, test/test_jit.py::TestAsync::test_async_script_capture, test/test_jit.py::TestAsync::test_async_script_error, test/test_jit.py::TestAsync::test_no_future_subtype_message, test/test_jit.py::TestAsync::test_trace_fork_wait, test/test_jit.py::TestAsync::test_trace_fork_wait_inline, test/test_jit.py::TestAsync::test_trace_fork_wait_leaking, test/test_jit.py::TestAsync::test_trace_modulecalls_with_different_output_types, test/test_jit.py::TestAwait::test_await_class_arg, test/test_jit.py::TestAwait::test_await_eager_lazy, test/test_jit.py::TestAwait::test_await_getattr_implicit_convertion, test/test_jit.py::TestAwait::test_await_isinstance, test/test_jit.py::TestAwait::test_await_python, test/test_jit.py::TestAwait::test_await_type_python, test/test_jit.py::TestAwait::test_awaitable_to_await, test/test_jit.py::TestAwait::test_nowait, test/test_jit.py::TestAwait::test_script, test/test_jit.py::TestDataParallel::test_tensor_sharing_with_forward, test/test_jit.py::TestModels::test_alexnet, test/test_jit.py::TestModels::test_dcgan_models, test/test_jit.py::TestModels::test_neural_style_cuda, test/test_jit.py::TestModels::test_reinforcement_learning_cuda, test/test_jit.py::TestModels::test_script_module_script_resnet, test/test_jit.py::TestModels::test_snli, test/test_jit.py::TestModels::test_super_resolution, test/test_jit.py::TestModels::test_time_sequence_prediction, test/test_jit.py::TestAutodiffJit::test_autodiff_requires_grad_nograd, test/test_jit.py::TestAutodiffJit::test_requires_grad_outputs_profiled_twice, test/test_jit.py::TestAutodiffJit::test_requires_grad_outputs_side_effects, test/test_jit.py::TestAutodiffSubgraphSlicing::test_bias_as_arg, test/test_jit.py::TestAutodiffSubgraphSlicing::test_chunk_constant_script_ad, test/test_jit.py::TestAutodiffSubgraphSlicing::test_constructed_bias, test/test_jit.py::TestAutodiffSubgraphSlicing::test_diff_graph_inline_threshold, test/test_jit.py::TestAutodiffSubgraphSlicing::test_differentiable_graph_ops_requires_grad, test/test_jit.py::TestAutodiffSubgraphSlicing::test_does_not_create_cycles, test/test_jit.py::TestAutodiffSubgraphSlicing::test_does_not_merge_unrelated, test/test_jit.py::TestAutodiffSubgraphSlicing::test_merge_respects_aliasing, test/test_jit.py::TestAutodiffSubgraphSlicing::test_merges_dense, test/test_jit.py::TestAutodiffSubgraphSlicing::test_merges_up, test/test_jit.py::TestAutodiffSubgraphSlicing::test_prune_grad, test/test_jit.py::TestAutodiffSubgraphSlicing::test_requires_grad_for_tensor_list, test/test_jit.py::TestAutodiffSubgraphSlicing::test_simple_merge, test/test_jit.py::TestAutodiffSubgraphSlicing::test_simple_no_merge, test/test_jit.py::TestCustomOperators::test_getting_invalid_attr, test/test_jit.py::TestCustomOperators::test_passing_and_returning_lists, test/test_jit.py::TestCustomOperators::test_passing_one_positional_but_not_the_second, test/test_jit.py::TestCustomOperators::test_script_graph_for_custom_ops_matches_traced_graph, test/test_jit.py::TestCustomOperators::test_where_no_scalar, test/test_jit.py::TestGraphRewritePasses::test_fuse_linear, test/test_jit.py::TestClassType::test_cast_overloads, test/test_jit.py::TestClassType::test_class_constant, test/test_jit.py::TestClassType::test_class_constructs_itself, test/test_jit.py::TestClassType::test_class_sorting, test/test_jit.py::TestClassType::test_class_specialization, test/test_jit.py::TestClassType::test_conditional_set_attr, test/test_jit.py::TestClassType::test_custom_delete, test/test_jit.py::TestClassType::test_default_args, test/test_jit.py::TestClassType::test_get_attr, test/test_jit.py::TestClassType::test_get_with_method, test/test_jit.py::TestClassType::test_imported_classes, test/test_jit.py::TestClassType::test_in, test/test_jit.py::TestClassType::test_init_compiled_first, test/test_jit.py::TestClassType::test_optional_type_promotion, test/test_jit.py::TestClassType::test_properties, test/test_jit.py::TestClassType::test_recursive_class, test/test_jit.py::TestClassType::test_recursive_script_builtin_type_resolution, test/test_jit.py::TestClassType::test_recursive_scripting, test/test_jit.py::TestClassType::test_recursive_scripting_failed, test/test_jit.py::TestClassType::test_save_load_with_classes, test/test_jit.py::TestClassType::test_save_load_with_classes_returned, test/test_jit.py::TestClassType::test_set_attr_in_method, test/test_jit.py::TestClassType::test_set_attr_non_initialized, test/test_jit.py::TestClassType::test_staticmethod, test/test_jit.py::TestClassType::test_type_annotation, test/test_jit.py::TestClassType::test_type_annotations, test/test_jit.py::TestClassType::test_unresolved_class_attributes, test/test_jit.py::TestBuiltins::test_del_multiple_operands, test/test_jit.py::TestBuiltins::test_has_attr_invalid_args, test/test_jit.py::TestTensorBuiltins::test_method_on_number, test/test_jit.py::TestTensorBuiltins::test_tensor_item, test/test_jit.py::TestTensorBuiltins::test_tensor_properties, test/test_jit.py::TestTensorBuiltins::test_tensor_subscript_assign, test/test_jit.py::TestTensorBuiltins::test_tensor_subscript_assign_device, test/test_jit.py::TestIgnoreContextManager::test_with_ignore_context_manager_with_just_inp, test/test_jit.py::TestIgnoreContextManager::test_with_ignore_context_manager_with_just_out, test/test_jit.py::TestSymbolicShapeAnalysis::test_arange_shape, test/test_jit.py::TestSymbolicShapeAnalysis::test_binary_shape_functions, test/test_jit.py::TestSymbolicShapeAnalysis::test_partial_eval_graph_conv, test/test_jit.py::TestSymbolicShapeAnalysis::test_shape_analysis, test/test_jit.py::TestSymbolicShapeAnalysis::test_shape_function_includes, test/test_jit.py::TestSymbolicShapeAnalysis::test_shared_shape_graph, test/test_jit.py::TestSymbolicShapeAnalysis::test_squeeze_dims, test/test_jit.py::TestSymbolicShapeAnalysis::test_sym_ir_parsing, test/test_jit.py::TestSymbolicShapeAnalysis::test_unary_shape_fns_inplace, test/test_jit.py::TestFreezing::test_freeze_interface_within_object, test/test_jit.py::TestFreezing::test_freeze_module_detach_gradient, test/test_jit.py::TestFreezing::test_freeze_module_in_training_mode, test/test_jit.py::TestFreezing::test_freeze_module_return_sub_module, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_attr, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_attr2, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_attr3, test/test_jit.py::TestFreezing::test_freeze_module_with_fork2, test/test_jit.py::TestFreezing::test_freeze_module_with_inplace_mutable, test/test_jit.py::TestFreezing::test_freeze_module_with_mutable_list, test/test_jit.py::TestFreezing::test_freeze_module_with_mutable_tensor, test/test_jit.py::TestFreezing::test_freeze_module_with_nestedaliasing, test/test_jit.py::TestFreezing::test_freeze_module_with_nestedaliasingscalar, test/test_jit.py::TestFreezing::test_freeze_module_with_submodule, test/test_jit.py::TestFreezing::test_freeze_module_with_tuple, test/test_jit.py::TestFreezing::test_freeze_module_with_tupleoutput_submodule, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_method, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_method2, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_method_on_submodule, test/test_jit.py::TestFreezing::test_freeze_recursive_interfaces_with_reassignment, test/test_jit.py::TestFreezing::test_module_getattr_indirection, test/test_jit.py::TestFrozenOptimizations::test_collapse_adjacent_conversions, test/test_jit.py::TestFrozenOptimizations::test_conv_add_folding, test/test_jit.py::TestFrozenOptimizations::test_conv_bn_folding_not_forward, test/test_jit.py::TestFrozenOptimizations::test_conv_mul_add_bn, test/test_jit.py::TestFrozenOptimizations::test_conv_to_mkldnn, test/test_jit.py::TestFrozenOptimizations::test_freeze_conv_relu_fusion, test/test_jit.py::TestFrozenOptimizations::test_freeze_conv_relu_fusion_not_forward, test/test_jit.py::TestFrozenOptimizations::test_freeze_remove_dropout, test/test_jit.py::TestFrozenOptimizations::test_freeze_remove_feature_dropout, test/test_jit.py::TestFrozenOptimizations::test_hardswish_hardsigmoid, test/test_jit.py::TestFrozenOptimizations::test_incompatible_perf_formats, test/test_jit.py::TestFrozenOptimizations::test_linear_bn_folding, test/test_jit.py::TestFrozenOptimizations::test_linear_bn_folding_autocast_scenario_cuda, test/test_jit.py::TestFrozenOptimizations::test_linear_concat_different_input, test/test_jit.py::TestFrozenOptimizations::test_linear_multiple_blocks, test/test_jit.py::TestFrozenOptimizations::test_linear_non_constant_weight, test/test_jit.py::TestFrozenOptimizations::test_mkldnn_fuser_broadcasting, test/test_jit.py::TestFrozenOptimizations::test_numel_less_than_size_with_padding, test/test_jit.py::TestFrozenOptimizations::test_remove_detach, test/test_jit.py::TestFrozenOptimizations::test_remove_detach_not_applied, test/test_jit.py::TestMKLDNNReinplacing::test_always_alive_values, test/test_jit.py::TestMKLDNNReinplacing::test_successful, test/test_jit.py::TestPeephole::test_conv_dim_folding, test/test_jit.py::TestPeephole::test_integer_refinement, test/test_jit.py::TestPeephole::test_noop_peephole, test/test_jit.py::TestPeephole::test_normalized_is_op, test/test_jit.py::TestPeephole::test_normalized_rsub, test/test_jit.py::TestPeephole::test_optimize_out_comparison_same_value, test/test_jit.py::TestPeephole::test_peephole, test/test_jit.py::TestPeephole::test_peephole_arith, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_dict_modified, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_get_input_arg, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_keys_might_overlap, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_missing_key, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_unsupported_type, test/test_jit.py::TestPeephole::test_peephole_dict_len, test/test_jit.py::TestPeephole::test_peephole_dict_len_no_optimization_keys_might_overlap, test/test_jit.py::TestPeephole::test_peephole_dict_len_no_optimization_overlapping_keys, test/test_jit.py::TestPeephole::test_peephole_dict_len_no_optimization_unsupported_type, test/test_jit.py::TestPeephole::test_peephole_dynamic, test/test_jit.py::TestPeephole::test_peephole_int, test/test_jit.py::TestPeephole::test_peephole_list_ops, test/test_jit.py::TestPeephole::test_peephole_no_output_aliasing, test/test_jit.py::TestPeephole::test_peephole_slice_optimization_not_applied_non_const_args, test/test_jit.py::TestPeephole::test_peephole_with_non_output_writes, test/test_jit.py::TestPeephole::test_peephole_with_writes, test/test_jit.py::TestPeephole::test_refine_integer_values, test/test_jit.py::TestAliasAnalysis::test_multiple_compilation_units, test/test_jit.py::TestAliasAnalysis::test_nested_list_construct_not_wildcard, test/test_jit.py::TestAliasAnalysis::test_recursive_calls, test/test_jit.py::TestSaveLoad::test_different_functions, test/test_jit.py::TestSaveLoad::test_save_load_meta_tensors_to_device, test/test_jit.py::TestSaveLoad::test_save_load_params_buffers_submodules, test/test_jit.py::TestSaveLoad::test_save_load_with_extra_files, test/test_jit.py::TestSaveLoad::test_save_namedtuple_input_only, test/test_jit.py::TestSaveLoad::test_save_namedtuple_input_only_forwardref, test/test_jit.py::TestSaveLoad::test_save_namedtuple_output_only, test/test_jit.py::TestSaveLoad::test_save_nonexit_file, test/test_jit.py::TestSaveLoadFlatbuffer::test_different_functions, test/test_jit.py::TestSaveLoadFlatbuffer::test_different_interfaces, test/test_jit.py::TestSaveLoadFlatbuffer::test_many_collisions, test/test_jit.py::TestSaveLoadFlatbuffer::test_module_info_flatbuffer, test/test_jit.py::TestSaveLoadFlatbuffer::test_save_load_with_extra_files, test/test_jit.py::TestSaveLoadFlatbuffer::test_save_namedtuple_input_only, test/test_jit.py::TestSaveLoadFlatbuffer::test_save_namedtuple_output_only, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_scalar_inplace, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_scalar_scalar, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_tensor, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_tensor_out, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_linspace, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_linspace_out, test/test_jit.py::TestModuleContainers::test_custom_container_forward, test/test_jit.py::TestModuleContainers::test_empty_dict_override_contains, test/test_jit.py::TestModuleContainers::test_moduledict, test/test_jit.py::TestModuleContainers::test_moduledict_getitem, test/test_jit.py::TestModuleContainers::test_moduledict_keyerror, test/test_jit.py::TestModuleContainers::test_normal_list_attribute_with_modules_error, test/test_jit.py::TestModuleContainers::test_parameterdict_script_getitem, test/test_jit.py::TestModuleContainers::test_parameterlist_script_iter, test/test_jit.py::TestModuleContainers::test_typed_module_dict, test/test_jit.py::TestModuleContainers::test_typed_module_list, test/test_jit.py::TestPythonBindings::test_add_input, test/test_jit.py::TestPythonBindings::test_cu_create_function, test/test_jit.py::TestPythonBindings::test_graph_iterator_keepalive, test/test_jit.py::TestPythonBindings::test_invalidation, test/test_jit.py::TestPythonIr::test_python_ir_utils, test/test_jit.py::TestPythonIr::test_python_ir_utils_graph, test/test_jit.py::TestRemoveMutation::test_aten_inplace, test/test_jit.py::TestRemoveMutation::test_if_output, test/test_jit.py::TestRemoveMutation::test_list_indexing_removal, test/test_jit.py::TestRemoveMutation::test_lists_append, test/test_jit.py::TestTorchbind::test_hasattr, test/test_jit.py::TestTorchbind::test_lambda_as_constructor, test/test_jit.py::TestTorchbind::test_torchbind_attr_exception, test/test_jit.py::TestTorchbind::test_torchbind_class_attribute, test/test_jit.py::TestTorchbind::test_torchbind_def_property_getter_setter, test/test_jit.py::TestTorchbind::test_torchbind_def_property_just_getter, test/test_jit.py::TestTorchbind::test_torchbind_lambda_method, test/test_jit.py::TestTorchbind::test_torchbind_take_as_arg, test/test_jit.py::TestTorchbind::test_torchbind_take_instance_as_method_arg, test/test_jit.py::TestTorchbind::test_torchbind_tracing, test/test_jit.py::TestModuleInterface::test_freeze_module_with_interface, test/test_jit.py::TestModuleInterface::test_freeze_module_with_mutated_interface, test/test_jit.py::TestModuleInterface::test_module_apis_interface, test/test_jit.py::TestModuleInterface::test_module_doc_string, test/test_jit.py::TestModuleInterface::test_module_interface, test/test_jit.py::TestModuleInterface::test_module_interface_inheritance, test/test_jit.py::TestModuleInterface::test_module_swap, test/test_jit.py::TestModuleInterface::test_module_swap_no_module_interface, test/test_jit.py::TestModuleInterface::test_module_swap_wrong_module, test/test_jit.py::TestModuleInterface::test_not_submodule_interface_call, test/test_jit.py::TestWith::test_with_errors, test/test_jit.py::TestWith::test_with_record_function, test/test_jit.py::TestEnum::test_enum_as_const, test/test_jit.py::TestEnum::test_enum_comp_diff_classes, test/test_jit.py::TestEnum::test_enum_explicit_script, test/test_jit.py::TestEnum::test_enum_iterate, test/test_jit.py::TestEnum::test_enum_value, test/test_jit.py::TestEnum::test_non_existent_enum_value, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_char_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_char_placeholder_and_true_string_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_digit_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_digit_placeholder_and_string_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_float_placeholder_and_float_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_multiple_placeholders, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_percent_in_string, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_string_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_string_placeholder_and_format_string_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_too_few_arguments, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_unknown_format_specifier, test/test_jit.py::TestProfiler::test_aliasing_merge, test/test_jit.py::TestProfiler::test_autograd_fallback_graph, test/test_jit.py::TestProfiler::test_fallback_graph_not_specialized, test/test_jit.py::TestProfiler::test_iterative_fusion, test/test_jit.py::TestProfiler::test_local_fusion_strategy, test/test_jit.py::TestProfiler::test_not_optimizing_property, test/test_jit.py::TestProfiler::test_tensor_constant, test/test_jit.py::TestProfiler::test_tensor_type_not_determined_by_inputs, test/test_jit.py::TestProfiler::test_use_not_profiled, test/test_jit.py::TestSlice::test_slice_dynamic_index, test/test_jit.py::TestSlice::test_slice_kwarg, test/test_jit.py::TestSlice::test_slice_one_none, test/test_jit.py::TestSlice::test_slice_stop_only_with_nones, test/test_jit.py::TestSlice::test_slice_string, test/test_jit.py::TestSlice::test_slice_tensor_multidim, test/test_jit.py::TestSlice::test_slice_three_nones, test/test_jit.py::TestSlice::test_tuple_slicing, test/test_jit.py::TestIgnorableArgs::test_add_out_ignorable_args, test/test_jit.py::TestIgnorableArgs::test_slice_ignorable_args_for_slice, test/test_jit.py::TestHooks::test_hook_compilation_hint, test/test_jit.py::TestHooks::test_hook_method_name_collision, test/test_jit.py::TestHooks::test_module_forward_multiple_inputs, test/test_jit.py::TestHooks::test_module_forward_single_input, test/test_jit.py::TestHooks::test_module_hook_return_nothing, test/test_jit.py::TestHooks::test_module_multiple_hooks_multiple_inputs, test/test_jit.py::TestHooks::test_module_multiple_hooks_single_input, test/test_jit.py::TestHooks::test_submodule_called_directly_with_hooks, test/test_jit.py::TestHooks::test_submodule_forward_multiple_inputs, test/test_jit.py::TestHooks::test_submodule_forward_single_input, test/test_jit.py::TestHooks::test_submodule_same_hook_repeated, test/test_jit.py::TestWarn::test_warn_once_per_func, test/test_jit.py::TestWarn::test_warn_only_once, test/test_jit.py::TestWarn::test_warn_only_once_in_loop_func, test/test_jit.py::TestIsinstance::test_dict_nested, test/test_jit.py::TestIsinstance::test_dict_tensor, test/test_jit.py::TestIsinstance::test_in_while_loop, test/test_jit.py::TestIsinstance::test_list, test/test_jit.py::TestIsinstance::test_list_nested, test/test_jit.py::TestIsinstance::test_list_no_contained_type, test/test_jit.py::TestIsinstance::test_list_tensor, test/test_jit.py::TestIsinstance::test_nontuple_container_rhs_throws_in_eager, test/test_jit.py::TestIsinstance::test_optional, test/test_jit.py::TestIsinstance::test_optional_nested, test/test_jit.py::TestIsinstance::test_optional_no_contained_type, test/test_jit.py::TestIsinstance::test_tensor_type_false, test/test_jit.py::TestIsinstance::test_tuple, test/test_jit.py::TestPythonBuiltinOP::test_advancedindex, test/test_jit.py::TestPythonBuiltinOP::test_matmul_py3, test/test_jit.py::TestPythonBuiltinOP::test_mul, test/test_jit.py::TestPythonBuiltinOP::test_stepped_tuple_slicing, test/test_jit.py::TestPythonBuiltinOP::test_str_to_float, test/test_jit.py::TestPythonBuiltinOP::test_triple, test/test_jit.py::TestTyping::test_dict_comprehension_scope, test/test_jit.py::TestTyping::test_dict_in_not_in, test/test_jit.py::TestTyping::test_dict_invalid_annotations, test/test_jit.py::TestTyping::test_dict_type_refinement_annotation_key_mismatch, test/test_jit.py::TestTyping::test_dict_type_refinement_annotation_value_mismatch, test/test_jit.py::TestTyping::test_for_in_dict, test/test_jit.py::TestTyping::test_for_in_string, test/test_jit.py::TestTyping::test_for_tuple_unpack, test/test_jit.py::TestTyping::test_inherited_annotations_python_310, test/test_jit.py::TestTyping::test_list_io, test/test_jit.py::TestTyping::test_list_type_refinement_annotation_element_mismatch, test/test_jit.py::TestTyping::test_multiple_assign, test/test_jit.py::TestTyping::test_namedtuple_error_source_attribution, test/test_jit.py::TestTyping::test_namedtuple_good_error, test/test_jit.py::TestTyping::test_namedtuple_redefine, test/test_jit.py::TestTyping::test_nested_list, test/test_jit.py::TestTyping::test_singleton_tuple_unpack, test/test_jit.py::TestTyping::test_sum_list_diff_elms, test/test_jit.py::TestTyping::test_sum_list_literal, test/test_jit.py::TestTyping::test_tuple_create_return, test/test_jit.py::TestTyping::test_tuple_io, test/test_jit.py::TestTyping::test_tuple_keyword, test/test_jit.py::TestTyping::test_tuple_specialization, test/test_jit.py::TestHash::test_hash_float, test/test_jit.py::TestHash::test_hash_int, test/test_jit.py::TestHash::test_hash_none, test/test_jit.py::TestHash::test_hash_string, test/test_jit.py::TestHash::test_hash_tuple_nested_unhashable_type, test/test_jit.py::TestComplex::test_binary_op_complex_tensor, test/test_jit.py::TestComplex::test_complex_constants_and_ops, test/test_jit.py::TestComplex::test_complex_constructor, test/test_jit.py::TestComplex::test_complex_parse, test/test_jit.py::TestComplex::test_complexdict, test/test_jit.py::TestComplex::test_infj_nanj_pickle, test/test_jit.py::TestComplex::test_torch_complex_constructor_with_tensor, test/test_jit.py::TestJitUtils::test_checkscriptassertraisesregex, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_hybrid, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_keyword_only, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_positional_only, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_positional_or_keyword, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_var_positional, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_class_level_jit_annotation, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_list, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_list_lowercase, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_optional, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_tensor, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_falsy_base_type, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_nonempty_container, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_attribute, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_empty_dict, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_empty_list, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_empty_list_lowercase, test/test_jit.py::TestTypesAndAnnotation::test_ignore_with_types, test/test_jit.py::TestTypesAndAnnotation::test_ignoring_fn_with_nonscriptable_types, test/test_jit.py::TestTypesAndAnnotation::test_ignoring_module_attributes, test/test_jit.py::TestTypesAndAnnotation::test_inferred_type_error_message, test/test_jit.py::TestTypesAndAnnotation::test_optional_no_element_type_annotation, test/test_jit.py::TestTypesAndAnnotation::test_parser_bug, test/test_jit.py::TestTypesAndAnnotation::test_pep585_type, test/test_jit.py::TestTypesAndAnnotation::test_type_annotate_py3, test/test_jit.py::TestMisc::test_broadcasting_list, test/test_jit.py::TestMisc::test_export_opnames_interface, test/test_jit.py::TestMisc::test_future_isinstance, test/test_jit.py::TestMisc::test_hacked_twin, test/test_jit.py::TestMisc::test_jit_get_operation_order, test/test_jit.py::TestMisc::test_kwarg_support, test/test_jit.py::TestMisc::test_legacy_tensor_constructor, test/test_jit.py::TestMisc::test_math_inf, test/test_jit.py::TestMisc::test_parse_ir_single_element_tensor_negative, test/test_jit.py::TestMisc::test_parse_ir_single_element_tensor_positive, test/test_jit.py::TestMisc::test_pow_multiple_dtype, test/test_jit.py::TestMisc::test_str_refine_any, test/test_jit.py::TestMisc::test_subexpression_Dict_int_Future, test/test_jit.py::TestMisc::test_subexpression_Future_annotate, test/test_jit.py::TestMisc::test_subexpression_List_Future, test/test_jit.py::TestMisc::test_subexpression_Optional, test/test_jit.py::TestMisc::test_subexpression_Tuple_int_int_Future, test/test_jit.py::TestUpgraders::test_aten_div_tensor_out_at_3, test/test_jit.py::TestUpgraders::test_aten_full_out_at_4, test/test_jit.py::TestUpgraders::test_aten_linspace_out, test/test_jit.py::TestUpgraders::test_aten_logspace_out, test/test_jit.py::TestUpgraders::test_populated_test_upgrader_graph, test/test_jit.py::TestTensorCreationOps::test_randperm_default_dtype, test/test_jit.py::TestTensorCreationOps::test_tril_indices_default_dtype, test/test_jit.py::TestTensorCreationOps::test_tril_indices_specified_dtype, test/test_jit.py::TestTensorCreationOps::test_triu_indices_specified_dtype, test/test_jit.py::TestScriptProfile::test_empty, test/test_jit.py::TestScriptProfile::test_multi, test/test_jit.py::TestScriptProfile::test_script, test/test_jit.py::TestScriptProfile::test_section, test/test_jit.py::TestFunctionalToInplaceActivation::test_check_no_type_promotion, test/test_jit.py::TestFunctionalToInplaceActivation::test_no_functional_to_inplace, test/test_jit.py::TestInplaceToFunctionalActivation::test_inplace_to_functional_activation, test/test_jit.py::TestGetDefaultAttr::test_getattr_named_tuple, test/test_jit.py::TestGetDefaultAttr::test_getattr_tuple, test/test_jit.py::TestGetDefaultAttr::test_getattr_with_default, test/test_jit.py::TestAtenPow::test_aten_pow_zero_negative_exponent, test/test_jit.py::TestOptimizeForMobilePreserveDebugInfo::test_fuse_activation_with_pack_ops_linear_conv2d_1, test/test_jit.py::TestOptimizeForMobilePreserveDebugInfo::test_fuse_activation_with_pack_ops_linear_conv2d_2, test/test_jit.py::TestOptimizeForMobilePreserveDebugInfo::test_fuse_activation_with_pack_ops_linear_conv2d_4, test/test_jit.py::TestOptimizeForMobilePreserveDebugInfo::test_replace_conv1d_with_conv2d, test/test_jit.py::TestUnion::test_union_as_annotation_py2, test/test_jit.py::TestUnion::test_union_as_dict_value, test/test_jit.py::TestUnion::test_union_branching_does_not_widen_existing_inferred_type, test/test_jit.py::TestUnion::test_union_branching_with_union_return_and_homogenous_types, test/test_jit.py::TestUnion::test_union_does_not_replace_existing_annotated_type, test/test_jit.py::TestUnion::test_union_does_not_replace_existing_annotated_type_empty_container, test/test_jit.py::TestUnion::test_union_does_not_replace_existing_annotated_type_union, test/test_jit.py::TestUnion::test_union_module_with_union_class_variable, test/test_jit.py::TestUnion::test_union_serialization_preserves_type_annotations, test/test_jit.py::TestUnion::test_union_subtractive_refinement_with_container, test/test_jit.py::TestUnion::test_union_type_refinement, test/test_jit.py::TestUnion::test_union_type_refinement_statically_false, test/test_jit.py::TestUnion::test_union_type_refinement_tuple_rhs_noncontained_type, test/test_jit.py::TestUnion::test_union_type_refinement_tuple_rhs_union, test/test_jit.py::TestUnion::test_union_with_dict_assignment, test/test_jit.py::TestUnion::test_union_with_scalar_values, test/test_jit.py::TestBatchMM::test_batch_mm_prohibited_mutation, test/test_jit.py::TestBatchMM::test_batch_mm_prohibited_mutation_if_node, test/test_jit.py::TestBatchMM::test_batch_mm_prohibited_mutation_multiple_adds, test/test_jit.py::TestBatchMM::test_batch_mm_side_permitted_mutation, test/test_jit.py::TestBatchMM::test_batch_mm_side_prohibited_mutation_common_side, test/test_jit.py::TestBatchMM::test_batch_mm_side_prohibited_mutation_uncommon_side, test/test_jit.py::TestDtypeAnalysis::test_binary_scalar, test/test_jit.py::TestDtypeAnalysis::test_binary_tensors, test/test_jit.py::TestDtypeAnalysis::test_combined, test/test_jit.py::TestDtypeAnalysis::test_conv_no_mixed_args, test/test_jit.py::TestDtypeAnalysis::test_custom_rules, test/test_jit.py::TestDtypeAnalysis::test_unary, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_adaptive_avg_pool1d_cpu_float16, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_adaptive_avg_pool2d_cpu_bfloat16, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_adaptive_avg_pool2d_cpu_float64, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_adaptive_avg_pool3d_cpu_bfloat16, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_adaptive_max_pool1d_cpu_bfloat16, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_adaptive_max_pool2d_cpu_bfloat16, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_avg_pool1d_cpu_bfloat16, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_avg_pool1d_cpu_float32, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_avg_pool1d_cpu_float64, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_avg_pool1d_cpu_int64, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_avg_pool2d_cpu_bfloat16, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_avg_pool2d_cpu_float16, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_avg_pool2d_cpu_float32, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_avg_pool3d_cpu_float32, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_avg_pool3d_cpu_float64, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_batch_norm_cpu_float16, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_batch_norm_cpu_float32, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_conv1d_cpu_complex128, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_conv1d_cpu_float32, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_conv2d_cpu_bfloat16, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_conv2d_cpu_complex128, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_conv2d_cpu_complex64, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_conv2d_cpu_float64, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_conv2d_cpu_int64, test/test_jit.py::TestDtypeCustomRulesCPU::test_custom_rules_ints_nn_functional_conv_transpose2d_cpu_complex128, 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test/test_jit.py::TestJitGeneratedModule::test_nn_SoftMarginLoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_SoftMarginLoss_no_batch_dim_sum, test/test_jit.py::TestJitGeneratedModule::test_nn_SoftMarginLoss_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_Softplus_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_Tanh_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_Tanhshrink_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_Threshold_no_batch_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_TransformerDecoderLayer_relu_activation, test/test_jit.py::TestJitGeneratedModule::test_nn_TransformerEncoderLayer_relu_activation, test/test_jit.py::TestJitGeneratedModule::test_nn_TripletMarginLoss_no_batch_dim_mean, test/test_jit.py::TestJitGeneratedModule::test_nn_TripletMarginLoss_no_batch_dim_none, test/test_jit.py::TestJitGeneratedModule::test_nn_Unfold, test/test_jit.py::TestJitGeneratedModule::test_nn_Unfold_int_input, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_bicubic_scale_tuple_skewed_2d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_bicubic_tuple_2d_align_corners, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_bilinear_2d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_bilinear_2d_zero_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_bilinear_tuple_2d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_linear_1d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_linear_scale_1d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_linear_tuple_1d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_nearest_1d_zero_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_nearest_2d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_nearest_2d_zero_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_nearest_3d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_nearest_tuple_1d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_nearest_tuple_3d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_trilinear_scale_3d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_trilinear_tuple_3d_align_corners, test/test_jit.py::TestJitGeneratedModule::test_nn_log_softmax_dim0, test/test_jit.py::TestJitGeneratedModule::test_nn_log_softmax_spatial, test/test_jit.py::TestJitGeneratedModule::test_nn_log_softmax_spatial_special, test/test_jit.py::TestJitGeneratedModule::test_nn_multimarginloss_1d_input_0d_target_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_softmax_spatial, test/test_jit.py::TestJitGeneratedModule::test_nn_softmax_spatial_special, test/test_jit.py::TestProducerVersion::test_version 2025-08-14T22:46:15.0021138Z 2025-08-14T22:46:18.0438133Z /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. 2025-08-14T22:46:18.0441000Z import pkg_resources 2025-08-14T22:46:18.1646015Z Running test_linalg 6/6 ... [2025-08-14 22:46:18.164239] 2025-08-14T22:46:18.1646446Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:46:18.1649828Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_linalg.py', '-m', 'not serial', '--shard-id=6', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:46:18.164668] 2025-08-14T22:53:24.8789082Z 2025-08-14T22:53:24.8790615Z test_linalg 1/6 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_1.6_0bd2537b3e6a2dd1_.log 2025-08-14T22:53:24.8925752Z Running 200 items in this shard: test/test_linalg.py::TestLinalgCPU::test__dyn_quant_pack_4bit_weight_k_256_n_128_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_32_k_32_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_32_n_48_compile_True_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_32_n_64_compile_False_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_48_compile_True_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_48_compile_True_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_64_compile_True_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_32_n_48_compile_False_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_32_n_48_compile_True_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_64_n_64_compile_True_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_16_n_16_use_transpose_a_True_use_transpose_b_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_16_n_32_use_transpose_a_True_use_transpose_b_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_32_n_32_use_transpose_a_True_use_transpose_b_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_32_n_32_use_transpose_a_True_use_transpose_b_True_cpu, test/test_linalg.py::TestLinalgCPU::test_addbmm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_gelu_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_gelu_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_0_beta_0_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_0_5_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_1_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_0_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_1_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_2_beta_0_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_1_0_beta_0_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_1_0_beta_0_5_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_0_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_0_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_2_beta_0_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_0_0_beta_0_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_0_0_beta_1_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_0_0_beta_1_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_1_0_beta_0_5_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_relu_tunableop_rocm_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_relu_tunableop_rocm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_relu_tunableop_rocm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmv_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmv_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_addmv_rowmajor_colmajor_incx_incy_lda_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addr_float_and_complex_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addr_integral_cpu_int16, test/test_linalg.py::TestLinalgCPU::test_addr_type_promotion_cpu, test/test_linalg.py::TestLinalgCPU::test_baddbmm_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_baddbmm_input_dtypes_compatibility_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_int32, test/test_linalg.py::TestLinalgCPU::test_bmm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_call_count_tunableop_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_chain_matmul_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_backward_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_32_k_32_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_32_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test_cond_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_uint8, test/test_linalg.py::TestLinalgCPU::test_cross_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_det_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_dot_vs_numpy_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_dump_results_on_exit_tunableop_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eig_compare_backends_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eig_numpy_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigh_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigh_svd_illcondition_matrix_input_should_not_crash_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_einsum_sublist_format_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_32_k_64_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_36_k_32_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_40_k_36_cpu, test/test_linalg.py::TestLinalgCPU::test_gemm_bias_offline_tunableop_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_inner_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_inv_ex_info_device_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_inverse_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_inverse_errors_large_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_kron_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_kron_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_ldl_solve_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_cross_with_and_without_dim_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_batch_broadcasting_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_cpu_errors_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_solve_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_boundary_cases_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_perverse_nan_values_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linear_algebra_scalar_raises_cpu, test/test_linalg.py::TestLinalgCPU::test_lobpcg_ortho_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_lobpcg_torchscript_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_broadcasting_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_broadcasting_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_broadcasting_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matmul_mv_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matmul_offline_mgpu_tunableop_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_1d_Nd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_1d_Nd_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_basic_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_out_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_mm_bmm_non_memory_dense_cpu, test/test_linalg.py::TestLinalgCPU::test_mm_cpu_int32, test/test_linalg.py::TestLinalgCPU::test_mm_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_multi_dot_errors_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_norm_bfloat16_and_half_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_norm_complex_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_norm_complex_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_norm_dtype_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_norm_matrix_degenerate_shapes_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_norm_old_nan_propagation_cpu, test/test_linalg.py::TestLinalgCPU::test_norm_vector_degenerate_shapes_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_upper_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_empty_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_empty_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_ormqr_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_ormqr_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_float16, test/test_linalg.py::TestLinalgCPU::test_pinv_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_preferred_linalg_library_cpu, test/test_linalg.py::TestLinalgCPU::test_qr_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_qr_vs_numpy_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_rotating_buffer_tunableop_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_scaled_gemm_tunableop_cpu_float8_e5m2fnuz, test/test_linalg.py::TestLinalgCPU::test_solve_batched_broadcasting_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_solve_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_svd_lowrank_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_svd_memory_allocation_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_svd_memory_allocation_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_tensordot_out_kernel_errors_with_autograd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_tensordot_out_kernel_errors_with_autograd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_tensorinv_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_tensorinv_singular_input_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_tensorinv_singular_input_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_empty_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_tf32_offline_tunableop_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_many_batches_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_out_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_out_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_vdot_invalid_args_cpu, test/test_linalg.py::TestLinalgCPU::test_vector_norm_reduce_over_1D_vector_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_vector_norm_reduce_over_1D_vector_cpu_float64 2025-08-14T22:53:24.9054887Z 2025-08-14T22:53:27.9624707Z /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. 2025-08-14T22:53:27.9627931Z import pkg_resources 2025-08-14T22:53:28.0821391Z Running test_futures 1/1 ... [2025-08-14 22:53:28.081700] 2025-08-14T22:53:28.0822010Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:53:28.0823850Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_futures.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'] ... [2025-08-14 22:53:28.082039] 2025-08-14T22:53:39.8653976Z 2025-08-14T22:53:39.8656235Z test_futures 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_futures_1.1_8d56fbe66dce2930_.log 2025-08-14T22:53:39.8673901Z Running 22 items in this shard: test/test_futures.py::TestFuture::test_add_done_callback_error_is_ignored, test/test_futures.py::TestFuture::test_add_done_callback_maintains_callback_order, test/test_futures.py::TestFuture::test_add_done_callback_no_arg_error_is_ignored, test/test_futures.py::TestFuture::test_add_done_callback_simple, test/test_futures.py::TestFuture::test_chained_then, test/test_futures.py::TestFuture::test_collect_all, test/test_futures.py::TestFuture::test_done, test/test_futures.py::TestFuture::test_done_exception, test/test_futures.py::TestFuture::test_interleaving_then_and_add_done_callback_maintains_callback_order, test/test_futures.py::TestFuture::test_interleaving_then_and_add_done_callback_propagates_error, test/test_futures.py::TestFuture::test_mark_future_twice, test/test_futures.py::TestFuture::test_pickle_future, test/test_futures.py::TestFuture::test_set_exception, test/test_futures.py::TestFuture::test_set_exception_multithreading, test/test_futures.py::TestFuture::test_then, test/test_futures.py::TestFuture::test_then_no_arg, test/test_futures.py::TestFuture::test_then_raise, test/test_futures.py::TestFuture::test_then_wrong_arg, test/test_futures.py::TestFuture::test_wait, test/test_futures.py::TestFuture::test_wait_all, test/test_futures.py::TestFuture::test_wait_multi_thread, test/test_futures.py::TestFuture::test_wait_none 2025-08-14T22:53:39.8688854Z 2025-08-14T22:53:44.3400606Z /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. 2025-08-14T22:53:44.3403644Z import pkg_resources 2025-08-14T22:53:44.5378172Z Running torch_np/numpy_tests/lib/test_histograms 1/1 ... [2025-08-14 22:53:44.536523] 2025-08-14T22:53:44.5379677Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:53:44.5382851Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_histograms.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:53:44.537063] 2025-08-14T22:53:53.0895598Z 2025-08-14T22:53:53.0897234Z test_linalg 5/6 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_5.6_b3267ef0e831ad79_.log 2025-08-14T22:53:53.0972395Z Running 209 items in this shard: test/test_linalg.py::TestLinalgCPU::test__dyn_quant_matmul_4bit_m_1_k_64_n_11008_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_matmul_4bit_m_32_k_64_n_11008_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_pack_4bit_weight_k_256_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_pack_4bit_weight_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_48_compile_False_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_32_n_64_compile_False_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_64_n_48_compile_True_slice_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_16_n_32_use_transpose_a_False_use_transpose_b_False_cpu, test/test_linalg.py::TestLinalgCPU::test_addbmm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_2_beta_0_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_2_beta_0_5_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_0_5_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_1_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_0_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_2_beta_0_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_2_beta_0_5_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_2_beta_0_5_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_2_beta_1_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_1_0_beta_1_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_1_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_2_beta_0_5_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_1_0_beta_0_5_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_0_0_beta_1_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_0_2_beta_0_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_0_2_beta_0_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_0_2_beta_1_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_True_alpha_1_0_beta_0_5_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_sizes_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmv_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addr_bool_cpu_bool, test/test_linalg.py::TestLinalgCPU::test_addr_float_and_complex_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_addr_integral_cpu_uint8, test/test_linalg.py::TestLinalgCPU::test_baddbmm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_baddbmm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_int8, test/test_linalg.py::TestLinalgCPU::test_blas_mv_large_input_cpu, test/test_linalg.py::TestLinalgCPU::test_blas_nan_out_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_broadcast_batched_matmul_cpu, test/test_linalg.py::TestLinalgCPU::test_cholesky_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_non_pd_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_broadcasting_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_out_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_out_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_out_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_1_k_128_n_11008_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_32_k_128_n_4096_cpu, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_int16, test/test_linalg.py::TestLinalgCPU::test_cross_error_cpu, test/test_linalg.py::TestLinalgCPU::test_eig_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eig_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eig_removed_error_cpu, test/test_linalg.py::TestLinalgCPU::test_eigh_lower_uplo_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigh_lower_uplo_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigvals_compare_backends_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigvals_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigvalsh_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigvalsh_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_32_k_40_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_36_k_35_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_36_k_36_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_36_k_40_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_40_k_40_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_64_k_32_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_64_k_40_cpu, test/test_linalg.py::TestLinalgCPU::test_geqrf_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_geqrf_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_householder_product_errors_and_warnings_cpu, test/test_linalg.py::TestLinalgCPU::test_invariance_error_spectral_decompositions_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_inverse_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_inverse_many_batches_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_inverse_many_batches_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_kron_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_large_bmm_mm_backward_cpu, test/test_linalg.py::TestLinalgCPU::test_ldl_factor_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_ldl_factor_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_ldl_solve_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_cross_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_batch_broadcasting_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_cpu_errors_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_cpu_errors_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_family_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_analytic_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_compare_with_taylor_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_compare_with_taylor_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_utils_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_large_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_broadcasting_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_lu_solve_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_lu_solve_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matmul_check_entries_tunableop_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_3d_Nd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matrix_norm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_atol_rtol_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_empty_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_out_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_mm_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_norm_extreme_values_cpu, test/test_linalg.py::TestLinalgCPU::test_norm_fused_type_promotion_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_norm_matrix_degenerate_shapes_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_norm_matrix_degenerate_shapes_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_norm_vector_degenerate_shapes_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_nuclear_norm_out_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_nuclear_norm_out_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_many_batches_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_upper_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_ormqr_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_ormqr_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_float64, test/test_linalg.py::TestLinalgCPU::test_permute_matmul_cpu, test/test_linalg.py::TestLinalgCPU::test_pinv_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_qr_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_qr_vs_numpy_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_renorm_cpu, test/test_linalg.py::TestLinalgCPU::test_rowwise_scaled_gemm_numerics_tunableop_cpu_float8_e4m3fnuz, test/test_linalg.py::TestLinalgCPU::test_solve_batched_broadcasting_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_solve_batched_broadcasting_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_solve_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_svd_memory_allocation_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_svd_memory_allocation_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_tensorinv_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_tensorinv_empty_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_tensorinv_singular_input_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_empty_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_tf32_tunableop_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_broadcasting_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_broadcasting_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_vector_norm_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_vector_norm_decom_unbacked_checks_cpu 2025-08-14T22:53:53.1044776Z 2025-08-14T22:53:56.5233679Z /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. 2025-08-14T22:53:56.5237018Z import pkg_resources 2025-08-14T22:53:56.6844761Z Running nn/test_embedding 1/1 ... [2025-08-14 22:53:56.684077] 2025-08-14T22:53:56.6845586Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:53:56.6847196Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_embedding.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'] ... [2025-08-14 22:53:56.684442] 2025-08-14T22:53:58.3848751Z 2025-08-14T22:53:58.3850770Z torch_np/numpy_tests/lib/test_histograms 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_histograms_1.1_89ed881f57141483_.log 2025-08-14T22:53:58.3874992Z Running 60 items in this shard: test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_arr_weights_mismatch, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_big_arrays, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_bin_array_dims, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_bin_edge_cases, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_bool_conversion, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_density, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_empty, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_error_binnum_type, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_exotic_weights, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_f32_rounding, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_finite_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_histogram_bin_edges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_invalid_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_last_bin_inclusive_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_no_side_effects, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_object_array_of_0d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_one_bin, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_outliers, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_precision, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_signed_overflow_bounds, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_signed_overflow_bounds_2, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_simple, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_some_nan_values, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_type, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_unsigned_monotonicity_check, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_weights, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_empty, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_incorrect_methods, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_limited_variance, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_novariance, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_outlier, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_scott_vs_stone, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_auto, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_doane, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_fd, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_rice, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_scott, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_stone, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_sturges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_simple, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_simple_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_simple_weighted, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_small, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_bins_array, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_bins_error_2, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_bins_errors, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_density_non_uniform_1d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_density_non_uniform_2d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_edge_dtype, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_empty, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_equal_edges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_finite_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_identical_samples, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_inf_edges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_large_integers, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_rightmost_binedge, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_shape_3d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_shape_4d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_simple, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_weights 2025-08-14T22:53:58.3898006Z 2025-08-14T22:54:01.5354542Z /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. 2025-08-14T22:54:01.5358084Z import pkg_resources 2025-08-14T22:54:01.6574638Z Running functorch/test_vmap 4/6 ... [2025-08-14 22:54:01.657017] 2025-08-14T22:54:01.6575807Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:54:01.6578010Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_vmap.py', '-m', 'not serial', '--shard-id=4', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:54:01.657398] 2025-08-14T22:54:26.1365431Z 2025-08-14T22:54:26.1366947Z test_linalg 6/6 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_6.6_5135b931cc87f797_.log 2025-08-14T22:54:26.1513346Z Running 212 items in this shard: test/test_linalg.py::TestLinalgCPU::test_1_sized_with_0_strided_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_1_sized_with_0_strided_cpu_float64, test/test_linalg.py::TestLinalgCPU::test__dyn_quant_pack_4bit_weight_k_256_n_32_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_32_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_32_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_32_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_32_n_64_compile_False_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_32_n_64_compile_True_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_64_n_48_compile_False_slice_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_16_n_16_use_transpose_a_False_use_transpose_b_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_16_n_16_use_transpose_a_True_use_transpose_b_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_32_n_32_use_transpose_a_False_use_transpose_b_True_cpu, test/test_linalg.py::TestLinalgCPU::test_addbmm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_0_beta_0_5_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_0_beta_0_5_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_0_2_beta_1_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_0_0_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_False_alpha_1_0_beta_1_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_0_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_0_beta_1_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_2_beta_1_0_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_0_2_beta_1_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_False_transpose_b_True_alpha_1_0_beta_1_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_0_0_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_mv_transpose_a_True_transpose_b_False_alpha_0_0_beta_0_5_cpu_float16, 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test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_blas_nan_out_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_cholesky_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_non_pd_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_non_pd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_non_pd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_broadcasting_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_many_batches_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_out_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_1_k_128_n_4096_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_1_k_64_n_4096_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_32_k_64_n_11008_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_dyn_quant_matmul_4bit_m_32_k_64_n_4096_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_64_k_32_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_64_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test_cond_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cond_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_int32, test/test_linalg.py::TestLinalgCPU::test_det_logdet_slogdet_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_det_logdet_slogdet_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_disable_tuning_tunableop_cpu_float32, 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test/test_linalg.py::TestLinalgCPU::test_inv_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_inv_ex_info_device_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_inv_ex_singular_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_inverse_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_inverse_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_inverse_errors_large_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_kron_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_kron_empty_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_input_checks_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_cpu_errors_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_solve_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_analytic_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_batch_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_boundary_cases_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_compare_with_taylor_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_qr_autograd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_broadcasting_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_large_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_lobpcg_scipy_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_lu_solve_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_lu_solve_large_matrices_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matmul_45724_cpu, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_1d_Nd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_2d_Nd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_2d_Nd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_tunableop_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_atol_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_empty_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_removed_error_cpu, test/test_linalg.py::TestLinalgCPU::test_multi_dot_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_norm_complexhalf_cpu, test/test_linalg.py::TestLinalgCPU::test_norm_dtype_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_norm_dtype_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_norm_errors_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_norm_fused_type_promotion_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_norm_matrix_degenerate_shapes_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_norm_vector_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_nuclear_norm_axes_small_brute_force_old_cpu, test/test_linalg.py::TestLinalgCPU::test_nuclear_norm_exceptions_old_cpu, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_bool, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_int16, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_int8, test/test_linalg.py::TestLinalgCPU::test_outer_ger_addr_legacy_tests_cpu, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_float32, test/test_linalg.py::TestLinalgCPU::test_pinverse_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_qr_batched_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_slogdet_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_strided_mm_bmm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_svd_lowrank_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_tensordot_cpu, test/test_linalg.py::TestLinalgCPU::test_tensorinv_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_empty_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_many_batches_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_out_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_vector_norm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_vector_norm_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_vector_norm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_vector_norm_dim_tuple_arg_cpu, test/test_linalg.py::TestLinalgCPU::test_vector_norm_reduce_over_1D_vector_cpu_float32 2025-08-14T22:54:26.1650149Z 2025-08-14T22:54:29.3747564Z /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. 2025-08-14T22:54:29.3749285Z import pkg_resources 2025-08-14T22:54:29.4938414Z Running functorch/test_vmap 5/6 ... [2025-08-14 22:54:29.493455] 2025-08-14T22:54:29.4939360Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:54:29.4941721Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_vmap.py', '-m', 'not serial', '--shard-id=5', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:54:29.493811] 2025-08-14T22:59:45.3929293Z 2025-08-14T22:59:45.3930606Z nn/test_embedding 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_embedding_1.1_dc079349a4df0295_.log 2025-08-14T22:59:45.4076588Z Running 165 items in this shard: test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_bag_from_pretrained, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_bag_from_pretrained_padding_idx, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_bag_functional, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_bag_padding_idx_error, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_float32, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_float64, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_int16, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_int32, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_int64, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_int8, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_options, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_padding_idx, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_from_pretrained_uint8, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_functional, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_max_norm, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_max_norm_unsorted_repeating_indices, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_sparse_basic, test/nn/test_embedding.py::TestEmbeddingNN::test_embedding_sparse_empty_tensor, test/nn/test_embedding.py::TestEmbeddingNN::test_embeddingbag_from_pretrained, test/nn/test_embedding.py::TestEmbeddingNN::test_embeddingbag_from_pretrained_options, test/nn/test_embedding.py::TestEmbeddingNN::test_embeddingbag_include_last_offset, test/nn/test_embedding.py::TestEmbeddingNN::test_large_tensors, test/nn/test_embedding.py::TestEmbeddingNN::test_move_sparse_half_embedding, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int32_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_empty_per_sample_weights_and_offsets_cpu_int64_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int32_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_new_offsets_cpu_int64_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_no_offsets_cpu_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_no_offsets_cpu_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_no_offsets_cpu_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_no_offsets_cpu_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int32_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_and_offsets_cpu_int64_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_failures_cpu_int32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_failures_cpu_int32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_failures_cpu_int64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_EmbeddingBag_per_sample_weights_failures_cpu_int64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_backward_cpu_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_1D_padding_idx_cpu_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_1D_padding_idx_cpu_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_2D_padding_idx_cpu_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_2D_padding_idx_cpu_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_bfloat16_cpu_int32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_bfloat16_cpu_int32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_bfloat16_cpu_int64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_bfloat16_cpu_int64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int32_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_device_cpu_int64_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_dimension_errors_cpu, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_empty_input_cpu_int32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_empty_input_cpu_int32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_empty_input_cpu_int64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_empty_input_cpu_int64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_half_cpu_int32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_half_cpu_int32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_half_cpu_int64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_half_cpu_int64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int32_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int32_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int32_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int32_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int32_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int64_bfloat16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int64_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int64_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_non_contiguous_weight_cpu_int64_int64_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_max_cpu_float32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_max_cpu_float32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_max_cpu_float64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_max_cpu_float64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_mean_cpu_float32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_mean_cpu_float32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_mean_cpu_float64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_mean_cpu_float64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_sum_cpu_float32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_sum_cpu_float32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_sum_cpu_float64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx0_mode_sum_cpu_float64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_max_cpu_float32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_max_cpu_float32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_max_cpu_float64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_max_cpu_float64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_mean_cpu_float32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_mean_cpu_float32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_mean_cpu_float64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_mean_cpu_float64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_sum_cpu_float32_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_sum_cpu_float32_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_sum_cpu_float64_int32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_out_of_bounds_idx_padding_idx_0_mode_sum_cpu_float64_int64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_per_sample_weights_grad_bag_use_grad_False_per_sample_weights_use_grad_False_cpu, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_per_sample_weights_grad_bag_use_grad_False_per_sample_weights_use_grad_True_cpu, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_per_sample_weights_grad_bag_use_grad_True_per_sample_weights_use_grad_False_cpu, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_bag_per_sample_weights_grad_bag_use_grad_True_per_sample_weights_use_grad_True_cpu, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_dense_grad_cpu, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_max_norm_backward_cpu_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_max_norm_device_cpu_float16, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_max_norm_device_cpu_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_max_norm_device_cpu_float64, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_max_norm_fwd_AD_cpu_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_padding_idx_cpu_float32, test/nn/test_embedding.py::TestEmbeddingNNDeviceTypeCPU::test_embedding_scalar_weight_error_cpu 2025-08-14T22:59:45.4264323Z 2025-08-14T22:59:48.4747125Z /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. 2025-08-14T22:59:48.4750295Z import pkg_resources 2025-08-14T22:59:48.5946226Z Running functorch/test_vmap 6/6 ... [2025-08-14 22:59:48.594178] 2025-08-14T22:59:48.5947048Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T22:59:48.5949088Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_vmap.py', '-m', 'not serial', '--shard-id=6', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 22:59:48.594520] 2025-08-14T23:02:35.5134548Z 2025-08-14T23:02:35.5135940Z functorch/test_vmap 5/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_vmap_5.6_5dbaf478f8df422e_.log 2025-08-14T23:02:35.5436590Z Running 355 items in this shard: test/functorch/test_vmap.py::TestVmapAPI::test_batch_rule_does_not_need_to_handle_no_batched_input, test/functorch/test_vmap.py::TestVmapAPI::test_constant_function, test/functorch/test_vmap.py::TestVmapAPI::test_decomposition_under_python_dispatcher, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_warning, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_with_undefined_grad, test/functorch/test_vmap.py::TestVmapAPI::test_grad_unsupported_interaction, test/functorch/test_vmap.py::TestVmapAPI::test_inplace_fallback_unary, test/functorch/test_vmap.py::TestVmapAPI::test_multiple_out_dims, test/functorch/test_vmap.py::TestVmapAPI::test_nested_negative_in_dims, test/functorch/test_vmap.py::TestVmapAPI::test_nested_with_diag_embed, test/functorch/test_vmap.py::TestVmapAPI::test_nested_with_same_map_dim, test/functorch/test_vmap.py::TestVmapAPI::test_non_zero_in_dims, test/functorch/test_vmap.py::TestVmapAPI::test_noop_in_inner_vmap, test/functorch/test_vmap.py::TestVmapAPI::test_not_enough_in_dims_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_odict_returns, test/functorch/test_vmap.py::TestVmapAPI::test_unsupported_op_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_vmap_autocast_cuda, test/functorch/test_vmap.py::TestVmapOperators::test_adaptive_avg_pool2d, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_sub_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_chunk, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_clamp_variant_clamp_max, test/functorch/test_vmap.py::TestVmapOperators::test_expand_as, test/functorch/test_vmap.py::TestVmapOperators::test_is_floating_point, test/functorch/test_vmap.py::TestVmapOperators::test_new_empty, test/functorch/test_vmap.py::TestVmapOperators::test_one_hot, test/functorch/test_vmap.py::TestVmapOperators::test_reshape, test/functorch/test_vmap.py::TestVmapOperators::test_silu_backward, test/functorch/test_vmap.py::TestVmapOperators::test_stride, test/functorch/test_vmap.py::TestVmapOperators::test_t, test/functorch/test_vmap.py::TestVmapOperators::test_to, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_abs, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_atan, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_cos, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log10, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_reciprocal, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sigmoid, test/functorch/test_vmap.py::TestVmapOperators::test_unfold, test/functorch/test_vmap.py::TestVmapOperators::test_view_as_real, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_weird_matmul_case, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_advanced_indexing_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_fill__Tensor_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_index_fill_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_index_put_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_isinf_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_isnan_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_namedtuple_returns_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_H_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_T_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_add_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_addbmm_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_addr_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_amin_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_argmax_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_atleast_1d_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_bitwise_and_cpu_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_bitwise_not_cpu_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_bitwise_right_shift_cpu_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_bucketize_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_cat_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_char_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_char_functorch_no_channels_last_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_column_stack_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_conj_physical_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_corrcoef_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_count_nonzero_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_cross_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_cummax_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_dot_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_double_functorch_no_channels_last_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_dsplit_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_equal_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_expand_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_floor_divide_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_fmod_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_frac_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_gather_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_hsplit_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_i0_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_index_reduce_amin_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_inner_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_isin_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_item_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_jiterator_binary_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_lerp_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_linalg_cholesky_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_linalg_inv_ex_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_linalg_matrix_rank_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_linalg_norm_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_linalg_qr_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_linalg_solve_triangular_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_linalg_svd_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_linspace_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_logdet_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_long_functorch_no_channels_last_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_lu_unpack_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_masked_fill_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_masked_mean_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_masked_median_cpu_float32, 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test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_triangular_solve_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_triu_indices_cpu_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_unbind_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_unfold_copy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_var_mean_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_var_mean_unbiased_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_xlogy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_cond_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_cross_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_eigvals_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_multi_dot_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_svd_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_svdvals_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_tensorinv_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_with_anomaly_detection_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_log_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_max_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_randomness_backend0_randomness_same_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_sub_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_vmap_fallback_check_ok, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_first_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_last_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_none_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_last_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_none_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_first_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_first_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_2_out_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_error_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_same_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_different_batched_input_first_dim_3_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_error_batched_input_first_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_error_batched_input_first_dim_3_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_error_batched_call_False_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_error_batched_call_False_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_error_batched_call_True_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_True_randomness_different_batched_call_False_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_first_batched_other_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_last_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_different_batched_input_first_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_error_batched_input_first_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_error_batched_input_none_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_last_batched_other_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_none_batched_other_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_False_randomness_different_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_False_randomness_different_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_True_randomness_error_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_True_randomness_same_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_False_randomness_error_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_False_randomness_error_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_different_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_same_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_randperm_randomness_different_use_generator_True_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_randperm_randomness_same_use_generator_False_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_1_out_dim_0_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_1_out_dim_2_cpu, test/functorch/test_vmap.py::TestVmapDeviceTypeCPU::test_vmap_fallback_check, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_fallback_binary_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_fallback_with_nt_and_batched_dense_with_nonzero_bdim_raises_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_multilevel_vmap_raises_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_nt_with_nonzero_in_dim_raises_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_nt_with_nonzero_out_dim_raises_cpu 2025-08-14T23:02:35.5735974Z 2025-08-14T23:02:37.8088331Z Uploading artifacts took 2.29 seconds 2025-08-14T23:02:38.6573471Z /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. 2025-08-14T23:02:38.6576361Z import pkg_resources 2025-08-14T23:02:38.7787166Z Running test_sparse_csr 2/2 ... [2025-08-14 23:02:38.778374] 2025-08-14T23:02:38.7787649Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:02:38.7790974Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_sparse_csr.py', '-m', 'not serial', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:02:38.778738] 2025-08-14T23:05:13.6629368Z 2025-08-14T23:05:13.6630779Z functorch/test_vmap 6/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_vmap_6.6_ea222789de3da42c_.log 2025-08-14T23:05:13.6869926Z Running 364 items in this shard: test/functorch/test_vmap.py::TestVmapAPI::test_fallback_warns_when_warnings_are_enabled, test/functorch/test_vmap.py::TestVmapAPI::test_func_with_no_inputs, test/functorch/test_vmap.py::TestVmapAPI::test_func_with_no_tensors, test/functorch/test_vmap.py::TestVmapAPI::test_integer_in_dim_but_not_tensor_input_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_nested_with_different_map_dim, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_must_be_int_or_collection_of_int_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_out_dims_none, test/functorch/test_vmap.py::TestVmapAPI::test_restore_vmap_pytree_input_output, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_add, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_clamp_variant_clamp_min, test/functorch/test_vmap.py::TestVmapOperators::test_conj_bit, test/functorch/test_vmap.py::TestVmapOperators::test_diagonal, test/functorch/test_vmap.py::TestVmapOperators::test_mm, test/functorch/test_vmap.py::TestVmapOperators::test_real, test/functorch/test_vmap.py::TestVmapOperators::test_reshape_as, test/functorch/test_vmap.py::TestVmapOperators::test_slogdet, test/functorch/test_vmap.py::TestVmapOperators::test_sum, test/functorch/test_vmap.py::TestVmapOperators::test_sum_dim, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_cosh, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_exp, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_frac, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_round, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_tanh, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_trunc, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_2_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_batch_norm_training_False_track_running_stats_False_affine_True_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_flatten_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_foo_like_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_group_norm_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_nested_advanced_indexing_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_MulGenVmapAutogradFunction_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_NumpyCubeNotComposableAutogradFunction_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_NumpyMulAutogradFunction_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_NumpyNonzeroCustomOp_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_NumpySortAutogradFunction_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_NumpySplitCopyWithIntCustomOp_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_NumpyTakeAutogradFunction_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_NumpyViewCopyCustomOp_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule___rdiv___cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule___rsub___cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule__native_batch_norm_legit_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule__segment_reduce_lengths_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule__segment_reduce_offsets_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule__unsafe_masked_index_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_abs_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_addmm_decomposed_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_all_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_allclose_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_as_strided_copy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_asinh_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_atanh_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_atleast_2d_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_bitwise_left_shift_cpu_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_bitwise_or_cpu_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_block_diag_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_broadcast_shapes_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_broadcast_tensors_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_byte_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_cartesian_prod_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_cfloat_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_chunk_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_clamp_max_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_clone_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_complex_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_cosh_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_diag_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_diagonal_scatter_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_dist_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_double_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_empty_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_exp2_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_expand_copy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_fft_fftshift_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_fft_hfft2_cpu_float32, 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test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_ops_aten__new_zeros_with_same_feature_meta_functorchonly_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_outer_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_polar_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_polygamma_polygamma_n_2_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_polygamma_polygamma_n_3_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_quantile_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_rad2deg_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_real_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_renorm_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_repeat_interleave_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_round_decimals_3_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_searchsorted_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_signbit_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_sparse_mm_reduce_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_chebyshev_polynomial_u_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_chebyshev_polynomial_v_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_log_ndtr_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_scaled_modified_bessel_k0_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_shifted_chebyshev_polynomial_v_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_shifted_chebyshev_polynomial_w_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_zeta_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_square_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_squeeze_copy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_squeeze_multiple_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_sum_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_t_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_take_along_dim_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_to_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_trapz_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_unbind_copy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_unsqueeze_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_view_as_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_view_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_zeros_like_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_cholesky_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_cholesky_ex_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_eigvalsh_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_householder_product_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_solve_triangular_cpu_float32, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_binary_cross_entropy_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_expand_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_log_softmax_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_logsumexp_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_randomness_backend0_randomness_error_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_reshape_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_slice_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_unrelated_output_multiple_grad_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_vmap_fallback_check, test/functorch/test_vmap.py::TestTransformFailureCPU::test_fails_with_autograd_function_transform_jacrev_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_error_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_last_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_none_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_none_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_last_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_none_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_none_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_first_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_first_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_same_batched_input_none_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_none_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_last_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_none_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_1_out_dim_1_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_1_out_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_2_out_dim_0_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_2_out_dim_1_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_randomness_different_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_unbatched_randomness_same_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_factory_ops_randomness_different_use_generator_False_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_factory_ops_randomness_error_use_generator_False_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_factory_ops_randomness_error_use_generator_True_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_different_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_error_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_different_batched_input_last_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_different_batched_input_last_dim_3_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_error_batched_input_none_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_same_batched_input_none_dim_3_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_jacfwd_with_random_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_like_functions_randomness_error_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_different_batched_call_False_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_error_batched_call_True_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_same_batched_call_False_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_True_randomness_different_batched_call_False_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_True_randomness_different_batched_call_True_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_True_randomness_error_batched_call_False_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_True_randomness_same_batched_call_False_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_True_randomness_same_batched_call_True_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_none_batched_other_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_last_batched_other_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_none_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_none_batched_other_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_error_batched_input_first_batched_other_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_first_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_first_batched_other_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_False_randomness_error_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_True_randomness_same_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_False_randomness_same_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_error_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_randperm_randomness_error_use_generator_True_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_randperm_randomness_same_use_generator_True_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_fallback_binary_nt_and_unbatched_dense_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_vmap_fallback_check 2025-08-14T23:05:13.7035424Z 2025-08-14T23:05:16.7638867Z /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. 2025-08-14T23:05:16.7641821Z import pkg_resources 2025-08-14T23:05:16.8842522Z Running test_view_ops 1/1 ... [2025-08-14 23:05:16.883861] 2025-08-14T23:05:16.8843257Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:05:16.8845495Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_view_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:05:16.884226] 2025-08-14T23:05:23.3611956Z 2025-08-14T23:05:23.3613403Z functorch/test_vmap 4/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_vmap_4.6_3a8ef804cc46ac17_.log 2025-08-14T23:05:23.3774810Z Running 348 items in this shard: test/functorch/test_vmap.py::TestVmapAPI::test_backward_unsupported_interaction, test/functorch/test_vmap.py::TestVmapAPI::test_batched_gradient_basic, test/functorch/test_vmap.py::TestVmapAPI::test_different_map_dim_size_raises, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_does_not_warn_by_default, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_zero_dim, test/functorch/test_vmap.py::TestVmapAPI::test_functools_partial, test/functorch/test_vmap.py::TestVmapAPI::test_in_dims_wrong_type_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_multiple_outputs, test/functorch/test_vmap.py::TestVmapAPI::test_nested_out_dims, test/functorch/test_vmap.py::TestVmapAPI::test_nn_module, test/functorch/test_vmap.py::TestVmapAPI::test_non_default_in_dims_out_dims, test/functorch/test_vmap.py::TestVmapAPI::test_non_tensor_output_raises, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_returns_broadcast_nested, test/functorch/test_vmap.py::TestVmapAPI::test_reshape_dim_into, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_mul, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_pow_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_sub, test/functorch/test_vmap.py::TestVmapOperators::test_as_strided, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_2_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_clamp, test/functorch/test_vmap.py::TestVmapOperators::test_clamp_inplace_variant_clamp_max_, test/functorch/test_vmap.py::TestVmapOperators::test_clamp_inplace_variant_clamp_min_, test/functorch/test_vmap.py::TestVmapOperators::test_conj, test/functorch/test_vmap.py::TestVmapOperators::test_contiguous, test/functorch/test_vmap.py::TestVmapOperators::test_conv2d, test/functorch/test_vmap.py::TestVmapOperators::test_imag, test/functorch/test_vmap.py::TestVmapOperators::test_mean_dim, test/functorch/test_vmap.py::TestVmapOperators::test_squeeze, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_asin, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_expm1, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_lgamma, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log1p, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_rsqrt, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sign, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sinh, test/functorch/test_vmap.py::TestVmapOperators::test_unbind, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_2_randomness_same, 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test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_masked_cumsum_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_masked_median_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_masked_softmax_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_mul_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nanmean_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nanquantile_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_native_dropout_backward_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_new_zeros_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_avg_pool1d_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_conv2d_stride_depthwise_with_bias_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_cross_entropy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_grid_sample_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_interpolate_area_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_interpolate_nearest_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_kl_div_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_l1_loss_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_leaky_relu_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_normalize_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_pad_reflect_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_smooth_l1_loss_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_softmin_with_dtype_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_norm_fro_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_ones_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_permute_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_roll_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_round_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_rsub_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_scatter_reduce_amax_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_scatter_reduce_amin_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_sgn_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_sigmoid_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_signal_windows_bartlett_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_signal_windows_hamming_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_signal_windows_hann_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_sinh_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_slice_scatter_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_bessel_y1_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_chebyshev_polynomial_t_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_entr_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_legendre_polynomial_p_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_ndtri_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_scaled_modified_bessel_k1_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_shifted_chebyshev_polynomial_t_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_spherical_bessel_j0_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_split_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_split_with_sizes_copy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_split_with_sizes_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_std_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_stft_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_take_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_tan_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_tanh_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_topk_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_tril_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_uniform_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_unsqueeze_copy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_view_as_real_cpu_complex64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_vsplit_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_vstack_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_det_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_inv_ex_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_lstsq_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_lu_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_lu_factor_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_lu_factor_ex_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_pinv_hermitian_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_solve_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_solve_ex_cpu_float32, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_add_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_index_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_inplace_view_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_permute_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_sdpa_backend0_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_sigmoid_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_unrelated_output_cpu, test/functorch/test_vmap.py::TestTransformFailureCPU::test_fails_with_autograd_function_transform_grad_cpu, test/functorch/test_vmap.py::TestTransformFailureCPU::test_fails_with_autograd_function_transform_vjp_cpu, test/functorch/test_vmap.py::TestTransformFailureCPU::test_fails_with_autograd_function_transform_vmap_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_different_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_different_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_same_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_last_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_last_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_first_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_last_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_last_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_first_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_last_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_last_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_0_out_dim_1_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_1_out_dim_0_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_unbatched_randomness_different_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_different_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_error_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_error_batched_input_none_dim_3_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_like_functions_randomness_different_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_like_functions_randomness_different_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_different_batched_call_False_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_same_batched_call_False_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_True_randomness_different_batched_call_False_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_True_randomness_error_batched_call_True_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_first_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_last_batched_other_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_first_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_same_batched_input_none_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_different_batched_input_first_batched_other_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_error_batched_input_last_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_error_batched_input_last_batched_other_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_first_batched_other_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_False_randomness_error_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_error_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_same_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_randperm_randomness_error_use_generator_False_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_0_out_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_1_out_dim_1_cpu, test/functorch/test_vmap.py::TestVmapDeviceTypeCPU::test__is_any_true_cpu 2025-08-14T23:05:23.3929441Z 2025-08-14T23:05:26.5470198Z /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. 2025-08-14T23:05:26.5473181Z import pkg_resources 2025-08-14T23:05:26.6684526Z Running test_accelerator 1/1 ... [2025-08-14 23:05:26.668020] 2025-08-14T23:05:26.6685299Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:05:26.6687202Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_accelerator.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'] ... [2025-08-14 23:05:26.668399] 2025-08-14T23:05:29.8510087Z 2025-08-14T23:05:29.8511450Z test_accelerator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_accelerator_1.1_c938159374e8e670_.log 2025-08-14T23:05:29.8512198Z 2025-08-14T23:05:32.9770702Z /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. 2025-08-14T23:05:32.9773321Z import pkg_resources 2025-08-14T23:05:33.0983689Z Running profiler/test_memory_profiler 1/1 ... [2025-08-14 23:05:33.097939] 2025-08-14T23:05:33.0984597Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:05:33.0986324Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_memory_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'] ... [2025-08-14 23:05:33.098291] 2025-08-14T23:05:36.4686237Z 2025-08-14T23:05:36.4687594Z profiler/test_memory_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_memory_profiler_1.1_f916fa08d3707a75_.log 2025-08-14T23:05:36.4712659Z Running 33 items in this shard: test/profiler/test_memory_profiler.py::TestMemoryProfiler::test_config_check, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_module, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_module_and_optimizer, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_optimizer, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_optimizer_set_to_none, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_low_level, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_complicated, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_non_op_allocations, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_simple, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_simple_backward, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_simple_inplace, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_stacked, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_with_annotations, test/profiler/test_memory_profiler.py::TestDataFlow::test_match_schemas, test/profiler/test_memory_profiler.py::TestDataFlow::test_match_schemas_backward, test/profiler/test_memory_profiler.py::TestDataFlow::test_match_schemas_tensorlist, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_sequential_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_sequential_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_fwd_bwd_step, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_module_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_module_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_module_fwd_bwd_step, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_inputs_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_inputs_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_inputs_fwd_lazy, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_lazily_initialized, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_manual_optimizer_step, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_memory_timeline, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_parameters_and_gradients, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_parameters_and_gradients_set_to_none, test/profiler/test_memory_profiler.py::TestMemoryProfilerTimelineCPU::test_memory_timeline_no_id_cpu 2025-08-14T23:05:36.4736259Z 2025-08-14T23:05:39.6714836Z /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. 2025-08-14T23:05:39.6717714Z import pkg_resources 2025-08-14T23:05:39.7924887Z Running torch_np/numpy_tests/fft/test_pocketfft 1/1 ... [2025-08-14 23:05:39.792126] 2025-08-14T23:05:39.7925453Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:05:39.7928194Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_pocketfft.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:05:39.792532] 2025-08-14T23:06:43.4079548Z 2025-08-14T23:06:43.4081209Z torch_np/numpy_tests/fft/test_pocketfft 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_pocketfft_1.1_723e093371addb93_.log 2025-08-14T23:06:43.4136008Z Running 79 items in this shard: test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTShift::test_fft_n, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_all_1d_norm_preserving, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_axes_op3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_dtypes_dtype0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_dtypes_dtype1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_dtypes_dtype2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype0_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype1_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype2_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_F_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft1, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft3, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft4, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fft_with_order_dtype3_order_non-contiguous_fft5, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_fftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_hfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_identity, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm0, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm_backward, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm_forward, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifft_norm_ortho, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ifftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_ihfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_irfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_irfft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_irfftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_rfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_rfft2, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFT1D::test_rfftn, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_fft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_ifft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_irfft, test/torch_np/numpy_tests/fft/test_pocketfft.py::TestFFTThreadSafe::test_rfft 2025-08-14T23:06:43.4189134Z 2025-08-14T23:06:46.5522807Z /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. 2025-08-14T23:06:46.5525517Z import pkg_resources 2025-08-14T23:06:46.6735586Z Running test_function_schema 1/1 ... [2025-08-14 23:06:46.673144] 2025-08-14T23:06:46.6736387Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:06:46.6738758Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_function_schema.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:06:46.673573] 2025-08-14T23:06:52.1600465Z 2025-08-14T23:06:52.1601862Z test_function_schema 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_function_schema_1.1_94ce4170be386c1b_.log 2025-08-14T23:06:52.1608309Z Running 15 items in this shard: test/test_function_schema.py::TestFunctionSchema::test_backward_compatible_arguments, test/test_function_schema.py::TestFunctionSchema::test_backward_compatible_outputs, test/test_function_schema.py::TestFunctionSchema::test_backward_compatible_structure, test/test_function_schema.py::TestFunctionSchema::test_backward_compatible_with_smart_serialization, test/test_function_schema.py::TestFunctionSchema::test_forward_compatible_arguments_real_use_case, test/test_function_schema.py::TestFunctionSchema::test_forward_compatible_arguments_with_out, test/test_function_schema.py::TestFunctionSchema::test_forward_compatible_arguments_without_out, test/test_function_schema.py::TestFunctionSchema::test_hash_schema, test/test_function_schema.py::TestFunctionSchema::test_out_schema, test/test_function_schema.py::TestFunctionSchema::test_schema_error, test/test_function_schema.py::TestFunctionSchema::test_serialize_and_deserialize, test/test_function_schema.py::TestFunctionSchema::test_string_optional_parameter_default_value, test/test_function_schema.py::TestFunctionSchema::test_sym_int_argument_properly_parsed, test/test_function_schema.py::TestFunctionSchema::test_tensor_list_alias_annotation_properly_parsed, test/test_function_schema.py::TestFunctionSchema::test_tensor_option_arguments_properly_parsed 2025-08-14T23:06:52.1614096Z 2025-08-14T23:06:55.4477416Z /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. 2025-08-14T23:06:55.4480052Z import pkg_resources 2025-08-14T23:06:55.6253792Z Running torch_np/numpy_tests/lib/test_index_tricks 1/1 ... [2025-08-14 23:06:55.624792] 2025-08-14T23:06:55.6254795Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:06:55.6261038Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_index_tricks.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:06:55.625228] 2025-08-14T23:07:08.8159231Z 2025-08-14T23:07:08.8160717Z torch_np/numpy_tests/lib/test_index_tricks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_index_tricks_1.1_a24080ebd1413e5e_.log 2025-08-14T23:07:08.8193805Z Running 47 items in this shard: test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_0d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_big_indices, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_clipmodes, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_dtypes, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_ravel_mode_clip, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_ravel_mode_raise, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_ravel_mode_wrap, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_array_unravel, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_empty_indices, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestRavelUnravelIndex::test_writeability, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_accepts_longdouble, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_accepts_npcomplexfloating, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_accepts_npfloating, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_linspace_equivalence, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_mgrid_size_none_handling_start0_stop_10_step0_expected0, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_mgrid_size_none_handling_start_-10_stop_20_step1_expected1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_nd, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestGrid::test_sparse, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_0d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_1d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_2d, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_complex_step, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_mixed_type, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestConcatenator::test_more_mixed_type, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestNdenumerate::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIndexExpression::test_regression_1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIndexExpression::test_simple_1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_1d_only, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_bool, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_regression_1, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_repeated_input, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestIx_::test_shape_and_dtype, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestC::test_c_, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_basic, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_hetero_shape_handling, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_low_dim_handling, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_operate_4d_array, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_tall_matrix, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_tall_matrix_wrap, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestFillDiagonal::test_wide_matrix, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndices::test_diag_indices, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndicesFrom::test_diag_indices_from, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndicesFrom::test_error_shape_mismatch, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestDiagIndicesFrom::test_error_small_input, test/torch_np/numpy_tests/lib/test_index_tricks.py::TestNdIndex::test_ndindex 2025-08-14T23:07:08.8222041Z 2025-08-14T23:07:11.9722222Z /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. 2025-08-14T23:07:11.9723752Z import pkg_resources 2025-08-14T23:07:12.0930331Z Running test_scatter_gather_ops 1/1 ... [2025-08-14 23:07:12.092636] 2025-08-14T23:07:12.0932746Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:07:12.0934013Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_scatter_gather_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'] ... [2025-08-14 23:07:12.093037] 2025-08-14T23:08:28.8965879Z 2025-08-14T23:08:28.8967043Z test_view_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_view_ops_1.1_70a5bd9c5fcc7e47_.log 2025-08-14T23:08:28.9195635Z Running 442 items in this shard: test/test_view_ops.py::TestViewOpsCPU::test_T_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_advanced_indexing_assignment_cpu, test/test_view_ops.py::TestViewOpsCPU::test_advanced_indexing_nonview_cpu, test/test_view_ops.py::TestViewOpsCPU::test_as_strided_gradients_cpu, test/test_view_ops.py::TestViewOpsCPU::test_as_strided_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_as_strided_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_basic_indexing_ellipses_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_basic_indexing_newaxis_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_basic_indexing_slice_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_chunk_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_conj_imag_view_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_conj_imag_view_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_conj_view_with_shared_memory_cpu, test/test_view_ops.py::TestViewOpsCPU::test_contiguous_nonview_cpu, test/test_view_ops.py::TestViewOpsCPU::test_contiguous_self_cpu, test/test_view_ops.py::TestViewOpsCPU::test_diagonal_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_expand_as_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_expand_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_flatten_nonview_cpu, test/test_view_ops.py::TestViewOpsCPU::test_flatten_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_movedim_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_narrow_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_permute_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_real_imag_view_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_real_imag_view_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_reshape_as_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_reshape_nonview_cpu, test/test_view_ops.py::TestViewOpsCPU::test_reshape_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_select_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_bool, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_complex128, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_complex64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_float16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_float32, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_float64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_int16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_int32, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_int64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_int8, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_uint8, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_bool, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_complex128, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_complex64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_float16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_float32, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_float64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_int16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_int32, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_int64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_int8, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_uint8, test/test_view_ops.py::TestViewOpsCPU::test_split_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_squeeze_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_squeeze_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_t_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_t_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_transpose_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_transpose_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unbind_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unbind_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unfold_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unsqueeze_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unsqueeze_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_as_complex_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_as_real_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_as_real_cpu_complex32, test/test_view_ops.py::TestViewOpsCPU::test_view_as_real_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_as_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_copy_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_copy_out_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_copy_output_contiguous_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_view_cpu, test/test_view_ops.py::TestViewOpsLAZY::test_T_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_advanced_indexing_assignment_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_advanced_indexing_nonview_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_as_strided_gradients_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_as_strided_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_as_strided_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_basic_indexing_ellipses_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_basic_indexing_newaxis_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_basic_indexing_slice_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_chunk_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_conj_imag_view_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_conj_imag_view_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_conj_view_with_shared_memory_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_contiguous_nonview_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_contiguous_self_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_diagonal_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_expand_as_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_expand_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_flatten_nonview_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_flatten_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_movedim_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_narrow_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_permute_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_real_imag_view_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_real_imag_view_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_reshape_as_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_reshape_nonview_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_reshape_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_select_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_bool, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_float16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_float32, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_float64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_int16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_int32, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_int64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_int8, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_bool, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_float16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_float32, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_float64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_int16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_int32, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_int64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_int8, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_split_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_squeeze_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_squeeze_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_t_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_t_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_transpose_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_transpose_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unbind_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unbind_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unfold_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unsqueeze_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unsqueeze_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_complex_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_real_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_real_lazy_complex32, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_real_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_copy_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_copy_out_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_copy_output_contiguous_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_view_lazy, test/test_view_ops.py::TestOldViewOpsCPU::test_T_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_as_strided_overflow_storage_offset_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_gradient_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_big_transpose_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_shapes_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_tensors_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_chunk_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_conj_neg_view_numpy_error_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_contiguous_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_crow_col_indices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_empty_reshape_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_expand_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_flatten_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_memory_format_resize__cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_memory_format_resize_as_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_narrow_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_narrow_tensor_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_python_types_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_ravel_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_bfloat16, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_all_dtypes_and_devices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_as_all_dtypes_and_devices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_as_preserves_strides_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_overflow_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_split_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_t_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_errors_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_invalid_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_invalid_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_invalid_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_vs_numpy_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_vs_numpy_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_vs_numpy_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_bfloat16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_bfloat16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_unsqueeze_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_view_all_dtypes_and_devices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_view_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_view_empty_cpu 2025-08-14T23:08:28.9421974Z 2025-08-14T23:08:32.1978576Z /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. 2025-08-14T23:08:32.1981576Z import pkg_resources 2025-08-14T23:08:32.3189936Z Running test_unary_ufuncs 1/3 ... [2025-08-14 23:08:32.318571] 2025-08-14T23:08:32.3190725Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:08:32.3193328Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_unary_ufuncs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:08:32.318990] 2025-08-14T23:10:11.0409419Z 2025-08-14T23:10:11.0410639Z test_sparse_csr 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_sparse_csr_2.2_7a0f51e2188a65a8_.log 2025-08-14T23:10:11.1874724Z Running 2357 items in this shard: test/test_sparse_csr.py::TestSparseCSRCPU::test_add_SparseCSC_cpu_float32, test/test_sparse_csr.py::TestSparseCSRCPU::test_add_SparseCSC_cpu_float64, test/test_sparse_csr.py::TestSparseCSRCPU::test_add_SparseCSR_cpu_float64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_all_sparse_csr_SparseCSC_cpu_float32, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_all_sparse_csr_SparseCSC_cpu_float64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_all_sparse_csr_SparseCSR_cpu_complex64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_all_sparse_csr_SparseCSR_cpu_float32, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_all_sparse_csr_SparseCSR_cpu_float64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_dense_result_SparseCSC_cpu_complex128, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_dense_result_SparseCSC_cpu_float64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_dense_result_SparseCSR_cpu_float32, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_dense_result_SparseCSR_cpu_float64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_0_cpu_complex128, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_0_cpu_float32, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_0_cpu_float64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_1_cpu_float32, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_25_cpu_complex64, test/test_sparse_csr.py::TestSparseCSRCPU::test_addmm_sizes_all_sparse_csr_k_0_n_0_m_25_cpu_float32, 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test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_tan_cpu_bool, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_tan_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_tan_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_tan_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_tan_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_tanh_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_tanh_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_tanh_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_tanh_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_tanh_cpu_int64, 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test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_trunc_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_trunc_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_trunc_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_trunc_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_trunc_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_zeros_like_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_zeros_like_cpu_complex32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_zeros_like_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSC_zeros_like_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_abs_cpu_complex32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_abs_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_abs_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_abs_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_abs_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_abs_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_abs_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_angle_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_angle_cpu_bool, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_angle_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_angle_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_angle_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_angle_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_angle_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asin_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asin_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asin_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asin_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asin_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asin_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asin_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asinh_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asinh_cpu_bool, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asinh_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asinh_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asinh_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asinh_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asinh_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asinh_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_asinh_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_atan_cpu_complex64, 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test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_atanh_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_atanh_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_ceil_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_ceil_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_ceil_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_ceil_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_ceil_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_ceil_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_conj_physical_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_conj_physical_cpu_bool, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_conj_physical_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_conj_physical_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_conj_physical_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_conj_physical_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_deg2rad_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_deg2rad_cpu_bool, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_deg2rad_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_deg2rad_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_deg2rad_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_deg2rad_cpu_int16, 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test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_floor_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_floor_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_frac_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_frac_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isinf_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isinf_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isinf_cpu_complex32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isinf_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isinf_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isinf_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isinf_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isinf_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isnan_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isnan_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isnan_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isnan_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isnan_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isnan_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isneginf_cpu_bool, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isneginf_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isneginf_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isneginf_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isneginf_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isneginf_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isneginf_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isposinf_cpu_bool, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isposinf_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isposinf_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isposinf_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isposinf_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_isposinf_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_log1p_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_log1p_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_log1p_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_log1p_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_amax_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_amax_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_amax_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_amax_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_amax_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_amax_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_amin_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_amin_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_amin_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_amin_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_amin_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_mean_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_mean_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_mean_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_prod_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_prod_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_prod_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_prod_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_prod_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_prod_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_prod_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_prod_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_prod_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_sum_cpu_bool, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_sum_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_sum_cpu_complex64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_sum_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_sum_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_sum_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_masked_sum_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_mul_cpu_bool, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_mul_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_mul_cpu_complex32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_mul_cpu_complex64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_mul_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_mul_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_neg_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_neg_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_neg_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_neg_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_neg_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_neg_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_neg_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_nn_functional_relu_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_nn_functional_relu_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_nn_functional_relu_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_nn_functional_relu_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_nn_functional_relu_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_positive_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_positive_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_rad2deg_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_rad2deg_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_rad2deg_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_rad2deg_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_randn_like_cpu_complex32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_randn_like_cpu_float16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_round_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_round_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_round_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_round_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_round_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_consistency_SparseCSR_round_cpu_uint8, 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test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSC_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSC_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSC_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_complex64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseBSR_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_float32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_float64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_int64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_int8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSC_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_bfloat16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_complex128, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_complex64, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_int16, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_int32, test/test_sparse_csr.py::TestSparseCompressedCPU::test_validate_SparseCSR_cpu_uint8, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_TensorAsKey, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_bsr_dense_addmm_meta, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_bsr_dense_bmm, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_sampled_addmm, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_scaled_dot_product_attention, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_scatter_mm, test/test_sparse_csr.py::TestSparseCompressedTritonKernelsCPU::test_triton_tune 2025-08-14T23:10:11.2809749Z 2025-08-14T23:10:14.1728626Z /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. 2025-08-14T23:10:14.1731336Z import pkg_resources 2025-08-14T23:10:14.2935436Z Running torch_np/numpy_tests/core/test_scalar_methods 1/1 ... [2025-08-14 23:10:14.293117] 2025-08-14T23:10:14.2936106Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:10:14.2937960Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalar_methods.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:10:14.293502] 2025-08-14T23:10:17.7136538Z 2025-08-14T23:10:17.7138186Z torch_np/numpy_tests/core/test_scalar_methods 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalar_methods_1.1_9e45072caa09e2e6_.log 2025-08-14T23:10:17.7180573Z Running 96 items in this shard: test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_against_known_values, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_errors_ftype0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_errors_ftype1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_errors_ftype2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_roundtrip_ftype0_frac_vals0_exp_vals0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_roundtrip_ftype1_frac_vals1_exp_vals1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_roundtrip_ftype2_frac_vals2_exp_vals2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_simple_fractions_ftype0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_simple_fractions_ftype1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_simple_fractions_ftype2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_-0_875_ratio1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_0_0_ratio2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_0_875_ratio0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype0_f_11_5_ratio3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_-0_875_ratio1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_0_0_ratio2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_0_875_ratio0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype1_f_11_5_ratio3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_-0_875_ratio1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_0_0_ratio2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_0_875_ratio0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestAsIntegerRatio::test_small_ftype2_f_11_5_ratio3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_b, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_h, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_i, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_false_code_l, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_inf_code_g, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_special_str_value_nan_code_g, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_B, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_b, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_h, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_i, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestIsInteger::test_true_code_l, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls4, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_cls5, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_complexfloating_subscript_tuple_arg_len_3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_abc_non_numeric_cls0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_?, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_B, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_D, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_F, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_G, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_H, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_I, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_L, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_M, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_N, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_O, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_P, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_Q, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_S, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_U, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_V, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_b, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_d, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_e, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_f, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_g, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_h, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_i, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_l, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_m, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_n, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_p, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_concrete_code_q, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_scalar, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestClassGetItem::test_subscript_tuple_arg_len_3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_bit_count, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype0, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype1, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype2, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype3, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype4, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype5, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype6, test/torch_np/numpy_tests/core/test_scalar_methods.py::TestBitCount::test_small_itype7 2025-08-14T23:10:17.7219447Z 2025-08-14T23:10:20.7918836Z /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. 2025-08-14T23:10:20.7921675Z import pkg_resources 2025-08-14T23:10:20.9138211Z Running nn/test_init 1/1 ... [2025-08-14 23:10:20.913405] 2025-08-14T23:10:20.9138968Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:10:20.9141186Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_init.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:10:20.913805] 2025-08-14T23:10:36.6037601Z 2025-08-14T23:10:36.6038896Z nn/test_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_init_1.1_3aedc7c40a01da3a_.log 2025-08-14T23:10:36.6047814Z Running 30 items in this shard: test/nn/test_init.py::TestNNInit::test_calculate_gain_leaky_relu, test/nn/test_init.py::TestNNInit::test_calculate_gain_leaky_relu_only_accepts_numbers, test/nn/test_init.py::TestNNInit::test_calculate_gain_linear, test/nn/test_init.py::TestNNInit::test_calculate_gain_nonlinear, test/nn/test_init.py::TestNNInit::test_calculate_gain_only_accepts_valid_nonlinearities, test/nn/test_init.py::TestNNInit::test_constant, test/nn/test_init.py::TestNNInit::test_deprecation, test/nn/test_init.py::TestNNInit::test_dirac_identity, test/nn/test_init.py::TestNNInit::test_dirac_only_works_on_3_4_5d_inputs, test/nn/test_init.py::TestNNInit::test_dirac_properties, test/nn/test_init.py::TestNNInit::test_eye, test/nn/test_init.py::TestNNInit::test_eye_only_works_on_2d_inputs, test/nn/test_init.py::TestNNInit::test_kaiming_normal, test/nn/test_init.py::TestNNInit::test_kaiming_normal_errors_on_inputs_smaller_than_2d, test/nn/test_init.py::TestNNInit::test_kaiming_normal_warning_on_0element_tensor, test/nn/test_init.py::TestNNInit::test_kaiming_uniform, test/nn/test_init.py::TestNNInit::test_kaiming_uniform_errors_on_inputs_smaller_than_2d, test/nn/test_init.py::TestNNInit::test_kaiming_uniform_warning_on_0element_tensor, test/nn/test_init.py::TestNNInit::test_normal, test/nn/test_init.py::TestNNInit::test_ones_and_zeros, test/nn/test_init.py::TestNNInit::test_orthogonal, test/nn/test_init.py::TestNNInit::test_sparse_default_std, test/nn/test_init.py::TestNNInit::test_sparse_only_works_on_2d_inputs, test/nn/test_init.py::TestNNInit::test_trunc_normal, test/nn/test_init.py::TestNNInit::test_trunc_normal_generator, test/nn/test_init.py::TestNNInit::test_uniform, test/nn/test_init.py::TestNNInit::test_xavier_normal, test/nn/test_init.py::TestNNInit::test_xavier_normal_errors_on_inputs_smaller_than_2d, test/nn/test_init.py::TestNNInit::test_xavier_uniform, test/nn/test_init.py::TestNNInit::test_xavier_uniform_errors_on_inputs_smaller_than_2d 2025-08-14T23:10:36.6055895Z 2025-08-14T23:10:39.7242356Z /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. 2025-08-14T23:10:39.7243885Z import pkg_resources 2025-08-14T23:10:39.8464217Z Running torch_np/numpy_tests/core/test_numerictypes 1/1 ... [2025-08-14 23:10:39.845963] 2025-08-14T23:10:39.8465041Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:10:39.8467089Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numerictypes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:10:39.846360] 2025-08-14T23:10:45.5193278Z 2025-08-14T23:10:45.5194935Z torch_np/numpy_tests/core/test_numerictypes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numerictypes_1.1_7584af104e9df888_.log 2025-08-14T23:10:45.5210437Z Running 34 items in this shard: test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_loses1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_loses2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_both_abstract, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_nondtype_nonscalartype, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_same, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_sibling_class, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_subclass, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_subclass_backwards, test/torch_np/numpy_tests/core/test_numerictypes.py::TestBitName::test_abstract, test/torch_np/numpy_tests/core/test_numerictypes.py::TestDocStrings::test_platform_dependent_aliases, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t0, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t4, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t5, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t6, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t7, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t8, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t9, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_unique, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t0, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t4, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t5, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t6, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t7, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t8, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t9 2025-08-14T23:10:45.5225174Z 2025-08-14T23:10:48.6621321Z /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. 2025-08-14T23:10:48.6624257Z import pkg_resources 2025-08-14T23:10:48.7828077Z Running torch_np/numpy_tests/lib/test_arraypad 1/1 ... [2025-08-14 23:10:48.782379] 2025-08-14T23:10:48.7829020Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:10:48.7831218Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraypad.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'] ... [2025-08-14 23:10:48.782724] 2025-08-14T23:10:54.1543589Z 2025-08-14T23:10:54.1544900Z torch_np/numpy_tests/lib/test_arraypad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraypad_1.1_191d902c5b9aa40e_.log 2025-08-14T23:10:54.1549021Z Running 9 items in this shard: test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_float, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_float2, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_float3, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_odd_pad_amount, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_pad_2d, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_zeros, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_large_integers, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_pad_empty_dimension 2025-08-14T23:10:54.1552399Z 2025-08-14T23:10:57.2075430Z /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. 2025-08-14T23:10:57.2078275Z import pkg_resources 2025-08-14T23:10:57.3282730Z Running torch_np/numpy_tests/lib/test_arraysetops 1/1 ... [2025-08-14 23:10:57.327864] 2025-08-14T23:10:57.3283677Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:10:57.3285842Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraysetops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:10:57.328235] 2025-08-14T23:11:03.0008214Z 2025-08-14T23:11:03.0009762Z torch_np/numpy_tests/lib/test_arraysetops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraysetops_1.1_120b4f0e22b37047_.log 2025-08-14T23:11:03.0035190Z Running 62 items in this shard: test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_forbidden_type_casts_ary0_prepend0_append_nan_expected_to_end, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_forbidden_type_casts_ary1_prepend1_append1_expected_to_begin, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_forbidden_type_casts_ary2_prepend_nan_append_nan_expected_to_begin, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary0_prepend_65536_append_65540_expected0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary1_prepend1_append1_expected1, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary2_prepend_0_append_0_expected2, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary3_prepend_3_append_-9_expected3, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_boolean_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_boolean_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_boolean_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_both_arrays_are_object, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_both_arrays_have_structured_dtype, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_char_array, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_errors, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_first_array_is_object, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_hit_alternate_algorithm, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_invert_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_invert_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_invert_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_boolean_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_boolean_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_boolean_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype10_dtype20_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype10_dtype20_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype10_dtype20_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype11_dtype21_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype11_dtype21_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype11_dtype21_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_ravel_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_ravel_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_ravel_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_second_array_is_object, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_table_timedelta_fails, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_timedelta_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_timedelta_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_with_arrays_containing_tuples, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_intersect1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_intersect1d_array_like, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_intersect1d_indices, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_isin_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_isin_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_isin_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_manyways, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setdiff1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setdiff1d_char_array, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setdiff1d_unique, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setxor1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_union1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d_2, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d_with_axis_axis_-1, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d_with_axis_axis_0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis_errors, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis_list, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis_zeros, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_nanequals, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_sort_order_with_axis 2025-08-14T23:11:03.0059037Z 2025-08-14T23:11:06.2117381Z /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. 2025-08-14T23:11:06.2120007Z import pkg_resources 2025-08-14T23:11:06.3324844Z Running test_sparse 1/2 ... [2025-08-14 23:11:06.332077] 2025-08-14T23:11:06.3325542Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-14T23:11:06.3327468Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_sparse.py', '-m', 'not serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-14 23:11:06.332425] 2025-08-14T23:11:09.7835894Z 2025-08-14T23:11:09.7837247Z test_scatter_gather_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_scatter_gather_ops_1.1_467a3dd7505f911b_.log 2025-08-14T23:11:09.7870925Z Running 84 items in this shard: test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_backward_with_empty_index_tensor_sparse_grad_False_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_backward_with_empty_index_tensor_sparse_grad_False_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_backward_with_empty_index_tensor_sparse_grad_True_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_backward_with_empty_index_tensor_sparse_grad_True_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_bool_cpu_bool, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_expanded_index_cpu_bfloat16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_expanded_index_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_expanded_index_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_large_cpu_bfloat16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_gather_large_cpu_int8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__reductions_cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__reductions_cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__reductions_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__scalar_cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__scalar_cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter__scalar_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_add__cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_add__cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_add__cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_add_broadcasted_index_deterministic_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_add_mult_index_base_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_expanded_index_cpu_bfloat16, 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test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amin_cpu_int64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amin_cpu_int8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_amin_cpu_uint8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_bfloat16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_complex128, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_mean_cpu_int16, 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test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_int16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_int32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_int64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_int8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_prod_cpu_uint8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_bfloat16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_complex128, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_complex64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_float16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_float32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_float64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_int16, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_int32, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_int64, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_int8, test/test_scatter_gather_ops.py::TestScatterGatherCPU::test_scatter_reduce_sum_cpu_uint8 2025-08-14T23:11:09.7902553Z 2025-08-14T23:11:12.9074024Z /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. 2025-08-14T23:11:12.9075545Z import pkg_resources 2025-08-14T23:15:24.0069742Z 2025-08-14T23:15:24.0071034Z test_unary_ufuncs 1/3 was successful, full logs can be found in artifacts with path test/test-reports/test_unary_ufuncs_1.3_c8e0057fe07dab32_.log 2025-08-14T23:15:24.6795479Z Running 8356 items in this shard: test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_angle_complex_to_float_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_big_number_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_signed_zero_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_signed_zero_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_zero_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_complex128, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_erfinv_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_exp_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_expm1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_fill_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_float_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_floor_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_frac_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_half_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_i0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_i0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_i0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_i0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_imag_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_int_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isfinite_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isfinite_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isfinite_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isfinite_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isfinite_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isfinite_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isinf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isinf_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isinf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isinf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isinf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isinf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isinf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isnan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isneginf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isposinf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isposinf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isposinf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isposinf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isposinf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_lgamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log10_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log1p_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_log_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logical_not_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logical_not_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logical_not_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logit_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_logit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_long_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nan_to_num_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_neg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_celu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_elu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_elu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_hardtanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_logsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_prelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_rrelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_silu_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softplus_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softplus_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_softsign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_tanhshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_tanhshrink_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_tanhshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_tanhshrink_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_tanhshrink_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_nn_functional_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_4_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_4_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_4_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_polygamma_polygamma_n_4_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_positive_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_positive_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_positive_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_positive_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_positive_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rad2deg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rad2deg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rad2deg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rad2deg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_real_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_real_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_real_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_real_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_real_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_reciprocal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_reciprocal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_round_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_round_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_round_decimals_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_round_decimals_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_round_decimals_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_round_decimals_neg_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_sgn_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_sgn_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_sgn_cpu_uint8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_ceil_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_ceil_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_ceil_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_ceil_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_conj_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_conj_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_conj_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_conj_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_conj_physical_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_conj_physical_cpu_float64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_exp2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_exp2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_exp2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_exp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_exp_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_exp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_exp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_expm1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_expm1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_fill_cpu_bfloat16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_isfinite_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_isfinite_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_isinf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_isinf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_isinf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_isinf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_isnan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_isnan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_isnan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_isneginf_cpu_bfloat16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log1p_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log1p_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log1p_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_logical_not_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_logical_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nan_to_num_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nan_to_num_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nan_to_num_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_neg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_celu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_celu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_celu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_elu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_elu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardtanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardtanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_mish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_prelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_selu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softplus_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_positive_cpu_complex128, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_reciprocal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_reciprocal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_reciprocal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_reciprocal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_round_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_round_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_round_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_round_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_round_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_rsqrt_cpu_float16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frexp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_imag_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_celu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_mish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_mish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_rrelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_rrelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softplus_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softplus_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_neg_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sgn_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_short_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_short_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sigmoid_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sigmoid_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_signbit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_signbit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_signbit_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sin_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sin_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sin_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sin_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinc_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sinh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_airy_ai_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_airy_ai_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_j0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_j1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_j1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_j1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_y0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_y0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_y0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_bessel_y1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_entr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_erfcx_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_erfcx_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_erfcx_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i0e_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i0e_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i0e_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i1e_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i1e_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i1e_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_i1e_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_log_ndtr_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_log_ndtr_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_log_ndtr_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_i0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_i0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_i1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_i1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_i1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_k0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_k0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_k1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_modified_bessel_k1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_ndtr_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_ndtr_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_ndtr_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_ndtri_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_ndtri_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_ndtri_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_polygamma_special_polygamma_n_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_polygamma_special_polygamma_n_0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_polygamma_special_polygamma_n_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_polygamma_special_polygamma_n_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_polygamma_special_polygamma_n_0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_polygamma_special_polygamma_n_0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_polygamma_special_polygamma_n_0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_scaled_modified_bessel_k0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_scaled_modified_bessel_k0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_scaled_modified_bessel_k1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_scaled_modified_bessel_k1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_scaled_modified_bessel_k1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_scaled_modified_bessel_k1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_spherical_bessel_j0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_spherical_bessel_j0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_special_spherical_bessel_j0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sqrt_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_sqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_square_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_square_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_square_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_square_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_square_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_square_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tan_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tanh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_tanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_trunc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_trunc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_trunc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_trunc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_trunc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bfloat16_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bfloat16_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bfloat16_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bfloat16_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bool_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bool_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_bool_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs__conversions_byte_cpu_bfloat16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nan_to_num_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_neg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_neg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_celu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_celu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_elu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_hardtanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_hardtanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_hardtanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_hardtanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_mish_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_prelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_prelu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_relu6_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_relu_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_relu_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_relu_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_softshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_softshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_tanhshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_tanhshrink_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_tanhshrink_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_nn_functional_threshold_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_positive_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_positive_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_positive_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_rad2deg_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_rad2deg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_rad2deg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_real_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_real_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_reciprocal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_reciprocal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_round_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_round_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_round_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_round_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_rsqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_rsqrt_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_rsqrt_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sgn_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sgn_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sgn_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sgn_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sgn_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sgn_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sin_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sin_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sin_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sinc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sinc_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sinc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sinc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sinc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sinh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sinh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sinh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sinh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_bessel_j0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_bessel_j0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_bessel_j0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_bessel_j1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_bessel_j1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_bessel_j1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_entr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_entr_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_erfcx_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_erfcx_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_erfcx_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_erfcx_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_erfcx_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_i0e_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_i0e_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_i0e_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_i0e_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_i1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_i1e_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_i1e_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_i1e_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_log_ndtr_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_log_ndtr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_logit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_logit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_logit_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_multigammaln_mvlgamma_p_1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_multigammaln_mvlgamma_p_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_multigammaln_mvlgamma_p_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_multigammaln_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_multigammaln_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_multigammaln_mvlgamma_p_5_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_ndtr_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_ndtr_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_ndtr_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_ndtri_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_ndtri_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_ndtri_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_ndtri_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_spherical_bessel_j0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_spherical_bessel_j0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_spherical_bessel_j0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_spherical_bessel_j0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_special_spherical_bessel_j0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sqrt_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sqrt_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sqrt_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_sqrt_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_square_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_square_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_square_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_square_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_square_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_square_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tan_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tanh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tanh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_tanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_trunc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_trunc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_trunc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed__refs_trunc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_abs_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_abs_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asin_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_asinh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_atanh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bitwise_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bitwise_not_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bitwise_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cdouble_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_imag_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_celu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_celu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_elu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_logsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_logsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_logsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_mish_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_mish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_prelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_prelu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_selu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softplus_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softplus_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_neg_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_neg_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i0e_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_i1e_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_log_ndtr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_log_ndtr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_log_ndtr_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_log_ndtr_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_i0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_i0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_i0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_i0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_i1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_i1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_i1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_k0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_k0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_k1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_modified_bessel_k1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_ndtr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_ndtr_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_ndtr_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_ndtri_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_ndtri_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_polygamma_special_polygamma_n_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_polygamma_special_polygamma_n_0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_polygamma_special_polygamma_n_0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_polygamma_special_polygamma_n_0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_scaled_modified_bessel_k0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_scaled_modified_bessel_k0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_scaled_modified_bessel_k0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_scaled_modified_bessel_k0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_scaled_modified_bessel_k1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_scaled_modified_bessel_k1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_scaled_modified_bessel_k1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_scaled_modified_bessel_k1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_spherical_bessel_j0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_spherical_bessel_j0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_spherical_bessel_j0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sqrt_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sqrt_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sqrt_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_square_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_square_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_trunc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_trunc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_acos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_acos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_acosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_atanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_atanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_atanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_rsqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_logit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_asin_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_asin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_atanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_atanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log10_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log10_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log1p_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_rsqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_rsqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_special_ndtri_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_special_ndtri_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_sqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_sqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_frexp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_frexp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_edge_cases_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_edge_cases_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_backward_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardswish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardswish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_special_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_common_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_edge_cases_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_common_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igammac_common_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_isposinf_isneginf_non_boolean_output_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_narrow_dtypes_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bfloat16_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bfloat16_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bfloat16_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bfloat16_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bool_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bool_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_bool_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_byte_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_byte_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cdouble_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cdouble_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cdouble_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cdouble_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cdouble_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cfloat_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cfloat_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cfloat_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_cfloat_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_chalf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_chalf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_chalf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_chalf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_chalf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_chalf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_char_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_char_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_double_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_double_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_double_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_double_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_double_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_float_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_float_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_float_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_half_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_half_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_half_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_int_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_int_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_int_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_int_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_long_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_long_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_long_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_long_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_short_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_short_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_short_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_short_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs__conversions_short_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_abs_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_abs_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_abs_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_abs_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_abs_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig__refs_acos_cpu_bool, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isinf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isinf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isnan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isnan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isnan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isneginf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isneginf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isneginf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isneginf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isposinf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isposinf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isposinf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isposinf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isposinf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isposinf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_lgamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log10_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log10_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log10_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log1p_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_log_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logical_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_logit_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_long_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_long_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_long_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nan_to_num_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_logsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_mish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_mish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_prelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_prelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_prelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_rrelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_rrelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_silu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_neg_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rsqrt_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rsqrt_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rsqrt_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sgn_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sgn_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sgn_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sgn_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sgn_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sgn_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_short_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_short_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_short_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sigmoid_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sigmoid_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sigmoid_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sign_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sign_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_signbit_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_signbit_cpu_uint8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_silu_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_sinc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_log_ndtr_vs_scipy_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_ndtr_vs_scipy_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_int64 2025-08-14T23:15:25.2311010Z 2025-08-14T23:15:28.0838506Z /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. 2025-08-14T23:15:28.0841450Z import pkg_resources 2025-08-14T23:18:15.3332470Z 2025-08-14T23:18:15.3333515Z test_sparse 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_sparse_1.2_75b264249c9e69b6_.log 2025-08-14T23:18:15.4246637Z Running 1573 items in this shard: test/test_sparse.py::TestSparseOneOff::test_cuda_from_cpu, test/test_sparse.py::TestSparseMeta::test_add_meta_SparseBSC_float64, test/test_sparse.py::TestSparseMeta::test_add_meta_SparseBSR_float64, test/test_sparse.py::TestSparseMeta::test_add_meta_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_add_meta_SparseCSC_float64, test/test_sparse.py::TestSparseMeta::test_fake_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_meta_SparseBSC_float64, test/test_sparse.py::TestSparseMeta::test_meta_SparseCSR_float64, test/test_sparse.py::TestSparseMeta::test_print_meta_SparseBSC_float64, test/test_sparse.py::TestSparseMeta::test_print_meta_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_sum_meta_SparseBSC_float64, test/test_sparse.py::TestSparseMeta::test_sum_meta_SparseBSR_float64, test/test_sparse.py::TestSparseMeta::test_sum_meta_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_sum_meta_SparseCSC_float64, test/test_sparse.py::TestSparseMeta::test_to_meta_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_fake_SparseBSR_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_fake_SparseCSC_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_meta_SparseCOO_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_meta_SparseCSC_float64, test/test_sparse.py::TestSparseMeta::test_zeros_like_meta_SparseCSR_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_abs_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_abs_cpu_int8, 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test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atanh_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atanh_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atanh_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_ceil_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_ceil_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_ceil_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_ceil_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_complex128, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_complex64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_complex128, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_complex64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_conj_physical_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_deg2rad_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_deg2rad_cpu_int16, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_deg2rad_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_erf_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_erf_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_erf_cpu_int8, 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test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseBSR_int64_cpu_int16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseBSR_int64_cpu_int64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseBSR_int64_cpu_int8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseBSR_int64_cpu_uint8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCOO_int32_cpu_bool, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCOO_int32_cpu_float16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCOO_int32_cpu_int32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCOO_int32_cpu_int8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCOO_int64_cpu_bool, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCOO_int64_cpu_complex64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCOO_int64_cpu_float16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCOO_int64_cpu_float32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCOO_int64_cpu_int16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCOO_int64_cpu_int8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCOO_int64_cpu_uint8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSC_int32_cpu_complex64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSC_int32_cpu_float16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSC_int32_cpu_int16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSC_int32_cpu_int64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSC_int32_cpu_uint8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSC_int64_cpu_complex64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSC_int64_cpu_float16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSC_int64_cpu_float32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSC_int64_cpu_int16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSC_int64_cpu_int8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int32_cpu_bfloat16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int32_cpu_complex64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int32_cpu_float16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int32_cpu_float32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int32_cpu_int16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int32_cpu_int32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int32_cpu_int8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int64_cpu_bfloat16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int64_cpu_float16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int64_cpu_float32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int64_cpu_int16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int64_cpu_int32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int64_cpu_int64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int64_cpu_int8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_SparseCSR_SparseCSR_int64_cpu_uint8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int32_cpu_bfloat16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int32_cpu_bool, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int32_cpu_float32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int32_cpu_float64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int32_cpu_int16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int32_cpu_int32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int32_cpu_int64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int32_cpu_uint8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int64_cpu_bfloat16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int64_cpu_complex128, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int64_cpu_float32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int64_cpu_int16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int64_cpu_int32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int64_cpu_int64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSC_int64_cpu_uint8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSR_int32_cpu_bfloat16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSR_int32_cpu_bool, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSR_int32_cpu_complex128, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSR_int32_cpu_complex64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSR_int32_cpu_int32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSR_int32_cpu_uint8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSR_int64_cpu_bfloat16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSR_int64_cpu_bool, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSR_int64_cpu_float16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSR_int64_cpu_int16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSR_int64_cpu_int32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseBSR_int64_cpu_int64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCOO_int32_cpu_bool, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCOO_int32_cpu_complex128, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCOO_int32_cpu_float64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCOO_int32_cpu_int64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCOO_int32_cpu_int8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCOO_int32_cpu_uint8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCOO_int64_cpu_bool, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCOO_int64_cpu_float32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCOO_int64_cpu_float64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCOO_int64_cpu_int16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCOO_int64_cpu_int32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCOO_int64_cpu_int64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCOO_int64_cpu_int8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int32_cpu_bfloat16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int32_cpu_bool, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int32_cpu_complex64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int32_cpu_float16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int32_cpu_float64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int32_cpu_int32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int32_cpu_uint8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int64_cpu_bfloat16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int64_cpu_complex128, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int64_cpu_complex64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int64_cpu_float16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int64_cpu_int16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int64_cpu_int32, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int64_cpu_int64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int64_cpu_int8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSC_int64_cpu_uint8, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSR_int32_cpu_bfloat16, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSR_int32_cpu_bool, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSR_int64_cpu_bool, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSR_int64_cpu_complex64, test/test_sparse.py::TestSparseAnyCPU::test_to_sparse_Strided_SparseCSR_int64_cpu_int64, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_ccol_indices_SparseBSC_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_ccol_indices_SparseBSR_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_ccol_indices_Strided_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_coalesce_SparseBSR_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_coalesce_SparseCOO_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_col_indices_SparseBSC_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_col_indices_SparseCOO_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_col_indices_Strided_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_indices_SparseBSR_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_indices_SparseCOO_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_indices_SparseCSC_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_indices_SparseCSR_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_indices_Strided_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_is_coalesced_SparseBSC_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_is_coalesced_SparseBSR_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_is_coalesced_SparseCOO_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_is_coalesced_SparseCSC_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_is_coalesced_Strided_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_row_indices_SparseCOO_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_row_indices_SparseCSC_cpu, test/test_sparse.py::TestSparseAnyCPU::test_unsupported_backend_error_message_values_Strided_cpu 2025-08-14T23:18:15.4798255Z 2025-08-14T23:18:16.1071931Z Running test batch 'tests to run' cost 7734.49 seconds 2025-08-14T23:18:17.0562295Z 2025-08-14T23:18:17.0562710Z real 129m0.057s 2025-08-14T23:18:17.0563003Z user 375m34.939s 2025-08-14T23:18:17.0563258Z sys 51m41.129s 2025-08-14T23:18:17.0563482Z + assert_git_not_dirty 2025-08-14T23:18:17.0569870Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-08-14T23:18:17.0570323Z + [[ linux-jammy-py3.13-clang12 != *xla* ]] 2025-08-14T23:18:17.0586040Z ++ git status --porcelain 2025-08-14T23:18:17.0586904Z ++ grep -v '?? third_party' 2025-08-14T23:18:58.2392088Z ++ true 2025-08-14T23:18:58.2436840Z + git_status= 2025-08-14T23:18:58.2437098Z + [[ -n '' ]] 2025-08-14T23:18:58.2438660Z + [[ 2 == 1 ]] 2025-08-14T23:18:58.2474592Z + sccache_epilogue 2025-08-14T23:18:58.2474951Z + echo '::group::Sccache Compilation Log' 2025-08-14T23:18:58.2475584Z ##[group]Sccache Compilation Log 2025-08-14T23:18:58.2475948Z + echo '=================== sccache compilation log ===================' 2025-08-14T23:18:58.2476391Z =================== sccache compilation log =================== 2025-08-14T23:18:58.2477014Z + python /var/lib/jenkins/workspace/.ci/pytorch/print_sccache_log.py /var/lib/jenkins/sccache_error.log 2025-08-14T23:18:58.2611227Z + echo '=========== If your build fails, please take a look at the log above for possible reasons ===========' 2025-08-14T23:18:58.2612001Z =========== If your build fails, please take a look at the log above for possible reasons =========== 2025-08-14T23:18:58.2612495Z + sccache --show-stats 2025-08-14T23:18:58.2637950Z Compile requests 3018 2025-08-14T23:18:58.2638413Z Compile requests executed 46 2025-08-14T23:18:58.2638769Z Cache hits 9 2025-08-14T23:18:58.2639115Z Cache hits (C/C++) 9 2025-08-14T23:18:58.2639435Z Cache misses 37 2025-08-14T23:18:58.2639751Z Cache misses (C/C++) 37 2025-08-14T23:18:58.2640125Z Cache hits rate 19.57 % 2025-08-14T23:18:58.2640443Z Cache hits rate (C/C++) 19.57 % 2025-08-14T23:18:58.2640904Z Cache timeouts 0 2025-08-14T23:18:58.2641246Z Cache read errors 0 2025-08-14T23:18:58.2641618Z Forced recaches 0 2025-08-14T23:18:58.2641936Z Cache write errors 1 2025-08-14T23:18:58.2642301Z Cache errors 0 2025-08-14T23:18:58.2642599Z Compilations 37 2025-08-14T23:18:58.2642944Z Compilation failures 0 2025-08-14T23:18:58.2643300Z Non-cacheable compilations 0 2025-08-14T23:18:58.2643626Z Non-cacheable calls 1 2025-08-14T23:18:58.2644077Z Non-compilation calls 2971 2025-08-14T23:18:58.2644418Z Unsupported compiler calls 0 2025-08-14T23:18:58.2644798Z Average cache write 0.043 s 2025-08-14T23:18:58.2645112Z Average compiler 12.103 s 2025-08-14T23:18:58.2645507Z Average cache read hit 0.038 s 2025-08-14T23:18:58.2645844Z Failed distributed compilations 0 2025-08-14T23:18:58.2646122Z 2025-08-14T23:18:58.2646219Z Non-cacheable reasons: 2025-08-14T23:18:58.2646480Z -E 1 2025-08-14T23:18:58.2646712Z 2025-08-14T23:18:58.2646986Z Cache location s3, name: ossci-compiler-cache-circleci-v2, prefix: / 2025-08-14T23:18:58.2647445Z Version (client) 0.10.0 2025-08-14T23:18:58.2647803Z + sccache --stop-server 2025-08-14T23:18:58.2658897Z Stopping sccache server... 2025-08-14T23:18:58.2661810Z Compile requests 3018 2025-08-14T23:18:58.2662215Z Compile requests executed 46 2025-08-14T23:18:58.2662534Z Cache hits 9 2025-08-14T23:18:58.2662848Z Cache hits (C/C++) 9 2025-08-14T23:18:58.2663165Z Cache misses 37 2025-08-14T23:18:58.2663540Z Cache misses (C/C++) 37 2025-08-14T23:18:58.2664769Z Cache hits rate 19.57 % 2025-08-14T23:18:58.2677215Z Cache hits rate (C/C++) 19.57 % 2025-08-14T23:18:58.2677577Z Cache timeouts 0 2025-08-14T23:18:58.2677917Z Cache read errors 0 2025-08-14T23:18:58.2678233Z Forced recaches 0 2025-08-14T23:18:58.2678551Z Cache write errors 1 2025-08-14T23:18:58.2678867Z Cache errors 0 2025-08-14T23:18:58.2679169Z Compilations 37 2025-08-14T23:18:58.2679495Z Compilation failures 0 2025-08-14T23:18:58.2679832Z Non-cacheable compilations 0 2025-08-14T23:18:58.2680154Z Non-cacheable calls 1 2025-08-14T23:18:58.2680479Z Non-compilation calls 2971 2025-08-14T23:18:58.2680812Z Unsupported compiler calls 0 2025-08-14T23:18:58.2681147Z Average cache write 0.043 s 2025-08-14T23:18:58.2681463Z Average compiler 12.103 s 2025-08-14T23:18:58.2681800Z Average cache read hit 0.038 s 2025-08-14T23:18:58.2682138Z Failed distributed compilations 0 2025-08-14T23:18:58.2682359Z 2025-08-14T23:18:58.2682454Z Non-cacheable reasons: 2025-08-14T23:18:58.2682796Z -E 1 2025-08-14T23:18:58.2683034Z 2025-08-14T23:18:58.2683286Z Cache location s3, name: ossci-compiler-cache-circleci-v2, prefix: / 2025-08-14T23:18:58.2683730Z Version (client) 0.10.0 2025-08-14T23:18:58.2684061Z + echo ::endgroup:: 2025-08-14T23:18:58.2684552Z ##[endgroup] 2025-08-14T23:18:58.2684780Z + cleanup_workspace 2025-08-14T23:18:58.2685284Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2025-08-14T23:18:58.2686055Z sudo may print the following warning message that can be ignored. The chown command will still run. 2025-08-14T23:18:58.2686684Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2025-08-14T23:18:58.2687153Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2025-08-14T23:18:58.2687687Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2025-08-14T23:18:58.2688287Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2025-08-14T23:18:58.2688763Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2025-08-14T23:19:01.7367336Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main 2025-08-14T23:19:01.7367812Z with: 2025-08-14T23:19:01.7368061Z benchmark-results-dir: test/test-reports 2025-08-14T23:19:01.7368385Z dry-run: false 2025-08-14T23:19:01.7368623Z schema-version: v3 2025-08-14T23:19:01.7369174Z github-token: *** 2025-08-14T23:19:01.7369407Z env: 2025-08-14T23:19:01.7369624Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:01.7370081Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:01.7370587Z ##[endgroup] 2025-08-14T23:19:01.7396041Z ##[group]Run set -eux 2025-08-14T23:19:01.7396347Z set -eux 2025-08-14T23:19:01.7396705Z python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0 2025-08-14T23:19:01.7397141Z  2025-08-14T23:19:01.7397366Z DEVICE_NAME="" 2025-08-14T23:19:01.7397630Z DEVICE_TYPE="" 2025-08-14T23:19:01.7397886Z  2025-08-14T23:19:01.7398164Z if command -v nvidia-smi; then 2025-08-14T23:19:01.7398623Z  # NB: I'm using PyTorch here to get the device name, however, it needs to 2025-08-14T23:19:01.7399214Z  # install the correct version of PyTorch manually for now. Any PyTorch 2025-08-14T23:19:01.7399766Z  # version is fine, I just use 2.7.1 to satify PYPIDEP linter 2025-08-14T23:19:01.7400188Z  python3 -mpip install torch==2.7.1 2025-08-14T23:19:01.7400540Z elif command -v rocminfo; then 2025-08-14T23:19:01.7400987Z  # NB: Installing torch on ROCm runner with pip here causes CI to fail 2025-08-14T23:19:01.7401553Z  # with a memoryview is too large error only on MI300 runners. Is pip 2025-08-14T23:19:01.7402105Z  # version on ROCm runner there too old? As a workaround, let's use the 2025-08-14T23:19:01.7402602Z  # GPU device name coming from rocminfo instead 2025-08-14T23:19:01.7402969Z  DEVICE_NAME=rocm 2025-08-14T23:19:01.7403444Z  DEVICE_TYPE=$(rocminfo | grep "Marketing Name" | tail -n1 | awk -F':' '{print $2}' | xargs) 2025-08-14T23:19:01.7403945Z fi 2025-08-14T23:19:01.7404157Z  2025-08-14T23:19:01.7404427Z echo "DEVICE_NAME=$DEVICE_NAME" >> $GITHUB_ENV 2025-08-14T23:19:01.7404830Z echo "DEVICE_TYPE=$DEVICE_TYPE" >> $GITHUB_ENV 2025-08-14T23:19:01.7413997Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:01.7414379Z env: 2025-08-14T23:19:01.7414599Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:01.7415060Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:01.7415569Z ##[endgroup] 2025-08-14T23:19:01.7445230Z + python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0 2025-08-14T23:19:02.0263957Z Defaulting to user installation because normal site-packages is not writeable 2025-08-14T23:19:03.1028465Z Collecting boto3==1.35.33 2025-08-14T23:19:03.1191725Z Downloading boto3-1.35.33-py3-none-any.whl (139 kB) 2025-08-14T23:19:03.4383346Z Collecting psutil==7.0.0 2025-08-14T23:19:03.4420560Z 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) 2025-08-14T23:19:03.4711532Z Collecting pynvml==12.0.0 2025-08-14T23:19:03.4750841Z Downloading pynvml-12.0.0-py3-none-any.whl (26 kB) 2025-08-14T23:19:04.6642210Z Collecting botocore<1.36.0,>=1.35.33 2025-08-14T23:19:04.6684027Z Downloading botocore-1.35.99-py3-none-any.whl (13.3 MB) 2025-08-14T23:19:04.8498474Z Collecting s3transfer<0.11.0,>=0.10.0 2025-08-14T23:19:04.8540749Z Downloading s3transfer-0.10.4-py3-none-any.whl (83 kB) 2025-08-14T23:19:04.8582863Z 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) 2025-08-14T23:19:04.9014243Z Collecting nvidia-ml-py<13.0.0a0,>=12.0.0 2025-08-14T23:19:04.9058951Z Downloading nvidia_ml_py-12.575.51-py3-none-any.whl (47 kB) 2025-08-14T23:19:04.9140316Z 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) 2025-08-14T23:19:04.9151202Z 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) 2025-08-14T23:19:05.1436207Z 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) 2025-08-14T23:19:05.2818195Z Installing collected packages: botocore, s3transfer, nvidia-ml-py, pynvml, psutil, boto3 2025-08-14T23:19:05.7761430Z Attempting uninstall: nvidia-ml-py 2025-08-14T23:19:05.7762622Z Found existing installation: nvidia-ml-py 11.525.84 2025-08-14T23:19:05.7782199Z Uninstalling nvidia-ml-py-11.525.84: 2025-08-14T23:19:05.7996253Z Successfully uninstalled nvidia-ml-py-11.525.84 2025-08-14T23:19:05.8602544Z Attempting uninstall: psutil 2025-08-14T23:19:05.8603422Z Found existing installation: psutil 5.9.8 2025-08-14T23:19:05.8678378Z Uninstalling psutil-5.9.8: 2025-08-14T23:19:05.8684410Z Successfully uninstalled psutil-5.9.8 2025-08-14T23:19:06.0357142Z 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 2025-08-14T23:19:06.1380930Z + DEVICE_NAME= 2025-08-14T23:19:06.1381379Z + DEVICE_TYPE= 2025-08-14T23:19:06.1381799Z + command -v nvidia-smi 2025-08-14T23:19:06.1382269Z + command -v rocminfo 2025-08-14T23:19:06.1382745Z + echo DEVICE_NAME= 2025-08-14T23:19:06.1383100Z + echo DEVICE_TYPE= 2025-08-14T23:19:06.1413016Z ##[group]Run set -eux 2025-08-14T23:19:06.1413296Z set -eux 2025-08-14T23:19:06.1413513Z  2025-08-14T23:19:06.1413756Z if [[ -z "${GITHUB_TOKEN}" ]]; then 2025-08-14T23:19:06.1414108Z  echo "Missing github-token input" 2025-08-14T23:19:06.1414431Z  exit 1 2025-08-14T23:19:06.1414665Z fi 2025-08-14T23:19:06.1421793Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:06.1422163Z env: 2025-08-14T23:19:06.1422381Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:06.1422854Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:06.1423466Z DEVICE_NAME: 2025-08-14T23:19:06.1423687Z DEVICE_TYPE: 2025-08-14T23:19:06.1424138Z GITHUB_TOKEN: *** 2025-08-14T23:19:06.1424378Z ##[endgroup] 2025-08-14T23:19:06.1447658Z + [[ -z *** ]] 2025-08-14T23:19:06.1503963Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main 2025-08-14T23:19:06.1504393Z with: 2025-08-14T23:19:06.1504755Z github-token: *** 2025-08-14T23:19:06.1504989Z env: 2025-08-14T23:19:06.1505192Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:06.1505693Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:06.1506299Z DEVICE_NAME: 2025-08-14T23:19:06.1506510Z DEVICE_TYPE: 2025-08-14T23:19:06.1506749Z ##[endgroup] 2025-08-14T23:19:06.1530270Z ##[group]Run set -eux 2025-08-14T23:19:06.1530545Z set -eux 2025-08-14T23:19:06.1530772Z  2025-08-14T23:19:06.1531240Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-08-14T23:19:06.1537052Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:06.1537431Z env: 2025-08-14T23:19:06.1537679Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:06.1538140Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:06.1538657Z DEVICE_NAME: 2025-08-14T23:19:06.1538883Z DEVICE_TYPE: 2025-08-14T23:19:06.1539268Z GITHUB_TOKEN: *** 2025-08-14T23:19:06.1539489Z ##[endgroup] 2025-08-14T23:19:06.1561221Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 16976255017 i-0c96d079ce61848eb 2025-08-14T23:19:08.3079610Z setting job-id=48127032091 2025-08-14T23:19:08.3080201Z setting job-name=linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge) 2025-08-14T23:19:08.3201507Z ##[group]Run set -eux 2025-08-14T23:19:08.3201790Z set -eux 2025-08-14T23:19:08.3202022Z  2025-08-14T23:19:08.3202523Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \ 2025-08-14T23:19:08.3203034Z  --schema-version "${SCHEMA_VERSION}" \ 2025-08-14T23:19:08.3203385Z  --repo "${REPO}" \ 2025-08-14T23:19:08.3203694Z  --head-branch "${HEAD_BRANCH}" \ 2025-08-14T23:19:08.3204015Z  --head-sha "${HEAD_SHA}" \ 2025-08-14T23:19:08.3204353Z  --workflow-id "${WORKFLOW_RUN_ID}" \ 2025-08-14T23:19:08.3204716Z  --run-attempt "${RUN_ATTEMPT}" \ 2025-08-14T23:19:08.3205044Z  --job-id "${JOB_ID}" \ 2025-08-14T23:19:08.3205345Z  --job-name "${JOB_NAME}" 2025-08-14T23:19:08.3211152Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:08.3211540Z env: 2025-08-14T23:19:08.3211771Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:08.3212244Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:08.3212742Z DEVICE_NAME: 2025-08-14T23:19:08.3212978Z DEVICE_TYPE: 2025-08-14T23:19:08.3213214Z SCHEMA_VERSION: v3 2025-08-14T23:19:08.3213455Z REPO: pytorch/pytorch 2025-08-14T23:19:08.3213726Z HEAD_BRANCH: refs/heads/main 2025-08-14T23:19:08.3214049Z HEAD_SHA: 1fc683cf17c8c673044538d10266c00f92987be2 2025-08-14T23:19:08.3214387Z WORKFLOW_RUN_ID: 16976255017 2025-08-14T23:19:08.3214671Z RUN_ATTEMPT: 1 2025-08-14T23:19:08.3214905Z JOB_ID: 48127032091 2025-08-14T23:19:08.3215312Z JOB_NAME: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge) 2025-08-14T23:19:08.3215794Z ##[endgroup] 2025-08-14T23:19:08.3243781Z + 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 1fc683cf17c8c673044538d10266c00f92987be2 --workflow-id 16976255017 --run-attempt 1 --job-id 48127032091 --job-name 'linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge)' 2025-08-14T23:19:08.3561480Z ##[group]Run set -eux 2025-08-14T23:19:08.3561762Z set -eux 2025-08-14T23:19:08.3561991Z  2025-08-14T23:19:08.3562380Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_runners_info.py" 2025-08-14T23:19:08.3568140Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:08.3568524Z env: 2025-08-14T23:19:08.3568736Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:08.3569211Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:08.3569839Z DEVICE_NAME: 2025-08-14T23:19:08.3570078Z DEVICE_TYPE: 2025-08-14T23:19:08.3570309Z ##[endgroup] 2025-08-14T23:19:08.3593047Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_runners_info.py 2025-08-14T23:19:08.3994941Z INFO:root:Fail to import torch to get the device name 2025-08-14T23:19:08.4101636Z ##[group]Run set -eux 2025-08-14T23:19:08.4101916Z set -eux 2025-08-14T23:19:08.4102144Z  2025-08-14T23:19:08.4102390Z # TODO (huydhn): Implement this part 2025-08-14T23:19:08.4102778Z echo "dependencies={}" >> "${GITHUB_OUTPUT}" 2025-08-14T23:19:08.4108561Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:08.4108947Z env: 2025-08-14T23:19:08.4109177Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:08.4109643Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:08.4110174Z DEVICE_NAME: 2025-08-14T23:19:08.4110408Z DEVICE_TYPE: 2025-08-14T23:19:08.4110802Z ##[endgroup] 2025-08-14T23:19:08.4132869Z + echo 'dependencies={}' 2025-08-14T23:19:08.4170431Z ##[group]Run set -eux 2025-08-14T23:19:08.4170910Z set -eux 2025-08-14T23:19:08.4171326Z  2025-08-14T23:19:08.4171794Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then 2025-08-14T23:19:08.4172739Z  echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping" 2025-08-14T23:19:08.4173613Z  # We don't want the job to fail if the directory doesn't exist 2025-08-14T23:19:08.4174340Z  exit 0 2025-08-14T23:19:08.4174739Z fi 2025-08-14T23:19:08.4175101Z  2025-08-14T23:19:08.4175514Z if [[ "${DRY_RUN}" == "true" ]]; then 2025-08-14T23:19:08.4176397Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-08-14T23:19:08.4177422Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-08-14T23:19:08.4178209Z  --metadata "${BENCHMARK_METADATA}" \ 2025-08-14T23:19:08.4178838Z  --runners "${RUNNER_INFO}" \ 2025-08-14T23:19:08.4179436Z  --dependencies "${DEPENDENCIES}" \ 2025-08-14T23:19:08.4180028Z  --dry-run 2025-08-14T23:19:08.4180482Z else 2025-08-14T23:19:08.4181188Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-08-14T23:19:08.4182262Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-08-14T23:19:08.4183037Z  --metadata "${BENCHMARK_METADATA}" \ 2025-08-14T23:19:08.4183757Z  --runners "${RUNNER_INFO}" \ 2025-08-14T23:19:08.4184382Z  --dependencies "${DEPENDENCIES}" 2025-08-14T23:19:08.4184946Z fi 2025-08-14T23:19:08.4192770Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:08.4193746Z env: 2025-08-14T23:19:08.4194111Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:08.4194901Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:08.4195440Z DEVICE_NAME: 2025-08-14T23:19:08.4195670Z DEVICE_TYPE: 2025-08-14T23:19:08.4195915Z BENCHMARK_RESULTS_DIR: test/test-reports 2025-08-14T23:19:08.4196235Z DRY_RUN: false 2025-08-14T23:19:08.4197549Z BENCHMARK_METADATA: {"timestamp": 1755213548, "schema_version": "v3", "name": "linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "1fc683cf17c8c673044538d10266c00f92987be2", "workflow_id": 16976255017, "run_attempt": 1, "job_id": 48127032091} 2025-08-14T23:19:08.4199270Z RUNNER_INFO: [{"cpu_info": "x86_64", "cpu_count": 8, "avail_mem_in_gb": 15, "extra_info": {"hostname": "ip-10-0-65-99.ec2.internal"}, "name": "", "type": ""}] 2025-08-14T23:19:08.4199891Z DEPENDENCIES: {} 2025-08-14T23:19:08.4200130Z ##[endgroup] 2025-08-14T23:19:08.4221708Z + [[ ! -d test/test-reports ]] 2025-08-14T23:19:08.4222185Z + [[ false == \t\r\u\e ]] 2025-08-14T23:19:08.4225024Z + 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": 1755213548, "schema_version": "v3", "name": "linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "1fc683cf17c8c673044538d10266c00f92987be2", "workflow_id": 16976255017, "run_attempt": 1, "job_id": 48127032091}' --runners '[{"cpu_info": "x86_64", "cpu_count": 8, "avail_mem_in_gb": 15, "extra_info": {"hostname": "ip-10-0-65-99.ec2.internal"}, "name": "", "type": ""}]' --dependencies '{}' 2025-08-14T23:19:08.5706510Z /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': 'dynamo/test_dynamic_shapes'}, {'test_file': 'dynamo/test_repros'}, {'test_file': 'dynamo/test_higher_order_ops'}, {'test_file': 'dynamo/test_logging'}, {'test_file': 'dynamo/test_misc'}, {'test_file': 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'dynamo/cpython/3_13/test_numeric_tower'}, {'test_file': 'dynamo/cpython/3_13/test_baseexception'}, {'test_file': 'dynamo/cpython/3_13/test_operator'}, {'test_file': 'dynamo/cpython/3_13/test_exceptions'}, {'test_file': 'dynamo/cpython/3_13/test_collections'}, {'test_file': 'dynamo/cpython/3_13/test_heapq'}, {'test_file': 'dynamo/cpython/3_13/test_int'}, {'test_file': 'dynamo/cpython/3_13/test_userlist'}, {'test_file': 'dynamo/cpython/3_13/test_generators'}, {'test_file': 'dynamo/cpython/3_13/test_set'}, {'test_file': 'dynamo/cpython/3_13/test_math'}, {'test_file': 'dynamo/cpython/3_13/test_userdict'}, {'test_file': 'dynamo/cpython/3_13/test_iter'}, {'test_file': 'dynamo/cpython/3_13/test_bool'}, {'test_file': 'dynamo/cpython/3_13/test_sort'}, {'test_file': 'dynamo/cpython/3_13/test_cmath'}, {'test_file': 'dynamo/cpython/3_13/test_sys'}, {'test_file': 'dynamo/cpython/3_13/test_list'}, {'test_file': 'dynamo/cpython/3_13/test_tuple'}, {'test_file': 'dynamo/cpython/3_13/test_dict'}, 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'test_namedtuple_return_api'}, {'test_file': 'profiler/test_record_function'}, {'test_file': 'test_multiprocessing'}, {'test_file': 'test_autograd_fallback'}, {'test_file': 'nn/test_lazy_modules'}, {'test_file': 'test_jit_fuser_te'}, {'test_file': 'test_segment_reductions'}, {'test_file': 'test_import_stats'}, {'test_file': 'test_masked'}, {'test_file': 'test_cuda_sanitizer'}, {'test_file': 'test_monitor'}, {'test_file': 'profiler/test_cpp_thread'}, {'test_file': 'test_subclass'}, {'test_file': 'profiler/test_profiler'}, {'test_file': 'functorch/test_vmap_registrations'}, {'test_file': 'nn/test_pruning'}, {'test_file': 'test_itt'}, {'test_file': 'optim/test_lrscheduler'}, {'test_file': 'test_jit_disabled'}, {'test_file': 'torch_np/numpy_tests/core/test_numeric'}, {'test_file': 'test_sympy_utils'}, {'test_file': 'torch_np/test_indexing'}, {'test_file': 'torch_np/numpy_tests/lib/test_function_base'}, {'test_file': 'test_jit'}, {'test_file': 'nn/test_packed_sequence'}, {'test_file': 'cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic'}, {'test_file': 'functorch/test_ops'}, {'test_file': 'test_schema_check'}, {'test_file': 'test_datapipe'}, {'test_file': 'test_autocast'}, {'test_file': 'test_bundled_inputs'}, {'test_file': 'profiler/test_execution_trace'}, {'test_file': 'lazy/test_generator'}, {'test_file': 'torch_np/numpy_tests/core/test_indexing'}, {'test_file': 'test_maskedtensor'}, {'test_file': 'test_tensorboard'}, {'test_file': 'torch_np/numpy_tests/lib/test_type_check'}, {'test_file': 'lazy/test_ts_opinfo'}, {'test_file': 'test_mkldnn_fusion'}, {'test_file': 'benchmark_utils/test_benchmark_utils'}, {'test_file': 'test_linalg'}, {'test_file': 'optim/test_swa_utils'}, {'test_file': 'test_futures'}, {'test_file': 'torch_np/numpy_tests/lib/test_histograms'}, {'test_file': 'optim/test_optim'}, {'test_file': 'nn/test_embedding'}, {'test_file': 'nn/test_dropout'}, {'test_file': 'torch_np/numpy_tests/core/test_scalarmath'}, {'test_file': 'test_functional_optim'}, {'test_file': 'test_vulkan'}, {'test_file': 'functorch/test_vmap'}, {'test_file': 'test_cuda_trace'}, {'test_file': 'nn/test_load_state_dict'}, {'test_file': 'test_shape_ops'}, {'test_file': 'torch_np/numpy_tests/core/test_dtype'}, {'test_file': 'torch_np/numpy_tests/core/test_shape_base'}, {'test_file': 'test_type_info'}, {'test_file': 'test_dataloader'}, {'test_file': 'test_jit_llga_fuser'}, {'test_file': 'torch_np/test_function_base'}, {'test_file': 'test_native_mha'}, {'test_file': 'test_sort_and_select'}, {'test_file': 'torch_np/numpy_tests/lib/test_twodim_base'}, {'test_file': 'torch_np/test_scalars_0D_arrays'}, {'test_file': 'nn/test_module_hooks'}, {'test_file': 'test_numa_binding'}, {'test_file': 'torch_np/numpy_tests/core/test_getlimits'}, {'test_file': 'torch_np/test_ndarray_methods'}, {'test_file': 'lazy/test_debug_util'}, {'test_file': 'test_cuda_nvml_based_avail'}, {'test_file': 'torch_np/numpy_tests/lib/test_shape_base_'}, {'test_file': 'torch_np/numpy_tests/linalg/test_linalg'}, {'test_file': 'test_sparse_csr'}, {'test_file': 'test_view_ops'}, {'test_file': 'test_accelerator'}, {'test_file': 'profiler/test_memory_profiler'}, {'test_file': 'torch_np/numpy_tests/fft/test_pocketfft'}, {'test_file': 'test_function_schema'}, {'test_file': 'torch_np/numpy_tests/lib/test_index_tricks'}, {'test_file': 'test_cpp_extensions_jit'}, {'test_file': 'test_scatter_gather_ops'}, {'test_file': 'test_serialization'}, {'test_file': 'torch_np/numpy_tests/core/test_dlpack'}, {'test_file': 'test_xnnpack_integration'}, {'test_file': 'torch_np/numpy_tests/fft/test_helper'}, {'test_file': 'test_mkldnn'}, {'test_file': 'test_unary_ufuncs'}, {'test_file': 'nn/test_pooling'}, {'test_file': 'torch_np/numpy_tests/core/test_scalar_methods'}, {'test_file': 'nn/test_init'}, {'test_file': 'test_cuda_primary_ctx'}, {'test_file': 'torch_np/numpy_tests/core/test_numerictypes'}, {'test_file': 'test_mobile_optimizer'}, {'test_file': 'torch_np/numpy_tests/lib/test_arraypad'}, {'test_file': 'test_cuda_multigpu'}, {'test_file': 'test_multiprocessing_spawn'}, {'test_file': 'torch_np/numpy_tests/lib/test_arraysetops'}, {'test_file': 'nn/test_convolution'}, {'test_file': 'test_sparse'}, {'test_file': 'test_jit_autocast'}, {'test_file': 'test_dlpack'}, {'test_file': 'profiler/test_profiler_tree'}, {'test_file': 'torch_np/numpy_tests/core/test_scalar_ctors'}, {'test_file': 'profiler/test_torch_tidy'}, {'test_file': 'torch_np/test_reductions'}, {'test_file': 'lazy/test_reuse_ir'}, {'test_file': 'test_functional_autograd_benchmark'}, {'test_file': 'test_prims'}, {'test_file': 'test_spectral_ops'}, {'test_file': 'profiler/test_python_tracer'}, {'test_file': 'distributions/test_distributions'}, {'test_file': 'test_autoload_enable'}, {'test_file': 'test_cpp_extensions_aot_no_ninja'}, {'test_file': 'test_autoload_disable'}, {'test_file': 'test_cpp_extensions_aot_ninja'}], 'excluded': []} from test/test-reports/td_exclusions-ed19be74378cf308a58c.json is not a benchmark record, skipping 2025-08-14T23:19:08.5757221Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-08-14T23:19:08.5936697Z ##[group]Run cat test/**/*_toprint.log || true 2025-08-14T23:19:08.5937109Z cat test/**/*_toprint.log || true 2025-08-14T23:19:08.5942830Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:08.5943204Z env: 2025-08-14T23:19:08.5943531Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:08.5944015Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:08.5944515Z DEVICE_NAME: 2025-08-14T23:19:08.5944746Z DEVICE_TYPE: 2025-08-14T23:19:08.5944978Z ##[endgroup] 2025-08-14T23:19:08.6026088Z cat: 'test/**/*_toprint.log': No such file or directory 2025-08-14T23:19:08.6061933Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2025-08-14T23:19:08.6062294Z kill "$MONITOR_SCRIPT_PID" 2025-08-14T23:19:08.6067702Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:08.6068077Z env: 2025-08-14T23:19:08.6068293Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:08.6068750Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:08.6069258Z DEVICE_NAME: 2025-08-14T23:19:08.6069489Z DEVICE_TYPE: 2025-08-14T23:19:08.6069726Z MONITOR_SCRIPT_PID: 41583 2025-08-14T23:19:08.6069977Z ##[endgroup] 2025-08-14T23:19:08.6212391Z Prepare all required actions 2025-08-14T23:19:08.6212826Z Getting action download info 2025-08-14T23:19:08.7678304Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-08-14T23:19:08.9962163Z Download action repository 'actions/upload-artifact@v4' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02) 2025-08-14T23:19:09.4197317Z ##[group]Run ./.github/actions/upload-test-artifacts 2025-08-14T23:19:09.4197685Z with: 2025-08-14T23:19:09.4198024Z file-suffix: test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091 2025-08-14T23:19:09.4198442Z s3-bucket: gha-artifacts 2025-08-14T23:19:09.4198732Z env: 2025-08-14T23:19:09.4198949Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:09.4199412Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:09.4199932Z DEVICE_NAME: 2025-08-14T23:19:09.4200173Z DEVICE_TYPE: 2025-08-14T23:19:09.4200396Z ##[endgroup] 2025-08-14T23:19:09.4227606Z ##[group]Run # Remove any previous test jsons if they exist 2025-08-14T23:19:09.4228079Z # Remove any previous test jsons if they exist 2025-08-14T23:19:09.4228451Z rm -f test-jsons-*.zip 2025-08-14T23:19:09.4228884Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json' 2025-08-14T23:19:09.4234624Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:09.4234993Z env: 2025-08-14T23:19:09.4235219Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:09.4235696Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:09.4236197Z DEVICE_NAME: 2025-08-14T23:19:09.4236428Z DEVICE_TYPE: 2025-08-14T23:19:09.4236778Z FILE_SUFFIX: test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091 2025-08-14T23:19:09.4237192Z ##[endgroup] 2025-08-14T23:19:09.4378740Z adding: test/test-reports/td_exclusions-ed19be74378cf308a58c.json (deflated 82%) 2025-08-14T23:19:09.4403134Z ##[group]Run # Remove any previous test reports if they exist 2025-08-14T23:19:09.4403624Z # Remove any previous test reports if they exist 2025-08-14T23:19:09.4404022Z rm -f test-reports-*.zip 2025-08-14T23:19:09.4404504Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv' 2025-08-14T23:19:09.4409972Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:09.4410350Z env: 2025-08-14T23:19:09.4410571Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:09.4411034Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:09.4411541Z DEVICE_NAME: 2025-08-14T23:19:09.4411773Z DEVICE_TYPE: 2025-08-14T23:19:09.4412107Z FILE_SUFFIX: test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091 2025-08-14T23:19:09.4412516Z ##[endgroup] 2025-08-14T23:19:09.4529466Z adding: test/test-reports/python-pytest/test_tensor_creation_ops/test_tensor_creation_ops-98676bcf7c269f4e.xml (deflated 92%) 2025-08-14T23:19:09.4547437Z adding: test/test-reports/python-pytest/test_nn/test_nn-8111998a9ecbb05c.xml (deflated 94%) 2025-08-14T23:19:09.4565125Z adding: test/test-reports/python-pytest/test_nn/test_nn-ff189a74d7a957e3.xml (deflated 94%) 2025-08-14T23:19:09.4589525Z adding: test/test-reports/python-pytest/test_overrides/test_overrides-a27fbea3743c8974.xml (deflated 96%) 2025-08-14T23:19:09.4608317Z adding: test/test-reports/python-pytest/test_cpp_api_parity/test_cpp_api_parity-0ac24ae58eda80eb.xml (deflated 98%) 2025-08-14T23:19:09.4609436Z adding: 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debug-*.zip 2025-08-14T23:19:09.9714773Z if [ -d 'test/debug' ]; then 2025-08-14T23:19:09.9715120Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2025-08-14T23:19:09.9715469Z fi 2025-08-14T23:19:09.9721469Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:09.9721846Z env: 2025-08-14T23:19:09.9722072Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:09.9722550Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:09.9723046Z DEVICE_NAME: 2025-08-14T23:19:09.9723273Z DEVICE_TYPE: 2025-08-14T23:19:09.9723615Z FILE_SUFFIX: test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091 2025-08-14T23:19:09.9724028Z ##[endgroup] 2025-08-14T23:19:09.9821264Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-08-14T23:19:09.9821679Z with: 2025-08-14T23:19:09.9821908Z s3-bucket: gha-artifacts 2025-08-14T23:19:09.9822247Z s3-prefix: pytorch/pytorch/16976255017/1/artifact 2025-08-14T23:19:09.9822601Z retention-days: 14 2025-08-14T23:19:09.9822847Z if-no-files-found: warn 2025-08-14T23:19:09.9823118Z path: test-jsons-*.zip 2025-08-14T23:19:09.9823474Z name: artifact 2025-08-14T23:19:09.9823704Z region: us-east-1 2025-08-14T23:19:09.9823937Z env: 2025-08-14T23:19:09.9824157Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:09.9824618Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:09.9825128Z DEVICE_NAME: 2025-08-14T23:19:09.9825355Z DEVICE_TYPE: 2025-08-14T23:19:09.9825566Z ##[endgroup] 2025-08-14T23:19:10.3350310Z NOTE: s3-prefix specified, ignoring name parameter 2025-08-14T23:19:10.3350985Z With the provided path, there will be 1 file uploaded 2025-08-14T23:19:10.3351448Z Uploading to s3 prefix: pytorch/pytorch/16976255017/1/artifact 2025-08-14T23:19:10.3391152Z Starting upload of test-jsons-test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091.zip 2025-08-14T23:19:10.4650265Z Finished upload of test-jsons-test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091.zip 2025-08-14T23:19:10.4847284Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-08-14T23:19:10.4847629Z with: 2025-08-14T23:19:10.4847845Z s3-bucket: gha-artifacts 2025-08-14T23:19:10.4848266Z s3-prefix: pytorch/pytorch/16976255017/1/artifact 2025-08-14T23:19:10.4848621Z retention-days: 14 2025-08-14T23:19:10.4848885Z if-no-files-found: error 2025-08-14T23:19:10.4849157Z path: test-reports-*.zip 2025-08-14T23:19:10.4849431Z name: artifact 2025-08-14T23:19:10.4849669Z region: us-east-1 2025-08-14T23:19:10.4849889Z env: 2025-08-14T23:19:10.4850099Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:10.4850568Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:10.4851060Z DEVICE_NAME: 2025-08-14T23:19:10.4851290Z DEVICE_TYPE: 2025-08-14T23:19:10.4851520Z ##[endgroup] 2025-08-14T23:19:10.8075774Z NOTE: s3-prefix specified, ignoring name parameter 2025-08-14T23:19:10.8076247Z With the provided path, there will be 1 file uploaded 2025-08-14T23:19:10.8076688Z Uploading to s3 prefix: pytorch/pytorch/16976255017/1/artifact 2025-08-14T23:19:10.8116478Z Starting upload of test-reports-test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091.zip 2025-08-14T23:19:11.0000520Z Finished upload of test-reports-test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091.zip 2025-08-14T23:19:11.0204619Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-08-14T23:19:11.0205234Z with: 2025-08-14T23:19:11.0205626Z s3-bucket: gha-artifacts 2025-08-14T23:19:11.0206223Z s3-prefix: pytorch/pytorch/16976255017/1/artifact 2025-08-14T23:19:11.0206877Z retention-days: 14 2025-08-14T23:19:11.0207302Z if-no-files-found: ignore 2025-08-14T23:19:11.0207791Z path: logs-*.zip 2025-08-14T23:19:11.0208188Z name: artifact 2025-08-14T23:19:11.0208583Z region: us-east-1 2025-08-14T23:19:11.0209016Z env: 2025-08-14T23:19:11.0209414Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:11.0210297Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:11.0211254Z DEVICE_NAME: 2025-08-14T23:19:11.0211678Z DEVICE_TYPE: 2025-08-14T23:19:11.0212093Z ##[endgroup] 2025-08-14T23:19:11.3446031Z NOTE: s3-prefix specified, ignoring name parameter 2025-08-14T23:19:11.3446771Z With the provided path, there will be 1 file uploaded 2025-08-14T23:19:11.3447235Z Uploading to s3 prefix: pytorch/pytorch/16976255017/1/artifact 2025-08-14T23:19:11.3486429Z Starting upload of logs-test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091.zip 2025-08-14T23:19:11.5416158Z Finished upload of logs-test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091.zip 2025-08-14T23:19:11.5604183Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-08-14T23:19:11.5604537Z with: 2025-08-14T23:19:11.5604765Z s3-bucket: gha-artifacts 2025-08-14T23:19:11.5605167Z s3-prefix: pytorch/pytorch/16976255017/1/artifact 2025-08-14T23:19:11.5605518Z retention-days: 14 2025-08-14T23:19:11.5605795Z if-no-files-found: ignore 2025-08-14T23:19:11.5606059Z path: debug-*.zip 2025-08-14T23:19:11.5606295Z name: artifact 2025-08-14T23:19:11.5606532Z region: us-east-1 2025-08-14T23:19:11.5606762Z env: 2025-08-14T23:19:11.5606964Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:11.5607443Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:11.5607956Z DEVICE_NAME: 2025-08-14T23:19:11.5608173Z DEVICE_TYPE: 2025-08-14T23:19:11.5608397Z ##[endgroup] 2025-08-14T23:19:11.8793288Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2025-08-14T23:19:11.8973575Z ##[group]Run # shellcheck disable=SC2156 2025-08-14T23:19:11.8973957Z # shellcheck disable=SC2156 2025-08-14T23:19:11.8974556Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2025-08-14T23:19:11.8981574Z shell: /usr/bin/bash -e {0} 2025-08-14T23:19:11.8981846Z env: 2025-08-14T23:19:11.8982076Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:11.8982552Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:11.8983068Z DEVICE_NAME: 2025-08-14T23:19:11.8983288Z DEVICE_TYPE: 2025-08-14T23:19:11.8983734Z ##[endgroup] 2025-08-14T23:19:12.1857438Z Prepare all required actions 2025-08-14T23:19:12.1857834Z Getting action download info 2025-08-14T23:19:12.2797608Z ##[group]Run ./.github/actions/upload-utilization-stats 2025-08-14T23:19:12.2797979Z with: 2025-08-14T23:19:12.2798202Z job_id: 48127032091 2025-08-14T23:19:12.2798633Z job_name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge) 2025-08-14T23:19:12.2799110Z workflow_name: pull 2025-08-14T23:19:12.2799366Z workflow_run_id: 16976255017 2025-08-14T23:19:12.2799648Z workflow_attempt: 1 2025-08-14T23:19:12.2799884Z env: 2025-08-14T23:19:12.2800098Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:12.2800570Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:12.2801075Z DEVICE_NAME: 2025-08-14T23:19:12.2801292Z DEVICE_TYPE: 2025-08-14T23:19:12.2801517Z ##[endgroup] 2025-08-14T23:19:12.2820271Z ##[group]Run echo "workflow_id: 16976255017" 2025-08-14T23:19:12.2820693Z echo "workflow_id: 16976255017" 2025-08-14T23:19:12.2821026Z echo "workflow_attempt: 1" 2025-08-14T23:19:12.2821332Z echo "workflow_Name: pull" 2025-08-14T23:19:12.2821638Z echo "job_id: 48127032091" 2025-08-14T23:19:12.2822165Z echo "job_name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge)" 2025-08-14T23:19:12.2822708Z echo "artifact_prefix: " 2025-08-14T23:19:12.2823019Z python3 --version 2025-08-14T23:19:12.2829314Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:12.2829713Z env: 2025-08-14T23:19:12.2829921Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:12.2830397Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:12.2830904Z DEVICE_NAME: 2025-08-14T23:19:12.2831119Z DEVICE_TYPE: 2025-08-14T23:19:12.2831344Z ##[endgroup] 2025-08-14T23:19:12.2853669Z workflow_id: 16976255017 2025-08-14T23:19:12.2854022Z workflow_attempt: 1 2025-08-14T23:19:12.2854289Z workflow_Name: pull 2025-08-14T23:19:12.2854511Z job_id: 48127032091 2025-08-14T23:19:12.2854927Z job_name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge) 2025-08-14T23:19:12.2855410Z artifact_prefix: 2025-08-14T23:19:12.2865616Z Python 3.9.23 2025-08-14T23:19:12.2926161Z ##[group]Run nick-fields/retry@v3.0.0 2025-08-14T23:19:12.2926673Z with: 2025-08-14T23:19:12.2927017Z shell: bash 2025-08-14T23:19:12.2927401Z timeout_minutes: 5 2025-08-14T23:19:12.2927807Z max_attempts: 5 2025-08-14T23:19:12.2928195Z retry_wait_seconds: 30 2025-08-14T23:19:12.2929341Z 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 2025-08-14T23:19:12.2930441Z polling_interval_seconds: 1 2025-08-14T23:19:12.2930944Z warning_on_retry: true 2025-08-14T23:19:12.2931405Z continue_on_error: false 2025-08-14T23:19:12.2931846Z env: 2025-08-14T23:19:12.2932218Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:12.2933085Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:12.2934034Z DEVICE_NAME: 2025-08-14T23:19:12.2934428Z DEVICE_TYPE: 2025-08-14T23:19:12.2934817Z ##[endgroup] 2025-08-14T23:19:12.6193447Z Defaulting to user installation because normal site-packages is not writeable 2025-08-14T23:19:12.6916126Z Collecting python-dateutil==2.8.2 2025-08-14T23:19:12.7084894Z Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB) 2025-08-14T23:19:13.6964199Z Collecting boto3==1.35.42 2025-08-14T23:19:13.7000901Z Downloading boto3-1.35.42-py3-none-any.whl (139 kB) 2025-08-14T23:19:14.2106277Z Collecting pandas==2.1.3 2025-08-14T23:19:14.2144570Z Downloading pandas-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB) 2025-08-14T23:19:14.3444647Z Requirement already satisfied: dataclasses_json==0.6.7 in /home/ec2-user/.local/lib/python3.9/site-packages (0.6.7) 2025-08-14T23:19:14.3461797Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil==2.8.2) (1.15.0) 2025-08-14T23:19:14.3507152Z 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) 2025-08-14T23:19:14.3512901Z 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) 2025-08-14T23:19:14.3516943Z 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) 2025-08-14T23:19:15.2437951Z Collecting numpy<2,>=1.22.4 2025-08-14T23:19:15.2475513Z Downloading numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB) 2025-08-14T23:19:15.4693808Z Collecting tzdata>=2022.1 2025-08-14T23:19:15.4731469Z Downloading tzdata-2025.2-py2.py3-none-any.whl (347 kB) 2025-08-14T23:19:15.4874518Z Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3.9/site-packages (from pandas==2.1.3) (2022.7.1) 2025-08-14T23:19:15.4906583Z 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) 2025-08-14T23:19:15.4910711Z 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) 2025-08-14T23:19:15.4995675Z 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) 2025-08-14T23:19:15.5089932Z 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) 2025-08-14T23:19:15.5191616Z 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) (4.14.1) 2025-08-14T23:19:15.5195795Z 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) 2025-08-14T23:19:15.6835794Z Installing collected packages: python-dateutil, tzdata, numpy, pandas, boto3 2025-08-14T23:19:20.6404836Z Attempting uninstall: boto3 2025-08-14T23:19:20.6405450Z Found existing installation: boto3 1.35.33 2025-08-14T23:19:20.6506395Z Uninstalling boto3-1.35.33: 2025-08-14T23:19:20.6518701Z Successfully uninstalled boto3-1.35.33 2025-08-14T23:19:20.7079249Z Successfully installed boto3-1.35.42 numpy-1.26.4 pandas-2.1.3 python-dateutil-2.8.2 tzdata-2025.2 2025-08-14T23:19:21.3738207Z Command completed after 1 attempt(s). 2025-08-14T23:19:21.3803489Z ##[group]Run python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-08-14T23:19:21.3804195Z python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-08-14T23:19:21.3804708Z  --workflow-run-id "16976255017" \ 2025-08-14T23:19:21.3805081Z  --workflow-name "pull" \ 2025-08-14T23:19:21.3805403Z  --workflow-run-attempt "1" \ 2025-08-14T23:19:21.3805723Z  --job-id "48127032091" \ 2025-08-14T23:19:21.3806233Z  --job-name "linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge)" \ 2025-08-14T23:19:21.3806752Z  --local-path "" \ 2025-08-14T23:19:21.3807048Z  --artifact-prefix "" 2025-08-14T23:19:21.3812756Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:21.3813144Z env: 2025-08-14T23:19:21.3813349Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:21.3813829Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:21.3814337Z DEVICE_NAME: 2025-08-14T23:19:21.3814552Z DEVICE_TYPE: 2025-08-14T23:19:21.3814785Z ##[endgroup] 2025-08-14T23:19:23.6438244Z repo: pytorch/pytorch 2025-08-14T23:19:23.6438695Z Search for test log in s3 bucket: ossci-utilization 2025-08-14T23:19:23.6439643Z Downloading logs-test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091.zip 2025-08-14T23:19:23.6440340Z extracting usage_log.txt from zip file logs-test-dynamo_wrapped-2-3-lf.linux.2xlarge_48127032091.zip 2025-08-14T23:19:23.6440930Z Converted Log Model: UtilizationMetadata: 2025-08-14T23:19:23.6442298Z UtilizationMetadata(level='metadata', workflow_id='16976255017', job_id='48127032091', workflow_name='pull', job_name='linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge)', usage_collect_interval=1.0, data_model_version=1.5, start_at=1755205608, gpu_count=0, cpu_count=8, gpu_type=None, error=None) 2025-08-14T23:19:23.6443738Z [Db Segments] detected pytest cmd: 12, generated segments: 12 2025-08-14T23:19:23.6444131Z [db model] Peek db timeseries 2025-08-14T23:19:23.6444405Z :{ 2025-08-14T23:19:23.6444613Z "created_at": 1755213563, 2025-08-14T23:19:23.6444879Z "type": "utilization", 2025-08-14T23:19:23.6445133Z "tags": [ 2025-08-14T23:19:23.6445349Z "record" 2025-08-14T23:19:23.6445563Z ], 2025-08-14T23:19:23.6445778Z "time_stamp": 1755205608, 2025-08-14T23:19:23.6446061Z "repo": "pytorch/pytorch", 2025-08-14T23:19:23.6446328Z "workflow_id": 16976255017, 2025-08-14T23:19:23.6446600Z "run_attempt": 1, 2025-08-14T23:19:23.6446843Z "job_id": 48127032091, 2025-08-14T23:19:23.6447095Z "workflow_name": "pull", 2025-08-14T23:19:23.6447561Z "job_name": "linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 2, 3, lf.linux.2xlarge)", 2025-08-14T23:19:23.6448047Z "json_data": "{}" 2025-08-14T23:19:23.6448281Z } 2025-08-14T23:19:23.6448768Z Writing 1 documents to S3 ossci-utilization/util_metadata/v_1.5/pytorch/pytorch/16976255017/1/48127032091/metadata 2025-08-14T23:19:23.6449697Z Done! Finish writing document to S3 ossci-utilization/util_metadata/v_1.5/pytorch/pytorch/16976255017/1/48127032091/metadata 2025-08-14T23:19:23.6450669Z Writing 1576 documents to S3 ossci-utilization/util_timeseries/v_1.5/pytorch/pytorch/16976255017/1/48127032091/time_series 2025-08-14T23:19:23.6451658Z Done! Finish writing document to S3 ossci-utilization/util_timeseries/v_1.5/pytorch/pytorch/16976255017/1/48127032091/time_series 2025-08-14T23:19:23.7554402Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2025-08-14T23:19:23.7554847Z with: 2025-08-14T23:19:23.7555059Z env: 2025-08-14T23:19:23.7555281Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:23.7555743Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:23.7556246Z DEVICE_NAME: 2025-08-14T23:19:23.7556476Z DEVICE_TYPE: 2025-08-14T23:19:23.7556778Z ##[endgroup] 2025-08-14T23:19:23.7582903Z ##[group]Run set -eou pipefail 2025-08-14T23:19:23.7583243Z set -eou pipefail 2025-08-14T23:19:23.7583619Z  2025-08-14T23:19:23.7583997Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2025-08-14T23:19:23.7584461Z for _ in $(seq 1440); do 2025-08-14T23:19:23.7584802Z  # Break if no ssh session exists anymore 2025-08-14T23:19:23.7585170Z  if [ "$(who)" = "" ]; then 2025-08-14T23:19:23.7585455Z  break 2025-08-14T23:19:23.7585735Z  fi 2025-08-14T23:19:23.7585948Z  echo "." 2025-08-14T23:19:23.7586190Z  sleep 5 2025-08-14T23:19:23.7586425Z done 2025-08-14T23:19:23.7591968Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:23.7592350Z env: 2025-08-14T23:19:23.7592568Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:23.7593273Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:23.7593780Z DEVICE_NAME: 2025-08-14T23:19:23.7594008Z DEVICE_TYPE: 2025-08-14T23:19:23.7594233Z ##[endgroup] 2025-08-14T23:19:23.7616683Z Holding runner for 2 hours until all ssh sessions have logged out 2025-08-14T23:19:23.7695928Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-08-14T23:19:23.7696583Z # ignore expansion of "docker ps -q" since it could be empty 2025-08-14T23:19:23.7697098Z # shellcheck disable=SC2046 2025-08-14T23:19:23.7697457Z docker stop $(docker ps -q) || true 2025-08-14T23:19:23.7697794Z # Prune all of the docker images 2025-08-14T23:19:23.7698126Z docker system prune -af 2025-08-14T23:19:23.7703941Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:23.7704306Z env: 2025-08-14T23:19:23.7704529Z GIT_DEFAULT_BRANCH: main 2025-08-14T23:19:23.7704998Z DOCKER_CONTAINER_ID: 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:23.7705498Z DEVICE_NAME: 2025-08-14T23:19:23.7705725Z DEVICE_TYPE: 2025-08-14T23:19:23.7705948Z ##[endgroup] 2025-08-14T23:19:34.7047319Z 3c4d0c0501a3 2025-08-14T23:19:35.2942884Z Deleted Containers: 2025-08-14T23:19:35.2943505Z 3c4d0c0501a3fcf384cc8b366f34ee1aff63f559ba50b49b6e15facb5e7db859 2025-08-14T23:19:35.2943878Z 2025-08-14T23:19:39.0205325Z Deleted Images: 2025-08-14T23:19:39.0206428Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-bfa89110622ba7202628e9faac705f183070defe 2025-08-14T23:19:39.0208357Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image@sha256:e5a191418e9f11a59a218b040bad54b9c3113ac41271ba5a6874e1c6d47db7c7 2025-08-14T23:19:39.0209277Z deleted: sha256:d23aa400d15c13cf4cd8d85388ce0304ef46d5163b945cf305903ce37fa5c8ba 2025-08-14T23:19:39.0209935Z deleted: sha256:6a9f2117a37a88c716c4ba00fc7d9eea6f842884b250b2cc8f3da411dd6b5a18 2025-08-14T23:19:39.0210593Z deleted: sha256:928a77ff77b53b8b159d2ba363b25a33b16ccd93cb7215cf62bb7c60a2952e3c 2025-08-14T23:19:39.0211372Z deleted: sha256:5dee72bfa24414d9e210a7838e3b34431872f20c366f00af7aa7fadde516421b 2025-08-14T23:19:39.0212387Z deleted: sha256:1a15d9fcdc8987a68a9549fc2819474e6ddea57ba42e24a01d0be1a6cbda7060 2025-08-14T23:19:39.0213407Z deleted: sha256:e2d8116b3ffb55e75c7f523650cb4c816e927f2076e7c19f7b2ef54e4f857e0c 2025-08-14T23:19:39.0214356Z deleted: sha256:b9c9ba2b294f2187caac77f5e0291821e746648f48bf2d0c1577f7eca51a8d62 2025-08-14T23:19:39.0215297Z deleted: 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2025-08-14T23:19:39.1790724Z git version 2.47.1 2025-08-14T23:19:39.1824666Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/b85ff70c-495d-4a3c-9e68-bcfc62dd35f5/.gitconfig' 2025-08-14T23:19:39.1833377Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/b85ff70c-495d-4a3c-9e68-bcfc62dd35f5' before making global git config changes 2025-08-14T23:19:39.1834484Z Adding repository directory to the temporary git global config as a safe directory 2025-08-14T23:19:39.1837886Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-08-14T23:19:39.1873556Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-08-14T23:19:39.1908442Z [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' || :" 2025-08-14T23:19:39.2203688Z Entering 'android/libs/fbjni' 2025-08-14T23:19:39.2256129Z Entering 'third_party/FP16' 2025-08-14T23:19:39.2306507Z Entering 'third_party/FXdiv' 2025-08-14T23:19:39.2356625Z Entering 'third_party/NNPACK' 2025-08-14T23:19:39.2407298Z Entering 'third_party/NVTX' 2025-08-14T23:19:39.2458945Z Entering 'third_party/VulkanMemoryAllocator' 2025-08-14T23:19:39.2510530Z Entering 'third_party/XNNPACK' 2025-08-14T23:19:39.2575181Z Entering 'third_party/aiter' 2025-08-14T23:19:39.2626779Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-08-14T23:19:39.2685404Z Entering 'third_party/benchmark' 2025-08-14T23:19:39.2736563Z Entering 'third_party/composable_kernel' 2025-08-14T23:19:39.2796511Z Entering 'third_party/cpp-httplib' 2025-08-14T23:19:39.2846674Z Entering 'third_party/cpuinfo' 2025-08-14T23:19:39.2897240Z Entering 'third_party/cudnn_frontend' 2025-08-14T23:19:39.2948016Z Entering 'third_party/cutlass' 2025-08-14T23:19:39.3006977Z Entering 'third_party/fbgemm' 2025-08-14T23:19:39.3058939Z Entering 'third_party/fbgemm/external/asmjit' 2025-08-14T23:19:39.3107555Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-08-14T23:19:39.3164107Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-08-14T23:19:39.3213625Z Entering 'third_party/fbgemm/external/cutlass' 2025-08-14T23:19:39.3270717Z Entering 'third_party/fbgemm/external/googletest' 2025-08-14T23:19:39.3320345Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-08-14T23:19:39.3368821Z Entering 'third_party/fbgemm/external/json' 2025-08-14T23:19:39.3420983Z Entering 'third_party/flash-attention' 2025-08-14T23:19:39.3472132Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-08-14T23:19:39.3528308Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-08-14T23:19:39.3585736Z Entering 'third_party/flatbuffers' 2025-08-14T23:19:39.3639337Z Entering 'third_party/fmt' 2025-08-14T23:19:39.3689848Z Entering 'third_party/gemmlowp/gemmlowp' 2025-08-14T23:19:39.3740483Z Entering 'third_party/gloo' 2025-08-14T23:19:39.3791190Z Entering 'third_party/googletest' 2025-08-14T23:19:39.3842524Z Entering 'third_party/ideep' 2025-08-14T23:19:39.3891283Z Entering 'third_party/ideep/mkl-dnn' 2025-08-14T23:19:39.3949167Z Entering 'third_party/ittapi' 2025-08-14T23:19:39.3999678Z Entering 'third_party/kineto' 2025-08-14T23:19:39.4049757Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-08-14T23:19:39.4098111Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-08-14T23:19:39.4148755Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-08-14T23:19:39.4199210Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-08-14T23:19:39.4249102Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-08-14T23:19:39.4297401Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-08-14T23:19:39.4349072Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-08-14T23:19:39.4397359Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-08-14T23:19:39.4446883Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-08-14T23:19:39.4497921Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-08-14T23:19:39.4547881Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-08-14T23:19:39.4596132Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-08-14T23:19:39.4646216Z Entering 'third_party/kleidiai' 2025-08-14T23:19:39.4698653Z Entering 'third_party/mimalloc' 2025-08-14T23:19:39.4749527Z Entering 'third_party/nlohmann' 2025-08-14T23:19:39.4801320Z Entering 'third_party/onnx' 2025-08-14T23:19:39.4868363Z Entering 'third_party/onnx/third_party/pybind11' 2025-08-14T23:19:39.4921354Z Entering 'third_party/opentelemetry-cpp' 2025-08-14T23:19:39.4972829Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-08-14T23:19:39.5022789Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-08-14T23:19:39.5071676Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-08-14T23:19:39.5120294Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-08-14T23:19:39.5171412Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-08-14T23:19:39.5220646Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-08-14T23:19:39.5269349Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-08-14T23:19:39.5317387Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-08-14T23:19:39.5367660Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-08-14T23:19:39.5419610Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-08-14T23:19:39.5487321Z Entering 'third_party/pocketfft' 2025-08-14T23:19:39.5538133Z Entering 'third_party/protobuf' 2025-08-14T23:19:39.5590726Z Entering 'third_party/protobuf/third_party/benchmark' 2025-08-14T23:19:39.5640094Z Entering 'third_party/protobuf/third_party/googletest' 2025-08-14T23:19:39.5690186Z Entering 'third_party/psimd' 2025-08-14T23:19:39.5740292Z Entering 'third_party/pthreadpool' 2025-08-14T23:19:39.5789294Z Entering 'third_party/pybind11' 2025-08-14T23:19:39.5839675Z Entering 'third_party/python-peachpy' 2025-08-14T23:19:39.5889822Z Entering 'third_party/sleef' 2025-08-14T23:19:39.5940949Z Entering 'third_party/tensorpipe' 2025-08-14T23:19:39.5990426Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-08-14T23:19:39.6039689Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-08-14T23:19:39.6088141Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-08-14T23:19:39.6137798Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-08-14T23:19:39.6185424Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-08-14T23:19:39.6252789Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-08-14T23:19:39.6270940Z http.https://github.com/.extraheader 2025-08-14T23:19:39.6279546Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2025-08-14T23:19:39.6306190Z [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' || :" 2025-08-14T23:19:39.6577239Z Entering 'android/libs/fbjni' 2025-08-14T23:19:39.6612161Z http.https://github.com/.extraheader 2025-08-14T23:19:39.6642964Z Entering 'third_party/FP16' 2025-08-14T23:19:39.6676740Z http.https://github.com/.extraheader 2025-08-14T23:19:39.6708407Z Entering 'third_party/FXdiv' 2025-08-14T23:19:39.6742716Z http.https://github.com/.extraheader 2025-08-14T23:19:39.6772238Z Entering 'third_party/NNPACK' 2025-08-14T23:19:39.6806994Z http.https://github.com/.extraheader 2025-08-14T23:19:39.6837374Z Entering 'third_party/NVTX' 2025-08-14T23:19:39.6871556Z http.https://github.com/.extraheader 2025-08-14T23:19:39.6902517Z Entering 'third_party/VulkanMemoryAllocator' 2025-08-14T23:19:39.6937062Z http.https://github.com/.extraheader 2025-08-14T23:19:39.6967077Z Entering 'third_party/XNNPACK' 2025-08-14T23:19:39.7001695Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7046183Z Entering 'third_party/aiter' 2025-08-14T23:19:39.7080632Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7113161Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-08-14T23:19:39.7146318Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7186484Z Entering 'third_party/benchmark' 2025-08-14T23:19:39.7220817Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7251082Z Entering 'third_party/composable_kernel' 2025-08-14T23:19:39.7284948Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7326311Z Entering 'third_party/cpp-httplib' 2025-08-14T23:19:39.7360974Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7391444Z Entering 'third_party/cpuinfo' 2025-08-14T23:19:39.7425400Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7455900Z Entering 'third_party/cudnn_frontend' 2025-08-14T23:19:39.7490248Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7521532Z Entering 'third_party/cutlass' 2025-08-14T23:19:39.7557430Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7596286Z Entering 'third_party/fbgemm' 2025-08-14T23:19:39.7630418Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7663651Z Entering 'third_party/fbgemm/external/asmjit' 2025-08-14T23:19:39.7697257Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7727493Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-08-14T23:19:39.7760027Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7797001Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-08-14T23:19:39.7830333Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7860051Z Entering 'third_party/fbgemm/external/cutlass' 2025-08-14T23:19:39.7892496Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7930138Z Entering 'third_party/fbgemm/external/googletest' 2025-08-14T23:19:39.7962845Z http.https://github.com/.extraheader 2025-08-14T23:19:39.7993269Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-08-14T23:19:39.8026019Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8055321Z Entering 'third_party/fbgemm/external/json' 2025-08-14T23:19:39.8088431Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8122141Z Entering 'third_party/flash-attention' 2025-08-14T23:19:39.8156415Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8188254Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-08-14T23:19:39.8221074Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8257770Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-08-14T23:19:39.8290755Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8330535Z Entering 'third_party/flatbuffers' 2025-08-14T23:19:39.8364355Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8398730Z Entering 'third_party/fmt' 2025-08-14T23:19:39.8433107Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8463307Z Entering 'third_party/gemmlowp/gemmlowp' 2025-08-14T23:19:39.8498289Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8528200Z Entering 'third_party/gloo' 2025-08-14T23:19:39.8562026Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8591854Z Entering 'third_party/googletest' 2025-08-14T23:19:39.8625971Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8656161Z Entering 'third_party/ideep' 2025-08-14T23:19:39.8689875Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8719280Z Entering 'third_party/ideep/mkl-dnn' 2025-08-14T23:19:39.8752267Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8788752Z Entering 'third_party/ittapi' 2025-08-14T23:19:39.8823557Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8854066Z Entering 'third_party/kineto' 2025-08-14T23:19:39.8887939Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8919768Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-08-14T23:19:39.8952514Z http.https://github.com/.extraheader 2025-08-14T23:19:39.8982579Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-08-14T23:19:39.9016101Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9048087Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-08-14T23:19:39.9081082Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9112657Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-08-14T23:19:39.9145736Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9176714Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-08-14T23:19:39.9210091Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9239880Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-08-14T23:19:39.9273579Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9305790Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-08-14T23:19:39.9338624Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9370002Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-08-14T23:19:39.9404392Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9434997Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-08-14T23:19:39.9468080Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9499913Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-08-14T23:19:39.9532627Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9564394Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-08-14T23:19:39.9597824Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9627668Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-08-14T23:19:39.9660328Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9692452Z Entering 'third_party/kleidiai' 2025-08-14T23:19:39.9727435Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9758777Z Entering 'third_party/mimalloc' 2025-08-14T23:19:39.9792646Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9823738Z Entering 'third_party/nlohmann' 2025-08-14T23:19:39.9857207Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9888518Z Entering 'third_party/onnx' 2025-08-14T23:19:39.9923525Z http.https://github.com/.extraheader 2025-08-14T23:19:39.9971194Z Entering 'third_party/onnx/third_party/pybind11' 2025-08-14T23:19:40.0004599Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0036548Z Entering 'third_party/opentelemetry-cpp' 2025-08-14T23:19:40.0070034Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0102596Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-08-14T23:19:40.0135449Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0165587Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-08-14T23:19:40.0199352Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0230001Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-08-14T23:19:40.0262744Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0292048Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-08-14T23:19:40.0325112Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0355955Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-08-14T23:19:40.0388588Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0418411Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-08-14T23:19:40.0451093Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0480872Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-08-14T23:19:40.0513729Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0542815Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-08-14T23:19:40.0576140Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0608285Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-08-14T23:19:40.0641166Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0672916Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-08-14T23:19:40.0706894Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0757409Z Entering 'third_party/pocketfft' 2025-08-14T23:19:40.0791514Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0821594Z Entering 'third_party/protobuf' 2025-08-14T23:19:40.0855183Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0888690Z Entering 'third_party/protobuf/third_party/benchmark' 2025-08-14T23:19:40.0921950Z http.https://github.com/.extraheader 2025-08-14T23:19:40.0951970Z Entering 'third_party/protobuf/third_party/googletest' 2025-08-14T23:19:40.0985132Z http.https://github.com/.extraheader 2025-08-14T23:19:40.1017957Z Entering 'third_party/psimd' 2025-08-14T23:19:40.1052054Z http.https://github.com/.extraheader 2025-08-14T23:19:40.1082306Z Entering 'third_party/pthreadpool' 2025-08-14T23:19:40.1116651Z http.https://github.com/.extraheader 2025-08-14T23:19:40.1147205Z Entering 'third_party/pybind11' 2025-08-14T23:19:40.1180515Z http.https://github.com/.extraheader 2025-08-14T23:19:40.1211184Z Entering 'third_party/python-peachpy' 2025-08-14T23:19:40.1244712Z http.https://github.com/.extraheader 2025-08-14T23:19:40.1275109Z Entering 'third_party/sleef' 2025-08-14T23:19:40.1309268Z http.https://github.com/.extraheader 2025-08-14T23:19:40.1339337Z Entering 'third_party/tensorpipe' 2025-08-14T23:19:40.1372897Z http.https://github.com/.extraheader 2025-08-14T23:19:40.1405666Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-08-14T23:19:40.1437992Z http.https://github.com/.extraheader 2025-08-14T23:19:40.1467626Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-08-14T23:19:40.1501100Z http.https://github.com/.extraheader 2025-08-14T23:19:40.1530118Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-08-14T23:19:40.1563239Z http.https://github.com/.extraheader 2025-08-14T23:19:40.1592997Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-08-14T23:19:40.1626361Z http.https://github.com/.extraheader 2025-08-14T23:19:40.1656109Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-08-14T23:19:40.1688889Z http.https://github.com/.extraheader 2025-08-14T23:19:40.1802791Z A job completed hook has been configured by the self-hosted runner administrator 2025-08-14T23:19:40.1826777Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2025-08-14T23:19:40.1831894Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-14T23:19:40.1832285Z ##[endgroup] 2025-08-14T23:19:48.4148958Z Cleaning up orphan processes