2024-06-26T04:56:16.7589793Z Current runner version: '2.317.0' 2024-06-26T04:56:16.7596332Z Runner name: 'i-0e3c0504b423c9909' 2024-06-26T04:56:16.7597131Z Runner group name: 'Default' 2024-06-26T04:56:16.7598077Z Machine name: 'ip-10-0-6-47' 2024-06-26T04:56:16.7602565Z ##[group]GITHUB_TOKEN Permissions 2024-06-26T04:56:16.7604558Z Actions: read 2024-06-26T04:56:16.7605174Z Attestations: read 2024-06-26T04:56:16.7605684Z Checks: read 2024-06-26T04:56:16.7606109Z Contents: read 2024-06-26T04:56:16.7606618Z Deployments: read 2024-06-26T04:56:16.7607119Z Discussions: read 2024-06-26T04:56:16.7607573Z Issues: read 2024-06-26T04:56:16.7608082Z Metadata: read 2024-06-26T04:56:16.7608565Z Packages: read 2024-06-26T04:56:16.7609001Z Pages: read 2024-06-26T04:56:16.7609504Z PullRequests: read 2024-06-26T04:56:16.7610031Z RepositoryProjects: read 2024-06-26T04:56:16.7610555Z SecurityEvents: read 2024-06-26T04:56:16.7611179Z Statuses: read 2024-06-26T04:56:16.7611785Z ##[endgroup] 2024-06-26T04:56:16.7615248Z Secret source: Actions 2024-06-26T04:56:16.7616026Z Prepare workflow directory 2024-06-26T04:56:16.8950533Z Prepare all required actions 2024-06-26T04:56:16.9118683Z Getting action download info 2024-06-26T04:56:17.0816330Z Download action repository 'pytorch/test-infra@main' (SHA:43a2ce341cc31288e9a38b65ce600a7f43021bd5) 2024-06-26T04:56:17.4504276Z Download action repository 'pytorch/pytorch@main' (SHA:6181e65cd81725efc6bc5d64ef3be607b0aa3ca1) 2024-06-26T04:56:20.2986509Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0) 2024-06-26T04:56:20.4235496Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2024-06-26T04:56:20.7168200Z Getting action download info 2024-06-26T04:56:20.8179979Z Download action repository 'malfet/checkout@silent-checkout' (SHA:e07af140b3ccefc05679e3755b9db68f4ee4589c) 2024-06-26T04:56:21.0153033Z Getting action download info 2024-06-26T04:56:21.1236276Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2024-06-26T04:56:21.2805645Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/pull/129470/merge (4b51b1a62a63a1add1b3a0f7882f5c7dc66b8f8d) 2024-06-26T04:56:21.2808008Z ##[group] Inputs 2024-06-26T04:56:21.2808594Z build-environment: linux-focal-py3.12-clang10-experimental-split-build 2024-06-26T04:56:21.2811749Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}]} 2024-06-26T04:56:21.2815235Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:56:21.2816246Z sync-tag: 2024-06-26T04:56:21.2817025Z timeout-minutes: 600 2024-06-26T04:56:21.2817334Z use-gha: 2024-06-26T04:56:21.2817603Z dashboard-tag: 2024-06-26T04:56:21.2817904Z s3-bucket: gha-artifacts 2024-06-26T04:56:21.2818231Z aws-role-to-assume: 2024-06-26T04:56:21.2818545Z ##[endgroup] 2024-06-26T04:56:21.2819532Z Complete job name: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, linux.2xlarge, unstable) 2024-06-26T04:56:21.3356627Z A job started hook has been configured by the self-hosted runner administrator 2024-06-26T04:56:21.3500178Z ##[group]Run '/home/ec2-user/runner-scripts/cleanup.sh' 2024-06-26T04:56:21.3511234Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:56:21.3511755Z ##[endgroup] 2024-06-26T04:56:22.0077707Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2024-06-26T04:56:22.0078289Z with: 2024-06-26T04:56:22.0079015Z github-secret: *** 2024-06-26T04:56:22.0079986Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2024-06-26T04:56:22.0081143Z activate-with-label: false 2024-06-26T04:56:22.0081515Z label: with-ssh 2024-06-26T04:56:22.0081823Z remove-existing-keys: true 2024-06-26T04:56:22.0082181Z fail-silently: true 2024-06-26T04:56:22.0082464Z env: 2024-06-26T04:56:22.0082719Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:56:22.0083042Z ##[endgroup] 2024-06-26T04:56:22.0998547Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2024-06-26T04:56:22.3391041Z Grabbing public ssh keys from https://github.com/leslie-fang-intel.keys 2024-06-26T04:56:22.4074173Z No SSH keys found for user leslie-fang-intel 2024-06-26T04:56:22.4075248Z Grabbing public ssh keys from https://github.com/leslie-fang-intel.keys 2024-06-26T04:56:22.4755371Z No SSH keys found for user leslie-fang-intel 2024-06-26T04:56:22.4866320Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2024-06-26T04:56:22.4866856Z with: 2024-06-26T04:56:22.4867122Z submodules: recursive 2024-06-26T04:56:22.4867438Z fetch-depth: 0 2024-06-26T04:56:22.4867692Z env: 2024-06-26T04:56:22.4867938Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:56:22.4868261Z ##[endgroup] 2024-06-26T04:56:22.5057837Z ##[group]Run retry () { 2024-06-26T04:56:22.5058206Z retry () { 2024-06-26T04:56:22.5058699Z  $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) 2024-06-26T04:56:22.5059260Z } 2024-06-26T04:56:22.5059540Z echo "${GITHUB_WORKSPACE}" 2024-06-26T04:56:22.5059946Z if [ -z "${NO_SUDO}" ]; then 2024-06-26T04:56:22.5060415Z  retry sudo rm -rf "${GITHUB_WORKSPACE}" 2024-06-26T04:56:22.5060859Z else 2024-06-26T04:56:22.5061183Z  retry rm -rf "${GITHUB_WORKSPACE}" 2024-06-26T04:56:22.5061598Z fi 2024-06-26T04:56:22.5061920Z mkdir "${GITHUB_WORKSPACE}" 2024-06-26T04:56:22.5069598Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:56:22.5070095Z env: 2024-06-26T04:56:22.5070353Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:56:22.5070665Z NO_SUDO: 2024-06-26T04:56:22.5070923Z ##[endgroup] 2024-06-26T04:56:22.5094201Z /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-06-26T04:56:24.2058833Z ##[group]Run malfet/checkout@silent-checkout 2024-06-26T04:56:24.2059315Z with: 2024-06-26T04:56:24.2059668Z ref: b8c4c54d347aa776934c60784e35936878ef18dc 2024-06-26T04:56:24.2060134Z fetch-depth: 0 2024-06-26T04:56:24.2060479Z submodules: recursive 2024-06-26T04:56:24.2060852Z quiet-checkout: true 2024-06-26T04:56:24.2061217Z repository: pytorch/pytorch 2024-06-26T04:56:24.2061754Z token: *** 2024-06-26T04:56:24.2062085Z ssh-strict: true 2024-06-26T04:56:24.2062431Z persist-credentials: true 2024-06-26T04:56:24.2062822Z clean: true 2024-06-26T04:56:24.2063189Z sparse-checkout-cone-mode: true 2024-06-26T04:56:24.2063601Z lfs: false 2024-06-26T04:56:24.2063933Z set-safe-directory: true 2024-06-26T04:56:24.2064300Z env: 2024-06-26T04:56:24.2064588Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:56:24.2064961Z ##[endgroup] 2024-06-26T04:56:24.3003379Z Syncing repository: pytorch/pytorch 2024-06-26T04:56:24.3005404Z ##[group]Getting Git version info 2024-06-26T04:56:24.3006217Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-06-26T04:56:24.3007337Z [command]/usr/bin/git version 2024-06-26T04:56:24.3007744Z git version 2.40.1 2024-06-26T04:56:24.3009358Z ##[endgroup] 2024-06-26T04:56:24.3021055Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/40ef5162-765e-430c-a066-3a4fea49402b' before making global git config changes 2024-06-26T04:56:24.3022481Z Adding repository directory to the temporary git global config as a safe directory 2024-06-26T04:56:24.3024571Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-06-26T04:56:24.3050557Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-06-26T04:56:24.3054638Z ##[group]Initializing the repository 2024-06-26T04:56:24.3057600Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-06-26T04:56:24.3146033Z hint: Using 'master' as the name for the initial branch. This default branch name 2024-06-26T04:56:24.3147059Z hint: is subject to change. To configure the initial branch name to use in all 2024-06-26T04:56:24.3147934Z hint: of your new repositories, which will suppress this warning, call: 2024-06-26T04:56:24.3148548Z hint: 2024-06-26T04:56:24.3149148Z hint: git config --global init.defaultBranch 2024-06-26T04:56:24.3149648Z hint: 2024-06-26T04:56:24.3150207Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2024-06-26T04:56:24.3151134Z hint: 'development'. The just-created branch can be renamed via this command: 2024-06-26T04:56:24.3151916Z hint: 2024-06-26T04:56:24.3152269Z hint: git branch -m 2024-06-26T04:56:24.3154843Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2024-06-26T04:56:24.3161205Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2024-06-26T04:56:24.3187174Z ##[endgroup] 2024-06-26T04:56:24.3187822Z ##[group]Disabling automatic garbage collection 2024-06-26T04:56:24.3190500Z [command]/usr/bin/git config --local gc.auto 0 2024-06-26T04:56:24.3215905Z ##[endgroup] 2024-06-26T04:56:24.3216474Z ##[group]Setting up auth 2024-06-26T04:56:24.3221537Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-06-26T04:56:24.3248826Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2024-06-26T04:56:24.3471497Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-06-26T04:56:24.3497387Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2024-06-26T04:56:24.3723856Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-06-26T04:56:24.3759629Z ##[endgroup] 2024-06-26T04:56:24.3760239Z ##[group]Fetching the repository 2024-06-26T04:56:24.3765690Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --progress --no-recurse-submodules --quiet origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2024-06-26T04:56:26.4934812Z remote: Enumerating objects: 987558 2024-06-26T04:56:26.4935438Z remote: Enumerating objects: 989963, done. 2024-06-26T04:56:26.4936829Z remote: 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2024-06-26T04:56:26.5004162Z remote: Counting objects: 99% (2381/2405) 2024-06-26T04:56:26.5004721Z remote: Counting objects: 100% (2405/2405) 2024-06-26T04:56:26.5005304Z remote: Counting objects: 100% (2405/2405), done. 2024-06-26T04:56:26.5099436Z remote: Compressing objects: 0% (1/962) 2024-06-26T04:56:26.5591401Z remote: Compressing objects: 1% (10/962) 2024-06-26T04:56:26.5895594Z remote: Compressing objects: 2% (20/962) 2024-06-26T04:56:26.6209924Z remote: Compressing objects: 3% (29/962) 2024-06-26T04:56:26.6444198Z remote: Compressing objects: 4% (39/962) 2024-06-26T04:56:26.6583199Z remote: Compressing objects: 5% (49/962) 2024-06-26T04:56:26.6762111Z remote: Compressing objects: 6% (58/962) 2024-06-26T04:56:26.6895344Z remote: Compressing objects: 7% (68/962) 2024-06-26T04:56:26.7227820Z remote: Compressing objects: 8% (77/962) 2024-06-26T04:56:26.8222562Z remote: Compressing objects: 9% (87/962) 2024-06-26T04:56:26.8859804Z remote: Compressing objects: 10% (97/962) 2024-06-26T04:56:26.9502641Z remote: Compressing objects: 11% (106/962) 2024-06-26T04:56:26.9695708Z remote: Compressing objects: 12% (116/962) 2024-06-26T04:56:26.9786683Z remote: Compressing objects: 13% (126/962) 2024-06-26T04:56:26.9833295Z remote: Compressing objects: 14% (135/962) 2024-06-26T04:56:26.9959252Z remote: Compressing objects: 15% (145/962) 2024-06-26T04:56:26.9980909Z remote: Compressing objects: 16% (154/962) 2024-06-26T04:56:26.9987209Z remote: Compressing objects: 17% (164/962) 2024-06-26T04:56:27.0009309Z remote: Compressing objects: 18% (174/962) 2024-06-26T04:56:27.0026815Z remote: Compressing objects: 19% (183/962) 2024-06-26T04:56:27.0049574Z remote: Compressing objects: 20% (193/962) 2024-06-26T04:56:27.0063830Z remote: Compressing objects: 21% (203/962) 2024-06-26T04:56:27.0083669Z remote: Compressing objects: 22% (212/962) 2024-06-26T04:56:27.0102740Z remote: Compressing objects: 23% (222/962) 2024-06-26T04:56:27.0110907Z remote: Compressing objects: 24% (231/962) 2024-06-26T04:56:27.0120174Z remote: Compressing objects: 25% (241/962) 2024-06-26T04:56:27.0128558Z remote: Compressing objects: 26% (251/962) 2024-06-26T04:56:27.0132066Z remote: Compressing objects: 27% (260/962) 2024-06-26T04:56:27.0136670Z remote: Compressing objects: 28% (270/962) 2024-06-26T04:56:27.0140950Z remote: Compressing objects: 29% (279/962) 2024-06-26T04:56:27.0151696Z remote: Compressing objects: 30% (289/962) 2024-06-26T04:56:27.0161369Z remote: Compressing objects: 31% (299/962) 2024-06-26T04:56:27.0162442Z remote: Compressing objects: 32% (308/962) 2024-06-26T04:56:27.0168709Z remote: Compressing objects: 33% (318/962) 2024-06-26T04:56:27.0176771Z remote: Compressing objects: 34% (328/962) 2024-06-26T04:56:27.0179841Z remote: Compressing objects: 35% (337/962) 2024-06-26T04:56:27.0188861Z remote: Compressing objects: 36% (347/962) 2024-06-26T04:56:27.0209013Z remote: Compressing objects: 37% (356/962) 2024-06-26T04:56:27.0222154Z remote: Compressing objects: 38% (366/962) 2024-06-26T04:56:27.0232157Z remote: Compressing objects: 39% (376/962) 2024-06-26T04:56:27.0243430Z remote: Compressing objects: 40% (385/962) 2024-06-26T04:56:27.0263740Z remote: Compressing objects: 41% (395/962) 2024-06-26T04:56:27.0283023Z remote: Compressing objects: 42% (405/962) 2024-06-26T04:56:27.0301177Z remote: Compressing objects: 43% (414/962) 2024-06-26T04:56:27.0312189Z remote: Compressing objects: 44% (424/962) 2024-06-26T04:56:27.0313874Z remote: Compressing objects: 45% (433/962) 2024-06-26T04:56:27.0330871Z remote: Compressing objects: 46% (443/962) 2024-06-26T04:56:27.0348274Z remote: Compressing objects: 47% (453/962) 2024-06-26T04:56:27.0366670Z remote: Compressing objects: 48% (462/962) 2024-06-26T04:56:27.0375441Z remote: Compressing objects: 49% (472/962) 2024-06-26T04:56:27.0380912Z remote: Compressing objects: 50% (481/962) 2024-06-26T04:56:27.0386603Z remote: Compressing objects: 51% (491/962) 2024-06-26T04:56:27.0389086Z remote: Compressing objects: 52% (501/962) 2024-06-26T04:56:27.0404345Z remote: Compressing objects: 53% (510/962) 2024-06-26T04:56:27.0418260Z remote: Compressing objects: 54% (520/962) 2024-06-26T04:56:27.0425289Z remote: Compressing objects: 55% (530/962) 2024-06-26T04:56:27.0440131Z remote: Compressing objects: 56% (539/962) 2024-06-26T04:56:27.0466725Z remote: Compressing objects: 57% (549/962) 2024-06-26T04:56:27.0483537Z remote: Compressing objects: 58% (558/962) 2024-06-26T04:56:27.0500685Z remote: Compressing objects: 59% (568/962) 2024-06-26T04:56:27.0511885Z remote: Compressing objects: 60% (578/962) 2024-06-26T04:56:27.0525256Z remote: Compressing objects: 61% (587/962) 2024-06-26T04:56:27.0530819Z remote: Compressing objects: 62% (597/962) 2024-06-26T04:56:27.0535753Z remote: Compressing objects: 63% (607/962) 2024-06-26T04:56:27.0546317Z remote: Compressing objects: 64% (616/962) 2024-06-26T04:56:27.0557232Z remote: Compressing objects: 65% (626/962) 2024-06-26T04:56:27.0565469Z remote: Compressing objects: 66% (635/962) 2024-06-26T04:56:27.0574305Z remote: Compressing objects: 67% (645/962) 2024-06-26T04:56:27.0583776Z remote: Compressing objects: 68% (655/962) 2024-06-26T04:56:27.0594382Z remote: Compressing objects: 69% (664/962) 2024-06-26T04:56:27.0605682Z remote: Compressing objects: 70% (674/962) 2024-06-26T04:56:27.0613634Z remote: Compressing objects: 71% (684/962) 2024-06-26T04:56:27.0621628Z remote: Compressing objects: 72% (693/962) 2024-06-26T04:56:27.0629591Z remote: Compressing objects: 73% (703/962) 2024-06-26T04:56:27.0641949Z remote: Compressing objects: 74% (712/962) 2024-06-26T04:56:27.0649231Z remote: Compressing objects: 75% (722/962) 2024-06-26T04:56:27.0654016Z remote: Compressing objects: 76% (732/962) 2024-06-26T04:56:27.0658415Z remote: Compressing objects: 77% (741/962) 2024-06-26T04:56:27.0661482Z remote: Compressing objects: 78% (751/962) 2024-06-26T04:56:27.0662840Z remote: Compressing objects: 79% (760/962) 2024-06-26T04:56:27.0664441Z remote: Compressing objects: 80% (770/962) 2024-06-26T04:56:27.0665027Z remote: Compressing objects: 81% (780/962) 2024-06-26T04:56:27.0665852Z remote: Compressing objects: 82% (789/962) 2024-06-26T04:56:27.0668756Z remote: Compressing objects: 83% (799/962) 2024-06-26T04:56:27.0672760Z remote: Compressing objects: 84% (809/962) 2024-06-26T04:56:27.0678739Z remote: Compressing objects: 85% (818/962) 2024-06-26T04:56:27.0689154Z remote: Compressing objects: 86% (828/962) 2024-06-26T04:56:27.0695379Z remote: Compressing objects: 87% (837/962) 2024-06-26T04:56:27.0696067Z remote: Compressing objects: 88% (847/962) 2024-06-26T04:56:27.0696649Z remote: Compressing objects: 89% (857/962) 2024-06-26T04:56:27.0697404Z remote: Compressing objects: 90% (866/962) 2024-06-26T04:56:27.0698057Z remote: Compressing objects: 91% (876/962) 2024-06-26T04:56:27.0699032Z remote: Compressing objects: 92% (886/962) 2024-06-26T04:56:27.0699609Z remote: Compressing objects: 93% (895/962) 2024-06-26T04:56:27.0700169Z remote: Compressing objects: 94% (905/962) 2024-06-26T04:56:27.0700741Z remote: Compressing objects: 95% (914/962) 2024-06-26T04:56:27.0701390Z remote: Compressing objects: 96% (924/962) 2024-06-26T04:56:27.0701952Z remote: Compressing objects: 97% (934/962) 2024-06-26T04:56:27.0702523Z remote: Compressing objects: 98% (943/962) 2024-06-26T04:56:27.0703095Z remote: Compressing objects: 99% (953/962) 2024-06-26T04:56:27.0703820Z remote: Compressing objects: 100% (962/962) 2024-06-26T04:56:27.0704438Z remote: Compressing objects: 100% (962/962), done. 2024-06-26T04:56:45.7686635Z remote: Total 989963 (delta 1981), reused 1707 (delta 1442), pack-reused 987558 2024-06-26T04:57:11.4962155Z [command]/usr/bin/git rev-parse --verify --quiet b8c4c54d347aa776934c60784e35936878ef18dc^{object} 2024-06-26T04:57:11.4985139Z b8c4c54d347aa776934c60784e35936878ef18dc 2024-06-26T04:57:11.4988741Z ##[endgroup] 2024-06-26T04:57:11.4989341Z ##[group]Determining the checkout info 2024-06-26T04:57:11.4991298Z ##[endgroup] 2024-06-26T04:57:11.4991896Z ##[group]Checking out the ref 2024-06-26T04:57:11.4993612Z [command]/usr/bin/git checkout --quiet --force b8c4c54d347aa776934c60784e35936878ef18dc 2024-06-26T04:57:12.8104440Z ##[endgroup] 2024-06-26T04:57:12.8105229Z ##[group]Setting up auth for fetching submodules 2024-06-26T04:57:12.8108918Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-06-26T04:57:12.8151624Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2024-06-26T04:57:12.8178594Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2024-06-26T04:57:12.8205510Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2024-06-26T04:57:12.8228735Z ##[endgroup] 2024-06-26T04:57:12.8229234Z ##[group]Fetching submodules 2024-06-26T04:57:12.8232757Z [command]/usr/bin/git submodule sync --recursive 2024-06-26T04:57:12.8475764Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2024-06-26T04:57:12.8705066Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2024-06-26T04:57:12.8706564Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2024-06-26T04:57:12.8708026Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2024-06-26T04:57:12.8709866Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2024-06-26T04:57:12.8712643Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2024-06-26T04:57:12.8714370Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2024-06-26T04:57:12.8716948Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2024-06-26T04:57:12.8719405Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2024-06-26T04:57:12.8721980Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2024-06-26T04:57:12.8724748Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2024-06-26T04:57:12.8727343Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2024-06-26T04:57:12.8730241Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen' 2024-06-26T04:57:12.8733131Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2024-06-26T04:57:12.8736256Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2024-06-26T04:57:12.8739477Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2024-06-26T04:57:12.8742784Z Submodule 'third_party/foxi' (https://github.com/houseroad/foxi.git) registered for path 'third_party/foxi' 2024-06-26T04:57:12.8746328Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2024-06-26T04:57:12.8749656Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo' 2024-06-26T04:57:12.8753263Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2024-06-26T04:57:12.8757856Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2024-06-26T04:57:12.8761948Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2024-06-26T04:57:12.8765728Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2024-06-26T04:57:12.8769920Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2024-06-26T04:57:12.8773905Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl' 2024-06-26T04:57:12.8778174Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2024-06-26T04:57:12.8782329Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2024-06-26T04:57:12.8786997Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2024-06-26T04:57:12.8791169Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2024-06-26T04:57:12.8796250Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2024-06-26T04:57:12.8800539Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2024-06-26T04:57:12.8805484Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2024-06-26T04:57:12.8810244Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2024-06-26T04:57:12.8815094Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2024-06-26T04:57:12.8819944Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2024-06-26T04:57:12.8825162Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2024-06-26T04:57:12.8847144Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2024-06-26T04:57:13.1635675Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2024-06-26T04:57:13.3091150Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2024-06-26T04:57:13.4928788Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2024-06-26T04:57:13.7039892Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2024-06-26T04:57:15.8855248Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2024-06-26T04:57:27.8744990Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2024-06-26T04:57:28.2517098Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2024-06-26T04:57:28.9187548Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2024-06-26T04:57:29.4860297Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2024-06-26T04:57:30.5528936Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2024-06-26T04:57:32.2379770Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'... 2024-06-26T04:57:37.5226128Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2024-06-26T04:57:38.8824595Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2024-06-26T04:57:40.6639780Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2024-06-26T04:57:41.9846428Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/foxi'... 2024-06-26T04:57:42.1405971Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2024-06-26T04:57:42.5103549Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2024-06-26T04:57:42.7887241Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2024-06-26T04:57:43.8591368Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2024-06-26T04:57:44.1773288Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2024-06-26T04:57:44.4330791Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2024-06-26T04:57:45.9580119Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2024-06-26T04:57:46.6936607Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nccl/nccl'... 2024-06-26T04:57:47.3315520Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2024-06-26T04:57:53.5713602Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2024-06-26T04:57:56.2389805Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2024-06-26T04:58:03.3923752Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2024-06-26T04:58:03.5910555Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2024-06-26T04:58:12.5321522Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2024-06-26T04:58:12.6865120Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2024-06-26T04:58:12.8571785Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2024-06-26T04:58:13.8265750Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2024-06-26T04:58:14.2872610Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2024-06-26T04:58:14.9085109Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2024-06-26T04:58:15.3389641Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2024-06-26T04:58:15.3485925Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2024-06-26T04:58:15.3557590Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2024-06-26T04:58:15.3757838Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2024-06-26T04:58:15.4094014Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191' 2024-06-26T04:58:16.2597537Z Submodule path 'third_party/XNNPACK': checked out 'fcbf55af6cf28a4627bcd1f703ab7ad843f0f3a2' 2024-06-26T04:58:16.2795462Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415' 2024-06-26T04:58:16.3145289Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006' 2024-06-26T04:58:16.4036910Z Submodule path 'third_party/cpuinfo': checked out '3c8b1533ac03dd6531ab6e7b9245d488f13a82a5' 2024-06-26T04:58:16.4331168Z Submodule path 'third_party/cudnn_frontend': checked out 'aa3abd4bc689d6412979c7f55f9cd132848c9c6a' 2024-06-26T04:58:16.8950189Z Submodule path 'third_party/cutlass': checked out 'bbe579a9e3beb6ea6626d9227ec32d0dae119a49' 2024-06-26T04:58:17.1253701Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c' 2024-06-26T04:58:17.1942945Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e' 2024-06-26T04:58:17.1958009Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit' 2024-06-26T04:58:17.1959748Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo' 2024-06-26T04:58:17.1961846Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass' 2024-06-26T04:58:17.1964054Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest' 2024-06-26T04:58:17.1966487Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch' 2024-06-26T04:58:17.1988184Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'... 2024-06-26T04:58:18.2428202Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'... 2024-06-26T04:58:18.8165126Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'... 2024-06-26T04:58:20.5146751Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'... 2024-06-26T04:58:21.5506129Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'... 2024-06-26T04:58:21.8589493Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81' 2024-06-26T04:58:21.9468276Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3' 2024-06-26T04:58:22.3145749Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6' 2024-06-26T04:58:22.3724076Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796' 2024-06-26T04:58:22.3829784Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1' 2024-06-26T04:58:22.4821157Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b' 2024-06-26T04:58:22.5126766Z Submodule path 'third_party/fmt': checked out 'e69e5f977d458f2650bb346dadf2ad30c5320281' 2024-06-26T04:58:22.5203532Z Submodule path 'third_party/foxi': checked out 'c278588e34e535f0bb8f00df3880d26928038cad' 2024-06-26T04:58:22.5555790Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2024-06-26T04:58:22.5765415Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509' 2024-06-26T04:58:22.6170412Z Submodule path 'third_party/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929' 2024-06-26T04:58:22.6278746Z Submodule path 'third_party/ideep': checked out '55ca0191687aaf19aca5cdb7881c791e3bea442b' 2024-06-26T04:58:22.6292908Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2024-06-26T04:58:22.6312004Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2024-06-26T04:58:35.7025106Z Submodule path 'third_party/ideep/mkl-dnn': checked out '1137e04ec0b5251ca2b4400a4fd3c667ce843d67' 2024-06-26T04:58:35.7178948Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42' 2024-06-26T04:58:35.8025685Z Submodule path 'third_party/kineto': checked out '8681ff11e1fa54da39023076c5c43eddd87b7a8a' 2024-06-26T04:58:35.8038663Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2024-06-26T04:58:35.8040487Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2024-06-26T04:58:35.8042531Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2024-06-26T04:58:35.8064764Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2024-06-26T04:58:36.3060831Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2024-06-26T04:58:37.6118983Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2024-06-26T04:58:38.7975829Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2024-06-26T04:58:38.7988382Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-06-26T04:58:38.7990588Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-06-26T04:58:38.7992984Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-06-26T04:58:38.7995811Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-06-26T04:58:38.7998264Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-06-26T04:58:38.8001151Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-06-26T04:58:38.8004027Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-06-26T04:58:38.8006752Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-06-26T04:58:38.8028538Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2024-06-26T04:58:39.7689521Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2024-06-26T04:58:40.1217225Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2024-06-26T04:58:41.5055882Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2024-06-26T04:58:41.7757983Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2024-06-26T04:58:42.2408945Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2024-06-26T04:58:43.3009670Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2024-06-26T04:58:50.6776190Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2024-06-26T04:58:51.0748984Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2024-06-26T04:58:51.0900900Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2024-06-26T04:58:51.1224675Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2024-06-26T04:58:51.1330748Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2024-06-26T04:58:51.1342382Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-06-26T04:58:51.1362909Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2024-06-26T04:58:51.4388997Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2024-06-26T04:58:51.4557335Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2024-06-26T04:58:51.4913798Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2024-06-26T04:58:51.5865797Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2024-06-26T04:58:51.6003047Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2024-06-26T04:58:51.6309893Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out 'a33701196adfad74917046096bf5a2aa0ab0bb50' 2024-06-26T04:58:51.6829124Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2024-06-26T04:58:51.7145977Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78' 2024-06-26T04:58:51.7351441Z Submodule path 'third_party/nccl/nccl': checked out '48bb7fec7953112ff37499a272317f6663f8f600' 2024-06-26T04:58:51.8295237Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806' 2024-06-26T04:58:52.1215365Z Submodule path 'third_party/onnx': checked out '990217f043af7222348ca8f0301e17fa7b841781' 2024-06-26T04:58:52.1244945Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/onnx/third_party/benchmark' 2024-06-26T04:58:52.1246859Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2024-06-26T04:58:52.1268287Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/benchmark'... 2024-06-26T04:58:52.5272516Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2024-06-26T04:58:53.5647625Z Submodule path 'third_party/onnx/third_party/benchmark': checked out '2dd015dfef425c866d9a43f2c67d8b52d709acb6' 2024-06-26T04:58:53.5942714Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '5b0a6fc2017fcc176545afe3e09c9f9885283242' 2024-06-26T04:58:53.6507335Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2024-06-26T04:58:53.6522896Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-06-26T04:58:53.6526228Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2024-06-26T04:58:53.6529686Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-06-26T04:58:53.6533150Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-06-26T04:58:53.6537320Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-06-26T04:58:53.6541752Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-06-26T04:58:53.6545566Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-06-26T04:58:53.6547380Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-06-26T04:58:53.6566418Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2024-06-26T04:58:54.0673991Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'... 2024-06-26T04:58:55.1377090Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'... 2024-06-26T04:58:55.4451723Z Cloning into 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config remote.origin.url 2024-06-26T04:59:20.2786386Z Entering 'third_party/ideep' 2024-06-26T04:59:20.2823745Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config remote.origin.url 2024-06-26T04:59:20.2836419Z Entering 'third_party/ideep/mkl-dnn' 2024-06-26T04:59:20.2873355Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config remote.origin.url 2024-06-26T04:59:20.2893551Z Entering 'third_party/ittapi' 2024-06-26T04:59:20.2931224Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config remote.origin.url 2024-06-26T04:59:20.2944335Z Entering 'third_party/kineto' 2024-06-26T04:59:20.2983323Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config remote.origin.url 2024-06-26T04:59:20.2996906Z Entering 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/fmt/config remote.origin.url 2024-06-26T04:59:20.3201724Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-06-26T04:59:20.3238654Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/config remote.origin.url 2024-06-26T04:59:20.3251844Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-06-26T04:59:20.3289927Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/modules/doc/config remote.origin.url 2024-06-26T04:59:20.3304759Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-06-26T04:59:20.3341728Z 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2024-06-26T04:59:20.3702489Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nccl/nccl/config remote.origin.url 2024-06-26T04:59:20.3717375Z Entering 'third_party/nlohmann' 2024-06-26T04:59:20.3753695Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config remote.origin.url 2024-06-26T04:59:20.3769081Z Entering 'third_party/onnx' 2024-06-26T04:59:20.3806708Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config remote.origin.url 2024-06-26T04:59:20.3834762Z Entering 'third_party/onnx/third_party/benchmark' 2024-06-26T04:59:20.3872374Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/benchmark/config remote.origin.url 2024-06-26T04:59:20.3887734Z Entering 'third_party/onnx/third_party/pybind11' 2024-06-26T04:59:20.3925660Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config remote.origin.url 2024-06-26T04:59:20.3941449Z Entering 'third_party/opentelemetry-cpp' 2024-06-26T04:59:20.3979692Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/config remote.origin.url 2024-06-26T04:59:20.3995331Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-06-26T04:59:20.4032291Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/benchmark/config remote.origin.url 2024-06-26T04:59:20.4046456Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-06-26T04:59:20.4083693Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/googletest/config remote.origin.url 2024-06-26T04:59:20.4097034Z Entering 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentracing-cpp/config remote.origin.url 2024-06-26T04:59:20.4299379Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-06-26T04:59:20.4336524Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config remote.origin.url 2024-06-26T04:59:20.4348909Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-06-26T04:59:20.4387221Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/civetweb/config remote.origin.url 2024-06-26T04:59:20.4403080Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-06-26T04:59:20.4440563Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/googletest/config remote.origin.url 2024-06-26T04:59:20.4454973Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-06-26T04:59:20.4492065Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config remote.origin.url 2024-06-26T04:59:20.4524296Z Entering 'third_party/pocketfft' 2024-06-26T04:59:20.4561668Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config remote.origin.url 2024-06-26T04:59:20.4575869Z Entering 'third_party/protobuf' 2024-06-26T04:59:20.4613470Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config remote.origin.url 2024-06-26T04:59:20.4629881Z Entering 'third_party/protobuf/third_party/benchmark' 2024-06-26T04:59:20.4667262Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config remote.origin.url 2024-06-26T04:59:20.4680644Z Entering 'third_party/protobuf/third_party/googletest' 2024-06-26T04:59:20.4717589Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config remote.origin.url 2024-06-26T04:59:20.4732849Z Entering 'third_party/psimd' 2024-06-26T04:59:20.4770968Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config remote.origin.url 2024-06-26T04:59:20.4785001Z Entering 'third_party/pthreadpool' 2024-06-26T04:59:20.4822217Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config remote.origin.url 2024-06-26T04:59:20.4836966Z Entering 'third_party/pybind11' 2024-06-26T04:59:20.4873844Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config remote.origin.url 2024-06-26T04:59:20.4888647Z Entering 'third_party/python-peachpy' 2024-06-26T04:59:20.4925133Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config remote.origin.url 2024-06-26T04:59:20.4939625Z Entering 'third_party/sleef' 2024-06-26T04:59:20.4978680Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config remote.origin.url 2024-06-26T04:59:20.4993186Z Entering 'third_party/tensorpipe' 2024-06-26T04:59:20.5030787Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config remote.origin.url 2024-06-26T04:59:20.5044872Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-06-26T04:59:20.5083549Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config remote.origin.url 2024-06-26T04:59:20.5097267Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-06-26T04:59:20.5134792Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config remote.origin.url 2024-06-26T04:59:20.5147895Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-06-26T04:59:20.5184897Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config remote.origin.url 2024-06-26T04:59:20.5199100Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-06-26T04:59:20.5237137Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config remote.origin.url 2024-06-26T04:59:20.5249148Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-06-26T04:59:20.5287349Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config remote.origin.url 2024-06-26T04:59:20.5906498Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2024-06-26T04:59:20.6150707Z Entering 'android/libs/fbjni' 2024-06-26T04:59:20.6183738Z Entering 'third_party/FP16' 2024-06-26T04:59:20.6217233Z Entering 'third_party/FXdiv' 2024-06-26T04:59:20.6249367Z Entering 'third_party/NNPACK' 2024-06-26T04:59:20.6284129Z Entering 'third_party/VulkanMemoryAllocator' 2024-06-26T04:59:20.6317123Z Entering 'third_party/XNNPACK' 2024-06-26T04:59:20.6367273Z Entering 'third_party/benchmark' 2024-06-26T04:59:20.6399621Z Entering 'third_party/cpp-httplib' 2024-06-26T04:59:20.6432048Z Entering 'third_party/cpuinfo' 2024-06-26T04:59:20.6464335Z Entering 'third_party/cudnn_frontend' 2024-06-26T04:59:20.6497543Z Entering 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[command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2024-06-26T04:59:20.8826439Z Entering 'android/libs/fbjni' 2024-06-26T04:59:20.8857821Z Entering 'third_party/FP16' 2024-06-26T04:59:20.8891007Z Entering 'third_party/FXdiv' 2024-06-26T04:59:20.8923971Z Entering 'third_party/NNPACK' 2024-06-26T04:59:20.8956693Z Entering 'third_party/VulkanMemoryAllocator' 2024-06-26T04:59:20.8989237Z Entering 'third_party/XNNPACK' 2024-06-26T04:59:20.9037840Z Entering 'third_party/benchmark' 2024-06-26T04:59:20.9070318Z Entering 'third_party/cpp-httplib' 2024-06-26T04:59:20.9102063Z Entering 'third_party/cpuinfo' 2024-06-26T04:59:20.9135351Z Entering 'third_party/cudnn_frontend' 2024-06-26T04:59:20.9166878Z Entering 'third_party/cutlass' 2024-06-26T04:59:20.9206944Z Entering 'third_party/eigen' 2024-06-26T04:59:20.9240995Z Entering 'third_party/fbgemm' 2024-06-26T04:59:20.9274982Z Entering 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2024-06-26T04:59:21.0496143Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-06-26T04:59:21.0528345Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-06-26T04:59:21.0559963Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-06-26T04:59:21.0591153Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-06-26T04:59:21.0621812Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-06-26T04:59:21.0654874Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-06-26T04:59:21.0688437Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-06-26T04:59:21.0737590Z Entering 'third_party/pocketfft' 2024-06-26T04:59:21.0769656Z Entering 'third_party/protobuf' 2024-06-26T04:59:21.0805121Z Entering 'third_party/protobuf/third_party/benchmark' 2024-06-26T04:59:21.0835805Z Entering 'third_party/protobuf/third_party/googletest' 2024-06-26T04:59:21.0868770Z Entering 'third_party/psimd' 2024-06-26T04:59:21.0901375Z Entering 'third_party/pthreadpool' 2024-06-26T04:59:21.0933393Z Entering 'third_party/pybind11' 2024-06-26T04:59:21.0965555Z Entering 'third_party/python-peachpy' 2024-06-26T04:59:21.0997474Z Entering 'third_party/sleef' 2024-06-26T04:59:21.1029147Z Entering 'third_party/tensorpipe' 2024-06-26T04:59:21.1061339Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-06-26T04:59:21.1093465Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-06-26T04:59:21.1124402Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-06-26T04:59:21.1155297Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-06-26T04:59:21.1186079Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-06-26T04:59:21.1229259Z ##[endgroup] 2024-06-26T04:59:21.1262534Z [command]/usr/bin/git log -1 --format='%H' 2024-06-26T04:59:21.1285890Z 'b8c4c54d347aa776934c60784e35936878ef18dc' 2024-06-26T04:59:21.1465073Z Prepare all required actions 2024-06-26T04:59:21.1465565Z Getting action download info 2024-06-26T04:59:21.2889570Z ##[group]Run ./.github/actions/setup-linux 2024-06-26T04:59:21.2889989Z env: 2024-06-26T04:59:21.2890249Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:21.2890565Z ##[endgroup] 2024-06-26T04:59:21.2952040Z ##[group]Run set -euo pipefail 2024-06-26T04:59:21.2952599Z set -euo pipefail 2024-06-26T04:59:21.2952958Z function get_ec2_metadata() { 2024-06-26T04:59:21.2953487Z  # Pulled from instance metadata endpoint for EC2 2024-06-26T04:59:21.2954399Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2024-06-26T04:59:21.2955334Z  category=$1 2024-06-26T04:59:21.2955852Z  # If it is GCP runner (runner name contains gcp), do not run this 2024-06-26T04:59:21.2956491Z  runner_name_str=i-0e3c0504b423c9909 2024-06-26T04:59:21.2956977Z  if [[ -f /.inarc ]]; then 2024-06-26T04:59:21.2957462Z  echo "ARC Runner, no info on ec2 metadata" 2024-06-26T04:59:21.2958007Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2024-06-26T04:59:21.2958705Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2024-06-26T04:59:21.2959318Z  else 2024-06-26T04:59:21.2959787Z  curl -fsSL "http://169.254.169.254/latest/meta-data/${category}" 2024-06-26T04:59:21.2960366Z  fi 2024-06-26T04:59:21.2960635Z } 2024-06-26T04:59:21.2961065Z echo "ami-id: $(get_ec2_metadata ami-id)" 2024-06-26T04:59:21.2961648Z echo "instance-id: $(get_ec2_metadata instance-id)" 2024-06-26T04:59:21.2962304Z echo "instance-type: $(get_ec2_metadata instance-type)" 2024-06-26T04:59:21.2962854Z echo "system info $(uname -a)" 2024-06-26T04:59:21.2970320Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:21.2970811Z env: 2024-06-26T04:59:21.2971061Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:21.2971385Z ##[endgroup] 2024-06-26T04:59:21.3051270Z ami-id: ami-0ce0c36d7a00b20e2 2024-06-26T04:59:21.3101576Z instance-id: i-0e3c0504b423c9909 2024-06-26T04:59:21.3150336Z instance-type: c5.2xlarge 2024-06-26T04:59:21.3157223Z system info Linux ip-10-0-6-47.ec2.internal 4.14.336-257.562.amzn2.x86_64 #1 SMP Sat Feb 24 09:50:35 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux 2024-06-26T04:59:21.3180115Z ##[group]Run echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> $GITHUB_OUTPUT 2024-06-26T04:59:21.3181134Z echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> $GITHUB_OUTPUT 2024-06-26T04:59:21.3188810Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:21.3189347Z env: 2024-06-26T04:59:21.3189636Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:21.3190010Z ##[endgroup] 2024-06-26T04:59:21.3273356Z ##[group]Run if systemctl is-active --quiet docker; then 2024-06-26T04:59:21.3273953Z if systemctl is-active --quiet docker; then 2024-06-26T04:59:21.3274485Z  echo "Docker daemon is running..."; 2024-06-26T04:59:21.3275089Z else 2024-06-26T04:59:21.3275567Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2024-06-26T04:59:21.3276132Z fi 2024-06-26T04:59:21.3283055Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:21.3283559Z env: 2024-06-26T04:59:21.3283811Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:21.3284122Z ##[endgroup] 2024-06-26T04:59:21.3367611Z Docker daemon is running... 2024-06-26T04:59:21.3415634Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2024-06-26T04:59:21.3416245Z with: 2024-06-26T04:59:21.3416538Z shell: bash 2024-06-26T04:59:21.3416850Z timeout_minutes: 5 2024-06-26T04:59:21.3417199Z max_attempts: 3 2024-06-26T04:59:21.3417539Z retry_wait_seconds: 30 2024-06-26T04:59:21.3419173Z command: AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" 2024-06-26T04:59:21.3420739Z polling_interval_seconds: 1 2024-06-26T04:59:21.3421149Z warning_on_retry: true 2024-06-26T04:59:21.3421534Z continue_on_error: false 2024-06-26T04:59:21.3422017Z env: 2024-06-26T04:59:21.3422315Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:21.3422707Z AWS_RETRY_MODE: standard 2024-06-26T04:59:21.3423074Z AWS_MAX_ATTEMPTS: 5 2024-06-26T04:59:21.3423445Z AWS_DEFAULT_REGION: us-east-1 2024-06-26T04:59:21.3423844Z ##[endgroup] 2024-06-26T04:59:22.4578831Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-06-26T04:59:22.4580126Z Configure a credential helper to remove this warning. See 2024-06-26T04:59:22.4581529Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-06-26T04:59:22.4582421Z 2024-06-26T04:59:22.4582630Z Login Succeeded 2024-06-26T04:59:23.3939966Z Command completed after 1 attempt(s). 2024-06-26T04:59:23.3985736Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-06-26T04:59:23.3986468Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-06-26T04:59:23.3987119Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-06-26T04:59:23.3994571Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:23.3995250Z env: 2024-06-26T04:59:23.3995508Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:23.3995836Z ##[endgroup] 2024-06-26T04:59:23.4064146Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-06-26T04:59:23.4064922Z # ignore expansion of "docker ps -q" since it could be empty 2024-06-26T04:59:23.4065510Z # shellcheck disable=SC2046 2024-06-26T04:59:23.4065956Z docker stop $(docker ps -q) || true 2024-06-26T04:59:23.4066427Z # Prune all of the docker images 2024-06-26T04:59:23.4066864Z docker system prune -af 2024-06-26T04:59:23.4073734Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:23.4074212Z env: 2024-06-26T04:59:23.4074466Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:23.4074992Z ##[endgroup] 2024-06-26T04:59:23.4321145Z "docker stop" requires at least 1 argument. 2024-06-26T04:59:23.4321897Z See 'docker stop --help'. 2024-06-26T04:59:23.4322150Z 2024-06-26T04:59:23.4322364Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2024-06-26T04:59:23.4322738Z 2024-06-26T04:59:23.4322886Z Stop one or more running containers 2024-06-26T04:59:23.4448284Z Total reclaimed space: 0B 2024-06-26T04:59:23.4478192Z ##[group]Run set +e 2024-06-26T04:59:23.4478538Z set +e 2024-06-26T04:59:23.4478807Z set -x 2024-06-26T04:59:23.4479081Z  2024-06-26T04:59:23.4479385Z PT_DOMAIN=download.pytorch.org 2024-06-26T04:59:23.4480180Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2024-06-26T04:59:23.4481366Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2024-06-26T04:59:23.4482134Z # one is returned at random 2024-06-26T04:59:23.4482649Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2024-06-26T04:59:23.4483171Z  2024-06-26T04:59:23.4483463Z if [ -z "${RESOLVED_IP}" ]; then 2024-06-26T04:59:23.4484060Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2024-06-26T04:59:23.4484826Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2024-06-26T04:59:23.4485377Z  2024-06-26T04:59:23.4485678Z  if [ -z "${RESOLVED_IP}" ]; then 2024-06-26T04:59:23.4486209Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2024-06-26T04:59:23.4486713Z  exit 1 2024-06-26T04:59:23.4486999Z  fi 2024-06-26T04:59:23.4487244Z fi 2024-06-26T04:59:23.4487598Z  2024-06-26T04:59:23.4487935Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2024-06-26T04:59:23.4488425Z  # Clean up any old records first 2024-06-26T04:59:23.4488920Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2024-06-26T04:59:23.4489360Z fi 2024-06-26T04:59:23.4489597Z  2024-06-26T04:59:23.4490138Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2024-06-26T04:59:23.4490677Z cat /etc/hosts 2024-06-26T04:59:23.4497835Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:23.4498327Z env: 2024-06-26T04:59:23.4498578Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:23.4498893Z ##[endgroup] 2024-06-26T04:59:23.4518490Z + PT_DOMAIN=download.pytorch.org 2024-06-26T04:59:23.4522968Z ++ dig -4 +short download.pytorch.org 2024-06-26T04:59:23.4523445Z ++ tail -n1 2024-06-26T04:59:23.5109518Z + RESOLVED_IP=18.160.10.36 2024-06-26T04:59:23.5109971Z + '[' -z 18.160.10.36 ']' 2024-06-26T04:59:23.5110404Z + grep -r download.pytorch.org /etc/hosts 2024-06-26T04:59:23.5116792Z 18.160.10.76 download.pytorch.org 2024-06-26T04:59:23.5117987Z + sudo sed -i /download.pytorch.org/d /etc/hosts 2024-06-26T04:59:23.5218843Z + echo '18.160.10.36 download.pytorch.org' 2024-06-26T04:59:23.5219682Z + sudo tee -a /etc/hosts 2024-06-26T04:59:23.5678019Z 18.160.10.36 download.pytorch.org 2024-06-26T04:59:23.5689737Z + cat /etc/hosts 2024-06-26T04:59:23.5696054Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2024-06-26T04:59:23.5711012Z ::1 localhost6 localhost6.localdomain6 2024-06-26T04:59:23.5711518Z 18.160.10.36 download.pytorch.org 2024-06-26T04:59:23.5871898Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2024-06-26T04:59:23.5872501Z with: 2024-06-26T04:59:23.5873412Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:23.5874477Z docker-build-dir: .ci/docker 2024-06-26T04:59:23.5875082Z working-directory: . 2024-06-26T04:59:23.5875532Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:59:23.5876065Z force-push: false 2024-06-26T04:59:23.5876352Z env: 2024-06-26T04:59:23.5876590Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:23.5876915Z ##[endgroup] 2024-06-26T04:59:23.5896514Z ##[group]Run set -ex 2024-06-26T04:59:23.5896850Z set -ex 2024-06-26T04:59:23.5897121Z  2024-06-26T04:59:23.5897658Z # If the docker build directory or the build script doesn't exist, the action will 2024-06-26T04:59:23.5898676Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2024-06-26T04:59:23.5899480Z # job could then download the pre-built image as usual 2024-06-26T04:59:23.5900225Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then 2024-06-26T04:59:23.5900897Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2024-06-26T04:59:23.5901522Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-06-26T04:59:23.5902060Z  2024-06-26T04:59:23.5902551Z  echo "There is no Docker build script in ${REPO_NAME} repo, skipping..." 2024-06-26T04:59:23.5903177Z  exit 0 2024-06-26T04:59:23.5903452Z else 2024-06-26T04:59:23.5903790Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2024-06-26T04:59:23.5904225Z fi 2024-06-26T04:59:23.5904463Z  2024-06-26T04:59:23.5904914Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2024-06-26T04:59:23.5905782Z  # The docker image name already includes the ECR prefix and tag, so we can just 2024-06-26T04:59:23.5906554Z  # use it as it is, but first let's extract the tag 2024-06-26T04:59:23.5907257Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2024-06-26T04:59:23.5907982Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-06-26T04:59:23.5908677Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-06-26T04:59:23.5909219Z else 2024-06-26T04:59:23.5909632Z  DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2024-06-26T04:59:23.5910290Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-06-26T04:59:23.5911303Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-06-26T04:59:23.5912075Z fi 2024-06-26T04:59:23.5919440Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:23.5919932Z env: 2024-06-26T04:59:23.5920186Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:23.5920508Z REPO_NAME: pytorch 2024-06-26T04:59:23.5921524Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:23.5922571Z DOCKER_BUILD_DIR: .ci/docker 2024-06-26T04:59:23.5923059Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:59:23.5923557Z ##[endgroup] 2024-06-26T04:59:23.5943754Z + [[ ! -d .ci/docker ]] 2024-06-26T04:59:23.5944173Z + [[ ! -f .ci/docker/build.sh ]] 2024-06-26T04:59:23.5944542Z + echo skip=false 2024-06-26T04:59:23.5946316Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 == *\3\0\8\5\3\5\3\8\5\1\1\4\.\d\k\r\.\e\c\r\.\u\s\-\e\a\s\t\-\1\.\a\m\a\z\o\n\a\w\s\.\c\o\m\/\p\y\t\o\r\c\h* ]] 2024-06-26T04:59:23.5950196Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:23.5951225Z ++ awk -F '[:,]' '{print $2}' 2024-06-26T04:59:23.5991254Z + DOCKER_TAG=91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:23.5991934Z + echo docker-tag=91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:23.5993876Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:23.6020348Z ##[group]Run set +e 2024-06-26T04:59:23.6020686Z set +e 2024-06-26T04:59:23.6020967Z set -x 2024-06-26T04:59:23.6021219Z  2024-06-26T04:59:23.6021483Z login() { 2024-06-26T04:59:23.6022136Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-06-26T04:59:23.6022860Z } 2024-06-26T04:59:23.6023097Z  2024-06-26T04:59:23.6023347Z retry () { 2024-06-26T04:59:23.6023708Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-06-26T04:59:23.6024140Z } 2024-06-26T04:59:23.6024385Z  2024-06-26T04:59:23.6024662Z retry login "${DOCKER_REGISTRY}" 2024-06-26T04:59:23.6025061Z  2024-06-26T04:59:23.6025524Z # Check if image already exists, if it does then skip building it 2024-06-26T04:59:23.6026237Z if docker manifest inspect "${DOCKER_IMAGE}"; then 2024-06-26T04:59:23.6026724Z  exit 0 2024-06-26T04:59:23.6027005Z fi 2024-06-26T04:59:23.6027257Z  2024-06-26T04:59:23.6027734Z # NB: This part requires a full checkout. Otherwise, the merge base will 2024-06-26T04:59:23.6028591Z # be empty. The default action would be to continue rebuild the image 2024-06-26T04:59:23.6029335Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2024-06-26T04:59:23.6029998Z  # if we're on the base branch then use the parent commit 2024-06-26T04:59:23.6030591Z  MERGE_BASE=$(git rev-parse HEAD~) 2024-06-26T04:59:23.6031012Z else 2024-06-26T04:59:23.6031452Z  # otherwise we're on a PR, so use the most recent base commit 2024-06-26T04:59:23.6032142Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2024-06-26T04:59:23.6032644Z fi 2024-06-26T04:59:23.6032887Z  2024-06-26T04:59:23.6033182Z if [[ -z "${MERGE_BASE}" ]]; then 2024-06-26T04:59:23.6033678Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-06-26T04:59:23.6034119Z  2024-06-26T04:59:23.6034956Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2024-06-26T04:59:23.6035864Z  exit 0 2024-06-26T04:59:23.6036145Z fi 2024-06-26T04:59:23.6036383Z  2024-06-26T04:59:23.6036798Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2024-06-26T04:59:23.6037804Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2024-06-26T04:59:23.6038618Z  exit 1 2024-06-26T04:59:23.6038895Z fi 2024-06-26T04:59:23.6039143Z  2024-06-26T04:59:23.6039607Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2024-06-26T04:59:23.6040576Z # If no image exists but the hash is the same as the previous hash then we should error out here 2024-06-26T04:59:23.6041550Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2024-06-26T04:59:23.6042542Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2024-06-26T04:59:23.6043667Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2024-06-26T04:59:23.6044319Z fi 2024-06-26T04:59:23.6044576Z  2024-06-26T04:59:23.6044990Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-06-26T04:59:23.6051954Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:23.6052442Z env: 2024-06-26T04:59:23.6052681Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:23.6053022Z DOCKER_BUILD_DIR: .ci/docker 2024-06-26T04:59:23.6053470Z BASE_REVISION: 4b9c9a9cc9c9283380a011310ba180c105c3dcb9 2024-06-26T04:59:23.6054560Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:23.6055645Z DOCKER_TAG: 91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:23.6056244Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:59:23.6056765Z ##[endgroup] 2024-06-26T04:59:23.6115521Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:59:23.6117541Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:59:23.6118191Z + aws ecr get-login-password --region us-east-1 2024-06-26T04:59:23.6118974Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:59:23.9818504Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-06-26T04:59:23.9819549Z Configure a credential helper to remove this warning. See 2024-06-26T04:59:23.9820782Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-06-26T04:59:23.9821586Z 2024-06-26T04:59:23.9821692Z Login Succeeded 2024-06-26T04:59:23.9832908Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:24.1758861Z { 2024-06-26T04:59:24.1759283Z "schemaVersion": 2, 2024-06-26T04:59:24.1759854Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2024-06-26T04:59:24.1760629Z "config": { 2024-06-26T04:59:24.1761310Z "mediaType": "application/vnd.docker.container.image.v1+json", 2024-06-26T04:59:24.1762288Z "size": 43620, 2024-06-26T04:59:24.1763230Z "digest": "sha256:c557aad093b3e05d2c865978f4d0f2394ea4fa88c1e2f3e78bbc35645582fa29" 2024-06-26T04:59:24.1764086Z }, 2024-06-26T04:59:24.1764457Z "layers": [ 2024-06-26T04:59:24.1764817Z { 2024-06-26T04:59:24.1765497Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1766430Z "size": 28584223, 2024-06-26T04:59:24.1767352Z "digest": "sha256:560c024910bebac6b404791af28ebd48a8289303b8377d17b67ffdfe52754f2a" 2024-06-26T04:59:24.1768450Z }, 2024-06-26T04:59:24.1768811Z { 2024-06-26T04:59:24.1769233Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1769775Z "size": 1822, 2024-06-26T04:59:24.1770547Z "digest": "sha256:d56c75e5f4d98716b0a5d7d6c0683f689a92d377a19b52e2b1fe4ea37d275ffb" 2024-06-26T04:59:24.1771205Z + exit 0 2024-06-26T04:59:24.1771437Z }, 2024-06-26T04:59:24.1771737Z { 2024-06-26T04:59:24.1772268Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1772836Z "size": 313359083, 2024-06-26T04:59:24.1773378Z "digest": "sha256:402182948e899a656a2f36e5dac9a5e518bfbb09b7ac88a68b8cd56fa0fb1f5d" 2024-06-26T04:59:24.1774002Z }, 2024-06-26T04:59:24.1774222Z { 2024-06-26T04:59:24.1774698Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1775702Z "size": 804, 2024-06-26T04:59:24.1776505Z "digest": "sha256:a07c3239eb667d17b1272a8fe8ee6e0a16fb5311e35a2edb710754b1b2204d6e" 2024-06-26T04:59:24.1777513Z }, 2024-06-26T04:59:24.1777742Z { 2024-06-26T04:59:24.1778165Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1778705Z "size": 79404485, 2024-06-26T04:59:24.1779277Z "digest": "sha256:cadd53e932c09e175e1dd71459e5df3e2fd0f06d364dd9ac85681f529dcdfc3c" 2024-06-26T04:59:24.1780232Z }, 2024-06-26T04:59:24.1780641Z { 2024-06-26T04:59:24.1781430Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1782430Z "size": 546, 2024-06-26T04:59:24.1783110Z "digest": "sha256:b9491f8e87658bf406a9098a6af3453d8ef49e3282ce6f56309d77eb8a9c09c7" 2024-06-26T04:59:24.1783735Z }, 2024-06-26T04:59:24.1784091Z { 2024-06-26T04:59:24.1784815Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1785712Z "size": 1283, 2024-06-26T04:59:24.1786583Z "digest": "sha256:9f184530f2e2aad5d626add328eda59c1c202a68475c8b37b4ba8d40da7de02e" 2024-06-26T04:59:24.1787502Z }, 2024-06-26T04:59:24.1787932Z { 2024-06-26T04:59:24.1788732Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1789923Z "size": 485, 2024-06-26T04:59:24.1790766Z "digest": "sha256:c6150e458a983b4722772b390dd307ae7db526c878eab9d12735ae73bc79be32" 2024-06-26T04:59:24.1791747Z }, 2024-06-26T04:59:24.1792173Z { 2024-06-26T04:59:24.1792972Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1793829Z "size": 110, 2024-06-26T04:59:24.1794390Z "digest": "sha256:bfaf1c5525d9cd0c4bfbd66acfb4408dd6fa22607e67875d19f67be0a47f2a16" 2024-06-26T04:59:24.1795243Z }, 2024-06-26T04:59:24.1795457Z { 2024-06-26T04:59:24.1796027Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1796974Z "size": 3686, 2024-06-26T04:59:24.1797799Z "digest": "sha256:869aa8bd3e9d0685ef206b56581e69a41a74b27a9540495c8e80f7db2fb6d94c" 2024-06-26T04:59:24.1798768Z }, 2024-06-26T04:59:24.1799192Z { 2024-06-26T04:59:24.1799974Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1801076Z "size": 1905, 2024-06-26T04:59:24.1802103Z "digest": "sha256:db1ea1e5664a7f25a65ffc51ae30639b7dd4bdf025aec490c770da0f511369fd" 2024-06-26T04:59:24.1802981Z }, 2024-06-26T04:59:24.1803204Z { 2024-06-26T04:59:24.1803620Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1804156Z "size": 700, 2024-06-26T04:59:24.1804703Z "digest": "sha256:f4fc602d79e2d965373aaaf2c8cfe6534d2b90c33a7c655586c1b70e0f6b4845" 2024-06-26T04:59:24.1805332Z }, 2024-06-26T04:59:24.1805539Z { 2024-06-26T04:59:24.1805958Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1806512Z "size": 2665563591, 2024-06-26T04:59:24.1807071Z "digest": "sha256:28d6bdf30db3cec4cb73a388385ec3e7ae3a8e668a35d7d3a7efb9e2f5402239" 2024-06-26T04:59:24.1807707Z }, 2024-06-26T04:59:24.1807927Z { 2024-06-26T04:59:24.1808335Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1808885Z "size": 32, 2024-06-26T04:59:24.1809422Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-06-26T04:59:24.1810050Z }, 2024-06-26T04:59:24.1810408Z { 2024-06-26T04:59:24.1810824Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1811365Z "size": 381, 2024-06-26T04:59:24.1811906Z "digest": "sha256:96c42d4a99b046ae057ee19b9ef481e45bbbbb21455e2a258b1680a2bdf97111" 2024-06-26T04:59:24.1812538Z }, 2024-06-26T04:59:24.1812747Z { 2024-06-26T04:59:24.1813161Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1813711Z "size": 104, 2024-06-26T04:59:24.1814226Z "digest": "sha256:19990b8b1757bd175f09eecf7749f395a6c1b4a897cb5e29cf162ba637cafe3a" 2024-06-26T04:59:24.1814843Z }, 2024-06-26T04:59:24.1815062Z { 2024-06-26T04:59:24.1815468Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1816016Z "size": 231, 2024-06-26T04:59:24.1816550Z "digest": "sha256:807599da5b1a88e6bbf37a02869d0fcd366dadf13b600d32369b410e25ca2a11" 2024-06-26T04:59:24.1817175Z }, 2024-06-26T04:59:24.1817381Z { 2024-06-26T04:59:24.1817799Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1818352Z "size": 2839017, 2024-06-26T04:59:24.1818890Z "digest": "sha256:3bc392ea68959eb298ae507bbc36afaf35192f3f5416bf81124b60bdbb94ccb6" 2024-06-26T04:59:24.1819512Z }, 2024-06-26T04:59:24.1819735Z { 2024-06-26T04:59:24.1820241Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1820798Z "size": 1989, 2024-06-26T04:59:24.1821335Z "digest": 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"sha256:f4fc602d79e2d965373aaaf2c8cfe6534d2b90c33a7c655586c1b70e0f6b4845" 2024-06-26T04:59:24.1937153Z }, 2024-06-26T04:59:24.1937373Z { 2024-06-26T04:59:24.1937776Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1938332Z "size": 139, 2024-06-26T04:59:24.1938872Z "digest": "sha256:c5f8bd342f184c62bdcd81b70f09e0e68f90c12e194ef32c0a1fc63455d2915f" 2024-06-26T04:59:24.1939483Z }, 2024-06-26T04:59:24.1939704Z { 2024-06-26T04:59:24.1940193Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1940733Z "size": 32, 2024-06-26T04:59:24.1941267Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-06-26T04:59:24.1941895Z }, 2024-06-26T04:59:24.1942103Z { 2024-06-26T04:59:24.1942524Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1943080Z "size": 158, 2024-06-26T04:59:24.1943598Z "digest": "sha256:63417469eaf6ea97e6fc339931afea4109714caadc4fbd7d2e347ff3434c95d2" 2024-06-26T04:59:24.1944217Z }, 2024-06-26T04:59:24.1944437Z { 2024-06-26T04:59:24.1944839Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1945395Z "size": 908, 2024-06-26T04:59:24.1945905Z "digest": "sha256:3b971402f4833579e12f197dc43266852344dcc3fb423e6548f6779c0807689c" 2024-06-26T04:59:24.1946492Z }, 2024-06-26T04:59:24.1946712Z { 2024-06-26T04:59:24.1947126Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1947668Z "size": 700, 2024-06-26T04:59:24.1948207Z "digest": "sha256:f4fc602d79e2d965373aaaf2c8cfe6534d2b90c33a7c655586c1b70e0f6b4845" 2024-06-26T04:59:24.1948834Z }, 2024-06-26T04:59:24.1949041Z { 2024-06-26T04:59:24.1949457Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1950005Z "size": 134, 2024-06-26T04:59:24.1950530Z "digest": "sha256:c9ba29c920afe8702e5392d6afe72b7b6f916eefdc433bd3e2820653273a114c" 2024-06-26T04:59:24.1951158Z }, 2024-06-26T04:59:24.1951375Z { 2024-06-26T04:59:24.1951778Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1952326Z "size": 32, 2024-06-26T04:59:24.1952869Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-06-26T04:59:24.1953491Z }, 2024-06-26T04:59:24.1953700Z { 2024-06-26T04:59:24.1954113Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1954798Z "size": 156, 2024-06-26T04:59:24.1955355Z "digest": "sha256:d706890df26378f388ccfcfde99f4b922e07c64147857bf2d11cc3da2a595453" 2024-06-26T04:59:24.1955977Z }, 2024-06-26T04:59:24.1956187Z { 2024-06-26T04:59:24.1956608Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1957156Z "size": 1579, 2024-06-26T04:59:24.1957700Z "digest": "sha256:3b1f57fd0104cbcbd9d7c8ec985cbc371f6f92249a01fe6698314459b9b969a9" 2024-06-26T04:59:24.1958312Z }, 2024-06-26T04:59:24.1958531Z { 2024-06-26T04:59:24.1958934Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1959484Z "size": 32, 2024-06-26T04:59:24.1960020Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-06-26T04:59:24.1960755Z }, 2024-06-26T04:59:24.1961030Z { 2024-06-26T04:59:24.1961452Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1962005Z "size": 128, 2024-06-26T04:59:24.1962521Z "digest": "sha256:565227b9e7141060948b22cc43c4d8a33df043204402bb61b9be3ecd0d97f89d" 2024-06-26T04:59:24.1963135Z }, 2024-06-26T04:59:24.1963363Z { 2024-06-26T04:59:24.1963767Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1964315Z "size": 379, 2024-06-26T04:59:24.1964962Z "digest": "sha256:f7be3bf4a877129e69b2058df1780e0009611b4ae187c91374bcbd9fdfe61e56" 2024-06-26T04:59:24.1965579Z }, 2024-06-26T04:59:24.1965802Z { 2024-06-26T04:59:24.1966220Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1966755Z "size": 32, 2024-06-26T04:59:24.1967294Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-06-26T04:59:24.1967924Z }, 2024-06-26T04:59:24.1968131Z { 2024-06-26T04:59:24.1968546Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1969105Z "size": 104, 2024-06-26T04:59:24.1969626Z "digest": "sha256:85b9d4888a5f6f97c3e87f46e35194ccc955e1b30f21bd8c7565b8ee71f2782b" 2024-06-26T04:59:24.1970349Z }, 2024-06-26T04:59:24.1970579Z { 2024-06-26T04:59:24.1970990Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1971543Z "size": 1841, 2024-06-26T04:59:24.1972073Z "digest": "sha256:c48375725932ae883d25e94c1a89cf516fa7d82d0778987a93daebb49f12d2ec" 2024-06-26T04:59:24.1972674Z }, 2024-06-26T04:59:24.1972899Z { 2024-06-26T04:59:24.1973325Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1973868Z "size": 7529642, 2024-06-26T04:59:24.1974424Z "digest": "sha256:2ee46f0bbde326e7461090d7ebe5298a109fc87f4c84c052fc233e56593ec958" 2024-06-26T04:59:24.1975055Z }, 2024-06-26T04:59:24.1975267Z { 2024-06-26T04:59:24.1975690Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1976246Z "size": 106, 2024-06-26T04:59:24.1976787Z "digest": "sha256:e1d9c59b8ecbef7acbf4af5846117cdf508c784a3515d9d0cd560c61eb256ab2" 2024-06-26T04:59:24.1977432Z }, 2024-06-26T04:59:24.1977656Z { 2024-06-26T04:59:24.1978061Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1978608Z "size": 164, 2024-06-26T04:59:24.1979169Z "digest": "sha256:48eeae0fe99ddcb85faf46544e9adcae44005d6daa9c5ccb3a0a2ae6a8eb16ae" 2024-06-26T04:59:24.1979805Z }, 2024-06-26T04:59:24.1980024Z { 2024-06-26T04:59:24.1980441Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1980976Z "size": 7944, 2024-06-26T04:59:24.1981512Z "digest": "sha256:80d1fccb27712d5d0857704d19ce3079e31961bccaad65c8f4a6b0838d2a783e" 2024-06-26T04:59:24.1982140Z }, 2024-06-26T04:59:24.1982345Z { 2024-06-26T04:59:24.1982763Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1983310Z "size": 8065, 2024-06-26T04:59:24.1983859Z "digest": "sha256:cdd9e4d7a0b1ae2d382df45cd7edb679c42951b3e8aeacbab693fa0fd6c14291" 2024-06-26T04:59:24.1984500Z }, 2024-06-26T04:59:24.1984719Z { 2024-06-26T04:59:24.1985127Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1985678Z "size": 301, 2024-06-26T04:59:24.1986219Z "digest": "sha256:f14381b4af2d3d15ac41db40423af7ce0d42df396771ed3a5fe5ec74e6eab1a7" 2024-06-26T04:59:24.1986837Z }, 2024-06-26T04:59:24.1987056Z { 2024-06-26T04:59:24.1987469Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1988007Z "size": 32, 2024-06-26T04:59:24.1988542Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-06-26T04:59:24.1989170Z }, 2024-06-26T04:59:24.1989380Z { 2024-06-26T04:59:24.1989795Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1990419Z "size": 108, 2024-06-26T04:59:24.1990926Z "digest": "sha256:f0c45732a66338f7cecf3516738862709286aa73f07ff2584e54c1f6d808e7ef" 2024-06-26T04:59:24.1991536Z }, 2024-06-26T04:59:24.1991758Z { 2024-06-26T04:59:24.1992166Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1992720Z "size": 54145775, 2024-06-26T04:59:24.1993268Z "digest": "sha256:60298c8310b13acaf88912bfae3a4ce350982bcc020cf95291453458642600aa" 2024-06-26T04:59:24.1993873Z }, 2024-06-26T04:59:24.1994092Z { 2024-06-26T04:59:24.1994507Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-06-26T04:59:24.1995219Z "size": 32, 2024-06-26T04:59:24.1995763Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-06-26T04:59:24.1996396Z } 2024-06-26T04:59:24.1996604Z ] 2024-06-26T04:59:24.1996823Z } 2024-06-26T04:59:24.2194874Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/} 2024-06-26T04:59:24.2195510Z tag=${ECR_DOCKER_IMAGE##*/} 2024-06-26T04:59:24.2196060Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2024-06-26T04:59:24.2204048Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:24.2204541Z env: 2024-06-26T04:59:24.2204800Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:24.2205775Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:24.2206780Z ##[endgroup] 2024-06-26T04:59:24.2229954Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-py3.12-clang10-91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:24.2282618Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2024-06-26T04:59:24.2283196Z with: 2024-06-26T04:59:24.2284082Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:24.2285254Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:59:24.2285752Z env: 2024-06-26T04:59:24.2286002Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:24.2286326Z ##[endgroup] 2024-06-26T04:59:24.2303730Z ##[group]Run set -x 2024-06-26T04:59:24.2304042Z set -x 2024-06-26T04:59:24.2304320Z set +e 2024-06-26T04:59:24.2304586Z  2024-06-26T04:59:24.2304828Z login() { 2024-06-26T04:59:24.2305484Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-06-26T04:59:24.2306211Z } 2024-06-26T04:59:24.2306450Z  2024-06-26T04:59:24.2306743Z retry () { 2024-06-26T04:59:24.2307097Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-06-26T04:59:24.2307529Z } 2024-06-26T04:59:24.2307785Z  2024-06-26T04:59:24.2308066Z retry login "${DOCKER_REGISTRY}" 2024-06-26T04:59:24.2308467Z  2024-06-26T04:59:24.2308719Z set -e 2024-06-26T04:59:24.2309177Z # ignore output since only exit code is used for conditional 2024-06-26T04:59:24.2309883Z # only pull docker image if it's not available locally 2024-06-26T04:59:24.2310657Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2024-06-26T04:59:24.2311358Z  retry docker pull "${DOCKER_IMAGE}" 2024-06-26T04:59:24.2311773Z fi 2024-06-26T04:59:24.2318907Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T04:59:24.2319397Z env: 2024-06-26T04:59:24.2319641Z GIT_DEFAULT_BRANCH: main 2024-06-26T04:59:24.2320607Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:24.2321821Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:59:24.2322323Z ##[endgroup] 2024-06-26T04:59:24.2341731Z + set +e 2024-06-26T04:59:24.2342592Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:59:24.2343421Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:59:24.2344349Z + aws ecr get-login-password --region us-east-1 2024-06-26T04:59:24.2345631Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-06-26T04:59:24.6028634Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-06-26T04:59:24.6029714Z Configure a credential helper to remove this warning. See 2024-06-26T04:59:24.6030621Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-06-26T04:59:24.6031156Z 2024-06-26T04:59:24.6031262Z Login Succeeded 2024-06-26T04:59:24.6038648Z + set -e 2024-06-26T04:59:24.6040756Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:24.6157846Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:24.6159678Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T04:59:24.8124488Z 91382da70d5719cd7007b6b80b71d2f48398f6b7: Pulling from pytorch/pytorch-linux-focal-py3.12-clang10 2024-06-26T04:59:24.8136081Z 560c024910be: Pulling fs layer 2024-06-26T04:59:24.8136902Z d56c75e5f4d9: Pulling fs layer 2024-06-26T04:59:24.8137588Z 402182948e89: Pulling fs layer 2024-06-26T04:59:24.8138246Z a07c3239eb66: Pulling fs layer 2024-06-26T04:59:24.8138864Z cadd53e932c0: Pulling fs layer 2024-06-26T04:59:24.8139710Z b9491f8e8765: Pulling fs layer 2024-06-26T04:59:24.8140374Z 9f184530f2e2: Pulling fs layer 2024-06-26T04:59:24.8141009Z c6150e458a98: Pulling fs layer 2024-06-26T04:59:24.8159342Z bfaf1c5525d9: Pulling fs layer 2024-06-26T04:59:24.8160044Z 869aa8bd3e9d: Pulling fs layer 2024-06-26T04:59:24.8160747Z db1ea1e5664a: Pulling fs layer 2024-06-26T04:59:24.8161484Z f4fc602d79e2: Pulling fs layer 2024-06-26T04:59:24.8162053Z 28d6bdf30db3: Pulling fs layer 2024-06-26T04:59:24.8162420Z 4f4fb700ef54: Pulling fs layer 2024-06-26T04:59:24.8162796Z cadd53e932c0: Waiting 2024-06-26T04:59:24.8163114Z 96c42d4a99b0: Pulling fs layer 2024-06-26T04:59:24.8163460Z b9491f8e8765: Waiting 2024-06-26T04:59:24.8163746Z a07c3239eb66: Waiting 2024-06-26T04:59:24.8164048Z 9f184530f2e2: Waiting 2024-06-26T04:59:24.8164390Z 19990b8b1757: Pulling fs layer 2024-06-26T04:59:24.8164794Z c6150e458a98: Waiting 2024-06-26T04:59:24.8165097Z 807599da5b1a: Pulling fs layer 2024-06-26T04:59:24.8165446Z 869aa8bd3e9d: Waiting 2024-06-26T04:59:24.8165796Z 3bc392ea6895: Pulling fs layer 2024-06-26T04:59:24.8166134Z db1ea1e5664a: Waiting 2024-06-26T04:59:24.8166432Z bfaf1c5525d9: Waiting 2024-06-26T04:59:24.8166713Z f4fc602d79e2: Waiting 2024-06-26T04:59:24.8167057Z 51d0409ee5a5: Pulling fs layer 2024-06-26T04:59:24.8167404Z 28d6bdf30db3: Waiting 2024-06-26T04:59:24.8167765Z 2ab4a482ebb6: Pulling fs layer 2024-06-26T04:59:24.8168111Z 4f4fb700ef54: Waiting 2024-06-26T04:59:24.8168424Z 7bd34ce32c4e: Pulling fs layer 2024-06-26T04:59:24.8168753Z 96c42d4a99b0: Waiting 2024-06-26T04:59:24.8169048Z 807599da5b1a: Waiting 2024-06-26T04:59:24.8169339Z 3bc392ea6895: Waiting 2024-06-26T04:59:24.8169617Z 19990b8b1757: Waiting 2024-06-26T04:59:24.8169909Z 51d0409ee5a5: Waiting 2024-06-26T04:59:24.8170217Z bc7869b23905: Pulling fs layer 2024-06-26T04:59:24.8170545Z 2ab4a482ebb6: Waiting 2024-06-26T04:59:24.8170836Z 7bd34ce32c4e: Waiting 2024-06-26T04:59:24.8171174Z 5309dec9ef4b: Pulling fs layer 2024-06-26T04:59:24.8171586Z f97cbbc932b6: Pulling fs layer 2024-06-26T04:59:24.8171947Z bc7869b23905: Waiting 2024-06-26T04:59:24.8172260Z 98cb4a93632e: Pulling fs layer 2024-06-26T04:59:24.8172602Z 5309dec9ef4b: Waiting 2024-06-26T04:59:24.8172886Z f97cbbc932b6: Waiting 2024-06-26T04:59:24.8173194Z 3fe82dcf5152: Pulling fs layer 2024-06-26T04:59:24.8173555Z ab9c373a715d: Pulling fs layer 2024-06-26T04:59:24.8174098Z 3fe82dcf5152: Waiting 2024-06-26T04:59:24.8174404Z aa2d7818db84: Pulling fs layer 2024-06-26T04:59:24.8174753Z f733c4f50868: Pulling fs layer 2024-06-26T04:59:24.8175137Z a84efe42f3a3: Pulling fs layer 2024-06-26T04:59:24.8175510Z 796fdea9be06: Pulling fs layer 2024-06-26T04:59:24.8175886Z 58e9d24c5602: Pulling fs layer 2024-06-26T04:59:24.8176211Z a84efe42f3a3: Waiting 2024-06-26T04:59:24.8176522Z 448b0bc764da: Pulling fs layer 2024-06-26T04:59:24.8176863Z ab9c373a715d: Waiting 2024-06-26T04:59:24.8177162Z ab1f0809c531: Pulling fs layer 2024-06-26T04:59:24.8177503Z f733c4f50868: Waiting 2024-06-26T04:59:24.8177813Z 403d548a9d3f: Pulling fs layer 2024-06-26T04:59:24.8178163Z 431f6d892eb4: Pulling fs layer 2024-06-26T04:59:24.8178503Z aa2d7818db84: Waiting 2024-06-26T04:59:24.8178790Z ab1f0809c531: Waiting 2024-06-26T04:59:24.8179088Z 5df7e38e5d56: Pulling fs layer 2024-06-26T04:59:24.8179446Z ed9cdcecda09: Pulling fs layer 2024-06-26T04:59:24.8179787Z 431f6d892eb4: Waiting 2024-06-26T04:59:24.8180074Z 796fdea9be06: Waiting 2024-06-26T04:59:24.8180387Z e1bf0caebceb: Pulling fs layer 2024-06-26T04:59:24.8180737Z fb2d6dc9808d: Pulling fs layer 2024-06-26T04:59:24.8181081Z 5df7e38e5d56: Waiting 2024-06-26T04:59:24.8181390Z 9068f3d11281: Pulling fs layer 2024-06-26T04:59:24.8181718Z ed9cdcecda09: Waiting 2024-06-26T04:59:24.8182028Z e01067f03626: Pulling fs layer 2024-06-26T04:59:24.8182371Z e1bf0caebceb: Waiting 2024-06-26T04:59:24.8182669Z be74a2d30609: Pulling fs layer 2024-06-26T04:59:24.8183013Z 403d548a9d3f: Waiting 2024-06-26T04:59:24.8183304Z e01067f03626: Waiting 2024-06-26T04:59:24.8183601Z bfb36cacae9a: Pulling fs layer 2024-06-26T04:59:24.8184078Z fb2d6dc9808d: Waiting 2024-06-26T04:59:24.8184478Z 9068f3d11281: Waiting 2024-06-26T04:59:24.8185015Z 144221b84a63: Pulling fs layer 2024-06-26T04:59:24.8185630Z be74a2d30609: Waiting 2024-06-26T04:59:24.8186147Z 6c30dfdcf5a2: Pulling fs layer 2024-06-26T04:59:24.8186721Z a78edc01be60: Pulling fs layer 2024-06-26T04:59:24.8187372Z b92d914a069c: Pulling fs layer 2024-06-26T04:59:24.8188049Z ef77b35f5994: Pulling fs layer 2024-06-26T04:59:24.8188697Z ddd5132eddf6: Pulling fs layer 2024-06-26T04:59:24.8189356Z 733134e2ad51: Pulling fs layer 2024-06-26T04:59:24.8190001Z 2fb71352883c: Pulling fs layer 2024-06-26T04:59:24.8190560Z a78edc01be60: Waiting 2024-06-26T04:59:24.8191061Z ddd5132eddf6: Waiting 2024-06-26T04:59:24.8191556Z 733134e2ad51: Waiting 2024-06-26T04:59:24.8192058Z ef77b35f5994: Waiting 2024-06-26T04:59:24.8192604Z bfb36cacae9a: Waiting 2024-06-26T04:59:24.8193141Z 2fb71352883c: Waiting 2024-06-26T04:59:24.8193692Z f3c560e868a6: Pulling fs layer 2024-06-26T04:59:24.8194318Z b92d914a069c: Waiting 2024-06-26T04:59:24.8194995Z 440771933ac6: Pulling fs layer 2024-06-26T04:59:24.8195546Z 144221b84a63: Waiting 2024-06-26T04:59:24.8196031Z f3c560e868a6: Waiting 2024-06-26T04:59:24.8196562Z 526b68d3127a: Pulling fs layer 2024-06-26T04:59:24.8197219Z db7fe98fd9ce: Pulling fs layer 2024-06-26T04:59:24.8197879Z 998e82e7f434: Pulling fs layer 2024-06-26T04:59:24.8198517Z 6c30dfdcf5a2: Waiting 2024-06-26T04:59:24.8199070Z c5f8bd342f18: Pulling fs layer 2024-06-26T04:59:24.8199671Z 63417469eaf6: Pulling fs layer 2024-06-26T04:59:24.8200245Z db7fe98fd9ce: Waiting 2024-06-26T04:59:24.8200752Z 3b971402f483: Pulling fs layer 2024-06-26T04:59:24.8201432Z 998e82e7f434: Waiting 2024-06-26T04:59:24.8201994Z c9ba29c920af: Pulling fs layer 2024-06-26T04:59:24.8202617Z c5f8bd342f18: Waiting 2024-06-26T04:59:24.8203151Z 526b68d3127a: Waiting 2024-06-26T04:59:24.8203670Z 63417469eaf6: Waiting 2024-06-26T04:59:24.8204172Z d706890df263: Pulling fs layer 2024-06-26T04:59:24.8204773Z 3b1f57fd0104: Pulling fs layer 2024-06-26T04:59:24.8205391Z 565227b9e714: Pulling fs layer 2024-06-26T04:59:24.8206000Z c9ba29c920af: Waiting 2024-06-26T04:59:24.8206578Z f7be3bf4a877: Pulling fs layer 2024-06-26T04:59:24.8207171Z 565227b9e714: Waiting 2024-06-26T04:59:24.8207719Z 85b9d4888a5f: Pulling fs layer 2024-06-26T04:59:24.8208351Z c48375725932: Pulling fs layer 2024-06-26T04:59:24.8209151Z 2ee46f0bbde3: Pulling fs layer 2024-06-26T04:59:24.8209718Z f7be3bf4a877: Waiting 2024-06-26T04:59:24.8210246Z d706890df263: Waiting 2024-06-26T04:59:24.8210813Z e1d9c59b8ecb: Pulling fs layer 2024-06-26T04:59:24.8211472Z 48eeae0fe99d: Pulling fs layer 2024-06-26T04:59:24.8212095Z c48375725932: Waiting 2024-06-26T04:59:24.8212653Z 80d1fccb2771: Pulling fs layer 2024-06-26T04:59:24.8213218Z 48eeae0fe99d: Waiting 2024-06-26T04:59:24.8213713Z cdd9e4d7a0b1: Pulling fs layer 2024-06-26T04:59:24.8214355Z e1d9c59b8ecb: Waiting 2024-06-26T04:59:24.8214912Z f14381b4af2d: Pulling fs layer 2024-06-26T04:59:24.8215565Z f0c45732a663: Pulling fs layer 2024-06-26T04:59:24.8216206Z 80d1fccb2771: Waiting 2024-06-26T04:59:24.8216727Z 60298c8310b1: Pulling fs layer 2024-06-26T04:59:24.8217285Z 60298c8310b1: Waiting 2024-06-26T04:59:24.8217766Z cdd9e4d7a0b1: Waiting 2024-06-26T04:59:24.8218264Z f0c45732a663: Waiting 2024-06-26T04:59:24.8218758Z f14381b4af2d: Waiting 2024-06-26T04:59:24.8816739Z d56c75e5f4d9: Download complete 2024-06-26T04:59:24.9556307Z a07c3239eb66: Verifying Checksum 2024-06-26T04:59:24.9556848Z a07c3239eb66: Download complete 2024-06-26T04:59:25.1503649Z 560c024910be: Verifying Checksum 2024-06-26T04:59:25.1504176Z 560c024910be: Download complete 2024-06-26T04:59:25.2278505Z b9491f8e8765: Verifying Checksum 2024-06-26T04:59:25.2279285Z b9491f8e8765: Download complete 2024-06-26T04:59:25.3164666Z 9f184530f2e2: Download complete 2024-06-26T04:59:25.3983466Z c6150e458a98: Verifying Checksum 2024-06-26T04:59:25.3984039Z c6150e458a98: Download complete 2024-06-26T04:59:25.4682964Z bfaf1c5525d9: Verifying Checksum 2024-06-26T04:59:25.4683984Z bfaf1c5525d9: Download complete 2024-06-26T04:59:25.5358455Z 869aa8bd3e9d: Download complete 2024-06-26T04:59:25.6262919Z db1ea1e5664a: Verifying Checksum 2024-06-26T04:59:25.6263443Z db1ea1e5664a: Download complete 2024-06-26T04:59:25.7021669Z f4fc602d79e2: Verifying Checksum 2024-06-26T04:59:25.7022614Z f4fc602d79e2: Download complete 2024-06-26T04:59:25.8739207Z cadd53e932c0: Verifying Checksum 2024-06-26T04:59:25.8739811Z cadd53e932c0: Download complete 2024-06-26T04:59:25.8842149Z 4f4fb700ef54: Verifying Checksum 2024-06-26T04:59:25.8843660Z 4f4fb700ef54: Download complete 2024-06-26T04:59:25.9806707Z 96c42d4a99b0: Verifying Checksum 2024-06-26T04:59:25.9807499Z 96c42d4a99b0: Download complete 2024-06-26T04:59:26.0142108Z 560c024910be: Pull complete 2024-06-26T04:59:26.0500601Z 19990b8b1757: Verifying Checksum 2024-06-26T04:59:26.0501172Z 19990b8b1757: Download complete 2024-06-26T04:59:26.0628861Z d56c75e5f4d9: Pull complete 2024-06-26T04:59:26.1178109Z 807599da5b1a: Verifying Checksum 2024-06-26T04:59:26.1178643Z 807599da5b1a: Download complete 2024-06-26T04:59:26.2095799Z 3bc392ea6895: Verifying Checksum 2024-06-26T04:59:26.2096406Z 3bc392ea6895: Download complete 2024-06-26T04:59:26.3028877Z 51d0409ee5a5: Verifying Checksum 2024-06-26T04:59:26.3029398Z 51d0409ee5a5: Download complete 2024-06-26T04:59:26.4244380Z 2ab4a482ebb6: Verifying Checksum 2024-06-26T04:59:26.4245564Z 2ab4a482ebb6: Download complete 2024-06-26T04:59:26.4919104Z 7bd34ce32c4e: Verifying Checksum 2024-06-26T04:59:26.4919772Z 7bd34ce32c4e: Download complete 2024-06-26T04:59:26.5572200Z bc7869b23905: Download complete 2024-06-26T04:59:26.6855896Z 5309dec9ef4b: Download complete 2024-06-26T04:59:27.9541848Z f97cbbc932b6: Verifying Checksum 2024-06-26T04:59:27.9543564Z f97cbbc932b6: Download complete 2024-06-26T04:59:28.0232001Z 98cb4a93632e: Verifying Checksum 2024-06-26T04:59:28.0232614Z 98cb4a93632e: Download complete 2024-06-26T04:59:28.0871264Z 3fe82dcf5152: Verifying Checksum 2024-06-26T04:59:28.0872020Z 3fe82dcf5152: Download complete 2024-06-26T04:59:28.0958105Z 402182948e89: Verifying Checksum 2024-06-26T04:59:28.0958555Z 402182948e89: Download complete 2024-06-26T04:59:28.1478419Z ab9c373a715d: Verifying Checksum 2024-06-26T04:59:28.1479080Z ab9c373a715d: Download complete 2024-06-26T04:59:28.1880547Z aa2d7818db84: Download complete 2024-06-26T04:59:28.2246488Z f733c4f50868: Download complete 2024-06-26T04:59:28.2944152Z 796fdea9be06: Verifying Checksum 2024-06-26T04:59:28.2944779Z 796fdea9be06: Download complete 2024-06-26T04:59:28.3691917Z 58e9d24c5602: Verifying Checksum 2024-06-26T04:59:28.3692669Z 58e9d24c5602: Download complete 2024-06-26T04:59:28.4304966Z 448b0bc764da: Verifying Checksum 2024-06-26T04:59:28.4305611Z 448b0bc764da: Download complete 2024-06-26T04:59:28.5033566Z ab1f0809c531: Verifying Checksum 2024-06-26T04:59:28.5034129Z ab1f0809c531: Download complete 2024-06-26T04:59:28.5613755Z 403d548a9d3f: Verifying Checksum 2024-06-26T04:59:28.5614369Z 403d548a9d3f: Download complete 2024-06-26T04:59:28.6359942Z 431f6d892eb4: Verifying Checksum 2024-06-26T04:59:28.6360514Z 431f6d892eb4: Download complete 2024-06-26T04:59:28.7013523Z 5df7e38e5d56: Download complete 2024-06-26T04:59:28.7679244Z ed9cdcecda09: Verifying Checksum 2024-06-26T04:59:28.7680051Z ed9cdcecda09: Download complete 2024-06-26T04:59:28.8377249Z e1bf0caebceb: Verifying Checksum 2024-06-26T04:59:28.8377901Z e1bf0caebceb: Download complete 2024-06-26T04:59:28.9052751Z fb2d6dc9808d: Verifying Checksum 2024-06-26T04:59:28.9053320Z fb2d6dc9808d: Download complete 2024-06-26T04:59:28.9973512Z 9068f3d11281: Verifying Checksum 2024-06-26T04:59:28.9973983Z 9068f3d11281: Download complete 2024-06-26T04:59:29.0762961Z e01067f03626: Verifying Checksum 2024-06-26T04:59:29.0763500Z e01067f03626: Download complete 2024-06-26T04:59:29.6405593Z be74a2d30609: Verifying Checksum 2024-06-26T04:59:29.6406378Z be74a2d30609: Download complete 2024-06-26T04:59:29.7169450Z bfb36cacae9a: Download complete 2024-06-26T04:59:29.8352250Z 144221b84a63: Verifying Checksum 2024-06-26T04:59:29.8353358Z 144221b84a63: Download complete 2024-06-26T04:59:29.9137798Z 6c30dfdcf5a2: Download complete 2024-06-26T04:59:29.9938915Z a78edc01be60: Download complete 2024-06-26T04:59:30.2730087Z b92d914a069c: Verifying Checksum 2024-06-26T04:59:30.2731494Z b92d914a069c: Download complete 2024-06-26T04:59:30.3667354Z ef77b35f5994: Verifying Checksum 2024-06-26T04:59:30.3668079Z ef77b35f5994: Download complete 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48eeae0fe99d: Pull complete 2024-06-26T05:00:54.9965612Z 80d1fccb2771: Pull complete 2024-06-26T05:00:55.0479377Z cdd9e4d7a0b1: Pull complete 2024-06-26T05:00:55.1031798Z f14381b4af2d: Pull complete 2024-06-26T05:00:55.2175186Z f0c45732a663: Pull complete 2024-06-26T05:00:56.7486806Z 60298c8310b1: Pull complete 2024-06-26T05:00:56.8094959Z Digest: sha256:7b399ecc0d0cb44dc26da833aa04b6714aa8a8efdabb4a4d766be163ffdd83c4 2024-06-26T05:00:56.8121872Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T05:00:56.8143631Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T05:00:56.8184481Z ##[group]Run echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT" 2024-06-26T05:00:56.8185434Z echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT" 2024-06-26T05:00:56.8196409Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T05:00:56.8196908Z env: 2024-06-26T05:00:56.8197171Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:00:56.8197492Z ##[endgroup] 2024-06-26T05:00:56.8262716Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-06-26T05:00:56.8263490Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-06-26T05:00:56.8264189Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 & 2024-06-26T05:00:56.8264849Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2024-06-26T05:00:56.8271998Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T05:00:56.8272494Z env: 2024-06-26T05:00:56.8272757Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:00:56.8273074Z ##[endgroup] 2024-06-26T05:00:57.2974751Z Defaulting to user installation because normal site-packages is not writeable 2024-06-26T05:00:57.3188101Z Requirement already satisfied: psutil==5.9.1 in /home/ec2-user/.local/lib/python3.7/site-packages (5.9.1) 2024-06-26T05:00:57.3287141Z Requirement already satisfied: nvidia-ml-py==11.525.84 in /home/ec2-user/.local/lib/python3.7/site-packages (11.525.84) 2024-06-26T05:00:57.4511112Z Prepare all required actions 2024-06-26T05:00:57.4511605Z Getting action download info 2024-06-26T05:00:57.6425469Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2024-06-26T05:00:57.8418863Z Download action repository 'actions/download-artifact@v3' (SHA:9bc31d5ccc31df68ecc42ccf4149144866c47d8a) 2024-06-26T05:00:57.9582149Z ##[group]Run ./.github/actions/download-build-artifacts 2024-06-26T05:00:57.9582627Z with: 2024-06-26T05:00:57.9583016Z name: linux-focal-py3.12-clang10-experimental-split-build 2024-06-26T05:00:57.9583551Z s3-bucket: gha-artifacts 2024-06-26T05:00:57.9583870Z env: 2024-06-26T05:00:57.9584105Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:00:57.9584425Z ##[endgroup] 2024-06-26T05:00:57.9617996Z ##[group]Run seemethere/download-artifact-s3@v4 2024-06-26T05:00:57.9618414Z with: 2024-06-26T05:00:57.9618819Z name: linux-focal-py3.12-clang10-experimental-split-build 2024-06-26T05:00:57.9619399Z s3-bucket: gha-artifacts 2024-06-26T05:00:57.9619859Z region: us-east-1 2024-06-26T05:00:57.9620132Z env: 2024-06-26T05:00:57.9620367Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:00:57.9620690Z ##[endgroup] 2024-06-26T05:00:58.4289711Z (node:16363) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-06-26T05:00:58.4290450Z 2024-06-26T05:00:58.4290717Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-06-26T05:00:58.4291435Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-06-26T05:00:58.4292433Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-06-26T05:00:58.5061655Z Found 1 objects with prefix pytorch/pytorch/9673645538/linux-focal-py3.12-clang10-experimental-split-build/ 2024-06-26T05:00:58.5063023Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-06-26T05:01:16.5837163Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-06-26T05:01:16.5843989Z Artifact download has finished successfully 2024-06-26T05:01:16.5981087Z ##[group]Run unzip -o artifacts.zip 2024-06-26T05:01:16.5981515Z unzip -o artifacts.zip 2024-06-26T05:01:16.5988799Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T05:01:16.5989276Z env: 2024-06-26T05:01:16.5989533Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:16.5989858Z ##[endgroup] 2024-06-26T05:01:16.6321638Z Archive: artifacts.zip 2024-06-26T05:01:16.6347901Z creating: dist/ 2024-06-26T05:01:18.3860899Z inflating: dist/torch_no_python-2.5.0a0+gitb8c4c54-py3-none-any.whl 2024-06-26T05:01:18.7780904Z inflating: 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inflating: build/lib/torch/include/ATen/LegacyVmapTransforms.h 2024-06-26T05:01:24.0605541Z inflating: build/lib/torch/include/ATen/LinalgBackend.h 2024-06-26T05:01:24.0606164Z inflating: build/lib/torch/include/ATen/MapAllocator.h 2024-06-26T05:01:24.0606757Z inflating: build/lib/torch/include/ATen/MatrixRef.h 2024-06-26T05:01:24.0607687Z inflating: build/lib/torch/include/ATen/MemoryOverlap.h 2024-06-26T05:01:24.0608315Z extracting: build/lib/torch/include/ATen/NamedTensor.h 2024-06-26T05:01:24.0608951Z inflating: build/lib/torch/include/ATen/NamedTensorUtils.h 2024-06-26T05:01:24.0609602Z inflating: build/lib/torch/include/ATen/NestedTensorImpl.h 2024-06-26T05:01:24.0610239Z inflating: build/lib/torch/include/ATen/NumericUtils.h 2024-06-26T05:01:24.0610839Z inflating: build/lib/torch/include/ATen/OpMathType.h 2024-06-26T05:01:24.0611968Z inflating: build/lib/torch/include/ATen/OpaqueTensorImpl.h 2024-06-26T05:01:24.0612801Z inflating: build/lib/torch/include/ATen/PTThreadPool.h 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build/lib/torch/include/ATen/SequenceNumber.h 2024-06-26T05:01:24.0622836Z extracting: build/lib/torch/include/ATen/SmallVector.h 2024-06-26T05:01:24.0623647Z inflating: build/lib/torch/include/ATen/SparseCsrTensorImpl.h 2024-06-26T05:01:24.0624437Z inflating: build/lib/torch/include/ATen/SparseCsrTensorUtils.h 2024-06-26T05:01:24.0625150Z inflating: build/lib/torch/include/ATen/SparseTensorImpl.h 2024-06-26T05:01:24.0625875Z extracting: build/lib/torch/include/ATen/Storage.h 2024-06-26T05:01:24.0626461Z inflating: build/lib/torch/include/ATen/StorageUtils.h 2024-06-26T05:01:24.0627039Z extracting: build/lib/torch/include/ATen/Tensor.h 2024-06-26T05:01:24.0627617Z inflating: build/lib/torch/include/ATen/TensorAccessor.h 2024-06-26T05:01:24.0628248Z inflating: build/lib/torch/include/ATen/TensorGeometry.h 2024-06-26T05:01:24.0629001Z inflating: build/lib/torch/include/ATen/TensorIndexing.h 2024-06-26T05:01:24.0629718Z inflating: build/lib/torch/include/ATen/TensorIterator.h 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build/lib/torch/include/ATen/Utils.h 2024-06-26T05:01:24.0639597Z inflating: build/lib/torch/include/ATen/Version.h 2024-06-26T05:01:24.0640180Z inflating: build/lib/torch/include/ATen/WrapDimUtils.h 2024-06-26T05:01:24.0641053Z inflating: build/lib/torch/include/ATen/WrapDimUtilsMulti.h 2024-06-26T05:01:24.0641885Z inflating: build/lib/torch/include/ATen/autocast_mode.h 2024-06-26T05:01:24.0642583Z inflating: build/lib/torch/include/ATen/ceil_div.h 2024-06-26T05:01:24.0643285Z inflating: build/lib/torch/include/ATen/code_template.h 2024-06-26T05:01:24.0643976Z inflating: build/lib/torch/include/ATen/cpp_custom_type_hack.h 2024-06-26T05:01:24.0644719Z inflating: build/lib/torch/include/ATen/div_rtn.h 2024-06-26T05:01:24.0645415Z inflating: build/lib/torch/include/ATen/dlpack.h 2024-06-26T05:01:24.0646018Z inflating: build/lib/torch/include/ATen/jit_macros.h 2024-06-26T05:01:24.0646625Z inflating: build/lib/torch/include/ATen/jiterator_macros.h 2024-06-26T05:01:24.0647467Z inflating: build/lib/torch/include/ATen/record_function.h 2024-06-26T05:01:24.0648177Z inflating: build/lib/torch/include/ATen/CPUFunctions.h 2024-06-26T05:01:24.0649786Z inflating: build/lib/torch/include/ATen/CPUFunctions_inl.h 2024-06-26T05:01:24.0650690Z inflating: build/lib/torch/include/ATen/CompositeExplicitAutogradFunctions.h 2024-06-26T05:01:24.0652712Z inflating: build/lib/torch/include/ATen/CompositeExplicitAutogradFunctions_inl.h 2024-06-26T05:01:24.0653877Z inflating: build/lib/torch/include/ATen/CompositeExplicitAutogradNonFunctionalFunctions.h 2024-06-26T05:01:24.0655221Z inflating: build/lib/torch/include/ATen/CompositeExplicitAutogradNonFunctionalFunctions_inl.h 2024-06-26T05:01:24.0656332Z inflating: build/lib/torch/include/ATen/CompositeImplicitAutogradFunctions.h 2024-06-26T05:01:24.0657343Z inflating: build/lib/torch/include/ATen/CompositeImplicitAutogradFunctions_inl.h 2024-06-26T05:01:24.0658454Z inflating: build/lib/torch/include/ATen/CompositeImplicitAutogradNestedTensorFunctions.h 2024-06-26T05:01:24.0659669Z inflating: build/lib/torch/include/ATen/CompositeImplicitAutogradNestedTensorFunctions_inl.h 2024-06-26T05:01:24.0661486Z inflating: build/lib/torch/include/ATen/Functions.h 2024-06-26T05:01:24.0662108Z inflating: build/lib/torch/include/ATen/MetaFunctions.h 2024-06-26T05:01:24.0663168Z inflating: build/lib/torch/include/ATen/MetaFunctions_inl.h 2024-06-26T05:01:24.0664366Z inflating: build/lib/torch/include/ATen/MethodOperators.h 2024-06-26T05:01:24.0667745Z inflating: build/lib/torch/include/ATen/NativeFunctions.h 2024-06-26T05:01:24.0670923Z inflating: build/lib/torch/include/ATen/NativeMetaFunctions.h 2024-06-26T05:01:24.0674251Z inflating: build/lib/torch/include/ATen/Operators.h 2024-06-26T05:01:24.0764736Z inflating: build/lib/torch/include/ATen/RedispatchFunctions.h 2024-06-26T05:01:24.0803930Z inflating: build/lib/torch/include/ATen/RegistrationDeclarations.h 2024-06-26T05:01:24.0877889Z inflating: build/lib/torch/include/ATen/VmapGeneratedPlumbing.h 2024-06-26T05:01:24.0878604Z inflating: build/lib/torch/include/ATen/CUDAFunctions.h 2024-06-26T05:01:24.0880364Z inflating: build/lib/torch/include/ATen/CUDAFunctions_inl.h 2024-06-26T05:01:24.0880999Z creating: build/lib/torch/include/ATen/cpu/ 2024-06-26T05:01:24.0881608Z inflating: build/lib/torch/include/ATen/cpu/FlushDenormal.h 2024-06-26T05:01:24.0882225Z inflating: build/lib/torch/include/ATen/cpu/Utils.h 2024-06-26T05:01:24.0882790Z inflating: build/lib/torch/include/ATen/cpu/vml.h 2024-06-26T05:01:24.0883319Z creating: build/lib/torch/include/ATen/cpu/vec/ 2024-06-26T05:01:24.0883860Z creating: build/lib/torch/include/ATen/cpu/vec/vec256/ 2024-06-26T05:01:24.0884593Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/missing_vld1_neon.h 2024-06-26T05:01:24.0885471Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/missing_vst1_neon.h 2024-06-26T05:01:24.0886275Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256.h 2024-06-26T05:01:24.0888672Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_bfloat16.h 2024-06-26T05:01:24.0890293Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_complex_double.h 2024-06-26T05:01:24.0892135Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_complex_float.h 2024-06-26T05:01:24.0893025Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_convert.h 2024-06-26T05:01:24.0893920Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_double.h 2024-06-26T05:01:24.0895797Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_float.h 2024-06-26T05:01:24.0897901Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_float_neon.h 2024-06-26T05:01:24.0899895Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_half_neon.h 2024-06-26T05:01:24.0903561Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_int.h 2024-06-26T05:01:24.0904347Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_mask.h 2024-06-26T05:01:24.0907406Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_qint.h 2024-06-26T05:01:24.0908104Z creating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/ 2024-06-26T05:01:24.0908899Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_bfloat16_vsx.h 2024-06-26T05:01:24.0909941Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_common_vsx.h 2024-06-26T05:01:24.0911003Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_complex_double_vsx.h 2024-06-26T05:01:24.0912729Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_complex_float_vsx.h 2024-06-26T05:01:24.0913910Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_double_vsx.h 2024-06-26T05:01:24.0915326Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_float_vsx.h 2024-06-26T05:01:24.0916525Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_int16_vsx.h 2024-06-26T05:01:24.0917550Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_int32_vsx.h 2024-06-26T05:01:24.0918525Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_int64_vsx.h 2024-06-26T05:01:24.0919535Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_qint32_vsx.h 2024-06-26T05:01:24.0920942Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_qint8_vsx.h 2024-06-26T05:01:24.0923038Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_quint8_vsx.h 2024-06-26T05:01:24.0924694Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vsx_helpers.h 2024-06-26T05:01:24.0925462Z creating: build/lib/torch/include/ATen/cpu/vec/vec256/zarch/ 2024-06-26T05:01:24.0931080Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/zarch/vec256_zarch.h 2024-06-26T05:01:24.0931821Z creating: build/lib/torch/include/ATen/cpu/vec/vec512/ 2024-06-26T05:01:24.0932475Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512.h 2024-06-26T05:01:24.0936369Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_bfloat16.h 2024-06-26T05:01:24.0938250Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_complex_double.h 2024-06-26T05:01:24.0940877Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_complex_float.h 2024-06-26T05:01:24.0941766Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_convert.h 2024-06-26T05:01:24.0942794Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_double.h 2024-06-26T05:01:24.0945074Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_float.h 2024-06-26T05:01:24.0948283Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_int.h 2024-06-26T05:01:24.0949067Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_mask.h 2024-06-26T05:01:24.0952161Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_qint.h 2024-06-26T05:01:24.0952894Z inflating: build/lib/torch/include/ATen/cpu/vec/functional.h 2024-06-26T05:01:24.0953617Z inflating: build/lib/torch/include/ATen/cpu/vec/functional_base.h 2024-06-26T05:01:24.0955330Z inflating: build/lib/torch/include/ATen/cpu/vec/functional_bfloat16.h 2024-06-26T05:01:24.0956187Z inflating: build/lib/torch/include/ATen/cpu/vec/intrinsics.h 2024-06-26T05:01:24.0956846Z inflating: build/lib/torch/include/ATen/cpu/vec/vec.h 2024-06-26T05:01:24.0959167Z inflating: build/lib/torch/include/ATen/cpu/vec/vec_base.h 2024-06-26T05:01:24.0959849Z inflating: build/lib/torch/include/ATen/cpu/vec/vec_convert.h 2024-06-26T05:01:24.0960513Z inflating: build/lib/torch/include/ATen/cpu/vec/vec_half.h 2024-06-26T05:01:24.0961233Z inflating: build/lib/torch/include/ATen/cpu/vec/vec_mask.h 2024-06-26T05:01:24.0962240Z inflating: build/lib/torch/include/ATen/cpu/vec/vec_n.h 2024-06-26T05:01:24.0962799Z creating: build/lib/torch/include/ATen/core/ 2024-06-26T05:01:24.0963359Z inflating: build/lib/torch/include/ATen/core/ATenGeneral.h 2024-06-26T05:01:24.0964014Z inflating: build/lib/torch/include/ATen/core/ATenOpList.h 2024-06-26T05:01:24.0964641Z inflating: build/lib/torch/include/ATen/core/ATen_fwd.h 2024-06-26T05:01:24.0965246Z inflating: build/lib/torch/include/ATen/core/ATen_pch.h 2024-06-26T05:01:24.0965927Z inflating: build/lib/torch/include/ATen/core/Array.h 2024-06-26T05:01:24.0966537Z inflating: build/lib/torch/include/ATen/core/Backtrace.h 2024-06-26T05:01:24.0967240Z inflating: build/lib/torch/include/ATen/core/CachingHostAllocator.h 2024-06-26T05:01:24.0968020Z inflating: build/lib/torch/include/ATen/core/CheckMemoryFormat.h 2024-06-26T05:01:24.0968839Z inflating: build/lib/torch/include/ATen/core/DeprecatedTypeProperties.h 2024-06-26T05:01:24.0969787Z inflating: build/lib/torch/include/ATen/core/DeprecatedTypePropertiesRegistry.h 2024-06-26T05:01:24.0970573Z inflating: 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build/lib/torch/include/ATen/core/List.h 2024-06-26T05:01:24.1000297Z inflating: build/lib/torch/include/ATen/core/List_inl.h 2024-06-26T05:01:24.1001022Z inflating: build/lib/torch/include/ATen/core/MT19937RNGEngine.h 2024-06-26T05:01:24.1001697Z inflating: build/lib/torch/include/ATen/core/NamedTensor.h 2024-06-26T05:01:24.1002429Z inflating: build/lib/torch/include/ATen/core/NestedIntSymNodeImpl.h 2024-06-26T05:01:24.1003181Z inflating: build/lib/torch/include/ATen/core/PhiloxRNGEngine.h 2024-06-26T05:01:24.1003949Z inflating: build/lib/torch/include/ATen/core/PythonFallbackKernel.h 2024-06-26T05:01:24.1004839Z inflating: build/lib/torch/include/ATen/core/PythonOpRegistrationTrampoline.h 2024-06-26T05:01:24.1005684Z inflating: build/lib/torch/include/ATen/core/QuantizerBase.h 2024-06-26T05:01:24.1006395Z inflating: build/lib/torch/include/ATen/core/Range.h 2024-06-26T05:01:24.1006995Z inflating: build/lib/torch/include/ATen/core/Reduction.h 2024-06-26T05:01:24.1007587Z extracting: 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build/lib/torch/include/ATen/cuda/tunable/GemmCommon.h 2024-06-26T05:01:24.1113816Z inflating: build/lib/torch/include/ATen/cuda/tunable/GemmHipblaslt.h 2024-06-26T05:01:24.1114892Z inflating: build/lib/torch/include/ATen/cuda/tunable/GemmRocblas.h 2024-06-26T05:01:24.1115748Z inflating: build/lib/torch/include/ATen/cuda/tunable/StreamTimer.h 2024-06-26T05:01:24.1116614Z inflating: build/lib/torch/include/ATen/cuda/tunable/Tunable.h 2024-06-26T05:01:24.1117384Z inflating: build/lib/torch/include/ATen/cuda/tunable/TunableGemm.h 2024-06-26T05:01:24.1118138Z inflating: build/lib/torch/include/ATen/cuda/tunable/TunableOp.h 2024-06-26T05:01:24.1118798Z creating: build/lib/torch/include/ATen/cudnn/ 2024-06-26T05:01:24.1119416Z inflating: build/lib/torch/include/ATen/cudnn/Descriptors.h 2024-06-26T05:01:24.1120092Z extracting: build/lib/torch/include/ATen/cudnn/Exceptions.h 2024-06-26T05:01:24.1120819Z inflating: build/lib/torch/include/ATen/cudnn/Handle.h 2024-06-26T05:01:24.1121466Z 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build/lib/torch/include/torch/csrc/distributed/c10d/reducer.hpp 2024-06-26T05:01:24.5882105Z inflating: build/lib/torch/include/torch/csrc/distributed/c10d/reducer_timer.hpp 2024-06-26T05:01:24.5882807Z inflating: build/lib/torch/include/torch/csrc/distributed/c10d/sequence_num.hpp 2024-06-26T05:01:24.5883072Z creating: build/lib/torch/include/torch/csrc/distributed/rpc/ 2024-06-26T05:01:24.5883797Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/agent_utils.h 2024-06-26T05:01:24.5884973Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/message.h 2024-06-26T05:01:24.5885666Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/py_rref.h 2024-06-26T05:01:24.5886306Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/python_call.h 2024-06-26T05:01:24.5886994Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/python_functions.h 2024-06-26T05:01:24.5887565Z inflating: 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build/lib/torch/include/torch/csrc/distributed/rpc/rref_context.h 2024-06-26T05:01:24.5897841Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/rref_impl.h 2024-06-26T05:01:24.5898646Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/rref_proto.h 2024-06-26T05:01:24.5899423Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/script_call.h 2024-06-26T05:01:24.5900135Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/script_remote_call.h 2024-06-26T05:01:24.5900643Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/script_resp.h 2024-06-26T05:01:24.5902676Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/tensorpipe_agent.h 2024-06-26T05:01:24.5903559Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/tensorpipe_utils.h 2024-06-26T05:01:24.5904258Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/torchscript_functions.h 2024-06-26T05:01:24.5904812Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/types.h 2024-06-26T05:01:24.5905588Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/unpickled_python_call.h 2024-06-26T05:01:24.5906359Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/unpickled_python_remote_call.h 2024-06-26T05:01:24.5906985Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/utils.h 2024-06-26T05:01:24.5907271Z creating: build/lib/torch/include/torch/csrc/distributed/autograd/ 2024-06-26T05:01:24.5907719Z creating: build/lib/torch/include/torch/csrc/distributed/autograd/context/ 2024-06-26T05:01:24.5909006Z inflating: build/lib/torch/include/torch/csrc/distributed/autograd/context/container.h 2024-06-26T05:01:24.5909957Z inflating: build/lib/torch/include/torch/csrc/distributed/autograd/context/context.h 2024-06-26T05:01:24.5910326Z creating: build/lib/torch/include/torch/csrc/distributed/autograd/functions/ 2024-06-26T05:01:24.5911075Z inflating: 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build/bin/CppSignature_test 2024-06-26T05:01:28.0824143Z inflating: build/bin/ivalue_test 2024-06-26T05:01:28.0873432Z inflating: build/bin/mobile_memory_cleanup 2024-06-26T05:01:28.0926589Z inflating: build/bin/scalar_tensor_test 2024-06-26T05:01:28.0975770Z inflating: build/bin/math_kernel_test 2024-06-26T05:01:28.1289908Z inflating: build/bin/op_registration_test 2024-06-26T05:01:28.1336434Z inflating: build/bin/reduce_ops_test 2024-06-26T05:01:28.1389468Z inflating: build/bin/native_test 2024-06-26T05:01:28.1438646Z inflating: build/bin/memory_format_test 2024-06-26T05:01:28.1486654Z inflating: build/bin/packedtensoraccessor_test 2024-06-26T05:01:28.1553419Z inflating: build/bin/pow_test 2024-06-26T05:01:28.1606511Z inflating: build/bin/quantized_test 2024-06-26T05:01:28.1653643Z inflating: build/bin/reportMemoryUsage_test 2024-06-26T05:01:28.1702920Z inflating: build/bin/test_edge_op_registration 2024-06-26T05:01:28.1720178Z inflating: build/bin/tutorial_tensorexpr 2024-06-26T05:01:28.2757630Z inflating: build/bin/test_tensorexpr 2024-06-26T05:01:28.3328622Z inflating: build/bin/test_jit 2024-06-26T05:01:28.3332894Z inflating: build/bin/torch_shm_manager 2024-06-26T05:01:28.3333719Z creating: .additional_ci_files/ 2024-06-26T05:01:28.3388361Z inflating: .additional_ci_files/test-times.json 2024-06-26T05:01:28.3604276Z inflating: .additional_ci_files/test-class-times.json 2024-06-26T05:01:28.3650949Z ##[group]Run rm artifacts.zip 2024-06-26T05:01:28.3651476Z rm artifacts.zip 2024-06-26T05:01:28.3658922Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T05:01:28.3659418Z env: 2024-06-26T05:01:28.3659661Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:28.3659990Z ##[endgroup] 2024-06-26T05:01:28.4371512Z ##[group]Run df -H 2024-06-26T05:01:28.4371807Z df -H 2024-06-26T05:01:28.4379140Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T05:01:28.4379621Z env: 2024-06-26T05:01:28.4379881Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:28.4380205Z ##[endgroup] 2024-06-26T05:01:28.4421522Z Filesystem Size Used Avail Use% Mounted on 2024-06-26T05:01:28.4422323Z devtmpfs 8.2G 0 8.2G 0% /dev 2024-06-26T05:01:28.4423058Z tmpfs 8.2G 4.4M 8.2G 1% /dev/shm 2024-06-26T05:01:28.4423514Z tmpfs 8.2G 410k 8.2G 1% /run 2024-06-26T05:01:28.4423972Z tmpfs 8.2G 0 8.2G 0% /sys/fs/cgroup 2024-06-26T05:01:28.4424423Z /dev/nvme0n1p1 162G 20G 142G 13% / 2024-06-26T05:01:28.4470330Z Prepare all required actions 2024-06-26T05:01:28.4470889Z Getting action download info 2024-06-26T05:01:28.5767961Z ##[group]Run ./.github/actions/download-td-artifacts 2024-06-26T05:01:28.5768413Z with: 2024-06-26T05:01:28.5768644Z env: 2024-06-26T05:01:28.5768893Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:28.5769202Z ##[endgroup] 2024-06-26T05:01:28.5802760Z ##[group]Run seemethere/download-artifact-s3@v4 2024-06-26T05:01:28.5803199Z with: 2024-06-26T05:01:28.5803430Z name: td_results 2024-06-26T05:01:28.5803726Z s3-bucket: gha-artifacts 2024-06-26T05:01:28.5804058Z region: us-east-1 2024-06-26T05:01:28.5804317Z env: 2024-06-26T05:01:28.5804566Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:28.5804887Z ##[endgroup] 2024-06-26T05:01:29.0462504Z (node:16447) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-06-26T05:01:29.0463245Z 2024-06-26T05:01:29.0463572Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-06-26T05:01:29.0464284Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-06-26T05:01:29.0465167Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-06-26T05:01:29.1236983Z Found 1 objects with prefix pytorch/pytorch/9673645538/td_results/ 2024-06-26T05:01:29.1238090Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-06-26T05:01:29.1741065Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-06-26T05:01:29.1746749Z Artifact download has finished successfully 2024-06-26T05:01:29.1899687Z ##[group]Run mkdir -p .additional_ci_files 2024-06-26T05:01:29.1900146Z mkdir -p .additional_ci_files 2024-06-26T05:01:29.1900702Z mv td_results.json .additional_ci_files/td_results.json 2024-06-26T05:01:29.1908423Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T05:01:29.1908917Z env: 2024-06-26T05:01:29.1909182Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:29.1909510Z ##[endgroup] 2024-06-26T05:01:29.1988805Z ##[group]Run .github/scripts/parse_ref.py 2024-06-26T05:01:29.1989264Z .github/scripts/parse_ref.py 2024-06-26T05:01:29.1996401Z shell: /usr/bin/bash -e {0} 2024-06-26T05:01:29.1996740Z env: 2024-06-26T05:01:29.1996993Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:29.1997305Z ##[endgroup] 2024-06-26T05:01:29.2242881Z Prepare all required actions 2024-06-26T05:01:29.2284127Z ##[group]Run ./.github/actions/get-workflow-job-id 2024-06-26T05:01:29.2284578Z with: 2024-06-26T05:01:29.2285149Z github-token: *** 2024-06-26T05:01:29.2285436Z env: 2024-06-26T05:01:29.2285681Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:29.2286007Z ##[endgroup] 2024-06-26T05:01:29.2303415Z ##[group]Run set -eux 2024-06-26T05:01:29.2303730Z set -eux 2024-06-26T05:01:29.2304312Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2024-06-26T05:01:29.2311772Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T05:01:29.2312260Z env: 2024-06-26T05:01:29.2312514Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:29.2313029Z GITHUB_TOKEN: *** 2024-06-26T05:01:29.2313306Z ##[endgroup] 2024-06-26T05:01:29.2333436Z + python3 .github/scripts/get_workflow_job_id.py 9673645538 i-0e3c0504b423c9909 2024-06-26T05:01:31.1183242Z setting job-id=26688306089 2024-06-26T05:01:31.1184348Z setting job-name=linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, linux.2xlarge, unstable) 2024-06-26T05:01:31.1407822Z Prepare all required actions 2024-06-26T05:01:31.1408262Z Getting action download info 2024-06-26T05:01:31.2541892Z ##[group]Run ./.github/actions/filter-test-configs 2024-06-26T05:01:31.2542345Z with: 2024-06-26T05:01:31.2542786Z github-token: *** 2024-06-26T05:01:31.2545575Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}]} 2024-06-26T05:01:31.2549100Z job-name: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, linux.2xlarge, unstable) 2024-06-26T05:01:31.2549911Z env: 2024-06-26T05:01:31.2550236Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:31.2550573Z ##[endgroup] 2024-06-26T05:01:31.2594384Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2024-06-26T05:01:31.2596487Z with: 2024-06-26T05:01:31.2596735Z shell: bash 2024-06-26T05:01:31.2596996Z timeout_minutes: 10 2024-06-26T05:01:31.2597301Z max_attempts: 5 2024-06-26T05:01:31.2597591Z retry_wait_seconds: 30 2024-06-26T05:01:31.2598744Z command: set -eux # PyYAML 6.0 doesn't work with MacOS x86 anymore # This must run on Python-3.7 (AmazonLinux2) so can't use request=3.32.2 python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-06-26T05:01:31.2599959Z polling_interval_seconds: 1 2024-06-26T05:01:31.2600313Z warning_on_retry: true 2024-06-26T05:01:31.2600639Z continue_on_error: false 2024-06-26T05:01:31.2601004Z env: 2024-06-26T05:01:31.2601251Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:31.2601783Z GITHUB_TOKEN: *** 2024-06-26T05:01:31.2602056Z ##[endgroup] 2024-06-26T05:01:31.3120943Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-06-26T05:01:31.5301847Z Defaulting to user installation because normal site-packages is not writeable 2024-06-26T05:01:31.5475389Z Requirement already satisfied: requests==2.27.1 in /home/ec2-user/.local/lib/python3.7/site-packages (2.27.1) 2024-06-26T05:01:31.5609650Z Requirement already satisfied: pyyaml==6.0.1 in /home/ec2-user/.local/lib/python3.7/site-packages (6.0.1) 2024-06-26T05:01:31.5618736Z Requirement already satisfied: charset-normalizer~=2.0.0; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.27.1) (2.0.12) 2024-06-26T05:01:31.5638789Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.27.1) (2024.6.2) 2024-06-26T05:01:31.5647296Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.27.1) (1.26.19) 2024-06-26T05:01:31.5827785Z Requirement already satisfied: idna<4,>=2.5; python_version >= "3" in /home/ec2-user/.local/lib/python3.7/site-packages (from requests==2.27.1) (3.7) 2024-06-26T05:01:32.3119766Z Command completed after 1 attempt(s). 2024-06-26T05:01:32.3164891Z ##[group]Run set -x 2024-06-26T05:01:32.3165208Z set -x 2024-06-26T05:01:32.3165484Z  2024-06-26T05:01:32.3166023Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-06-26T05:01:32.3166711Z # in runner workspace 2024-06-26T05:01:32.3167228Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2024-06-26T05:01:32.3175013Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T05:01:32.3175495Z env: 2024-06-26T05:01:32.3175754Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:32.3176082Z ##[endgroup] 2024-06-26T05:01:32.3198199Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2024-06-26T05:01:32.3413291Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2024-06-26T05:01:32.3429245Z echo "Workflow: ${GITHUB_WORKFLOW}" 2024-06-26T05:01:32.3429749Z echo "Job name: ${JOB_NAME}" 2024-06-26T05:01:32.3430140Z  2024-06-26T05:01:32.3430677Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-06-26T05:01:32.3431349Z # in runner workspace 2024-06-26T05:01:32.3431918Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2024-06-26T05:01:32.3432739Z  --workflow "${GITHUB_WORKFLOW}" \ 2024-06-26T05:01:32.3433173Z  --job-name "${JOB_NAME}" \ 2024-06-26T05:01:32.3436492Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}]}" \ 2024-06-26T05:01:32.3439478Z  --selected-test-configs "" \ 2024-06-26T05:01:32.3439919Z  --pr-number "${PR_NUMBER}" \ 2024-06-26T05:01:32.3440329Z  --tag "${TAG}" \ 2024-06-26T05:01:32.3440688Z  --event-name "${EVENT_NAME}" \ 2024-06-26T05:01:32.3441201Z  --schedule "${SCHEDULE}" \ 2024-06-26T05:01:32.3441607Z  --branch "${HEAD_BRANCH}" 2024-06-26T05:01:32.3448715Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T05:01:32.3449190Z env: 2024-06-26T05:01:32.3449458Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:32.3449998Z GITHUB_TOKEN: *** 2024-06-26T05:01:32.3450729Z JOB_NAME: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, linux.2xlarge, unstable) 2024-06-26T05:01:32.3451569Z PR_NUMBER: 129470 2024-06-26T05:01:32.3451854Z TAG: 2024-06-26T05:01:32.3452098Z EVENT_NAME: pull_request 2024-06-26T05:01:32.3452420Z SCHEDULE: 2024-06-26T05:01:32.3452681Z HEAD_BRANCH: 2024-06-26T05:01:32.3452937Z ##[endgroup] 2024-06-26T05:01:32.3472500Z Workflow: pull 2024-06-26T05:01:32.3473452Z Job name: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, linux.2xlarge, unstable) 2024-06-26T05:01:32.5513020Z INFO:root:Found no test-config label on the PR, so all test configs are included 2024-06-26T05:01:33.1851373Z ##[group]Run echo "Filtered matrix:" 2024-06-26T05:01:33.1851818Z echo "Filtered matrix:" 2024-06-26T05:01:33.1854936Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge", "unstable": "unstable"}]}" 2024-06-26T05:01:33.1857808Z  2024-06-26T05:01:33.1858051Z echo 2024-06-26T05:01:33.1858405Z echo "Is the current job unstable? True" 2024-06-26T05:01:33.1858853Z  2024-06-26T05:01:33.1859089Z echo 2024-06-26T05:01:33.1859430Z echo "Is keep-going label set? False" 2024-06-26T05:01:33.1859874Z  2024-06-26T05:01:33.1860104Z echo 2024-06-26T05:01:33.1860399Z echo "Renabled issues? " 2024-06-26T05:01:33.1867942Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T05:01:33.1868426Z env: 2024-06-26T05:01:33.1868689Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:33.1869029Z ##[endgroup] 2024-06-26T05:01:33.1890169Z Filtered matrix: 2024-06-26T05:01:33.1894079Z {include: [{config: default, shard: 1, num_shards: 3, runner: linux.2xlarge, unstable: unstable}, {config: default, shard: 2, num_shards: 3, runner: linux.2xlarge, unstable: unstable}, {config: default, shard: 3, num_shards: 3, runner: linux.2xlarge, unstable: unstable}, {config: dynamo, shard: 1, num_shards: 3, runner: linux.2xlarge, unstable: unstable}, {config: dynamo, shard: 2, num_shards: 3, runner: linux.2xlarge, unstable: unstable}, {config: dynamo, shard: 3, num_shards: 3, runner: linux.2xlarge, unstable: unstable}]} 2024-06-26T05:01:33.1896857Z 2024-06-26T05:01:33.1896997Z Is the current job unstable? True 2024-06-26T05:01:33.1897277Z 2024-06-26T05:01:33.1897527Z Is keep-going label set? False 2024-06-26T05:01:33.1897781Z 2024-06-26T05:01:33.1897901Z Renabled issues? 2024-06-26T05:01:33.1941212Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-06-26T05:01:33.1941931Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-06-26T05:01:33.1949149Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T05:01:33.1949631Z env: 2024-06-26T05:01:33.1949899Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:33.1950237Z JOB_TIMEOUT: 600 2024-06-26T05:01:33.1950516Z ##[endgroup] 2024-06-26T05:01:33.2022963Z ##[group]Run set -x 2024-06-26T05:01:33.2023339Z set -x 2024-06-26T05:01:33.2023614Z  2024-06-26T05:01:33.2023947Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2024-06-26T05:01:33.2024506Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2024-06-26T05:01:33.2025127Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2024-06-26T05:01:33.2025630Z  TEST_COMMAND=.ci/onnx/test.sh 2024-06-26T05:01:33.2026014Z else 2024-06-26T05:01:33.2026337Z  TEST_COMMAND=.ci/pytorch/test.sh 2024-06-26T05:01:33.2026745Z fi 2024-06-26T05:01:33.2026981Z  2024-06-26T05:01:33.2027435Z # detached container should get cleaned up by teardown_ec2_linux 2024-06-26T05:01:33.2028203Z # TODO: Stop building test binaries as part of the build phase 2024-06-26T05:01:33.2028864Z # Used for GPU_FLAG since that doesn't play nice 2024-06-26T05:01:33.2029451Z # shellcheck disable=SC2086,SC2090 2024-06-26T05:01:33.2029897Z container_name=$(docker run \ 2024-06-26T05:01:33.2030299Z  ${GPU_FLAG:-} \ 2024-06-26T05:01:33.2030648Z  -e BUILD_ENVIRONMENT \ 2024-06-26T05:01:33.2031019Z  -e PR_NUMBER \ 2024-06-26T05:01:33.2031364Z  -e GITHUB_ACTIONS \ 2024-06-26T05:01:33.2031743Z  -e GITHUB_REPOSITORY \ 2024-06-26T05:01:33.2032115Z  -e GITHUB_WORKFLOW \ 2024-06-26T05:01:33.2032480Z  -e GITHUB_JOB \ 2024-06-26T05:01:33.2032818Z  -e GITHUB_RUN_ID \ 2024-06-26T05:01:33.2033181Z  -e GITHUB_RUN_NUMBER \ 2024-06-26T05:01:33.2033552Z  -e GITHUB_RUN_ATTEMPT \ 2024-06-26T05:01:33.2033928Z  -e JOB_ID \ 2024-06-26T05:01:33.2034243Z  -e JOB_NAME \ 2024-06-26T05:01:33.2034553Z  -e BASE_SHA \ 2024-06-26T05:01:33.2035052Z  -e BRANCH \ 2024-06-26T05:01:33.2035370Z  -e SHA1 \ 2024-06-26T05:01:33.2035683Z  -e AWS_DEFAULT_REGION \ 2024-06-26T05:01:33.2036077Z  -e IN_WHEEL_TEST \ 2024-06-26T05:01:33.2036438Z  -e SHARD_NUMBER \ 2024-06-26T05:01:33.2036773Z  -e TEST_CONFIG \ 2024-06-26T05:01:33.2037122Z  -e NUM_TEST_SHARDS \ 2024-06-26T05:01:33.2037495Z  -e REENABLED_ISSUES \ 2024-06-26T05:01:33.2037876Z  -e CONTINUE_THROUGH_ERROR \ 2024-06-26T05:01:33.2038278Z  -e VERBOSE_TEST_LOGS \ 2024-06-26T05:01:33.2038659Z  -e NO_TEST_TIMEOUT \ 2024-06-26T05:01:33.2039000Z  -e NO_TD \ 2024-06-26T05:01:33.2039318Z  -e TD_DISTRIBUTED \ 2024-06-26T05:01:33.2039681Z  -e PR_LABELS \ 2024-06-26T05:01:33.2040052Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2024-06-26T05:01:33.2040492Z  -e SCCACHE_BUCKET \ 2024-06-26T05:01:33.2040929Z  -e SCCACHE_S3_KEY_PREFIX \ 2024-06-26T05:01:33.2041308Z  -e XLA_CUDA \ 2024-06-26T05:01:33.2041692Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2024-06-26T05:01:33.2042371Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2024-06-26T05:01:33.2042872Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2024-06-26T05:01:33.2043380Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2024-06-26T05:01:33.2043834Z  -e HUGGING_FACE_HUB_TOKEN \ 2024-06-26T05:01:33.2044222Z  -e DASHBOARD_TAG \ 2024-06-26T05:01:33.2044680Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2024-06-26T05:01:33.2045221Z  --security-opt seccomp=unconfined \ 2024-06-26T05:01:33.2045656Z  --cap-add=SYS_PTRACE \ 2024-06-26T05:01:33.2046027Z  --ipc=host \ 2024-06-26T05:01:33.2046358Z  --shm-size="${SHM_SIZE}" \ 2024-06-26T05:01:33.2046736Z  --tty \ 2024-06-26T05:01:33.2047011Z  --detach \ 2024-06-26T05:01:33.2047344Z  --name="${container_name}" \ 2024-06-26T05:01:33.2047742Z  --user jenkins \ 2024-06-26T05:01:33.2048290Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2024-06-26T05:01:33.2048847Z  -w /var/lib/jenkins/workspace \ 2024-06-26T05:01:33.2049271Z  "${DOCKER_IMAGE}" 2024-06-26T05:01:33.2049588Z ) 2024-06-26T05:01:33.2049979Z # Propagate download.pytorch.org IP to container 2024-06-26T05:01:33.2050927Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2024-06-26T05:01:33.2051909Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2024-06-26T05:01:33.2052850Z docker exec -t "${container_name}" sh -c "pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2024-06-26T05:01:33.2060107Z shell: /usr/bin/bash -e {0} 2024-06-26T05:01:33.2060439Z env: 2024-06-26T05:01:33.2060699Z GIT_DEFAULT_BRANCH: main 2024-06-26T05:01:33.2061245Z BUILD_ENVIRONMENT: linux-focal-py3.12-clang10-experimental-split-build 2024-06-26T05:01:33.2061856Z PR_NUMBER: 129470 2024-06-26T05:01:33.2062192Z GITHUB_REPOSITORY: pytorch/pytorch 2024-06-26T05:01:33.2062589Z GITHUB_WORKFLOW: pull 2024-06-26T05:01:33.2062902Z GITHUB_JOB: test 2024-06-26T05:01:33.2063198Z GITHUB_RUN_ID: 9673645538 2024-06-26T05:01:33.2063524Z GITHUB_RUN_NUMBER: 219936 2024-06-26T05:01:33.2063868Z GITHUB_RUN_ATTEMPT: 1 2024-06-26T05:01:33.2064181Z JOB_ID: 26688306089 2024-06-26T05:01:33.2064914Z JOB_NAME: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, linux.2xlarge, unstable) 2024-06-26T05:01:33.2065755Z BRANCH: pull/129470 2024-06-26T05:01:33.2066113Z SHA1: b8c4c54d347aa776934c60784e35936878ef18dc 2024-06-26T05:01:33.2066603Z BASE_SHA: 4b9c9a9cc9c9283380a011310ba180c105c3dcb9 2024-06-26T05:01:33.2067052Z TEST_CONFIG: dynamo 2024-06-26T05:01:33.2067349Z SHARD_NUMBER: 1 2024-06-26T05:01:33.2067617Z NUM_TEST_SHARDS: 3 2024-06-26T05:01:33.2067914Z REENABLED_ISSUES: 2024-06-26T05:01:33.2068233Z CONTINUE_THROUGH_ERROR: False 2024-06-26T05:01:33.2068590Z VERBOSE_TEST_LOGS: False 2024-06-26T05:01:33.2068926Z NO_TEST_TIMEOUT: False 2024-06-26T05:01:33.2069235Z NO_TD: False 2024-06-26T05:01:33.2069498Z TD_DISTRIBUTED: False 2024-06-26T05:01:33.2069900Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2024-06-26T05:01:33.2070377Z SCCACHE_S3_KEY_PREFIX: pull 2024-06-26T05:01:33.2070705Z SHM_SIZE: 1g 2024-06-26T05:01:33.2071604Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T05:01:33.2072598Z XLA_CUDA: 2024-06-26T05:01:33.2073065Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2024-06-26T05:01:33.2073665Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2024-06-26T05:01:33.2074080Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2024-06-26T05:01:33.2074468Z DASHBOARD_TAG: 2024-06-26T05:01:33.2074907Z HUGGING_FACE_HUB_TOKEN: 2024-06-26T05:01:33.2075231Z ##[endgroup] 2024-06-26T05:01:33.2095384Z + [[ dynamo == \m\u\l\t\i\g\p\u ]] 2024-06-26T05:01:33.2096425Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *onnx* ]] 2024-06-26T05:01:33.2097190Z + TEST_COMMAND=.ci/pytorch/test.sh 2024-06-26T05:01:33.2103877Z +++ nproc --ignore=2 2024-06-26T05:01:33.2131754Z ++ 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 NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=6 -e SCCACHE_BUCKET -e SCCACHE_S3_KEY_PREFIX -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e DASHBOARD_TAG --env-file=/tmp/github_env_9673645538 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=1g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T05:01:43.3014502Z + container_name=b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T05:01:43.3016158Z + grep download.pytorch.org /etc/hosts 2024-06-26T05:01:43.3017354Z + docker exec -i b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf sudo bash -c '/bin/cat >> /etc/hosts' 2024-06-26T05:01:43.3919107Z + echo DOCKER_CONTAINER_ID=b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T05:01:43.3922181Z ++ echo dist/torch-2.5.0a0+gitb8c4c54-cp312-cp312-linux_x86_64.whl dist/torch_no_python-2.5.0a0+gitb8c4c54-py3-none-any.whl 2024-06-26T05:01:43.3924605Z + docker exec -t b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf sh -c 'pip install dist/torch-2.5.0a0+gitb8c4c54-cp312-cp312-linux_x86_64.whl dist/torch_no_python-2.5.0a0+gitb8c4c54-py3-none-any.whl[opt-einsum] && .ci/pytorch/test.sh' 2024-06-26T05:01:43.9242193Z Processing ./dist/torch-2.5.0a0+gitb8c4c54-cp312-cp312-linux_x86_64.whl 2024-06-26T05:01:44.1422161Z Processing ./dist/torch_no_python-2.5.0a0+gitb8c4c54-py3-none-any.whl (from torch-no-python==2.5.0a0+gitb8c4c54) 2024-06-26T05:01:44.8366857Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54) (3.13.1) 2024-06-26T05:01:44.8376653Z Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54) (4.12.2) 2024-06-26T05:01:44.8389143Z Requirement already satisfied: sympy in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54) (1.12) 2024-06-26T05:01:44.8397726Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54) (2.8.8) 2024-06-26T05:01:44.8403800Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54) (3.1.4) 2024-06-26T05:01:44.8412955Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54) (2024.2.0) 2024-06-26T05:01:44.8421857Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+gitb8c4c54) (69.5.1) 2024-06-26T05:01:44.8551551Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch-no-python==2.5.0a0+gitb8c4c54->torch-no-python==2.5.0a0+gitb8c4c54) (3.3.0) 2024-06-26T05:01:44.8606648Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from opt-einsum>=3.3->torch-no-python==2.5.0a0+gitb8c4c54->torch-no-python==2.5.0a0+gitb8c4c54) (1.26.0) 2024-06-26T05:01:44.8905350Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch==2.5.0a0+gitb8c4c54) (2.1.5) 2024-06-26T05:01:44.9435363Z Requirement already satisfied: mpmath>=0.19 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy->torch==2.5.0a0+gitb8c4c54) (1.2.1) 2024-06-26T05:01:45.6238254Z Installing collected packages: torch-no-python, torch 2024-06-26T05:02:01.8739416Z Successfully installed torch-2.5.0a0+gitb8c4c54 torch-no-python-2.5.0a0+gitb8c4c54 2024-06-26T05:02:01.9885201Z ++ dirname .ci/pytorch/test.sh 2024-06-26T05:02:01.9897175Z + source .ci/pytorch/common.sh 2024-06-26T05:02:01.9903064Z +++ dirname .ci/pytorch/common.sh 2024-06-26T05:02:01.9908338Z ++ source .ci/pytorch/common_utils.sh 2024-06-26T05:02:01.9913502Z +++ declare -f -t trap_add 2024-06-26T05:02:01.9919274Z ++ set -ex 2024-06-26T05:02:01.9920183Z ++ [[ linux-focal-py3.12-clang10-experimental-split-build == *rocm* ]] 2024-06-26T05:02:01.9922942Z ++ BUILD_TEST_LIBTORCH=0 2024-06-26T05:02:01.9923757Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *rocm* ]] 2024-06-26T05:02:01.9924444Z ++ stat -c %u /var/lib/jenkins/workspace 2024-06-26T05:02:01.9945842Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2024-06-26T05:02:01.9946561Z + trap_add cleanup_workspace EXIT 2024-06-26T05:02:01.9947113Z + trap_add_cmd=cleanup_workspace 2024-06-26T05:02:01.9947452Z + shift 2024-06-26T05:02:01.9947708Z + for trap_add_name in "$@" 2024-06-26T05:02:01.9952335Z +++ trap -p EXIT 2024-06-26T05:02:01.9954924Z ++ eval 'extract_trap_cmd ' 2024-06-26T05:02:01.9955403Z +++ extract_trap_cmd 2024-06-26T05:02:01.9955746Z +++ printf '%s\n' '' 2024-06-26T05:02:01.9956097Z ++ printf '%s\n' cleanup_workspace 2024-06-26T05:02:01.9957692Z + trap -- ' 2024-06-26T05:02:01.9958015Z cleanup_workspace' EXIT 2024-06-26T05:02:01.9958467Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2024-06-26T05:02:02.5812619Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2024-06-26T05:02:02.6060090Z + echo 'Environment variables:' 2024-06-26T05:02:02.6060607Z Environment variables: 2024-06-26T05:02:02.6060902Z + env 2024-06-26T05:02:02.6084615Z INSTALLED_DB=yes 2024-06-26T05:02:02.6085591Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-06-26T05:02:02.6086531Z CONTINUE_THROUGH_ERROR=False 2024-06-26T05:02:02.6087470Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10-experimental-split-build 2024-06-26T05:02:02.6088483Z HOSTNAME=b1cbc8a302fe 2024-06-26T05:02:02.6089690Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_cde60ecd-3ebe-49fb-9067-9e1a21939aac 2024-06-26T05:02:02.6090760Z GITHUB_ACTION=__self 2024-06-26T05:02:02.6091273Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-06-26T05:02:02.6091916Z GITHUB_RUN_NUMBER=219936 2024-06-26T05:02:02.6092475Z TEST_CONFIG=dynamo 2024-06-26T05:02:02.6093031Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-06-26T05:02:02.6093820Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-06-26T05:02:02.6094772Z GITHUB_TRIGGERING_ACTOR=leslie-fang-intel 2024-06-26T05:02:02.6095615Z GITHUB_REF_TYPE=branch 2024-06-26T05:02:02.6096313Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-06-26T05:02:02.6097204Z BASE_SHA=4b9c9a9cc9c9283380a011310ba180c105c3dcb9 2024-06-26T05:02:02.6098182Z XLA_CUDA= 2024-06-26T05:02:02.6098781Z HUGGING_FACE_HUB_TOKEN= 2024-06-26T05:02:02.6103671Z *** 2024-06-26T05:02:02.6104135Z GITHUB_REPOSITORY_ID=65600975 2024-06-26T05:02:02.6104676Z GITHUB_ACTIONS=true 2024-06-26T05:02:02.6105321Z SHA1=b8c4c54d347aa776934c60784e35936878ef18dc 2024-06-26T05:02:02.6106084Z GITHUB_SHA=4b51b1a62a63a1add1b3a0f7882f5c7dc66b8f8d 2024-06-26T05:02:02.6107229Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/129470/merge 2024-06-26T05:02:02.6107862Z UCC_HOME=/usr 2024-06-26T05:02:02.6108137Z VERBOSE_TEST_LOGS=False 2024-06-26T05:02:02.6108468Z GITHUB_REF=refs/pull/129470/merge 2024-06-26T05:02:02.6108810Z SHARD_NUMBER=1 2024-06-26T05:02:02.6109096Z GITHUB_REF_PROTECTED=false 2024-06-26T05:02:02.6109437Z HOME=/var/lib/jenkins 2024-06-26T05:02:02.6110040Z GITHUB_API_URL=https://api.github.com 2024-06-26T05:02:02.6110452Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-06-26T05:02:02.6110825Z UCX_COMMIT= 2024-06-26T05:02:02.6111097Z SCCACHE_S3_KEY_PREFIX=pull 2024-06-26T05:02:02.6111410Z NUM_TEST_SHARDS=3 2024-06-26T05:02:02.6111687Z UCX_HOME=/usr 2024-06-26T05:02:02.6112576Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_cde60ecd-3ebe-49fb-9067-9e1a21939aac 2024-06-26T05:02:02.6113956Z JOB_NAME=linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, linux.2xlarge, unstable) 2024-06-26T05:02:02.6115562Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_cde60ecd-3ebe-49fb-9067-9e1a21939aac 2024-06-26T05:02:02.6116737Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-06-26T05:02:02.6117395Z GITHUB_EVENT_NAME=pull_request 2024-06-26T05:02:02.6117744Z DASHBOARD_TAG= 2024-06-26T05:02:02.6118135Z GITHUB_RUN_ID=9673645538 2024-06-26T05:02:02.6119085Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_cde60ecd-3ebe-49fb-9067-9e1a21939aac 2024-06-26T05:02:02.6120035Z GITHUB_ACTOR=leslie-fang-intel 2024-06-26T05:02:02.6120388Z PR_NUMBER=129470 2024-06-26T05:02:02.6120651Z DESIRED_CUDA= 2024-06-26T05:02:02.6121019Z GITHUB_RUN_ATTEMPT=1 2024-06-26T05:02:02.6121334Z ANACONDA_PYTHON_VERSION=3.12 2024-06-26T05:02:02.6121753Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-06-26T05:02:02.6122198Z TERM=xterm 2024-06-26T05:02:02.6122455Z INSTALLED_VISION=yes 2024-06-26T05:02:02.6122741Z BRANCH=pull/129470 2024-06-26T05:02:02.6123042Z OPENSSL_ROOT_DIR=/opt/openssl 2024-06-26T05:02:02.6123392Z CUDA_PATH=/usr/local/cuda 2024-06-26T05:02:02.6124135Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-06-26T05:02:02.6124896Z GITHUB_SERVER_URL=https://github.com 2024-06-26T05:02:02.6125278Z UCC_COMMIT= 2024-06-26T05:02:02.6125528Z REENABLED_ISSUES= 2024-06-26T05:02:02.6125803Z DOCS= 2024-06-26T05:02:02.6126044Z INSTALLED_ANDROID= 2024-06-26T05:02:02.6126307Z SHLVL=1 2024-06-26T05:02:02.6126539Z MAX_JOBS=6 2024-06-26T05:02:02.6126802Z GITHUB_ACTOR_ID=53841472 2024-06-26T05:02:02.6127236Z GITHUB_WORKFLOW_SHA=4b51b1a62a63a1add1b3a0f7882f5c7dc66b8f8d 2024-06-26T05:02:02.6127751Z GITHUB_REF_NAME=129470/merge 2024-06-26T05:02:02.6128346Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-06-26T05:02:02.6128908Z GITHUB_JOB=test 2024-06-26T05:02:02.6129187Z NO_TEST_TIMEOUT=False 2024-06-26T05:02:02.6129493Z TD_DISTRIBUTED=False 2024-06-26T05:02:02.6129805Z GITHUB_REPOSITORY=pytorch/pytorch 2024-06-26T05:02:02.6130185Z GITHUB_RETENTION_DAYS=90 2024-06-26T05:02:02.6130510Z OPENSSL_DIR=/opt/openssl 2024-06-26T05:02:02.6130824Z GITHUB_ACTION_REPOSITORY= 2024-06-26T05:02:02.6131902Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-06-26T05:02:02.6133134Z GITHUB_BASE_REF=gh/leslie-fang-intel/126/base 2024-06-26T05:02:02.6133548Z INSTALLED_ACL= 2024-06-26T05:02:02.6133807Z CI=true 2024-06-26T05:02:02.6134070Z GITHUB_REPOSITORY_OWNER=pytorch 2024-06-26T05:02:02.6134411Z JOB_ID=26688306089 2024-06-26T05:02:02.6134698Z INSTALLED_PROTOBUF=yes 2024-06-26T05:02:02.6135109Z GITHUB_HEAD_REF=gh/leslie-fang-intel/126/head 2024-06-26T05:02:02.6135522Z GITHUB_ACTION_REF= 2024-06-26T05:02:02.6135934Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-06-26T05:02:02.6136390Z GITHUB_WORKFLOW=pull 2024-06-26T05:02:02.6136696Z DEBIAN_FRONTEND=noninteractive 2024-06-26T05:02:02.6137632Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_cde60ecd-3ebe-49fb-9067-9e1a21939aac 2024-06-26T05:02:02.6138483Z NO_TD=False 2024-06-26T05:02:02.6138750Z SKIP_SCCACHE_INITIALIZATION=1 2024-06-26T05:02:02.6139090Z _=/usr/bin/env 2024-06-26T05:02:02.6139555Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2024-06-26T05:02:02.6255846Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch 2024-06-26T05:02:02.6257539Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-06-26T05:02:02.6259038Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib 2024-06-26T05:02:02.6260584Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/test 2024-06-26T05:02:02.6261381Z + BUILD_DIR=build 2024-06-26T05:02:02.6261691Z + BUILD_RENAMED_DIR=build_renamed 2024-06-26T05:02:02.6262074Z + BUILD_BIN_DIR=build/bin 2024-06-26T05:02:02.6262378Z + SHARD_NUMBER=1 2024-06-26T05:02:02.6262662Z + NUM_TEST_SHARDS=3 2024-06-26T05:02:02.6262959Z + export VALGRIND=ON 2024-06-26T05:02:02.6263242Z + VALGRIND=ON 2024-06-26T05:02:02.6263815Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *clang9* ]] 2024-06-26T05:02:02.6264414Z + [[ 0 == \1 ]] 2024-06-26T05:02:02.6264672Z + [[ False == \1 ]] 2024-06-26T05:02:02.6265409Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *bazel* ]] 2024-06-26T05:02:02.6266044Z ++ realpath build/custom_test_artifacts 2024-06-26T05:02:02.6284503Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2024-06-26T05:02:02.6285567Z + [[ -n '' ]] 2024-06-26T05:02:02.6285900Z + echo 'Environment variables' 2024-06-26T05:02:02.6286249Z Environment variables 2024-06-26T05:02:02.6286540Z + env 2024-06-26T05:02:02.6290806Z INSTALLED_DB=yes 2024-06-26T05:02:02.6291681Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-06-26T05:02:02.6292724Z CONTINUE_THROUGH_ERROR=False 2024-06-26T05:02:02.6293770Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10-experimental-split-build 2024-06-26T05:02:02.6294756Z HOSTNAME=b1cbc8a302fe 2024-06-26T05:02:02.6295626Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_cde60ecd-3ebe-49fb-9067-9e1a21939aac 2024-06-26T05:02:02.6296465Z GITHUB_ACTION=__self 2024-06-26T05:02:02.6296807Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-06-26T05:02:02.6297185Z GITHUB_RUN_NUMBER=219936 2024-06-26T05:02:02.6297503Z TEST_CONFIG=dynamo 2024-06-26T05:02:02.6297812Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-06-26T05:02:02.6298255Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-06-26T05:02:02.6298739Z GITHUB_TRIGGERING_ACTOR=leslie-fang-intel 2024-06-26T05:02:02.6299152Z GITHUB_REF_TYPE=branch 2024-06-26T05:02:02.6299457Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-06-26T05:02:02.6299859Z BASE_SHA=4b9c9a9cc9c9283380a011310ba180c105c3dcb9 2024-06-26T05:02:02.6300290Z XLA_CUDA= 2024-06-26T05:02:02.6300539Z HUGGING_FACE_HUB_TOKEN= 2024-06-26T05:02:02.6300944Z *** 2024-06-26T05:02:02.6301194Z GITHUB_REPOSITORY_ID=65600975 2024-06-26T05:02:02.6301525Z GITHUB_ACTIONS=true 2024-06-26T05:02:02.6301866Z SHA1=b8c4c54d347aa776934c60784e35936878ef18dc 2024-06-26T05:02:02.6302362Z GITHUB_SHA=4b51b1a62a63a1add1b3a0f7882f5c7dc66b8f8d 2024-06-26T05:02:02.6303078Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/129470/merge 2024-06-26T05:02:02.6303728Z UCC_HOME=/usr 2024-06-26T05:02:02.6303998Z VERBOSE_TEST_LOGS=False 2024-06-26T05:02:02.6304314Z GITHUB_REF=refs/pull/129470/merge 2024-06-26T05:02:02.6304670Z SHARD_NUMBER=1 2024-06-26T05:02:02.6304957Z GITHUB_REF_PROTECTED=false 2024-06-26T05:02:02.6305272Z HOME=/var/lib/jenkins 2024-06-26T05:02:02.6305613Z GITHUB_API_URL=https://api.github.com 2024-06-26T05:02:02.6307581Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-06-26T05:02:02.6308129Z UCX_COMMIT= 2024-06-26T05:02:02.6308561Z SCCACHE_S3_KEY_PREFIX=pull 2024-06-26T05:02:02.6309152Z NUM_TEST_SHARDS=3 2024-06-26T05:02:02.6309671Z UCX_HOME=/usr 2024-06-26T05:02:02.6311326Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_cde60ecd-3ebe-49fb-9067-9e1a21939aac 2024-06-26T05:02:02.6313991Z JOB_NAME=linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 1, 3, linux.2xlarge, unstable) 2024-06-26T05:02:02.6316372Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_cde60ecd-3ebe-49fb-9067-9e1a21939aac 2024-06-26T05:02:02.6317708Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-06-26T05:02:02.6318370Z GITHUB_EVENT_NAME=pull_request 2024-06-26T05:02:02.6318716Z DASHBOARD_TAG= 2024-06-26T05:02:02.6318994Z GITHUB_RUN_ID=9673645538 2024-06-26T05:02:02.6319928Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_cde60ecd-3ebe-49fb-9067-9e1a21939aac 2024-06-26T05:02:02.6320949Z GITHUB_ACTOR=leslie-fang-intel 2024-06-26T05:02:02.6321326Z PR_NUMBER=129470 2024-06-26T05:02:02.6321589Z DESIRED_CUDA= 2024-06-26T05:02:02.6321860Z GITHUB_RUN_ATTEMPT=1 2024-06-26T05:02:02.6322150Z VALGRIND=ON 2024-06-26T05:02:02.6322412Z ANACONDA_PYTHON_VERSION=3.12 2024-06-26T05:02:02.6322842Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-06-26T05:02:02.6323290Z TERM=xterm 2024-06-26T05:02:02.6323535Z INSTALLED_VISION=yes 2024-06-26T05:02:02.6323932Z BRANCH=pull/129470 2024-06-26T05:02:02.6324244Z OPENSSL_ROOT_DIR=/opt/openssl 2024-06-26T05:02:02.6324583Z CUDA_PATH=/usr/local/cuda 2024-06-26T05:02:02.6325337Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-06-26T05:02:02.6326106Z GITHUB_SERVER_URL=https://github.com 2024-06-26T05:02:02.6326474Z UCC_COMMIT= 2024-06-26T05:02:02.6326729Z REENABLED_ISSUES= 2024-06-26T05:02:02.6326998Z DOCS= 2024-06-26T05:02:02.6327224Z INSTALLED_ANDROID= 2024-06-26T05:02:02.6327499Z SHLVL=1 2024-06-26T05:02:02.6327731Z MAX_JOBS=6 2024-06-26T05:02:02.6327977Z GITHUB_ACTOR_ID=53841472 2024-06-26T05:02:02.6328422Z GITHUB_WORKFLOW_SHA=4b51b1a62a63a1add1b3a0f7882f5c7dc66b8f8d 2024-06-26T05:02:02.6328937Z GITHUB_REF_NAME=129470/merge 2024-06-26T05:02:02.6329515Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-06-26T05:02:02.6330088Z GITHUB_JOB=test 2024-06-26T05:02:02.6330365Z NO_TEST_TIMEOUT=False 2024-06-26T05:02:02.6330657Z TD_DISTRIBUTED=False 2024-06-26T05:02:02.6330984Z GITHUB_REPOSITORY=pytorch/pytorch 2024-06-26T05:02:02.6331370Z GITHUB_RETENTION_DAYS=90 2024-06-26T05:02:02.6331683Z OPENSSL_DIR=/opt/openssl 2024-06-26T05:02:02.6332012Z GITHUB_ACTION_REPOSITORY= 2024-06-26T05:02:02.6333088Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-06-26T05:02:02.6334296Z GITHUB_BASE_REF=gh/leslie-fang-intel/126/base 2024-06-26T05:02:02.6334724Z INSTALLED_ACL= 2024-06-26T05:02:02.6334986Z CI=true 2024-06-26T05:02:02.6335238Z GITHUB_REPOSITORY_OWNER=pytorch 2024-06-26T05:02:02.6335594Z JOB_ID=26688306089 2024-06-26T05:02:02.6335882Z INSTALLED_PROTOBUF=yes 2024-06-26T05:02:02.6336280Z GITHUB_HEAD_REF=gh/leslie-fang-intel/126/head 2024-06-26T05:02:02.6336705Z GITHUB_ACTION_REF= 2024-06-26T05:02:02.6337119Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-06-26T05:02:02.6337563Z GITHUB_WORKFLOW=pull 2024-06-26T05:02:02.6337886Z DEBIAN_FRONTEND=noninteractive 2024-06-26T05:02:02.6338827Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_cde60ecd-3ebe-49fb-9067-9e1a21939aac 2024-06-26T05:02:02.6339665Z NO_TD=False 2024-06-26T05:02:02.6339941Z SKIP_SCCACHE_INITIALIZATION=1 2024-06-26T05:02:02.6340280Z _=/usr/bin/env 2024-06-26T05:02:02.6340577Z + echo 'Testing pytorch' 2024-06-26T05:02:02.6340896Z Testing pytorch 2024-06-26T05:02:02.6341189Z + export LANG=C.UTF-8 2024-06-26T05:02:02.6341485Z + LANG=C.UTF-8 2024-06-26T05:02:02.6363511Z + PR_NUMBER=129470 2024-06-26T05:02:02.6364062Z + [[ dynamo == \d\e\f\a\u\l\t ]] 2024-06-26T05:02:02.6364640Z + [[ dynamo == \d\i\s\t\r\i\b\u\t\e\d ]] 2024-06-26T05:02:02.6365200Z + [[ dynamo == \s\l\o\w ]] 2024-06-26T05:02:02.6366263Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *slow-gradcheck* ]] 2024-06-26T05:02:02.6367403Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *cuda* ]] 2024-06-26T05:02:02.6368277Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *rocm* ]] 2024-06-26T05:02:02.6369310Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *xpu* ]] 2024-06-26T05:02:02.6369907Z + [[ dynamo == *crossref* ]] 2024-06-26T05:02:02.6370500Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *rocm* ]] 2024-06-26T05:02:02.6371345Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *xpu* ]] 2024-06-26T05:02:02.6372206Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *-bazel-* ]] 2024-06-26T05:02:02.6372857Z + pip_install --user ninja==1.10.2 2024-06-26T05:02:02.6373376Z + pip install --progress-bar off --user ninja==1.10.2 2024-06-26T05:02:03.0838017Z Collecting ninja==1.10.2 2024-06-26T05:02:03.1026944Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2024-06-26T05:02:03.1156758Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2024-06-26T05:02:03.6998769Z Installing collected packages: ninja 2024-06-26T05:02:03.7069457Z  WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH. 2024-06-26T05:02:03.7070808Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2024-06-26T05:02:03.7112434Z Successfully installed ninja-1.10.2 2024-06-26T05:02:03.8275943Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-06-26T05:02:03.8278226Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-06-26T05:02:03.8279981Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *aarch64* ]] 2024-06-26T05:02:03.8280594Z + install_tlparse 2024-06-26T05:02:03.8281014Z + pip_install --user tlparse==0.3.7 2024-06-26T05:02:03.8281571Z + pip install --progress-bar off --user tlparse==0.3.7 2024-06-26T05:02:04.2532179Z Collecting tlparse==0.3.7 2024-06-26T05:02:04.2682284Z Downloading tlparse-0.3.7-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (346 bytes) 2024-06-26T05:02:04.2783464Z Downloading tlparse-0.3.7-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB) 2024-06-26T05:02:04.9073661Z Installing collected packages: tlparse 2024-06-26T05:02:04.9457184Z Successfully installed tlparse-0.3.7 2024-06-26T05:02:05.0642285Z ++ python -m site --user-base 2024-06-26T05:02:05.0813549Z + PATH=/var/lib/jenkins/.local/bin:/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-06-26T05:02:05.0815397Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *asan* ]] 2024-06-26T05:02:05.0816590Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *-debug* ]] 2024-06-26T05:02:05.0817654Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *-bazel-* ]] 2024-06-26T05:02:05.0818950Z + echo 'We are not in debug mode: linux-focal-py3.12-clang10-experimental-split-build. Expect the assertion to pass' 2024-06-26T05:02:05.0820516Z We are not in debug mode: linux-focal-py3.12-clang10-experimental-split-build. Expect the assertion to pass 2024-06-26T05:02:05.0821424Z + cd test 2024-06-26T05:02:05.0822012Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2024-06-26T05:02:06.4514037Z + [[ dynamo == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2024-06-26T05:02:06.4514820Z + [[ dynamo == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2024-06-26T05:02:06.4515629Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *-bazel-* ]] 2024-06-26T05:02:06.4516400Z + pushd test 2024-06-26T05:02:06.4516714Z ~/workspace/test ~/workspace 2024-06-26T05:02:06.4517628Z ++ python -c 'import torch; print(torch.version.cuda)' 2024-06-26T05:02:07.8042482Z + CUDA_VERSION=None 2024-06-26T05:02:07.8043207Z + '[' None == 12.4 ']' 2024-06-26T05:02:07.8043791Z + ISCUDA124= 2024-06-26T05:02:07.8044060Z + popd 2024-06-26T05:02:07.8044303Z ~/workspace 2024-06-26T05:02:07.8047162Z + DYNAMO_BENCHMARK_FLAGS=() 2024-06-26T05:02:07.8048063Z + [[ dynamo == *dynamo_eager* ]] 2024-06-26T05:02:07.8048819Z + [[ dynamo == *aot_eager* ]] 2024-06-26T05:02:07.8049491Z + [[ dynamo == *aot_inductor* ]] 2024-06-26T05:02:07.8050201Z + [[ dynamo == *inductor* ]] 2024-06-26T05:02:07.8050872Z + [[ dynamo == *dynamic* ]] 2024-06-26T05:02:07.8051537Z + [[ dynamo == *cpu_inductor* ]] 2024-06-26T05:02:07.8052430Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2024-06-26T05:02:07.8079353Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *libtorch* ]] 2024-06-26T05:02:07.8080297Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *-bazel-* ]] 2024-06-26T05:02:07.8081857Z + cd test 2024-06-26T05:02:07.8082363Z + python -c 'import torch; print(torch.__config__.show())' 2024-06-26T05:02:08.9558124Z PyTorch built with: 2024-06-26T05:02:08.9559111Z - GCC 4.2 2024-06-26T05:02:08.9559450Z - C++ Version: 201703 2024-06-26T05:02:08.9559806Z - clang 10.0.0 2024-06-26T05:02:08.9560640Z - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-06-26T05:02:08.9561806Z - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) 2024-06-26T05:02:08.9562470Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-06-26T05:02:08.9562969Z - LAPACK is enabled (usually provided by MKL) 2024-06-26T05:02:08.9563436Z - NNPACK is enabled 2024-06-26T05:02:08.9563789Z - CPU capability usage: AVX512 2024-06-26T05:02:08.9572917Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CXX_COMPILER=/opt/cache/bin/clang++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=1 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=braced-scalar-init -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wvla-extension -Wnewline-eof -Winconsistent-missing-override -Winconsistent-missing-destructor-override -Wno-pass-failed -Wno-error=pedantic -Wno-error=old-style-cast -Wno-error=inconsistent-missing-override -Wno-error=inconsistent-missing-destructor-override -Wconstant-conversion -Wno-invalid-partial-specialization -Wno-missing-braces -Qunused-arguments -fcolor-diagnostics -faligned-new -fno-math-errno -fno-trapping-math -Werror=format, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.5.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 2024-06-26T05:02:08.9581483Z 2024-06-26T05:02:09.1592817Z + cd test 2024-06-26T05:02:09.1593576Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2024-06-26T05:02:10.2830501Z ATen/Parallel: 2024-06-26T05:02:10.2831038Z at::get_num_threads() : 4 2024-06-26T05:02:10.2831424Z at::get_num_interop_threads() : 4 2024-06-26T05:02:10.2831886Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-06-26T05:02:10.2832275Z omp_get_max_threads() : 4 2024-06-26T05:02:10.2833365Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-06-26T05:02:10.2834241Z mkl_get_max_threads() : 4 2024-06-26T05:02:10.2835089Z Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) 2024-06-26T05:02:10.2849812Z std::thread::hardware_concurrency() : 8 2024-06-26T05:02:10.2850354Z Environment variables: 2024-06-26T05:02:10.2850778Z OMP_NUM_THREADS : [not set] 2024-06-26T05:02:10.2851158Z MKL_NUM_THREADS : [not set] 2024-06-26T05:02:10.2851822Z ATen parallel backend: OpenMP 2024-06-26T05:02:10.2852068Z 2024-06-26T05:02:10.4873803Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *aarch64* ]] 2024-06-26T05:02:10.4874507Z + [[ dynamo == *backward* ]] 2024-06-26T05:02:10.4875028Z + [[ dynamo == *xla* ]] 2024-06-26T05:02:10.4875351Z + [[ dynamo == *executorch* ]] 2024-06-26T05:02:10.4875730Z + [[ dynamo == \j\i\t\_\l\e\g\a\c\y ]] 2024-06-26T05:02:10.4876685Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *libtorch* ]] 2024-06-26T05:02:10.4877431Z + [[ dynamo == distributed ]] 2024-06-26T05:02:10.4877857Z + [[ dynamo == *inductor_distributed* ]] 2024-06-26T05:02:10.4878380Z + [[ dynamo == *inductor-halide* ]] 2024-06-26T05:02:10.4878853Z + [[ dynamo == *inductor-micro-benchmark* ]] 2024-06-26T05:02:10.4879354Z + [[ dynamo == *huggingface* ]] 2024-06-26T05:02:10.4879740Z + [[ dynamo == *timm* ]] 2024-06-26T05:02:10.4880092Z + [[ dynamo == *torchbench* ]] 2024-06-26T05:02:10.4880890Z + [[ dynamo == *inductor_cpp_wrapper_abi_compatible* ]] 2024-06-26T05:02:10.4881448Z + [[ dynamo == *inductor* ]] 2024-06-26T05:02:10.4881789Z + [[ dynamo == *inductor* ]] 2024-06-26T05:02:10.4882184Z + [[ dynamo == *dynamo* ]] 2024-06-26T05:02:10.4882509Z + [[ 1 == 1 ]] 2024-06-26T05:02:10.4882841Z + [[ 3 -gt 1 ]] 2024-06-26T05:02:10.4883136Z + install_torchvision 2024-06-26T05:02:10.4883447Z + local orig_preload 2024-06-26T05:02:10.4883786Z + local commit 2024-06-26T05:02:10.4884083Z ++ get_pinned_commit vision 2024-06-26T05:02:10.4884507Z ++ cat .github/ci_commit_pins/vision.txt 2024-06-26T05:02:10.4893217Z + commit=d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T05:02:10.4894007Z + orig_preload= 2024-06-26T05:02:10.4894388Z + '[' -n '' ']' 2024-06-26T05:02:10.4895254Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T05:02:10.4896766Z + pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T05:02:10.8783656Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T05:02:10.8788942Z Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-h228gmv9 2024-06-26T05:02:10.8809507Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-h228gmv9 2024-06-26T05:02:12.3904257Z Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f' 2024-06-26T05:02:12.3919803Z Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T05:02:13.6659066Z Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T05:02:13.9016940Z Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f 2024-06-26T05:02:15.7827010Z Preparing metadata (setup.py) ... [?25l- \ done 2024-06-26T05:02:15.7879349Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (1.26.0) 2024-06-26T05:02:15.7892815Z Requirement already satisfied: torch in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.5.0a0+gitb8c4c54) 2024-06-26T05:02:15.7904313Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (10.3.0) 2024-06-26T05:02:15.8219932Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.13.1) 2024-06-26T05:02:15.8228533Z Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2) 2024-06-26T05:02:15.8238653Z Requirement already satisfied: sympy in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.12) 2024-06-26T05:02:15.8246848Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8) 2024-06-26T05:02:15.8253006Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.4) 2024-06-26T05:02:15.8262184Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.2.0) 2024-06-26T05:02:15.8271141Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (69.5.1) 2024-06-26T05:02:15.8288160Z Requirement already satisfied: torch-no-python==2.5.0a0+gitb8c4c54 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.5.0a0+gitb8c4c54) 2024-06-26T05:02:15.8656641Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (2.1.5) 2024-06-26T05:02:15.9054748Z Requirement already satisfied: mpmath>=0.19 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy->torch->torchvision==0.19.0a0+d23a6e1) (1.2.1) 2024-06-26T05:02:15.9113184Z Building wheels for collected packages: torchvision 2024-06-26T05:03:20.7567825Z Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done 2024-06-26T05:03:20.7604891Z [?25h Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp312-cp312-linux_x86_64.whl size=1115257 sha256=6836b36f6612091b7890d1de30f7988c9df4315868b89066c1783a2d2519fda6 2024-06-26T05:03:20.7607433Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/b9/aa/81/39d3509ec629531316195ffac7a7b05ff7603f393064d63ec9 2024-06-26T05:03:20.7642079Z Successfully built torchvision 2024-06-26T05:03:21.3434017Z Installing collected packages: torchvision 2024-06-26T05:03:21.7804070Z Successfully installed torchvision-0.19.0a0+d23a6e1 2024-06-26T05:03:21.9285929Z + '[' -n '' ']' 2024-06-26T05:03:21.9286314Z + test_dynamo_shard 1 2024-06-26T05:03:21.9286816Z + [[ -z 3 ]] 2024-06-26T05:03:21.9288503Z + python tools/dynamo/verify_dynamo.py 2024-06-26T05:03:23.0828570Z Python version: 3.12.4 2024-06-26T05:03:23.0829115Z `torch` version: 2.5.0a0+gitb8c4c54 2024-06-26T05:03:23.0829789Z CUDA version: None 2024-06-26T05:03:23.0830349Z ROCM version: None 2024-06-26T05:03:23.0830535Z 2024-06-26T05:03:23.7894283Z CUDA not available -- skipping CUDA check on eager backend 2024-06-26T05:03:23.7894722Z 2024-06-26T05:03:24.1806442Z CUDA not available -- skipping CUDA check on aot_eager backend 2024-06-26T05:03:24.1806910Z 2024-06-26T05:03:29.0925762Z CUDA not available -- skipping CUDA check on inductor backend 2024-06-26T05:03:29.0926245Z 2024-06-26T05:03:29.0926387Z All required checks passed 2024-06-26T05:03:29.6417816Z + python test/run_test.py --dynamo --exclude-inductor-tests --exclude-jit-executor --exclude-distributed-tests --exclude-torch-export-tests --shard 1 3 --verbose 2024-06-26T05:03:29.7419219Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-06-26T05:03:29.7420519Z import pkg_resources 2024-06-26T05:03:31.6062956Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:03:32.5429616Z Downloading https://ossci-metrics.s3.amazonaws.com/slow-tests.json to /var/lib/jenkins/workspace/test/.pytorch-slow-tests.json 2024-06-26T05:03:32.5942537Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-06-26T05:03:32.6690301Z Ignoring disabled issues: [''] 2024-06-26T05:03:32.6790668Z Found test times from artifacts 2024-06-26T05:03:32.7179669Z Found test times from artifacts 2024-06-26T05:03:32.7192162Z Running all tests 2024-06-26T05:03:32.7291748Z Running parallel tests on 3 processes 2024-06-26T05:03:32.7295164Z Name: tests to run (est. time: 50.54min) 2024-06-26T05:03:32.7295887Z Serial tests (34): 2024-06-26T05:03:32.7296478Z test_nn 1/2 2024-06-26T05:03:32.7296792Z test_cpp_api_parity 1/1 2024-06-26T05:03:32.7297131Z test_torch 1/1 2024-06-26T05:03:32.7297421Z test_ci_sanity_check_fail 1/1 2024-06-26T05:03:32.7297786Z test_show_pickle 1/1 2024-06-26T05:03:32.7298112Z test_autocast 1/1 2024-06-26T05:03:32.7298396Z test_utils 1/1 2024-06-26T05:03:32.7298686Z test_tensorexpr 1/1 2024-06-26T05:03:32.7299018Z test_autograd_fallback 1/1 2024-06-26T05:03:32.7299365Z test_python_dispatch 1/1 2024-06-26T05:03:32.7299756Z test_cpp_extensions_stream_and_event 1/1 2024-06-26T05:03:32.7300435Z test_cpp_extensions_mtia_backend 1/1 2024-06-26T05:03:32.7300840Z test_overrides 1/1 2024-06-26T05:03:32.7301154Z test_jit_disabled 1/1 2024-06-26T05:03:32.7301481Z test_native_mha 1/1 2024-06-26T05:03:32.7301795Z test_cpp_extensions_jit 1/1 2024-06-26T05:03:32.7302236Z test_cpp_extensions_open_device_registration 1/1 2024-06-26T05:03:32.7302703Z test_sort_and_select 1/1 2024-06-26T05:03:32.7303042Z test_multiprocessing 1/1 2024-06-26T05:03:32.7303392Z test_mobile_optimizer 1/1 2024-06-26T05:03:32.7303740Z nn/test_pooling 1/1 2024-06-26T05:03:32.7304056Z test_tensor_creation_ops 1/1 2024-06-26T05:03:32.7304417Z test_reductions 1/1 2024-06-26T05:03:32.7304740Z test_cuda_primary_ctx 1/1 2024-06-26T05:03:32.7305069Z test_dispatch 1/1 2024-06-26T05:03:32.7305373Z test_cuda_trace 1/1 2024-06-26T05:03:32.7305708Z test_multiprocessing_spawn 1/1 2024-06-26T05:03:32.7306086Z test_cuda_nvml_based_avail 1/1 2024-06-26T05:03:32.7306466Z test_spectral_ops 1/1 2024-06-26T05:03:32.7306839Z distributions/test_distributions 1/2 2024-06-26T05:03:32.7307266Z distributions/test_distributions 2/2 2024-06-26T05:03:32.7307668Z doctests 1/1 2024-06-26T05:03:32.7307979Z test_cpp_extensions_aot_no_ninja 1/1 2024-06-26T05:03:32.7308388Z test_cpp_extensions_aot_ninja 1/1 2024-06-26T05:03:32.7308782Z Parallel tests (27): 2024-06-26T05:03:32.7309112Z dynamo/test_dynamic_shapes 1/1 2024-06-26T05:03:32.7309497Z dynamo/test_fx_passes_pre_grad 1/1 2024-06-26T05:03:32.7309898Z dynamo/test_frame_init 1/1 2024-06-26T05:03:32.7310248Z dynamo/test_sdpa 1/1 2024-06-26T05:03:32.7310567Z dynamo/test_exceptions 1/1 2024-06-26T05:03:32.7310930Z dynamo/test_repros 1/1 2024-06-26T05:03:32.7311264Z dynamo/test_nops 1/1 2024-06-26T05:03:32.7311575Z dynamo/test_config 1/1 2024-06-26T05:03:32.7311900Z test_jiterator 1/1 2024-06-26T05:03:32.7312209Z test_matmul_cuda 1/1 2024-06-26T05:03:32.7312521Z dynamo/test_sources 1/1 2024-06-26T05:03:32.7312855Z xpu/test_conv 1/1 2024-06-26T05:03:32.7313141Z test_cuda 1/1 2024-06-26T05:03:32.7313450Z dynamo/test_verify_correctness 1/1 2024-06-26T05:03:32.7313855Z dynamo/test_profiler 1/1 2024-06-26T05:03:32.7314197Z dynamo/test_reorder_logs 1/1 2024-06-26T05:03:32.7314550Z test_hub 1/1 2024-06-26T05:03:32.7315035Z dynamo/test_minifier 1/1 2024-06-26T05:03:32.7315419Z dynamo/test_activation_checkpointing 1/1 2024-06-26T05:03:32.7315867Z dynamo/test_recompile_ux 1/1 2024-06-26T05:03:32.7316244Z dynamo/test_subclasses 1/1 2024-06-26T05:03:32.7316614Z lazy/test_extract_compiled_graph 1/1 2024-06-26T05:03:32.7317043Z dynamo/test_aot_autograd_cache 1/1 2024-06-26T05:03:32.7317439Z test_cuda_multigpu 1/1 2024-06-26T05:03:32.7317807Z torch_np/numpy_tests/lib/test_arraypad 1/1 2024-06-26T05:03:32.7318261Z dynamo/test_python_autograd 1/1 2024-06-26T05:03:32.7318638Z test_sparse 1/2 2024-06-26T05:03:32.7318945Z Name: excluded (est. time: 0.0min) 2024-06-26T05:03:32.7319469Z Serial tests (0): 2024-06-26T05:03:32.7319770Z Parallel tests (0): 2024-06-26T05:03:32.7320416Z Starting test batch 'tests to run' 0.0 seconds after initiating testing 2024-06-26T05:03:32.7403270Z Running test_nn 1/2 ... [2024-06-26 05:03:32.739975] 2024-06-26T05:03:32.7407719Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_nn.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:03:32.740308] 2024-06-26T05:03:34.7899622Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:03:34.7938581Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:03:34.8069096Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:09:34.6334247Z 2024-06-26T05:09:34.6335482Z test_nn 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.2_32f9faf793a94c9f_.log 2024-06-26T05:09:34.7119402Z Running 1041 items in this shard: test/test_nn.py::TestNN::test_AdaptiveLogSoftmax, test/test_nn.py::TestNN::test_AdaptiveLogSoftmax_cuda, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_CELU_no_batch_dim, test/test_nn.py::TestNN::test_CELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_CTCLoss_zero_lengths, test/test_nn.py::TestNN::test_Conv1d, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_dilated, test/test_nn.py::TestNN::test_Conv1d_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_groups_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1, test/test_nn.py::TestNN::test_Conv1d_pad1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same2, test/test_nn.py::TestNN::test_Conv1d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_stride, test/test_nn.py::TestNN::test_Conv1d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise, test/test_nn.py::TestNN::test_Conv2d_depthwise_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated, test/test_nn.py::TestNN::test_Conv2d_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups, test/test_nn.py::TestNN::test_Conv2d_groups_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_no_bias, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_pad_same, test/test_nn.py::TestNN::test_Conv2d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated_strided_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_pad_same, test/test_nn.py::TestNN::test_Conv3d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_valid, test/test_nn.py::TestNN::test_Conv3d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_stride, test/test_nn.py::TestNN::test_Conv3d_stride_padding, test/test_nn.py::TestNN::test_Conv3d_stride_padding_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_zero_batch, test/test_nn.py::TestNN::test_Conv3d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_groups, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_CrossMapLRN2d, test/test_nn.py::TestNN::test_ELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean, test/test_nn.py::TestNN::test_EmbeddingBag_mean_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_sparse, test/test_nn.py::TestNN::test_EmbeddingBag_sparse_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum, test/test_nn.py::TestNN::test_Embedding_discontiguous, test/test_nn.py::TestNN::test_Embedding_discontiguous_cuda, test/test_nn.py::TestNN::test_Embedding_sparse, test/test_nn.py::TestNN::test_Flatten_cuda, test/test_nn.py::TestNN::test_Fold_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input_cuda, test/test_nn.py::TestNN::test_GELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_reduce, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_LSTM_cell, test/test_nn.py::TestNN::test_LSTM_cell_forward_input_size, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_cuda, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear_no_bias_cuda, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MSELoss_no_reduce, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MaxUnpool1d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool3d_net, test/test_nn.py::TestNN::test_MaxUnpool3d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Mish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ModuleList, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_reduce, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg_cuda, test/test_nn.py::TestNN::test_PReLU_no_batch_dim, test/test_nn.py::TestNN::test_PReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args_cuda, test/test_nn.py::TestNN::test_ParameterDict, test/test_nn.py::TestNN::test_ParameterList, test/test_nn.py::TestNN::test_ParameterList_meta, test/test_nn.py::TestNN::test_PixelShuffle_cuda, test/test_nn.py::TestNN::test_PixelUnshuffle, test/test_nn.py::TestNN::test_PixelUnshuffle_cuda, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce, test/test_nn.py::TestNN::test_RNN_cell_forward_zero_hidden_size, test/test_nn.py::TestNN::test_RNN_cell_no_broadcasting, test/test_nn.py::TestNN::test_RNN_dropout_state, test/test_nn.py::TestNN::test_RReLU_cuda, test/test_nn.py::TestNN::test_RReLU_no_batch_dim, test/test_nn.py::TestNN::test_RReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar, test/test_nn.py::TestNN::test_ReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_complex_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sequential_extend, test/test_nn.py::TestNN::test_Sequential_getitem, test/test_nn.py::TestNN::test_Sequential_iadd, test/test_nn.py::TestNN::test_Sequential_rmul, test/test_nn.py::TestNN::test_SiLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_beta, test/test_nn.py::TestNN::test_SmoothL1Loss_beta_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta_cuda, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_Softplus_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim, test/test_nn.py::TestNN::test_Softsign_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_Transformer_cell, test/test_nn.py::TestNN::test_Transformer_multilayer_coder, test/test_nn.py::TestNN::test_Transformer_multilayer_coder_cuda, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_Unfold, test/test_nn.py::TestNN::test_Unfold_cuda, test/test_nn.py::TestNN::test_add_module_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_affine_grid_3d, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_2, test/test_nn.py::TestNN::test_affine_grid_error_checking, test/test_nn.py::TestNN::test_assignment, test/test_nn.py::TestNN::test_batch_norm_update_stats, test/test_nn.py::TestNN::test_batchnorm_buffer_update_when_stats_are_not_tracked, test/test_nn.py::TestNN::test_batchnorm_cudnn_half, test/test_nn.py::TestNN::test_batchnorm_cudnn_nhwc, test/test_nn.py::TestNN::test_batchnorm_load_state_dict, test/test_nn.py::TestNN::test_batchnorm_nhwc_cpu, test/test_nn.py::TestNN::test_batchnorm_nhwc_cuda, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_BatchNorm2d, test/test_nn.py::TestNN::test_batchnorm_nonaffine_cuda_half_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_bias_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_less_than_one_value_per_channel, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_weights, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss_large_tensors_with_grad, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_forward_grad, test/test_nn.py::TestNN::test_bce_with_logits_ones_in_pos_weights_are_the_same_as_none, test/test_nn.py::TestNN::test_bce_with_logits_with_pos_weight_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bilinear, test/test_nn.py::TestNN::test_broadcast_double_backwards_gpu, test/test_nn.py::TestNN::test_broadcast_no_grad, test/test_nn.py::TestNN::test_buffer_not_persistent_load, test/test_nn.py::TestNN::test_buffers_and_named_buffers, test/test_nn.py::TestNN::test_cosine_embedding_loss_margin_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_with_diff_type, test/test_nn.py::TestNN::test_cross_entropy_loss_precision, test/test_nn.py::TestNN::test_cudnn_rnn_dropout_states_device, test/test_nn.py::TestNN::test_cudnn_weight_tying, test/test_nn.py::TestNN::test_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_set_extra_state, test/test_nn.py::TestNN::test_fb_fc_packed, test/test_nn.py::TestNN::test_flatten, test/test_nn.py::TestNN::test_fractional_max_pool2d_invalid_output_ratio, test/test_nn.py::TestNN::test_gaussian_nll_loss_args, test/test_nn.py::TestNN::test_gaussian_nll_loss_broadcasting, test/test_nn.py::TestNN::test_get_buffer, test/test_nn.py::TestNN::test_grid_sample, test/test_nn.py::TestNN::test_grid_sample_3d, test/test_nn.py::TestNN::test_grid_sample_error_checking, test/test_nn.py::TestNN::test_hardtanh_backward, test/test_nn.py::TestNN::test_hardtanh_inplace_gradgrad, test/test_nn.py::TestNN::test_huber_loss_invalid_delta, test/test_nn.py::TestNN::test_inplace_thnn, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_3d, test/test_nn.py::TestNN::test_interpolate_nearest_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_undefined_behavior_casting, test/test_nn.py::TestNN::test_l1_loss_correct, test/test_nn.py::TestNN::test_layer_norm_grads_with_create_graph_flag, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightStrided, test/test_nn.py::TestNN::test_log_softmax_scalar, test/test_nn.py::TestNN::test_log_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_loss_equal_input_target_shape, test/test_nn.py::TestNN::test_margin_ranking_loss_no_reduce, test/test_nn.py::TestNN::test_module_backcompat, test/test_nn.py::TestNN::test_module_super_init, test/test_nn.py::TestNN::test_modules, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce, test/test_nn.py::TestNN::test_named_modules, test/test_nn.py::TestNN::test_named_parameters_remove_duplicate, test/test_nn.py::TestNN::test_nested_tensor_from_mask, test/test_nn.py::TestNN::test_overwrite_module_params_on_conversion, test/test_nn.py::TestNN::test_pack_sequence_batch_sizes_throw, test/test_nn.py::TestNN::test_padding_list, test/test_nn.py::TestNN::test_parameterlistdict_setting_attributes, test/test_nn.py::TestNN::test_pdist_empty_col, test/test_nn.py::TestNN::test_pixel_shuffle_nhwc_cpu, test/test_nn.py::TestNN::test_pixel_shuffle_unshuffle, test/test_nn.py::TestNN::test_pointwise_loss_broadcast, test/test_nn.py::TestNN::test_projections_errors_on_gru_and_rnn, test/test_nn.py::TestNN::test_projections_lstm_args_check, test/test_nn.py::TestNN::test_projections_lstm_initial_hidden_state, test/test_nn.py::TestNN::test_register_buffer_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_relu_inplace_on_view, test/test_nn.py::TestNN::test_rnn_check_device, test/test_nn.py::TestNN::test_rnn_initial_hidden_state, test/test_nn.py::TestNN::test_rnn_weight_norm, test/test_nn.py::TestNN::test_smoothl1loss_intergral_target, test/test_nn.py::TestNN::test_softmax_functional_dim0, test/test_nn.py::TestNN::test_softmax_functional_dim0_cuda, test/test_nn.py::TestNN::test_softmax_functional_dim3, test/test_nn.py::TestNN::test_softmax_lastdim, test/test_nn.py::TestNN::test_softmax_lastdim_dtype, test/test_nn.py::TestNN::test_softmax_spatial, test/test_nn.py::TestNN::test_softmax_spatial_dtype_cuda, test/test_nn.py::TestNN::test_softmax_spatial_special, test/test_nn.py::TestNN::test_softmin, test/test_nn.py::TestNN::test_spectral_norm, test/test_nn.py::TestNN::test_spectral_norm_dim, test/test_nn.py::TestNN::test_spectral_norm_forward, test/test_nn.py::TestNN::test_spectral_norm_load_state_dict, test/test_nn.py::TestNN::test_spectral_norm_pickle, test/test_nn.py::TestNN::test_state_dict, test/test_nn.py::TestNN::test_swap_module_params_poisons_acc_grad, test/test_nn.py::TestNN::test_to, test/test_nn.py::TestNN::test_transformer_args_check, test/test_nn.py::TestNN::test_transformerdecoderlayer_gelu, test/test_nn.py::TestNN::test_triplet_margin_loss_no_reduce, test/test_nn.py::TestNN::test_triplet_margin_loss_swap, test/test_nn.py::TestNN::test_type, test/test_nn.py::TestNN::test_unflatten, test/test_nn.py::TestNN::test_unfold_invalid_arg, test/test_nn.py::TestNN::test_upsamplingBilinear2d_spatial_invariance, test/test_nn.py::TestNN::test_upsamplingLinear1d_spatial_invariance, test/test_nn.py::TestNN::test_upsampling_bfloat16, test/test_nn.py::TestNN::test_upsampling_not_recompute_scale_factor, test/test_nn.py::TestNN::test_upsampling_small_scale, test/test_nn.py::TestFusionEval::test_fuse_module_eval_numerics, test/test_nn.py::TestConstantPadNd::test_constant_pad_nd, test/test_nn.py::TestAddRelu::test_add_relu, test/test_nn.py::TestFunctionalPickle::test_pickle_softsign, test/test_nn.py::TestFusionUtils::test_fuse_linear_bn_requires_grad, test/test_nn.py::TestUtils::test_consume_prefix_in_state_dict_if_present, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_memory_format_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm1d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm2d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm3d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_warnings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Unfold_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_adaptiveavg_pool1d_shmem_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_3d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_errors_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_weight_ignore_indices_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_one_hot_target_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_with_neg_alpha_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_glu_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_for_single_spatial_element_during_training_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_less_than_one_value_per_channel_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_invalid_reduction_strings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_neg_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_zero_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_linear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_big_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_forward_with_nans_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_transformer_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_byte_target_matches_long_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_out_of_bounds_ignore_index_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_total_weight_is_zero_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nonlinearity_propagate_nan_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_one_hot_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_overwrite_module_params_on_conversion_cpu_device_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_prelu_backward_32bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_skip_init_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_vs_huber_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_results_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_threshold_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_fast_path_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_default_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_correctness_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_fail_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_rocm_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsampling_64bit_indexing_channels_last_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingnearest2d_backward_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_variable_sequence_cpu_float32 2024-06-26T05:09:34.7794898Z 2024-06-26T05:09:34.7795349Z Running test_cpp_api_parity 1/1 ... [2024-06-26 05:09:34.635849] 2024-06-26T05:09:34.7797139Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_api_parity.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:09:34.636164] 2024-06-26T05:11:44.1740842Z 2024-06-26T05:11:44.1742523Z 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_236058b2b41e3050_.log 2024-06-26T05:11:44.1985589Z Running 488 items in this shard: test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean, 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test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special_cuda 2024-06-26T05:11:44.2221110Z 2024-06-26T05:11:44.2221478Z Running test_torch 1/1 ... [2024-06-26 05:11:44.175121] 2024-06-26T05:11:44.2223182Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_torch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:11:44.175408] 2024-06-26T05:15:52.8173884Z 2024-06-26T05:15:52.8175144Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_1ed48d86b87d723d_.log 2024-06-26T05:15:52.8584367Z Running 1020 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals, test/test_torch.py::TestBasicVitalSigns::test_basic_vitals_read_write, test/test_torch.py::TestBasicVitalSigns::test_dataloader_vitals, test/test_torch.py::TestTorch::test_RNGState, test/test_torch.py::TestTorch::test_RNGStateAliasing, test/test_torch.py::TestTorch::test_RNG_after_pickle, test/test_torch.py::TestTorch::test_Size, test/test_torch.py::TestTorch::test_Size_iter, test/test_torch.py::TestTorch::test_Size_scalar, test/test_torch.py::TestTorch::test_add_meta_scalar, test/test_torch.py::TestTorch::test_allow_tensor_metadata_change, test/test_torch.py::TestTorch::test_apply, test/test_torch.py::TestTorch::test_as_subclass, test/test_torch.py::TestTorch::test_assert_async, test/test_torch.py::TestTorch::test_backward_hooks_traverse, test/test_torch.py::TestTorch::test_batch_norm_cpu_inference, test/test_torch.py::TestTorch::test_bmm_multithreaded, test/test_torch.py::TestTorch::test_boxMullerState, test/test_torch.py::TestTorch::test_cat_neg_dim, test/test_torch.py::TestTorch::test_check, test/test_torch.py::TestTorch::test_chunk_neg_dim, test/test_torch.py::TestTorch::test_conj_neg_tolist, test/test_torch.py::TestTorch::test_contains, test/test_torch.py::TestTorch::test_copy_broadcast, test/test_torch.py::TestTorch::test_copy_dtypes, test/test_torch.py::TestTorch::test_copy_float16, test/test_torch.py::TestTorch::test_copy_many_to_one, test/test_torch.py::TestTorch::test_copy_transpose, test/test_torch.py::TestTorch::test_cuda_not_built, test/test_torch.py::TestTorch::test_cummax_neg_dim, test/test_torch.py::TestTorch::test_cummin_neg_dim, test/test_torch.py::TestTorch::test_cumprod_neg_dim, test/test_torch.py::TestTorch::test_cumsum_neg_dim, test/test_torch.py::TestTorch::test_cxx_flags, test/test_torch.py::TestTorch::test_data_ptr_of_empty_tensor_with_storage, test/test_torch.py::TestTorch::test_data_ptr_of_empty_view_with_storage, test/test_torch.py::TestTorch::test_deepcopy_gradient, test/test_torch.py::TestTorch::test_deepcopy_parameter, test/test_torch.py::TestTorch::test_deterministic_fill_uninitialized_memory, test/test_torch.py::TestTorch::test_deterministic_flag, test/test_torch.py::TestTorch::test_device, test/test_torch.py::TestTorch::test_dim_order, test/test_torch.py::TestTorch::test_dir, test/test_torch.py::TestTorch::test_doc, test/test_torch.py::TestTorch::test_doc_template, test/test_torch.py::TestTorch::test_dot_data_use, test/test_torch.py::TestTorch::test_dtype_is_signed, test/test_torch.py::TestTorch::test_element_size, test/test_torch.py::TestTorch::test_empty_meta, test/test_torch.py::TestTorch::test_empty_storage_view, test/test_torch.py::TestTorch::test_equal, test/test_torch.py::TestTorch::test_error_msg_type_translation, test/test_torch.py::TestTorch::test_fill_diagonal, test/test_torch.py::TestTorch::test_format_scalar_meta, test/test_torch.py::TestTorch::test_from_buffer, test/test_torch.py::TestTorch::test_from_file, test/test_torch.py::TestTorch::test_gather_neg_dim, test/test_torch.py::TestTorch::test_generator_cpu, test/test_torch.py::TestTorch::test_get_cpu_capability, test/test_torch.py::TestTorch::test_has_internal_overlap, test/test_torch.py::TestTorch::test_has_storage, test/test_torch.py::TestTorch::test_index_add, test/test_torch.py::TestTorch::test_index_add_all_dtypes, test/test_torch.py::TestTorch::test_index_add_cornercase, test/test_torch.py::TestTorch::test_index_add_correctness, test/test_torch.py::TestTorch::test_index_add_neg_dim, test/test_torch.py::TestTorch::test_index_copy_neg_dim, test/test_torch.py::TestTorch::test_index_fill_neg_dim, test/test_torch.py::TestTorch::test_index_select_neg_dim, test/test_torch.py::TestTorch::test_invalid_arg_error_handling, test/test_torch.py::TestTorch::test_invalid_generator_raises, test/test_torch.py::TestTorch::test_is_nonzero, test/test_torch.py::TestTorch::test_is_same_size, test/test_torch.py::TestTorch::test_iter, test/test_torch.py::TestTorch::test_kthvalue_neg_dim, test/test_torch.py::TestTorch::test_linspace_logspace, test/test_torch.py::TestTorch::test_logcumsumexp_neg_dim, test/test_torch.py::TestTorch::test_manual_seed, test/test_torch.py::TestTorch::test_map, test/test_torch.py::TestTorch::test_map2, test/test_torch.py::TestTorch::test_max_neg_dim, test/test_torch.py::TestTorch::test_mean_neg_dim, test/test_torch.py::TestTorch::test_median_neg_dim, test/test_torch.py::TestTorch::test_memory_format, test/test_torch.py::TestTorch::test_memory_format_contiguous_returns_same_tensor_if_already_satisfies, test/test_torch.py::TestTorch::test_memory_format_empty, test/test_torch.py::TestTorch::test_min_neg_dim, test/test_torch.py::TestTorch::test_mode_neg_dim, test/test_torch.py::TestTorch::test_multinomial_invalid_probs, test/test_torch.py::TestTorch::test_nanmedian_neg_dim, test/test_torch.py::TestTorch::test_narrow_neg_dim, test/test_torch.py::TestTorch::test_nbytes, test/test_torch.py::TestTorch::test_ndim, test/test_torch.py::TestTorch::test_new, test/test_torch.py::TestTorch::test_newaxis_numpy_comparison, test/test_torch.py::TestTorch::test_newindex, test/test_torch.py::TestTorch::test_no_cuda_monkeypatch, test/test_torch.py::TestTorch::test_norm_neg_dim, test/test_torch.py::TestTorch::test_normal_shape, test/test_torch.py::TestTorch::test_numel, test/test_torch.py::TestTorch::test_parallel_info, test/test_torch.py::TestTorch::test_parsing_double, test/test_torch.py::TestTorch::test_parsing_int64, test/test_torch.py::TestTorch::test_parsing_intlist, test/test_torch.py::TestTorch::test_permute, test/test_torch.py::TestTorch::test_pickle, test/test_torch.py::TestTorch::test_pickle_dtype, test/test_torch.py::TestTorch::test_pickle_function, test/test_torch.py::TestTorch::test_pickle_generator, test/test_torch.py::TestTorch::test_pickle_parameter, test/test_torch.py::TestTorch::test_pickle_parameter_no_requires_grad, test/test_torch.py::TestTorch::test_pickle_size, test/test_torch.py::TestTorch::test_pin_memory, test/test_torch.py::TestTorch::test_print, test/test_torch.py::TestTorch::test_prod_neg_dim, test/test_torch.py::TestTorch::test_pyobj_preserved, test/test_torch.py::TestTorch::test_qengine, test/test_torch.py::TestTorch::test_renorm_neg_dim, test/test_torch.py::TestTorch::test_resizable, test/test_torch.py::TestTorch::test_reversed, test/test_torch.py::TestTorch::test_scatter_neg_dim, test/test_torch.py::TestTorch::test_select_neg_dim, test/test_torch.py::TestTorch::test_set_flush_denormal, test/test_torch.py::TestTorch::test_setting_real_imag_to_a_number, test/test_torch.py::TestTorch::test_show_config, test/test_torch.py::TestTorch::test_size_neg_dim, test/test_torch.py::TestTorch::test_size_stride, test/test_torch.py::TestTorch::test_sizeof, test/test_torch.py::TestTorch::test_slice, test/test_torch.py::TestTorch::test_slow_test, test/test_torch.py::TestTorch::test_sobolengine_bounds, test/test_torch.py::TestTorch::test_sobolengine_bounds_scrambled, test/test_torch.py::TestTorch::test_sobolengine_continuing, test/test_torch.py::TestTorch::test_sobolengine_continuing_scrambled, test/test_torch.py::TestTorch::test_sobolengine_default_dtype, test/test_torch.py::TestTorch::test_sobolengine_distribution, test/test_torch.py::TestTorch::test_sobolengine_distribution_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw, test/test_torch.py::TestTorch::test_sobolengine_draw_base2, test/test_torch.py::TestTorch::test_sobolengine_draw_base2_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw_scrambled, test/test_torch.py::TestTorch::test_sobolengine_fast_forward, test/test_torch.py::TestTorch::test_sobolengine_fast_forward_scrambled, test/test_torch.py::TestTorch::test_sobolengine_first_point, test/test_torch.py::TestTorch::test_sobolengine_high_dim, test/test_torch.py::TestTorch::test_sobolengine_raise, test/test_torch.py::TestTorch::test_sobolengine_reset, test/test_torch.py::TestTorch::test_sobolengine_reset_scrambled, test/test_torch.py::TestTorch::test_sort_neg_dim, test/test_torch.py::TestTorch::test_split_neg_dim, test/test_torch.py::TestTorch::test_split_with_sizes_copy_out, test/test_torch.py::TestTorch::test_squeeze_neg_dim, test/test_torch.py::TestTorch::test_std_neg_dim, test/test_torch.py::TestTorch::test_storage_base_init, test/test_torch.py::TestTorch::test_storage_base_new, test/test_torch.py::TestTorch::test_storage_byteswap, test/test_torch.py::TestTorch::test_storage_casts, test/test_torch.py::TestTorch::test_storage_cycle_via_dict, test/test_torch.py::TestTorch::test_storage_cycle_via_slots, test/test_torch.py::TestTorch::test_storage_dead_weak_ref, test/test_torch.py::TestTorch::test_storage_dealloc, test/test_torch.py::TestTorch::test_storage_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_zombie, test/test_torch.py::TestTorch::test_storage_dict_dealloc, test/test_torch.py::TestTorch::test_storage_error, test/test_torch.py::TestTorch::test_storage_error_no_attribute, test/test_torch.py::TestTorch::test_storage_finalizer_dealloc, test/test_torch.py::TestTorch::test_storage_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_zombie, test/test_torch.py::TestTorch::test_storage_preserve_nonhermetic_in_hermetic_context, test/test_torch.py::TestTorch::test_storage_resurrected_weak_ref, test/test_torch.py::TestTorch::test_storage_slot_dealloc, test/test_torch.py::TestTorch::test_storage_weakref_dealloc, test/test_torch.py::TestTorch::test_structseq_repr, test/test_torch.py::TestTorch::test_subclass_preserved, test/test_torch.py::TestTorch::test_subclass_tensors, test/test_torch.py::TestTorch::test_sum_neg_dim, test/test_torch.py::TestTorch::test_swap_basic, test/test_torch.py::TestTorch::test_swap_fail_slots, test/test_torch.py::TestTorch::test_t_not_2d_error, test/test_torch.py::TestTorch::test_tensor_base_init, test/test_torch.py::TestTorch::test_tensor_base_new, test/test_torch.py::TestTorch::test_tensor_ctor_scalar, test/test_torch.py::TestTorch::test_tensor_cycle_via_dict, test/test_torch.py::TestTorch::test_tensor_cycle_via_slots, test/test_torch.py::TestTorch::test_tensor_dead_weak_ref, test/test_torch.py::TestTorch::test_tensor_dict_dealloc, test/test_torch.py::TestTorch::test_tensor_finalizer_dealloc, test/test_torch.py::TestTorch::test_tensor_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_tensor_resurrected_weak_ref, test/test_torch.py::TestTorch::test_tensor_set, test/test_torch.py::TestTorch::test_tensor_set_errors, test/test_torch.py::TestTorch::test_tensor_slot_dealloc, test/test_torch.py::TestTorch::test_tensor_weakref_dealloc, test/test_torch.py::TestTorch::test_tensor_where_scalar, test/test_torch.py::TestTorch::test_tensoriterator_output_setup, test/test_torch.py::TestTorch::test_terminate_handler_on_crash, test/test_torch.py::TestTorch::test_to, test/test_torch.py::TestTorch::test_to_with_tensor, test/test_torch.py::TestTorch::test_topk_neg_dim, test/test_torch.py::TestTorch::test_torch_from_file, test/test_torch.py::TestTorch::test_transpose_neg_dim, test/test_torch.py::TestTorch::test_type, test/test_torch.py::TestTorch::test_type_alias, test/test_torch.py::TestTorch::test_type_conversion_via_dtype_name, test/test_torch.py::TestTorch::test_typed_storage_deprecation_warning, test/test_torch.py::TestTorch::test_typed_storage_internal_no_warning, test/test_torch.py::TestTorch::test_unbind_neg_dim, test/test_torch.py::TestTorch::test_unflatten, test/test_torch.py::TestTorch::test_unfold_neg_dim, test/test_torch.py::TestTorch::test_unsqueeze_neg_dim, test/test_torch.py::TestTorch::test_upsample_nearest1d_meta, test/test_torch.py::TestTorch::test_upsample_nearest2d_meta, test/test_torch.py::TestTorch::test_var_neg_dim, test/test_torch.py::TestTorch::test_warn_types, test/test_torch.py::TestTorch::test_wildcard_import, test/test_torch.py::TestVitalSignsCudaCPU::test_cuda_vitals_gpu_only_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_assertRaisesRegex_ignore_msg_non_native_device_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bfloat16_neg_abs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bool_tensor_value_change_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_add_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcdiv_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcmul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_atan2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_div_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_eq_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_fmod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ge_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_gt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_le_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lerp_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_fill_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_scatter_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_select_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_max_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_min_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_mul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ne_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_pow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_remainder_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_sub_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_cuda_backward_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_euclidean_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_grad_p_lt_1_no_nan_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_same_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_all_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_internal_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_not_memory_dense_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_zero_stride_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_complex_half_experimental_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_constants_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_backward_agnostic_to_memory_format_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_math_view_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cov_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cpp_warnings_have_python_context_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cublas_config_nondeterministic_alert_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_cummin_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummin_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumprod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int16, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_device_guard_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex128, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_separate_unscale_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_multiple_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_penalty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_state_dict_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_sparse_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_update_scale_cpu_float32, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_prod_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_prod_cpu_float16, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int8, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_logcumsumexp_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_lognormal_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_bool_tensor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bfloat16_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bfloat16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bool_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bool_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex128_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex128_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex64_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float16_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float32_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float32_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float64_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int16_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int32_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int32_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int64_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int8_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int8_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_uint8_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_uint8_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_bool_tensor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_complex64, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_clone_cpu, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_deterministic_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_deterministic_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_deterministic_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_device_constrain_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_empty_w_replacement_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_empty_wo_replacement_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_gpu_device_constrain_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_rng_state_advance_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_narrow_copy_non_contiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_narrow_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AdaptiveAvgPool2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AdaptiveAvgPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AdaptiveMaxPool2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AvgPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_CTCLoss_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_EmbeddingBag_max_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_FractionalMaxPool2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_FractionalMaxPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_NLLLoss_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReflectionPad1d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReflectionPad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReflectionPad3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReplicationPad1d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReplicationPad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReplicationPad3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_bincount_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_cumsum_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_grid_sample_2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_grid_sample_3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_histc_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_bicubic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_bilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_linear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_trilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_kthvalue_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_median_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_put_accumulate_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_put_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_qint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_qint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_quint2x4, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_quint4x2, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_quint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_normal_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_normal_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_normal_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nullary_op_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pairwise_distance_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_pdist_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pdist_norm_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pickle_gradscaler_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pin_memory_from_constructor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_reduced_type_float_copy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_reduced_type_float_copy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_repeat_interleave_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scalar_check_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_bool_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_non_unique_index_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_one_dim_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_to_large_input_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_bool_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_multiply_unsupported_dtypes_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_multiply_unsupported_dtypes_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_to_large_input_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_zero_size_index_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_serialization_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_default_tensor_type_warnings_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_shift_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_skip_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_all_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint4x2, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_strides_propagation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_sync_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_set_errors_multigpu_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_shape_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_type_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_ternary_op_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_unfold_all_devices_and_dtypes_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_unfold_scalars_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_untyped_storage_meta_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_warn_always_caught_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_where_scalar_handcrafted_values_cpu 2024-06-26T05:15:52.8978146Z 2024-06-26T05:15:52.8978619Z Running test_ci_sanity_check_fail 1/1 ... [2024-06-26 05:15:52.819927] 2024-06-26T05:15:52.8980474Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_ci_sanity_check_fail.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:15:52.820230] 2024-06-26T05:16:00.5821791Z Running test_show_pickle 1/1 ... [2024-06-26 05:16:00.581795] 2024-06-26T05:16:00.5824361Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_show_pickle.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:16:00.582084] 2024-06-26T05:16:03.1998992Z 2024-06-26T05:16:03.2000563Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_b760876b2caaa831_.log 2024-06-26T05:16:03.2002144Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model 2024-06-26T05:16:03.2004127Z 2024-06-26T05:16:03.2004949Z Running test_autocast 1/1 ... [2024-06-26 05:16:03.199943] 2024-06-26T05:16:03.2006718Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_autocast.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:16:03.200280] 2024-06-26T05:16:16.7322113Z 2024-06-26T05:16:16.7323446Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_13e659eb0fd2f0b2_.log 2024-06-26T05:16:16.7330481Z Running 16 items in this shard: test/test_autocast.py::TestAutocastCPU::test_autocast_disabled_with_fp32_dtype, test/test_autocast.py::TestAutocastCPU::test_autocast_methods_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_16, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_rnn, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_16, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_need_autocast_promote, test/test_autocast.py::TestAutocastCPU::test_cpu_autocast_deprecated_warning, test/test_autocast.py::TestAutocastCPU::test_generic_autocast, test/test_autocast.py::TestAutocastGPU::test_cache_disabled, test/test_autocast.py::TestAutocastGPU::test_cast_cache_is_global, test/test_autocast.py::TestTorchAutocast::test_autocast_fast_dtype, test/test_autocast.py::TestTorchAutocast::test_invalid_device, test/test_autocast.py::TestTorchAutocast::test_non_string_device 2024-06-26T05:16:16.7336652Z 2024-06-26T05:16:16.7336923Z Running test_utils 1/1 ... [2024-06-26 05:16:16.732355] 2024-06-26T05:16:16.7340719Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_utils.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:16:16.733769] 2024-06-26T05:17:35.4098923Z 2024-06-26T05:17:35.4155734Z test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_1.1_a1ca395dc4e6cfc9_.log 2024-06-26T05:17:35.7593068Z Running 5895 items in this shard: test/test_utils.py::TestCheckpoint::test_checkpoint, test/test_utils.py::TestCheckpoint::test_checkpoint_module_list, test/test_utils.py::TestCheckpoint::test_checkpoint_no_tensors, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor_inputs_outputs, test/test_utils.py::TestCheckpoint::test_checkpoint_not_preserve_rng_state_and_without_reentrant, test/test_utils.py::TestCheckpoint::test_checkpoint_partial_grad, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cpu, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cuda, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_multiple_args, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_no_args, test/test_utils.py::TestCheckpoint::test_checkpoint_trigger, test/test_utils.py::TestCheckpoint::test_checkpoint_valid, test/test_utils.py::TestCheckpoint::test_checkpointing_without_reentrant_early_free, test/test_utils.py::TestCheckpoint::test_get_device_states_recursive, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_meta, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_multi_cuda, test/test_utils.py::TestDataLoaderUtils::test_multi_drop, test/test_utils.py::TestDataLoaderUtils::test_multi_keep, test/test_utils.py::TestDataLoaderUtils::test_random_seed, test/test_utils.py::TestDataLoaderUtils::test_single_drop, test/test_utils.py::TestDataLoaderUtils::test_single_keep, test/test_utils.py::TestBottleneck::test_bottleneck_cpu_only, test/test_utils.py::TestBottleneck::test_bottleneck_cuda, test/test_utils.py::TestCollectEnv::test_smoke, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_acosh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_add_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addbmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcdiv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addcmul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bool, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_and_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_left_shift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_not_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_or_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_right_shift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bitwise_xor_cpu_uint8, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_shapes_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bucketize_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex64, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float64, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float32, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_uint8, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_singular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_or_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_xor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_fill_cpu_int16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_bfloat16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_ctc_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_ctc_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_one_hot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pdist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pdist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_complex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_complex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polar_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polar_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_quantile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_quantile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int32, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float32, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int32, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_j1_cpu_uint8, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_bessel_y1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_chebyshev_polynomial_t_cpu_int32, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_v_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_shifted_chebyshev_polynomial_w_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_spherical_bessel_j0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_xlog1py_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_zeta_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_list_args_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sqrt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_square_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_multiple_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_more_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_nn_module_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_set_default_device_cpu, test/test_utils.py::TestCppExtensionUtils::test_cc_compiler_is_ok, test/test_utils.py::TestCppExtensionUtils::test_cpp_compiler_is_ok, test/test_utils.py::TestTraceback::test_basic, test/test_utils.py::TestTraceback::test_captured_traceback, test/test_utils.py::TestTraceback::test_captured_traceback_format_all, test/test_utils.py::TestTraceback::test_captured_traceback_format_all_cached, test/test_utils.py::TestTraceback::test_format_traceback_short 2024-06-26T05:17:36.0185805Z 2024-06-26T05:17:36.0186251Z Running test_tensorexpr 1/1 ... [2024-06-26 05:17:35.417483] 2024-06-26T05:17:36.0188030Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_tensorexpr.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:17:35.417772] 2024-06-26T05:17:37.8358450Z 2024-06-26T05:17:37.8359963Z test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_1.1_7d70cb09213ddc0b_.log 2024-06-26T05:17:37.8403156Z Running 74 items in this shard: test/test_tensorexpr.py::TestTensorExprFuser::test_add_const_rhs, test/test_tensorexpr.py::TestTensorExprFuser::test_add_sub, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_input_and_module, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_module, test/test_tensorexpr.py::TestTensorExprFuser::test_all_combos, test/test_tensorexpr.py::TestTensorExprFuser::test_alpha, test/test_tensorexpr.py::TestTensorExprFuser::test_binary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_bitwise_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast3, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_2, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_big2, test/test_tensorexpr.py::TestTensorExprFuser::test_cat, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_empty_tensors, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_negative_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_only, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_promote_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_with_constant_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_char, test/test_tensorexpr.py::TestTensorExprFuser::test_chunk, test/test_tensorexpr.py::TestTensorExprFuser::test_clamp, test/test_tensorexpr.py::TestTensorExprFuser::test_constant, test/test_tensorexpr.py::TestTensorExprFuser::test_double, test/test_tensorexpr.py::TestTensorExprFuser::test_double_intrinsics, test/test_tensorexpr.py::TestTensorExprFuser::test_dynamic_shape, test/test_tensorexpr.py::TestTensorExprFuser::test_easy, test/test_tensorexpr.py::TestTensorExprFuser::test_eq, test/test_tensorexpr.py::TestTensorExprFuser::test_exp_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_four_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_ge, test/test_tensorexpr.py::TestTensorExprFuser::test_gt, test/test_tensorexpr.py::TestTensorExprFuser::test_guard_fails, test/test_tensorexpr.py::TestTensorExprFuser::test_half_bn_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_half_gelu, test/test_tensorexpr.py::TestTensorExprFuser::test_int64_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_int_output, test/test_tensorexpr.py::TestTensorExprFuser::test_le, test/test_tensorexpr.py::TestTensorExprFuser::test_loop, test/test_tensorexpr.py::TestTensorExprFuser::test_lt, test/test_tensorexpr.py::TestTensorExprFuser::test_mask, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction2, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1_2, test/test_tensorexpr.py::TestTensorExprFuser::test_multi_rand, test/test_tensorexpr.py::TestTensorExprFuser::test_multioutput, test/test_tensorexpr.py::TestTensorExprFuser::test_multiple_outputs, test/test_tensorexpr.py::TestTensorExprFuser::test_nans, test/test_tensorexpr.py::TestTensorExprFuser::test_ne, test/test_tensorexpr.py::TestTensorExprFuser::test_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_propagated_mem_layout, test/test_tensorexpr.py::TestTensorExprFuser::test_rand_like, test/test_tensorexpr.py::TestTensorExprFuser::test_rank_two, test/test_tensorexpr.py::TestTensorExprFuser::test_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_remainder, test/test_tensorexpr.py::TestTensorExprFuser::test_reps, test/test_tensorexpr.py::TestTensorExprFuser::test_round_2, test/test_tensorexpr.py::TestTensorExprFuser::test_scalar, test/test_tensorexpr.py::TestTensorExprFuser::test_short, test/test_tensorexpr.py::TestTensorExprFuser::test_simple_add, test/test_tensorexpr.py::TestTensorExprFuser::test_sin_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_slice, test/test_tensorexpr.py::TestTensorExprFuser::test_sliced_stride, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cpu, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cuda, test/test_tensorexpr.py::TestTensorExprFuser::test_strided_output_preserved, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg2, test/test_tensorexpr.py::TestTensorExprFuser::test_transpose, test/test_tensorexpr.py::TestTensorExprFuser::test_unary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_unsqueeze, test/test_tensorexpr.py::TestTensorExprFuser::test_where 2024-06-26T05:17:37.8449806Z 2024-06-26T05:17:37.8450529Z Running test_autograd_fallback 1/1 ... [2024-06-26 05:17:37.836039] 2024-06-26T05:17:37.8453943Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_autograd_fallback.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:17:37.836348] 2024-06-26T05:17:41.8557762Z 2024-06-26T05:17:41.8559501Z test_autograd_fallback 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_fallback_1.1_8c5bdbad9d5fa7eb_.log 2024-06-26T05:17:41.8576313Z Running 28 items in this shard: test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_warn 2024-06-26T05:17:41.8591239Z 2024-06-26T05:17:41.8591610Z Running test_python_dispatch 1/1 ... [2024-06-26 05:17:41.855934] 2024-06-26T05:17:41.8593536Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_python_dispatch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:17:41.856204] 2024-06-26T05:17:53.9861954Z 2024-06-26T05:17:53.9864157Z test_python_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_python_dispatch_1.1_f6aceebc50cacbef_.log 2024-06-26T05:17:53.9918410Z Running 111 items in this shard: test/test_python_dispatch.py::TestDispatcherPythonBindings::test_call_boxed, test/test_python_dispatch.py::TestPythonRegistration::test_alias_analysis, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_no_existing, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_with_existing, test/test_python_dispatch.py::TestPythonRegistration::test_error_for_unsupported_ns_or_kind, test/test_python_dispatch.py::TestPythonRegistration::test_error_if_fn_not_callable, test/test_python_dispatch.py::TestPythonRegistration::test_extend_library_with_dispatch_key_arg, test/test_python_dispatch.py::TestPythonRegistration::test_finalizer, test/test_python_dispatch.py::TestPythonRegistration::test_override_aten_ops_with_multiple_libraries, test/test_python_dispatch.py::TestPythonRegistration::test_override_cpu_sum, test/test_python_dispatch.py::TestPythonRegistration::test_override_cuda_with_jiterator, test/test_python_dispatch.py::TestPythonRegistration::test_register_fallthrough, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_error_cases, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_multiple_returns, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_no_returns, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_one_return, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_with_optional, test/test_python_dispatch.py::TestPythonRegistration::test_returning_symint, test/test_python_dispatch.py::TestPythonDispatch::test_all_same_mode, test/test_python_dispatch.py::TestPythonDispatch::test_autograd_in_attr, test/test_python_dispatch.py::TestPythonDispatch::test_basic, test/test_python_dispatch.py::TestPythonDispatch::test_capture_logs_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_construct_int_tensor, test/test_python_dispatch.py::TestPythonDispatch::test_custom_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_custom_size_policy_dynamic_shapes, test/test_python_dispatch.py::TestPythonDispatch::test_data_ptr_respects_numel_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_non_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass_with_clone_returning_different_type, test/test_python_dispatch.py::TestPythonDispatch::test_detach_appears_twice_when_called_once, test/test_python_dispatch.py::TestPythonDispatch::test_device_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dim_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call_list_arg, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_dont_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_error_using_class_method_on_mode, test/test_python_dispatch.py::TestPythonDispatch::test_exception_handling, test/test_python_dispatch.py::TestPythonDispatch::test_fancy_strides, test/test_python_dispatch.py::TestPythonDispatch::test_format, test/test_python_dispatch.py::TestPythonDispatch::test_get_cur_mode, test/test_python_dispatch.py::TestPythonDispatch::test_get_mode_stack, test/test_python_dispatch.py::TestPythonDispatch::test_index_put_where_only_index_is_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_invalid_ret, test/test_python_dispatch.py::TestPythonDispatch::test_is_contiguous_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only_and_positional_default, test/test_python_dispatch.py::TestPythonDispatch::test_layout_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_like, test/test_python_dispatch.py::TestPythonDispatch::test_list_ret, test/test_python_dispatch.py::TestPythonDispatch::test_make_fx_with_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_make_subclass_with_modes, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_noalloc, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_propagates_metadata, test/test_python_dispatch.py::TestPythonDispatch::test_maybe_tuple_bug, test/test_python_dispatch.py::TestPythonDispatch::test_mode_with_make_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_multiple_ops_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_nested_push_logging_tensor_mode, test/test_python_dispatch.py::TestPythonDispatch::test_nesting_same_mode, test/test_python_dispatch.py::TestPythonDispatch::test_new_ones, test/test_python_dispatch.py::TestPythonDispatch::test_none_wrapping, test/test_python_dispatch.py::TestPythonDispatch::test_notimplemented_mode, test/test_python_dispatch.py::TestPythonDispatch::test_optional_tensor_list, test/test_python_dispatch.py::TestPythonDispatch::test_out, test/test_python_dispatch.py::TestPythonDispatch::test_produce_real_type, test/test_python_dispatch.py::TestPythonDispatch::test_record_stream, test/test_python_dispatch.py::TestPythonDispatch::test_return_and_correct_aliasing_gives_correct_stride, test/test_python_dispatch.py::TestPythonDispatch::test_return_stream, test/test_python_dispatch.py::TestPythonDispatch::test_set_data, test/test_python_dispatch.py::TestPythonDispatch::test_shallow_copy_and_detach, test/test_python_dispatch.py::TestPythonDispatch::test_sizes_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_standard_is_not_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_storage, test/test_python_dispatch.py::TestPythonDispatch::test_storage_can_be_converted_to_python_object, test/test_python_dispatch.py::TestPythonDispatch::test_strides_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_autograd_device_check, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_creation, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_sym_sizes_strides_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_tolist_numpy_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_basic, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_respects_no_dispatch, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_unrelated_tensors, test/test_python_dispatch.py::TestPythonDispatch::test_version, test/test_python_dispatch.py::TestPythonDispatch::test_with_mode_created_separately, test/test_python_dispatch.py::TestPythonDispatch::test_with_nested_modes, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_extra_dispatch_keys, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_multiprocessing_preserves_dtype, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_serializes, test/test_python_dispatch.py::TestPythonDispatcher::test_basic, test/test_python_dispatch.py::TestPythonDispatcher::test_lstsq, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_cat_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_conv2d_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCatCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCubeCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulScalarCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNMSCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNonzeroCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySortCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyTakeCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyViewCopyCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_mul_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_native_batch_norm_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_out_op_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_list_args_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_view_cpu_float32 2024-06-26T05:17:53.9970509Z 2024-06-26T05:17:53.9970975Z Running test_cpp_extensions_stream_and_event 1/1 ... [2024-06-26 05:17:53.986451] 2024-06-26T05:17:53.9972953Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_stream_and_event.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:17:53.986767] 2024-06-26T05:17:57.3056226Z 2024-06-26T05:17:57.3058115Z test_cpp_extensions_stream_and_event 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_stream_and_event_1.1_e04d91513478882f_.log 2024-06-26T05:17:57.3060123Z Running 1 items in this shard: test/test_cpp_extensions_stream_and_event.py::TestCppExtensionStreamAndEvent::test_stream_event 2024-06-26T05:17:57.3060916Z 2024-06-26T05:17:57.3061379Z Running test_cpp_extensions_mtia_backend 1/1 ... [2024-06-26 05:17:57.305814] 2024-06-26T05:17:57.3064081Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_mtia_backend.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:17:57.306105] 2024-06-26T05:18:00.6246950Z 2024-06-26T05:18:00.6248493Z test_cpp_extensions_mtia_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_mtia_backend_1.1_3510886faba92112_.log 2024-06-26T05:18:00.6252899Z Running 5 items in this shard: test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_device_context, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_get_device_module, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_basic, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_context, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_context_different_device 2024-06-26T05:18:00.6255718Z 2024-06-26T05:18:00.6256020Z Running test_overrides 1/1 ... [2024-06-26 05:18:00.624852] 2024-06-26T05:18:00.6257767Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_overrides.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:18:00.625118] 2024-06-26T05:22:00.1955446Z 2024-06-26T05:22:00.1956743Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_2af9e0332f42b2cc_.log 2024-06-26T05:22:00.2646070Z Running 1456 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__, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_argmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_argmin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_argsort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_argwhere, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_as_strided, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_as_strided_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_as_strided_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_asin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_asin_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_asinh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_asinh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atan2, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cpu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cross, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_crow_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cuda, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cummax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cummin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumprod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumprod_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumsum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumsum_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_data_ptr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_deg2rad, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_deg2rad_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dense_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dequantize, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_det, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_detach, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_detach_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diag, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diag_embed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diagflat, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diagonal_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diff, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_digamma_, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_divide_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmod_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frac, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gather, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gcd, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gcd_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ge, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_has_names, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_heaviside, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_heaviside_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_histc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_histogram, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hypot_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_i0, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_i0_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inner, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ipu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_coalesced, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_contiguous, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_pinned, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_set_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_shared, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_istft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_item, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kron, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_long, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_max, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_median, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_min, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mode, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_module_load, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_msort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_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_, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_spherical_bessel_j0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_xlog1py, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_zeta, test/test_overrides.py::TestTorchFunctionOverride::test_torch__assert_async, test/test_overrides.py::TestTorchFunctionOverride::test_torch__conj_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__functional_assert_async, test/test_overrides.py::TestTorchFunctionOverride::test_torch__fw_primal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lobpcg_lobpcg, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lowrank_pca_lowrank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lowrank_svd_lowrank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__make_dual_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__native_batch_norm_legit, test/test_overrides.py::TestTorchFunctionOverride::test_torch__neg_view_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__reshape_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__rowwise_prune, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sparse_broadcast_to_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cos, 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_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_gradient, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hardshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_heaviside, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogram, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogramdd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_i0, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_imag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inner, test/test_overrides.py::TestTorchFunctionOverride::test_torch_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_istft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kron, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_torch_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_le, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log10, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_unpack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_median, test/test_overrides.py::TestTorchFunctionOverride::test_torch_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_add_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_depthwise_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_rnn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mode, test/test_overrides.py::TestTorchFunctionOverride::test_torch_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_msort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ne, test/test_overrides.py::TestTorchFunctionOverride::test_torch_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_negative, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional__threshold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_affine_grid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_elu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gaussian_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_glu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_grid_sample, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gumbel_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hardtanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_huber_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_interpolate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_leaky_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_local_response_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mish, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mse_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_head_attention_forward, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_normalize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_pad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu6, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_silu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_smooth_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softsign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_tanhshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_with_distance_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_unfold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_constant_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_kaiming_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_torch_norm_except_dim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nuclear_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_numel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ones_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_outer, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pairwise_distance, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_unshuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polar, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_positive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_qr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_channel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor_dynamic, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rand_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randint_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randn_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_real, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_torch_renorm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_repeat_interleave, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reshape, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_roll, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rot90, test/test_overrides.py::TestTorchFunctionOverride::test_torch_round, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_saddmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_searchsorted, test/test_overrides.py::TestTorchFunctionOverride::test_torch_segment_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sgn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_signbit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_smm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_square, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_svd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_float, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_int, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_ite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_t, test/test_overrides.py::TestTorchFunctionOverride::test_torch_t_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_take, test/test_overrides.py::TestTorchFunctionOverride::test_torch_take_along_dim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_threshold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_topk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trace, test/test_overrides.py::TestTorchFunctionOverride::test_torch_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_transpose_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trapz, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triangular_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tril, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triplet_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_true_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trunc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unbind, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unbind_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unflatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unfold_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsqueeze, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsqueeze_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_values_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_var, test/test_overrides.py::TestTorchFunctionOverride::test_torch_var_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vdot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_complex_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_real, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_real_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_where, test/test_overrides.py::TestTorchFunctionOverride::test_torch_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_zeros_like, test/test_overrides.py::TestTorchFunctionOverride::test_user_implementation_raises, test/test_overrides.py::TestEinsumOverride::test_wrapper, test/test_overrides.py::TestGradCheckOverride::test_gradcheck, test/test_overrides.py::TestNamedTuple::test_max, test/test_overrides.py::TestGradNewOnesOverride::test_newones, test/test_overrides.py::TestPickle::test_pickle, test/test_overrides.py::TestBroadcastAllOverride::test_broadcast_all, test/test_overrides.py::TestWrapTorchFunction::test_wrap_torch_function, test/test_overrides.py::TestIndexing::test_getitem, test/test_overrides.py::TestIndexing::test_getitem_subclass, test/test_overrides.py::TestIndexing::test_setitem, test/test_overrides.py::TestIndexing::test_setitem_subclass, test/test_overrides.py::TestIndexing::test_setitem_val, test/test_overrides.py::TestIterator::test_iterator, test/test_overrides.py::TestRNN::test_rnn, test/test_overrides.py::TestDisabledTorchFunction::test_parameter_does_not_prevent_dispatch, test/test_overrides.py::TestResolveName::test_resolve_name, test/test_overrides.py::TestTorchFunctionWarning::test_warn_on_invalid_torch_function, 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_disable_enable_subclass, 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_with_mode, test/test_overrides.py::TestTorchFunctionMode::test_with_mode_created_separately, test/test_overrides.py::TestTorchFunctionMode::test_with_nested_modes 2024-06-26T05:22:00.3231861Z 2024-06-26T05:22:00.3232325Z Running test_jit_disabled 1/1 ... [2024-06-26 05:22:00.197377] 2024-06-26T05:22:00.3234092Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jit_disabled.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:22:00.197659] 2024-06-26T05:22:02.9155289Z 2024-06-26T05:22:02.9157174Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_51a947bea9d98691_.log 2024-06-26T05:22:02.9160846Z Running 3 items in this shard: test/test_jit_disabled.py::TestJitDisabled::test_attribute, test/test_jit_disabled.py::TestJitDisabled::test_recursive_script, test/test_jit_disabled.py::TestJitDisabled::test_script_module_construction 2024-06-26T05:22:02.9163151Z 2024-06-26T05:22:02.9163648Z Running test_native_mha 1/1 ... [2024-06-26 05:22:02.915750] 2024-06-26T05:22:02.9165955Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_native_mha.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:22:02.916102] 2024-06-26T05:22:28.1635481Z 2024-06-26T05:22:28.1637360Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_ee159f4f2f8d3959_.log 2024-06-26T05:22:28.1667101Z Running 28 items in this shard: test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_encoder_decoder_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_nested_cpu_float32 2024-06-26T05:22:28.1693337Z 2024-06-26T05:22:28.1693763Z Running test_cpp_extensions_jit 1/1 ... [2024-06-26 05:22:28.163744] 2024-06-26T05:22:28.1695689Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_jit.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:22:28.164108] 2024-06-26T05:23:00.1723671Z 2024-06-26T05:23:00.1725058Z test_cpp_extensions_jit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_jit_1.1_f8cdaf8083388ec1_.log 2024-06-26T05:23:00.1739100Z Running 26 items in this shard: test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_autograd_from_cpp, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_compilation_error_formatting, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_same_output_as_python, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_up_to_date_attributes, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op_with_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_compound_op_autograd, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_functorch_error, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_gen_extension_h_pch, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_half_support, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_custom_op_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_multiple_sources_and_no_functions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_throws_when_functions_is_bad, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_dict, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_list, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_compile_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_archflags, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cudnn_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_lenient_flag_handling_in_jit_extensions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_mps_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_reload_jit_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_returns_shared_library_path_when_is_python_module_is_true, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_set_default_type_also_changes_aten_default_type, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_warning 2024-06-26T05:23:00.1751920Z 2024-06-26T05:23:00.1752396Z Running test_cpp_extensions_open_device_registration 1/1 ... [2024-06-26 05:23:00.172533] 2024-06-26T05:23:00.1754462Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_open_device_registration.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:23:00.172886] 2024-06-26T05:23:07.1963760Z 2024-06-26T05:23:07.1965621Z test_cpp_extensions_open_device_registration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_open_device_registration_1.1_bf0727c56301faaf_.log 2024-06-26T05:23:07.1981734Z Running 21 items in this shard: test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_base_device_registration, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_common_registration, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_compile_autograd_function_aliasing, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_compile_autograd_function_returns_self, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_dispatchstub, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_faketensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_generator_registration_and_hooks, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_named_tensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_numpy_serialization_map_location, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_packed_sequence, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_quantized, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_random, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_scalar_type_fallback, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_serialization, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_pin_memory, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_resize, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_type, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensor_type_fallback, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensorlist_type_fallback 2024-06-26T05:23:07.1995810Z 2024-06-26T05:23:07.1996146Z Running test_sort_and_select 1/1 ... [2024-06-26 05:23:07.196523] 2024-06-26T05:23:07.1997948Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_sort_and_select.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:23:07.196812] 2024-06-26T05:23:49.9660281Z 2024-06-26T05:23:49.9662453Z test_sort_and_select 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_sort_and_select_1.1_0373feb47a8d53a3_.log 2024-06-26T05:23:49.9722894Z Running 110 items in this shard: test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_dtypes_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_scalar_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_slow_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_expanded_tensor_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_slice_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_restride_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_stable_none_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_4d_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_arguments_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_noncontiguous_gpu_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_quantized_scalar_input_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_dim_cpu 2024-06-26T05:23:49.9769881Z 2024-06-26T05:23:49.9770311Z Running test_multiprocessing 1/1 ... [2024-06-26 05:23:49.966774] 2024-06-26T05:23:49.9772225Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_multiprocessing.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:23:49.967030] 2024-06-26T05:24:17.8231296Z 2024-06-26T05:24:17.8233329Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_595e60d151ca65fd_.log 2024-06-26T05:24:17.8253221Z Running 38 items in this shard: test/test_multiprocessing.py::TestMultiprocessing::test_autograd_errors, test/test_multiprocessing.py::TestMultiprocessing::test_autograd_fine_with_spawn, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_bad_call, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_ipc_deadlock, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_memory_allocation, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_send_many, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_simple, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_small_tensors, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_shared, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_event, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_exporter, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_importer, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_multi_gpu, test/test_multiprocessing.py::TestMultiprocessing::test_event_multiprocess, test/test_multiprocessing.py::TestMultiprocessing::test_fd_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fd_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fd_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs, test/test_multiprocessing.py::TestMultiprocessing::test_fs_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_fs_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fs_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_inherit_tensor, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cpu, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_mixed_types_cuda_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_non_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_set_thread_name, test/test_multiprocessing.py::TestMultiprocessing::test_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_wrong_cuda_fork 2024-06-26T05:24:17.8268921Z 2024-06-26T05:24:17.8269330Z Running test_mobile_optimizer 1/1 ... [2024-06-26 05:24:17.823414] 2024-06-26T05:24:17.8271199Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mobile_optimizer.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:24:17.823781] 2024-06-26T05:24:24.8978747Z 2024-06-26T05:24:24.8980329Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_05a1c40e14de60f1_.log 2024-06-26T05:24:24.8985226Z Running 7 items in this shard: test/test_mobile_optimizer.py::TestOptimizer::test_clone_module_with_class, test/test_mobile_optimizer.py::TestOptimizer::test_generate_mobile_module_lints, test/test_mobile_optimizer.py::TestOptimizer::test_hoist_conv_packed_params, test/test_mobile_optimizer.py::TestOptimizer::test_mobilenet_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_preserve_bundled_inputs_methods, test/test_mobile_optimizer.py::TestOptimizer::test_quantized_conv_no_asan_failures 2024-06-26T05:24:24.8988323Z 2024-06-26T05:24:24.8988654Z Running nn/test_pooling 1/1 ... [2024-06-26 05:24:24.898005] 2024-06-26T05:24:24.8990402Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'nn/test_pooling.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:24:24.898280] 2024-06-26T05:25:21.9014011Z 2024-06-26T05:25:21.9015670Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_6e708dea08703455_.log 2024-06-26T05:25:21.9067343Z Running 100 items in this shard: test/nn/test_pooling.py::TestAvgPool::test_avg_pool1d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool2d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool3d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d_with_divisor, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d_with_divisor, test/nn/test_pooling.py::TestPoolingNN::test_MaxUnpool2d_output_size, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_nhwc_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_backward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_forward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_non_contiguous, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_lower_precision, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_none, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_overflow, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool2d_nhwc_cpu, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool3d_input_check, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool1d_empty_kernel, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool2d_empty_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool3d_backward_after_cat_dim1_device_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case10_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case4_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case5_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case6_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case7_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case8_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case9_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool2d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool3d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pool_odd_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_uint8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool3d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_indices_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool3d_non_square_backward_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool_indices_no_batch_dim_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_large_size_int64_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_size_one_feature_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_invalid_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_large_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_bfloat16_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_large_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_zero_stride_cpu 2024-06-26T05:25:21.9114544Z 2024-06-26T05:25:21.9115023Z Running test_tensor_creation_ops 1/1 ... [2024-06-26 05:25:21.901852] 2024-06-26T05:25:21.9116874Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_tensor_creation_ops.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:25:21.902168] 2024-06-26T05:28:43.4872532Z 2024-06-26T05:28:43.4874258Z 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_37c28843da56dc06_.log 2024-06-26T05:28:43.5184180Z Running 625 items in this shard: test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_strided_neg_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_scipy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cartesian_prod_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_big_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_in_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_memory_format_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_preserve_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_stack_cross_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_combinations_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_complex_type_conversions_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_device_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_dtypes_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ctor_with_numpy_array_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diag_embed_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diagflat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_full_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_overflow_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_tensor_props_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_eye_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_fill_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_False_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_True_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_like_fn_stride_proparation_vs_tensoriterator_unary_op_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linlogspace_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_default_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_is_default_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_dtype_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_non_1d_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_unsupported_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_vs_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_warns_if_no_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_xy_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_methods_requires_grad_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_offset_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ones_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_factories_64bit_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_warning_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_repeat_interleave_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_roll_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_simple_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_storage_filename_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_strided_mismatched_stride_shape_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_ctor_device_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factories_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_copy_var_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_non_writable_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_sequence_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_out_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_std_error_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_inference_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_bfloat16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_random_neg_values_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_device_compatibility_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float64, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_empty_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_full_like_inference_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_ones_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_ones_like_multiple_device_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_zeros_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_zeros_like_multiple_device_cpu, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_byte_to_int_cpu, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_astensor_consistency_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_default_device_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_device_without_index_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_numpy_scalars_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_retain_autograd_history_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_retain_autograd_history_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_unsupported_alias_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_unsupported_alias_mult_devices_cpu_float32 2024-06-26T05:28:43.5470232Z 2024-06-26T05:28:43.5470657Z Running test_reductions 1/1 ... [2024-06-26 05:28:43.489179] 2024-06-26T05:28:43.5472420Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_reductions.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:28:43.489492] 2024-06-26T05:41:15.3551520Z 2024-06-26T05:41:15.3554101Z test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.1_9348e0682fc734de_.log 2024-06-26T05:41:15.5690250Z Running 4585 items in this shard: test/test_reductions.py::TestReductionsCPU::test_accreal_type_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_all_any_with_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_all_issue117215_cpu, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_amin_amax_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_argminmax_axis_with_dim_one_cpu, test/test_reductions.py::TestReductionsCPU::test_argminmax_large_axis_cpu, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_bincount_cpu, test/test_reductions.py::TestReductionsCPU::test_bucketization_cpu, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_cumprod_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_cumsum_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int16, 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test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_cpu_device_mismatch_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_reduction_uint8_overflow_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_out_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_parallel_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_argmax_argmix_kthvalue_dim_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_reduce_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_cpu, test/test_reductions.py::TestReductionsCPU::test_var_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_var_large_input_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability2_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability_cpu, test/test_reductions.py::TestReductionsCPU::test_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float64 2024-06-26T05:41:15.7927940Z 2024-06-26T05:41:15.7928805Z Running test_cuda_primary_ctx 1/1 ... [2024-06-26 05:41:15.361792] 2024-06-26T05:41:15.7932762Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_primary_ctx.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:41:15.362080] 2024-06-26T05:41:17.5793627Z 2024-06-26T05:41:17.5795287Z test_cuda_primary_ctx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_primary_ctx_1.1_c205288fcc92d968_.log 2024-06-26T05:41:17.5796550Z Running 0 items in this shard: 2024-06-26T05:41:17.5796842Z 2024-06-26T05:41:17.5797653Z Running test_dispatch 1/1 ... [2024-06-26 05:41:17.579542] 2024-06-26T05:41:17.5801660Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_dispatch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:41:17.579895] 2024-06-26T05:41:57.4466888Z 2024-06-26T05:41:57.4468189Z test_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_dispatch_1.1_606692c0e6e75d96_.log 2024-06-26T05:41:57.4482239Z Running 32 items in this shard: test/test_dispatch.py::TestDispatch::test_all_invariants, test/test_dispatch.py::TestDispatch::test_computed_table, test/test_dispatch.py::TestDispatch::test_computed_table_with_ambiguous_autogradother, test/test_dispatch.py::TestDispatch::test_computed_table_with_autograd, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_math, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_math_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_math, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_math_autogradcpu_fallthrough, test/test_dispatch.py::TestDispatch::test_computed_table_with_math, test/test_dispatch.py::TestDispatch::test_def, test/test_dispatch.py::TestDispatch::test_def_impl_schema_mismatch, test/test_dispatch.py::TestDispatch::test_def_only, test/test_dispatch.py::TestDispatch::test_def_with_explicit_alias, test/test_dispatch.py::TestDispatch::test_def_with_inference, test/test_dispatch.py::TestDispatch::test_dispatch_print_registrations_for_dispatch_key_invalid, test/test_dispatch.py::TestDispatch::test_find_dangling_impls, test/test_dispatch.py::TestDispatch::test_find_dangling_impls_ext, test/test_dispatch.py::TestDispatch::test_impl_only, test/test_dispatch.py::TestDispatch::test_multiple_def_alias_defaulting, test/test_dispatch.py::TestDispatch::test_multiple_def_alias_mismatch, test/test_dispatch.py::TestDispatch::test_multiple_def_error, test/test_dispatch.py::TestDispatch::test_multiple_fallback, test/test_dispatch.py::TestDispatch::test_overwrite_math, test/test_dispatch.py::TestPythonDispatcher::test_autogradother, test/test_dispatch.py::TestPythonDispatcher::test_basic, test/test_dispatch.py::TestPythonDispatcher::test_defaultbackend_autogradcpu, test/test_dispatch.py::TestPythonDispatcher::test_defaultbackend_math, test/test_dispatch.py::TestPythonDispatcher::test_duplicate_registrations, test/test_dispatch.py::TestPythonDispatcher::test_math_autogradcpu, test/test_dispatch.py::TestPythonDispatcher::test_quantized_structured_not_implemented 2024-06-26T05:41:57.4494482Z 2024-06-26T05:41:57.4494862Z Running test_cuda_trace 1/1 ... [2024-06-26 05:41:57.446864] 2024-06-26T05:41:57.4496676Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_trace.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:41:57.447182] 2024-06-26T05:41:59.6196182Z 2024-06-26T05:41:59.6197870Z test_cuda_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_trace_1.1_3ab94be7f228fb4c_.log 2024-06-26T05:41:59.6198996Z Running 0 items in this shard: 2024-06-26T05:41:59.6199286Z 2024-06-26T05:41:59.6199710Z Running test_multiprocessing_spawn 1/1 ... [2024-06-26 05:41:59.619753] 2024-06-26T05:41:59.6204324Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_multiprocessing_spawn.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:41:59.620100] 2024-06-26T05:42:25.8224112Z 2024-06-26T05:42:25.8225781Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_5b1cd19f6eca9d60_.log 2024-06-26T05:42:25.8244716Z Running 19 items in this shard: test/test_multiprocessing_spawn.py::SpawnTest::test_exception_all, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_single, test/test_multiprocessing_spawn.py::SpawnTest::test_first_argument_index, test/test_multiprocessing_spawn.py::SpawnTest::test_signal_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_success, test/test_multiprocessing_spawn.py::SpawnTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::SpawnTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ForkTest::test_exception_all, test/test_multiprocessing_spawn.py::ForkTest::test_exception_single, test/test_multiprocessing_spawn.py::ForkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ForkTest::test_success, test/test_multiprocessing_spawn.py::ForkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ForkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ErrorTest::test_errors_pickleable 2024-06-26T05:42:25.8257750Z 2024-06-26T05:42:25.8258188Z Running test_cuda_nvml_based_avail 1/1 ... [2024-06-26 05:42:25.822667] 2024-06-26T05:42:25.8260291Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_nvml_based_avail.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:42:25.823062] 2024-06-26T05:42:28.0374674Z 2024-06-26T05:42:28.0376993Z test_cuda_nvml_based_avail 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_nvml_based_avail_1.1_2b713415fbafd198_.log 2024-06-26T05:42:28.0379099Z Running 0 items in this shard: 2024-06-26T05:42:28.0379376Z 2024-06-26T05:42:28.0379688Z Running test_spectral_ops 1/1 ... [2024-06-26 05:42:28.037634] 2024-06-26T05:42:28.0382638Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_spectral_ops.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:42:28.037945] 2024-06-26T05:43:00.1446698Z 2024-06-26T05:43:00.1452991Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_3db11754b54ca28b_.log 2024-06-26T05:43:00.1659970Z Running 280 items in this shard: test/test_spectral_ops.py::TestFFTCPU::test_batch_istft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_istft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_definition_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_onesided_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_plan_cache_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_ifft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft2_fftn_equivalence_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft2_fftn_equivalence_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft2_invalid_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft2_numpy_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft2_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft2_cpu_bfloat16, 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test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_stft_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_stft_requires_complex_cpu, test/test_spectral_ops.py::TestFFTCPU::test_stft_requires_window_cpu, test/test_spectral_ops.py::TestFFTCPU::test_stft_roundtrip_complex_window_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_stft_roundtrip_complex_window_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_stft_window_device_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftfreq_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftshift_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_hfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifftshift_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ihfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfftfreq_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfftn_cpu 2024-06-26T05:43:00.1775845Z 2024-06-26T05:43:00.1776385Z Running distributions/test_distributions 1/2 ... [2024-06-26 05:43:00.145415] 2024-06-26T05:43:00.1778353Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:43:00.145715] 2024-06-26T05:48:20.0390116Z 2024-06-26T05:48:20.0391949Z distributions/test_distributions 1/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.2_628516c5706c4109_.log 2024-06-26T05:48:20.0457274Z Running 130 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_argmax_relaxed_categorical, test/distributions/test_distributions.py::TestDistributions::test_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_beta_log_prob, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow, test/distributions/test_distributions.py::TestDistributions::test_binomial, test/distributions/test_distributions.py::TestDistributions::test_binomial_half, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_binomial_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_cauchy, test/distributions/test_distributions.py::TestDistributions::test_cdf_icdf_inverse, test/distributions/test_distributions.py::TestDistributions::test_cdf_log_prob, test/distributions/test_distributions.py::TestDistributions::test_chi2_shape, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_distribution_expand, test/distributions/test_distributions.py::TestDistributions::test_enumerate_support_type, test/distributions/test_distributions.py::TestDistributions::test_exponential, test/distributions/test_distributions.py::TestDistributions::test_exponential_sample, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor, test/distributions/test_distributions.py::TestDistributions::test_gamma_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_shape, test/distributions/test_distributions.py::TestDistributions::test_geometric, test/distributions/test_distributions.py::TestDistributions::test_geometric_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_geometric_sample, test/distributions/test_distributions.py::TestDistributions::test_gumbel_sample, test/distributions/test_distributions.py::TestDistributions::test_halfcauchy, test/distributions/test_distributions.py::TestDistributions::test_halfnormal, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_has_examples, test/distributions/test_distributions.py::TestDistributions::test_independent_expand, test/distributions/test_distributions.py::TestDistributions::test_independent_shape, test/distributions/test_distributions.py::TestDistributions::test_invalid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_inversegamma, test/distributions/test_distributions.py::TestDistributions::test_inversegamma_sample, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_mean_variance, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_shape, test/distributions/test_distributions.py::TestDistributions::test_lkj_cholesky_log_prob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lognormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_lognormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_log_prob, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_multinomial_2d, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial, test/distributions/test_distributions.py::TestDistributions::test_normal, test/distributions/test_distributions.py::TestDistributions::test_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_pareto, test/distributions/test_distributions.py::TestDistributions::test_pareto_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_forward_ad, test/distributions/test_distributions.py::TestDistributions::test_poisson_log_prob, test/distributions/test_distributions.py::TestDistributions::test_repr, test/distributions/test_distributions.py::TestDistributions::test_rounded_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_studentT, test/distributions/test_distributions.py::TestDistributions::test_studentT_log_prob, test/distributions/test_distributions.py::TestDistributions::test_studentT_sample, test/distributions/test_distributions.py::TestDistributions::test_support_attributes, test/distributions/test_distributions.py::TestDistributions::test_vonmises_logprob, test/distributions/test_distributions.py::TestDistributions::test_vonmises_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_log_prob, test/distributions/test_distributions.py::TestDistributions::test_wishart_moments, test/distributions/test_distributions.py::TestDistributions::test_wishart_properties, test/distributions/test_distributions.py::TestDistributions::test_wishart_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_zero_excluded_binomial, test/distributions/test_distributions.py::TestRsample::test_beta_wrt_alpha, test/distributions/test_distributions.py::TestRsample::test_beta_wrt_beta, test/distributions/test_distributions.py::TestRsample::test_dirichlet_on_diagonal, test/distributions/test_distributions.py::TestRsample::test_dirichlet_tangent_field, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape_vectorized_n, test/distributions/test_distributions.py::TestDistributionShapes::test_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_cauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_cauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_scalar_param, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_tensor_param, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gumbel_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_mean_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_multinomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_one_hot_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_scalar_params, test/distributions/test_distributions.py::TestKL::test_entropy_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched_broadcasted, test/distributions/test_distributions.py::TestKL::test_kl_transformed, test/distributions/test_distributions.py::TestConstraints::test_support_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob_with_logits, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_logits_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_icdf, test/distributions/test_distributions.py::TestAgainstScipy::test_mean, test/distributions/test_distributions.py::TestFunctors::test_cat_event_dim, test/distributions/test_distributions.py::TestFunctors::test_stack_transform, test/distributions/test_distributions.py::TestValidation::test_invalid_log_probs_arg, test/distributions/test_distributions.py::TestValidation::test_valid, test/distributions/test_distributions.py::TestValidation::test_warning_unimplemented_constraints, test/distributions/test_distributions.py::TestJit::test_cdf, test/distributions/test_distributions.py::TestJit::test_mean, test/distributions/test_distributions.py::TestJit::test_sample 2024-06-26T05:48:20.0519080Z 2024-06-26T05:48:20.0519547Z Running distributions/test_distributions 2/2 ... [2024-06-26 05:48:20.039450] 2024-06-26T05:48:20.0521543Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:48:20.039779] 2024-06-26T05:54:38.9375566Z 2024-06-26T05:54:38.9379045Z distributions/test_distributions 2/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_2.2_c40381becc40fff9_.log 2024-06-26T05:54:38.9427094Z Running 95 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_beta_sample, test/distributions/test_distributions.py::TestDistributions::test_beta_shape, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow_gpu, test/distributions/test_distributions.py::TestDistributions::test_binomial_bfloat16, test/distributions/test_distributions.py::TestDistributions::test_binomial_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_binomial_extreme_vals, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_binomial_sample, test/distributions/test_distributions.py::TestDistributions::test_binomial_stable, test/distributions/test_distributions.py::TestDistributions::test_chi2_sample, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob_zero, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_mode, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_sample, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributions::test_distribution_subclass_expand, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_shape, test/distributions/test_distributions.py::TestDistributions::test_gamma_log_prob_at_boundary, test/distributions/test_distributions.py::TestDistributions::test_gumbel, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_laplace, test/distributions/test_distributions.py::TestDistributions::test_laplace_sample, test/distributions/test_distributions.py::TestDistributions::test_lazy_property_grad, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal, test/distributions/test_distributions.py::TestDistributions::test_lognormal, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_sample, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributions::test_mode, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_poisson_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_shape, test/distributions/test_distributions.py::TestDistributions::test_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_rsample_requires_grad, test/distributions/test_distributions.py::TestDistributions::test_sample_detached, test/distributions/test_distributions.py::TestDistributions::test_uniform, test/distributions/test_distributions.py::TestDistributions::test_valid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_wishart_shape, test/distributions/test_distributions.py::TestRsample::test_chi2, test/distributions/test_distributions.py::TestRsample::test_dirichlet_multivariate, test/distributions/test_distributions.py::TestRsample::test_gamma, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_entropy_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_kumaraswamy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_pareto_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_weibull_scale_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_tensor_params, test/distributions/test_distributions.py::TestKL::test_entropy_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_edgecases, test/distributions/test_distributions.py::TestKL::test_kl_infinite, test/distributions/test_distributions.py::TestKL::test_kl_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_shape, test/distributions/test_distributions.py::TestConstraints::test_params_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob_with_logits, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_probs_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_cdf, test/distributions/test_distributions.py::TestAgainstScipy::test_variance_stddev, test/distributions/test_distributions.py::TestFunctors::test_cat_transform, test/distributions/test_distributions.py::TestFunctors::test_cat_transform_non_uniform, test/distributions/test_distributions.py::TestValidation::test_invalid, test/distributions/test_distributions.py::TestJit::test_entropy, test/distributions/test_distributions.py::TestJit::test_enumerate_support, test/distributions/test_distributions.py::TestJit::test_log_prob, test/distributions/test_distributions.py::TestJit::test_rsample, test/distributions/test_distributions.py::TestJit::test_variance 2024-06-26T05:54:38.9472972Z 2024-06-26T05:54:38.9473295Z Running doctests 1/1 ... [2024-06-26 05:54:38.937924] 2024-06-26T05:54:38.9474103Z Start doctest_module('/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch') 2024-06-26T05:54:38.9474836Z Listing tests 2024-06-26T05:54:39.1436992Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=426. 2024-06-26T05:54:39.1438321Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:39.1439238Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-06-26T05:54:39.1439807Z 2024-06-26T05:54:39.1440099Z This is helpful when you want to visualize data over some 2024-06-26T05:54:39.1440713Z range of inputs. See below for a plotting example. 2024-06-26T05:54:39.1441217Z 2024-06-26T05:54:39.1441529Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-06-26T05:54:39.1442274Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-06-26T05:54:39.1443074Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-06-26T05:54:39.1443842Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-06-26T05:54:39.1444544Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-06-26T05:54:39.1445141Z to the result shape. 2024-06-26T05:54:39.1445388Z 2024-06-26T05:54:39.1445556Z .. note:: 2024-06-26T05:54:39.1445953Z 0D inputs are treated equivalently to 1D inputs of a 2024-06-26T05:54:39.1446502Z single element. 2024-06-26T05:54:39.1446734Z 2024-06-26T05:54:39.1446838Z .. warning:: 2024-06-26T05:54:39.1447335Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-06-26T05:54:39.1448068Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-06-26T05:54:39.1448464Z 2024-06-26T05:54:39.1448727Z In the future `torch.meshgrid` will transition to 2024-06-26T05:54:39.1449303Z `indexing='xy'` as the default. 2024-06-26T05:54:39.1449646Z 2024-06-26T05:54:39.1449896Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-06-26T05:54:39.1450647Z this issue with the goal of migrating to NumPy's behavior. 2024-06-26T05:54:39.1451128Z 2024-06-26T05:54:39.1451234Z .. seealso:: 2024-06-26T05:54:39.1451423Z 2024-06-26T05:54:39.1451705Z :func:`torch.cartesian_prod` has the same effect but it 2024-06-26T05:54:39.1452272Z collects the data in a tensor of vectors. 2024-06-26T05:54:39.1452683Z 2024-06-26T05:54:39.1452778Z Args: 2024-06-26T05:54:39.1453370Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-06-26T05:54:39.1454187Z treated as tensors of size :math:`(1,)` automatically 2024-06-26T05:54:39.1454663Z 2024-06-26T05:54:39.1455260Z indexing: (str, optional): the indexing mode, either "xy" 2024-06-26T05:54:39.1456215Z or "ij", defaults to "ij". See warning for future changes. 2024-06-26T05:54:39.1456962Z 2024-06-26T05:54:39.1457388Z If "xy" is selected, the first dimension corresponds 2024-06-26T05:54:39.1458152Z to the cardinality of the second input and the second 2024-06-26T05:54:39.1458846Z dimension corresponds to the cardinality of the first 2024-06-26T05:54:39.1459344Z input. 2024-06-26T05:54:39.1459536Z 2024-06-26T05:54:39.1459745Z If "ij" is selected, the dimensions are in the same 2024-06-26T05:54:39.1460292Z order as the cardinality of the inputs. 2024-06-26T05:54:39.1460638Z 2024-06-26T05:54:39.1460738Z Returns: 2024-06-26T05:54:39.1461135Z seq (sequence of Tensors): If the input has :math:`N` 2024-06-26T05:54:39.1461940Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-06-26T05:54:39.1462569Z output will also have :math:`N` tensors, where each tensor 2024-06-26T05:54:39.1463203Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-06-26T05:54:39.1463529Z 2024-06-26T05:54:39.1463641Z Example:: 2024-06-26T05:54:39.1463830Z 2024-06-26T05:54:39.1463972Z >>> x = torch.tensor([1, 2, 3]) 2024-06-26T05:54:39.1464406Z >>> y = torch.tensor([4, 5, 6]) 2024-06-26T05:54:39.1464697Z 2024-06-26T05:54:39.1465009Z Observe the element-wise pairings across the grid, (1, 4), 2024-06-26T05:54:39.1465610Z (1, 5), ..., (3, 6). This is the same thing as the 2024-06-26T05:54:39.1466075Z cartesian product. 2024-06-26T05:54:39.1466582Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-06-26T05:54:39.1467071Z >>> grid_x 2024-06-26T05:54:39.1467374Z tensor([[1, 1, 1], 2024-06-26T05:54:39.1467702Z [2, 2, 2], 2024-06-26T05:54:39.1468035Z [3, 3, 3]]) 2024-06-26T05:54:39.1468371Z >>> grid_y 2024-06-26T05:54:39.1468663Z tensor([[4, 5, 6], 2024-06-26T05:54:39.1469001Z [4, 5, 6], 2024-06-26T05:54:39.1469330Z [4, 5, 6]]) 2024-06-26T05:54:39.1469560Z 2024-06-26T05:54:39.1469794Z This correspondence can be seen when these grids are 2024-06-26T05:54:39.1470281Z stacked properly. 2024-06-26T05:54:39.1470785Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-06-26T05:54:39.1471388Z ... torch.cartesian_prod(x, y)) 2024-06-26T05:54:39.1471806Z True 2024-06-26T05:54:39.1471984Z 2024-06-26T05:54:39.1472213Z `torch.meshgrid` is commonly used to produce a grid for 2024-06-26T05:54:39.1472712Z plotting. 2024-06-26T05:54:39.1473071Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-06-26T05:54:39.1473587Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-06-26T05:54:39.1474092Z >>> import matplotlib.pyplot as plt 2024-06-26T05:54:39.1474826Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-06-26T05:54:39.1475381Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-06-26T05:54:39.1475948Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-06-26T05:54:39.1476459Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-06-26T05:54:39.1476999Z >>> ax = plt.axes(projection='3d') 2024-06-26T05:54:39.1477511Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-06-26T05:54:39.1477971Z >>> plt.show() 2024-06-26T05:54:39.1478197Z 2024-06-26T05:54:39.1478353Z .. image:: ../_static/img/meshgrid.png 2024-06-26T05:54:39.1478767Z :width: 512 2024-06-26T05:54:39.1478966Z 2024-06-26T05:54:39.1479075Z 2024-06-26T05:54:39.1479597Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:39.1480205Z 2024-06-26T05:54:39.1481116Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=815. 2024-06-26T05:54:39.1482409Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:39.1483507Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-06-26T05:54:39.1484170Z 2024-06-26T05:54:39.1484375Z Returns the unique elements of the input tensor. 2024-06-26T05:54:39.1484727Z 2024-06-26T05:54:39.1485132Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-06-26T05:54:39.1486035Z this function also eliminates non-consecutive duplicate values. 2024-06-26T05:54:39.1486486Z 2024-06-26T05:54:39.1486798Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-06-26T05:54:39.1487766Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-06-26T05:54:39.1488735Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-06-26T05:54:39.1489555Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-06-26T05:54:39.1489954Z 2024-06-26T05:54:39.1490064Z Args: 2024-06-26T05:54:39.1490344Z input (Tensor): the input tensor 2024-06-26T05:54:39.1490934Z sorted (bool): Whether to sort the unique elements in ascending order 2024-06-26T05:54:39.1491534Z before returning as output. 2024-06-26T05:54:39.1492086Z return_inverse (bool): Whether to also return the indices for where 2024-06-26T05:54:39.1492835Z elements in the original input ended up in the returned unique list. 2024-06-26T05:54:39.1493598Z return_counts (bool): Whether to also return the counts for each unique 2024-06-26T05:54:39.1494168Z element. 2024-06-26T05:54:39.1494635Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-06-26T05:54:39.1495378Z unique of the flattened input is returned. Otherwise, each of the 2024-06-26T05:54:39.1496103Z tensors indexed by the given dimension is treated as one of the 2024-06-26T05:54:39.1496819Z elements to apply the unique operation upon. See examples for more 2024-06-26T05:54:39.1497404Z details. Default: ``None`` 2024-06-26T05:54:39.1497679Z 2024-06-26T05:54:39.1497788Z Returns: 2024-06-26T05:54:39.1498320Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-06-26T05:54:39.1498883Z 2024-06-26T05:54:39.1499205Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-06-26T05:54:39.1499869Z - **inverse_indices** (*Tensor*): (optional) if 2024-06-26T05:54:39.1500445Z :attr:`return_inverse` is True, there will be an additional 2024-06-26T05:54:39.1526782Z returned tensor (same shape as input) representing the indices 2024-06-26T05:54:39.1527528Z for where elements in the original input map to in the output; 2024-06-26T05:54:39.1528215Z otherwise, this function will only return a single tensor. 2024-06-26T05:54:39.1528886Z - **counts** (*Tensor*): (optional) if 2024-06-26T05:54:39.1529455Z :attr:`return_counts` is True, there will be an additional 2024-06-26T05:54:39.1530105Z returned tensor (same shape as output or output.size(dim), 2024-06-26T05:54:39.1530770Z if dim was specified) representing the number of occurrences 2024-06-26T05:54:39.1531353Z for each unique value or tensor. 2024-06-26T05:54:39.1531660Z 2024-06-26T05:54:39.1531785Z Example:: 2024-06-26T05:54:39.1531948Z 2024-06-26T05:54:39.1532236Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-06-26T05:54:39.1532791Z >>> output 2024-06-26T05:54:39.1533082Z tensor([1, 2, 3]) 2024-06-26T05:54:39.1533427Z 2024-06-26T05:54:39.1533592Z >>> output, inverse_indices = torch.unique( 2024-06-26T05:54:39.1534265Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-06-26T05:54:39.1534933Z >>> output 2024-06-26T05:54:39.1535205Z tensor([1, 2, 3]) 2024-06-26T05:54:39.1535532Z >>> inverse_indices 2024-06-26T05:54:39.1535865Z tensor([0, 2, 1, 2]) 2024-06-26T05:54:39.1536087Z 2024-06-26T05:54:39.1536256Z >>> output, inverse_indices = torch.unique( 2024-06-26T05:54:39.1536913Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-06-26T05:54:39.1537521Z >>> output 2024-06-26T05:54:39.1537811Z tensor([1, 2, 3]) 2024-06-26T05:54:39.1538120Z >>> inverse_indices 2024-06-26T05:54:39.1538450Z tensor([[0, 2], 2024-06-26T05:54:39.1538753Z [1, 2]]) 2024-06-26T05:54:39.1538950Z 2024-06-26T05:54:39.1539152Z >>> a = torch.tensor([ 2024-06-26T05:54:39.1539488Z ... [ 2024-06-26T05:54:39.1539775Z ... [1, 1, 0, 0], 2024-06-26T05:54:39.1540121Z ... [1, 1, 0, 0], 2024-06-26T05:54:39.1540479Z ... [0, 0, 1, 1], 2024-06-26T05:54:39.1540817Z ... ], 2024-06-26T05:54:39.1541077Z ... [ 2024-06-26T05:54:39.1541355Z ... [0, 0, 1, 1], 2024-06-26T05:54:39.1541697Z ... [0, 0, 1, 1], 2024-06-26T05:54:39.1542017Z ... [1, 1, 1, 1], 2024-06-26T05:54:39.1542335Z ... ], 2024-06-26T05:54:39.1542595Z ... [ 2024-06-26T05:54:39.1542850Z ... [1, 1, 0, 0], 2024-06-26T05:54:39.1543188Z ... [1, 1, 0, 0], 2024-06-26T05:54:39.1543527Z ... [0, 0, 1, 1], 2024-06-26T05:54:39.1543846Z ... ], 2024-06-26T05:54:39.1544108Z ... ]) 2024-06-26T05:54:39.1544266Z 2024-06-26T05:54:39.1544590Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-06-26T05:54:39.1545351Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-06-26T05:54:39.1545996Z >>> # each other, so one of them will be removed. 2024-06-26T05:54:39.1546475Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-06-26T05:54:39.1546858Z tensor(True) 2024-06-26T05:54:39.1547207Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-06-26T05:54:39.1547636Z >>> a_unique_dim0 2024-06-26T05:54:39.1547947Z tensor([[[0, 0, 1, 1], 2024-06-26T05:54:39.1548279Z [0, 0, 1, 1], 2024-06-26T05:54:39.1548611Z [1, 1, 1, 1]], 2024-06-26T05:54:39.1548936Z [[1, 1, 0, 0], 2024-06-26T05:54:39.1549265Z [1, 1, 0, 0], 2024-06-26T05:54:39.1549595Z [0, 0, 1, 1]]]) 2024-06-26T05:54:39.1549829Z 2024-06-26T05:54:39.1550195Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-06-26T05:54:39.1550773Z >>> # `a_unique_dim0`: 2024-06-26T05:54:39.1551182Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-06-26T05:54:39.1551600Z tensor(True) 2024-06-26T05:54:39.1551960Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-06-26T05:54:39.1552390Z tensor(True) 2024-06-26T05:54:39.1552574Z 2024-06-26T05:54:39.1552861Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-06-26T05:54:39.1553573Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-06-26T05:54:39.1554123Z >>> # them will be removed. 2024-06-26T05:54:39.1554510Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-06-26T05:54:39.1555033Z tensor(True) 2024-06-26T05:54:39.1555345Z >>> torch.unique(a, dim=1) 2024-06-26T05:54:39.1555710Z tensor([[[0, 0, 1, 1], 2024-06-26T05:54:39.1556051Z [1, 1, 0, 0]], 2024-06-26T05:54:39.1556396Z [[1, 1, 1, 1], 2024-06-26T05:54:39.1556718Z [0, 0, 1, 1]], 2024-06-26T05:54:39.1557156Z [[0, 0, 1, 1], 2024-06-26T05:54:39.1557492Z [1, 1, 0, 0]]]) 2024-06-26T05:54:39.1557776Z 2024-06-26T05:54:39.1558086Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-06-26T05:54:39.1558784Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-06-26T05:54:39.1559442Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-06-26T05:54:39.1560056Z >>> # sub-tensors will be removed. 2024-06-26T05:54:39.1560476Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-06-26T05:54:39.1560869Z tensor(True) 2024-06-26T05:54:39.1561267Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-06-26T05:54:39.1561650Z tensor(True) 2024-06-26T05:54:39.1561963Z >>> torch.unique(a, dim=2) 2024-06-26T05:54:39.1562332Z tensor([[[0, 1], 2024-06-26T05:54:39.1562721Z [0, 1], 2024-06-26T05:54:39.1563031Z [1, 0]], 2024-06-26T05:54:39.1563342Z [[1, 0], 2024-06-26T05:54:39.1563634Z [1, 0], 2024-06-26T05:54:39.1563941Z [1, 1]], 2024-06-26T05:54:39.1564253Z [[0, 1], 2024-06-26T05:54:39.1564539Z [0, 1], 2024-06-26T05:54:39.1564838Z [1, 0]]]) 2024-06-26T05:54:39.1565136Z 2024-06-26T05:54:39.1565662Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:39.1566163Z 2024-06-26T05:54:39.1767059Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=931. 2024-06-26T05:54:39.1768262Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:39.1769058Z Given an operator and some sample arguments, tests if the operator is 2024-06-26T05:54:39.1769684Z registered correctly. 2024-06-26T05:54:39.1769922Z 2024-06-26T05:54:39.1770238Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-06-26T05:54:39.1771050Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-06-26T05:54:39.1771870Z and these APIs require that the functions you pass them satisfy certain 2024-06-26T05:54:39.1772650Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-06-26T05:54:39.1773316Z ``opcheck`` tests these metadata and properties. 2024-06-26T05:54:39.1773663Z 2024-06-26T05:54:39.1773813Z Concretely, we test the following: 2024-06-26T05:54:39.1774339Z - test_schema: if the operator's schema is correct. 2024-06-26T05:54:39.1775031Z - test_autograd_registration: if autograd was registered correctly. 2024-06-26T05:54:39.1775760Z - test_faketensor: If the operator has a FakeTensor kernel 2024-06-26T05:54:39.1776386Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-06-26T05:54:39.1777102Z but not sufficient) for the operator to work with PyTorch compilation 2024-06-26T05:54:39.1777706Z APIs (torch.compile/export/FX). 2024-06-26T05:54:39.1778292Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-06-26T05:54:39.1778956Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-06-26T05:54:39.1779635Z This checks that the outputs (and gradients, if applicable) are the 2024-06-26T05:54:39.1780314Z same under eager-mode PyTorch and torch.compile. 2024-06-26T05:54:39.1780850Z This test is a superset of ``test_faketensor``. 2024-06-26T05:54:39.1781204Z 2024-06-26T05:54:39.1781465Z For best results, please call ``opcheck`` multiple times with a 2024-06-26T05:54:39.1782121Z representative set of inputs. If your operator supports 2024-06-26T05:54:39.1782814Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-06-26T05:54:39.1783601Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-06-26T05:54:39.1784280Z use ``opcheck`` with inputs on all supported devices. 2024-06-26T05:54:39.1784778Z 2024-06-26T05:54:39.1784873Z Args: 2024-06-26T05:54:39.1785267Z op: The operator. Must either be a function decorated with 2024-06-26T05:54:39.1786027Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-06-26T05:54:39.1786774Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-06-26T05:54:39.1787382Z args: The args to the operator 2024-06-26T05:54:39.1787811Z kwargs: The kwargs to the operator 2024-06-26T05:54:39.1788345Z test_utils: Tests that we should run. Default: all of them. 2024-06-26T05:54:39.1788935Z Example: ("test_schema", "test_faketensor") 2024-06-26T05:54:39.1789534Z raise_exception: If we should raise an exception on the first 2024-06-26T05:54:39.1790182Z error. If False, we will return a dict with information 2024-06-26T05:54:39.1790702Z on if each test passed or not. 2024-06-26T05:54:39.1791099Z 2024-06-26T05:54:39.1791218Z .. warning:: 2024-06-26T05:54:39.1791390Z 2024-06-26T05:54:39.1791702Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-06-26T05:54:39.1792448Z opcheck tests if your usage of torch.library APIs is correct while 2024-06-26T05:54:39.1793191Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-06-26T05:54:39.1793934Z mathematically correct. Use both to test custom ops that support 2024-06-26T05:54:39.1794491Z gradient computation. 2024-06-26T05:54:39.1794963Z 2024-06-26T05:54:39.1795061Z Example: 2024-06-26T05:54:39.1795236Z 2024-06-26T05:54:39.1795420Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:39.1796041Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-06-26T05:54:39.1796704Z >>> def numpy_add(x: Tensor, y: float) -> Tensor: 2024-06-26T05:54:39.1797188Z >>> x_np = x.numpy(force=True) 2024-06-26T05:54:39.1797601Z >>> z_np = x_np + y 2024-06-26T05:54:39.1798009Z >>> return torch.from_numpy(z_np).to(x.device) 2024-06-26T05:54:39.1798441Z >>> 2024-06-26T05:54:39.1798722Z >>> @numpy_sin.register_fake 2024-06-26T05:54:39.1799092Z >>> def _(x, y): 2024-06-26T05:54:39.1799436Z >>> return torch.empty_like(x) 2024-06-26T05:54:39.1799824Z >>> 2024-06-26T05:54:39.1800123Z >>> def setup_context(ctx, inputs, output): 2024-06-26T05:54:39.1800559Z >>> y, = inputs 2024-06-26T05:54:39.1800883Z >>> ctx.y = y 2024-06-26T05:54:39.1801254Z >>> 2024-06-26T05:54:39.1801531Z >>> def backward(ctx, grad): 2024-06-26T05:54:39.1801946Z >>> return grad * ctx.y, None 2024-06-26T05:54:39.1802317Z >>> 2024-06-26T05:54:39.1802760Z >>> numpy_sin.register_autograd(backward, setup_context=setup_context) 2024-06-26T05:54:39.1803312Z >>> 2024-06-26T05:54:39.1803569Z >>> sample_inputs = [ 2024-06-26T05:54:39.1803936Z >>> (torch.randn(3), 3.14), 2024-06-26T05:54:39.1804455Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-06-26T05:54:39.1804974Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-06-26T05:54:39.1805673Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-06-26T05:54:39.1806209Z >>> ] 2024-06-26T05:54:39.1806453Z >>> 2024-06-26T05:54:39.1806734Z >>> for args in sample_inputs: 2024-06-26T05:54:39.1807176Z >>> torch.library.opcheck(foo, args) 2024-06-26T05:54:39.1807497Z 2024-06-26T05:54:39.1807583Z 2024-06-26T05:54:39.1808104Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:39.1808589Z 2024-06-26T05:54:39.3755342Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=340. 2024-06-26T05:54:39.3756649Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-06-26T05:54:39.3757486Z Retrieves the CUDA runtime API module. 2024-06-26T05:54:39.3757840Z 2024-06-26T05:54:39.3757876Z 2024-06-26T05:54:39.3758312Z This function initializes the CUDA runtime environment if it is not already 2024-06-26T05:54:39.3759212Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-06-26T05:54:39.3760050Z runtime API module provides access to various CUDA runtime functions. 2024-06-26T05:54:39.3760517Z 2024-06-26T05:54:39.3760647Z Args: 2024-06-26T05:54:39.3760987Z ``None`` 2024-06-26T05:54:39.3761158Z 2024-06-26T05:54:39.3761268Z Returns: 2024-06-26T05:54:39.3761671Z module: The CUDA runtime API module (_cudart). 2024-06-26T05:54:39.3762034Z 2024-06-26T05:54:39.3762135Z Raises: 2024-06-26T05:54:39.3762725Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-06-26T05:54:39.3763911Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-06-26T05:54:39.3764654Z 2024-06-26T05:54:39.3764825Z Example of CUDA operations with profiling: 2024-06-26T05:54:39.3765299Z >>> import torch 2024-06-26T05:54:39.3765726Z >>> from torch.cuda import cudart, check_error 2024-06-26T05:54:39.3766223Z >>> import os 2024-06-26T05:54:39.3766515Z >>> 2024-06-26T05:54:39.3766917Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-06-26T05:54:39.3767323Z >>> 2024-06-26T05:54:39.3767716Z >>> def perform_cuda_operations_with_streams(): 2024-06-26T05:54:39.3768219Z >>> stream = torch.cuda.Stream() 2024-06-26T05:54:39.3768736Z >>> with torch.cuda.stream(stream): 2024-06-26T05:54:39.3769344Z >>> x = torch.randn(100, 100, device='cuda') 2024-06-26T05:54:39.3769926Z >>> y = torch.randn(100, 100, device='cuda') 2024-06-26T05:54:39.3770426Z >>> z = torch.mul(x, y) 2024-06-26T05:54:39.3770876Z >>> return z 2024-06-26T05:54:39.3771181Z >>> 2024-06-26T05:54:39.3771451Z >>> torch.cuda.synchronize() 2024-06-26T05:54:39.3771977Z >>> print("====== Start nsys profiling ======") 2024-06-26T05:54:39.3772562Z >>> check_error(cudart().cudaProfilerStart()) 2024-06-26T05:54:39.3773121Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-06-26T05:54:39.3773684Z >>> result = perform_cuda_operations_with_streams() 2024-06-26T05:54:39.3774283Z >>> print("CUDA operations completed.") 2024-06-26T05:54:39.3774874Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-06-26T05:54:39.3775477Z >>> print("====== End nsys profiling ======") 2024-06-26T05:54:39.3775809Z 2024-06-26T05:54:39.3776094Z To run this example and save the profiling information, execute: 2024-06-26T05:54:39.3777210Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-06-26T05:54:39.3777927Z 2024-06-26T05:54:39.3778263Z This command profiles the CUDA operations in the provided script and saves 2024-06-26T05:54:39.3779081Z the profiling information to a file named `trace_name.prof`. 2024-06-26T05:54:39.3779941Z The `--profile-from-start off` option ensures that profiling starts only 2024-06-26T05:54:39.3780673Z after the `cudaProfilerStart` call in the script. 2024-06-26T05:54:39.3781472Z The `--csv` and `--print-summary` options format the profiling output as a 2024-06-26T05:54:39.3782190Z CSV file and print a summary, respectively. 2024-06-26T05:54:39.3782977Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-06-26T05:54:39.3783743Z overwrite of the output file if it already exists. 2024-06-26T05:54:39.3784261Z 2024-06-26T05:54:39.3785452Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2024-06-26T05:54:39.3786627Z 2024-06-26T05:54:39.3787227Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-06-26T05:54:39.3788054Z ^ 2024-06-26T05:54:39.3860145Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101. 2024-06-26T05:54:39.3861595Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:39.3862146Z 2024-06-26T05:54:39.3862453Z Append the given callback function to this ``Future``, which will be run 2024-06-26T05:54:39.3863216Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-06-26T05:54:39.3863958Z the same ``Future``, but the order in which they will be executed cannot 2024-06-26T05:54:39.3864649Z be guaranteed (to enforce a certain order consider chaining: 2024-06-26T05:54:39.3865344Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-06-26T05:54:39.3866283Z is the reference to this ``Future``. The callback function can use the 2024-06-26T05:54:39.3867023Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-06-26T05:54:39.3867769Z already completed, the given callback will be run immediately inline. 2024-06-26T05:54:39.3868227Z 2024-06-26T05:54:39.3868566Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-06-26T05:54:39.3869305Z callback might be invoked while the async kernels that are populating 2024-06-26T05:54:39.3870264Z those tensors haven't yet finished executing on the device. However, the 2024-06-26T05:54:39.3871080Z callback will be invoked with some dedicated streams set as current 2024-06-26T05:54:39.3871983Z (fetched from a global pool) which will be synchronized with those 2024-06-26T05:54:39.3873035Z kernels. Hence any operation performed by the callback on these tensors 2024-06-26T05:54:39.3873792Z will be scheduled on the device after the kernels complete. In other 2024-06-26T05:54:39.3874596Z words, as long as the callback doesn't switch streams, it can safely 2024-06-26T05:54:39.3875497Z manipulate the result without any additional synchronization. This is 2024-06-26T05:54:39.3876220Z similar to the non-blocking behavior of :meth:`wait`. 2024-06-26T05:54:39.3876583Z 2024-06-26T05:54:39.3876883Z Similarly, if the callback returns a value that contains tensors that 2024-06-26T05:54:39.3877608Z reside on a GPU, it can do so even if the kernels that are producing 2024-06-26T05:54:39.3878351Z these tensors are still running on the device, as long as the callback 2024-06-26T05:54:39.3879132Z didn't change streams during its execution. If one wants to change 2024-06-26T05:54:39.3879917Z streams, one must be careful to re-synchronize them with the original 2024-06-26T05:54:39.3880659Z streams, that is, those that were current when the callback was invoked. 2024-06-26T05:54:39.3881211Z 2024-06-26T05:54:39.3881300Z Args: 2024-06-26T05:54:39.3881718Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-06-26T05:54:39.3882289Z the only argument. 2024-06-26T05:54:39.3882599Z 2024-06-26T05:54:39.3882691Z Returns: 2024-06-26T05:54:39.3883072Z A new ``Future`` object that holds the return value of the 2024-06-26T05:54:39.3883692Z ``callback`` and will be marked as completed when the given 2024-06-26T05:54:39.3884208Z ``callback`` finishes. 2024-06-26T05:54:39.3884429Z 2024-06-26T05:54:39.3884683Z .. note:: Note that if the callback function throws, either 2024-06-26T05:54:39.3885338Z through the original future being completed with an exception and 2024-06-26T05:54:39.3886052Z calling ``fut.wait()``, or through other code in the callback, the 2024-06-26T05:54:39.3886756Z future returned by ``then`` will be marked appropriately with the 2024-06-26T05:54:39.3887444Z encountered error. However, if this callback later completes 2024-06-26T05:54:39.3888133Z additional futures, those futures are not marked as completed with 2024-06-26T05:54:39.3889002Z an error and the user is responsible for handling completion/waiting 2024-06-26T05:54:39.3889596Z on those futures independently. 2024-06-26T05:54:39.3889925Z 2024-06-26T05:54:39.3890023Z Example:: 2024-06-26T05:54:39.3890369Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-06-26T05:54:39.3890841Z >>> def callback(fut): 2024-06-26T05:54:39.3891233Z ... print(f"RPC return value is {fut.wait()}.") 2024-06-26T05:54:39.3891726Z >>> fut = torch.futures.Future() 2024-06-26T05:54:39.3892233Z >>> # The inserted callback will print the return value when 2024-06-26T05:54:39.3892778Z >>> # receiving the response from "worker1" 2024-06-26T05:54:39.3893224Z >>> cb_fut = fut.then(callback) 2024-06-26T05:54:39.3893618Z >>> chain_cb_fut = cb_fut.then( 2024-06-26T05:54:39.3894073Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-06-26T05:54:39.3894534Z ... ) 2024-06-26T05:54:39.3894792Z >>> fut.set_result(5) 2024-06-26T05:54:39.3895207Z RPC return value is 5. 2024-06-26T05:54:39.3895555Z Chained cb done. None 2024-06-26T05:54:39.3895768Z 2024-06-26T05:54:39.3896179Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:39.3896693Z 2024-06-26T05:54:39.3897707Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=209. 2024-06-26T05:54:39.3898985Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:39.3899502Z 2024-06-26T05:54:39.3899788Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-06-26T05:54:39.3900537Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-06-26T05:54:39.3901126Z cannot be marked completed twice. 2024-06-26T05:54:39.3901391Z 2024-06-26T05:54:39.3901685Z If the result contains tensors that reside on GPUs, this method can be 2024-06-26T05:54:39.3902425Z called even if the asynchronous kernels that are populating those 2024-06-26T05:54:39.3903222Z tensors haven't yet completed running on the device, provided that the 2024-06-26T05:54:39.3903973Z streams on which those kernels were enqueued are set as the current ones 2024-06-26T05:54:39.3904800Z when this method is called. Put simply, it's safe to call this method 2024-06-26T05:54:39.3905526Z immediately after launching those kernels, without any additional 2024-06-26T05:54:39.3906319Z synchronization, as long as one doesn't change streams in between. This 2024-06-26T05:54:39.3907093Z method will record events on all the relevant current streams and will 2024-06-26T05:54:39.3907830Z use them to ensure proper scheduling for all the consumers of this 2024-06-26T05:54:39.3908352Z ``Future``. 2024-06-26T05:54:39.3908520Z 2024-06-26T05:54:39.3908609Z Args: 2024-06-26T05:54:39.3908958Z result (object): the result object of this ``Future``. 2024-06-26T05:54:39.3909326Z 2024-06-26T05:54:39.3909437Z Example:: 2024-06-26T05:54:39.3909777Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-06-26T05:54:39.3910249Z >>> import threading 2024-06-26T05:54:39.3910568Z >>> import time 2024-06-26T05:54:39.3910879Z >>> def slow_set_future(fut, value): 2024-06-26T05:54:39.3911282Z ... time.sleep(0.5) 2024-06-26T05:54:39.3911631Z ... fut.set_result(value) 2024-06-26T05:54:39.3912009Z >>> fut = torch.futures.Future() 2024-06-26T05:54:39.3912410Z >>> t = threading.Thread( 2024-06-26T05:54:39.3912775Z ... target=slow_set_future, 2024-06-26T05:54:39.3913162Z ... args=(fut, torch.ones(2) * 3) 2024-06-26T05:54:39.3913545Z ... ) 2024-06-26T05:54:39.3913778Z >>> t.start() 2024-06-26T05:54:39.3914065Z >>> print(fut.wait()) 2024-06-26T05:54:39.3914387Z tensor([3., 3.]) 2024-06-26T05:54:39.3914787Z >>> t.join() 2024-06-26T05:54:39.3914976Z 2024-06-26T05:54:39.3915366Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:39.3915868Z 2024-06-26T05:54:39.4099413Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=191. 2024-06-26T05:54:39.4100673Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:39.4101521Z Return the sum of each row of the given sparse tensor. 2024-06-26T05:54:39.4101892Z 2024-06-26T05:54:39.4102214Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-06-26T05:54:39.4102973Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-06-26T05:54:39.4103698Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-06-26T05:54:39.4104349Z returns a dense tensor instead of a sparse tensor. 2024-06-26T05:54:39.4104719Z 2024-06-26T05:54:39.4105079Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-06-26T05:54:39.4105863Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-06-26T05:54:39.4106381Z 2024-06-26T05:54:39.4106693Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-06-26T05:54:39.4107469Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-06-26T05:54:39.4107977Z 2024-06-26T05:54:39.4108072Z Args: 2024-06-26T05:54:39.4108385Z input (Tensor): the input sparse tensor 2024-06-26T05:54:39.4109097Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-06-26T05:54:39.4109779Z over all dims. 2024-06-26T05:54:39.4110353Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-06-26T05:54:39.4111010Z Default: dtype of :attr:`input`. 2024-06-26T05:54:39.4111327Z 2024-06-26T05:54:39.4111441Z Example:: 2024-06-26T05:54:39.4111601Z 2024-06-26T05:54:39.4111711Z >>> nnz = 3 2024-06-26T05:54:39.4111993Z >>> dims = [5, 5, 2, 3] 2024-06-26T05:54:39.4112456Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-06-26T05:54:39.4113095Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-06-26T05:54:39.4113692Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-06-26T05:54:39.4114128Z >>> size = torch.Size(dims) 2024-06-26T05:54:39.4114817Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:39.4115339Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-06-26T05:54:39.4115754Z >>> S 2024-06-26T05:54:39.4116053Z tensor(indices=tensor([[2, 0, 3], 2024-06-26T05:54:39.4116469Z [2, 4, 1]]), 2024-06-26T05:54:39.4116975Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-06-26T05:54:39.4117524Z [ 0.3411, 0.0918, -0.2312]], 2024-06-26T05:54:39.4117842Z 2024-06-26T05:54:39.4118064Z [[ 0.5348, 0.0634, -2.0494], 2024-06-26T05:54:39.4118581Z [-0.7125, -1.0646, 2.1844]], 2024-06-26T05:54:39.4118908Z 2024-06-26T05:54:39.4119114Z [[ 0.1276, 0.1874, -0.6334], 2024-06-26T05:54:39.4119644Z [-1.9682, -0.5340, 0.7483]]]), 2024-06-26T05:54:39.4120150Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-06-26T05:54:39.4120522Z 2024-06-26T05:54:39.4120790Z # when sum over only part of sparse_dims, return a sparse tensor 2024-06-26T05:54:39.4121435Z >>> torch.sparse.sum(S, [1, 3]) 2024-06-26T05:54:39.4121864Z tensor(indices=tensor([[0, 2, 3]]), 2024-06-26T05:54:39.4122349Z values=tensor([[-1.4512, 0.4073], 2024-06-26T05:54:39.4122840Z [-0.8901, 0.2017], 2024-06-26T05:54:39.4123321Z [-0.3183, -1.7539]]), 2024-06-26T05:54:39.4123794Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-06-26T05:54:39.4124146Z 2024-06-26T05:54:39.4124359Z # when sum over all sparse dim, return a dense tensor 2024-06-26T05:54:39.4124931Z # with summed dims squeezed 2024-06-26T05:54:39.4125328Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-06-26T05:54:39.4125830Z tensor([-2.6596, -1.1450]) 2024-06-26T05:54:39.4126179Z 2024-06-26T05:54:39.4126691Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:39.4127191Z 2024-06-26T05:54:39.9412562Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=38. 2024-06-26T05:54:39.9413840Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:39.9414353Z 2024-06-26T05:54:39.9414654Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-06-26T05:54:39.9415467Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-06-26T05:54:39.9416188Z pushes the map into PyTorch operations called by ``func``, effectively 2024-06-26T05:54:39.9417070Z vectorizing those operations. 2024-06-26T05:54:39.9417325Z 2024-06-26T05:54:39.9417636Z vmap is useful for handling batch dimensions: one can write a function 2024-06-26T05:54:39.9418373Z ``func`` that runs on examples and then lift it to a function that can 2024-06-26T05:54:39.9419108Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-06-26T05:54:39.9419779Z compute batched gradients when composed with autograd. 2024-06-26T05:54:39.9420151Z 2024-06-26T05:54:39.9420278Z .. note:: 2024-06-26T05:54:39.9420658Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-06-26T05:54:39.9421281Z convenience. Use whichever one you'd like. 2024-06-26T05:54:39.9421603Z 2024-06-26T05:54:39.9421707Z Args: 2024-06-26T05:54:39.9422119Z func (function): A Python function that takes one or more arguments. 2024-06-26T05:54:39.9422710Z Must return one or more Tensors. 2024-06-26T05:54:39.9423287Z in_dims (int or nested structure): Specifies which dimension of the 2024-06-26T05:54:39.9423951Z inputs should be mapped over. ``in_dims`` should have a 2024-06-26T05:54:39.9424591Z structure like the inputs. If the ``in_dim`` for a particular 2024-06-26T05:54:39.9425274Z input is None, then that indicates there is no map dimension. 2024-06-26T05:54:39.9425789Z Default: 0. 2024-06-26T05:54:39.9426243Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-06-26T05:54:39.9426944Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-06-26T05:54:39.9427558Z it should have one element per output. Default: 0. 2024-06-26T05:54:39.9428158Z randomness (str): Specifies whether the randomness in this 2024-06-26T05:54:39.9428921Z vmap should be the same or different across batches. If 'different', 2024-06-26T05:54:39.9429712Z the randomness for each batch will be different. If 'same', the 2024-06-26T05:54:39.9430492Z randomness will be the same across batches. If 'error', any calls to 2024-06-26T05:54:39.9431291Z random functions will error. Default: 'error'. WARNING: this flag 2024-06-26T05:54:39.9432008Z only applies to random PyTorch operations and does not apply to 2024-06-26T05:54:39.9432656Z Python's random module or numpy randomness. 2024-06-26T05:54:39.9433324Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-06-26T05:54:39.9434122Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-06-26T05:54:39.9435221Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-06-26T05:54:39.9436199Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-06-26T05:54:39.9436758Z 2024-06-26T05:54:39.9436849Z Returns: 2024-06-26T05:54:39.9437253Z Returns a new "batched" function. It takes the same inputs as 2024-06-26T05:54:39.9437899Z ``func``, except each input has an extra dimension at the index 2024-06-26T05:54:39.9438571Z specified by ``in_dims``. It takes returns the same outputs as 2024-06-26T05:54:39.9439342Z ``func``, except each output has an extra dimension at the index 2024-06-26T05:54:39.9439929Z specified by ``out_dims``. 2024-06-26T05:54:39.9440185Z 2024-06-26T05:54:39.9440282Z .. warning: 2024-06-26T05:54:39.9440779Z :func:`vmap` works best with functional-style code. Please do not 2024-06-26T05:54:39.9441580Z perform any side-effects in ``func``, with the exception of 2024-06-26T05:54:39.9442370Z in-place PyTorch operations. Examples of side-effects include mutating 2024-06-26T05:54:39.9443147Z Python data structures and assigning values to variables not captured 2024-06-26T05:54:39.9443700Z in ``func``. 2024-06-26T05:54:39.9443885Z 2024-06-26T05:54:39.9444214Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-06-26T05:54:39.9445063Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-06-26T05:54:39.9445907Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-06-26T05:54:39.9446362Z 2024-06-26T05:54:39.9446614Z >>> torch.dot # [D], [D] -> [] 2024-06-26T05:54:39.9447307Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-06-26T05:54:39.9447914Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-06-26T05:54:39.9448348Z >>> batched_dot(x, y) 2024-06-26T05:54:39.9448574Z 2024-06-26T05:54:39.9448892Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-06-26T05:54:39.9449504Z model authoring experience. 2024-06-26T05:54:39.9449735Z 2024-06-26T05:54:39.9449865Z >>> batch_size, feature_size = 3, 5 2024-06-26T05:54:39.9450369Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-06-26T05:54:39.9450846Z >>> 2024-06-26T05:54:39.9451099Z >>> def model(feature_vec): 2024-06-26T05:54:39.9451523Z >>> # Very simple linear model with activation 2024-06-26T05:54:39.9452026Z >>> return feature_vec.dot(weights).relu() 2024-06-26T05:54:39.9452434Z >>> 2024-06-26T05:54:39.9452767Z >>> examples = torch.randn(batch_size, feature_size) 2024-06-26T05:54:39.9453274Z >>> result = torch.vmap(model)(examples) 2024-06-26T05:54:39.9453579Z 2024-06-26T05:54:39.9453937Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-06-26T05:54:39.9454816Z or impossible to batch. One example is higher-order gradient computation. 2024-06-26T05:54:39.9455650Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-06-26T05:54:39.9456475Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-06-26T05:54:39.9457276Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-06-26T05:54:39.9458103Z we can vectorize the whole computation, computing the Jacobian in a single 2024-06-26T05:54:39.9458702Z call to ``autograd.grad``. 2024-06-26T05:54:39.9458923Z 2024-06-26T05:54:39.9459024Z >>> # Setup 2024-06-26T05:54:39.9459290Z >>> N = 5 2024-06-26T05:54:39.9459568Z >>> f = lambda x: x ** 2 2024-06-26T05:54:39.9459940Z >>> x = torch.randn(N, requires_grad=True) 2024-06-26T05:54:39.9460355Z >>> y = f(x) 2024-06-26T05:54:39.9460639Z >>> I_N = torch.eye(N) 2024-06-26T05:54:39.9460937Z >>> 2024-06-26T05:54:39.9461195Z >>> # Sequential approach 2024-06-26T05:54:39.9461718Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-06-26T05:54:39.9462304Z >>> for v in I_N.unbind()] 2024-06-26T05:54:39.9462759Z >>> jacobian = torch.stack(jacobian_rows) 2024-06-26T05:54:39.9463163Z >>> 2024-06-26T05:54:39.9463430Z >>> # vectorized gradient computation 2024-06-26T05:54:39.9463835Z >>> def get_vjp(v): 2024-06-26T05:54:39.9464198Z >>> return torch.autograd.grad(y, x, v) 2024-06-26T05:54:39.9464650Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-06-26T05:54:39.9464963Z 2024-06-26T05:54:39.9465333Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-06-26T05:54:39.9465927Z 2024-06-26T05:54:39.9466191Z >>> torch.dot # [D], [D] -> [] 2024-06-26T05:54:39.9467058Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-06-26T05:54:39.9467785Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-06-26T05:54:39.9468294Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-06-26T05:54:39.9468612Z 2024-06-26T05:54:39.9468956Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-06-26T05:54:39.9469647Z the dimension that each inputs are batched along as 2024-06-26T05:54:39.9470017Z 2024-06-26T05:54:39.9470263Z >>> torch.dot # [N], [N] -> [] 2024-06-26T05:54:39.9470977Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-06-26T05:54:39.9471677Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-06-26T05:54:39.9472362Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-06-26T05:54:39.9472886Z 2024-06-26T05:54:39.9473238Z If there are multiple inputs each of which is batched along different dimensions, 2024-06-26T05:54:39.9474049Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-06-26T05:54:39.9474488Z 2024-06-26T05:54:39.9474856Z >>> torch.dot # [D], [D] -> [] 2024-06-26T05:54:39.9475607Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-06-26T05:54:39.9476268Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-06-26T05:54:39.9477005Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-06-26T05:54:39.9477540Z 2024-06-26T05:54:39.9477878Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-06-26T05:54:39.9478525Z matching the shape of the input: 2024-06-26T05:54:39.9478787Z 2024-06-26T05:54:39.9479043Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-06-26T05:54:39.9479535Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-06-26T05:54:39.9480018Z >>> input = {'x': x, 'y': y} 2024-06-26T05:54:39.9480567Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-06-26T05:54:39.9481151Z >>> batched_dot(input) 2024-06-26T05:54:39.9481383Z 2024-06-26T05:54:39.9481766Z By default, the output is batched along the first dimension. However, it can be batched 2024-06-26T05:54:39.9482474Z along any dimension by using ``out_dims`` 2024-06-26T05:54:39.9482772Z 2024-06-26T05:54:39.9482887Z >>> f = lambda x: x ** 2 2024-06-26T05:54:39.9483241Z >>> x = torch.randn(2, 5) 2024-06-26T05:54:39.9483645Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-06-26T05:54:39.9484069Z >>> batched_pow(x) # [5, 2] 2024-06-26T05:54:39.9484322Z 2024-06-26T05:54:39.9484733Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-06-26T05:54:39.9485420Z accept kwargs 2024-06-26T05:54:39.9485583Z 2024-06-26T05:54:39.9485699Z >>> x = torch.randn([2, 5]) 2024-06-26T05:54:39.9486057Z >>> def fn(x, scale=4.): 2024-06-26T05:54:39.9486509Z >>> return x * scale 2024-06-26T05:54:39.9486810Z >>> 2024-06-26T05:54:39.9487081Z >>> batched_pow = torch.vmap(fn) 2024-06-26T05:54:39.9487539Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-06-26T05:54:39.9488183Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-06-26T05:54:39.9488674Z 2024-06-26T05:54:39.9488778Z .. note:: 2024-06-26T05:54:39.9489379Z vmap does not provide general autobatching or handle variable-length 2024-06-26T05:54:39.9489961Z sequences out of the box. 2024-06-26T05:54:39.9490197Z 2024-06-26T05:54:39.9490591Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:39.9491097Z 2024-06-26T05:54:40.9747810Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=329. 2024-06-26T05:54:40.9750068Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:40.9750657Z 2024-06-26T05:54:40.9751008Z Raises an AssertionError if two items are not equal up to desired 2024-06-26T05:54:40.9751539Z precision. 2024-06-26T05:54:40.9751707Z 2024-06-26T05:54:40.9751955Z .. note:: It is recommended to use one of `assert_allclose`, 2024-06-26T05:54:40.9752582Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-06-26T05:54:40.9753334Z instead of this function for more consistent floating point 2024-06-26T05:54:40.9754025Z comparisons. 2024-06-26T05:54:40.9754338Z 2024-06-26T05:54:40.9754844Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-06-26T05:54:40.9755473Z 2024-06-26T05:54:40.9755977Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-06-26T05:54:40.9756768Z 2024-06-26T05:54:40.9757096Z That is a looser test than originally documented, but agrees with what the 2024-06-26T05:54:40.9757890Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-06-26T05:54:40.9758802Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-06-26T05:54:40.9759719Z delegates to assert_array_almost_equal 2024-06-26T05:54:40.9760205Z 2024-06-26T05:54:40.9760401Z Parameters 2024-06-26T05:54:40.9760855Z ---------- 2024-06-26T05:54:40.9761192Z actual : array_like 2024-06-26T05:54:40.9761488Z The object to check. 2024-06-26T05:54:40.9761814Z desired : array_like 2024-06-26T05:54:40.9762126Z The expected object. 2024-06-26T05:54:40.9762439Z decimal : int, optional 2024-06-26T05:54:40.9762781Z Desired precision, default is 7. 2024-06-26T05:54:40.9763174Z err_msg : str, optional 2024-06-26T05:54:40.9763566Z The error message to be printed in case of failure. 2024-06-26T05:54:40.9764044Z verbose : bool, optional 2024-06-26T05:54:40.9764550Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:40.9764996Z 2024-06-26T05:54:40.9765083Z Raises 2024-06-26T05:54:40.9765334Z ------ 2024-06-26T05:54:40.9765579Z AssertionError 2024-06-26T05:54:40.9766003Z If actual and desired are not equal up to specified precision. 2024-06-26T05:54:40.9766439Z 2024-06-26T05:54:40.9766534Z See Also 2024-06-26T05:54:40.9766793Z -------- 2024-06-26T05:54:40.9767231Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:40.9767871Z relative and/or absolute precision. 2024-06-26T05:54:40.9768462Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:40.9768883Z 2024-06-26T05:54:40.9768975Z Examples 2024-06-26T05:54:40.9769233Z -------- 2024-06-26T05:54:40.9769584Z >>> from torch._numpy.testing import assert_almost_equal 2024-06-26T05:54:40.9770107Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-06-26T05:54:40.9770666Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-06-26T05:54:40.9771190Z Traceback (most recent call last): 2024-06-26T05:54:40.9771538Z ... 2024-06-26T05:54:40.9771785Z AssertionError: 2024-06-26T05:54:40.9772113Z Arrays are not almost equal to 10 decimals 2024-06-26T05:54:40.9772518Z ACTUAL: 2.3333333333333 2024-06-26T05:54:40.9772830Z DESIRED: 2.33333334 2024-06-26T05:54:40.9773016Z 2024-06-26T05:54:40.9773214Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-06-26T05:54:40.9773714Z ... np.array([1.0,2.33333334]), decimal=9) 2024-06-26T05:54:40.9774185Z Traceback (most recent call last): 2024-06-26T05:54:40.9774547Z ... 2024-06-26T05:54:40.9774780Z AssertionError: 2024-06-26T05:54:40.9775106Z Arrays are not almost equal to 9 decimals 2024-06-26T05:54:40.9775505Z 2024-06-26T05:54:40.9775772Z Mismatched elements: 1 / 2 (50%) 2024-06-26T05:54:40.9776245Z Max absolute difference: 6.666699636781459e-09 2024-06-26T05:54:40.9776784Z Max relative difference: 2.8571569790287484e-09 2024-06-26T05:54:40.9777382Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-06-26T05:54:40.9777892Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-06-26T05:54:40.9778274Z 2024-06-26T05:54:40.9778279Z 2024-06-26T05:54:40.9778684Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:40.9779170Z 2024-06-26T05:54:40.9780052Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=454. 2024-06-26T05:54:40.9781360Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:40.9781881Z 2024-06-26T05:54:40.9782174Z Raises an AssertionError if two items are not equal up to significant 2024-06-26T05:54:40.9782732Z digits. 2024-06-26T05:54:40.9782871Z 2024-06-26T05:54:40.9783108Z .. note:: It is recommended to use one of `assert_allclose`, 2024-06-26T05:54:40.9783808Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-06-26T05:54:40.9784463Z instead of this function for more consistent floating point 2024-06-26T05:54:40.9784980Z comparisons. 2024-06-26T05:54:40.9785195Z 2024-06-26T05:54:40.9785431Z Given two numbers, check that they are approximately equal. 2024-06-26T05:54:40.9786112Z Approximately equal is defined as the number of significant digits 2024-06-26T05:54:40.9786659Z that agree. 2024-06-26T05:54:40.9786809Z 2024-06-26T05:54:40.9786906Z Parameters 2024-06-26T05:54:40.9787174Z ---------- 2024-06-26T05:54:40.9787426Z actual : scalar 2024-06-26T05:54:40.9787701Z The object to check. 2024-06-26T05:54:40.9788017Z desired : scalar 2024-06-26T05:54:40.9788304Z The expected object. 2024-06-26T05:54:40.9788625Z significant : int, optional 2024-06-26T05:54:40.9788990Z Desired precision, default is 7. 2024-06-26T05:54:40.9789380Z err_msg : str, optional 2024-06-26T05:54:40.9789781Z The error message to be printed in case of failure. 2024-06-26T05:54:40.9790259Z verbose : bool, optional 2024-06-26T05:54:40.9790751Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:40.9791190Z 2024-06-26T05:54:40.9791280Z Raises 2024-06-26T05:54:40.9791526Z ------ 2024-06-26T05:54:40.9791765Z AssertionError 2024-06-26T05:54:40.9792186Z If actual and desired are not equal up to specified precision. 2024-06-26T05:54:40.9792616Z 2024-06-26T05:54:40.9792708Z See Also 2024-06-26T05:54:40.9792965Z -------- 2024-06-26T05:54:40.9793398Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:40.9794038Z relative and/or absolute precision. 2024-06-26T05:54:40.9794764Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:40.9795213Z 2024-06-26T05:54:40.9795308Z Examples 2024-06-26T05:54:40.9795576Z -------- 2024-06-26T05:54:40.9796224Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-06-26T05:54:40.9797161Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-06-26T05:54:40.9797816Z ... significant=8) 2024-06-26T05:54:40.9798534Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-06-26T05:54:40.9799169Z ... significant=8) 2024-06-26T05:54:40.9799609Z Traceback (most recent call last): 2024-06-26T05:54:40.9799973Z ... 2024-06-26T05:54:40.9800207Z AssertionError: 2024-06-26T05:54:40.9800545Z Items are not equal to 8 significant digits: 2024-06-26T05:54:40.9801076Z ACTUAL: 1.234567e-21 2024-06-26T05:54:40.9801415Z DESIRED: 1.2345672e-21 2024-06-26T05:54:40.9801633Z 2024-06-26T05:54:40.9801835Z the evaluated condition that raises the exception is 2024-06-26T05:54:40.9802196Z 2024-06-26T05:54:40.9802505Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-06-26T05:54:40.9802991Z True 2024-06-26T05:54:40.9803120Z 2024-06-26T05:54:40.9803130Z 2024-06-26T05:54:40.9803604Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:40.9804105Z 2024-06-26T05:54:40.9805053Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=733. 2024-06-26T05:54:40.9806402Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:40.9806923Z 2024-06-26T05:54:40.9807197Z Raises an AssertionError if two array_like objects are not equal. 2024-06-26T05:54:40.9807629Z 2024-06-26T05:54:40.9807917Z Given two array_like objects, check that the shape is equal and all 2024-06-26T05:54:40.9808642Z elements of these objects are equal (but see the Notes for the special 2024-06-26T05:54:40.9809379Z handling of a scalar). An exception is raised at shape mismatch or 2024-06-26T05:54:40.9810121Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-06-26T05:54:40.9811158Z are compared like numbers, no assertion is raised if both objects have 2024-06-26T05:54:40.9811868Z NaNs in the same positions. 2024-06-26T05:54:40.9812330Z 2024-06-26T05:54:40.9812767Z The usual caution for verifying equality with floating point numbers is 2024-06-26T05:54:40.9813338Z advised. 2024-06-26T05:54:40.9813480Z 2024-06-26T05:54:40.9813577Z Parameters 2024-06-26T05:54:40.9813856Z ---------- 2024-06-26T05:54:40.9814104Z x : array_like 2024-06-26T05:54:40.9814384Z The actual object to check. 2024-06-26T05:54:40.9814736Z y : array_like 2024-06-26T05:54:40.9815033Z The desired, expected object. 2024-06-26T05:54:40.9815391Z err_msg : str, optional 2024-06-26T05:54:40.9815792Z The error message to be printed in case of failure. 2024-06-26T05:54:40.9816272Z verbose : bool, optional 2024-06-26T05:54:40.9816753Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:40.9817307Z strict : bool, optional 2024-06-26T05:54:40.9817801Z If True, raise an AssertionError when either the shape or the data 2024-06-26T05:54:40.9818466Z type of the array_like objects does not match. The special 2024-06-26T05:54:40.9819136Z handling for scalars mentioned in the Notes section is disabled. 2024-06-26T05:54:40.9819570Z 2024-06-26T05:54:40.9819669Z Raises 2024-06-26T05:54:40.9819904Z ------ 2024-06-26T05:54:40.9820145Z AssertionError 2024-06-26T05:54:40.9820491Z If actual and desired objects are not equal. 2024-06-26T05:54:40.9820818Z 2024-06-26T05:54:40.9820909Z See Also 2024-06-26T05:54:40.9821165Z -------- 2024-06-26T05:54:40.9821610Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:40.9822238Z relative and/or absolute precision. 2024-06-26T05:54:40.9822830Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:40.9823254Z 2024-06-26T05:54:40.9823353Z Notes 2024-06-26T05:54:40.9823583Z ----- 2024-06-26T05:54:40.9823992Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-06-26T05:54:40.9824726Z function checks that each element of the array_like object is equal to 2024-06-26T05:54:40.9825481Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-06-26T05:54:40.9825958Z 2024-06-26T05:54:40.9826049Z Examples 2024-06-26T05:54:40.9826307Z -------- 2024-06-26T05:54:40.9826615Z The first assert does not raise an exception: 2024-06-26T05:54:40.9826940Z 2024-06-26T05:54:40.9827135Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-06-26T05:54:40.9827660Z ... [np.exp(0),2.33333, np.nan]) 2024-06-26T05:54:40.9827988Z 2024-06-26T05:54:40.9828308Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-06-26T05:54:40.9828900Z functions for these cases instead: 2024-06-26T05:54:40.9829180Z 2024-06-26T05:54:40.9829358Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-06-26T05:54:40.9829879Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-06-26T05:54:40.9830401Z ... rtol=1e-10, atol=0) 2024-06-26T05:54:40.9830775Z 2024-06-26T05:54:40.9831051Z As mentioned in the Notes section, `assert_array_equal` has special 2024-06-26T05:54:40.9831859Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-06-26T05:54:40.9832324Z 2024-06-26T05:54:40.9832466Z >>> x = np.full((2, 5), fill_value=3) 2024-06-26T05:54:40.9832870Z >>> np.testing.assert_array_equal(x, 3) 2024-06-26T05:54:40.9833176Z 2024-06-26T05:54:40.9833469Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-06-26T05:54:40.9834022Z array: 2024-06-26T05:54:40.9834156Z 2024-06-26T05:54:40.9834345Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-06-26T05:54:40.9835021Z Traceback (most recent call last): 2024-06-26T05:54:40.9835392Z ... 2024-06-26T05:54:40.9835629Z AssertionError: 2024-06-26T05:54:40.9835919Z Arrays are not equal 2024-06-26T05:54:40.9836215Z 2024-06-26T05:54:40.9836475Z (shapes (2, 5), () mismatch) 2024-06-26T05:54:40.9836966Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-06-26T05:54:40.9837350Z [3, 3, 3, 3, 3]]) 2024-06-26T05:54:40.9837654Z y: torch.ndarray(3) 2024-06-26T05:54:40.9837855Z 2024-06-26T05:54:40.9838136Z The `strict` parameter also ensures that the array data types match: 2024-06-26T05:54:40.9838583Z 2024-06-26T05:54:40.9838706Z >>> x = np.array([2, 2, 2]) 2024-06-26T05:54:40.9839080Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-06-26T05:54:40.9839596Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-06-26T05:54:40.9840080Z Traceback (most recent call last): 2024-06-26T05:54:40.9840429Z ... 2024-06-26T05:54:40.9840675Z AssertionError: 2024-06-26T05:54:40.9841038Z Arrays are not equal 2024-06-26T05:54:40.9841324Z 2024-06-26T05:54:40.9841659Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-06-26T05:54:40.9842129Z x: torch.ndarray([2, 2, 2]) 2024-06-26T05:54:40.9842469Z y: torch.ndarray([2., 2., 2.]) 2024-06-26T05:54:40.9842732Z 2024-06-26T05:54:40.9843139Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:40.9843631Z 2024-06-26T05:54:40.9844546Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=839. 2024-06-26T05:54:40.9845893Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:40.9846400Z 2024-06-26T05:54:40.9846680Z Raises an AssertionError if two objects are not equal up to desired 2024-06-26T05:54:40.9847229Z precision. 2024-06-26T05:54:40.9847378Z 2024-06-26T05:54:40.9847624Z .. note:: It is recommended to use one of `assert_allclose`, 2024-06-26T05:54:40.9848233Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-06-26T05:54:40.9848886Z instead of this function for more consistent floating point 2024-06-26T05:54:40.9849412Z comparisons. 2024-06-26T05:54:40.9849608Z 2024-06-26T05:54:40.9849926Z The test verifies identical shapes and that the elements of ``actual`` and 2024-06-26T05:54:40.9850520Z ``desired`` satisfy. 2024-06-26T05:54:40.9850722Z 2024-06-26T05:54:40.9850934Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-06-26T05:54:40.9851262Z 2024-06-26T05:54:40.9851586Z That is a looser test than originally documented, but agrees with what the 2024-06-26T05:54:40.9852380Z actual implementation did up to rounding vagaries. An exception is raised 2024-06-26T05:54:40.9853181Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-06-26T05:54:40.9853969Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-06-26T05:54:40.9854576Z objects have NaNs in the same positions. 2024-06-26T05:54:40.9854885Z 2024-06-26T05:54:40.9854980Z Parameters 2024-06-26T05:54:40.9855247Z ---------- 2024-06-26T05:54:40.9855481Z x : array_like 2024-06-26T05:54:40.9855773Z The actual object to check. 2024-06-26T05:54:40.9856128Z y : array_like 2024-06-26T05:54:40.9856414Z The desired, expected object. 2024-06-26T05:54:40.9856853Z decimal : int, optional 2024-06-26T05:54:40.9857200Z Desired precision, default is 6. 2024-06-26T05:54:40.9857579Z err_msg : str, optional 2024-06-26T05:54:40.9858035Z The error message to be printed in case of failure. 2024-06-26T05:54:40.9858508Z verbose : bool, optional 2024-06-26T05:54:40.9858991Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:40.9859442Z 2024-06-26T05:54:40.9859528Z Raises 2024-06-26T05:54:40.9859781Z ------ 2024-06-26T05:54:40.9860011Z AssertionError 2024-06-26T05:54:40.9860450Z If actual and desired are not equal up to specified precision. 2024-06-26T05:54:40.9860870Z 2024-06-26T05:54:40.9860975Z See Also 2024-06-26T05:54:40.9861219Z -------- 2024-06-26T05:54:40.9861670Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:40.9862311Z relative and/or absolute precision. 2024-06-26T05:54:40.9862952Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:40.9863392Z 2024-06-26T05:54:40.9863484Z Examples 2024-06-26T05:54:40.9863739Z -------- 2024-06-26T05:54:40.9864032Z the first assert does not raise an exception 2024-06-26T05:54:40.9864372Z 2024-06-26T05:54:40.9864591Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-06-26T05:54:40.9865137Z ... [1.0,2.333,np.nan]) 2024-06-26T05:54:40.9865455Z 2024-06-26T05:54:40.9865695Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-06-26T05:54:40.9866250Z ... [1.0,2.33339,np.nan], decimal=5) 2024-06-26T05:54:40.9866730Z Traceback (most recent call last): 2024-06-26T05:54:40.9867091Z ... 2024-06-26T05:54:40.9867325Z AssertionError: 2024-06-26T05:54:40.9867648Z Arrays are not almost equal to 5 decimals 2024-06-26T05:54:40.9868049Z 2024-06-26T05:54:40.9868317Z Mismatched elements: 1 / 3 (33.3%) 2024-06-26T05:54:40.9868794Z Max absolute difference: 5.999999999994898e-05 2024-06-26T05:54:40.9869331Z Max relative difference: 2.5713661239633743e-05 2024-06-26T05:54:40.9869855Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-06-26T05:54:40.9870437Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-06-26T05:54:40.9870801Z 2024-06-26T05:54:40.9871038Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-06-26T05:54:40.9871588Z ... [1.0,2.33333, 5], decimal=5) 2024-06-26T05:54:40.9872057Z Traceback (most recent call last): 2024-06-26T05:54:40.9872422Z ... 2024-06-26T05:54:40.9872655Z AssertionError: 2024-06-26T05:54:40.9872979Z Arrays are not almost equal to 5 decimals 2024-06-26T05:54:40.9873381Z 2024-06-26T05:54:40.9873648Z x and y nan location mismatch: 2024-06-26T05:54:40.9874106Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-06-26T05:54:40.9874834Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-06-26T05:54:40.9875206Z 2024-06-26T05:54:40.9875216Z 2024-06-26T05:54:40.9875616Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:40.9876122Z 2024-06-26T05:54:40.9877064Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1789. 2024-06-26T05:54:40.9878405Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:40.9879188Z Context manager that resets warning registry for catching warnings 2024-06-26T05:54:40.9879623Z 2024-06-26T05:54:40.9879964Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-06-26T05:54:40.9880746Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-06-26T05:54:40.9881613Z it impossible to retrigger the warning in this module, whatever you put in 2024-06-26T05:54:40.9882436Z the warnings filters. This context manager accepts a sequence of `modules` 2024-06-26T05:54:40.9883097Z as a keyword argument to its constructor and: 2024-06-26T05:54:40.9883523Z 2024-06-26T05:54:40.9883828Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-06-26T05:54:40.9884452Z on entry; 2024-06-26T05:54:40.9884855Z * resets ``__warningregistry__`` to its previous state on exit. 2024-06-26T05:54:40.9885279Z 2024-06-26T05:54:40.9885584Z This makes it possible to trigger any warning afresh inside the context 2024-06-26T05:54:40.9886293Z manager without disturbing the state of warnings outside. 2024-06-26T05:54:40.9886685Z 2024-06-26T05:54:40.9887004Z For compatibility with Python 3.0, please consider all arguments to be 2024-06-26T05:54:40.9887607Z keyword-only. 2024-06-26T05:54:40.9887792Z 2024-06-26T05:54:40.9887888Z Parameters 2024-06-26T05:54:40.9888175Z ---------- 2024-06-26T05:54:40.9888441Z record : bool, optional 2024-06-26T05:54:40.9888982Z Specifies whether warnings should be captured by a custom 2024-06-26T05:54:40.9889700Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-06-26T05:54:40.9890448Z returned by the context manager. Otherwise None is returned by the 2024-06-26T05:54:40.9891206Z context manager. The objects appended to the list are arguments whose 2024-06-26T05:54:40.9891890Z attributes mirror the arguments to ``showwarning()``. 2024-06-26T05:54:40.9892384Z modules : sequence, optional 2024-06-26T05:54:40.9892945Z Sequence of modules for which to reset warnings registry on entry and 2024-06-26T05:54:40.9893742Z restore on exit. To work correctly, all 'ignore' filters should 2024-06-26T05:54:40.9894308Z filter by one of these modules. 2024-06-26T05:54:40.9894593Z 2024-06-26T05:54:40.9894686Z Examples 2024-06-26T05:54:40.9894962Z -------- 2024-06-26T05:54:40.9895226Z >>> import warnings 2024-06-26T05:54:40.9895671Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-06-26T05:54:40.9896254Z ... modules=[np.core.fromnumeric]): 2024-06-26T05:54:40.9896768Z ... warnings.simplefilter('always') 2024-06-26T05:54:40.9897417Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-06-26T05:54:40.9898117Z ... # do something that raises a warning but ignore those in 2024-06-26T05:54:40.9898648Z ... # np.core.fromnumeric 2024-06-26T05:54:40.9898984Z 2024-06-26T05:54:40.9899504Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:40.9899990Z 2024-06-26T05:54:41.1270345Z msg = Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=273. 2024-06-26T05:54:41.1272137Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.1272936Z Applies a 1D convolution over a quantized input signal composed of 2024-06-26T05:54:41.1273562Z several quantized input planes. 2024-06-26T05:54:41.1273850Z 2024-06-26T05:54:41.1274147Z For details on input arguments, parameters, and implementation see 2024-06-26T05:54:41.1274964Z :class:`~torch.nn.Conv1d`. 2024-06-26T05:54:41.1275242Z 2024-06-26T05:54:41.1275365Z .. note:: 2024-06-26T05:54:41.1275801Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-06-26T05:54:41.1276234Z 2024-06-26T05:54:41.1276346Z .. note:: 2024-06-26T05:54:41.1276737Z Only `torch.quint8` is supported for the input data type. 2024-06-26T05:54:41.1277145Z 2024-06-26T05:54:41.1277149Z 2024-06-26T05:54:41.1277249Z Attributes: 2024-06-26T05:54:41.1277707Z weight (Tensor): packed tensor derived from the learnable weight 2024-06-26T05:54:41.1278264Z parameter. 2024-06-26T05:54:41.1278726Z scale (Tensor): scalar for the output scale 2024-06-26T05:54:41.1279290Z zero_point (Tensor): scalar for the output zero point 2024-06-26T05:54:41.1279661Z 2024-06-26T05:54:41.1280060Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-06-26T05:54:41.1280428Z 2024-06-26T05:54:41.1280532Z Examples:: 2024-06-26T05:54:41.1280698Z 2024-06-26T05:54:41.1281094Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-06-26T05:54:41.1281640Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-06-26T05:54:41.1282117Z >>> input = torch.randn(20, 16, 100) 2024-06-26T05:54:41.1282556Z >>> # quantize input to quint8 2024-06-26T05:54:41.1282957Z >>> # xdoctest: +SKIP 2024-06-26T05:54:41.1283462Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-06-26T05:54:41.1284093Z ... dtype=torch.quint8) 2024-06-26T05:54:41.1284559Z >>> output = m(q_input) 2024-06-26T05:54:41.1284802Z 2024-06-26T05:54:41.1284889Z 2024-06-26T05:54:41.1285436Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.1286056Z 2024-06-26T05:54:41.1458963Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=9. 2024-06-26T05:54:41.1460591Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.1461297Z A quantized long short-term memory (LSTM). 2024-06-26T05:54:41.1461644Z 2024-06-26T05:54:41.1462027Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-06-26T05:54:41.1462570Z 2024-06-26T05:54:41.1462682Z Attributes: 2024-06-26T05:54:41.1463004Z layers : instances of the `_LSTMLayer` 2024-06-26T05:54:41.1463329Z 2024-06-26T05:54:41.1463438Z .. note:: 2024-06-26T05:54:41.1463903Z To access the weights and biases, you need to access them per layer. 2024-06-26T05:54:41.1464584Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-06-26T05:54:41.1464986Z 2024-06-26T05:54:41.1465085Z Examples:: 2024-06-26T05:54:41.1465386Z >>> # xdoctest: +SKIP 2024-06-26T05:54:41.1465752Z >>> custom_module_config = { 2024-06-26T05:54:41.1466253Z ... 'float_to_observed_custom_module_class': { 2024-06-26T05:54:41.1466761Z ... nn.LSTM: nn.quantizable.LSTM, 2024-06-26T05:54:41.1467178Z ... }, 2024-06-26T05:54:41.1467590Z ... 'observed_to_quantized_custom_module_class': { 2024-06-26T05:54:41.1468144Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-06-26T05:54:41.1468595Z ... } 2024-06-26T05:54:41.1468849Z ... } 2024-06-26T05:54:41.1469298Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-06-26T05:54:41.1478889Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-06-26T05:54:41.1479454Z 2024-06-26T05:54:41.1480013Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.1480525Z 2024-06-26T05:54:41.2287810Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=230. 2024-06-26T05:54:41.2289346Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.2290091Z Squashes the sparse masks into the appropriate tensors. 2024-06-26T05:54:41.2290501Z 2024-06-26T05:54:41.2290799Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-06-26T05:54:41.2291487Z the module will have a `sparse_params` dict attached to it. 2024-06-26T05:54:41.2291914Z 2024-06-26T05:54:41.2292008Z Args: 2024-06-26T05:54:41.2292425Z params_to_keep: List of keys to save in the module or a dict 2024-06-26T05:54:41.2293052Z representing the modules and keys that will have 2024-06-26T05:54:41.2293843Z sparsity parameters saved 2024-06-26T05:54:41.2294541Z params_to_keep_per_layer: Dict to specify the params that should be 2024-06-26T05:54:41.2295494Z saved for specific layers. The keys in the dict 2024-06-26T05:54:41.2296175Z should be the module fqn, while the values should 2024-06-26T05:54:41.2296932Z be a list of strings with the names of the variables 2024-06-26T05:54:41.2297563Z to save in the `sparse_params` 2024-06-26T05:54:41.2297893Z 2024-06-26T05:54:41.2297995Z Examples: 2024-06-26T05:54:41.2298424Z >>> # xdoctest: +SKIP("locals are undefined") 2024-06-26T05:54:41.2299039Z >>> # Don't save any sparse params 2024-06-26T05:54:41.2299488Z >>> sparsifier.squash_mask() 2024-06-26T05:54:41.2300094Z >>> hasattr(model.submodule1, 'sparse_params') 2024-06-26T05:54:41.2300596Z False 2024-06-26T05:54:41.2300782Z 2024-06-26T05:54:41.2300935Z >>> # Keep sparse params per layer 2024-06-26T05:54:41.2301565Z >>> sparsifier.squash_mask( 2024-06-26T05:54:41.2302012Z ... params_to_keep_per_layer={ 2024-06-26T05:54:41.2302619Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-06-26T05:54:41.2303269Z ... 'submodule2.linear42': ('baz',) 2024-06-26T05:54:41.2303744Z ... }) 2024-06-26T05:54:41.2304167Z >>> print(model.submodule1.linear1.sparse_params) 2024-06-26T05:54:41.2304775Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:41.2305248Z >>> print(model.submodule2.linear42.sparse_params) 2024-06-26T05:54:41.2305815Z {'baz': 0.1} 2024-06-26T05:54:41.2306037Z 2024-06-26T05:54:41.2306256Z >>> # Keep sparse params for all layers 2024-06-26T05:54:41.2306919Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-06-26T05:54:41.2307549Z >>> print(model.submodule1.linear1.sparse_params) 2024-06-26T05:54:41.2308138Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:41.2308673Z >>> print(model.submodule2.linear42.sparse_params) 2024-06-26T05:54:41.2309217Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:41.2309524Z 2024-06-26T05:54:41.2309801Z >>> # Keep some sparse params for all layers, and specific ones for 2024-06-26T05:54:41.2310436Z >>> # some other layers 2024-06-26T05:54:41.2310873Z >>> sparsifier.squash_mask( 2024-06-26T05:54:41.2311380Z ... params_to_keep=('foo', 'bar'), 2024-06-26T05:54:41.2311917Z ... params_to_keep_per_layer={ 2024-06-26T05:54:41.2312499Z ... 'submodule2.linear42': ('baz',) 2024-06-26T05:54:41.2312931Z ... }) 2024-06-26T05:54:41.2313401Z >>> print(model.submodule1.linear1.sparse_params) 2024-06-26T05:54:41.2313938Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:41.2314463Z >>> print(model.submodule2.linear42.sparse_params) 2024-06-26T05:54:41.2315277Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-06-26T05:54:41.2315731Z 2024-06-26T05:54:41.2316260Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.2316821Z 2024-06-26T05:54:41.3037175Z msg = Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=178. 2024-06-26T05:54:41.3038659Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.3039198Z 2024-06-26T05:54:41.3039550Z Config object that specifies the supported data types passed as arguments to 2024-06-26T05:54:41.3040374Z quantize ops in the reference model spec, for input and output activations, 2024-06-26T05:54:41.3041044Z weights, and biases. 2024-06-26T05:54:41.3041244Z 2024-06-26T05:54:41.3041449Z For example, consider the following reference model: 2024-06-26T05:54:41.3041807Z 2024-06-26T05:54:41.3042074Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-06-26T05:54:41.3042445Z 2024-06-26T05:54:41.3042891Z The pattern in the square brackets refers to the reference pattern of 2024-06-26T05:54:41.3043652Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-06-26T05:54:41.3044496Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-06-26T05:54:41.3045268Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-06-26T05:54:41.3046041Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-06-26T05:54:41.3046630Z the second quantize op (quant2). 2024-06-26T05:54:41.3046885Z 2024-06-26T05:54:41.3047182Z Note that the dtype here does not refer to the interface dtypes of the 2024-06-26T05:54:41.3047929Z op. For example, the "input dtype" here is not the dtype of the input 2024-06-26T05:54:41.3048663Z tensor passed to the quantized linear op. Though it can still be the 2024-06-26T05:54:41.3049389Z same as the interface dtype, this is not always the case, e.g. the 2024-06-26T05:54:41.3050199Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-06-26T05:54:41.3050952Z specified in the DTypeConfig would still be quint8. The semantics of 2024-06-26T05:54:41.3051695Z dtypes here are the same as the semantics of the dtypes specified in 2024-06-26T05:54:41.3052242Z the observers. 2024-06-26T05:54:41.3052428Z 2024-06-26T05:54:41.3052729Z These dtypes are matched against the ones specified in the user's 2024-06-26T05:54:41.3053475Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-06-26T05:54:41.3054226Z specified in the DTypeConfig (if any), then we will quantize the given 2024-06-26T05:54:41.3054979Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-06-26T05:54:41.3055565Z the pattern will not be quantized. 2024-06-26T05:54:41.3055832Z 2024-06-26T05:54:41.3055960Z Example usage:: 2024-06-26T05:54:41.3056142Z 2024-06-26T05:54:41.3056264Z >>> # xdoctest: +SKIP(failing) 2024-06-26T05:54:41.3056657Z >>> dtype_config1 = DTypeConfig( 2024-06-26T05:54:41.3057057Z ... input_dtype=torch.quint8, 2024-06-26T05:54:41.3057462Z ... output_dtype=torch.quint8, 2024-06-26T05:54:41.3057866Z ... weight_dtype=torch.qint8, 2024-06-26T05:54:41.3058262Z ... bias_dtype=torch.float) 2024-06-26T05:54:41.3058523Z 2024-06-26T05:54:41.3058647Z >>> dtype_config2 = DTypeConfig( 2024-06-26T05:54:41.3059085Z ... input_dtype=DTypeWithConstraints( 2024-06-26T05:54:41.3059527Z ... dtype=torch.quint8, 2024-06-26T05:54:41.3059906Z ... quant_min_lower_bound=0, 2024-06-26T05:54:41.3060319Z ... quant_max_upper_bound=255, 2024-06-26T05:54:41.3060710Z ... ), 2024-06-26T05:54:41.3061024Z ... output_dtype=DTypeWithConstraints( 2024-06-26T05:54:41.3061465Z ... dtype=torch.quint8, 2024-06-26T05:54:41.3061851Z ... quant_min_lower_bound=0, 2024-06-26T05:54:41.3062251Z ... quant_max_upper_bound=255, 2024-06-26T05:54:41.3062648Z ... ), 2024-06-26T05:54:41.3062974Z ... weight_dtype=DTypeWithConstraints( 2024-06-26T05:54:41.3063403Z ... dtype=torch.qint8, 2024-06-26T05:54:41.3063843Z ... quant_min_lower_bound=-128, 2024-06-26T05:54:41.3064272Z ... quant_max_upper_bound=127, 2024-06-26T05:54:41.3064650Z ... ), 2024-06-26T05:54:41.3064934Z ... bias_dtype=torch.float) 2024-06-26T05:54:41.3065196Z 2024-06-26T05:54:41.3065332Z >>> dtype_config1.input_dtype 2024-06-26T05:54:41.3065683Z torch.quint8 2024-06-26T05:54:41.3065866Z 2024-06-26T05:54:41.3065987Z >>> dtype_config2.input_dtype 2024-06-26T05:54:41.3066344Z torch.quint8 2024-06-26T05:54:41.3066514Z 2024-06-26T05:54:41.3066680Z >>> dtype_config2.input_dtype_with_constraints 2024-06-26T05:54:41.3067719Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-06-26T05:54:41.3068575Z 2024-06-26T05:54:41.3068981Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.3069519Z 2024-06-26T05:54:41.4065023Z msg = Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=287. 2024-06-26T05:54:41.4067747Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.4068261Z 2024-06-26T05:54:41.4068683Z Takes in optional filter values and generates two tables with desired information. 2024-06-26T05:54:41.4069217Z 2024-06-26T05:54:41.4069567Z The generated tables are presented in both a list-of-lists format 2024-06-26T05:54:41.4070122Z 2024-06-26T05:54:41.4070593Z The reason for the two tables are that they handle different things: 2024-06-26T05:54:41.4071616Z 1.) the first table handles all tensor level information 2024-06-26T05:54:41.4072664Z 2.) the second table handles and displays all channel based information 2024-06-26T05:54:41.4073136Z 2024-06-26T05:54:41.4073735Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-06-26T05:54:41.4075295Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-06-26T05:54:41.4076857Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-06-26T05:54:41.4077524Z 2024-06-26T05:54:41.4077632Z Tensor table columns: 2024-06-26T05:54:41.4078097Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:41.4078771Z ---- --------- --------- --------- --------- --------- 2024-06-26T05:54:41.4079141Z 2024-06-26T05:54:41.4079293Z Per-Channel table columns: 2024-06-26T05:54:41.4079829Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:41.4080571Z ---- --------- ------- --------- --------- --------- --------- 2024-06-26T05:54:41.4081027Z 2024-06-26T05:54:41.4081117Z Args: 2024-06-26T05:54:41.4081614Z feature_filter (str, optional): Filters the features presented to only those that 2024-06-26T05:54:41.4082385Z contain this filter substring 2024-06-26T05:54:41.4083296Z Default = "", results in all the features being printed 2024-06-26T05:54:41.4084031Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:41.4084895Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:41.4085409Z 2024-06-26T05:54:41.4085541Z Returns a dictionary with two keys: 2024-06-26T05:54:41.4086047Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-06-26T05:54:41.4086588Z "tensor_level_info", "channel_level_info" 2024-06-26T05:54:41.4087025Z Each key maps to a tuple with: 2024-06-26T05:54:41.4087471Z A list of the headers of each table 2024-06-26T05:54:41.4088054Z A list of lists containing the table information row by row 2024-06-26T05:54:41.4088708Z The 0th index row will contain the headers of the columns 2024-06-26T05:54:41.4089283Z The rest of the rows will contain data 2024-06-26T05:54:41.4089608Z 2024-06-26T05:54:41.4089716Z Example Use: 2024-06-26T05:54:41.4090035Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:41.4090556Z >>> mod_report_visualizer.generate_filtered_tables( 2024-06-26T05:54:41.4091078Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:41.4091518Z ... module_fqn_filter = "block1" 2024-06-26T05:54:41.4092176Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-06-26T05:54:41.4092723Z 2024-06-26T05:54:41.4093126Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.4093616Z 2024-06-26T05:54:41.4094966Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=380. 2024-06-26T05:54:41.4096817Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.4097433Z 2024-06-26T05:54:41.4097802Z Takes in optional filter values and prints out formatted tables of the information. 2024-06-26T05:54:41.4098346Z 2024-06-26T05:54:41.4098834Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-06-26T05:54:41.4099709Z 1.) the first table handles all tensor level information 2024-06-26T05:54:41.4100369Z 2.) the second table handles and displays all channel based information 2024-06-26T05:54:41.4100850Z 2024-06-26T05:54:41.4101321Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-06-26T05:54:41.4102575Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-06-26T05:54:41.4103681Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-06-26T05:54:41.4104345Z 2024-06-26T05:54:41.4104455Z Tensor table columns: 2024-06-26T05:54:41.4104923Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:41.4105593Z ---- --------- --------- --------- --------- --------- 2024-06-26T05:54:41.4105947Z 2024-06-26T05:54:41.4106099Z Per-Channel table columns: 2024-06-26T05:54:41.4106341Z 2024-06-26T05:54:41.4106644Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:41.4107377Z ---- --------- ------- --------- --------- --------- --------- 2024-06-26T05:54:41.4107752Z 2024-06-26T05:54:41.4107839Z Args: 2024-06-26T05:54:41.4108334Z feature_filter (str, optional): Filters the features presented to only those that 2024-06-26T05:54:41.4108999Z contain this filter substring 2024-06-26T05:54:41.4109491Z Default = "", results in all the features being printed 2024-06-26T05:54:41.4110226Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:41.4111102Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:41.4111611Z 2024-06-26T05:54:41.4111725Z Example Use: 2024-06-26T05:54:41.4112039Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:41.4112579Z >>> mod_report_visualizer.generate_table_visualization( 2024-06-26T05:54:41.4113113Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:41.4113553Z ... module_fqn_filter = "block1" 2024-06-26T05:54:41.4113930Z ... ) 2024-06-26T05:54:41.4114329Z >>> # prints out neatly formatted table with per_channel_min info 2024-06-26T05:54:41.4115056Z >>> # for all modules in block 1 of the model 2024-06-26T05:54:41.4115392Z 2024-06-26T05:54:41.4115791Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.4116282Z 2024-06-26T05:54:41.4117678Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=533. 2024-06-26T05:54:41.4119403Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.4119909Z 2024-06-26T05:54:41.4120249Z Takes in a feature and optional module_filter and plots of the desired data. 2024-06-26T05:54:41.4120738Z 2024-06-26T05:54:41.4121180Z For per channel features, it averages the value across the channels and plots a point 2024-06-26T05:54:41.4122096Z per module. The reason for this is that for models with hundreds of channels, it can 2024-06-26T05:54:41.4123020Z be hard to differentiate one channel line from another, and so the point of generating 2024-06-26T05:54:41.4123944Z a single average point per module is to give a sense of general trends that encourage 2024-06-26T05:54:41.4124674Z further deep dives. 2024-06-26T05:54:41.4124879Z 2024-06-26T05:54:41.4124972Z Note: 2024-06-26T05:54:41.4125489Z Only features in the report that have tensor value data are plottable by this class 2024-06-26T05:54:41.4126292Z When the tensor information is plotted, it will plot: 2024-06-26T05:54:41.4126850Z idx as the x val, feature value as the y_val 2024-06-26T05:54:41.4127408Z When the channel information is plotted, it will plot: 2024-06-26T05:54:41.4128174Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-06-26T05:54:41.4129066Z The reason for this is that we want to be able to compare values across the 2024-06-26T05:54:41.4129897Z channels for same layer, and it will be hard if values are staggered by idx 2024-06-26T05:54:41.4130612Z This means each module is represented by only 1 x value 2024-06-26T05:54:41.4131094Z Args: 2024-06-26T05:54:41.4131681Z feature_filter (str): Filters the features presented to only those that 2024-06-26T05:54:41.4132278Z contain this filter substring 2024-06-26T05:54:41.4132908Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:41.4133781Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:41.4134293Z 2024-06-26T05:54:41.4134409Z Example Use: 2024-06-26T05:54:41.4134732Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:41.4135268Z >>> mod_report_visualizer.generate_plot_visualization( 2024-06-26T05:54:41.4135797Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:41.4136233Z ... module_fqn_filter = "block1" 2024-06-26T05:54:41.4136619Z ... ) 2024-06-26T05:54:41.4137004Z >>> # outputs line plot of per_channel_min information for all 2024-06-26T05:54:41.4137717Z >>> # modules in block1 of model each channel gets it's own line, 2024-06-26T05:54:41.4138441Z >>> # and it's plotted across the in-order modules on the x-axis 2024-06-26T05:54:41.4138848Z 2024-06-26T05:54:41.4139250Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.4139745Z 2024-06-26T05:54:41.4141104Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=601. 2024-06-26T05:54:41.4142847Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.4143363Z 2024-06-26T05:54:41.4143743Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-06-26T05:54:41.4144293Z 2024-06-26T05:54:41.4144391Z Note: 2024-06-26T05:54:41.4144892Z Only features in the report that have tensor value data can be viewed as a histogram 2024-06-26T05:54:41.4145822Z If you want to plot a histogram from all the channel values of a specific feature for 2024-06-26T05:54:41.4146719Z a specific model, make sure to specify both the model and the feature properly 2024-06-26T05:54:41.4147577Z in the filters and you should be able to see a distribution of the channel data 2024-06-26T05:54:41.4148111Z 2024-06-26T05:54:41.4148200Z Args: 2024-06-26T05:54:41.4148694Z feature_filter (str, optional): Filters the features presented to only those that 2024-06-26T05:54:41.4149346Z contain this filter substring 2024-06-26T05:54:41.4149834Z Default = "", results in all the features being printed 2024-06-26T05:54:41.4150564Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:41.4151433Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:41.4152242Z num_bins (int, optional): The number of bins to create the histogram with 2024-06-26T05:54:41.4152983Z Default = 10, the values will be split into 10 equal sized bins 2024-06-26T05:54:41.4153447Z 2024-06-26T05:54:41.4153560Z Example Use: 2024-06-26T05:54:41.4153832Z >>> # xdoctest: +SKIP 2024-06-26T05:54:41.4154430Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-06-26T05:54:41.4155321Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:41.4155763Z ... module_fqn_filter = "block1" 2024-06-26T05:54:41.4156149Z ... ) 2024-06-26T05:54:41.4156672Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-06-26T05:54:41.4157563Z information is gathered across all channels for all modules in block 1 for the 2024-06-26T05:54:41.4158370Z per_channel_min and is displayed in a histogram of equally sized bins 2024-06-26T05:54:41.4158842Z 2024-06-26T05:54:41.4159242Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.4159727Z 2024-06-26T05:54:41.6579118Z msg = Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2736. 2024-06-26T05:54:41.6580546Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.6581066Z 2024-06-26T05:54:41.6581385Z Gathers picklable objects from the whole group in a single process. 2024-06-26T05:54:41.6581852Z 2024-06-26T05:54:41.6582172Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-06-26T05:54:41.6582850Z object must be picklable in order to be gathered. 2024-06-26T05:54:41.6583192Z 2024-06-26T05:54:41.6583293Z Args: 2024-06-26T05:54:41.6583587Z obj (Any): Input object. Must be picklable. 2024-06-26T05:54:41.6584194Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-06-26T05:54:41.6584890Z should be correctly sized as the size of the group for this 2024-06-26T05:54:41.6585646Z collective and will contain the output. Must be ``None`` on non-dst 2024-06-26T05:54:41.6586233Z ranks. (default is ``None``) 2024-06-26T05:54:41.6587035Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0) 2024-06-26T05:54:41.6588013Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-06-26T05:54:41.6588741Z the default process group will be used. Default is ``None``. 2024-06-26T05:54:41.6589153Z 2024-06-26T05:54:41.6589255Z Returns: 2024-06-26T05:54:41.6589654Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-06-26T05:54:41.6590207Z output of the collective. 2024-06-26T05:54:41.6590447Z 2024-06-26T05:54:41.6590770Z .. note:: Note that this API differs slightly from the gather collective 2024-06-26T05:54:41.6591540Z since it does not provide an async_op handle and thus will be a blocking 2024-06-26T05:54:41.6592103Z call. 2024-06-26T05:54:41.6592262Z 2024-06-26T05:54:41.6592642Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-06-26T05:54:41.6593422Z of objects must be moved to the GPU device before communication takes 2024-06-26T05:54:41.6594069Z place. In this case, the device used is given by 2024-06-26T05:54:41.6594982Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-06-26T05:54:41.6595730Z ensure that this is set so that each rank has an individual GPU, via 2024-06-26T05:54:41.6596302Z ``torch.cuda.set_device()``. 2024-06-26T05:54:41.6596568Z 2024-06-26T05:54:41.6596668Z .. warning:: 2024-06-26T05:54:41.6597105Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-06-26T05:54:41.6597832Z known to be insecure. It is possible to construct malicious pickle data 2024-06-26T05:54:41.6598602Z which will execute arbitrary code during unpickling. Only call this 2024-06-26T05:54:41.6599181Z function with data you trust. 2024-06-26T05:54:41.6599438Z 2024-06-26T05:54:41.6599552Z .. warning:: 2024-06-26T05:54:41.6599991Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-06-26T05:54:41.6600922Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-06-26T05:54:41.6601762Z pickled. Please consider using :func:`gather` instead. 2024-06-26T05:54:41.6602139Z 2024-06-26T05:54:41.6602237Z Example:: 2024-06-26T05:54:41.6602573Z >>> # xdoctest: +SKIP("need process group init") 2024-06-26T05:54:41.6603153Z >>> # Note: Process group initialization omitted on each rank. 2024-06-26T05:54:41.6603689Z >>> import torch.distributed as dist 2024-06-26T05:54:41.6604112Z >>> # Assumes world_size of 3. 2024-06-26T05:54:41.6604612Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-06-26T05:54:41.6605159Z >>> output = [None for _ in gather_objects] 2024-06-26T05:54:41.6605594Z >>> dist.gather_object( 2024-06-26T05:54:41.6605968Z ... gather_objects[dist.get_rank()], 2024-06-26T05:54:41.6606435Z ... output if dist.get_rank() == 0 else None, 2024-06-26T05:54:41.6606966Z ... dst=0 2024-06-26T05:54:41.6607239Z ... ) 2024-06-26T05:54:41.6607472Z >>> # On rank 0 2024-06-26T05:54:41.6607753Z >>> output 2024-06-26T05:54:41.6608070Z ['foo', 12, {1: 2}] 2024-06-26T05:54:41.6608269Z 2024-06-26T05:54:41.6608656Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.6609152Z 2024-06-26T05:54:41.6747025Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2024-06-26T05:54:41.6748533Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.6749058Z 2024-06-26T05:54:41.6749202Z Module ``torch.distributed.launch``. 2024-06-26T05:54:41.6749507Z 2024-06-26T05:54:41.6749842Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-06-26T05:54:41.6750531Z training processes on each of the training nodes. 2024-06-26T05:54:41.6750874Z 2024-06-26T05:54:41.6750992Z .. warning:: 2024-06-26T05:54:41.6751171Z 2024-06-26T05:54:41.6751602Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-06-26T05:54:41.6752127Z 2024-06-26T05:54:41.6752537Z The utility can be used for single-node distributed training, in which one or 2024-06-26T05:54:41.6753352Z more processes per node will be spawned. The utility can be used for either 2024-06-26T05:54:41.6754144Z CPU training or GPU training. If the utility is used for GPU training, 2024-06-26T05:54:41.6755092Z each distributed process will be operating on a single GPU. This can achieve 2024-06-26T05:54:41.6755963Z well-improved single-node training performance. It can also be used in 2024-06-26T05:54:41.6756839Z multi-node distributed training, by spawning up multiple processes on each node 2024-06-26T05:54:41.6757728Z for well-improved multi-node distributed training performance as well. 2024-06-26T05:54:41.6758512Z This will especially be beneficial for systems with multiple Infiniband 2024-06-26T05:54:41.6759379Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-06-26T05:54:41.6760029Z aggregated communication bandwidth. 2024-06-26T05:54:41.6760305Z 2024-06-26T05:54:41.6760698Z In both cases of single-node distributed training or multi-node distributed 2024-06-26T05:54:41.6761583Z training, this utility will launch the given number of processes per node 2024-06-26T05:54:41.6762454Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-06-26T05:54:41.6763257Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-06-26T05:54:41.6764010Z and each process will be operating on a single GPU from *GPU 0 to 2024-06-26T05:54:41.6764593Z GPU (nproc_per_node - 1)*. 2024-06-26T05:54:41.6764829Z 2024-06-26T05:54:41.6764945Z **How to use this module:** 2024-06-26T05:54:41.6765168Z 2024-06-26T05:54:41.6765411Z 1. Single-Node multi-process distributed training 2024-06-26T05:54:41.6765763Z 2024-06-26T05:54:41.6765861Z :: 2024-06-26T05:54:41.6766150Z 2024-06-26T05:54:41.6766520Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-06-26T05:54:41.6767314Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-06-26T05:54:41.6767944Z arguments of your training script) 2024-06-26T05:54:41.6768275Z 2024-06-26T05:54:41.6768608Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-06-26T05:54:41.6769055Z 2024-06-26T05:54:41.6769060Z 2024-06-26T05:54:41.6769248Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-06-26T05:54:41.6769597Z 2024-06-26T05:54:41.6769699Z :: 2024-06-26T05:54:41.6769827Z 2024-06-26T05:54:41.6770192Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-06-26T05:54:41.6770937Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-06-26T05:54:41.6771647Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-06-26T05:54:41.6772363Z and all other arguments of your training script) 2024-06-26T05:54:41.6772745Z 2024-06-26T05:54:41.6772835Z Node 2: 2024-06-26T05:54:41.6772971Z 2024-06-26T05:54:41.6773074Z :: 2024-06-26T05:54:41.6773200Z 2024-06-26T05:54:41.6773579Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-06-26T05:54:41.6774304Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-06-26T05:54:41.6775009Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-06-26T05:54:41.6775653Z and all other arguments of your training script) 2024-06-26T05:54:41.6776015Z 2024-06-26T05:54:41.6776238Z 3. To look up what optional arguments this module offers: 2024-06-26T05:54:41.6776631Z 2024-06-26T05:54:41.6776720Z :: 2024-06-26T05:54:41.6776845Z 2024-06-26T05:54:41.6777071Z python -m torch.distributed.launch --help 2024-06-26T05:54:41.6777398Z 2024-06-26T05:54:41.6777402Z 2024-06-26T05:54:41.6777531Z **Important Notices:** 2024-06-26T05:54:41.6777737Z 2024-06-26T05:54:41.6778031Z 1. This utility and multi-process distributed (single-node or 2024-06-26T05:54:41.6778817Z multi-node) GPU training currently only achieves the best performance using 2024-06-26T05:54:41.6779651Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-06-26T05:54:41.6780255Z use for GPU training. 2024-06-26T05:54:41.6780465Z 2024-06-26T05:54:41.6780817Z 2. In your training program, you must parse the command-line argument: 2024-06-26T05:54:41.6781640Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-06-26T05:54:41.6782411Z If your training program uses GPUs, you should ensure that your code only 2024-06-26T05:54:41.6783159Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-06-26T05:54:41.6783596Z 2024-06-26T05:54:41.6783732Z Parsing the local_rank argument 2024-06-26T05:54:41.6783982Z 2024-06-26T05:54:41.6784082Z :: 2024-06-26T05:54:41.6784208Z 2024-06-26T05:54:41.6784321Z >>> # xdoctest: +SKIP 2024-06-26T05:54:41.6784652Z >>> import argparse 2024-06-26T05:54:41.6785016Z >>> parser = argparse.ArgumentParser() 2024-06-26T05:54:41.6785631Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-06-26T05:54:41.6786179Z >>> args = parser.parse_args() 2024-06-26T05:54:41.6786443Z 2024-06-26T05:54:41.6786603Z Set your device to local rank using either 2024-06-26T05:54:41.6786911Z 2024-06-26T05:54:41.6786999Z :: 2024-06-26T05:54:41.6787138Z 2024-06-26T05:54:41.6787406Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-06-26T05:54:41.6787836Z 2024-06-26T05:54:41.6787934Z or 2024-06-26T05:54:41.6788057Z 2024-06-26T05:54:41.6788144Z :: 2024-06-26T05:54:41.6788279Z 2024-06-26T05:54:41.6788441Z >>> with torch.cuda.device(args.local_rank): 2024-06-26T05:54:41.6788887Z >>> # your code to run 2024-06-26T05:54:41.6789207Z >>> ... 2024-06-26T05:54:41.6789381Z 2024-06-26T05:54:41.6789502Z .. versionchanged:: 2.0.0 2024-06-26T05:54:41.6789783Z 2024-06-26T05:54:41.6790182Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-06-26T05:54:41.6791086Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-06-26T05:54:41.6791834Z previously used underscored ``--local_rank``. 2024-06-26T05:54:41.6792180Z 2024-06-26T05:54:41.6792499Z For backward compatibility, it may be necessary for users to handle both 2024-06-26T05:54:41.6793418Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-06-26T05:54:41.6794295Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-06-26T05:54:41.6795247Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-06-26T05:54:41.6796139Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-06-26T05:54:41.6796865Z including ``"--local-rank"`` should be sufficient. 2024-06-26T05:54:41.6797369Z 2024-06-26T05:54:41.6797698Z 3. In your training program, you are supposed to call the following function 2024-06-26T05:54:41.6798529Z at the beginning to start the distributed backend. It is strongly recommended 2024-06-26T05:54:41.6799345Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-06-26T05:54:41.6800069Z but ``env://`` is the one that is officially supported by this module. 2024-06-26T05:54:41.6800518Z 2024-06-26T05:54:41.6800608Z :: 2024-06-26T05:54:41.6800739Z 2024-06-26T05:54:41.6801148Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-06-26T05:54:41.6801818Z >>> init_method='env://') 2024-06-26T05:54:41.6802169Z 2024-06-26T05:54:41.6802497Z 4. In your training program, you can either use regular distributed functions 2024-06-26T05:54:41.6803324Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-06-26T05:54:41.6804102Z training program uses GPUs for training and you would like to use 2024-06-26T05:54:41.6804782Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-06-26T05:54:41.6805323Z here is how to configure it. 2024-06-26T05:54:41.6805557Z 2024-06-26T05:54:41.6805662Z :: 2024-06-26T05:54:41.6805790Z 2024-06-26T05:54:41.6806046Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-06-26T05:54:41.6806669Z >>> device_ids=[args.local_rank], 2024-06-26T05:54:41.6807236Z >>> output_device=args.local_rank) 2024-06-26T05:54:41.6807605Z 2024-06-26T05:54:41.6807928Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-06-26T05:54:41.6808754Z that your code will be operating on. This is generally the local rank of the 2024-06-26T05:54:41.6809572Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-06-26T05:54:41.6810361Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-06-26T05:54:41.6810902Z utility 2024-06-26T05:54:41.6811047Z 2024-06-26T05:54:41.6811394Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-06-26T05:54:41.6812199Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-06-26T05:54:41.6813002Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-06-26T05:54:41.6813794Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-06-26T05:54:41.6814498Z will not pass ``--local-rank`` when you specify this flag. 2024-06-26T05:54:41.6814883Z 2024-06-26T05:54:41.6814982Z .. warning:: 2024-06-26T05:54:41.6815149Z 2024-06-26T05:54:41.6815423Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-06-26T05:54:41.6816187Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-06-26T05:54:41.6816764Z write to a networked filesystem. See 2024-06-26T05:54:41.6817340Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-06-26T05:54:41.6818136Z how things can go wrong if you don't do this correctly. 2024-06-26T05:54:41.6818518Z 2024-06-26T05:54:41.6818569Z 2024-06-26T05:54:41.6818573Z 2024-06-26T05:54:41.6818578Z 2024-06-26T05:54:41.6818985Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.6819470Z 2024-06-26T05:54:41.7332472Z msg = Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2024-06-26T05:54:41.7333946Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.7334472Z 2024-06-26T05:54:41.7334826Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-06-26T05:54:41.7335508Z Needs to be called on all ranks in an SPMD fashion. 2024-06-26T05:54:41.7335860Z 2024-06-26T05:54:41.7335961Z Args: 2024-06-26T05:54:41.7336640Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-06-26T05:54:41.7337415Z of shards that represent the local shards on this rank. 2024-06-26T05:54:41.7338131Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-06-26T05:54:41.7338758Z shape of the overall sharded tensor. 2024-06-26T05:54:41.7339085Z 2024-06-26T05:54:41.7339182Z Keyword args: 2024-06-26T05:54:41.7339694Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-06-26T05:54:41.7340357Z the default process group will be used. 2024-06-26T05:54:41.7340906Z init_rrefs (bool, optional): Whether or not to initialize 2024-06-26T05:54:41.7341585Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-06-26T05:54:41.7342303Z Need to initialize the RPC Framework if specified as ``True``. 2024-06-26T05:54:41.7342832Z Default: ``False``. 2024-06-26T05:54:41.7343054Z 2024-06-26T05:54:41.7343179Z Returns: 2024-06-26T05:54:41.7343514Z A :class:`ShardedTensor` object handle on this rank 2024-06-26T05:54:41.7343883Z 2024-06-26T05:54:41.7343888Z 2024-06-26T05:54:41.7343980Z Examples: 2024-06-26T05:54:41.7344477Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-06-26T05:54:41.7345233Z each shard have a (5, 5) local tensor, we can do it like below: 2024-06-26T05:54:41.7345662Z 2024-06-26T05:54:41.7345756Z on rank 0: 2024-06-26T05:54:41.7346074Z >>> # xdoctest: +SKIP("not distributed") 2024-06-26T05:54:41.7346528Z >>> local_shard_metadata = ShardMetadata( 2024-06-26T05:54:41.7346959Z >>> shard_offsets=[0, 0], 2024-06-26T05:54:41.7347329Z >>> shard_lengths=[5, 5], 2024-06-26T05:54:41.7347695Z >>> placement="rank:0/cuda:0" 2024-06-26T05:54:41.7348057Z >>> ) 2024-06-26T05:54:41.7348455Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-06-26T05:54:41.7349121Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-06-26T05:54:41.7349546Z 2024-06-26T05:54:41.7349643Z on rank 1: 2024-06-26T05:54:41.7349959Z >>> # xdoctest: +SKIP("not distributed") 2024-06-26T05:54:41.7350418Z >>> local_shard_metadata = ShardMetadata( 2024-06-26T05:54:41.7350846Z >>> shard_offsets=[5, 0], 2024-06-26T05:54:41.7351211Z >>> shard_lengths=[5, 5], 2024-06-26T05:54:41.7351573Z >>> placement="rank:1/cuda:1" 2024-06-26T05:54:41.7351938Z >>> ) 2024-06-26T05:54:41.7352339Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-06-26T05:54:41.7353008Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-06-26T05:54:41.7353416Z 2024-06-26T05:54:41.7353817Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.7354312Z 2024-06-26T05:54:41.7434079Z msg = Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784. 2024-06-26T05:54:41.7435859Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.7436454Z 2024-06-26T05:54:41.7436805Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-06-26T05:54:41.7437439Z size and sharding spec on each rank. 2024-06-26T05:54:41.7437735Z 2024-06-26T05:54:41.7437822Z Args: 2024-06-26T05:54:41.7438264Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-06-26T05:54:41.7439058Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-06-26T05:54:41.7439996Z The specification describing how to shard the Tensor. 2024-06-26T05:54:41.7440785Z global_size (Sequence[int]): Size of the sharded tensor. 2024-06-26T05:54:41.7441891Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-06-26T05:54:41.7442822Z Default: None 2024-06-26T05:54:41.7443366Z init_rrefs (bool, optional): Whether or not to initialize 2024-06-26T05:54:41.7444042Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-06-26T05:54:41.7444743Z Need to initialize the RPC Framework if specified as ``True``. 2024-06-26T05:54:41.7445294Z Default: ``False``. 2024-06-26T05:54:41.7445516Z 2024-06-26T05:54:41.7445625Z Returns: 2024-06-26T05:54:41.7446099Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-06-26T05:54:41.7446751Z tensor stored in the current rank. 2024-06-26T05:54:41.7447052Z 2024-06-26T05:54:41.7447162Z Examples: 2024-06-26T05:54:41.7447415Z >>> # xdoctest: +SKIP 2024-06-26T05:54:41.7447818Z >>> # All tensors below are of torch.int64 type. 2024-06-26T05:54:41.7448313Z >>> # We have 2 process groups, 2 ranks. 2024-06-26T05:54:41.7448852Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-06-26T05:54:41.7449530Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-06-26T05:54:41.7450071Z >>> local_tensor 2024-06-26T05:54:41.7450373Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-06-26T05:54:41.7450764Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-06-26T05:54:41.7451143Z >>> sharding_dim = 0 2024-06-26T05:54:41.7451495Z >>> sharding_spec = ChunkShardingSpec( 2024-06-26T05:54:41.7451926Z dim=sharding_dim, 2024-06-26T05:54:41.7452278Z placements=[ 2024-06-26T05:54:41.7452592Z "rank:0/cuda:0", 2024-06-26T05:54:41.7452946Z "rank:1/cuda:1", 2024-06-26T05:54:41.7453281Z ], 2024-06-26T05:54:41.7453520Z ) 2024-06-26T05:54:41.7453999Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-06-26T05:54:41.7454604Z >>> st 2024-06-26T05:54:41.7454844Z ShardedTensor( 2024-06-26T05:54:41.7455153Z ShardedTensorMetadata( 2024-06-26T05:54:41.7455511Z shards_metadata=[ 2024-06-26T05:54:41.7456083Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-06-26T05:54:41.7456945Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-06-26T05:54:41.7457557Z ], 2024-06-26T05:54:41.7457839Z size=torch.Size([2, 4]) 2024-06-26T05:54:41.7458183Z ) 2024-06-26T05:54:41.7458426Z >>> st.local_tensor() 2024-06-26T05:54:41.7458739Z tensor([1, 2, 3, 4]) # Rank 0 2024-06-26T05:54:41.7459103Z tensor([3, 4, 5, 6]) # Rank 1 2024-06-26T05:54:41.7459360Z 2024-06-26T05:54:41.7459722Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-06-26T05:54:41.7460586Z rank validations, and we only validate the local shard on the current rank. 2024-06-26T05:54:41.7461387Z We fully rely on the user to ensure local tensor is sharded based on the 2024-06-26T05:54:41.7461976Z sharding spec. 2024-06-26T05:54:41.7462176Z 2024-06-26T05:54:41.7462613Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.7463168Z 2024-06-26T05:54:41.7464249Z msg = Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023. 2024-06-26T05:54:41.7465728Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.7466249Z 2024-06-26T05:54:41.7466591Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-06-26T05:54:41.7467210Z single local shard. 2024-06-26T05:54:41.7467395Z 2024-06-26T05:54:41.7467766Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-06-26T05:54:41.7468545Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-06-26T05:54:41.7469169Z we swap local shards directly. 2024-06-26T05:54:41.7469776Z For more generic cases, we merge different shards across different ranks and split 2024-06-26T05:54:41.7470701Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-06-26T05:54:41.7471230Z 2024-06-26T05:54:41.7471317Z Args: 2024-06-26T05:54:41.7471832Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-06-26T05:54:41.7472580Z specification describing how the tensor is sharded. 2024-06-26T05:54:41.7472964Z 2024-06-26T05:54:41.7473055Z Returns: 2024-06-26T05:54:41.7473474Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-06-26T05:54:41.7473906Z 2024-06-26T05:54:41.7474011Z Examples: 2024-06-26T05:54:41.7474261Z >>> # xdoctest: +SKIP 2024-06-26T05:54:41.7474761Z >>> # We have 2 process groups, 2 ranks. 2024-06-26T05:54:41.7475320Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-06-26T05:54:41.7475861Z >>> tensor = torch.stack([tensor, tensor]) 2024-06-26T05:54:41.7476278Z >>> tensor 2024-06-26T05:54:41.7476601Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-06-26T05:54:41.7477063Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-06-26T05:54:41.7477525Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-06-26T05:54:41.7477989Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-06-26T05:54:41.7478411Z >>> sharding_dim = 0 2024-06-26T05:54:41.7478751Z >>> spec = ChunkShardingSpec( 2024-06-26T05:54:41.7479126Z dim=sharding_dim, 2024-06-26T05:54:41.7479452Z placements=[ 2024-06-26T05:54:41.7479772Z "rank:0/cuda:0", 2024-06-26T05:54:41.7480121Z "rank:1/cuda:1", 2024-06-26T05:54:41.7480454Z "rank:2/cuda:2", 2024-06-26T05:54:41.7480798Z "rank:3/cuda:3", 2024-06-26T05:54:41.7481211Z ], 2024-06-26T05:54:41.7481457Z ) 2024-06-26T05:54:41.7481731Z >>> current_offsets = [0] * 2 2024-06-26T05:54:41.7482115Z >>> current_offsets[0] = rank * 2 2024-06-26T05:54:41.7482515Z >>> shard_metadata = ShardMetadata( 2024-06-26T05:54:41.7482998Z shard_offsets=copy.deepcopy(current_offsets), 2024-06-26T05:54:41.7483479Z shard_sizes=tensor.size(), 2024-06-26T05:54:41.7483897Z placement=spec.placements[rank], 2024-06-26T05:54:41.7484304Z ) 2024-06-26T05:54:41.7484558Z >>> local_shards = [ 2024-06-26T05:54:41.7484854Z Shard( 2024-06-26T05:54:41.7485137Z tensor=tensor, 2024-06-26T05:54:41.7485499Z metadata=shard_metadata, 2024-06-26T05:54:41.7485867Z ) 2024-06-26T05:54:41.7486118Z ] 2024-06-26T05:54:41.7486561Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-06-26T05:54:41.7487126Z >>> sharding_dim = 1 2024-06-26T05:54:41.7487501Z >>> resharding_spec = ChunkShardingSpec( 2024-06-26T05:54:41.7487929Z dim=sharding_dim, 2024-06-26T05:54:41.7488258Z placements=[ 2024-06-26T05:54:41.7488576Z "rank:0/cuda:0", 2024-06-26T05:54:41.7488925Z "rank:1/cuda:1", 2024-06-26T05:54:41.7489348Z "rank:2/cuda:2", 2024-06-26T05:54:41.7489698Z "rank:3/cuda:3", 2024-06-26T05:54:41.7490041Z ], 2024-06-26T05:54:41.7490351Z ) 2024-06-26T05:54:41.7490635Z >>> st.reshard(resharding_spec) 2024-06-26T05:54:41.7491059Z >>> tensor = st.local_shards()[0].tensor 2024-06-26T05:54:41.7491451Z >>> tensor 2024-06-26T05:54:41.7491812Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-06-26T05:54:41.7492355Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-06-26T05:54:41.7492884Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-06-26T05:54:41.7493433Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-06-26T05:54:41.7493790Z 2024-06-26T05:54:41.7494209Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.7494698Z 2024-06-26T05:54:41.7571287Z msg = Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2024-06-26T05:54:41.7572715Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.7573267Z 2024-06-26T05:54:41.7573548Z Representation of a sharding plan, describes how to shard a module 2024-06-26T05:54:41.7574379Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-06-26T05:54:41.7575308Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-06-26T05:54:41.7576210Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-06-26T05:54:41.7576746Z 2024-06-26T05:54:41.7576837Z Args: 2024-06-26T05:54:41.7577347Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-06-26T05:54:41.7578089Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-06-26T05:54:41.7578944Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-06-26T05:54:41.7579861Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-06-26T05:54:41.7580531Z a parameter to a `ShardingSpec`. 2024-06-26T05:54:41.7581209Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-06-26T05:54:41.7581860Z to a `Sharder` object. 2024-06-26T05:54:41.7582565Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-06-26T05:54:41.7583604Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-06-26T05:54:41.7584480Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-06-26T05:54:41.7585098Z Default: `None` 2024-06-26T05:54:41.7585632Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-06-26T05:54:41.7586553Z a module's sharded output to be returned as a Tensor from its local shards to 2024-06-26T05:54:41.7587400Z ensure further processing in a data parallel fashion. ("" in list means the 2024-06-26T05:54:41.7587989Z root module). 2024-06-26T05:54:41.7588364Z Default: None 2024-06-26T05:54:41.7588651Z Example: 2024-06-26T05:54:41.7589189Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-06-26T05:54:41.7590176Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-06-26T05:54:41.7590748Z 2024-06-26T05:54:41.7590980Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-06-26T05:54:41.7591484Z >>> class MyModule(nn.Module): 2024-06-26T05:54:41.7591862Z >>> def __init__(self): 2024-06-26T05:54:41.7592221Z >>> super().__init__() 2024-06-26T05:54:41.7592582Z >>> self.fc1 = nn.Linear() 2024-06-26T05:54:41.7592975Z >>> self.gelu = nn.GELU() 2024-06-26T05:54:41.7593370Z >>> self.fc2 = nn.Linear() 2024-06-26T05:54:41.7593833Z >>> self.relu = nn.Linear() 2024-06-26T05:54:41.7594190Z >>> 2024-06-26T05:54:41.7594456Z >>> def forward(self, input): 2024-06-26T05:54:41.7595185Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-06-26T05:54:41.7595578Z 2024-06-26T05:54:41.7595583Z 2024-06-26T05:54:41.7595750Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-06-26T05:54:41.7596220Z >>> sharding_plan = ShardingPlan( 2024-06-26T05:54:41.7596605Z >>> plan={ 2024-06-26T05:54:41.7596888Z >>> "fc1.weight": spec1, 2024-06-26T05:54:41.7597271Z >>> "fc2.weight": spec2 2024-06-26T05:54:41.7597622Z >>> }, 2024-06-26T05:54:41.7597876Z >>> output_plan={ 2024-06-26T05:54:41.7598200Z >>> "fc2": output_spec 2024-06-26T05:54:41.7598543Z >>> }, 2024-06-26T05:54:41.7598905Z >>> return_local_tensor=["fc2"] 2024-06-26T05:54:41.7599278Z >>> ) 2024-06-26T05:54:41.7599510Z 2024-06-26T05:54:41.7599935Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.7600426Z 2024-06-26T05:54:41.8543321Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/experimental/local_map.py line=30. 2024-06-26T05:54:41.8544774Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:41.8545347Z 2024-06-26T05:54:41.8545703Z ``local_map`` is an experimental API that allows users to apply on :class:`DTensors` 2024-06-26T05:54:41.8546517Z a function that is written to be applied on :class:`~torch.Tensors`. 2024-06-26T05:54:41.8546968Z 2024-06-26T05:54:41.8547069Z Args: 2024-06-26T05:54:41.8547475Z func (Callable): the function to be applied on each local shard of 2024-06-26T05:54:41.8548034Z :class:`DTensor`s. 2024-06-26T05:54:41.8548544Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-06-26T05:54:41.8549408Z the desired placements of the :class:`DTensor`s in `func`'s flattened output. 2024-06-26T05:54:41.8550253Z If the flattened `output` is a single value, the `out_placements` should be 2024-06-26T05:54:41.8551064Z of type `PlacementType`. Otherwise if the flattened `output` has multiple 2024-06-26T05:54:41.8551865Z values, the `out_placements` should be a tuple of `PlacementType` values 1:1 2024-06-26T05:54:41.8552508Z mapping to the flattened `output`. 2024-06-26T05:54:41.8553085Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-06-26T05:54:41.8553884Z placements (a `Tuple[Placement]` value). For non-:class:`Tensor` output, 2024-06-26T05:54:41.8554513Z the `PlacementType` should be `None`. 2024-06-26T05:54:41.8555336Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-06-26T05:54:41.8556176Z in. In this case, even if `out_placements` is not `None`, the result function 2024-06-26T05:54:41.8556975Z should ignore the desired placements because the application is not on 2024-06-26T05:54:41.8557561Z :class:`DTensors`. 2024-06-26T05:54:41.8557995Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-06-26T05:54:41.8558766Z the required placements of the :class:`DTensor`s in `func`'s flattened input. 2024-06-26T05:54:41.8559567Z If `in_placements` is specified, `local_map` would examine whether the 2024-06-26T05:54:41.8560331Z placements of each :class:`DTensor` argument is the same as the required 2024-06-26T05:54:41.8561133Z placements or not. If the placements are not the same and 2024-06-26T05:54:41.8561865Z `redistribute_inputs` is `False`, an exception will be raised. Otherwise if 2024-06-26T05:54:41.8562684Z `redistribute_inputs` is `True`, the argument will be first redistributed to 2024-06-26T05:54:41.8563505Z the required sharding placements before passing its local tensor to `func`. 2024-06-26T05:54:41.8564475Z The only exception is when required placements are not `None` and the 2024-06-26T05:54:41.8565281Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-06-26T05:54:41.8566135Z will be skipped and the argument will be directly passed to `func`. 2024-06-26T05:54:41.8566890Z If `in_placements` is `None`, no placements examination will be performed. 2024-06-26T05:54:41.8567478Z Default: `None` 2024-06-26T05:54:41.8567849Z device_mesh (:class:`DeviceMesh`, optional): 2024-06-26T05:54:41.8568456Z the device mesh that all the :class:`DTensor`s are placed on. If not 2024-06-26T05:54:41.8569303Z specified, this will be inferred from the input :class:`DTensor`s' device 2024-06-26T05:54:41.8570104Z mesh. `local_map` requires every :class:`DTensor`s to be placed on the same 2024-06-26T05:54:41.8570711Z device mesh. Default: `None`. 2024-06-26T05:54:41.8571236Z redistribute_inputs (bool, optional): 2024-06-26T05:54:41.8571888Z the bool value indicating whether to reshard the input :class:`DTensor`s when 2024-06-26T05:54:41.8572719Z their placements are different from the required input placements. If this 2024-06-26T05:54:41.8573538Z value is `False` and some :class:`DTensor` input has a different placement, 2024-06-26T05:54:41.8574212Z an exception will be raised. Default: `False`. 2024-06-26T05:54:41.8574555Z 2024-06-26T05:54:41.8574661Z Returns: 2024-06-26T05:54:41.8575145Z A `Callable` that applies `func` to each local shard of the input :class:`DTensor` 2024-06-26T05:54:41.8575984Z and returns a :class:`DTensor` constructed from the return value of `func`. 2024-06-26T05:54:41.8576468Z 2024-06-26T05:54:41.8576568Z Raises: 2024-06-26T05:54:41.8577051Z AssertionError: If the input :class:`DTensor`s are not placed on the same device 2024-06-26T05:54:41.8577908Z mesh, or if they are placed on a different device mesh than the `device_mesh` 2024-06-26T05:54:41.8578523Z argument passed in. 2024-06-26T05:54:41.8578731Z 2024-06-26T05:54:41.8579153Z AssertionError: For any non-:class:`DTensor` output, we require its corresponding 2024-06-26T05:54:41.8580028Z output placement in `out_placements` be `None`. An AssertionError will be raised 2024-06-26T05:54:41.8580661Z if this is not the case. 2024-06-26T05:54:41.8580890Z 2024-06-26T05:54:41.8581241Z ValueError: If `redistribute_inputs=False` but the input :class:`DTensor` needs 2024-06-26T05:54:41.8581920Z a redistribution according to `in_placements`. 2024-06-26T05:54:41.8582276Z 2024-06-26T05:54:41.8582367Z Example: 2024-06-26T05:54:41.8582653Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:41.8583108Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-06-26T05:54:41.8583601Z >>> partial_sum_tensor = torch.mm(W, X) 2024-06-26T05:54:41.8584230Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-06-26T05:54:41.8584831Z >>> return reduced_tensor 2024-06-26T05:54:41.8585180Z >>> 2024-06-26T05:54:41.8585483Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-06-26T05:54:41.8585967Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-06-26T05:54:41.8586406Z >>> Y = torch.mm(W, X) 2024-06-26T05:54:41.8586959Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-06-26T05:54:41.8587690Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-06-26T05:54:41.8588201Z >>> 2024-06-26T05:54:41.8588704Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-06-26T05:54:41.8589383Z >>> local_mm_allreduce_forward = local_map( 2024-06-26T05:54:41.8589821Z >>> mm_allreduce_forward, 2024-06-26T05:54:41.8590210Z >>> out_placements=[Replicate()], 2024-06-26T05:54:41.8590641Z >>> in_placements=[col_wise, row_wise], 2024-06-26T05:54:41.8591072Z >>> device_mesh=device_mesh, 2024-06-26T05:54:41.8591440Z >>> ) 2024-06-26T05:54:41.8591715Z >>> 2024-06-26T05:54:41.8592285Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-06-26T05:54:41.8593275Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-06-26T05:54:41.8594264Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-06-26T05:54:41.8595143Z 2024-06-26T05:54:41.8595398Z NOTE: This API is currently experimental and subject to change 2024-06-26T05:54:41.8595824Z 2024-06-26T05:54:41.8596219Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:41.8596709Z 2024-06-26T05:54:42.0128099Z msg = Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2024-06-26T05:54:42.0129941Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.0130466Z 2024-06-26T05:54:42.0130648Z Run post-localSGD algorithm. 2024-06-26T05:54:42.0130904Z 2024-06-26T05:54:42.0131283Z This DDP communication hook is used for running post-localSGD algorithm, 2024-06-26T05:54:42.0131962Z by combining with a model averaging component (e.g., 2024-06-26T05:54:42.0132767Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-06-26T05:54:42.0133498Z that runs after the optimizer step. 2024-06-26T05:54:42.0133787Z 2024-06-26T05:54:42.0133873Z Args: 2024-06-26T05:54:42.0134361Z state (PostLocalSGDState): State information to run post-localSGD. 2024-06-26T05:54:42.0135186Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-06-26T05:54:42.0136404Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-06-26T05:54:42.0137492Z Note that since DDP comm hook only supports single process single device mode, 2024-06-26T05:54:42.0138206Z only exactly one tensor is stored in this bucket. 2024-06-26T05:54:42.0138576Z 2024-06-26T05:54:42.0138670Z Returns: 2024-06-26T05:54:42.0139146Z Future handler of the communication, which updates the gradients in place. 2024-06-26T05:54:42.0139634Z 2024-06-26T05:54:42.0139763Z Example:: 2024-06-26T05:54:42.0140013Z >>> # xdoctest: +SKIP 2024-06-26T05:54:42.0140552Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-06-26T05:54:42.0141198Z start_localSGD_iter=10) 2024-06-26T05:54:42.0141731Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:42.0142595Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-06-26T05:54:42.0143713Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-06-26T05:54:42.0144359Z 2024-06-26T05:54:42.0144746Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.0145246Z 2024-06-26T05:54:42.0174959Z msg = Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2024-06-26T05:54:42.0176480Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.0177027Z 2024-06-26T05:54:42.0177157Z Implement PowerSGD algorithm. 2024-06-26T05:54:42.0177401Z 2024-06-26T05:54:42.0177705Z This DDP communication hook implements PowerSGD gradient compression 2024-06-26T05:54:42.0178467Z algorithm described in the `paper `_. 2024-06-26T05:54:42.0179255Z Once gradient tensors are aggregated across all workers, this hook applies 2024-06-26T05:54:42.0179858Z compression as follows: 2024-06-26T05:54:42.0180068Z 2024-06-26T05:54:42.0180796Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-06-26T05:54:42.0181713Z 2024-06-26T05:54:42.0182299Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-06-26T05:54:42.0183106Z 2024-06-26T05:54:42.0183675Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-06-26T05:54:42.0184402Z 2024-06-26T05:54:42.0184543Z 2. Handles uncompressed tensors: 2024-06-26T05:54:42.0184799Z 2024-06-26T05:54:42.0185490Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-06-26T05:54:42.0186347Z 2024-06-26T05:54:42.0186813Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-06-26T05:54:42.0187451Z 2024-06-26T05:54:42.0187892Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-06-26T05:54:42.0188369Z 2024-06-26T05:54:42.0188795Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-06-26T05:54:42.0189751Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-06-26T05:54:42.0190372Z 2024-06-26T05:54:42.0190564Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-06-26T05:54:42.0190925Z 2024-06-26T05:54:42.0191053Z 3.3. Allreduces Ps as a batch; 2024-06-26T05:54:42.0191314Z 2024-06-26T05:54:42.0191463Z 3.4. Orthogonalizes each P in Ps; 2024-06-26T05:54:42.0191739Z 2024-06-26T05:54:42.0192009Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-06-26T05:54:42.0192454Z 2024-06-26T05:54:42.0192576Z 3.6. Allreduces Qs as a batch; 2024-06-26T05:54:42.0192836Z 2024-06-26T05:54:42.0193267Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-06-26T05:54:42.0193852Z 2024-06-26T05:54:42.0194408Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-06-26T05:54:42.0195714Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-06-26T05:54:42.0196882Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-06-26T05:54:42.0197624Z 2024-06-26T05:54:42.0197724Z Args: 2024-06-26T05:54:42.0198424Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-06-26T05:54:42.0199658Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-06-26T05:54:42.0200447Z and ``min_compression_rate``. 2024-06-26T05:54:42.0201450Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-06-26T05:54:42.0202545Z Note that since DDP comm hook only supports single process single device mode, 2024-06-26T05:54:42.0203267Z only exactly one tensor is stored in this bucket. 2024-06-26T05:54:42.0203623Z 2024-06-26T05:54:42.0203729Z Returns: 2024-06-26T05:54:42.0204188Z Future handler of the communication, which updates the gradients in place. 2024-06-26T05:54:42.0204689Z 2024-06-26T05:54:42.0204794Z Example:: 2024-06-26T05:54:42.0205060Z >>> # xdoctest: +SKIP 2024-06-26T05:54:42.0205617Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-06-26T05:54:42.0206354Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-06-26T05:54:42.0206948Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-06-26T05:54:42.0207310Z 2024-06-26T05:54:42.0207698Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.0208200Z 2024-06-26T05:54:42.0213756Z msg = Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=36. 2024-06-26T05:54:42.0215406Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.0216002Z 2024-06-26T05:54:42.0216283Z Averages parameters periodically after the warm-up stage. 2024-06-26T05:54:42.0216672Z 2024-06-26T05:54:42.0217115Z This can be used for running `post-local SGD `_, 2024-06-26T05:54:42.0217875Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-06-26T05:54:42.0218599Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-06-26T05:54:42.0219070Z 2024-06-26T05:54:42.0219171Z Args: 2024-06-26T05:54:42.0219516Z period (int): The number of steps per model averaging. 2024-06-26T05:54:42.0220264Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-06-26T05:54:42.0221072Z Otherwise, only DDP needs to be used. 2024-06-26T05:54:42.0221749Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-06-26T05:54:42.0222338Z model averaging is skipped. 2024-06-26T05:54:42.0222958Z process_group: The process group to be used for all-reduce. 2024-06-26T05:54:42.0223556Z If ``None``, the default process group, which 2024-06-26T05:54:42.0224163Z is created by :func:`torch.distributed.init_process_group`, 2024-06-26T05:54:42.0224749Z will be used. (default: ``None``) 2024-06-26T05:54:42.0225066Z 2024-06-26T05:54:42.0225182Z Example:: 2024-06-26T05:54:42.0225328Z 2024-06-26T05:54:42.0225487Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:42.0225915Z >>> import torch 2024-06-26T05:54:42.0226247Z >>> import torch.distributed as dist 2024-06-26T05:54:42.0227172Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-06-26T05:54:42.0228626Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-06-26T05:54:42.0229470Z >>> import torch.nn as nn 2024-06-26T05:54:42.0229793Z >>> 2024-06-26T05:54:42.0230163Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-06-26T05:54:42.0230682Z >>> torch.cuda.set_device(rank) 2024-06-26T05:54:42.0231104Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-06-26T05:54:42.0231641Z >>> model = nn.parallel.DistributedDataParallel( 2024-06-26T05:54:42.0232186Z >>> module, device_ids=[rank], output_device=rank 2024-06-26T05:54:42.0232614Z >>> ) 2024-06-26T05:54:42.0233013Z >>> # Register a post-localSGD communication hook. 2024-06-26T05:54:42.0233748Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-06-26T05:54:42.0234503Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:42.0235168Z >>> 2024-06-26T05:54:42.0235695Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-06-26T05:54:42.0236458Z >>> # After 100 steps, run model averaging every 4 steps. 2024-06-26T05:54:42.0237272Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-06-26T05:54:42.0238207Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-06-26T05:54:42.0238826Z >>> for step in range(0, 200): 2024-06-26T05:54:42.0239202Z >>> optimizer.zero_grad() 2024-06-26T05:54:42.0239595Z >>> loss = loss_fn(output, labels) 2024-06-26T05:54:42.0239999Z >>> loss.backward() 2024-06-26T05:54:42.0240324Z >>> optimizer.step() 2024-06-26T05:54:42.0240809Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-06-26T05:54:42.0241657Z >>> # inter-node communication only occurs every 4 iterations after 2024-06-26T05:54:42.0242244Z >>> # the initial ``warmup_steps`` period. 2024-06-26T05:54:42.0242868Z >>> averager.average_parameters(model.parameters()) 2024-06-26T05:54:42.0243230Z 2024-06-26T05:54:42.0243632Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.0244170Z 2024-06-26T05:54:42.0245474Z msg = Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18. 2024-06-26T05:54:42.0247125Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.0247629Z 2024-06-26T05:54:42.0248065Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-06-26T05:54:42.0248668Z 2024-06-26T05:54:42.0249079Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-06-26T05:54:42.0249991Z by using different periods concurrently after the warm-up stage. 2024-06-26T05:54:42.0251094Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-06-26T05:54:42.0252349Z that supports `post-local SGD `_, which essentially only supports 2024-06-26T05:54:42.0253464Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-06-26T05:54:42.0254539Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-06-26T05:54:42.0255636Z Similarly, the process groups within this class do not have such an intra-machine process 2024-06-26T05:54:42.0256641Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-06-26T05:54:42.0257195Z 2024-06-26T05:54:42.0257283Z Args: 2024-06-26T05:54:42.0257771Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-06-26T05:54:42.0258553Z process group size, used for initializing process groups of 2024-06-26T05:54:42.0259308Z different sizes in a hierarchy to average parameters concurrently. 2024-06-26T05:54:42.0260071Z Particularly, at each iteration, there will be at most a single 2024-06-26T05:54:42.0260909Z process group that runs averaging -- the period of such group should 2024-06-26T05:54:42.0261690Z have the largest period which the current step can be divided by. 2024-06-26T05:54:42.0271466Z For example, if the dict has three keys: 2, 4, and 8, 2024-06-26T05:54:42.0272196Z then this means totally three process groups will be created to 2024-06-26T05:54:42.0272939Z average parameters every 2, 4, and 8 iterations, respectively. 2024-06-26T05:54:42.0273674Z At the 4th iteration, only the second process group will run 2024-06-26T05:54:42.0274379Z averaging, because the first process group should be a 2024-06-26T05:54:42.0275242Z subset of the second process group, and no need to execute the first 2024-06-26T05:54:42.0275903Z process group redundantly. 2024-06-26T05:54:42.0276529Z On the other hand, the third process group can only be triggered 2024-06-26T05:54:42.0277299Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-06-26T05:54:42.0278337Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-06-26T05:54:42.0279524Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-06-26T05:54:42.0280536Z If ``None``, the default process group, which is created 2024-06-26T05:54:42.0281334Z by :func:`torch.distributed.init_process_group`, will be used. 2024-06-26T05:54:42.0282075Z (default: ``None``) 2024-06-26T05:54:42.0282407Z 2024-06-26T05:54:42.0282599Z Example:: 2024-06-26T05:54:42.0282939Z >>> # xdoctest: +SKIP('undefined rank') 2024-06-26T05:54:42.0283405Z >>> from collections import OrderedDict 2024-06-26T05:54:42.0283826Z >>> import torch 2024-06-26T05:54:42.0284152Z >>> import torch.distributed as dist 2024-06-26T05:54:42.0284815Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-06-26T05:54:42.0285479Z >>> PostLocalSGDState, 2024-06-26T05:54:42.0285833Z >>> post_localSGD_hook, 2024-06-26T05:54:42.0286176Z >>> ) 2024-06-26T05:54:42.0286820Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-06-26T05:54:42.0287586Z >>> import torch.nn as nn 2024-06-26T05:54:42.0287921Z >>> 2024-06-26T05:54:42.0288375Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-06-26T05:54:42.0288892Z >>> torch.cuda.set_device(rank) 2024-06-26T05:54:42.0289339Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-06-26T05:54:42.0289884Z >>> model = nn.parallel.DistributedDataParallel( 2024-06-26T05:54:42.0290439Z >>> module, device_ids=[rank], output_device=rank 2024-06-26T05:54:42.0290867Z >>> ) 2024-06-26T05:54:42.0291260Z >>> # Register a post-localSGD communication hook. 2024-06-26T05:54:42.0292085Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-06-26T05:54:42.0292780Z >>> subgroup, _ = dist.new_subgroups() 2024-06-26T05:54:42.0293492Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-06-26T05:54:42.0294273Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:42.0294727Z >>> 2024-06-26T05:54:42.0295266Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-06-26T05:54:42.0295983Z >>> # the 16 processes every 16 iterations. 2024-06-26T05:54:42.0296545Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-06-26T05:54:42.0297273Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-06-26T05:54:42.0298200Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-06-26T05:54:42.0299181Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-06-26T05:54:42.0299934Z >>> # After 100 steps, run model averaging at two levels. 2024-06-26T05:54:42.0300429Z >>> for step in range(0, 200): 2024-06-26T05:54:42.0300819Z >>> optimizer.zero_grad() 2024-06-26T05:54:42.0301197Z >>> loss = loss_fn(output, labels) 2024-06-26T05:54:42.0301601Z >>> loss.backward() 2024-06-26T05:54:42.0301939Z >>> optimizer.step() 2024-06-26T05:54:42.0302359Z >>> # Average parameters after ``optimizer.step()``. 2024-06-26T05:54:42.0303187Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-06-26T05:54:42.0303952Z >>> averager.average_parameters(model.parameters()) 2024-06-26T05:54:42.0304320Z 2024-06-26T05:54:42.0304423Z .. warning :: 2024-06-26T05:54:42.0304966Z The last group size in the dict must be the size of the provided ``process_group``, 2024-06-26T05:54:42.0305809Z which indicates model averaging at the highest level of the hierarchy. 2024-06-26T05:54:42.0306697Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-06-26T05:54:42.0307298Z 2024-06-26T05:54:42.0307398Z .. warning :: 2024-06-26T05:54:42.0307865Z `HierarchicalModelAverager` is experimental and subject to change. 2024-06-26T05:54:42.0308320Z 2024-06-26T05:54:42.0308724Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.0309221Z 2024-06-26T05:54:42.0421987Z msg = Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2024-06-26T05:54:42.0423603Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.0424288Z 2024-06-26T05:54:42.0424689Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-06-26T05:54:42.0425923Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-06-26T05:54:42.0426494Z 2024-06-26T05:54:42.0426710Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-06-26T05:54:42.0427169Z 2024-06-26T05:54:42.0427278Z .. warning:: 2024-06-26T05:54:42.0427663Z Current implementation only supports loading Tensors. 2024-06-26T05:54:42.0428114Z 2024-06-26T05:54:42.0428266Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:42.0428727Z >>> sd = {"mode": model} 2024-06-26T05:54:42.0429155Z >>> dcp.load( 2024-06-26T05:54:42.0429468Z >>> sd, 2024-06-26T05:54:42.0429831Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-06-26T05:54:42.0430417Z >>> planner=DynamicMetaLoadPlanner(), 2024-06-26T05:54:42.0430851Z >>> checkpoint_id="path_to_model.pt" 2024-06-26T05:54:42.0431306Z >>> ) 2024-06-26T05:54:42.0431458Z 2024-06-26T05:54:42.0431926Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.0432417Z 2024-06-26T05:54:42.0433612Z msg = Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=148. 2024-06-26T05:54:42.0435362Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.0435937Z 2024-06-26T05:54:42.0436427Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-06-26T05:54:42.0437728Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-06-26T05:54:42.0438557Z metadata file, like Torch Save files. 2024-06-26T05:54:42.0438893Z 2024-06-26T05:54:42.0439139Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-06-26T05:54:42.0439611Z 2024-06-26T05:54:42.0439719Z .. warning:: 2024-06-26T05:54:42.0440103Z Current implementation only supports loading Tensors. 2024-06-26T05:54:42.0440536Z 2024-06-26T05:54:42.0440673Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:42.0441186Z >>> sd = {"mode": model} 2024-06-26T05:54:42.0441505Z >>> dcp.load( 2024-06-26T05:54:42.0441768Z >>> sd, 2024-06-26T05:54:42.0442156Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-06-26T05:54:42.0442715Z >>> planner=DynamicMetaLoadPlanner(), 2024-06-26T05:54:42.0443145Z >>> checkpoint_id="path_to_model.pt" 2024-06-26T05:54:42.0443581Z >>> ) 2024-06-26T05:54:42.0443713Z 2024-06-26T05:54:42.0444180Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.0444667Z 2024-06-26T05:54:42.0476769Z msg = Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=213. 2024-06-26T05:54:42.0478244Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.0478790Z 2024-06-26T05:54:42.0479071Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-06-26T05:54:42.0479530Z 2024-06-26T05:54:42.0479745Z This is the current recommended way to checkpoint FSDP. 2024-06-26T05:54:42.0480235Z >>> # xdoctest: +SKIP 2024-06-26T05:54:42.0480615Z >>> import torch.distributed.checkpoint as dist_cp 2024-06-26T05:54:42.0481146Z >>> # Save 2024-06-26T05:54:42.0481413Z >>> model: torch.nn.Model 2024-06-26T05:54:42.0481764Z >>> optim_params = model.parameters() 2024-06-26T05:54:42.0482236Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-06-26T05:54:42.0482675Z >>> # Save 2024-06-26T05:54:42.0483116Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-06-26T05:54:42.0483781Z >>> state_dict = { 2024-06-26T05:54:42.0484185Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-06-26T05:54:42.0484718Z >>> "model": model.state_dict() 2024-06-26T05:54:42.0485098Z >>> } 2024-06-26T05:54:42.0485365Z >>> dist_cp.save_state_dict( 2024-06-26T05:54:42.0485725Z >>> state_dict=optim_state, 2024-06-26T05:54:42.0486304Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-06-26T05:54:42.0486872Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-06-26T05:54:42.0487282Z >>> ) 2024-06-26T05:54:42.0487519Z >>> 2024-06-26T05:54:42.0487748Z >>> # Load 2024-06-26T05:54:42.0488191Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-06-26T05:54:42.0488827Z >>> model_state_dict = model_tp.state_dict() 2024-06-26T05:54:42.0489265Z >>> checkpoint = { 2024-06-26T05:54:42.0489671Z >>> "model": model_state_dict 2024-06-26T05:54:42.0490044Z >>> } 2024-06-26T05:54:42.0490313Z >>> dist_cp.load_state_dict( 2024-06-26T05:54:42.0490674Z >>> state_dict=checkpoint, 2024-06-26T05:54:42.0491190Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-06-26T05:54:42.0491772Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-06-26T05:54:42.0492187Z >>> ) 2024-06-26T05:54:42.0492529Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-06-26T05:54:42.0492977Z >>> 2024-06-26T05:54:42.0493324Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-06-26T05:54:42.0493830Z >>> model_state_dict, 2024-06-26T05:54:42.0494196Z >>> optimizer_key="optimizer", 2024-06-26T05:54:42.0494692Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-06-26T05:54:42.0495184Z >>> ) 2024-06-26T05:54:42.0495422Z >>> 2024-06-26T05:54:42.0495729Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-06-26T05:54:42.0496248Z >>> model, optim, optim_state["optimizer"] 2024-06-26T05:54:42.0496670Z >>> ) 2024-06-26T05:54:42.0496895Z >>> 2024-06-26T05:54:42.0497178Z >>> optim.load_state_dict(flattened_osd) 2024-06-26T05:54:42.0497491Z 2024-06-26T05:54:42.0497992Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.0498677Z 2024-06-26T05:54:42.0500714Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=110. 2024-06-26T05:54:42.0502101Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.0502661Z 2024-06-26T05:54:42.0503044Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-06-26T05:54:42.0503612Z 2024-06-26T05:54:42.0503998Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-06-26T05:54:42.0504559Z 2024-06-26T05:54:42.0504965Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-06-26T05:54:42.0505671Z will be visible to the whole process. 2024-06-26T05:54:42.0505956Z 2024-06-26T05:54:42.0506371Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-06-26T05:54:42.0507117Z 2024-06-26T05:54:42.0507307Z 1) set_up_planner - called on all ranks. 2024-06-26T05:54:42.0507821Z Signals the start of a checkpoint save. 2024-06-26T05:54:42.0508359Z 2024-06-26T05:54:42.0508733Z 2) create_local_plan - called on all ranks. 2024-06-26T05:54:42.0509516Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-06-26T05:54:42.0510073Z 2024-06-26T05:54:42.0510373Z 3) create_global_plan - called on the coordinator rank only. 2024-06-26T05:54:42.0511028Z Takes the SavePlan from all ranks and make any global decision. 2024-06-26T05:54:42.0511469Z 2024-06-26T05:54:42.0511643Z 4) finish_plan - called on all ranks. 2024-06-26T05:54:42.0512266Z This gives each rank a chance to adjust to global planning decisions. 2024-06-26T05:54:42.0512843Z 2024-06-26T05:54:42.0513099Z 5) resolve_data - called multiple times on each rank 2024-06-26T05:54:42.0513768Z Lookups a value on the `state_dict` for the storage layer to write. 2024-06-26T05:54:42.0514233Z 2024-06-26T05:54:42.0514793Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-06-26T05:54:42.0515625Z most changes can be expressed by changes in a single method. 2024-06-26T05:54:42.0516029Z 2024-06-26T05:54:42.0516177Z There are 3 usual patterns of extension: 2024-06-26T05:54:42.0516491Z 2024-06-26T05:54:42.0516834Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-06-26T05:54:42.0517702Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-06-26T05:54:42.0518158Z 2024-06-26T05:54:42.0518313Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:42.0518877Z >>> class RenamePlanner(DefaultSavePlanner): 2024-06-26T05:54:42.0519332Z >>> def set_up_planner( 2024-06-26T05:54:42.0519662Z >>> self, 2024-06-26T05:54:42.0519960Z >>> state_dict: STATE_DICT_TYPE, 2024-06-26T05:54:42.0520410Z >>> storage_meta: Optional[StorageMeta], 2024-06-26T05:54:42.0520857Z >>> is_coordinator: bool, 2024-06-26T05:54:42.0521300Z >>> ) -> None: 2024-06-26T05:54:42.0521624Z >>> # prefix all keys with `foo_`` 2024-06-26T05:54:42.0522352Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-06-26T05:54:42.0522937Z 2024-06-26T05:54:42.0523399Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-06-26T05:54:42.0524037Z 2024-06-26T05:54:42.0524177Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:42.0524629Z >>> class FP16Planner(DefaultSavePlanner): 2024-06-26T05:54:42.0525062Z >>> def create_local_plan(self): 2024-06-26T05:54:42.0525499Z >>> plan = super().create_local_plan() 2024-06-26T05:54:42.0525925Z >>> for p in plan: 2024-06-26T05:54:42.0526294Z >>> if p.tensor_data is not None: 2024-06-26T05:54:42.0526807Z >>> p.tensor_data.properties.dtype = torch.float16 2024-06-26T05:54:42.0527294Z >>> return plan 2024-06-26T05:54:42.0527589Z >>> 2024-06-26T05:54:42.0527852Z >>> def resolve_data(self, write_item): 2024-06-26T05:54:42.0528315Z >>> item = super().resolve_data(write_item) 2024-06-26T05:54:42.0529023Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-06-26T05:54:42.0529566Z 2024-06-26T05:54:42.0530115Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-06-26T05:54:42.0530763Z 2024-06-26T05:54:42.0530899Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:42.0531326Z >>> from itertools import islice 2024-06-26T05:54:42.0531709Z >>> from dataclasses import replace 2024-06-26T05:54:42.0532206Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-06-26T05:54:42.0533054Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-06-26T05:54:42.0533824Z >>> # This sample doesn't handle ShardedTensors 2024-06-26T05:54:42.0534325Z >>> def create_global_plan(self, all_plans): 2024-06-26T05:54:42.0534768Z >>> def chunk(it, size): 2024-06-26T05:54:42.0535114Z >>> it = iter(it) 2024-06-26T05:54:42.0535569Z >>> return list(iter(lambda: tuple(islice(it, size)), ())) 2024-06-26T05:54:42.0536064Z >>> all_plans = [ 2024-06-26T05:54:42.0536459Z >>> replace(plan, items=items) for plan, items in 2024-06-26T05:54:42.0537061Z >>> zip(all_plans, chunk(all_plans[0].items, len(all_plans))) 2024-06-26T05:54:42.0537568Z >>> ] 2024-06-26T05:54:42.0537910Z >>> return super().create_global_plan(all_plans) 2024-06-26T05:54:42.0538250Z 2024-06-26T05:54:42.0538616Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-06-26T05:54:42.0539578Z accomplished by having each rank contribute their data items in the local plan and 2024-06-26T05:54:42.0540299Z the global planner aggregate them: 2024-06-26T05:54:42.0540566Z 2024-06-26T05:54:42.0540700Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:42.0541193Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-06-26T05:54:42.0541766Z >>> def create_local_plan(self) -> SavePlan: 2024-06-26T05:54:42.0542229Z >>> plan = super().create_local_plan() 2024-06-26T05:54:42.0542812Z >>> return replace(plan, planner_data="per-rank-data") 2024-06-26T05:54:42.0543282Z >>> 2024-06-26T05:54:42.0543886Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-06-26T05:54:42.0544707Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-06-26T05:54:42.0545410Z >>> merged_data = [p.planner_data for p in global_plan] 2024-06-26T05:54:42.0546012Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-06-26T05:54:42.0546526Z >>> return global_plan, metadata 2024-06-26T05:54:42.0546829Z 2024-06-26T05:54:42.0547219Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.0547708Z 2024-06-26T05:54:42.0548674Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=275. 2024-06-26T05:54:42.0550037Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.0550539Z 2024-06-26T05:54:42.0550920Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-06-26T05:54:42.0551486Z 2024-06-26T05:54:42.0551868Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-06-26T05:54:42.0552432Z 2024-06-26T05:54:42.0552825Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-06-26T05:54:42.0553532Z will be visible to the whole process. 2024-06-26T05:54:42.0553816Z 2024-06-26T05:54:42.0554198Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-06-26T05:54:42.0554941Z 2024-06-26T05:54:42.0555136Z 1) set_up_planner - called on all ranks. 2024-06-26T05:54:42.0555614Z Signals the start of loading a checkpoint. 2024-06-26T05:54:42.0555935Z 2024-06-26T05:54:42.0556130Z 2) create_local_plan - called on all ranks. 2024-06-26T05:54:42.0556842Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-06-26T05:54:42.0557417Z 2024-06-26T05:54:42.0557702Z 3) create_global_plan - called on the coordinator rank only. 2024-06-26T05:54:42.0558373Z Takes the LoadPlan from all ranks and make any global decision. 2024-06-26T05:54:42.0558800Z 2024-06-26T05:54:42.0559030Z 4) load_bytes - called multiple times on each rank 2024-06-26T05:54:42.0559659Z This is called once per non-tensor value in state_dict. 2024-06-26T05:54:42.0560042Z 2024-06-26T05:54:42.0560408Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-06-26T05:54:42.0561187Z They are called in pair for each Tensor value in state_dict. 2024-06-26T05:54:42.0561616Z 2024-06-26T05:54:42.0562021Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-06-26T05:54:42.0562845Z most changes can be expressed by changes in a single method. 2024-06-26T05:54:42.0563247Z 2024-06-26T05:54:42.0563418Z There are two usual patterns of extension: 2024-06-26T05:54:42.0563730Z 2024-06-26T05:54:42.0564104Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-06-26T05:54:42.0565082Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-06-26T05:54:42.0565911Z to keep a reference to the original state_dict as load happens in place so 2024-06-26T05:54:42.0566543Z we need to be able to perform it in place 2024-06-26T05:54:42.0566929Z 2024-06-26T05:54:42.0567065Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:42.0567531Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-06-26T05:54:42.0568048Z >>> def set_up_planner( 2024-06-26T05:54:42.0568377Z >>> self, 2024-06-26T05:54:42.0568688Z >>> state_dict: STATE_DICT_TYPE, 2024-06-26T05:54:42.0569080Z >>> metadata: Metadata, 2024-06-26T05:54:42.0569450Z >>> is_coordinator: bool, 2024-06-26T05:54:42.0569838Z >>> ) -> None: 2024-06-26T05:54:42.0570166Z >>> self.original_state_dict = state_dict 2024-06-26T05:54:42.0570729Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-06-26T05:54:42.0571224Z >>> 2024-06-26T05:54:42.0571496Z >>> if self.flatten_sharded_tensors: 2024-06-26T05:54:42.0572009Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-06-26T05:54:42.0572468Z >>> 2024-06-26T05:54:42.0572792Z >>> if self.flatten_state_dict: 2024-06-26T05:54:42.0573328Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-06-26T05:54:42.0573836Z >>> 2024-06-26T05:54:42.0574092Z >>> self.state_dict = state_dict 2024-06-26T05:54:42.0574508Z >>> self.metadata = metadata 2024-06-26T05:54:42.0574947Z >>> self.is_coordinator = is_coordinator 2024-06-26T05:54:42.0575345Z >>> 2024-06-26T05:54:42.0575640Z >>> def load_bytes(self, read_item, value): 2024-06-26T05:54:42.0576091Z >>> # Remove the "foo_" prefix 2024-06-26T05:54:42.0576694Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value) 2024-06-26T05:54:42.0577185Z 2024-06-26T05:54:42.0577190Z 2024-06-26T05:54:42.0577525Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-06-26T05:54:42.0578042Z 2024-06-26T05:54:42.0578179Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:42.0578674Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-06-26T05:54:42.0579181Z >>> def resolve_tensor(self, read_item): 2024-06-26T05:54:42.0579671Z >>> tensor = super().resolve_tensor(read_item) 2024-06-26T05:54:42.0580200Z >>> return torch.empty_like(tensor, device="cpu") 2024-06-26T05:54:42.0580634Z >>> 2024-06-26T05:54:42.0580936Z >>> def commit_tensor(self, read_item, tensor): 2024-06-26T05:54:42.0581478Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-06-26T05:54:42.0581844Z 2024-06-26T05:54:42.0582232Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.0582732Z 2024-06-26T05:54:42.0679208Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=60. 2024-06-26T05:54:42.0680608Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.0681229Z 2024-06-26T05:54:42.0681411Z Load a distributed ``state_dict`` in SPMD style. 2024-06-26T05:54:42.0681759Z 2024-06-26T05:54:42.0682026Z Each rank will try to read the least amount of data necessary 2024-06-26T05:54:42.0682749Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-06-26T05:54:42.0683579Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-06-26T05:54:42.0684103Z 2024-06-26T05:54:42.0684457Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-06-26T05:54:42.0685333Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-06-26T05:54:42.0686061Z ``load_state_dict`` once the deserialization is complete. 2024-06-26T05:54:42.0686449Z 2024-06-26T05:54:42.0686558Z .. warning:: 2024-06-26T05:54:42.0686943Z All tensors in ``state_dict`` must be allocated on their 2024-06-26T05:54:42.0687523Z destination device *prior to* calling this function. 2024-06-26T05:54:42.0687903Z 2024-06-26T05:54:42.0688286Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-06-26T05:54:42.0688983Z on state_dict. 2024-06-26T05:54:42.0689163Z 2024-06-26T05:54:42.0689265Z .. warning:: 2024-06-26T05:54:42.0689712Z Users must call `load_state_dict` on the root module to ensure load 2024-06-26T05:54:42.0690497Z pos-processing and non-tensor data properly propagates. 2024-06-26T05:54:42.0690889Z 2024-06-26T05:54:42.0690996Z .. note: 2024-06-26T05:54:42.0691445Z If no process group is initialized, this function will assume the intent 2024-06-26T05:54:42.0692247Z is to load a checkpoint into the local process. This can be useful in the 2024-06-26T05:54:42.0693073Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-06-26T05:54:42.0693682Z or ShardedTensor) 2024-06-26T05:54:42.0693898Z 2024-06-26T05:54:42.0693990Z .. note: 2024-06-26T05:54:42.0694319Z Rank 0 is assumed to be the coordinator rank. 2024-06-26T05:54:42.0694652Z 2024-06-26T05:54:42.0694740Z Args: 2024-06-26T05:54:42.0695154Z state_dict (Dict[str, Any]): The state_dict to save. 2024-06-26T05:54:42.0695697Z checkpoint_id (Union[str, os.PathLike, None]): 2024-06-26T05:54:42.0696328Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-06-26T05:54:42.0697085Z depends on the storage. It can be a path to a folder or to a file. 2024-06-26T05:54:42.0697837Z It can also be a key if the storage is a key-value store. 2024-06-26T05:54:42.0698338Z (Default: ``None``) 2024-06-26T05:54:42.0698731Z storage_reader (Optional[StorageReader]): 2024-06-26T05:54:42.0699333Z Instance of StorageWriter used to perform reads. If this is not 2024-06-26T05:54:42.0700046Z specified, DCP will automatically infer the reader based on the 2024-06-26T05:54:42.0700735Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-06-26T05:54:42.0701303Z be raised. (Default: ``None``) 2024-06-26T05:54:42.0701724Z planner (Optional[LoadPlanner]): 2024-06-26T05:54:42.0702273Z Instance of LoadPlanner. If this is not specificed, the default 2024-06-26T05:54:42.0702878Z planner will be used. (Default: ``None``) 2024-06-26T05:54:42.0703363Z process_group (Optional[ProcessGroup]): 2024-06-26T05:54:42.0704121Z ProcessGroup to be used for cross-rank synchronization. 2024-06-26T05:54:42.0704787Z (Default: ``None``) 2024-06-26T05:54:42.0705055Z 2024-06-26T05:54:42.0705215Z Returns: 2024-06-26T05:54:42.0705608Z None. 2024-06-26T05:54:42.0705855Z 2024-06-26T05:54:42.0706035Z Examples 2024-06-26T05:54:42.0706536Z >>> # xdoctest: +SKIP 2024-06-26T05:54:42.0706884Z >>> my_model = MyModule() 2024-06-26T05:54:42.0707299Z >>> optimizer = Adagrad(my_model.parameters()) 2024-06-26T05:54:42.0707806Z >>> model_state_dict = my_model.state_dict() 2024-06-26T05:54:42.0708525Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-06-26T05:54:42.0709105Z 2024-06-26T05:54:42.0709315Z >>> torch.distributed.checkpoint.load_state_dict( 2024-06-26T05:54:42.0709822Z >>> state_dict=model_state_dict, 2024-06-26T05:54:42.0710246Z >>> storage_reader=fs_storage_reader, 2024-06-26T05:54:42.0710654Z >>> ) 2024-06-26T05:54:42.0710798Z 2024-06-26T05:54:42.0711074Z >>> # module.load_state_dict() function might have customized steps 2024-06-26T05:54:42.0711837Z >>> # to flush the state_dict, must call it to 2024-06-26T05:54:42.0712297Z >>> # ensure correct behavior. 2024-06-26T05:54:42.0712791Z >>> my_model.load_state_dict(model_state_dict) 2024-06-26T05:54:42.0713394Z 2024-06-26T05:54:42.0713607Z .. note:: 2024-06-26T05:54:42.0714093Z load_state_dict uses collectives to coordinate reads across ranks. 2024-06-26T05:54:42.0715099Z For NCCL-based process groups, internal tensor representations of 2024-06-26T05:54:42.0715878Z objects must be moved to the GPU device before communication takes place. 2024-06-26T05:54:42.0716677Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-06-26T05:54:42.0717659Z and it is the user's responsibility to ensure that this is set so that each 2024-06-26T05:54:42.0718412Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-06-26T05:54:42.0718896Z 2024-06-26T05:54:42.0719284Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.0719787Z 2024-06-26T05:54:42.0720767Z msg = Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=66. 2024-06-26T05:54:42.0722198Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.0722719Z 2024-06-26T05:54:42.0722865Z Save a distributed model in SPMD style. 2024-06-26T05:54:42.0723163Z 2024-06-26T05:54:42.0723423Z This function is different from ``torch.save()`` as it handles 2024-06-26T05:54:42.0724253Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-06-26T05:54:42.0724786Z 2024-06-26T05:54:42.0725140Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-06-26T05:54:42.0725865Z save will call ``state_dict`` before serialization. 2024-06-26T05:54:42.0726221Z 2024-06-26T05:54:42.0726337Z .. warning:: 2024-06-26T05:54:42.0726815Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-06-26T05:54:42.0727422Z for saved state_dicts. 2024-06-26T05:54:42.0727645Z 2024-06-26T05:54:42.0727757Z .. warning:: 2024-06-26T05:54:42.0728191Z If using the `process_group` argument, make sure that only its ranks 2024-06-26T05:54:42.0728926Z call `save_state_dict` and that all data in state_dict belong to it. 2024-06-26T05:54:42.0729367Z 2024-06-26T05:54:42.0729475Z .. note:: 2024-06-26T05:54:42.0730042Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-06-26T05:54:42.0730933Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-06-26T05:54:42.0731596Z group needs to be passed in. 2024-06-26T05:54:42.0731851Z 2024-06-26T05:54:42.0731944Z .. note:: 2024-06-26T05:54:42.0732475Z If no process group is available, this function assumes the intention is to save the 2024-06-26T05:54:42.0733163Z state_dict in the local process. 2024-06-26T05:54:42.0733441Z 2024-06-26T05:54:42.0733550Z .. note: 2024-06-26T05:54:42.0733862Z Rank 0 is assumed to be the coordinator rank. 2024-06-26T05:54:42.0734205Z 2024-06-26T05:54:42.0734210Z 2024-06-26T05:54:42.0734298Z Args: 2024-06-26T05:54:42.0734641Z state_dict (Dict[str, Any]): The state_dict to save. 2024-06-26T05:54:42.0735169Z checkpoint_id (Union[str, os.PathLike, None]): 2024-06-26T05:54:42.0735812Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-06-26T05:54:42.0736560Z depends on the storage. It can be a path to a folder or to a file. 2024-06-26T05:54:42.0737300Z It can also be a key if the storage is a key-value store. 2024-06-26T05:54:42.0737818Z (Default: ``None``) 2024-06-26T05:54:42.0738206Z storage_writer (Optional[StorageWriter]): 2024-06-26T05:54:42.0738794Z Instance of StorageWriter used to perform writes. If this is not 2024-06-26T05:54:42.0739515Z specified, DCP will automatically infer the writer based on the 2024-06-26T05:54:42.0740225Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-06-26T05:54:42.0740773Z be raised. (Default: ``None``) 2024-06-26T05:54:42.0741192Z planner (Optional[SavePlanner]): 2024-06-26T05:54:42.0741753Z Instance of SavePlanner. If this is not specificed, the default 2024-06-26T05:54:42.0742342Z planner will be used. (Default: ``None``) 2024-06-26T05:54:42.0742827Z process_group (Optional[ProcessGroup]): 2024-06-26T05:54:42.0743437Z ProcessGroup to be used for cross-rank synchronization. 2024-06-26T05:54:42.0743949Z (Default: ``None``) 2024-06-26T05:54:42.0744167Z 2024-06-26T05:54:42.0744304Z Returns: 2024-06-26T05:54:42.0744659Z Metadata: Metadata object for the saved checkpoint. 2024-06-26T05:54:42.0745019Z 2024-06-26T05:54:42.0745127Z Example: 2024-06-26T05:54:42.0745413Z >>> # xdoctest: +SKIP 2024-06-26T05:54:42.0745750Z >>> my_model = MyModule() 2024-06-26T05:54:42.0745988Z 2024-06-26T05:54:42.0746132Z >>> state_dict = {"model": my_model} 2024-06-26T05:54:42.0746413Z 2024-06-26T05:54:42.0746814Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-06-26T05:54:42.0747559Z >>> torch.distributed.checkpoint.save( 2024-06-26T05:54:42.0748005Z >>> state_dict=state_dict, 2024-06-26T05:54:42.0748403Z >>> storage_writer=fs_storage_writer, 2024-06-26T05:54:42.0748807Z >>> ) 2024-06-26T05:54:42.0748948Z 2024-06-26T05:54:42.0749058Z .. note:: 2024-06-26T05:54:42.0749480Z save_state_dict uses collectives to coordinate writes across ranks. 2024-06-26T05:54:42.0750339Z For NCCL-based process groups, internal tensor representations of 2024-06-26T05:54:42.0751120Z objects must be moved to the GPU device before communication takes place. 2024-06-26T05:54:42.0751910Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-06-26T05:54:42.0752755Z and it is the user's responsibility to ensure that this is set so that 2024-06-26T05:54:42.0753503Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-06-26T05:54:42.0753946Z 2024-06-26T05:54:42.0754346Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.0754966Z 2024-06-26T05:54:42.0755929Z msg = Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=169. 2024-06-26T05:54:42.0757334Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.0758272Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-06-26T05:54:42.0759201Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-06-26T05:54:42.0759764Z 2024-06-26T05:54:42.0759872Z .. warning:: 2024-06-26T05:54:42.0760261Z This feature is experimental and subject to change. 2024-06-26T05:54:42.0760628Z 2024-06-26T05:54:42.0760737Z Args: 2024-06-26T05:54:42.0761150Z state_dict (Dict[str, Any]): The state_dict to save. 2024-06-26T05:54:42.0761710Z checkpoint_id (Union[str, os.PathLike, None]): 2024-06-26T05:54:42.0762364Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-06-26T05:54:42.0763112Z depends on the storage. It can be a path to a folder or to a file. 2024-06-26T05:54:42.0763873Z It can also be a key if the storage is a key-value store. 2024-06-26T05:54:42.0764394Z (Default: ``None``) 2024-06-26T05:54:42.0764795Z storage_writer (Optional[StorageWriter]): 2024-06-26T05:54:42.0765481Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-06-26T05:54:42.0766269Z this is not specified, DCP will automatically infer the writer based on the 2024-06-26T05:54:42.0767058Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-06-26T05:54:42.0767627Z be raised. (Default: ``None``) 2024-06-26T05:54:42.0768067Z planner (Optional[SavePlanner]): 2024-06-26T05:54:42.0768646Z Instance of SavePlanner. If this is not specificed, the default 2024-06-26T05:54:42.0769256Z planner will be used. (Default: ``None``) 2024-06-26T05:54:42.0769751Z process_group (Optional[ProcessGroup]): 2024-06-26T05:54:42.0770373Z ProcessGroup to be used for cross-rank synchronization. 2024-06-26T05:54:42.0770880Z (Default: ``None``) 2024-06-26T05:54:42.0771130Z 2024-06-26T05:54:42.0771226Z Returns: 2024-06-26T05:54:42.0771676Z Future: A future holding the resultant Metadata object from `save`. 2024-06-26T05:54:42.0772192Z 2024-06-26T05:54:42.0772286Z Example: 2024-06-26T05:54:42.0772569Z >>> # xdoctest: +SKIP 2024-06-26T05:54:42.0772986Z >>> my_model = MyModule() 2024-06-26T05:54:42.0773238Z 2024-06-26T05:54:42.0773377Z >>> state_dict = {"model": my_model} 2024-06-26T05:54:42.0773688Z 2024-06-26T05:54:42.0774097Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-06-26T05:54:42.0774946Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-06-26T05:54:42.0775621Z >>> state_dict=state_dict, 2024-06-26T05:54:42.0776036Z >>> storage_writer=fs_storage_writer, 2024-06-26T05:54:42.0776446Z >>> ) 2024-06-26T05:54:42.0776698Z >>> 2024-06-26T05:54:42.0776953Z >>> # ... do some work ... 2024-06-26T05:54:42.0777304Z >>> 2024-06-26T05:54:42.0777586Z >>> checkpoint_future.result() 2024-06-26T05:54:42.0777863Z 2024-06-26T05:54:42.0778052Z 2024-06-26T05:54:42.0778589Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.0779081Z 2024-06-26T05:54:42.0805074Z msg = Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2024-06-26T05:54:42.0806549Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.0807090Z 2024-06-26T05:54:42.0807338Z Initialize rendezvous event object and record its operations. 2024-06-26T05:54:42.0807767Z 2024-06-26T05:54:42.0807855Z Args: 2024-06-26T05:54:42.0808161Z run_id (str): The run id of the rendezvous. 2024-06-26T05:54:42.0808665Z message (str): The message describing the event. 2024-06-26T05:54:42.0809358Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-06-26T05:54:42.0810129Z name (str): Event name. (E.g. Current action being performed). 2024-06-26T05:54:42.0810681Z hostname (str): Hostname of the node. 2024-06-26T05:54:42.0811171Z pid (Optional[int]): The process id of the node. 2024-06-26T05:54:42.0811857Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-06-26T05:54:42.0812734Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-06-26T05:54:42.0813471Z rank (Optional[int]): The rank of the node, if known. 2024-06-26T05:54:42.0813931Z Returns: 2024-06-26T05:54:42.0814171Z None 2024-06-26T05:54:42.0814393Z Example: 2024-06-26T05:54:42.0814704Z >>> # See DynamicRendezvousHandler class 2024-06-26T05:54:42.0815132Z >>> def _record( 2024-06-26T05:54:42.0815409Z ... self, 2024-06-26T05:54:42.0815700Z ... message: str, 2024-06-26T05:54:42.0816104Z ... node_state: NodeState = NodeState.RUNNING, 2024-06-26T05:54:42.0816574Z ... rank: Optional[int] = None, 2024-06-26T05:54:42.0816999Z ... ) -> None: 2024-06-26T05:54:42.0817333Z ... construct_and_record_rdzv_event( 2024-06-26T05:54:42.0817850Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-06-26T05:54:42.0818385Z ... run_id=self._settings.run_id, 2024-06-26T05:54:42.0818812Z ... message=message, 2024-06-26T05:54:42.0819172Z ... node_state=node_state, 2024-06-26T05:54:42.0819597Z ... hostname=self._this_node.addr, 2024-06-26T05:54:42.0820040Z ... pid=self._this_node.pid, 2024-06-26T05:54:42.0820469Z ... local_id=self._this_node.local_id, 2024-06-26T05:54:42.0820905Z ... rank=rank, 2024-06-26T05:54:42.0821221Z ... ) 2024-06-26T05:54:42.0821379Z 2024-06-26T05:54:42.0821770Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.0822275Z 2024-06-26T05:54:42.2363636Z msg = Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=113. 2024-06-26T05:54:42.2365721Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.2366230Z 2024-06-26T05:54:42.2366620Z This configures FSDP-native mixed precision training. 2024-06-26T05:54:42.2366986Z 2024-06-26T05:54:42.2367099Z Attributes: 2024-06-26T05:54:42.2367554Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-06-26T05:54:42.2368295Z parameters during forward and backward and thus the dtype for 2024-06-26T05:54:42.2369024Z forward and backward computation. Outside forward and backward, the 2024-06-26T05:54:42.2369731Z *sharded* parameters are kept in full precision (e.g. for the 2024-06-26T05:54:42.2370428Z optimizer step), and for model checkpointing, the parameters are 2024-06-26T05:54:42.2371082Z always saved in full precision. (Default: ``None``) 2024-06-26T05:54:42.2371717Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-06-26T05:54:42.2372648Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-06-26T05:54:42.2373357Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-06-26T05:54:42.2374050Z the ``param_dtype`` value, still running gradient reduction in low 2024-06-26T05:54:42.2374757Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-06-26T05:54:42.2375475Z to force gradient reduction to run in full precision. (Default: 2024-06-26T05:54:42.2376009Z ``None``) 2024-06-26T05:54:42.2376450Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-06-26T05:54:42.2377173Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-06-26T05:54:42.2377876Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-06-26T05:54:42.2378575Z dtype thereafter. For model checkpointing, the buffers are saved 2024-06-26T05:54:42.2379267Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-06-26T05:54:42.2379777Z ``None``) 2024-06-26T05:54:42.2380197Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-06-26T05:54:42.2380909Z gradients to full precision after the backward pass in preparation 2024-06-26T05:54:42.2381643Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-06-26T05:54:42.2382358Z in the dtype used for gradient reduction, which can save memory if 2024-06-26T05:54:42.2383087Z using a custom optimizer that supports running in low precision. 2024-06-26T05:54:42.2383640Z (Default: ``False``) 2024-06-26T05:54:42.2384144Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-06-26T05:54:42.2384848Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-06-26T05:54:42.2385570Z that parameter and input dtypes match for forward computation, as 2024-06-26T05:54:42.2386314Z required by many ops. This may need to be set to ``True`` when only 2024-06-26T05:54:42.2387052Z applying mixed precision to some but not all FSDP modules, in which 2024-06-26T05:54:42.2387859Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-06-26T05:54:42.2388427Z (Default: ``False``) 2024-06-26T05:54:42.2388927Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-06-26T05:54:42.2389651Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-06-26T05:54:42.2390409Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-06-26T05:54:42.2391006Z this does not do anything. (Default: ``True``) 2024-06-26T05:54:42.2391637Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-06-26T05:54:42.2392333Z module classes to ignore for mixed precision when using an 2024-06-26T05:54:42.2392985Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-06-26T05:54:42.2393662Z applied to them separately with mixed precision disabled (meaning 2024-06-26T05:54:42.2394473Z that the final FSDP construction would deviate from the specified 2024-06-26T05:54:42.2395513Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-06-26T05:54:42.2396203Z not do anything. This API is experimental and subject to change. 2024-06-26T05:54:42.2396774Z (Default: ``(_BatchNorm,)``) 2024-06-26T05:54:42.2397044Z 2024-06-26T05:54:42.2397294Z .. note:: This API is experimental and subject to change. 2024-06-26T05:54:42.2397667Z 2024-06-26T05:54:42.2397962Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-06-26T05:54:42.2398429Z 2024-06-26T05:54:42.2398675Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-06-26T05:54:42.2399216Z precision, but buffers are not. 2024-06-26T05:54:42.2399486Z 2024-06-26T05:54:42.2399877Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-06-26T05:54:42.2400610Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-06-26T05:54:42.2401527Z Disabling FSDP's mixed precision for those norm modules only means that 2024-06-26T05:54:42.2402297Z the affine parameters are kept in ``float32``. However, this incurs 2024-06-26T05:54:42.2403111Z separate all-gathers and reduce-scatters for those norm modules, which 2024-06-26T05:54:42.2403892Z may be inefficient, so if the workload permits, the user should prefer 2024-06-26T05:54:42.2404551Z to still apply mixed precision to those modules. 2024-06-26T05:54:42.2404900Z 2024-06-26T05:54:42.2405204Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-06-26T05:54:42.2405920Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-06-26T05:54:42.2406664Z modules will have FSDP applied to them separately with mixed precision 2024-06-26T05:54:42.2407365Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-06-26T05:54:42.2407758Z 2024-06-26T05:54:42.2408028Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-06-26T05:54:42.2408746Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-06-26T05:54:42.2409423Z its ``cast_root_forward_inputs`` takes precedence over its 2024-06-26T05:54:42.2410084Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-06-26T05:54:42.2410765Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-06-26T05:54:42.2411516Z sufficient for the typical case where each FSDP instance has the same 2024-06-26T05:54:42.2412258Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-06-26T05:54:42.2413009Z ``param_dtype`` at the beginning of the model's forward pass. 2024-06-26T05:54:42.2413425Z 2024-06-26T05:54:42.2413702Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-06-26T05:54:42.2414460Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-06-26T05:54:42.2415226Z values to configure casting inputs or not before each instance's 2024-06-26T05:54:42.2415927Z forward. In such a case, since the casts happen before each FSDP 2024-06-26T05:54:42.2416705Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-06-26T05:54:42.2417455Z submodules run before its FSDP submodules to avoid the activation dtype 2024-06-26T05:54:42.2418222Z being changed due to a different ``MixedPrecision`` configuration. 2024-06-26T05:54:42.2418669Z 2024-06-26T05:54:42.2418787Z Example:: 2024-06-26T05:54:42.2418949Z 2024-06-26T05:54:42.2419136Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:42.2419685Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-06-26T05:54:42.2420194Z >>> model[1] = FSDP( 2024-06-26T05:54:42.2420533Z >>> model[1], 2024-06-26T05:54:42.2421130Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-06-26T05:54:42.2421852Z >>> ) 2024-06-26T05:54:42.2422123Z >>> model = FSDP( 2024-06-26T05:54:42.2422426Z >>> model, 2024-06-26T05:54:42.2423069Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-06-26T05:54:42.2423734Z >>> ) 2024-06-26T05:54:42.2423892Z 2024-06-26T05:54:42.2424187Z The above shows a working example. On the other hand, if ``model[1]`` 2024-06-26T05:54:42.2424922Z were replaced with ``model[0]``, meaning that the submodule using 2024-06-26T05:54:42.2425650Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-06-26T05:54:42.2426386Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-06-26T05:54:42.2426944Z ones. 2024-06-26T05:54:42.2427089Z 2024-06-26T05:54:42.2427094Z 2024-06-26T05:54:42.2427509Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.2428063Z 2024-06-26T05:54:42.2503885Z msg = Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=648. 2024-06-26T05:54:42.2505576Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.2506472Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-06-26T05:54:42.2507077Z 2024-06-26T05:54:42.2507544Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-06-26T05:54:42.2508782Z The target module does not have to be a FSDP module. If the target 2024-06-26T05:54:42.2509514Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-06-26T05:54:42.2509984Z 2024-06-26T05:54:42.2510326Z .. note:: This API should be called for only the top-level (root) 2024-06-26T05:54:42.2510853Z module. 2024-06-26T05:54:42.2511036Z 2024-06-26T05:54:42.2511344Z .. note:: This API enables users to transparently use the conventional 2024-06-26T05:54:42.2512057Z ``state_dict`` API to take model checkpoints in cases where the 2024-06-26T05:54:42.2512771Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-06-26T05:54:42.2513563Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-06-26T05:54:42.2514298Z instances, while dispatching into `sharded_state_dict` implementation 2024-06-26T05:54:42.2515086Z for FSDP: 2024-06-26T05:54:42.2515277Z 2024-06-26T05:54:42.2515395Z Example:: 2024-06-26T05:54:42.2515568Z 2024-06-26T05:54:42.2515742Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:42.2516214Z >>> model = DDP(FSDP(...)) 2024-06-26T05:54:42.2516624Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:42.2517000Z >>> model, 2024-06-26T05:54:42.2517389Z >>> StateDictType.SHARDED_STATE_DICT, 2024-06-26T05:54:42.2518017Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-06-26T05:54:42.2518783Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-06-26T05:54:42.2519343Z >>> ) 2024-06-26T05:54:42.2519681Z >>> param_state_dict = model.state_dict() 2024-06-26T05:54:42.2520238Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-06-26T05:54:42.2520625Z 2024-06-26T05:54:42.2520718Z Args: 2024-06-26T05:54:42.2521099Z module (torch.nn.Module): Root module. 2024-06-26T05:54:42.2521742Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-06-26T05:54:42.2522519Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-06-26T05:54:42.2523137Z target ``state_dict_type``. 2024-06-26T05:54:42.2523774Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-06-26T05:54:42.2524583Z for the optimizer state dict. 2024-06-26T05:54:42.2524902Z 2024-06-26T05:54:42.2525050Z Returns: 2024-06-26T05:54:42.2525522Z A StateDictSettings that include the previous state_dict type and 2024-06-26T05:54:42.2526117Z configuration for the module. 2024-06-26T05:54:42.2526494Z 2024-06-26T05:54:42.2527044Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.2527528Z 2024-06-26T05:54:42.2528769Z msg = Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=804. 2024-06-26T05:54:42.2530364Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.2531220Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-06-26T05:54:42.2531834Z 2024-06-26T05:54:42.2532277Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-06-26T05:54:42.2533046Z :meth:`set_state_dict_type` for the detail. 2024-06-26T05:54:42.2533376Z 2024-06-26T05:54:42.2533484Z Example:: 2024-06-26T05:54:42.2533675Z 2024-06-26T05:54:42.2533850Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:42.2534320Z >>> model = DDP(FSDP(...)) 2024-06-26T05:54:42.2534725Z >>> with FSDP.state_dict_type( 2024-06-26T05:54:42.2535122Z >>> model, 2024-06-26T05:54:42.2535510Z >>> StateDictType.SHARDED_STATE_DICT, 2024-06-26T05:54:42.2535928Z >>> ): 2024-06-26T05:54:42.2536257Z >>> checkpoint = model.state_dict() 2024-06-26T05:54:42.2536575Z 2024-06-26T05:54:42.2536681Z Args: 2024-06-26T05:54:42.2536988Z module (torch.nn.Module): Root module. 2024-06-26T05:54:42.2537629Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-06-26T05:54:42.2538415Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-06-26T05:54:42.2539104Z configuration for the target ``state_dict_type``. 2024-06-26T05:54:42.2539776Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-06-26T05:54:42.2540518Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-06-26T05:54:42.2541046Z 2024-06-26T05:54:42.2541574Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.2542072Z 2024-06-26T05:54:42.2563275Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1801. 2024-06-26T05:54:42.2564911Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.2565472Z 2024-06-26T05:54:42.2565862Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-06-26T05:54:42.2566412Z 2024-06-26T05:54:42.2566869Z The given state-dict can be transformed to one of three types: 2024-06-26T05:54:42.2568021Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-06-26T05:54:42.2569033Z 2024-06-26T05:54:42.2569416Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-06-26T05:54:42.2570204Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-06-26T05:54:42.2570756Z avoid OOM. 2024-06-26T05:54:42.2570920Z 2024-06-26T05:54:42.2571230Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-06-26T05:54:42.2571993Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-06-26T05:54:42.2572539Z memory. 2024-06-26T05:54:42.2572674Z 2024-06-26T05:54:42.2572976Z For local state_dict, no transformation will be performed. But a state 2024-06-26T05:54:42.2573875Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-06-26T05:54:42.2574510Z nature (this is not supported yet). 2024-06-26T05:54:42.2574865Z 2024-06-26T05:54:42.2574974Z Example:: 2024-06-26T05:54:42.2575139Z 2024-06-26T05:54:42.2575301Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:42.2575948Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-06-26T05:54:42.2576627Z >>> from torch.distributed.fsdp import StateDictType 2024-06-26T05:54:42.2577239Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-06-26T05:54:42.2577908Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-06-26T05:54:42.2578448Z >>> # Save a checkpoint 2024-06-26T05:54:42.2578793Z >>> model, optim = ... 2024-06-26T05:54:42.2579142Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:42.2579489Z >>> model, 2024-06-26T05:54:42.2579899Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:42.2580381Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:42.2580899Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:42.2581355Z >>> ) 2024-06-26T05:54:42.2581641Z >>> state_dict = model.state_dict() 2024-06-26T05:54:42.2582139Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-06-26T05:54:42.2582707Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-06-26T05:54:42.2583174Z >>> # Load a checkpoint 2024-06-26T05:54:42.2583504Z >>> model, optim = ... 2024-06-26T05:54:42.2583927Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-06-26T05:54:42.2584407Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:42.2584760Z >>> model, 2024-06-26T05:54:42.2585065Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:42.2585533Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:42.2586154Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:42.2586595Z >>> ) 2024-06-26T05:54:42.2586876Z >>> model.load_state_dict(state_dict) 2024-06-26T05:54:42.2587368Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-06-26T05:54:42.2587851Z >>> model, optim, optim_state_dict 2024-06-26T05:54:42.2588240Z >>> ) 2024-06-26T05:54:42.2588542Z >>> optim.load_state_dict(optim_state_dict) 2024-06-26T05:54:42.2588852Z 2024-06-26T05:54:42.2588940Z Args: 2024-06-26T05:54:42.2589339Z model (torch.nn.Module): Root module (which may or may not be a 2024-06-26T05:54:42.2590028Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-06-26T05:54:42.2590612Z were passed into the optimizer ``optim``. 2024-06-26T05:54:42.2591238Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-06-26T05:54:42.2591744Z parameters. 2024-06-26T05:54:42.2592204Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-06-26T05:54:42.2592947Z transform. If the value is None, optim.state_dict() will be used. ( 2024-06-26T05:54:42.2593514Z Default: ``None``) 2024-06-26T05:54:42.2594107Z group (dist.ProcessGroup): Model's process group across which parameters 2024-06-26T05:54:42.2595043Z are sharded or ``None`` if using the default process group. ( 2024-06-26T05:54:42.2595578Z Default: ``None``) 2024-06-26T05:54:42.2595792Z 2024-06-26T05:54:42.2595884Z Returns: 2024-06-26T05:54:42.2596306Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-06-26T05:54:42.2597104Z ``model``. The sharding of the optimizer state is based on 2024-06-26T05:54:42.2597586Z ``state_dict_type``. 2024-06-26T05:54:42.2597803Z 2024-06-26T05:54:42.2598200Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.2598699Z 2024-06-26T05:54:42.2599986Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1899. 2024-06-26T05:54:42.2601808Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.2602365Z 2024-06-26T05:54:42.2602963Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-06-26T05:54:42.2603629Z 2024-06-26T05:54:42.2603858Z Given a ``optim_state_dict`` that is transformed through 2024-06-26T05:54:42.2604502Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-06-26T05:54:42.2605239Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-06-26T05:54:42.2605948Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-06-26T05:54:42.2606361Z 2024-06-26T05:54:42.2606521Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:42.2607162Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-06-26T05:54:42.2608061Z >>> from torch.distributed.fsdp import StateDictType 2024-06-26T05:54:42.2608664Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-06-26T05:54:42.2609330Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-06-26T05:54:42.2609881Z >>> # Save a checkpoint 2024-06-26T05:54:42.2610209Z >>> model, optim = ... 2024-06-26T05:54:42.2610553Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:42.2610905Z >>> model, 2024-06-26T05:54:42.2611211Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:42.2611684Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:42.2612212Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:42.2612648Z >>> ) 2024-06-26T05:54:42.2612925Z >>> state_dict = model.state_dict() 2024-06-26T05:54:42.2613348Z >>> original_osd = optim.state_dict() 2024-06-26T05:54:42.2613791Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-06-26T05:54:42.2614212Z >>> model, 2024-06-26T05:54:42.2614482Z >>> optim, 2024-06-26T05:54:42.2614781Z >>> optim_state_dict=original_osd 2024-06-26T05:54:42.2615168Z >>> ) 2024-06-26T05:54:42.2615496Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-06-26T05:54:42.2615948Z >>> # Load a checkpoint 2024-06-26T05:54:42.2616285Z >>> model, optim = ... 2024-06-26T05:54:42.2616703Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-06-26T05:54:42.2617171Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:42.2617518Z >>> model, 2024-06-26T05:54:42.2617840Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:42.2618298Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:42.2618822Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:42.2619271Z >>> ) 2024-06-26T05:54:42.2619542Z >>> model.load_state_dict(state_dict) 2024-06-26T05:54:42.2620030Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-06-26T05:54:42.2620526Z >>> model, optim, optim_state_dict 2024-06-26T05:54:42.2620904Z >>> ) 2024-06-26T05:54:42.2621207Z >>> optim.load_state_dict(optim_state_dict) 2024-06-26T05:54:42.2621532Z 2024-06-26T05:54:42.2621619Z Args: 2024-06-26T05:54:42.2622010Z model (torch.nn.Module): Root module (which may or may not be a 2024-06-26T05:54:42.2622706Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-06-26T05:54:42.2623302Z were passed into the optimizer ``optim``. 2024-06-26T05:54:42.2623913Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-06-26T05:54:42.2624404Z parameters. 2024-06-26T05:54:42.2624875Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-06-26T05:54:42.2625592Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-06-26T05:54:42.2626300Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-06-26T05:54:42.2627007Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-06-26T05:54:42.2627672Z load_directly (bool): If this is set to True, this API will also 2024-06-26T05:54:42.2628402Z call optim.load_state_dict(result) before returning the result. 2024-06-26T05:54:42.2629134Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-06-26T05:54:42.2629753Z (Default: ``False``) 2024-06-26T05:54:42.2630355Z group (dist.ProcessGroup): Model's process group across which parameters 2024-06-26T05:54:42.2631093Z are sharded or ``None`` if using the default process group. ( 2024-06-26T05:54:42.2631624Z Default: ``None``) 2024-06-26T05:54:42.2631840Z 2024-06-26T05:54:42.2632244Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.2632733Z 2024-06-26T05:54:42.2757122Z msg = Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=137. 2024-06-26T05:54:42.2758712Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.2759288Z 2024-06-26T05:54:42.2759577Z RemoteModule instance can only be created after RPC initialization. 2024-06-26T05:54:42.2760029Z 2024-06-26T05:54:42.2760363Z It creates a user-specified module on a specified remote node. 2024-06-26T05:54:42.2761182Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-06-26T05:54:42.2761792Z executed on the remote node. 2024-06-26T05:54:42.2762361Z It takes care of autograd recording to ensure the backward pass propagates 2024-06-26T05:54:42.2763039Z gradients back to the corresponding remote module. 2024-06-26T05:54:42.2763871Z It can be shared across processors using `RPC framework `__, 2024-06-26T05:54:42.2764910Z without incurring any overheads of copying the actual module, 2024-06-26T05:54:42.2765721Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-06-26T05:54:42.2766622Z pointing to the remote module. 2024-06-26T05:54:42.2767057Z 2024-06-26T05:54:42.2767322Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-06-26T05:54:42.2767591Z the ``forward`` method of the module returned by the ``module_cls``. 2024-06-26T05:54:42.2767600Z 2024-06-26T05:54:42.2768041Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-06-26T05:54:42.2768047Z 2024-06-26T05:54:42.2768386Z Particularly, to create a hybrid model, typically the local modules should be 2024-06-26T05:54:42.2768906Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-06-26T05:54:42.2769007Z Hybrid Example: 2024-06-26T05:54:42.2769144Z >>> class HybridModel(nn.Module): 2024-06-26T05:54:42.2769271Z >>> def __init__(self): 2024-06-26T05:54:42.2769409Z >>> nn.Module.__init__(self) 2024-06-26T05:54:42.2769594Z >>> self.remote_embedding = RemoteModule(...) 2024-06-26T05:54:42.2769772Z >>> self.local_linear = nn.Linear(...) 2024-06-26T05:54:42.2769780Z 2024-06-26T05:54:42.2770047Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-06-26T05:54:42.2770472Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-06-26T05:54:42.2770742Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-06-26T05:54:42.2770948Z ``def forward(input: Tensor) -> Tensor:`` and 2024-06-26T05:54:42.2771217Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-06-26T05:54:42.2771223Z 2024-06-26T05:54:42.2771328Z .. note:: 2024-06-26T05:54:42.2771515Z If the remote module is placed on a cuda device, 2024-06-26T05:54:42.2771847Z any input CPU tensors will be automatically moved to the same cuda device, 2024-06-26T05:54:42.2772417Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-06-26T05:54:42.2772423Z 2024-06-26T05:54:42.2772523Z Args: 2024-06-26T05:54:42.2773006Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-06-26T05:54:42.2773674Z The device can be a local device or a remote device specified by one of the following remote 2024-06-26T05:54:42.2773903Z formats: 2024-06-26T05:54:42.2773913Z 2024-06-26T05:54:42.2774186Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-06-26T05:54:42.2774461Z 2. "/" (ex: "trainer0/cuda:0"). 2024-06-26T05:54:42.2774466Z 2024-06-26T05:54:42.2774992Z In addition, the device field can be optional and the default value is "cpu". 2024-06-26T05:54:42.2775215Z module_cls (nn.Module): For example, 2024-06-26T05:54:42.2775455Z >>> class MyModule(nn.Module): 2024-06-26T05:54:42.2775711Z >>> def forward(input): 2024-06-26T05:54:42.2775942Z >>> return input + 1 2024-06-26T05:54:42.2776093Z >>> 2024-06-26T05:54:42.2776209Z >>> module_cls = MyModule 2024-06-26T05:54:42.2776570Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-06-26T05:54:42.2776840Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-06-26T05:54:42.2777205Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-06-26T05:54:42.2777542Z to be created. The type object should be decorated by @torch.jit.interface. 2024-06-26T05:54:42.2777927Z If not provided, the generated RemoteModule is not torchscript-able. 2024-06-26T05:54:42.2778248Z Warning, this is an experimental API and susceptible to frequent changes. 2024-06-26T05:54:42.2778253Z 2024-06-26T05:54:42.2778345Z Returns: 2024-06-26T05:54:42.2778679Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-06-26T05:54:42.2779044Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-06-26T05:54:42.2779408Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-06-26T05:54:42.2779638Z on the user-provided module on the remote side. 2024-06-26T05:54:42.2779645Z 2024-06-26T05:54:42.2779747Z Example:: 2024-06-26T05:54:42.2779963Z Run the following code in two different processes: 2024-06-26T05:54:42.2779971Z 2024-06-26T05:54:42.2780108Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:42.2780214Z >>> # On worker 0: 2024-06-26T05:54:42.2780328Z >>> import torch 2024-06-26T05:54:42.2780480Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:42.2780605Z >>> from torch import nn, Tensor 2024-06-26T05:54:42.2780908Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-06-26T05:54:42.2780997Z >>> 2024-06-26T05:54:42.2781184Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-06-26T05:54:42.2781332Z >>> remote_linear_module = RemoteModule( 2024-06-26T05:54:42.2781496Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-06-26T05:54:42.2781597Z >>> ) 2024-06-26T05:54:42.2781725Z >>> input = torch.randn(128, 20) 2024-06-26T05:54:42.2781927Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-06-26T05:54:42.2782051Z >>> ret = ret_fut.wait() 2024-06-26T05:54:42.2782160Z >>> rpc.shutdown() 2024-06-26T05:54:42.2782165Z 2024-06-26T05:54:42.2782267Z >>> # On worker 1: 2024-06-26T05:54:42.2782380Z >>> import torch 2024-06-26T05:54:42.2782531Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:42.2782621Z >>> 2024-06-26T05:54:42.2782804Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-06-26T05:54:42.2782907Z >>> rpc.shutdown() 2024-06-26T05:54:42.2782912Z 2024-06-26T05:54:42.2783303Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.2783322Z 2024-06-26T05:54:42.2784449Z msg = Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=514. 2024-06-26T05:54:42.2784870Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.2784911Z 2024-06-26T05:54:42.2785336Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-06-26T05:54:42.2785376Z 2024-06-26T05:54:42.2785809Z This alternate initialization method can be particularly useful if we want to create multiple 2024-06-26T05:54:42.2786221Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-06-26T05:54:42.2786227Z 2024-06-26T05:54:42.2786591Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-06-26T05:54:42.2786837Z which is not supported. The recommended way is as follows: 2024-06-26T05:54:42.2786843Z 2024-06-26T05:54:42.2786985Z 1. the sender creates a RemoteModule; 2024-06-26T05:54:42.2787184Z 2. the sender sends its ``module_rref`` over RPC; 2024-06-26T05:54:42.2787703Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-06-26T05:54:42.2787712Z 2024-06-26T05:54:42.2787809Z Example:: 2024-06-26T05:54:42.2788020Z Run the following code in two different processes: 2024-06-26T05:54:42.2788027Z 2024-06-26T05:54:42.2788165Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:42.2788268Z >>> # On worker 0: 2024-06-26T05:54:42.2788382Z >>> import torch 2024-06-26T05:54:42.2788535Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:42.2788660Z >>> from torch import nn, Tensor 2024-06-26T05:54:42.2788962Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-06-26T05:54:42.2789051Z >>> 2024-06-26T05:54:42.2789238Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-06-26T05:54:42.2789366Z >>> remote_module = RemoteModule( 2024-06-26T05:54:42.2789529Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-06-26T05:54:42.2789634Z >>> ) 2024-06-26T05:54:42.2789723Z >>> 2024-06-26T05:54:42.2789859Z >>> remote_module1 = rpc.rpc_sync( 2024-06-26T05:54:42.2789982Z >>> "worker1/cpu", 2024-06-26T05:54:42.2790138Z >>> RemoteModule.init_from_module_rref, 2024-06-26T05:54:42.2790337Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-06-26T05:54:42.2790441Z >>> ) 2024-06-26T05:54:42.2790544Z >>> rpc.shutdown() 2024-06-26T05:54:42.2790550Z 2024-06-26T05:54:42.2790652Z >>> # On worker 1: 2024-06-26T05:54:42.2790763Z >>> import torch 2024-06-26T05:54:42.2790915Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:42.2791005Z >>> 2024-06-26T05:54:42.2791190Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-06-26T05:54:42.2791293Z >>> rpc.shutdown() 2024-06-26T05:54:42.2791298Z 2024-06-26T05:54:42.2791397Z Args: 2024-06-26T05:54:42.2791875Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-06-26T05:54:42.2792304Z The device can be a local device or a remote device specified by one of the following remote 2024-06-26T05:54:42.2792413Z formats: 2024-06-26T05:54:42.2792417Z 2024-06-26T05:54:42.2792602Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-06-26T05:54:42.2792800Z 2. "/" (ex: "trainer0/cuda:0"). 2024-06-26T05:54:42.2792805Z 2024-06-26T05:54:42.2793157Z In addition, the device field can be optional and the default value is "cpu". 2024-06-26T05:54:42.2793500Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-06-26T05:54:42.2793635Z the created remote module. 2024-06-26T05:54:42.2793996Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-06-26T05:54:42.2794326Z to be created. The type object should be decorated by @torch.jit.interface. 2024-06-26T05:54:42.2794952Z If not provided, the generated RemoteModule is not torchscript-able. 2024-06-26T05:54:42.2795286Z Warning, this is an experimental API and susceptible to frequent changes. 2024-06-26T05:54:42.2795371Z 2024-06-26T05:54:42.2795465Z Returns: 2024-06-26T05:54:42.2795804Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-06-26T05:54:42.2796225Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-06-26T05:54:42.2796572Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-06-26T05:54:42.2796814Z on the user-provided module on the remote side. 2024-06-26T05:54:42.2796820Z 2024-06-26T05:54:42.2797205Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.2797210Z 2024-06-26T05:54:42.2798145Z msg = Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=606. 2024-06-26T05:54:42.2798549Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.2798555Z 2024-06-26T05:54:42.2798921Z A RemoteModule instance can only be created after RPC initialization. 2024-06-26T05:54:42.2798943Z 2024-06-26T05:54:42.2799263Z It creates a user-specified module on a specified remote node. 2024-06-26T05:54:42.2799587Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-06-26T05:54:42.2799721Z executed on the remote node. 2024-06-26T05:54:42.2800042Z It takes care of autograd recording to ensure the backward pass propagates 2024-06-26T05:54:42.2800236Z gradients back to the corresponding remote module. 2024-06-26T05:54:42.2800241Z 2024-06-26T05:54:42.2800545Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-06-26T05:54:42.2800839Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-06-26T05:54:42.2801241Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-06-26T05:54:42.2801528Z and ``forward`` are the same as the ``forward`` method of the module 2024-06-26T05:54:42.2801660Z returned by the ``module_cls``. 2024-06-26T05:54:42.2801667Z 2024-06-26T05:54:42.2801953Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-06-26T05:54:42.2802369Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-06-26T05:54:42.2802668Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-06-26T05:54:42.2802674Z 2024-06-26T05:54:42.2802898Z | ``def forward(input: Tensor) -> Tensor:`` 2024-06-26T05:54:42.2803162Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-06-26T05:54:42.2803167Z 2024-06-26T05:54:42.2803270Z Args: 2024-06-26T05:54:42.2803747Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-06-26T05:54:42.2804216Z The format should be "/", where the device field can be parsed as torch.device type. 2024-06-26T05:54:42.2804416Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-06-26T05:54:42.2804759Z In addition, the device field can be optional and the default value is "cpu". 2024-06-26T05:54:42.2805107Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-06-26T05:54:42.2805115Z 2024-06-26T05:54:42.2805261Z >>> class MyModule(nn.Module): 2024-06-26T05:54:42.2805380Z >>> def forward(input): 2024-06-26T05:54:42.2805498Z >>> return input + 1 2024-06-26T05:54:42.2816123Z >>> 2024-06-26T05:54:42.2816345Z >>> module_cls = MyModule 2024-06-26T05:54:42.2816353Z 2024-06-26T05:54:42.2816644Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-06-26T05:54:42.2816898Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-06-26T05:54:42.2816903Z 2024-06-26T05:54:42.2816996Z Returns: 2024-06-26T05:54:42.2817342Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-06-26T05:54:42.2817758Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-06-26T05:54:42.2818207Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-06-26T05:54:42.2818457Z on the user-provided module on the remote side. 2024-06-26T05:54:42.2818510Z 2024-06-26T05:54:42.2818615Z Example:: 2024-06-26T05:54:42.2818815Z Run the following code in two different processes: 2024-06-26T05:54:42.2818833Z 2024-06-26T05:54:42.2818970Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:42.2819074Z >>> # On worker 0: 2024-06-26T05:54:42.2819189Z >>> import torch 2024-06-26T05:54:42.2819342Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:42.2819469Z >>> from torch import nn, Tensor 2024-06-26T05:54:42.2819774Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-06-26T05:54:42.2819866Z >>> 2024-06-26T05:54:42.2820043Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-06-26T05:54:42.2820307Z >>> remote_linear_module = RemoteModule( 2024-06-26T05:54:42.2820477Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-06-26T05:54:42.2820572Z >>> ) 2024-06-26T05:54:42.2820712Z >>> input = torch.randn(128, 20) 2024-06-26T05:54:42.2820918Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-06-26T05:54:42.2821033Z >>> ret = ret_fut.wait() 2024-06-26T05:54:42.2821157Z >>> rpc.shutdown() 2024-06-26T05:54:42.2821163Z 2024-06-26T05:54:42.2821266Z >>> # On worker 1: 2024-06-26T05:54:42.2821367Z >>> import torch 2024-06-26T05:54:42.2821539Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:42.2821630Z >>> 2024-06-26T05:54:42.2821818Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-06-26T05:54:42.2821921Z >>> rpc.shutdown() 2024-06-26T05:54:42.2821926Z 2024-06-26T05:54:42.2822167Z Furthermore, a more practical example that is combined with 2024-06-26T05:54:42.2822813Z `DistributedDataParallel `__ (DDP) 2024-06-26T05:54:42.2823267Z can be found in this `tutorial `__. 2024-06-26T05:54:42.2823273Z 2024-06-26T05:54:42.2823667Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.2823686Z 2024-06-26T05:54:42.2924296Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=129. 2024-06-26T05:54:42.2925764Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.2926290Z 2024-06-26T05:54:42.2926590Z DistributedOptimizer takes remote references to parameters scattered 2024-06-26T05:54:42.2927381Z across workers and applies the given optimizer locally for each parameter. 2024-06-26T05:54:42.2927866Z 2024-06-26T05:54:42.2928186Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-06-26T05:54:42.2928880Z to retrieve the gradients for specific parameters. 2024-06-26T05:54:42.2929247Z 2024-06-26T05:54:42.2929354Z Concurrent calls to 2024-06-26T05:54:42.2929823Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-06-26T05:54:42.2930432Z either from the same or different clients, will 2024-06-26T05:54:42.2931137Z be serialized on each worker -- as each worker's optimizer can only work 2024-06-26T05:54:42.2931908Z on one set of gradients at a time. However, there is no guarantee that 2024-06-26T05:54:42.2932739Z the full forward-backward-optimizer sequence will execute for one client 2024-06-26T05:54:42.2933545Z at a time. This means that the gradients being applied may not correspond 2024-06-26T05:54:42.2934401Z to the latest forward pass executed on a given worker. Also, there is no 2024-06-26T05:54:42.2935162Z guaranteed ordering across workers. 2024-06-26T05:54:42.2935551Z 2024-06-26T05:54:42.2935951Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-06-26T05:54:42.2937403Z by default, so that optimizer updates are not blocked by the Python Global 2024-06-26T05:54:42.2938894Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-06-26T05:54:42.2939774Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-06-26T05:54:42.2940610Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-06-26T05:54:42.2941253Z for your own custom optimizers. 2024-06-26T05:54:42.2941505Z 2024-06-26T05:54:42.2941592Z Args: 2024-06-26T05:54:42.2941984Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-06-26T05:54:42.2942532Z instantiate on each worker. 2024-06-26T05:54:42.2943076Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-06-26T05:54:42.2943634Z to optimize. 2024-06-26T05:54:42.2944119Z args: arguments to pass to the optimizer constructor on each worker. 2024-06-26T05:54:42.2944939Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-06-26T05:54:42.2945421Z 2024-06-26T05:54:42.2945527Z Example:: 2024-06-26T05:54:42.2945820Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:42.2946321Z >>> import torch.distributed.autograd as dist_autograd 2024-06-26T05:54:42.2946861Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:42.2947300Z >>> from torch import optim 2024-06-26T05:54:42.2947792Z >>> from torch.distributed.optim import DistributedOptimizer 2024-06-26T05:54:42.2948297Z >>> 2024-06-26T05:54:42.2948608Z >>> with dist_autograd.context() as context_id: 2024-06-26T05:54:42.2949045Z >>> # Forward pass. 2024-06-26T05:54:42.2949530Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-06-26T05:54:42.2950231Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-06-26T05:54:42.2950903Z >>> loss = rref1.to_here() + rref2.to_here() 2024-06-26T05:54:42.2951392Z >>> 2024-06-26T05:54:42.2951648Z >>> # Backward pass. 2024-06-26T05:54:42.2952100Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-06-26T05:54:42.2952575Z >>> 2024-06-26T05:54:42.2952834Z >>> # Optimizer. 2024-06-26T05:54:42.2953243Z >>> dist_optim = DistributedOptimizer( 2024-06-26T05:54:42.2953733Z >>> optim.SGD, 2024-06-26T05:54:42.2954060Z >>> [rref1, rref2], 2024-06-26T05:54:42.2954373Z >>> lr=0.05, 2024-06-26T05:54:42.2954896Z >>> ) 2024-06-26T05:54:42.2955182Z >>> dist_optim.step(context_id) 2024-06-26T05:54:42.2955527Z 2024-06-26T05:54:42.2955721Z __ https://github.com/pytorch/tutorials/pull/1465 2024-06-26T05:54:42.2956129Z 2024-06-26T05:54:42.2956546Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.2957105Z 2024-06-26T05:54:42.2958231Z msg = Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2024-06-26T05:54:42.2959715Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.2960225Z 2024-06-26T05:54:42.2960855Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-06-26T05:54:42.2961796Z This optimizer runs local optimizer at every step. 2024-06-26T05:54:42.2962683Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-06-26T05:54:42.2963301Z 2024-06-26T05:54:42.2963405Z Args: 2024-06-26T05:54:42.2963659Z optim: The local optimizer. 2024-06-26T05:54:42.2964270Z averager: A model averager instance to run post-localSGD algorithm. 2024-06-26T05:54:42.2964719Z 2024-06-26T05:54:42.2964834Z Example:: 2024-06-26T05:54:42.2964980Z 2024-06-26T05:54:42.2965156Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:42.2965574Z >>> import torch 2024-06-26T05:54:42.2965911Z >>> import torch.distributed as dist 2024-06-26T05:54:42.2966571Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-06-26T05:54:42.2967293Z >>> import torch.nn as nn 2024-06-26T05:54:42.2967794Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-06-26T05:54:42.2968622Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-06-26T05:54:42.2969263Z >>> PostLocalSGDState, 2024-06-26T05:54:42.2969620Z >>> post_localSGD_hook, 2024-06-26T05:54:42.2969953Z >>> ) 2024-06-26T05:54:42.2970185Z >>> 2024-06-26T05:54:42.2970534Z >>> model = nn.parallel.DistributedDataParallel( 2024-06-26T05:54:42.2971088Z >>> module, device_ids=[rank], output_device=rank 2024-06-26T05:54:42.2971516Z >>> ) 2024-06-26T05:54:42.2971753Z >>> 2024-06-26T05:54:42.2972136Z >>> # Register a post-localSGD communication hook. 2024-06-26T05:54:42.2972862Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-06-26T05:54:42.2973696Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:42.2974166Z >>> 2024-06-26T05:54:42.2974630Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-06-26T05:54:42.2975433Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-06-26T05:54:42.2976170Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-06-26T05:54:42.2976818Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-06-26T05:54:42.2977405Z >>> opt = PostLocalSGDOptimizer( 2024-06-26T05:54:42.2977812Z >>> optim=local_optim, 2024-06-26T05:54:42.2978364Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-06-26T05:54:42.2978948Z >>> ) 2024-06-26T05:54:42.2979188Z >>> 2024-06-26T05:54:42.2979635Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-06-26T05:54:42.2980633Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-06-26T05:54:42.2981804Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-06-26T05:54:42.2982608Z >>> for step in range(0, 200): 2024-06-26T05:54:42.2982970Z >>> opt.zero_grad() 2024-06-26T05:54:42.2983326Z >>> loss = loss_fn(output, labels) 2024-06-26T05:54:42.2983728Z >>> loss.backward() 2024-06-26T05:54:42.2984038Z >>> opt.step() 2024-06-26T05:54:42.2984242Z 2024-06-26T05:54:42.2984627Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.2985118Z 2024-06-26T05:54:42.3042952Z msg = Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282. 2024-06-26T05:54:42.3044490Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.3045053Z 2024-06-26T05:54:42.3045623Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-06-26T05:54:42.3046340Z 2024-06-26T05:54:42.3046499Z The sharing is done as described by ZeRO_. 2024-06-26T05:54:42.3046827Z 2024-06-26T05:54:42.3047008Z The local optimizer instance in each rank is only 2024-06-26T05:54:42.3047668Z responsible for updating approximately ``1 / world_size`` parameters and 2024-06-26T05:54:42.3048408Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-06-26T05:54:42.3049175Z parameters are updated locally, each rank will broadcast its parameters to 2024-06-26T05:54:42.3049919Z all other peers to keep all model replicas in the same state. 2024-06-26T05:54:42.3050563Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-06-26T05:54:42.3051392Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-06-26T05:54:42.3052020Z memory consumption. 2024-06-26T05:54:42.3052209Z 2024-06-26T05:54:42.3052614Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-06-26T05:54:42.3053552Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-06-26T05:54:42.3054422Z not divided among ranks. The partition is arbitrary and might not match the 2024-06-26T05:54:42.3055081Z the parameter registration or usage order. 2024-06-26T05:54:42.3055392Z 2024-06-26T05:54:42.3055487Z Arguments: 2024-06-26T05:54:42.3055899Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-06-26T05:54:42.3056582Z or :class:`dict` s giving all parameters, which will be sharded 2024-06-26T05:54:42.3057103Z across ranks. 2024-06-26T05:54:42.3057309Z 2024-06-26T05:54:42.3057410Z Keyword Args: 2024-06-26T05:54:42.3057870Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-06-26T05:54:42.3058422Z optimizer. 2024-06-26T05:54:42.3058877Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-06-26T05:54:42.3059647Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-06-26T05:54:42.3060249Z :meth:`torch.distributed.init_process_group`). 2024-06-26T05:54:42.3060897Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-06-26T05:54:42.3061658Z packed into buckets to speed up communication, and ``param.data`` 2024-06-26T05:54:42.3062388Z fields point to bucket views at different offsets; if ``False``, 2024-06-26T05:54:42.3063089Z each individual parameter is communicated separately, and each 2024-06-26T05:54:42.3063725Z ``params.data`` stays intact (default: ``False``). 2024-06-26T05:54:42.3064339Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-06-26T05:54:42.3065072Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-06-26T05:54:42.3065777Z synchronization; this requires (1) either a functional optimizer 2024-06-26T05:54:42.3066472Z for the ``optimizer_class`` argument or one with a functional 2024-06-26T05:54:42.3067110Z equivalent and (2) registering a DDP communication hook 2024-06-26T05:54:42.3067769Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-06-26T05:54:42.3068415Z parameters are packed into buckets matching those in 2024-06-26T05:54:42.3068996Z :class:`DistributedDataParallel`, meaning that the 2024-06-26T05:54:42.3069573Z ``parameters_as_bucket_view`` argument is ignored. 2024-06-26T05:54:42.3070193Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-06-26T05:54:42.3070729Z (per normal). 2024-06-26T05:54:42.3071027Z (default: ``False``) 2024-06-26T05:54:42.3071540Z **defaults: any trailing arguments, which are forwarded to the local 2024-06-26T05:54:42.3072093Z optimizer. 2024-06-26T05:54:42.3072270Z 2024-06-26T05:54:42.3072374Z Example:: 2024-06-26T05:54:42.3072534Z 2024-06-26T05:54:42.3072646Z >>> # xdoctest: +SKIP 2024-06-26T05:54:42.3072992Z >>> import torch.nn as nn 2024-06-26T05:54:42.3073494Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-06-26T05:54:42.3074209Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-06-26T05:54:42.3075189Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-06-26T05:54:42.3075807Z >>> ddp = DDP(model, device_ids=[rank]) 2024-06-26T05:54:42.3076268Z >>> opt = ZeroRedundancyOptimizer( 2024-06-26T05:54:42.3076694Z >>> ddp.parameters(), 2024-06-26T05:54:42.3077077Z >>> optimizer_class=torch.optim.Adam, 2024-06-26T05:54:42.3077489Z >>> lr=0.01 2024-06-26T05:54:42.3077766Z >>> ) 2024-06-26T05:54:42.3078025Z >>> ddp(inputs).sum().backward() 2024-06-26T05:54:42.3078405Z >>> opt.step() 2024-06-26T05:54:42.3078583Z 2024-06-26T05:54:42.3078695Z .. warning:: 2024-06-26T05:54:42.3079123Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-06-26T05:54:42.3079811Z passed-in parameters are the same dense type. 2024-06-26T05:54:42.3080232Z 2024-06-26T05:54:42.3080345Z .. warning:: 2024-06-26T05:54:42.3080794Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-06-26T05:54:42.3081633Z the way that overlapping :class:`DistributedDataParallel` with 2024-06-26T05:54:42.3082377Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-06-26T05:54:42.3083147Z two or three training iterations do not perform parameter updates in 2024-06-26T05:54:42.3083856Z the optimizer step, depending on if ``static_graph=False`` or 2024-06-26T05:54:42.3084519Z ``static_graph=True``, respectively. This is because it needs 2024-06-26T05:54:42.3085158Z information about the gradient bucketing strategy used by 2024-06-26T05:54:42.3085834Z :class:`DistributedDataParallel`, which is not finalized until the 2024-06-26T05:54:42.3086557Z second forward pass if ``static_graph=False`` or until the third 2024-06-26T05:54:42.3087353Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-06-26T05:54:42.3087925Z is to prepend dummy inputs. 2024-06-26T05:54:42.3088192Z 2024-06-26T05:54:42.3088517Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-06-26T05:54:42.3089005Z 2024-06-26T05:54:42.3089177Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-06-26T05:54:42.3089486Z 2024-06-26T05:54:42.3089490Z 2024-06-26T05:54:42.3089898Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.3090388Z 2024-06-26T05:54:42.3203102Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2024-06-26T05:54:42.3204531Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.3205054Z 2024-06-26T05:54:42.3205362Z Custom reducer class that can be used to specify a custom operation that 2024-06-26T05:54:42.3206076Z reduces losses of multiple microbatches into one value. 2024-06-26T05:54:42.3206459Z 2024-06-26T05:54:42.3206553Z Example: 2024-06-26T05:54:42.3206815Z >>> # xdoctest: +SKIP 2024-06-26T05:54:42.3207150Z >>> sum_reducer = _CustomReducer( 2024-06-26T05:54:42.3207514Z >>> torch.tensor(0.0), 2024-06-26T05:54:42.3207853Z >>> lambda a, b: a + b 2024-06-26T05:54:42.3208169Z >>> ) 2024-06-26T05:54:42.3208300Z 2024-06-26T05:54:42.3208690Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.3209189Z 2024-06-26T05:54:42.3529045Z msg = Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6. 2024-06-26T05:54:42.3530407Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.3530918Z 2024-06-26T05:54:42.3531322Z A decorator for a function indicating that the return value of the function 2024-06-26T05:54:42.3532125Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-06-26T05:54:42.3532906Z function can run asynchronously on the RPC callee. More specifically, the 2024-06-26T05:54:42.3533707Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-06-26T05:54:42.3534497Z function and installs subsequent processing steps as a callback to that 2024-06-26T05:54:42.3535283Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-06-26T05:54:42.3536031Z from the :class:`~torch.futures.Future` when completed and send the 2024-06-26T05:54:42.3536730Z value back as the RPC response. That also means the returned 2024-06-26T05:54:42.3537453Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-06-26T05:54:42.3538257Z sent through RPC. This decorator is useful when the wrapped function's 2024-06-26T05:54:42.3538986Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-06-26T05:54:42.3539721Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-06-26T05:54:42.3540384Z 2024-06-26T05:54:42.3540699Z .. note:: To enable asynchronous execution, applications must pass the 2024-06-26T05:54:42.3541504Z function object returned by this decorator to RPC APIs. If RPC detected 2024-06-26T05:54:42.3542279Z attributes installed by this decorator, it knows that this function 2024-06-26T05:54:42.3542982Z returns a ``Future`` object and will handle that accordingly. 2024-06-26T05:54:42.3543675Z However, this does not mean this decorator has to be outmost one when 2024-06-26T05:54:42.3544443Z defining a function. For example, when combined with ``@staticmethod`` 2024-06-26T05:54:42.3545202Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-06-26T05:54:42.3545943Z inner decorator to allow the target function be recognized as a static 2024-06-26T05:54:42.3546729Z or class function. This target function can still execute asynchronously 2024-06-26T05:54:42.3547619Z because, when accessed, the static or class method preserves attributes 2024-06-26T05:54:42.3548289Z installed by ``@rpc.functions.async_execution``. 2024-06-26T05:54:42.3548657Z 2024-06-26T05:54:42.3548665Z 2024-06-26T05:54:42.3548764Z Example:: 2024-06-26T05:54:42.3549191Z The returned :class:`~torch.futures.Future` object can come from 2024-06-26T05:54:42.3549802Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-06-26T05:54:42.3550422Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-06-26T05:54:42.3551119Z constructor. The example below shows directly using the 2024-06-26T05:54:42.3551680Z :class:`~torch.futures.Future` returned by 2024-06-26T05:54:42.3552136Z :meth:`~torch.futures.Future.then`. 2024-06-26T05:54:42.3552443Z 2024-06-26T05:54:42.3552591Z >>> from torch.distributed import rpc 2024-06-26T05:54:42.3552985Z >>> 2024-06-26T05:54:42.3553253Z >>> # omitting setup and shutdown RPC 2024-06-26T05:54:42.3553640Z >>> 2024-06-26T05:54:42.3553894Z >>> # On all workers 2024-06-26T05:54:42.3554233Z >>> @rpc.functions.async_execution 2024-06-26T05:54:42.3554965Z >>> def async_add_chained(to, x, y, z): 2024-06-26T05:54:42.3555594Z >>> # This function runs on "worker1" and returns immediately when 2024-06-26T05:54:42.3556280Z >>> # the callback is installed through the `then(cb)` API. In the 2024-06-26T05:54:42.3556968Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-06-26T05:54:42.3557622Z >>> # When the return value of that `rpc_async` arrives at 2024-06-26T05:54:42.3558245Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-06-26T05:54:42.3558941Z >>> # and set the value for the previously returned `Future`, which 2024-06-26T05:54:42.3559627Z >>> # will then trigger RPC to send the result back to "worker0". 2024-06-26T05:54:42.3560273Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:42.3560800Z >>> lambda fut: fut.wait() + z 2024-06-26T05:54:42.3561289Z >>> ) 2024-06-26T05:54:42.3561549Z >>> 2024-06-26T05:54:42.3561788Z >>> # On worker0 2024-06-26T05:54:42.3562102Z >>> # xdoctest: +SKIP 2024-06-26T05:54:42.3562439Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:42.3562758Z >>> "worker1", 2024-06-26T05:54:42.3563070Z >>> async_add_chained, 2024-06-26T05:54:42.3563446Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-06-26T05:54:42.3563860Z >>> ) 2024-06-26T05:54:42.3564162Z >>> print(ret) # prints tensor([3., 3.]) 2024-06-26T05:54:42.3564470Z 2024-06-26T05:54:42.3564788Z When combined with TorchScript decorators, this decorator must be the 2024-06-26T05:54:42.3565408Z outmost one. 2024-06-26T05:54:42.3565706Z 2024-06-26T05:54:42.3565863Z >>> from torch import Tensor 2024-06-26T05:54:42.3566336Z >>> from torch.futures import Future 2024-06-26T05:54:42.3566835Z >>> from torch.distributed import rpc 2024-06-26T05:54:42.3567496Z >>> 2024-06-26T05:54:42.3568112Z >>> # omitting setup and shutdown RPC 2024-06-26T05:54:42.3568498Z >>> 2024-06-26T05:54:42.3568755Z >>> # On all workers 2024-06-26T05:54:42.3569135Z >>> @torch.jit.script 2024-06-26T05:54:42.3569630Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-06-26T05:54:42.3570093Z >>> return x + y 2024-06-26T05:54:42.3570405Z >>> 2024-06-26T05:54:42.3570677Z >>> @rpc.functions.async_execution 2024-06-26T05:54:42.3571089Z >>> @torch.jit.script 2024-06-26T05:54:42.3571636Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-06-26T05:54:42.3572242Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-06-26T05:54:42.3572686Z >>> 2024-06-26T05:54:42.3572932Z >>> # On worker0 2024-06-26T05:54:42.3573226Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:42.3573553Z >>> "worker1", 2024-06-26T05:54:42.3573854Z >>> async_add, 2024-06-26T05:54:42.3574279Z >>> args=("worker2", torch.ones(2), 1) 2024-06-26T05:54:42.3574688Z >>> ) 2024-06-26T05:54:42.3574992Z >>> print(ret) # prints tensor([2., 2.]) 2024-06-26T05:54:42.3575299Z 2024-06-26T05:54:42.3575602Z When combined with static or class method, this decorator must be the 2024-06-26T05:54:42.3576168Z inner one. 2024-06-26T05:54:42.3576332Z 2024-06-26T05:54:42.3576493Z >>> from torch.distributed import rpc 2024-06-26T05:54:42.3576874Z >>> 2024-06-26T05:54:42.3577157Z >>> # omitting setup and shutdown RPC 2024-06-26T05:54:42.3577545Z >>> 2024-06-26T05:54:42.3577781Z >>> # On all workers 2024-06-26T05:54:42.3578125Z >>> class AsyncExecutionClass: 2024-06-26T05:54:42.3578489Z >>> 2024-06-26T05:54:42.3578729Z >>> @staticmethod 2024-06-26T05:54:42.3579088Z >>> @rpc.functions.async_execution 2024-06-26T05:54:42.3579548Z >>> def static_async_add(to, x, y, z): 2024-06-26T05:54:42.3580081Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:42.3580628Z >>> lambda fut: fut.wait() + z 2024-06-26T05:54:42.3581040Z >>> ) 2024-06-26T05:54:42.3581294Z >>> 2024-06-26T05:54:42.3581542Z >>> @classmethod 2024-06-26T05:54:42.3581897Z >>> @rpc.functions.async_execution 2024-06-26T05:54:42.3582344Z >>> def class_async_add(cls, to, x, y, z): 2024-06-26T05:54:42.3582828Z >>> ret_fut = torch.futures.Future() 2024-06-26T05:54:42.3583346Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:42.3583895Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-06-26T05:54:42.3584360Z >>> ) 2024-06-26T05:54:42.3584646Z >>> return ret_fut 2024-06-26T05:54:42.3584961Z >>> 2024-06-26T05:54:42.3585246Z >>> @rpc.functions.async_execution 2024-06-26T05:54:42.3585713Z >>> def bound_async_add(self, to, x, y, z): 2024-06-26T05:54:42.3586256Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:42.3586800Z >>> lambda fut: fut.wait() + z 2024-06-26T05:54:42.3587206Z >>> ) 2024-06-26T05:54:42.3587458Z >>> 2024-06-26T05:54:42.3587699Z >>> # On worker0 2024-06-26T05:54:42.3588006Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:42.3588324Z >>> "worker1", 2024-06-26T05:54:42.3588693Z >>> AsyncExecutionClass.static_async_add, 2024-06-26T05:54:42.3589182Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-06-26T05:54:42.3589572Z >>> ) 2024-06-26T05:54:42.3589874Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:42.3590283Z >>> 2024-06-26T05:54:42.3590527Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:42.3590859Z >>> "worker1", 2024-06-26T05:54:42.3591220Z >>> AsyncExecutionClass.class_async_add, 2024-06-26T05:54:42.3591690Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-06-26T05:54:42.3592102Z >>> ) 2024-06-26T05:54:42.3592400Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:42.3592714Z 2024-06-26T05:54:42.3592996Z This decorator also works with RRef helpers, i.e., . 2024-06-26T05:54:42.3593555Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-06-26T05:54:42.3594152Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-06-26T05:54:42.3594904Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-06-26T05:54:42.3595320Z 2024-06-26T05:54:42.3595470Z >>> from torch.distributed import rpc 2024-06-26T05:54:42.3595873Z >>> 2024-06-26T05:54:42.3596182Z >>> # reuse the AsyncExecutionClass class above 2024-06-26T05:54:42.3596736Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-06-26T05:54:42.3597389Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-06-26T05:54:42.3598000Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:42.3598390Z >>> 2024-06-26T05:54:42.3598735Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-06-26T05:54:42.3599521Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-06-26T05:54:42.3600159Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:42.3600568Z >>> 2024-06-26T05:54:42.3600915Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-06-26T05:54:42.3601667Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-06-26T05:54:42.3602319Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:42.3602628Z 2024-06-26T05:54:42.3603062Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.3603561Z 2024-06-26T05:54:42.3604692Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=108. 2024-06-26T05:54:42.3606171Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.3606690Z 2024-06-26T05:54:42.3606969Z Set device mapping between each RPC caller and callee pair. This 2024-06-26T05:54:42.3607647Z function can be called multiple times to incrementally add 2024-06-26T05:54:42.3608171Z device placement configurations. 2024-06-26T05:54:42.3608444Z 2024-06-26T05:54:42.3608534Z Args: 2024-06-26T05:54:42.3608785Z to (str): Callee name. 2024-06-26T05:54:42.3609265Z device_map (Dict of int, str, or torch.device): Device placement 2024-06-26T05:54:42.3609937Z mappings from this worker to the callee. This map must be 2024-06-26T05:54:42.3610444Z invertible. 2024-06-26T05:54:42.3610626Z 2024-06-26T05:54:42.3610731Z Example: 2024-06-26T05:54:42.3611003Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:42.3611403Z >>> # both workers 2024-06-26T05:54:42.3611707Z >>> def add(x, y): 2024-06-26T05:54:42.3612128Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-06-26T05:54:42.3612602Z >>> return x + y, (x + y).to(2) 2024-06-26T05:54:42.3612980Z >>> 2024-06-26T05:54:42.3613211Z >>> # on worker 0 2024-06-26T05:54:42.3613594Z >>> options = TensorPipeRpcBackendOptions( 2024-06-26T05:54:42.3614050Z >>> num_worker_threads=8, 2024-06-26T05:54:42.3614430Z >>> device_maps={"worker1": {0: 1}} 2024-06-26T05:54:42.3614961Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-06-26T05:54:42.3615396Z >>> ) 2024-06-26T05:54:42.3615692Z >>> options.set_device_map("worker1", {1: 2}) 2024-06-26T05:54:42.3616238Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-06-26T05:54:42.3616666Z >>> 2024-06-26T05:54:42.3616898Z >>> rpc.init_rpc( 2024-06-26T05:54:42.3617194Z >>> "worker0", 2024-06-26T05:54:42.3617489Z >>> rank=0, 2024-06-26T05:54:42.3617765Z >>> world_size=2, 2024-06-26T05:54:42.3618144Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-06-26T05:54:42.3618601Z >>> rpc_backend_options=options 2024-06-26T05:54:42.3618970Z >>> ) 2024-06-26T05:54:42.3619208Z >>> 2024-06-26T05:54:42.3619450Z >>> x = torch.ones(2) 2024-06-26T05:54:42.3619882Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-06-26T05:54:42.3620586Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-06-26T05:54:42.3621323Z >>> # sending the return value back, it will follow the invert of 2024-06-26T05:54:42.3621975Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-06-26T05:54:42.3622501Z >>> # cuda:1 on worker0 2024-06-26T05:54:42.3622988Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-06-26T05:54:42.3623577Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-06-26T05:54:42.3623939Z 2024-06-26T05:54:42.3624329Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.3624816Z 2024-06-26T05:54:42.3772850Z msg = Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=366. 2024-06-26T05:54:42.3774449Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.3774964Z 2024-06-26T05:54:42.3775615Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-06-26T05:54:42.3776907Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-06-26T05:54:42.3777624Z 2024-06-26T05:54:42.3777725Z Keyword Args: 2024-06-26T05:54:42.3778403Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-06-26T05:54:42.3779534Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-06-26T05:54:42.3780686Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-06-26T05:54:42.3781514Z as a placeholder. default: None. 2024-06-26T05:54:42.3782084Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-06-26T05:54:42.3783087Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-06-26T05:54:42.3784339Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-06-26T05:54:42.3785203Z input_kwarg_layouts (Dict[str, Placement]): 2024-06-26T05:54:42.3786079Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-06-26T05:54:42.3786894Z default: None 2024-06-26T05:54:42.3787290Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-06-26T05:54:42.3788170Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-06-26T05:54:42.3789056Z have the desired DTensor layouts. default: None. 2024-06-26T05:54:42.3789548Z use_local_output (bool, optional): 2024-06-26T05:54:42.3790333Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-06-26T05:54:42.3791090Z Returns: 2024-06-26T05:54:42.3791754Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-06-26T05:54:42.3792342Z 2024-06-26T05:54:42.3792455Z Example:: 2024-06-26T05:54:42.3792727Z >>> # xdoctest: +SKIP(failing) 2024-06-26T05:54:42.3793407Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-06-26T05:54:42.3794231Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-06-26T05:54:42.3795011Z >>> ... 2024-06-26T05:54:42.3795627Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-06-26T05:54:42.3796379Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-06-26T05:54:42.3796785Z >>> 2024-06-26T05:54:42.3797397Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-06-26T05:54:42.3798241Z >>> # and then redistributed to Replicated DTensor. 2024-06-26T05:54:42.3798797Z >>> parallelize_module( 2024-06-26T05:54:42.3799212Z >>> block, # this can be a submodule or module 2024-06-26T05:54:42.3799716Z >>> tp_mesh, 2024-06-26T05:54:42.3800010Z >>> parallelize_plan={ 2024-06-26T05:54:42.3800402Z >>> "attn": PrepareModuleInput( 2024-06-26T05:54:42.3800898Z >>> input_layouts=(Shard(0), None, None, ...), 2024-06-26T05:54:42.3801524Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-06-26T05:54:42.3802013Z >>> ), 2024-06-26T05:54:42.3802289Z >>> } 2024-06-26T05:54:42.3802532Z >>> ) 2024-06-26T05:54:42.3802686Z 2024-06-26T05:54:42.3803085Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.3803571Z 2024-06-26T05:54:42.3804703Z msg = Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=521. 2024-06-26T05:54:42.3806147Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.3806649Z 2024-06-26T05:54:42.3807284Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-06-26T05:54:42.3808434Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-06-26T05:54:42.3809043Z 2024-06-26T05:54:42.3809144Z Keyword Args: 2024-06-26T05:54:42.3809524Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-06-26T05:54:42.3810365Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-06-26T05:54:42.3811553Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-06-26T05:54:42.3812454Z ``None`` need to be specified as a placeholder. 2024-06-26T05:54:42.3813041Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-06-26T05:54:42.3814011Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-06-26T05:54:42.3814876Z have the desired DTensor layouts. 2024-06-26T05:54:42.3815302Z use_local_output (bool, optional): 2024-06-26T05:54:42.3816101Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-06-26T05:54:42.3816990Z Returns: 2024-06-26T05:54:42.3817751Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-06-26T05:54:42.3818741Z 2024-06-26T05:54:42.3818918Z Example:: 2024-06-26T05:54:42.3819213Z >>> # xdoctest: +SKIP(failing) 2024-06-26T05:54:42.3819902Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-06-26T05:54:42.3820728Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-06-26T05:54:42.3821244Z >>> ... 2024-06-26T05:54:42.3821824Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-06-26T05:54:42.3822557Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-06-26T05:54:42.3822969Z >>> 2024-06-26T05:54:42.3823662Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-06-26T05:54:42.3824544Z >>> # and then redistributed to Sharded DTensor. 2024-06-26T05:54:42.3825002Z >>> parallelize_module( 2024-06-26T05:54:42.3825406Z >>> block, # this can be a submodule or module 2024-06-26T05:54:42.3825837Z >>> tp_mesh, 2024-06-26T05:54:42.3826198Z >>> parallelize_plan = PrepareModuleOutput( 2024-06-26T05:54:42.3826680Z >>> output_layouts=Replicate(), 2024-06-26T05:54:42.3827114Z >>> desired_output_layouts=Shard(0) 2024-06-26T05:54:42.3827515Z >>> ) 2024-06-26T05:54:42.3827764Z >>> ) 2024-06-26T05:54:42.3827907Z 2024-06-26T05:54:42.3828370Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.3828872Z 2024-06-26T05:54:42.3829848Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=109. 2024-06-26T05:54:42.3831372Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.3831988Z 2024-06-26T05:54:42.3832255Z Creates a RelaxedBernoulli distribution, parametrized by 2024-06-26T05:54:42.3833328Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-06-26T05:54:42.3834051Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-06-26T05:54:42.3834974Z so the values are in (0, 1), and has reparametrizable samples. 2024-06-26T05:54:42.3835388Z 2024-06-26T05:54:42.3835508Z Example:: 2024-06-26T05:54:42.3835654Z 2024-06-26T05:54:42.3836009Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:42.3836531Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-06-26T05:54:42.3837038Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-06-26T05:54:42.3837472Z >>> m.sample() 2024-06-26T05:54:42.3837797Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-06-26T05:54:42.3838102Z 2024-06-26T05:54:42.3838205Z Args: 2024-06-26T05:54:42.3838502Z temperature (Tensor): relaxation temperature 2024-06-26T05:54:42.3839057Z probs (Number, Tensor): the probability of sampling `1` 2024-06-26T05:54:42.3839699Z logits (Number, Tensor): the log-odds of sampling `1` 2024-06-26T05:54:42.3840065Z 2024-06-26T05:54:42.3840451Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.3841019Z 2024-06-26T05:54:42.3842058Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=97. 2024-06-26T05:54:42.3843512Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.3844020Z 2024-06-26T05:54:42.3844309Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-06-26T05:54:42.3844997Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-06-26T05:54:42.3845742Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-06-26T05:54:42.3846439Z its samples are on simplex, and are reparametrizable. 2024-06-26T05:54:42.3846805Z 2024-06-26T05:54:42.3846916Z Example:: 2024-06-26T05:54:42.3847059Z 2024-06-26T05:54:42.3847287Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:42.3847832Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-06-26T05:54:42.3848382Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-06-26T05:54:42.3848822Z >>> m.sample() 2024-06-26T05:54:42.3849146Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-06-26T05:54:42.3849448Z 2024-06-26T05:54:42.3849551Z Args: 2024-06-26T05:54:42.3849848Z temperature (Tensor): relaxation temperature 2024-06-26T05:54:42.3850320Z probs (Tensor): event probabilities 2024-06-26T05:54:42.3850862Z logits (Tensor): unnormalized log probability for each event 2024-06-26T05:54:42.3851267Z 2024-06-26T05:54:42.3851649Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.3852144Z 2024-06-26T05:54:42.4629710Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=12. 2024-06-26T05:54:42.4631263Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.4631871Z 2024-06-26T05:54:42.4632207Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-06-26T05:54:42.4633048Z distribution where all component are from different parameterizations of 2024-06-26T05:54:42.4633875Z the same distribution type. It is parameterized by a `Categorical` 2024-06-26T05:54:42.4635092Z "selecting distribution" (over `k` component) and a component 2024-06-26T05:54:42.4635853Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-06-26T05:54:42.4636640Z (equal to `[k]`) which indexes each (batch of) component. 2024-06-26T05:54:42.4637089Z 2024-06-26T05:54:42.4637206Z Examples:: 2024-06-26T05:54:42.4637357Z 2024-06-26T05:54:42.4637515Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:42.4638143Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-06-26T05:54:42.4638783Z >>> # weighted normal distributions 2024-06-26T05:54:42.4639280Z >>> mix = D.Categorical(torch.ones(5,)) 2024-06-26T05:54:42.4639474Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-06-26T05:54:42.4639632Z >>> gmm = MixtureSameFamily(mix, comp) 2024-06-26T05:54:42.4639637Z 2024-06-26T05:54:42.4639925Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-06-26T05:54:42.4640247Z >>> # weighted bivariate normal distributions 2024-06-26T05:54:42.4640409Z >>> mix = D.Categorical(torch.ones(5,)) 2024-06-26T05:54:42.4640541Z >>> comp = D.Independent(D.Normal( 2024-06-26T05:54:42.4640771Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-06-26T05:54:42.4640933Z >>> gmm = MixtureSameFamily(mix, comp) 2024-06-26T05:54:42.4641023Z 2024-06-26T05:54:42.4641271Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-06-26T05:54:42.4641599Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-06-26T05:54:42.4641762Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-06-26T05:54:42.4641894Z >>> comp = D.Independent(D.Normal( 2024-06-26T05:54:42.4642087Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-06-26T05:54:42.4642254Z >>> gmm = MixtureSameFamily(mix, comp) 2024-06-26T05:54:42.4642263Z 2024-06-26T05:54:42.4642385Z Args: 2024-06-26T05:54:42.4642745Z mixture_distribution: `torch.distributions.Categorical`-like 2024-06-26T05:54:42.4642998Z instance. Manages the probability of selecting component. 2024-06-26T05:54:42.4643273Z The number of categories must match the rightmost batch 2024-06-26T05:54:42.4643539Z dimension of the `component_distribution`. Must have either 2024-06-26T05:54:42.4643719Z scalar `batch_shape` or `batch_shape` matching 2024-06-26T05:54:42.4643999Z `component_distribution.batch_shape[:-1]` 2024-06-26T05:54:42.4644348Z component_distribution: `torch.distributions.Distribution`-like 2024-06-26T05:54:42.4644688Z instance. Right-most batch dimension indexes component. 2024-06-26T05:54:42.4644694Z 2024-06-26T05:54:42.4645096Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.4645102Z 2024-06-26T05:54:42.7212114Z msg = Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=230. 2024-06-26T05:54:42.7212685Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.7213138Z Return a new dict with new, potentially nested, key value pair 2024-06-26T05:54:42.7213158Z 2024-06-26T05:54:42.7213390Z >>> purchase = {'name': 'Alice', 2024-06-26T05:54:42.7213659Z ... 'order': {'items': ['Apple', 'Orange'], 2024-06-26T05:54:42.7213915Z ... 'costs': [0.50, 1.25]}, 2024-06-26T05:54:42.7214132Z ... 'credit card': '5555-1234-1234-1234'} 2024-06-26T05:54:42.7214553Z >>> assoc_in(purchase, ['order', 'costs'], [0.25, 1.00]) # doctest: +SKIP 2024-06-26T05:54:42.7214740Z {'credit card': '5555-1234-1234-1234', 2024-06-26T05:54:42.7214871Z 'name': 'Alice', 2024-06-26T05:54:42.7215172Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-06-26T05:54:42.7215302Z 2024-06-26T05:54:42.7215726Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.7215913Z 2024-06-26T05:54:42.7217261Z msg = Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2024-06-26T05:54:42.7217742Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.7217942Z Update value in a (potentially) nested dictionary 2024-06-26T05:54:42.7217976Z 2024-06-26T05:54:42.7218071Z inputs: 2024-06-26T05:54:42.7218261Z d - dictionary on which to operate 2024-06-26T05:54:42.7218627Z keys - list or tuple giving the location of the value to be changed in d 2024-06-26T05:54:42.7218822Z func - function to operate on that value 2024-06-26T05:54:42.7218827Z 2024-06-26T05:54:42.7219133Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-06-26T05:54:42.7219497Z original dictionary with v replaced by func(v), but does not mutate the 2024-06-26T05:54:42.7219807Z original dictionary. 2024-06-26T05:54:42.7219817Z 2024-06-26T05:54:42.7220297Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-06-26T05:54:42.7220727Z specified by the keys, with the innermost value set to func(default). 2024-06-26T05:54:42.7220741Z 2024-06-26T05:54:42.7220941Z >>> inc = lambda x: x + 1 2024-06-26T05:54:42.7221118Z >>> update_in({'a': 0}, ['a'], inc) 2024-06-26T05:54:42.7221238Z {'a': 1} 2024-06-26T05:54:42.7221243Z 2024-06-26T05:54:42.7221424Z >>> transaction = {'name': 'Alice', 2024-06-26T05:54:42.7221739Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-06-26T05:54:42.7221959Z ... 'costs': [0.50, 1.25]}, 2024-06-26T05:54:42.7222189Z ... 'credit card': '5555-1234-1234-1234'} 2024-06-26T05:54:42.7222603Z >>> update_in(transaction, ['purchase', 'costs'], sum) # doctest: +SKIP 2024-06-26T05:54:42.7222795Z {'credit card': '5555-1234-1234-1234', 2024-06-26T05:54:42.7222930Z 'name': 'Alice', 2024-06-26T05:54:42.7223237Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-06-26T05:54:42.7223245Z 2024-06-26T05:54:42.7223440Z >>> # updating a value when k0 is not in d 2024-06-26T05:54:42.7223600Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-06-26T05:54:42.7223735Z {1: {2: {3: 'bar'}}} 2024-06-26T05:54:42.7223940Z >>> update_in({1: 'foo'}, [2, 3, 4], inc, 0) 2024-06-26T05:54:42.7224150Z {1: 'foo', 2: {3: {4: 1}}} 2024-06-26T05:54:42.7224238Z 2024-06-26T05:54:42.7224635Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.7224640Z 2024-06-26T05:54:42.7225761Z msg = Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=303. 2024-06-26T05:54:42.7226177Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.7226442Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-06-26T05:54:42.7226456Z 2024-06-26T05:54:42.7226725Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-06-26T05:54:42.7227001Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-06-26T05:54:42.7227008Z 2024-06-26T05:54:42.7227357Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-06-26T05:54:42.7227519Z structures such as dictionaries and lists. 2024-06-26T05:54:42.7227537Z 2024-06-26T05:54:42.7227705Z >>> transaction = {'name': 'Alice', 2024-06-26T05:54:42.7227983Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-06-26T05:54:42.7228241Z ... 'costs': [0.50, 1.25]}, 2024-06-26T05:54:42.7228456Z ... 'credit card': '5555-1234-1234-1234'} 2024-06-26T05:54:42.7228678Z >>> get_in(['purchase', 'items', 0], transaction) 2024-06-26T05:54:42.7228871Z 'Apple' 2024-06-26T05:54:42.7229035Z >>> get_in(['name'], transaction) 2024-06-26T05:54:42.7229151Z 'Alice' 2024-06-26T05:54:42.7229437Z >>> get_in(['purchase', 'total'], transaction) 2024-06-26T05:54:42.7229748Z >>> get_in(['purchase', 'items', 'apple'], transaction) 2024-06-26T05:54:42.7230014Z >>> get_in(['purchase', 'items', 10], transaction) 2024-06-26T05:54:42.7230246Z >>> get_in(['purchase', 'total'], transaction, 0) 2024-06-26T05:54:42.7230373Z 0 2024-06-26T05:54:42.7230577Z >>> get_in(['y'], {}, no_default=True) 2024-06-26T05:54:42.7230727Z Traceback (most recent call last): 2024-06-26T05:54:42.7230821Z ... 2024-06-26T05:54:42.7230946Z KeyError: 'y' 2024-06-26T05:54:42.7230967Z 2024-06-26T05:54:42.7231060Z See Also: 2024-06-26T05:54:42.7231227Z itertoolz.get 2024-06-26T05:54:42.7231350Z operator.getitem 2024-06-26T05:54:42.7231435Z 2024-06-26T05:54:42.7231823Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.7231829Z 2024-06-26T05:54:42.7233072Z msg = Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=355. 2024-06-26T05:54:42.7233523Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:42.7233707Z Group a collection by a key function 2024-06-26T05:54:42.7233713Z 2024-06-26T05:54:42.7234002Z >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank'] 2024-06-26T05:54:42.7234154Z >>> groupby(len, names) # doctest: +SKIP 2024-06-26T05:54:42.7234523Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-06-26T05:54:42.7234529Z 2024-06-26T05:54:42.7234862Z >>> iseven = lambda x: x % 2 == 0 2024-06-26T05:54:42.7235217Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-06-26T05:54:42.7235386Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-06-26T05:54:42.7235392Z 2024-06-26T05:54:42.7235617Z Non-callable keys imply grouping on a member. 2024-06-26T05:54:42.7235623Z 2024-06-26T05:54:42.7235900Z >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'}, 2024-06-26T05:54:42.7236181Z ... {'name': 'Bob', 'gender': 'M'}, 2024-06-26T05:54:42.7236459Z ... {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP 2024-06-26T05:54:42.7236663Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-06-26T05:54:42.7236899Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-06-26T05:54:42.7237090Z {'gender': 'M', 'name': 'Charlie'}]} 2024-06-26T05:54:42.7237095Z 2024-06-26T05:54:42.7237280Z Not to be confused with ``itertools.groupby`` 2024-06-26T05:54:42.7237285Z 2024-06-26T05:54:42.7237377Z See Also: 2024-06-26T05:54:42.7237473Z countby 2024-06-26T05:54:42.7237629Z 2024-06-26T05:54:42.7238024Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:42.7238029Z 2024-06-26T05:54:43.0988522Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601. 2024-06-26T05:54:43.0989875Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.0990763Z Applies Batch Normalization over a N-Dimensional input. 2024-06-26T05:54:43.0991340Z 2024-06-26T05:54:43.0992395Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-06-26T05:54:43.0993376Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-06-26T05:54:43.0994112Z Internal Covariate Shift `__ . 2024-06-26T05:54:43.0994545Z 2024-06-26T05:54:43.0994850Z .. math:: 2024-06-26T05:54:43.0995032Z 2024-06-26T05:54:43.0995448Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-06-26T05:54:43.0995966Z 2024-06-26T05:54:43.0996335Z The mean and standard-deviation are calculated per-dimension over all 2024-06-26T05:54:43.0997204Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-06-26T05:54:43.0998212Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-06-26T05:54:43.0998940Z By default, the elements of :math:`\gamma` are sampled from 2024-06-26T05:54:43.0999704Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-06-26T05:54:43.1000558Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-06-26T05:54:43.1001277Z `torch.var(input, unbiased=False)`. 2024-06-26T05:54:43.1001563Z 2024-06-26T05:54:43.1001895Z Also by default, during training this layer keeps running estimates of its 2024-06-26T05:54:43.1002700Z computed mean and variance, which are then used for normalization during 2024-06-26T05:54:43.1003489Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-06-26T05:54:43.1004077Z of 0.1. 2024-06-26T05:54:43.1004223Z 2024-06-26T05:54:43.1004646Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-06-26T05:54:43.1005411Z keep running estimates, and batch statistics are instead used during 2024-06-26T05:54:43.1005987Z evaluation time as well. 2024-06-26T05:54:43.1006220Z 2024-06-26T05:54:43.1006334Z .. note:: 2024-06-26T05:54:43.1006780Z This :attr:`momentum` argument is different from one used in optimizer 2024-06-26T05:54:43.1007546Z classes and the conventional notion of momentum. Mathematically, the 2024-06-26T05:54:43.1008182Z update rule for running statistics here is 2024-06-26T05:54:43.1008993Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-06-26T05:54:43.1009859Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-06-26T05:54:43.1010437Z new observed value. 2024-06-26T05:54:43.1010662Z 2024-06-26T05:54:43.1011081Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-06-26T05:54:43.1012068Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-06-26T05:54:43.1012857Z Normalization or Spatio-temporal Batch Normalization. 2024-06-26T05:54:43.1013238Z 2024-06-26T05:54:43.1013426Z Currently :class:`SyncBatchNorm` only supports 2024-06-26T05:54:43.1014136Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-06-26T05:54:43.1014961Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-06-26T05:54:43.1015684Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-06-26T05:54:43.1016221Z Network with DDP. 2024-06-26T05:54:43.1016430Z 2024-06-26T05:54:43.1016522Z Args: 2024-06-26T05:54:43.1016885Z num_features: :math:`C` from an expected input of size 2024-06-26T05:54:43.1017377Z :math:`(N, C, +)` 2024-06-26T05:54:43.1017859Z eps: a value added to the denominator for numerical stability. 2024-06-26T05:54:43.1018440Z Default: ``1e-5`` 2024-06-26T05:54:43.1018933Z momentum: the value used for the running_mean and running_var 2024-06-26T05:54:43.1019619Z computation. Can be set to ``None`` for cumulative moving average 2024-06-26T05:54:43.1020226Z (i.e. simple average). Default: 0.1 2024-06-26T05:54:43.1020815Z affine: a boolean value that when set to ``True``, this module has 2024-06-26T05:54:43.1021440Z learnable affine parameters. Default: ``True`` 2024-06-26T05:54:43.1022086Z track_running_stats: a boolean value that when set to ``True``, this 2024-06-26T05:54:43.1022857Z module tracks the running mean and variance, and when set to ``False``, 2024-06-26T05:54:43.1023636Z this module does not track such statistics, and initializes statistics 2024-06-26T05:54:43.1024386Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-06-26T05:54:43.1025155Z When these buffers are ``None``, this module always uses batch statistics. 2024-06-26T05:54:43.1025911Z in both training and eval modes. Default: ``True`` 2024-06-26T05:54:43.1026588Z process_group: synchronization of stats happen within each process group 2024-06-26T05:54:43.1027412Z individually. Default behavior is synchronization across the whole 2024-06-26T05:54:43.1027971Z world 2024-06-26T05:54:43.1028139Z 2024-06-26T05:54:43.1028232Z Shape: 2024-06-26T05:54:43.1028542Z - Input: :math:`(N, C, +)` 2024-06-26T05:54:43.1029037Z - Output: :math:`(N, C, +)` (same shape as input) 2024-06-26T05:54:43.1029382Z 2024-06-26T05:54:43.1029480Z .. note:: 2024-06-26T05:54:43.1029963Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-06-26T05:54:43.1030716Z synchronization is disabled when ``model.eval()`` is set or if 2024-06-26T05:54:43.1031289Z ``self.training`` is otherwise ``False``. 2024-06-26T05:54:43.1031619Z 2024-06-26T05:54:43.1031795Z Examples:: 2024-06-26T05:54:43.1031963Z 2024-06-26T05:54:43.1032093Z >>> # xdoctest: +SKIP 2024-06-26T05:54:43.1032451Z >>> # With Learnable Parameters 2024-06-26T05:54:43.1032875Z >>> m = nn.SyncBatchNorm(100) 2024-06-26T05:54:43.1033311Z >>> # creating process group (optional) 2024-06-26T05:54:43.1033816Z >>> # ranks is a list of int identifying rank ids. 2024-06-26T05:54:43.1034273Z >>> ranks = list(range(8)) 2024-06-26T05:54:43.1034799Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-06-26T05:54:43.1035288Z >>> # Note: every rank calls into new_group for every 2024-06-26T05:54:43.1035838Z >>> # process group created, even if that rank is not 2024-06-26T05:54:43.1036326Z >>> # part of the group. 2024-06-26T05:54:43.1036903Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-06-26T05:54:43.1037650Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-06-26T05:54:43.1038236Z >>> # Without Learnable Parameters 2024-06-26T05:54:43.1038825Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-06-26T05:54:43.1039424Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-06-26T05:54:43.1039864Z >>> output = m(input) 2024-06-26T05:54:43.1040094Z 2024-06-26T05:54:43.1040246Z >>> # network is nn.BatchNorm layer 2024-06-26T05:54:43.1040907Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-06-26T05:54:43.1041735Z >>> # only single gpu per process is currently supported 2024-06-26T05:54:43.1042421Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-06-26T05:54:43.1043041Z >>> sync_bn_network, 2024-06-26T05:54:43.1043512Z >>> device_ids=[args.local_rank], 2024-06-26T05:54:43.1044029Z >>> output_device=args.local_rank) 2024-06-26T05:54:43.1044461Z 2024-06-26T05:54:43.1044995Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.1045491Z 2024-06-26T05:54:43.1046480Z msg = Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=824. 2024-06-26T05:54:43.1047907Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.1048863Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-06-26T05:54:43.1049452Z 2024-06-26T05:54:43.1049545Z Args: 2024-06-26T05:54:43.1050034Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-06-26T05:54:43.1050823Z process_group (optional): process group to scope synchronization, 2024-06-26T05:54:43.1051399Z default is the whole world 2024-06-26T05:54:43.1051705Z 2024-06-26T05:54:43.1051803Z Returns: 2024-06-26T05:54:43.1052509Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-06-26T05:54:43.1053316Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-06-26T05:54:43.1054128Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-06-26T05:54:43.1054766Z instead. 2024-06-26T05:54:43.1054950Z 2024-06-26T05:54:43.1055066Z Example:: 2024-06-26T05:54:43.1055238Z 2024-06-26T05:54:43.1055391Z >>> # Network with nn.BatchNorm layer 2024-06-26T05:54:43.1055899Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:43.1056404Z >>> module = torch.nn.Sequential( 2024-06-26T05:54:43.1056858Z >>> torch.nn.Linear(20, 100), 2024-06-26T05:54:43.1057338Z >>> torch.nn.BatchNorm1d(100), 2024-06-26T05:54:43.1057769Z >>> ).cuda() 2024-06-26T05:54:43.1058246Z >>> # creating process group (optional) 2024-06-26T05:54:43.1058778Z >>> # ranks is a list of int identifying rank ids. 2024-06-26T05:54:43.1059263Z >>> ranks = list(range(8)) 2024-06-26T05:54:43.1059668Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-06-26T05:54:43.1060166Z >>> # Note: every rank calls into new_group for every 2024-06-26T05:54:43.1060739Z >>> # process group created, even if that rank is not 2024-06-26T05:54:43.1061216Z >>> # part of the group. 2024-06-26T05:54:43.1061644Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:43.1062292Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-06-26T05:54:43.1063064Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-06-26T05:54:43.1063892Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-06-26T05:54:43.1064462Z 2024-06-26T05:54:43.1064557Z 2024-06-26T05:54:43.1065101Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.1065588Z 2024-06-26T05:54:43.1230379Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60. 2024-06-26T05:54:43.1231659Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.1232200Z 2024-06-26T05:54:43.1232624Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-06-26T05:54:43.1233212Z 2024-06-26T05:54:43.1233595Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-06-26T05:54:43.1234457Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-06-26T05:54:43.1235148Z 2024-06-26T05:54:43.1235586Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-06-26T05:54:43.1236581Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-06-26T05:54:43.1237352Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-06-26T05:54:43.1237732Z 2024-06-26T05:54:43.1237821Z Shape: 2024-06-26T05:54:43.1238367Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-06-26T05:54:43.1239201Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-06-26T05:54:43.1240078Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-06-26T05:54:43.1240714Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-06-26T05:54:43.1241118Z 2024-06-26T05:54:43.1241206Z Args: 2024-06-26T05:54:43.1241541Z dim (Union[int, str]): Dimension to be unflattened 2024-06-26T05:54:43.1242343Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-06-26T05:54:43.1242981Z 2024-06-26T05:54:43.1243080Z Examples: 2024-06-26T05:54:43.1243479Z >>> input = torch.randn(2, 50) 2024-06-26T05:54:43.1243847Z >>> # With tuple of ints 2024-06-26T05:54:43.1244194Z >>> m = nn.Sequential( 2024-06-26T05:54:43.1244594Z >>> nn.Linear(50, 50), 2024-06-26T05:54:43.1244940Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-06-26T05:54:43.1245310Z >>> ) 2024-06-26T05:54:43.1245565Z >>> output = m(input) 2024-06-26T05:54:43.1245872Z >>> output.size() 2024-06-26T05:54:43.1246181Z torch.Size([2, 2, 5, 5]) 2024-06-26T05:54:43.1246518Z >>> # With torch.Size 2024-06-26T05:54:43.1246830Z >>> m = nn.Sequential( 2024-06-26T05:54:43.1247159Z >>> nn.Linear(50, 50), 2024-06-26T05:54:43.1247548Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-06-26T05:54:43.1247948Z >>> ) 2024-06-26T05:54:43.1248205Z >>> output = m(input) 2024-06-26T05:54:43.1248525Z >>> output.size() 2024-06-26T05:54:43.1248818Z torch.Size([2, 2, 5, 5]) 2024-06-26T05:54:43.1249311Z >>> # With namedshape (tuple of tuples) 2024-06-26T05:54:43.1249889Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-06-26T05:54:43.1250584Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-06-26T05:54:43.1251155Z >>> output = unflatten(input) 2024-06-26T05:54:43.1251526Z >>> output.size() 2024-06-26T05:54:43.1251820Z torch.Size([2, 2, 5, 5]) 2024-06-26T05:54:43.1252061Z 2024-06-26T05:54:43.1252445Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.1252933Z 2024-06-26T05:54:43.1567027Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1696. 2024-06-26T05:54:43.1568434Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.1569238Z Creates a criterion that measures the triplet loss given input 2024-06-26T05:54:43.1569915Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-06-26T05:54:43.1570625Z positive, and negative examples, respectively), and a nonnegative, 2024-06-26T05:54:43.1571464Z real-valued function ("distance function") used to compute the relationship 2024-06-26T05:54:43.1572245Z between the anchor and positive example ("positive distance") and the 2024-06-26T05:54:43.1572903Z anchor and negative example ("negative distance"). 2024-06-26T05:54:43.1573261Z 2024-06-26T05:54:43.1573614Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-06-26T05:54:43.1574159Z can be described as: 2024-06-26T05:54:43.1574385Z 2024-06-26T05:54:43.1574496Z .. math:: 2024-06-26T05:54:43.1574850Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-06-26T05:54:43.1575480Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-06-26T05:54:43.1575877Z 2024-06-26T05:54:43.1576294Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-06-26T05:54:43.1577214Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-06-26T05:54:43.1578112Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-06-26T05:54:43.1578940Z between the positive and negative distances that is required for the loss to 2024-06-26T05:54:43.1579759Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-06-26T05:54:43.1580392Z that the distance function can handle. 2024-06-26T05:54:43.1580692Z 2024-06-26T05:54:43.1580871Z If :attr:`reduction` is not ``'none'`` 2024-06-26T05:54:43.1581323Z (default ``'mean'``), then: 2024-06-26T05:54:43.1581565Z 2024-06-26T05:54:43.1581674Z .. math:: 2024-06-26T05:54:43.1581922Z \ell(x, y) = 2024-06-26T05:54:43.1582210Z \begin{cases} 2024-06-26T05:54:43.1582746Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-06-26T05:54:43.1583502Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-06-26T05:54:43.1584161Z \end{cases} 2024-06-26T05:54:43.1584353Z 2024-06-26T05:54:43.1584674Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-06-26T05:54:43.1585564Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-06-26T05:54:43.1586067Z 2024-06-26T05:54:43.1586159Z Args: 2024-06-26T05:54:43.1586733Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-06-26T05:54:43.1587512Z quantifies the closeness of two tensors. If not specified, 2024-06-26T05:54:43.1588141Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-06-26T05:54:43.1588889Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-06-26T05:54:43.1589785Z between the positive and negative distances required for the loss to be 0. Larger 2024-06-26T05:54:43.1590788Z margins penalize cases where the negative examples are not distant enough from the 2024-06-26T05:54:43.1591564Z anchors, relative to the positives. Default: :math:`1`. 2024-06-26T05:54:43.1592301Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-06-26T05:54:43.1593162Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-06-26T05:54:43.1594012Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-06-26T05:54:43.1595101Z negative example than the anchor is, swaps the positive example and the anchor in 2024-06-26T05:54:43.1595814Z the loss computation. Default: ``False``. 2024-06-26T05:54:43.1596506Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-06-26T05:54:43.1597394Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-06-26T05:54:43.1598165Z ``'mean'``: the sum of the output will be divided by the number of 2024-06-26T05:54:43.1599002Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-06-26T05:54:43.1599506Z 2024-06-26T05:54:43.1599511Z 2024-06-26T05:54:43.1599604Z Shape: 2024-06-26T05:54:43.1600284Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-06-26T05:54:43.1601028Z as supported by the distance function. 2024-06-26T05:54:43.1601782Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-06-26T05:54:43.1602422Z otherwise. 2024-06-26T05:54:43.1602608Z 2024-06-26T05:54:43.1602729Z Examples:: 2024-06-26T05:54:43.1602893Z 2024-06-26T05:54:43.1603012Z >>> # Initialize embeddings 2024-06-26T05:54:43.1603409Z >>> embedding = nn.Embedding(1000, 128) 2024-06-26T05:54:43.1603875Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-06-26T05:54:43.1604358Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-06-26T05:54:43.1604858Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-06-26T05:54:43.1605323Z >>> anchor = embedding(anchor_ids) 2024-06-26T05:54:43.1605741Z >>> positive = embedding(positive_ids) 2024-06-26T05:54:43.1606185Z >>> negative = embedding(negative_ids) 2024-06-26T05:54:43.1606572Z >>> 2024-06-26T05:54:43.1606866Z >>> # Built-in Distance Function 2024-06-26T05:54:43.1607257Z >>> triplet_loss = \ 2024-06-26T05:54:43.1607831Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-06-26T05:54:43.1608558Z >>> output = triplet_loss(anchor, positive, negative) 2024-06-26T05:54:43.1609021Z >>> output.backward() 2024-06-26T05:54:43.1609331Z >>> 2024-06-26T05:54:43.1609599Z >>> # Custom Distance Function 2024-06-26T05:54:43.1609969Z >>> def l_infinity(x1, x2): 2024-06-26T05:54:43.1610550Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-06-26T05:54:43.1611105Z >>> 2024-06-26T05:54:43.1611498Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-06-26T05:54:43.1612117Z >>> triplet_loss = ( 2024-06-26T05:54:43.1612685Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-06-26T05:54:43.1613451Z >>> output = triplet_loss(anchor, positive, negative) 2024-06-26T05:54:43.1613934Z >>> output.backward() 2024-06-26T05:54:43.1625147Z >>> 2024-06-26T05:54:43.1625479Z >>> # Custom Distance Function (Lambda) 2024-06-26T05:54:43.1625926Z >>> triplet_loss = ( 2024-06-26T05:54:43.1626308Z >>> nn.TripletMarginWithDistanceLoss( 2024-06-26T05:54:43.1627038Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-06-26T05:54:43.1627696Z >>> output = triplet_loss(anchor, positive, negative) 2024-06-26T05:54:43.1628165Z >>> output.backward() 2024-06-26T05:54:43.1628398Z 2024-06-26T05:54:43.1628497Z Reference: 2024-06-26T05:54:43.1629248Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-06-26T05:54:43.1630071Z http://www.bmva.org/bmvc/2016/papers/paper119/index.html 2024-06-26T05:54:43.1630572Z 2024-06-26T05:54:43.1631110Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-06-26T05:54:43.1631606Z 2024-06-26T05:54:43.2120916Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=395. 2024-06-26T05:54:43.2122721Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.2123426Z Computes a partial inverse of :class:`MaxPool2d`. 2024-06-26T05:54:43.2123826Z 2024-06-26T05:54:43.2124253Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-06-26T05:54:43.2124778Z 2024-06-26T05:54:43.2125093Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-06-26T05:54:43.2125894Z including the indices of the maximal values and computes a partial inverse 2024-06-26T05:54:43.2126638Z in which all non-maximal values are set to zero. 2024-06-26T05:54:43.2127000Z 2024-06-26T05:54:43.2127100Z Note: 2024-06-26T05:54:43.2127672Z This operation may behave nondeterministically when the input indices has repeat values. 2024-06-26T05:54:43.2128754Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-06-26T05:54:43.2129425Z 2024-06-26T05:54:43.2129745Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-06-26T05:54:43.2130454Z sizes. Hence, the inversion process can get ambiguous. 2024-06-26T05:54:43.2131099Z To accommodate this, you can provide the needed output size 2024-06-26T05:54:43.2131810Z as an additional argument :attr:`output_size` in the forward call. 2024-06-26T05:54:43.2132428Z See the Inputs and Example below. 2024-06-26T05:54:43.2132747Z 2024-06-26T05:54:43.2132861Z Args: 2024-06-26T05:54:43.2133238Z kernel_size (int or tuple): Size of the max pooling window. 2024-06-26T05:54:43.2133878Z stride (int or tuple): Stride of the max pooling window. 2024-06-26T05:54:43.2134459Z It is set to :attr:`kernel_size` by default. 2024-06-26T05:54:43.2135036Z padding (int or tuple): Padding that was added to the input 2024-06-26T05:54:43.2135460Z 2024-06-26T05:54:43.2135555Z Inputs: 2024-06-26T05:54:43.2135910Z - `input`: the input Tensor to invert 2024-06-26T05:54:43.2136545Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-06-26T05:54:43.2137243Z - `output_size` (optional): the targeted output size 2024-06-26T05:54:43.2137610Z 2024-06-26T05:54:43.2137720Z Shape: 2024-06-26T05:54:43.2138188Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-06-26T05:54:43.2139001Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-06-26T05:54:43.2139617Z 2024-06-26T05:54:43.2139735Z .. math:: 2024-06-26T05:54:43.2140434Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-06-26T05:54:43.2141070Z 2024-06-26T05:54:43.2141170Z .. math:: 2024-06-26T05:54:43.2141853Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-06-26T05:54:43.2142420Z 2024-06-26T05:54:43.2142663Z or as given by :attr:`output_size` in the call operator 2024-06-26T05:54:43.2143046Z 2024-06-26T05:54:43.2143146Z Example:: 2024-06-26T05:54:43.2143325Z 2024-06-26T05:54:43.2143542Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-06-26T05:54:43.2144087Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-06-26T05:54:43.2144570Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-06-26T05:54:43.2145221Z [ 5., 6., 7., 8.], 2024-06-26T05:54:43.2145694Z [ 9., 10., 11., 12.], 2024-06-26T05:54:43.2146167Z [13., 14., 15., 16.]]]]) 2024-06-26T05:54:43.2146617Z >>> output, indices = pool(input) 2024-06-26T05:54:43.2147038Z >>> unpool(output, indices) 2024-06-26T05:54:43.2147440Z tensor([[[[ 0., 0., 0., 0.], 2024-06-26T05:54:43.2147842Z [ 0., 6., 0., 8.], 2024-06-26T05:54:43.2148248Z [ 0., 0., 0., 0.], 2024-06-26T05:54:43.2148665Z [ 0., 14., 0., 16.]]]]) 2024-06-26T05:54:43.2149236Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-06-26T05:54:43.2149888Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-06-26T05:54:43.2150400Z [ 6., 7., 8., 9., 10.], 2024-06-26T05:54:43.2150873Z [11., 12., 13., 14., 15.], 2024-06-26T05:54:43.2151362Z [16., 17., 18., 19., 20.]]]]) 2024-06-26T05:54:43.2151833Z >>> output, indices = pool(input) 2024-06-26T05:54:43.2152351Z >>> # This call will not work without specifying output_size 2024-06-26T05:54:43.2152953Z >>> unpool(output, indices, output_size=input.size()) 2024-06-26T05:54:43.2153460Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-06-26T05:54:43.2153877Z [ 0., 7., 0., 9., 0.], 2024-06-26T05:54:43.2154289Z [ 0., 0., 0., 0., 0.], 2024-06-26T05:54:43.2154923Z [ 0., 17., 0., 19., 0.]]]]) 2024-06-26T05:54:43.2155217Z 2024-06-26T05:54:43.2155223Z 2024-06-26T05:54:43.2155322Z 2024-06-26T05:54:43.2155844Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.2156345Z 2024-06-26T05:54:43.2397426Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=270. 2024-06-26T05:54:43.2398752Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.2399822Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-06-26T05:54:43.2400442Z 2024-06-26T05:54:43.2400914Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-06-26T05:54:43.2401704Z and with 2D inputs, this class 2024-06-26T05:54:43.2401983Z 2024-06-26T05:54:43.2402422Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-06-26T05:54:43.2403473Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-06-26T05:54:43.2404500Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-06-26T05:54:43.2405101Z 2024-06-26T05:54:43.2405600Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-06-26T05:54:43.2406538Z operations. 2024-06-26T05:54:43.2406709Z 2024-06-26T05:54:43.2407189Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-06-26T05:54:43.2408013Z pass. This scales the output of the Embedding before performing a weighted 2024-06-26T05:54:43.2408852Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-06-26T05:54:43.2409688Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-06-26T05:54:43.2410294Z :attr:`per_sample_weights`. 2024-06-26T05:54:43.2410555Z 2024-06-26T05:54:43.2410648Z Args: 2024-06-26T05:54:43.2411039Z num_embeddings (int): size of the dictionary of embeddings 2024-06-26T05:54:43.2411650Z embedding_dim (int): the size of each embedding vector 2024-06-26T05:54:43.2412629Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-06-26T05:54:43.2413457Z is renormalized to have norm :attr:`max_norm`. 2024-06-26T05:54:43.2414435Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-06-26T05:54:43.2415546Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-06-26T05:54:43.2416482Z the words in the mini-batch. Default ``False``. 2024-06-26T05:54:43.2417149Z Note: this option is not supported when ``mode="max"``. 2024-06-26T05:54:43.2417925Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-06-26T05:54:43.2418766Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-06-26T05:54:43.2419547Z into consideration. ``"mean"`` computes the average of the values 2024-06-26T05:54:43.2420288Z in the bag, ``"max"`` computes the max value over each bag. 2024-06-26T05:54:43.2420853Z Default: ``"mean"`` 2024-06-26T05:54:43.2421630Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-06-26T05:54:43.2422611Z Notes for more details regarding sparse gradients. Note: this option is not 2024-06-26T05:54:43.2423289Z supported when ``mode="max"``. 2024-06-26T05:54:43.2424142Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-06-26T05:54:43.2425137Z is equivalent to the size of `indices`. This matches the CSR format. 2024-06-26T05:54:43.2426096Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-06-26T05:54:43.2427083Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-06-26T05:54:43.2427976Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-06-26T05:54:43.2428859Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-06-26T05:54:43.2429734Z zeros, but can be updated to another value to be used as the padding vector. 2024-06-26T05:54:43.2430608Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-06-26T05:54:43.2431255Z reduction. 2024-06-26T05:54:43.2431550Z 2024-06-26T05:54:43.2431665Z Attributes: 2024-06-26T05:54:43.2432263Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-06-26T05:54:43.2433103Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-06-26T05:54:43.2433467Z 2024-06-26T05:54:43.2433598Z Examples:: 2024-06-26T05:54:43.2433796Z 2024-06-26T05:54:43.2434033Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-06-26T05:54:43.2434942Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-06-26T05:54:43.2435493Z >>> # a batch of 2 samples of 4 indices each 2024-06-26T05:54:43.2436079Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-06-26T05:54:43.2436699Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-06-26T05:54:43.2437302Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:43.2437795Z >>> embedding_sum(input, offsets) 2024-06-26T05:54:43.2438272Z tensor([[-0.8861, -5.4350, -0.0523], 2024-06-26T05:54:43.2438749Z [ 1.1306, -2.5798, -1.0044]]) 2024-06-26T05:54:43.2439121Z 2024-06-26T05:54:43.2439270Z >>> # Example with padding_idx 2024-06-26T05:54:43.2439891Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-06-26T05:54:43.2440601Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-06-26T05:54:43.2441302Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-06-26T05:54:43.2441785Z >>> embedding_sum(input, offsets) 2024-06-26T05:54:43.2442213Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-06-26T05:54:43.2442693Z [-0.7082, 3.2145, -2.6251]]) 2024-06-26T05:54:43.2442980Z 2024-06-26T05:54:43.2443217Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-06-26T05:54:43.2443818Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-06-26T05:54:43.2444385Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-06-26T05:54:43.2444867Z embedding.weight, 2024-06-26T05:54:43.2445293Z padding_idx=embedding.padding_idx, 2024-06-26T05:54:43.2445770Z mode='sum') 2024-06-26T05:54:43.2446070Z 2024-06-26T05:54:43.2446599Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.2447103Z 2024-06-26T05:54:43.2739195Z msg = Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1743. 2024-06-26T05:54:43.2740663Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.2741210Z 2024-06-26T05:54:43.2741534Z Context manager for training with uneven inputs across processes in DDP. 2024-06-26T05:54:43.2742007Z 2024-06-26T05:54:43.2742361Z This context manager will keep track of already-joined DDP processes, 2024-06-26T05:54:43.2743109Z and "shadow" the forward and backward passes by inserting collective 2024-06-26T05:54:43.2743918Z communication operations to match with the ones created by non-joined 2024-06-26T05:54:43.2744706Z DDP processes. This will ensure each collective call has a corresponding 2024-06-26T05:54:43.2745536Z call by already-joined DDP processes, preventing hangs or errors that 2024-06-26T05:54:43.2746258Z would otherwise happen when training with uneven inputs across 2024-06-26T05:54:43.2746984Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-06-26T05:54:43.2747734Z specified to be ``True``, all trainers will throw an error once one rank 2024-06-26T05:54:43.2748475Z runs out of inputs, allowing these errors to be caught and handled 2024-06-26T05:54:43.2749045Z according to application logic. 2024-06-26T05:54:43.2749301Z 2024-06-26T05:54:43.2749594Z Once all DDP processes have joined, the context manager will broadcast 2024-06-26T05:54:43.2750361Z the model corresponding to the last joined process to all processes to 2024-06-26T05:54:43.2751016Z ensure the model is the same across all processes 2024-06-26T05:54:43.2751477Z (which is guaranteed by DDP). 2024-06-26T05:54:43.2751741Z 2024-06-26T05:54:43.2752174Z To use this to enable training with uneven inputs across processes, 2024-06-26T05:54:43.2752922Z simply wrap this context manager around your training loop. No further 2024-06-26T05:54:43.2753665Z modifications to the model or data loading is required. 2024-06-26T05:54:43.2754043Z 2024-06-26T05:54:43.2754156Z .. warning:: 2024-06-26T05:54:43.2754865Z If the model or training loop this context manager is wrapped around 2024-06-26T05:54:43.2755577Z has additional distributed collective operations, such as 2024-06-26T05:54:43.2756274Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-06-26T05:54:43.2756957Z ``throw_on_early_termination`` must be enabled. This is because this 2024-06-26T05:54:43.2757736Z context manager is not aware of non-DDP collective communication. 2024-06-26T05:54:43.2758397Z This flag will cause all ranks to throw when any one rank 2024-06-26T05:54:43.2759164Z exhausts inputs, allowing these errors to be caught and recovered 2024-06-26T05:54:43.2759729Z from across all ranks. 2024-06-26T05:54:43.2759948Z 2024-06-26T05:54:43.2760037Z Args: 2024-06-26T05:54:43.2760419Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-06-26T05:54:43.2761182Z gradients by the initial ``world_size`` DDP training was launched 2024-06-26T05:54:43.2761854Z with. If ``False``, will compute the effective world size 2024-06-26T05:54:43.2762495Z (number of ranks that have not depleted their inputs yet) and 2024-06-26T05:54:43.2763105Z divide gradients by that during allreduce. Set 2024-06-26T05:54:43.2763696Z ``divide_by_initial_world_size=True`` to ensure every input 2024-06-26T05:54:43.2764363Z sample including the uneven inputs have equal weight in terms of 2024-06-26T05:54:43.2765162Z how much they contribute to the global gradient. This is 2024-06-26T05:54:43.2765926Z achieved by always dividing the gradient by the initial 2024-06-26T05:54:43.2766789Z ``world_size`` even when we encounter uneven inputs. If you set 2024-06-26T05:54:43.2767950Z this to ``False``, we divide the gradient by the remaining 2024-06-26T05:54:43.2768627Z number of nodes. This ensures parity with training on a smaller 2024-06-26T05:54:43.2769295Z ``world_size`` although it also means the uneven inputs would 2024-06-26T05:54:43.2769961Z contribute more towards the global gradient. Typically, you 2024-06-26T05:54:43.2770640Z would want to set this to ``True`` for cases where the last few 2024-06-26T05:54:43.2771338Z inputs of your training job are uneven. In extreme cases, where 2024-06-26T05:54:43.2772017Z there is a large discrepancy in the number of inputs, setting 2024-06-26T05:54:43.2772631Z this to ``False`` might provide better results. 2024-06-26T05:54:43.2773267Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-06-26T05:54:43.2773945Z in ``enable=False`` to disable in cases where you know that 2024-06-26T05:54:43.2774599Z inputs are even across participating processes. Default is 2024-06-26T05:54:43.2775112Z ``True``. 2024-06-26T05:54:43.2775520Z throw_on_early_termination (bool): Whether to throw an error 2024-06-26T05:54:43.2776161Z or continue training when at least one rank has exhausted 2024-06-26T05:54:43.2776823Z inputs. If ``True``, will throw upon the first rank reaching end 2024-06-26T05:54:43.2777478Z of data. If ``False``, will continue training with a smaller 2024-06-26T05:54:43.2778147Z effective world size until all ranks are joined. Note that if 2024-06-26T05:54:43.2778723Z this flag is specified, then the flag 2024-06-26T05:54:43.2779252Z ``divide_by_initial_world_size`` would be ignored. Default 2024-06-26T05:54:43.2779747Z is ``False``. 2024-06-26T05:54:43.2779939Z 2024-06-26T05:54:43.2779958Z 2024-06-26T05:54:43.2780062Z Example:: 2024-06-26T05:54:43.2780210Z 2024-06-26T05:54:43.2780367Z >>> # xdoctest: +SKIP("Distributed") 2024-06-26T05:54:43.2780848Z >>> import torch 2024-06-26T05:54:43.2781187Z >>> import torch.distributed as dist 2024-06-26T05:54:43.2781636Z >>> import os 2024-06-26T05:54:43.2781952Z >>> import torch.multiprocessing as mp 2024-06-26T05:54:43.2782384Z >>> import torch.nn as nn 2024-06-26T05:54:43.2782748Z >>> # On each spawned worker 2024-06-26T05:54:43.2783099Z >>> def worker(rank): 2024-06-26T05:54:43.2783546Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-06-26T05:54:43.2784076Z >>> torch.cuda.set_device(rank) 2024-06-26T05:54:43.2784534Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-06-26T05:54:43.2785112Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-06-26T05:54:43.2785700Z >>> model, device_ids=[rank], output_device=rank 2024-06-26T05:54:43.2786132Z >>> ) 2024-06-26T05:54:43.2786534Z >>> # Rank 1 gets one more input than rank 0. 2024-06-26T05:54:43.2787129Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-06-26T05:54:43.2787652Z >>> with model.join(): 2024-06-26T05:54:43.2788013Z >>> for _ in range(5): 2024-06-26T05:54:43.2788392Z >>> for inp in inputs: 2024-06-26T05:54:43.2788801Z >>> loss = model(inp).sum() 2024-06-26T05:54:43.2789231Z >>> loss.backward() 2024-06-26T05:54:43.2789770Z >>> # Without the join() API, the below synchronization will hang 2024-06-26T05:54:43.2790462Z >>> # blocking for rank 1's allreduce to complete. 2024-06-26T05:54:43.2790981Z >>> torch.cuda.synchronize(device=rank) 2024-06-26T05:54:43.2791302Z 2024-06-26T05:54:43.2791704Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.2792194Z 2024-06-26T05:54:43.2793314Z msg = Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2034. 2024-06-26T05:54:43.2794935Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.2795464Z 2024-06-26T05:54:43.2795881Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-06-26T05:54:43.2796477Z 2024-06-26T05:54:43.2796744Z Registers an optimizer with DDP such that the optimization for a 2024-06-26T05:54:43.2797501Z parameter will run immediately when that parameter's gradient is 2024-06-26T05:54:43.2798181Z finished with reduction, instead of waiting for all parameters' 2024-06-26T05:54:43.2798899Z gradients to finish reduction. This can result in a training speedup 2024-06-26T05:54:43.2799646Z depending on your workload since the optimizer can run while gradient 2024-06-26T05:54:43.2800398Z reduction for other parameters are still ongoing. In addition, this has 2024-06-26T05:54:43.2801286Z the potential to reduce peak memory consumption during training, as it 2024-06-26T05:54:43.2802092Z only needs to load the per-parameter optimizer states of a single 2024-06-26T05:54:43.2802860Z parameter at a time, instead of loading all per-parameter optimizer 2024-06-26T05:54:43.2803419Z states at once. 2024-06-26T05:54:43.2803610Z 2024-06-26T05:54:43.2803698Z Args: 2024-06-26T05:54:43.2804104Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-06-26T05:54:43.2804641Z as a fused optimizer. 2024-06-26T05:54:43.2805081Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-06-26T05:54:43.2805738Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-06-26T05:54:43.2806468Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-06-26T05:54:43.2807201Z Optimizers. If this is omitted, all DDP model parameters will be 2024-06-26T05:54:43.2807739Z optimized. 2024-06-26T05:54:43.2808169Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-06-26T05:54:43.2808623Z 2024-06-26T05:54:43.2808795Z .. warning :: 2024-06-26T05:54:43.2809240Z _register_fused_optim should only be called once on a DDP instance, 2024-06-26T05:54:43.2810020Z and registering multiple fused optimizers for the same DDP model 2024-06-26T05:54:43.2810624Z is not currently supported. Please ping 2024-06-26T05:54:43.2811258Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-06-26T05:54:43.2811821Z for your use case. 2024-06-26T05:54:43.2812033Z 2024-06-26T05:54:43.2812135Z .. warning :: 2024-06-26T05:54:43.2812550Z _register_fused_optim and register_comm_hook currently do not 2024-06-26T05:54:43.2813224Z compose together, meaning that custom DDP communication hooks are 2024-06-26T05:54:43.2813885Z not supported with overlapped optimizers. Please ping 2024-06-26T05:54:43.2814568Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-06-26T05:54:43.2815145Z for your use case. 2024-06-26T05:54:43.2815417Z 2024-06-26T05:54:43.2815520Z .. warning :: 2024-06-26T05:54:43.2815980Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-06-26T05:54:43.2816592Z with overlapped optimizer. Please ping 2024-06-26T05:54:43.2817190Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-06-26T05:54:43.2817311Z for your use case. 2024-06-26T05:54:43.2817317Z 2024-06-26T05:54:43.2817412Z Example:: 2024-06-26T05:54:43.2817417Z 2024-06-26T05:54:43.2817583Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-06-26T05:54:43.2818063Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-06-26T05:54:43.2818336Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-06-26T05:54:43.2818463Z >>> lr = 1e-2 2024-06-26T05:54:43.2818571Z >>> betas = (0.9, 0.99) 2024-06-26T05:54:43.2818684Z >>> eps = 1e-6 2024-06-26T05:54:43.2818997Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-06-26T05:54:43.2819145Z >>> # Example with subset of parameters 2024-06-26T05:54:43.2819312Z >>> params_to_opt = [list(net.parameters())[0]] 2024-06-26T05:54:43.2819451Z >>> net._register_fused_optim( 2024-06-26T05:54:43.2819765Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-06-26T05:54:43.2819857Z ... ) 2024-06-26T05:54:43.2819863Z 2024-06-26T05:54:43.2820263Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.2820268Z 2024-06-26T05:54:43.3017866Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=6. 2024-06-26T05:54:43.3019326Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.3020152Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-06-26T05:54:43.3020610Z 2024-06-26T05:54:43.3020985Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-06-26T05:54:43.3021914Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-06-26T05:54:43.3023032Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-06-26T05:54:43.3024157Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-06-26T05:54:43.3025222Z 2024-06-26T05:54:43.3025357Z .. note:: 2024-06-26T05:54:43.3025822Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-06-26T05:54:43.3026609Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-06-26T05:54:43.3027372Z layer with 4d weight will be affected by ``model.to``, which does not 2024-06-26T05:54:43.3028128Z necessarily benefit from conversion to specified ``memory_format``. 2024-06-26T05:54:43.3028897Z One place we are confident in is that NHWC(channels_last) conversion for 2024-06-26T05:54:43.3029810Z convolution in cuDNN, As it is beneficial to run convolution in NHWC, 2024-06-26T05:54:43.3030563Z even in cases where we have to apply permutation to input tensors. 2024-06-26T05:54:43.3031064Z 2024-06-26T05:54:43.3031372Z Hence our strategy here is to convert only the weight of convolution to 2024-06-26T05:54:43.3031994Z channels_last. This ensures that; 2024-06-26T05:54:43.3032583Z 1. Fast convolution kernels will be used, the benefit of which could 2024-06-26T05:54:43.3033336Z outweigh overhead of permutation (if input is not in the same format) 2024-06-26T05:54:43.3034131Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-06-26T05:54:43.3034937Z from memory_format conversion. 2024-06-26T05:54:43.3035219Z 2024-06-26T05:54:43.3035545Z The optimal case is that, layers between convolution layers are channels 2024-06-26T05:54:43.3036422Z last compatible. Input tensor would be permuted to channels last when it 2024-06-26T05:54:43.3037221Z encounters the first convolution layer and stay in that memory format. 2024-06-26T05:54:43.3038019Z Hence following convolutions will not need to permute its input tensor. 2024-06-26T05:54:43.3038495Z 2024-06-26T05:54:43.3038808Z In case where a channels last incompatible layer is between convolution 2024-06-26T05:54:43.3039589Z layers, we need to permute the input tensor back to contiguous format 2024-06-26T05:54:43.3040371Z for that layer. The input tensor will go through the remaining layers in 2024-06-26T05:54:43.3041230Z contiguous format and be permuted to channels last when it encounters 2024-06-26T05:54:43.3042059Z another convolution layer. There's no point in propagating that 2024-06-26T05:54:43.3042810Z permutation to an earlier layer, as most layers are quite agnostic to 2024-06-26T05:54:43.3043393Z ``memory_format``. 2024-06-26T05:54:43.3043614Z 2024-06-26T05:54:43.3043932Z This claim might change when PyTorch supports fusion of permutation, as 2024-06-26T05:54:43.3044727Z there might have been a better spot to fuse the permutation other than 2024-06-26T05:54:43.3045353Z immediately before a convolution. 2024-06-26T05:54:43.3045647Z 2024-06-26T05:54:43.3045738Z Args: 2024-06-26T05:54:43.3046183Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-06-26T05:54:43.3046763Z ``nn.Module`` 2024-06-26T05:54:43.3047223Z memory_format: user specified ``memory_format``, 2024-06-26T05:54:43.3047819Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-06-26T05:54:43.3048239Z 2024-06-26T05:54:43.3048334Z Returns: 2024-06-26T05:54:43.3048675Z The original module with updated ``nn.Conv2d`` 2024-06-26T05:54:43.3049020Z 2024-06-26T05:54:43.3049114Z Example: 2024-06-26T05:54:43.3049463Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:43.3050014Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-06-26T05:54:43.3050694Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-06-26T05:54:43.3051313Z >>> model = nn.Sequential( 2024-06-26T05:54:43.3051723Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-06-26T05:54:43.3052146Z >>> # This is identical to: 2024-06-26T05:54:43.3052730Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:43.3053576Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:43.3054204Z >>> out = model(input) 2024-06-26T05:54:43.3054533Z 2024-06-26T05:54:43.3055062Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.3055551Z 2024-06-26T05:54:43.3056568Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=81. 2024-06-26T05:54:43.3058011Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.3058836Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-06-26T05:54:43.3059647Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-06-26T05:54:43.3060551Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-06-26T05:54:43.3061462Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-06-26T05:54:43.3062409Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-06-26T05:54:43.3062995Z 2024-06-26T05:54:43.3063108Z .. note:: 2024-06-26T05:54:43.3063578Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-06-26T05:54:43.3064424Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-06-26T05:54:43.3065196Z layer with 4d weight will be affected by ``model.to``, which does not 2024-06-26T05:54:43.3065943Z necessarily benefit from conversion to specified ``memory_format``. 2024-06-26T05:54:43.3066719Z One place we are confident in is that NHWC(channels_last) conversion for 2024-06-26T05:54:43.3067498Z convolution in cuDNN, As it is beneficial to run convolution in NHWC, 2024-06-26T05:54:43.3068253Z even in cases where we have to apply permutation to input tensors. 2024-06-26T05:54:43.3068703Z 2024-06-26T05:54:43.3069013Z Hence our strategy here is to convert only the weight of convolution to 2024-06-26T05:54:43.3069631Z channels_last. This ensures that; 2024-06-26T05:54:43.3070217Z 1. Fast convolution kernels will be used, the benefit of which could 2024-06-26T05:54:43.3070967Z outweigh overhead of permutation (if input is not in the same format) 2024-06-26T05:54:43.3071763Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-06-26T05:54:43.3072380Z from memory_format conversion. 2024-06-26T05:54:43.3072663Z 2024-06-26T05:54:43.3072977Z The optimal case is that, layers between convolution layers are channels 2024-06-26T05:54:43.3073781Z last compatible. Input tensor would be permuted to channels last when it 2024-06-26T05:54:43.3074567Z encounters the first convolution layer and stay in that memory format. 2024-06-26T05:54:43.3075475Z Hence following convolutions will not need to permute its input tensor. 2024-06-26T05:54:43.3075954Z 2024-06-26T05:54:43.3076264Z In case where a channels last incompatible layer is between convolution 2024-06-26T05:54:43.3077045Z layers, we need to permute the input tensor back to contiguous format 2024-06-26T05:54:43.3077824Z for that layer. The input tensor will go through the remaining layers in 2024-06-26T05:54:43.3078606Z contiguous format and be permuted to channels last when it encounters 2024-06-26T05:54:43.3079426Z another convolution layer. There's no point in propagating that 2024-06-26T05:54:43.3080169Z permutation to an earlier layer, as most layers are quite agnostic to 2024-06-26T05:54:43.3080737Z ``memory_format``. 2024-06-26T05:54:43.3081022Z 2024-06-26T05:54:43.3081341Z This claim might change when PyTorch supports fusion of permutation, as 2024-06-26T05:54:43.3082138Z there might have been a better spot to fuse the permutation other than 2024-06-26T05:54:43.3082760Z immediately before a convolution. 2024-06-26T05:54:43.3083056Z 2024-06-26T05:54:43.3083150Z Args: 2024-06-26T05:54:43.3083598Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-06-26T05:54:43.3084184Z ``nn.Module`` 2024-06-26T05:54:43.3084649Z memory_format: user specified ``memory_format``, 2024-06-26T05:54:43.3085254Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-06-26T05:54:43.3085720Z 2024-06-26T05:54:43.3085832Z Returns: 2024-06-26T05:54:43.3086168Z The original module with updated ``nn.Conv3d`` 2024-06-26T05:54:43.3086570Z 2024-06-26T05:54:43.3086666Z Example: 2024-06-26T05:54:43.3087012Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:43.3087548Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-06-26T05:54:43.3088251Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-06-26T05:54:43.3088880Z >>> model = nn.Sequential( 2024-06-26T05:54:43.3089290Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-06-26T05:54:43.3089715Z >>> # This is identical to: 2024-06-26T05:54:43.3090304Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:43.3091147Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:43.3091862Z >>> out = model(input) 2024-06-26T05:54:43.3092194Z 2024-06-26T05:54:43.3092725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.3093217Z 2024-06-26T05:54:43.3236329Z msg = Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=937. 2024-06-26T05:54:43.3237637Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.3238454Z Prune tensor by removing random channels along the specified dimension. 2024-06-26T05:54:43.3238923Z 2024-06-26T05:54:43.3239313Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-06-26T05:54:43.3240306Z by removing the specified ``amount`` of (currently unpruned) channels 2024-06-26T05:54:43.3241558Z along the specified ``dim`` selected at random. 2024-06-26T05:54:43.3242298Z Modifies module in place (and also return the modified module) 2024-06-26T05:54:43.3242813Z by: 2024-06-26T05:54:43.3242961Z 2024-06-26T05:54:43.3243325Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:43.3244076Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:43.3244826Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:43.3245539Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:43.3246274Z ``name+'_orig'``. 2024-06-26T05:54:43.3246487Z 2024-06-26T05:54:43.3246593Z Args: 2024-06-26T05:54:43.3247017Z module (nn.Module): module containing the tensor to prune 2024-06-26T05:54:43.3248055Z name (str): parameter name within ``module`` on which pruning 2024-06-26T05:54:43.3248678Z will act. 2024-06-26T05:54:43.3249104Z amount (int or float): quantity of parameters to prune. 2024-06-26T05:54:43.3249740Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-06-26T05:54:43.3250420Z fraction of parameters to prune. If ``int``, it represents the 2024-06-26T05:54:43.3251142Z absolute number of parameters to prune. 2024-06-26T05:54:43.3251877Z dim (int): index of the dim along which we define channels to prune. 2024-06-26T05:54:43.3252675Z 2024-06-26T05:54:43.3252871Z Returns: 2024-06-26T05:54:43.3253728Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-06-26T05:54:43.3254511Z 2024-06-26T05:54:43.3254608Z Examples: 2024-06-26T05:54:43.3254889Z >>> # xdoctest: +SKIP 2024-06-26T05:54:43.3255251Z >>> m = prune.random_structured( 2024-06-26T05:54:43.3255795Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-06-26T05:54:43.3256231Z ... ) 2024-06-26T05:54:43.3256617Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-06-26T05:54:43.3257134Z >>> print(columns_pruned) 2024-06-26T05:54:43.3257489Z 3 2024-06-26T05:54:43.3257828Z 2024-06-26T05:54:43.3258363Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.3258867Z 2024-06-26T05:54:43.3259774Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=978. 2024-06-26T05:54:43.3261020Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.3262032Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-06-26T05:54:43.3262619Z 2024-06-26T05:54:43.3262936Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-06-26T05:54:43.3263686Z by removing the specified ``amount`` of (currently unpruned) channels 2024-06-26T05:54:43.3264428Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-06-26T05:54:43.3265081Z Modifies module in place (and also return the modified module) 2024-06-26T05:54:43.3265670Z by: 2024-06-26T05:54:43.3265828Z 2024-06-26T05:54:43.3266184Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:43.3266935Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:43.3267667Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:43.3268393Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:43.3268973Z ``name+'_orig'``. 2024-06-26T05:54:43.3269179Z 2024-06-26T05:54:43.3269286Z Args: 2024-06-26T05:54:43.3269660Z module (nn.Module): module containing the tensor to prune 2024-06-26T05:54:43.3270306Z name (str): parameter name within ``module`` on which pruning 2024-06-26T05:54:43.3270832Z will act. 2024-06-26T05:54:43.3271259Z amount (int or float): quantity of parameters to prune. 2024-06-26T05:54:43.3271895Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-06-26T05:54:43.3272583Z fraction of parameters to prune. If ``int``, it represents the 2024-06-26T05:54:43.3273175Z absolute number of parameters to prune. 2024-06-26T05:54:43.3273852Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-06-26T05:54:43.3274501Z entries for argument ``p`` in :func:`torch.norm`. 2024-06-26T05:54:43.3275281Z dim (int): index of the dim along which we define channels to prune. 2024-06-26T05:54:43.3276045Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-06-26T05:54:43.3276786Z shape as module parameter) used to compute mask for pruning. 2024-06-26T05:54:43.3277517Z The values in this tensor indicate the importance of the corresponding 2024-06-26T05:54:43.3278166Z elements in the parameter being pruned. 2024-06-26T05:54:43.3278820Z If unspecified or None, the module parameter will be used in its place. 2024-06-26T05:54:43.3279307Z 2024-06-26T05:54:43.3279422Z Returns: 2024-06-26T05:54:43.3279879Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-06-26T05:54:43.3280357Z 2024-06-26T05:54:43.3280458Z Examples: 2024-06-26T05:54:43.3280773Z >>> from torch.nn.utils import prune 2024-06-26T05:54:43.3281261Z >>> m = prune.ln_structured( 2024-06-26T05:54:43.3281875Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-06-26T05:54:43.3282405Z ... ) 2024-06-26T05:54:43.3282641Z 2024-06-26T05:54:43.3283164Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.3283649Z 2024-06-26T05:54:43.3284507Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1025. 2024-06-26T05:54:43.3285774Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.3286286Z 2024-06-26T05:54:43.3286852Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-06-26T05:54:43.3287648Z 2024-06-26T05:54:43.3287822Z Modifies modules in place by: 2024-06-26T05:54:43.3288065Z 2024-06-26T05:54:43.3288425Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:43.3289155Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:43.3289895Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:43.3290613Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:43.3291167Z ``name+'_orig'``. 2024-06-26T05:54:43.3291365Z 2024-06-26T05:54:43.3291451Z Args: 2024-06-26T05:54:43.3291842Z parameters (Iterable of (module, name) tuples): parameters of 2024-06-26T05:54:43.3292527Z the model to prune in a global fashion, i.e. by aggregating all 2024-06-26T05:54:43.3293297Z weights prior to deciding which ones to prune. module must be of 2024-06-26T05:54:43.3293943Z type :class:`nn.Module`, and name must be a string. 2024-06-26T05:54:43.3294595Z pruning_method (function): a valid pruning function from this module, 2024-06-26T05:54:43.3295287Z or a custom one implemented by the user that satisfies the 2024-06-26T05:54:43.3296048Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-06-26T05:54:43.3296810Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-06-26T05:54:43.3297617Z the corresponding parameter's importance scores tensor. The tensor 2024-06-26T05:54:43.3298366Z should be the same shape as the parameter, and is used for computing 2024-06-26T05:54:43.3298941Z mask for pruning. 2024-06-26T05:54:43.3299432Z If unspecified or None, the parameter will be used in place of its 2024-06-26T05:54:43.3299996Z importance scores. 2024-06-26T05:54:43.3300378Z kwargs: other keyword arguments such as: 2024-06-26T05:54:43.3300956Z amount (int or float): quantity of parameters to prune across the 2024-06-26T05:54:43.3301516Z specified parameters. 2024-06-26T05:54:43.3301993Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-06-26T05:54:43.3302659Z fraction of parameters to prune. If ``int``, it represents the 2024-06-26T05:54:43.3303243Z absolute number of parameters to prune. 2024-06-26T05:54:43.3303575Z 2024-06-26T05:54:43.3303666Z Raises: 2024-06-26T05:54:43.3304048Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-06-26T05:54:43.3304398Z 2024-06-26T05:54:43.3304484Z Note: 2024-06-26T05:54:43.3304962Z Since global structured pruning doesn't make much sense unless the 2024-06-26T05:54:43.3305685Z norm is normalized by the size of the parameter, we now limit the 2024-06-26T05:54:43.3306299Z scope of global pruning to unstructured methods. 2024-06-26T05:54:43.3306657Z 2024-06-26T05:54:43.3306751Z Examples: 2024-06-26T05:54:43.3307046Z >>> from torch.nn.utils import prune 2024-06-26T05:54:43.3307480Z >>> from collections import OrderedDict 2024-06-26T05:54:43.3307927Z >>> net = nn.Sequential(OrderedDict([ 2024-06-26T05:54:43.3308396Z ... ('first', nn.Linear(10, 4)), 2024-06-26T05:54:43.3308835Z ... ('second', nn.Linear(4, 1)), 2024-06-26T05:54:43.3309211Z ... ])) 2024-06-26T05:54:43.3309480Z >>> parameters_to_prune = ( 2024-06-26T05:54:43.3309873Z ... (net.first, 'weight'), 2024-06-26T05:54:43.3310288Z ... (net.second, 'weight'), 2024-06-26T05:54:43.3310641Z ... ) 2024-06-26T05:54:43.3310899Z >>> prune.global_unstructured( 2024-06-26T05:54:43.3311282Z ... parameters_to_prune, 2024-06-26T05:54:43.3311698Z ... pruning_method=prune.L1Unstructured, 2024-06-26T05:54:43.3312115Z ... amount=10, 2024-06-26T05:54:43.3312398Z ... ) 2024-06-26T05:54:43.3312837Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-06-26T05:54:43.3313393Z tensor(10) 2024-06-26T05:54:43.3313615Z 2024-06-26T05:54:43.3313620Z 2024-06-26T05:54:43.3314005Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.3314542Z 2024-06-26T05:54:43.3315485Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1144. 2024-06-26T05:54:43.3316752Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.3317871Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-06-26T05:54:43.3318587Z 2024-06-26T05:54:43.3318870Z Modifies module in place (and also return the modified module) by: 2024-06-26T05:54:43.3319314Z 2024-06-26T05:54:43.3319677Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:43.3320429Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:43.3321320Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:43.3322054Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:43.3322639Z ``name+'_orig'``. 2024-06-26T05:54:43.3322846Z 2024-06-26T05:54:43.3322940Z Args: 2024-06-26T05:54:43.3323328Z module (nn.Module): module containing the tensor to prune 2024-06-26T05:54:43.3323979Z name (str): parameter name within ``module`` on which pruning 2024-06-26T05:54:43.3324485Z will act. 2024-06-26T05:54:43.3324914Z mask (Tensor): binary mask to be applied to the parameter. 2024-06-26T05:54:43.3325312Z 2024-06-26T05:54:43.3325419Z Returns: 2024-06-26T05:54:43.3325869Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-06-26T05:54:43.3326348Z 2024-06-26T05:54:43.3326444Z Examples: 2024-06-26T05:54:43.3326761Z >>> from torch.nn.utils import prune 2024-06-26T05:54:43.3327199Z >>> m = prune.custom_from_mask( 2024-06-26T05:54:43.3327805Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-06-26T05:54:43.3328314Z ... ) 2024-06-26T05:54:43.3328583Z >>> print(m.bias_mask) 2024-06-26T05:54:43.3328935Z tensor([0., 1., 0.]) 2024-06-26T05:54:43.3329157Z 2024-06-26T05:54:43.3329260Z 2024-06-26T05:54:43.3329771Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.3330273Z 2024-06-26T05:54:43.4257725Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=103. 2024-06-26T05:54:43.4259248Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.4260376Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-06-26T05:54:43.4261122Z 2024-06-26T05:54:43.4261473Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-06-26T05:54:43.4262318Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-06-26T05:54:43.4263136Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-06-26T05:54:43.4263760Z (UAI 2018). 2024-06-26T05:54:43.4263928Z 2024-06-26T05:54:43.4264280Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-06-26T05:54:43.4265101Z but using exponential weights instead of equal weights across iterations. 2024-06-26T05:54:43.4265603Z 2024-06-26T05:54:43.4265978Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-06-26T05:54:43.4266875Z on the device :attr:`device` and allows to compute running averages of the 2024-06-26T05:54:43.4267573Z parameters of the :attr:`model`. 2024-06-26T05:54:43.4268054Z 2024-06-26T05:54:43.4268193Z Args: 2024-06-26T05:54:43.4268657Z model (torch.nn.Module): model to use with SWA/EMA 2024-06-26T05:54:43.4269389Z device (torch.device, optional): if provided, the averaged model will be 2024-06-26T05:54:43.4270245Z stored on the :attr:`device` 2024-06-26T05:54:43.4270872Z avg_fn (function, optional): the averaging function used to update 2024-06-26T05:54:43.4271716Z parameters; the function must take in the current value of the 2024-06-26T05:54:43.4272503Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-06-26T05:54:43.4273289Z parameter, and the number of models already averaged; if None, 2024-06-26T05:54:43.4273996Z an equally weighted average is used (default: None) 2024-06-26T05:54:43.4274902Z multi_avg_fn (function, optional): the averaging function used to update 2024-06-26T05:54:43.4275764Z parameters inplace; the function must take in the current values of the 2024-06-26T05:54:43.4276673Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-06-26T05:54:43.4277704Z parameters as a list, and the number of models already averaged; if None, 2024-06-26T05:54:43.4278471Z an equally weighted average is used (default: None) 2024-06-26T05:54:43.4279150Z use_buffers (bool): if ``True``, it will compute running averages for 2024-06-26T05:54:43.4279969Z both the parameters and the buffers of the model. (default: ``False``) 2024-06-26T05:54:43.4280491Z 2024-06-26T05:54:43.4280587Z Example: 2024-06-26T05:54:43.4281062Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:43.4281627Z >>> loader, optimizer, model, loss_fn = ... 2024-06-26T05:54:43.4282182Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-06-26T05:54:43.4282941Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-06-26T05:54:43.4283634Z >>> T_max=300) 2024-06-26T05:54:43.4284114Z >>> swa_start = 160 2024-06-26T05:54:43.4284528Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-06-26T05:54:43.4285059Z >>> for i in range(300): 2024-06-26T05:54:43.4285506Z >>> for input, target in loader: 2024-06-26T05:54:43.4285960Z >>> optimizer.zero_grad() 2024-06-26T05:54:43.4286496Z >>> loss_fn(model(input), target).backward() 2024-06-26T05:54:43.4286973Z >>> optimizer.step() 2024-06-26T05:54:43.4287406Z >>> if i > swa_start: 2024-06-26T05:54:43.4287886Z >>> swa_model.update_parameters(model) 2024-06-26T05:54:43.4288338Z >>> swa_scheduler.step() 2024-06-26T05:54:43.4288793Z >>> else: 2024-06-26T05:54:43.4289109Z >>> scheduler.step() 2024-06-26T05:54:43.4289528Z >>> 2024-06-26T05:54:43.4289885Z >>> # Update bn statistics for the swa_model at the end 2024-06-26T05:54:43.4290528Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-06-26T05:54:43.4290957Z 2024-06-26T05:54:43.4291378Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-06-26T05:54:43.4292296Z If no averaging function is provided, the default is to compute 2024-06-26T05:54:43.4293053Z equally-weighted average of the weights (SWA). 2024-06-26T05:54:43.4293465Z 2024-06-26T05:54:43.4293574Z Example: 2024-06-26T05:54:43.4293895Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:43.4294567Z >>> # Compute exponential moving averages of the weights and buffers 2024-06-26T05:54:43.4295237Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-06-26T05:54:43.4295979Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-06-26T05:54:43.4296453Z 2024-06-26T05:54:43.4296566Z .. note:: 2024-06-26T05:54:43.4297030Z When using SWA/EMA with models containing Batch Normalization you may 2024-06-26T05:54:43.4297837Z need to update the activation statistics for Batch Normalization. 2024-06-26T05:54:43.4298704Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-06-26T05:54:43.4299533Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-06-26T05:54:43.4300474Z statistics in a post-training step by passing data through the model. The 2024-06-26T05:54:43.4301288Z second does it during the parameter update phase by averaging all buffers. 2024-06-26T05:54:43.4302187Z Empirical evidence has shown that updating the statistics in normalization 2024-06-26T05:54:43.4302994Z layers increases accuracy, but you may wish to empirically test which 2024-06-26T05:54:43.4303648Z approach yields the best results in your problem. 2024-06-26T05:54:43.4304019Z 2024-06-26T05:54:43.4304117Z .. note:: 2024-06-26T05:54:43.4304643Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-06-26T05:54:43.4305171Z 2024-06-26T05:54:43.4305333Z .. note:: 2024-06-26T05:54:43.4305765Z When :meth:`update_parameters` is called for the first time (i.e. 2024-06-26T05:54:43.4306456Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-06-26T05:54:43.4307148Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-06-26T05:54:43.4307835Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-06-26T05:54:43.4308382Z to update the parameters. 2024-06-26T05:54:43.4308706Z 2024-06-26T05:54:43.4309010Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-06-26T05:54:43.4309606Z https://arxiv.org/abs/1803.05407 2024-06-26T05:54:43.4310220Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-06-26T05:54:43.4310805Z Average: 2024-06-26T05:54:43.4311105Z https://arxiv.org/abs/1806.05594 2024-06-26T05:54:43.4311731Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-06-26T05:54:43.4312303Z https://arxiv.org/abs/1904.11943 2024-06-26T05:54:43.4312934Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-06-26T05:54:43.4313505Z Generalizes Well: 2024-06-26T05:54:43.4313856Z https://arxiv.org/abs/2001.02312 2024-06-26T05:54:43.4314248Z .. _Polyak averaging: 2024-06-26T05:54:43.4314849Z https://paperswithcode.com/method/polyak-averaging 2024-06-26T05:54:43.4315327Z 2024-06-26T05:54:43.4315844Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.4316350Z 2024-06-26T05:54:43.4317148Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=354. 2024-06-26T05:54:43.4318372Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.4319164Z Anneals the learning rate in each parameter group to a fixed value. 2024-06-26T05:54:43.4319609Z 2024-06-26T05:54:43.4319920Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-06-26T05:54:43.4320689Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-06-26T05:54:43.4321244Z 2024-06-26T05:54:43.4321340Z Args: 2024-06-26T05:54:43.4321714Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-06-26T05:54:43.4322457Z swa_lrs (float or list): the learning rate value for all param groups 2024-06-26T05:54:43.4323088Z together or separately for each group. 2024-06-26T05:54:43.4323691Z annealing_epochs (int): number of epochs in the annealing phase 2024-06-26T05:54:43.4324222Z (default: 10) 2024-06-26T05:54:43.4324725Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-06-26T05:54:43.4325464Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-06-26T05:54:43.4326017Z (default: "cos") 2024-06-26T05:54:43.4326558Z last_epoch (int): the index of the last epoch (default: -1) 2024-06-26T05:54:43.4327037Z 2024-06-26T05:54:43.4327292Z The :class:`SWALR` scheduler can be used together with other 2024-06-26T05:54:43.4327983Z schedulers to switch to a constant learning rate late in the training 2024-06-26T05:54:43.4328614Z as in the example below. 2024-06-26T05:54:43.4328844Z 2024-06-26T05:54:43.4328952Z Example: 2024-06-26T05:54:43.4329269Z >>> # xdoctest: +SKIP("Undefined variables") 2024-06-26T05:54:43.4329749Z >>> loader, optimizer, model = ... 2024-06-26T05:54:43.4330209Z >>> lr_lambda = lambda epoch: 0.9 2024-06-26T05:54:43.4330803Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-06-26T05:54:43.4331369Z >>> lr_lambda=lr_lambda) 2024-06-26T05:54:43.4331873Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-06-26T05:54:43.4332495Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-06-26T05:54:43.4333114Z >>> swa_start = 160 2024-06-26T05:54:43.4333461Z >>> for i in range(300): 2024-06-26T05:54:43.4333864Z >>> for input, target in loader: 2024-06-26T05:54:43.4334296Z >>> optimizer.zero_grad() 2024-06-26T05:54:43.4334769Z >>> loss_fn(model(input), target).backward() 2024-06-26T05:54:43.4335234Z >>> optimizer.step() 2024-06-26T05:54:43.4335609Z >>> if i > swa_start: 2024-06-26T05:54:43.4335995Z >>> swa_scheduler.step() 2024-06-26T05:54:43.4336377Z >>> else: 2024-06-26T05:54:43.4336681Z >>> scheduler.step() 2024-06-26T05:54:43.4336956Z 2024-06-26T05:54:43.4337244Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-06-26T05:54:43.4337837Z https://arxiv.org/abs/1803.05407 2024-06-26T05:54:43.4338204Z 2024-06-26T05:54:43.4338724Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.4339207Z 2024-06-26T05:54:43.7835947Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1268. 2024-06-26T05:54:43.7838651Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:43.7839955Z Asserts that ``actual`` and ``expected`` are close. 2024-06-26T05:54:43.7840601Z 2024-06-26T05:54:43.7841805Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-06-26T05:54:43.7843070Z 2024-06-26T05:54:43.7843267Z .. math:: 2024-06-26T05:54:43.7843585Z 2024-06-26T05:54:43.7844813Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-06-26T05:54:43.7846131Z 2024-06-26T05:54:43.7847202Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-06-26T05:54:43.7848935Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-06-26T05:54:43.7849787Z 2024-06-26T05:54:43.7850336Z In addition, they are only considered close if they have the same 2024-06-26T05:54:43.7851191Z 2024-06-26T05:54:43.7851791Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-06-26T05:54:43.7852931Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-06-26T05:54:43.7853887Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-06-26T05:54:43.7854864Z - stride (if ``check_stride`` is ``True``). 2024-06-26T05:54:43.7855458Z 2024-06-26T05:54:43.7856264Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-06-26T05:54:43.7857326Z 2024-06-26T05:54:43.7858218Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-06-26T05:54:43.7860173Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-06-26T05:54:43.7862112Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-06-26T05:54:43.7864064Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-06-26T05:54:43.7865552Z 2024-06-26T05:54:43.7866332Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-06-26T05:54:43.7868335Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-06-26T05:54:43.7869761Z definition above. 2024-06-26T05:54:43.7870130Z 2024-06-26T05:54:43.7871153Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-06-26T05:54:43.7873307Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-06-26T05:54:43.7875969Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-06-26T05:54:43.7878361Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-06-26T05:54:43.7880249Z their elements are considered close according to the above definition. 2024-06-26T05:54:43.7881159Z 2024-06-26T05:54:43.7881337Z .. note:: 2024-06-26T05:54:43.7881623Z 2024-06-26T05:54:43.7882449Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-06-26T05:54:43.7884628Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-06-26T05:54:43.7886335Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-06-26T05:54:43.7887359Z 2024-06-26T05:54:43.7887526Z Args: 2024-06-26T05:54:43.7887992Z actual (Any): Actual input. 2024-06-26T05:54:43.7888721Z expected (Any): Expected input. 2024-06-26T05:54:43.7890125Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-06-26T05:54:43.7891699Z are allowed. Otherwise type equality is required. 2024-06-26T05:54:43.7893329Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-06-26T05:54:43.7895207Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-06-26T05:54:43.7897086Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-06-26T05:54:43.7899013Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-06-26T05:54:43.7900632Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-06-26T05:54:43.7902311Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-06-26T05:54:43.7903997Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-06-26T05:54:43.7905694Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-06-26T05:54:43.7907531Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-06-26T05:54:43.7909805Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-06-26T05:54:43.7911310Z :func:`torch.promote_types`) before being compared. 2024-06-26T05:54:43.7912859Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-06-26T05:54:43.7915175Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-06-26T05:54:43.7916520Z compared. 2024-06-26T05:54:43.7917821Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-06-26T05:54:43.7920102Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-06-26T05:54:43.7922449Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-06-26T05:54:43.7923967Z should return the new message. 2024-06-26T05:54:43.7924513Z 2024-06-26T05:54:43.7924685Z Raises: 2024-06-26T05:54:43.7925541Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-06-26T05:54:43.7926834Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-06-26T05:54:43.7928332Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-06-26T05:54:43.7930372Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-06-26T05:54:43.7931912Z different types. 2024-06-26T05:54:43.7933519Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-06-26T05:54:43.7935889Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-06-26T05:54:43.7937943Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-06-26T05:54:43.7939807Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-06-26T05:54:43.7941145Z :attr:`~torch.Tensor.layout`. 2024-06-26T05:54:43.7942216Z AssertionError: If only one of corresponding tensors is quantized. 2024-06-26T05:54:43.7944239Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-06-26T05:54:43.7946300Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-06-26T05:54:43.7947610Z :attr:`~torch.Tensor.device`. 2024-06-26T05:54:43.7948937Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-06-26T05:54:43.7951021Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-06-26T05:54:43.7953077Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-06-26T05:54:43.7954323Z 2024-06-26T05:54:43.7955633Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-06-26T05:54:43.7957377Z ``dtype``'s, the maximum of both tolerances is used. 2024-06-26T05:54:43.7958043Z 2024-06-26T05:54:43.7958443Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7959336Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-06-26T05:54:43.7960223Z +===========================+============+==========+ 2024-06-26T05:54:43.7961316Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-06-26T05:54:43.7962329Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7963371Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-06-26T05:54:43.7964403Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7965412Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:43.7966421Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7967442Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-06-26T05:54:43.7968452Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7969431Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-06-26T05:54:43.7970403Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7971391Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:43.7972359Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7973547Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-06-26T05:54:43.7974539Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7975629Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:43.7976623Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7977638Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:43.7978646Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7979681Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:43.7980676Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7981644Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:43.7982630Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7983824Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:43.7984833Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7985676Z | other | ``0.0`` | ``0.0`` | 2024-06-26T05:54:43.7986668Z +---------------------------+------------+----------+ 2024-06-26T05:54:43.7987252Z 2024-06-26T05:54:43.7987452Z .. note:: 2024-06-26T05:54:43.7987729Z 2024-06-26T05:54:43.7988703Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-06-26T05:54:43.7990936Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-06-26T05:54:43.7992886Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-06-26T05:54:43.7993838Z 2024-06-26T05:54:43.7994050Z >>> import functools 2024-06-26T05:54:43.7995257Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-06-26T05:54:43.7996556Z >>> assert_equal(1e-9, 1e-10) 2024-06-26T05:54:43.7997321Z Traceback (most recent call last): 2024-06-26T05:54:43.7998041Z ... 2024-06-26T05:54:43.7998605Z AssertionError: Scalars are not equal! 2024-06-26T05:54:43.7999371Z 2024-06-26T05:54:43.8000011Z Expected 1e-10 but got 1e-09. 2024-06-26T05:54:43.8000904Z Absolute difference: 9.000000000000001e-10 2024-06-26T05:54:43.8001804Z Relative difference: 9.0 2024-06-26T05:54:43.8002242Z 2024-06-26T05:54:43.8002401Z Examples: 2024-06-26T05:54:43.8002951Z >>> # tensor to tensor comparison 2024-06-26T05:54:43.8003909Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-06-26T05:54:43.8004820Z >>> actual = torch.acos(torch.cos(expected)) 2024-06-26T05:54:43.8005775Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:43.8006433Z 2024-06-26T05:54:43.8006690Z >>> # scalar to scalar comparison 2024-06-26T05:54:43.8007425Z >>> import math 2024-06-26T05:54:43.8008037Z >>> expected = math.sqrt(2.0) 2024-06-26T05:54:43.8008781Z >>> actual = 2.0 / math.sqrt(2.0) 2024-06-26T05:54:43.8009625Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:43.8010295Z 2024-06-26T05:54:43.8010587Z >>> # numpy array to numpy array comparison 2024-06-26T05:54:43.8011408Z >>> import numpy as np 2024-06-26T05:54:43.8012210Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-06-26T05:54:43.8013099Z >>> actual = np.arccos(np.cos(expected)) 2024-06-26T05:54:43.8014029Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:43.8014671Z 2024-06-26T05:54:43.8014939Z >>> # sequence to sequence comparison 2024-06-26T05:54:43.8015749Z >>> import numpy as np 2024-06-26T05:54:43.8016856Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-06-26T05:54:43.8018170Z >>> # length and their elements have to match. 2024-06-26T05:54:43.8019169Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-06-26T05:54:43.8020003Z >>> actual = tuple(expected) 2024-06-26T05:54:43.8020834Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:43.8021552Z 2024-06-26T05:54:43.8021813Z >>> # mapping to mapping comparison 2024-06-26T05:54:43.8022664Z >>> from collections import OrderedDict 2024-06-26T05:54:43.8023463Z >>> import numpy as np 2024-06-26T05:54:43.8024116Z >>> foo = torch.tensor(1.0) 2024-06-26T05:54:43.8024800Z >>> bar = 2.0 2024-06-26T05:54:43.8025344Z >>> baz = np.array(3.0) 2024-06-26T05:54:43.8026429Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-06-26T05:54:43.8027928Z >>> # have to have the same set of keys and their elements have to match. 2024-06-26T05:54:43.8029290Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-06-26T05:54:43.8030586Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-06-26T05:54:43.8031565Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:43.8032227Z 2024-06-26T05:54:43.8032547Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-06-26T05:54:43.8033397Z >>> actual = expected.clone() 2024-06-26T05:54:43.8034321Z >>> # By default, directly related instances can be compared 2024-06-26T05:54:43.8035744Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-06-26T05:54:43.8052674Z >>> # This check can be made more strict with allow_subclasses=False 2024-06-26T05:54:43.8053843Z >>> torch.testing.assert_close( 2024-06-26T05:54:43.8054853Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-06-26T05:54:43.8055814Z ... ) 2024-06-26T05:54:43.8056386Z Traceback (most recent call last): 2024-06-26T05:54:43.8057067Z ... 2024-06-26T05:54:43.8057849Z TypeError: No comparison pair was able to handle inputs of type 2024-06-26T05:54:43.8059362Z and . 2024-06-26T05:54:43.8060784Z >>> # If the inputs are not directly related, they are never considered close 2024-06-26T05:54:43.8062037Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-06-26T05:54:43.8062965Z Traceback (most recent call last): 2024-06-26T05:54:43.8063661Z ... 2024-06-26T05:54:43.8064781Z TypeError: No comparison pair was able to handle inputs of type 2024-06-26T05:54:43.8066132Z and . 2024-06-26T05:54:43.8067303Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-06-26T05:54:43.8068564Z >>> # their type if check_dtype=False. 2024-06-26T05:54:43.8069556Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-06-26T05:54:43.8070278Z 2024-06-26T05:54:43.8070529Z >>> # NaN != NaN by default. 2024-06-26T05:54:43.8071303Z >>> expected = torch.tensor(float("Nan")) 2024-06-26T05:54:43.8072123Z >>> actual = expected.clone() 2024-06-26T05:54:43.8072989Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:43.8073906Z Traceback (most recent call last): 2024-06-26T05:54:43.8074784Z ... 2024-06-26T05:54:43.8075367Z AssertionError: Scalars are not close! 2024-06-26T05:54:43.8076125Z 2024-06-26T05:54:43.8076692Z Expected nan but got nan. 2024-06-26T05:54:43.8077653Z Absolute difference: nan (up to 1e-05 allowed) 2024-06-26T05:54:43.8078751Z Relative difference: nan (up to 1.3e-06 allowed) 2024-06-26T05:54:43.8079922Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-06-26T05:54:43.8080677Z 2024-06-26T05:54:43.8081066Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-06-26T05:54:43.8081943Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-06-26T05:54:43.8082915Z >>> # The default error message can be overwritten. 2024-06-26T05:54:43.8084440Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-06-26T05:54:43.8085833Z Traceback (most recent call last): 2024-06-26T05:54:43.8086563Z ... 2024-06-26T05:54:43.8087219Z AssertionError: Argh, the tensors are not close! 2024-06-26T05:54:43.8088511Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-06-26T05:54:43.8089670Z >>> # extra information 2024-06-26T05:54:43.8090382Z >>> torch.testing.assert_close( 2024-06-26T05:54:43.8091416Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-06-26T05:54:43.8092377Z ... ) 2024-06-26T05:54:43.8092922Z Traceback (most recent call last): 2024-06-26T05:54:43.8093657Z ... 2024-06-26T05:54:43.8094139Z AssertionError: Header 2024-06-26T05:54:43.8094924Z 2024-06-26T05:54:43.8095528Z Tensor-likes are not close! 2024-06-26T05:54:43.8096121Z 2024-06-26T05:54:43.8096707Z Mismatched elements: 2 / 3 (66.7%) 2024-06-26T05:54:43.8097960Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-06-26T05:54:43.8099498Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-06-26T05:54:43.8100580Z 2024-06-26T05:54:43.8101089Z Footer 2024-06-26T05:54:43.8101530Z 2024-06-26T05:54:43.8102514Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:43.8103450Z 2024-06-26T05:54:44.8907060Z msg = Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61. 2024-06-26T05:54:44.8909767Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:44.8910750Z 2024-06-26T05:54:44.8911566Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-06-26T05:54:44.8912680Z 2024-06-26T05:54:44.8913404Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-06-26T05:54:44.8915367Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-06-26T05:54:44.8916905Z server like load. It can emulate multiple calling threads to a single module 2024-06-26T05:54:44.8918483Z provided. In the future we plan to enhance this component to support inter and 2024-06-26T05:54:44.8920237Z intra-op parallelism as well as multiple models running in a single process. 2024-06-26T05:54:44.8921264Z 2024-06-26T05:54:44.8921917Z Please note that even though nn.Module is supported, it might incur an overhead 2024-06-26T05:54:44.8923486Z from the need to hold GIL every time we execute Python code or pass around 2024-06-26T05:54:44.8925020Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-06-26T05:54:44.8926581Z model for inference deployment it is better to switch to using it in this 2024-06-26T05:54:44.8927661Z benchmark. 2024-06-26T05:54:44.8927956Z 2024-06-26T05:54:44.8928147Z Example:: 2024-06-26T05:54:44.8928413Z 2024-06-26T05:54:44.8928688Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:44.8929539Z >>> from torch.utils import ThroughputBenchmark 2024-06-26T05:54:44.8930485Z >>> bench = ThroughputBenchmark(my_module) 2024-06-26T05:54:44.8931599Z >>> # Pre-populate benchmark's data set with the inputs 2024-06-26T05:54:44.8932499Z >>> for input in inputs: 2024-06-26T05:54:44.8933483Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-06-26T05:54:44.8934663Z ... bench.add_input(input[0], x2=input[1]) 2024-06-26T05:54:44.8935736Z >>> # Inputs supplied above are randomly used during the execution 2024-06-26T05:54:44.8936734Z >>> stats = bench.benchmark( 2024-06-26T05:54:44.8937435Z ... num_calling_threads=4, 2024-06-26T05:54:44.8938416Z ... num_warmup_iters = 100, 2024-06-26T05:54:44.8939082Z ... num_iters = 1000, 2024-06-26T05:54:44.8939658Z ... ) 2024-06-26T05:54:44.8940472Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-06-26T05:54:44.8941581Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-06-26T05:54:44.8942305Z 2024-06-26T05:54:44.8943089Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:44.8944009Z 2024-06-26T05:54:44.9054426Z msg = Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=115. 2024-06-26T05:54:44.9056971Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:44.9058387Z Register a container-like type as pytree node. 2024-06-26T05:54:44.9059045Z 2024-06-26T05:54:44.9059221Z Args: 2024-06-26T05:54:44.9060252Z cls (type): A Python type to treat as an internal pytree node. 2024-06-26T05:54:44.9061731Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-06-26T05:54:44.9063438Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-06-26T05:54:44.9065132Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-06-26T05:54:44.9066409Z passed to the ``unflatten_fn``. 2024-06-26T05:54:44.9067650Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-06-26T05:54:44.9069357Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-06-26T05:54:44.9070740Z The function should return an instance of ``cls``. 2024-06-26T05:54:44.9072020Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-06-26T05:54:44.9073419Z qualified name used when serializing the tree spec. 2024-06-26T05:54:44.9075129Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-06-26T05:54:44.9076968Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-06-26T05:54:44.9078703Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-06-26T05:54:44.9080435Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-06-26T05:54:44.9082293Z how to convert the custom json dumpable representation of the context back to the 2024-06-26T05:54:44.9083979Z original context. This is used for json deserialization, which is being used in 2024-06-26T05:54:44.9085232Z :mod:`torch.export` right now. 2024-06-26T05:54:44.9085789Z 2024-06-26T05:54:44.9085985Z Example:: 2024-06-26T05:54:44.9086274Z 2024-06-26T05:54:44.9086480Z >>> # xdoctest: +SKIP 2024-06-26T05:54:44.9087275Z >>> # Registry a Python type with lambda functions 2024-06-26T05:54:44.9088154Z >>> register_pytree_node( 2024-06-26T05:54:44.9088784Z ... set, 2024-06-26T05:54:44.9089411Z ... lambda s: (sorted(s), None, None), 2024-06-26T05:54:44.9090264Z ... lambda children, _: set(children), 2024-06-26T05:54:44.9091012Z ... ) 2024-06-26T05:54:44.9091464Z 2024-06-26T05:54:44.9092478Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:44.9093414Z 2024-06-26T05:54:44.9534049Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1186. 2024-06-26T05:54:44.9536699Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:44.9537669Z 2024-06-26T05:54:44.9538165Z Context passed to policy function during selective checkpointing. 2024-06-26T05:54:44.9539002Z 2024-06-26T05:54:44.9539633Z This class is used to pass relevant metadata to the policy function during 2024-06-26T05:54:44.9541304Z selective checkpointing. The metadata includes whether the current invocation 2024-06-26T05:54:44.9542828Z of the policy function is during recomputation or not. 2024-06-26T05:54:44.9543547Z 2024-06-26T05:54:44.9543738Z Example: 2024-06-26T05:54:44.9544200Z >>> # xdoctest: +SKIP(stub) 2024-06-26T05:54:44.9544832Z >>> 2024-06-26T05:54:44.9545384Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-06-26T05:54:44.9546196Z >>> print(ctx.is_recompute) 2024-06-26T05:54:44.9546829Z >>> 2024-06-26T05:54:44.9547700Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-06-26T05:54:44.9548821Z >>> 2024-06-26T05:54:44.9549375Z >>> out = torch.utils.checkpoint.checkpoint( 2024-06-26T05:54:44.9550196Z >>> fn, x, y, 2024-06-26T05:54:44.9550736Z >>> use_reentrant=False, 2024-06-26T05:54:44.9551688Z >>> context_fn=context_fn, 2024-06-26T05:54:44.9552363Z >>> ) 2024-06-26T05:54:44.9552624Z 2024-06-26T05:54:44.9553400Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:44.9554328Z 2024-06-26T05:54:44.9556440Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1319. 2024-06-26T05:54:44.9559074Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:44.9560061Z 2024-06-26T05:54:44.9560650Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-06-26T05:54:44.9561638Z 2024-06-26T05:54:44.9562198Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-06-26T05:54:44.9563415Z operations are recomputed during the backward pass. 2024-06-26T05:54:44.9564104Z 2024-06-26T05:54:44.9564262Z Args: 2024-06-26T05:54:44.9564766Z policy_fn_or_list (Callable or List): 2024-06-26T05:54:44.9565806Z - If a policy function is provided, it should accept a 2024-06-26T05:54:44.9567038Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-06-26T05:54:44.9568463Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-06-26T05:54:44.9569876Z indicating whether the execution of the op should be recomputed or not. 2024-06-26T05:54:44.9571436Z - If a list of operations is provided, it is equivalent to a policy 2024-06-26T05:54:44.9572725Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-06-26T05:54:44.9573995Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-06-26T05:54:44.9575029Z operations. 2024-06-26T05:54:44.9575881Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-06-26T05:54:44.9577271Z raised if any tensors cached by selective activation checkpoint are 2024-06-26T05:54:44.9578683Z mutated in order to ensure correctness. If set to `True`, this check 2024-06-26T05:54:44.9579743Z is disabled. 2024-06-26T05:54:44.9580257Z Returns: 2024-06-26T05:54:44.9580753Z A tuple of two context managers. 2024-06-26T05:54:44.9581296Z 2024-06-26T05:54:44.9581466Z Example: 2024-06-26T05:54:44.9581956Z >>> # xdoctest: +REQUIRES(LINUX) 2024-06-26T05:54:44.9582648Z >>> import functools 2024-06-26T05:54:44.9583202Z >>> 2024-06-26T05:54:44.9583742Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-06-26T05:54:44.9584616Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-06-26T05:54:44.9585389Z >>> 2024-06-26T05:54:44.9585835Z >>> ops_to_save = [ 2024-06-26T05:54:44.9586441Z >>> torch.ops.aten.mm.default, 2024-06-26T05:54:44.9587129Z >>> ] 2024-06-26T05:54:44.9587539Z >>> 2024-06-26T05:54:44.9588071Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-06-26T05:54:44.9588862Z >>> if op in ops_to_save: 2024-06-26T05:54:44.9589555Z >>> return CheckpointPolicy.MUST_SAVE 2024-06-26T05:54:44.9590323Z >>> else: 2024-06-26T05:54:44.9591133Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-06-26T05:54:44.9591932Z >>> 2024-06-26T05:54:44.9592817Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-06-26T05:54:44.9594116Z >>> 2024-06-26T05:54:44.9594566Z >>> # or equivalently 2024-06-26T05:54:44.9595758Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-06-26T05:54:44.9596925Z >>> 2024-06-26T05:54:44.9597373Z >>> def fn(x, y): 2024-06-26T05:54:44.9598183Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-06-26T05:54:44.9599148Z >>> 2024-06-26T05:54:44.9599725Z >>> out = torch.utils.checkpoint.checkpoint( 2024-06-26T05:54:44.9600534Z >>> fn, x, y, 2024-06-26T05:54:44.9601171Z >>> use_reentrant=False, 2024-06-26T05:54:44.9601828Z >>> context_fn=context_fn, 2024-06-26T05:54:44.9602400Z >>> ) 2024-06-26T05:54:44.9602662Z 2024-06-26T05:54:44.9603617Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:44.9604555Z 2024-06-26T05:54:44.9769249Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=923. 2024-06-26T05:54:44.9771713Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:44.9772656Z 2024-06-26T05:54:44.9773017Z Create a :class:`setuptools.Extension` for C++. 2024-06-26T05:54:44.9773687Z 2024-06-26T05:54:44.9774274Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-06-26T05:54:44.9775726Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-06-26T05:54:44.9776482Z 2024-06-26T05:54:44.9776936Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-06-26T05:54:44.9777931Z constructor. Full list arguments can be found at 2024-06-26T05:54:44.9779531Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-06-26T05:54:44.9780668Z 2024-06-26T05:54:44.9780866Z Example: 2024-06-26T05:54:44.9781344Z >>> # xdoctest: +SKIP 2024-06-26T05:54:44.9782111Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:44.9783032Z >>> from setuptools import setup 2024-06-26T05:54:44.9784091Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-06-26T05:54:44.9785145Z >>> setup( 2024-06-26T05:54:44.9785742Z ... name='extension', 2024-06-26T05:54:44.9786368Z ... ext_modules=[ 2024-06-26T05:54:44.9786930Z ... CppExtension( 2024-06-26T05:54:44.9787630Z ... name='extension', 2024-06-26T05:54:44.9788441Z ... sources=['extension.cpp'], 2024-06-26T05:54:44.9789392Z ... extra_compile_args=['-g'], 2024-06-26T05:54:44.9790446Z ... extra_link_flags=['-Wl,--no-as-needed', '-lm']) 2024-06-26T05:54:44.9791302Z ... ], 2024-06-26T05:54:44.9791788Z ... cmdclass={ 2024-06-26T05:54:44.9792430Z ... 'build_ext': BuildExtension 2024-06-26T05:54:44.9793065Z ... }) 2024-06-26T05:54:44.9793349Z 2024-06-26T05:54:44.9794095Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:44.9795236Z 2024-06-26T05:54:44.9797039Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=974. 2024-06-26T05:54:44.9799536Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:44.9800465Z 2024-06-26T05:54:44.9800847Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-06-26T05:54:44.9801577Z 2024-06-26T05:54:44.9802129Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-06-26T05:54:44.9803493Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-06-26T05:54:44.9804927Z extension. This includes the CUDA include path, library path and runtime 2024-06-26T05:54:44.9806197Z library. 2024-06-26T05:54:44.9806464Z 2024-06-26T05:54:44.9806943Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-06-26T05:54:44.9808049Z constructor. Full list arguments can be found at 2024-06-26T05:54:44.9809571Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-06-26T05:54:44.9810703Z 2024-06-26T05:54:44.9810875Z Example: 2024-06-26T05:54:44.9811349Z >>> # xdoctest: +SKIP 2024-06-26T05:54:44.9812109Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:44.9813004Z >>> from setuptools import setup 2024-06-26T05:54:44.9814076Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-06-26T05:54:44.9815095Z >>> setup( 2024-06-26T05:54:44.9815648Z ... name='cuda_extension', 2024-06-26T05:54:44.9816276Z ... ext_modules=[ 2024-06-26T05:54:44.9816855Z ... CUDAExtension( 2024-06-26T05:54:44.9817799Z ... name='cuda_extension', 2024-06-26T05:54:44.9818837Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-06-26T05:54:44.9819928Z ... extra_compile_args={'cxx': ['-g'], 2024-06-26T05:54:44.9820893Z ... 'nvcc': ['-O2']}, 2024-06-26T05:54:44.9821958Z ... extra_link_flags=['-Wl,--no-as-needed', '-lcuda']) 2024-06-26T05:54:44.9822780Z ... ], 2024-06-26T05:54:44.9823212Z ... cmdclass={ 2024-06-26T05:54:44.9823899Z ... 'build_ext': BuildExtension 2024-06-26T05:54:44.9824614Z ... }) 2024-06-26T05:54:44.9824902Z 2024-06-26T05:54:44.9825122Z Compute capabilities: 2024-06-26T05:54:44.9825483Z 2024-06-26T05:54:44.9826275Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-06-26T05:54:44.9828133Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-06-26T05:54:44.9830065Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-06-26T05:54:44.9832120Z newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch 2024-06-26T05:54:44.9834006Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-06-26T05:54:44.9835660Z support (see below for details on PTX). 2024-06-26T05:54:44.9835995Z 2024-06-26T05:54:44.9836442Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-06-26T05:54:44.9837175Z CCs you want the extension to support: 2024-06-26T05:54:44.9837474Z 2024-06-26T05:54:44.9837713Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-06-26T05:54:44.9838469Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-06-26T05:54:44.9838987Z 2024-06-26T05:54:44.9839421Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-06-26T05:54:44.9840576Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-06-26T05:54:44.9841767Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-06-26T05:54:44.9842858Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-06-26T05:54:44.9843930Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-06-26T05:54:44.9845018Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-06-26T05:54:44.9846034Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-06-26T05:54:44.9847144Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-06-26T05:54:44.9847860Z "8.0 8.6" would be better. 2024-06-26T05:54:44.9848075Z 2024-06-26T05:54:44.9848578Z Note that while it's possible to include all supported archs, the more archs get included the 2024-06-26T05:54:44.9849677Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-06-26T05:54:44.9850330Z 2024-06-26T05:54:44.9850873Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-06-26T05:54:44.9851797Z To workaround the issue, move python binding logic to pure C++ file. 2024-06-26T05:54:44.9852244Z 2024-06-26T05:54:44.9852356Z Example use: 2024-06-26T05:54:44.9852620Z #include 2024-06-26T05:54:44.9853053Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-06-26T05:54:44.9853399Z 2024-06-26T05:54:44.9853506Z Instead of: 2024-06-26T05:54:44.9853776Z #include 2024-06-26T05:54:44.9854226Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-06-26T05:54:44.9854581Z 2024-06-26T05:54:44.9854959Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-06-26T05:54:44.9856239Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-06-26T05:54:44.9857071Z 2024-06-26T05:54:44.9857199Z Relocatable device code linking: 2024-06-26T05:54:44.9857476Z 2024-06-26T05:54:44.9857859Z If you want to reference device symbols across compilation units (across object files), 2024-06-26T05:54:44.9858857Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-06-26T05:54:44.9859887Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-06-26T05:54:44.9861016Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-06-26T05:54:44.9862156Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-06-26T05:54:44.9863033Z help reduce the protentional perf degradation of `-rdc`. 2024-06-26T05:54:44.9863634Z Note that it needs to be used at both steps to be useful. 2024-06-26T05:54:44.9864028Z 2024-06-26T05:54:44.9864651Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-06-26T05:54:44.9865636Z There is also a case where `-dlink` is used without `-rdc`: 2024-06-26T05:54:44.9866440Z when an extension is linked against a static lib containing rdc-compiled objects 2024-06-26T05:54:44.9867235Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-06-26T05:54:44.9867675Z 2024-06-26T05:54:44.9867964Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-06-26T05:54:44.9868405Z 2024-06-26T05:54:44.9868499Z Example: 2024-06-26T05:54:44.9868760Z >>> # xdoctest: +SKIP 2024-06-26T05:54:44.9869165Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:44.9869621Z >>> CUDAExtension( 2024-06-26T05:54:44.9869992Z ... name='cuda_extension', 2024-06-26T05:54:44.9870537Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-06-26T05:54:44.9871023Z ... dlink=True, 2024-06-26T05:54:44.9871374Z ... dlink_libraries=["dlink_lib"], 2024-06-26T05:54:44.9871881Z ... extra_compile_args={'cxx': ['-g'], 2024-06-26T05:54:44.9872429Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-06-26T05:54:44.9872762Z 2024-06-26T05:54:44.9873146Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:44.9873645Z 2024-06-26T05:54:44.9874427Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1232. 2024-06-26T05:54:44.9875856Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:44.9876360Z 2024-06-26T05:54:44.9876592Z Load a PyTorch C++ extension just-in-time (JIT). 2024-06-26T05:54:44.9876950Z 2024-06-26T05:54:44.9877237Z To load an extension, a Ninja build file is emitted, which is used to 2024-06-26T05:54:44.9877964Z compile the given sources into a dynamic library. This library is 2024-06-26T05:54:44.9878749Z subsequently loaded into the current Python process as a module and 2024-06-26T05:54:44.9879398Z returned from this function, ready for use. 2024-06-26T05:54:44.9879723Z 2024-06-26T05:54:44.9880003Z By default, the directory to which the build file is emitted and the 2024-06-26T05:54:44.9880761Z resulting library compiled to is ``/torch_extensions/``, where 2024-06-26T05:54:44.9881594Z ```` is the temporary folder on the current platform and ```` 2024-06-26T05:54:44.9882351Z the name of the extension. This location can be overridden in two ways. 2024-06-26T05:54:44.9883109Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-06-26T05:54:44.9883832Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-06-26T05:54:44.9884579Z into subfolders of this directory. Second, if the ``build_directory`` 2024-06-26T05:54:44.9885434Z argument to this function is supplied, it overrides the entire path, i.e. 2024-06-26T05:54:44.9886137Z the library will be compiled into that folder directly. 2024-06-26T05:54:44.9886512Z 2024-06-26T05:54:44.9886800Z To compile the sources, the default system compiler (``c++``) is used, 2024-06-26T05:54:44.9887583Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-06-26T05:54:44.9888369Z additional arguments to the compilation process, ``extra_cflags`` or 2024-06-26T05:54:44.9889118Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-06-26T05:54:44.9889926Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-06-26T05:54:44.9890556Z ``extra_cflags`` to pass further include directories. 2024-06-26T05:54:44.9890907Z 2024-06-26T05:54:44.9891222Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-06-26T05:54:44.9891972Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-06-26T05:54:44.9892736Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-06-26T05:54:44.9893521Z passing the CUDA lib64 directory as a library directory, and linking 2024-06-26T05:54:44.9894164Z ``cudart``. You can pass additional flags to nvcc via 2024-06-26T05:54:44.9894801Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-06-26T05:54:44.9895557Z heuristics for finding the CUDA install directory are used, which usually 2024-06-26T05:54:44.9896318Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-06-26T05:54:44.9896887Z safest option. 2024-06-26T05:54:44.9897053Z 2024-06-26T05:54:44.9897150Z Args: 2024-06-26T05:54:44.9897574Z name: The name of the extension to build. This MUST be the same as the 2024-06-26T05:54:44.9898172Z name of the pybind11 module! 2024-06-26T05:54:44.9898720Z sources: A list of relative or absolute paths to C++ source files. 2024-06-26T05:54:44.9899446Z extra_cflags: optional list of compiler flags to forward to the build. 2024-06-26T05:54:44.9900195Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-06-26T05:54:44.9900780Z when building CUDA sources. 2024-06-26T05:54:44.9901340Z extra_ldflags: optional list of linker flags to forward to the build. 2024-06-26T05:54:44.9902087Z extra_include_paths: optional list of include directories to forward 2024-06-26T05:54:44.9902641Z to the build. 2024-06-26T05:54:44.9903063Z build_directory: optional path to use as build workspace. 2024-06-26T05:54:44.9903689Z verbose: If ``True``, turns on verbose logging of load steps. 2024-06-26T05:54:44.9904382Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-06-26T05:54:44.9905054Z the build. If set to ``None`` (default), this value is 2024-06-26T05:54:44.9905686Z automatically determined based on the existence of ``.cu`` or 2024-06-26T05:54:44.9906364Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-06-26T05:54:44.9906917Z and libraries to be included. 2024-06-26T05:54:44.9907512Z is_python_module: If ``True`` (default), imports the produced shared 2024-06-26T05:54:44.9908252Z library as a Python module. If ``False``, behavior depends on 2024-06-26T05:54:44.9908786Z ``is_standalone``. 2024-06-26T05:54:44.9909281Z is_standalone: If ``False`` (default) loads the constructed extension 2024-06-26T05:54:44.9910007Z into the process as a plain dynamic library. If ``True``, build a 2024-06-26T05:54:44.9910568Z standalone executable. 2024-06-26T05:54:44.9910806Z 2024-06-26T05:54:44.9910895Z Returns: 2024-06-26T05:54:44.9911176Z If ``is_python_module`` is ``True``: 2024-06-26T05:54:44.9911703Z Returns the loaded PyTorch extension as a Python module. 2024-06-26T05:54:44.9912095Z 2024-06-26T05:54:44.9912387Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-06-26T05:54:44.9913168Z Returns nothing. (The shared library is loaded into the process as 2024-06-26T05:54:44.9913729Z a side effect.) 2024-06-26T05:54:44.9913928Z 2024-06-26T05:54:44.9914067Z If ``is_standalone`` is ``True``. 2024-06-26T05:54:44.9914708Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-06-26T05:54:44.9915400Z added to the PATH environment variable as a side effect.) 2024-06-26T05:54:44.9915798Z 2024-06-26T05:54:44.9915907Z Example: 2024-06-26T05:54:44.9916157Z >>> # xdoctest: +SKIP 2024-06-26T05:54:44.9916552Z >>> from torch.utils.cpp_extension import load 2024-06-26T05:54:44.9917001Z >>> module = load( 2024-06-26T05:54:44.9917359Z ... name='extension', 2024-06-26T05:54:44.9917853Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-06-26T05:54:44.9918377Z ... extra_cflags=['-O2'], 2024-06-26T05:54:44.9918724Z ... verbose=True) 2024-06-26T05:54:44.9918940Z 2024-06-26T05:54:44.9919329Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:44.9919834Z 2024-06-26T05:54:44.9920709Z msg = Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1524. 2024-06-26T05:54:44.9922066Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:44.9922571Z 2024-06-26T05:54:44.9922924Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-06-26T05:54:44.9923373Z 2024-06-26T05:54:44.9923678Z This function behaves exactly like :func:`load`, but takes its sources as 2024-06-26T05:54:44.9924457Z strings rather than filenames. These strings are stored to files in the 2024-06-26T05:54:44.9925199Z build directory, after which the behavior of :func:`load_inline` is 2024-06-26T05:54:44.9925744Z identical to :func:`load`. 2024-06-26T05:54:44.9925979Z 2024-06-26T05:54:44.9926073Z See `the 2024-06-26T05:54:44.9926646Z tests `_ 2024-06-26T05:54:44.9927385Z for good examples of using this function. 2024-06-26T05:54:44.9927687Z 2024-06-26T05:54:44.9928059Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-06-26T05:54:44.9928885Z the necessary header includes, as well as the (pybind11) binding code. More 2024-06-26T05:54:44.9929694Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-06-26T05:54:44.9930437Z single ``.cpp`` file. This file is then prepended with ``#include 2024-06-26T05:54:44.9930959Z ``. 2024-06-26T05:54:44.9931174Z 2024-06-26T05:54:44.9931470Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-06-26T05:54:44.9932235Z automatically generated for each function specified. ``functions`` can 2024-06-26T05:54:44.9932998Z either be a list of function names, or a dictionary mapping from function 2024-06-26T05:54:44.9933792Z names to docstrings. If a list is given, the name of each function is used 2024-06-26T05:54:44.9934384Z as its docstring. 2024-06-26T05:54:44.9934643Z 2024-06-26T05:54:44.9934938Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-06-26T05:54:44.9935630Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-06-26T05:54:44.9936346Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-06-26T05:54:44.9937074Z separately, but ultimately linked into a single library. Note that no 2024-06-26T05:54:44.9937840Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-06-26T05:54:44.9938634Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-06-26T05:54:44.9939410Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-06-26T05:54:44.9939988Z include its name in ``functions``). 2024-06-26T05:54:44.9940270Z 2024-06-26T05:54:44.9940510Z See :func:`load` for a description of arguments omitted below. 2024-06-26T05:54:44.9940918Z 2024-06-26T05:54:44.9941018Z Args: 2024-06-26T05:54:44.9941511Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-06-26T05:54:44.9942278Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-06-26T05:54:44.9943028Z functions: A list of function names for which to generate function 2024-06-26T05:54:44.9943744Z bindings. If a dictionary is given, it should map function names to 2024-06-26T05:54:44.9944440Z docstrings (which are otherwise just the function names). 2024-06-26T05:54:44.9945135Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-06-26T05:54:44.9945797Z the build. If set to ``None`` (default), this value is 2024-06-26T05:54:44.9946439Z automatically determined based on whether ``cuda_sources`` is 2024-06-26T05:54:44.9947067Z provided. Set it to ``True`` to force CUDA headers 2024-06-26T05:54:44.9947563Z and libraries to be included. 2024-06-26T05:54:44.9948098Z with_pytorch_error_handling: Determines whether pytorch error and 2024-06-26T05:54:44.9948801Z warning macros are handled by pytorch instead of pybind. To do 2024-06-26T05:54:44.9949534Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-06-26T05:54:44.9950255Z function. This redirection might cause issues in obscure cases 2024-06-26T05:54:44.9950950Z of cpp. This flag should be set to ``False`` when this redirect 2024-06-26T05:54:44.9951483Z causes issues. 2024-06-26T05:54:44.9951676Z 2024-06-26T05:54:44.9951767Z Example: 2024-06-26T05:54:44.9952102Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:44.9952661Z >>> from torch.utils.cpp_extension import load_inline 2024-06-26T05:54:44.9953116Z >>> source = """ 2024-06-26T05:54:44.9953502Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-06-26T05:54:44.9953965Z return x.sin() + y.sin(); 2024-06-26T05:54:44.9954294Z } 2024-06-26T05:54:44.9954527Z """ 2024-06-26T05:54:44.9955045Z >>> module = load_inline(name='inline_extension', 2024-06-26T05:54:44.9955538Z ... cpp_sources=[source], 2024-06-26T05:54:44.9956061Z ... functions=['sin_add']) 2024-06-26T05:54:44.9956376Z 2024-06-26T05:54:44.9956487Z .. note:: 2024-06-26T05:54:44.9956902Z By default, the Ninja backend uses #CPUS + 2 workers to build the 2024-06-26T05:54:44.9957636Z extension. This may use up too many resources on some systems. One 2024-06-26T05:54:44.9958387Z can control the number of workers by setting the `MAX_JOBS` environment 2024-06-26T05:54:44.9959042Z variable to a non-negative number. 2024-06-26T05:54:44.9959328Z 2024-06-26T05:54:44.9959716Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:44.9960221Z 2024-06-26T05:54:45.0759633Z msg = Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=14. 2024-06-26T05:54:45.0761122Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:45.0762130Z Sampler that restricts data loading to a subset of the dataset. 2024-06-26T05:54:45.0762648Z 2024-06-26T05:54:45.0762821Z It is especially useful in conjunction with 2024-06-26T05:54:45.0763506Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-06-26T05:54:45.0764379Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-06-26T05:54:45.0765223Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-06-26T05:54:45.0765858Z original dataset that is exclusive to it. 2024-06-26T05:54:45.0766187Z 2024-06-26T05:54:45.0766299Z .. note:: 2024-06-26T05:54:45.0766796Z Dataset is assumed to be of constant size and that any instance of it always 2024-06-26T05:54:45.0767472Z returns the same elements in the same order. 2024-06-26T05:54:45.0767820Z 2024-06-26T05:54:45.0767914Z Args: 2024-06-26T05:54:45.0768304Z dataset: Dataset used for sampling. 2024-06-26T05:54:45.0768890Z num_replicas (int, optional): Number of processes participating in 2024-06-26T05:54:45.0769673Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-06-26T05:54:45.0770309Z current distributed group. 2024-06-26T05:54:45.0770910Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-06-26T05:54:45.0771696Z By default, :attr:`rank` is retrieved from the current distributed 2024-06-26T05:54:45.0772239Z group. 2024-06-26T05:54:45.0772708Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-06-26T05:54:45.0773281Z indices. 2024-06-26T05:54:45.0773734Z seed (int, optional): random seed used to shuffle the sampler if 2024-06-26T05:54:45.0774438Z :attr:`shuffle=True`. This number should be identical across all 2024-06-26T05:54:45.0775085Z processes in the distributed group. Default: ``0``. 2024-06-26T05:54:45.0775766Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-06-26T05:54:45.0776512Z tail of the data to make it evenly divisible across the number of 2024-06-26T05:54:45.0777219Z replicas. If ``False``, the sampler will add extra indices to make 2024-06-26T05:54:45.0777946Z the data evenly divisible across the replicas. Default: ``False``. 2024-06-26T05:54:45.0778392Z 2024-06-26T05:54:45.0778507Z .. warning:: 2024-06-26T05:54:45.0778922Z In distributed mode, calling the :meth:`set_epoch` method at 2024-06-26T05:54:45.0779695Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-06-26T05:54:45.0780576Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-06-26T05:54:45.0781245Z the same ordering will be always used. 2024-06-26T05:54:45.0781571Z 2024-06-26T05:54:45.0781674Z Example:: 2024-06-26T05:54:45.0781847Z 2024-06-26T05:54:45.0781960Z >>> # xdoctest: +SKIP 2024-06-26T05:54:45.0782486Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-06-26T05:54:45.0783160Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-06-26T05:54:45.0783700Z ... sampler=sampler) 2024-06-26T05:54:45.0784184Z >>> for epoch in range(start_epoch, n_epochs): 2024-06-26T05:54:45.0784632Z ... if is_distributed: 2024-06-26T05:54:45.0785033Z ... sampler.set_epoch(epoch) 2024-06-26T05:54:45.0785442Z ... train(loader) 2024-06-26T05:54:45.0785748Z 2024-06-26T05:54:45.0786290Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:45.0786774Z 2024-06-26T05:54:45.2604466Z gathering tests 2024-06-26T05:54:45.2617512Z running 696 test(s) 2024-06-26T05:54:45.2632576Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0, line 893 <- wrt source file 2024-06-26T05:54:45.2640713Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0 2024-06-26T05:54:45.2642404Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0, line 929 <- wrt source file 2024-06-26T05:54:45.2645591Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0 2024-06-26T05:54:45.2647427Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0, line 998 <- wrt source file 2024-06-26T05:54:45.2649149Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0 2024-06-26T05:54:45.2651375Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1047 <- wrt source file 2024-06-26T05:54:45.2653594Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0 2024-06-26T05:54:45.2655271Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0, line 1084 <- wrt source file 2024-06-26T05:54:45.2656826Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0 2024-06-26T05:54:45.2658452Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1239 <- wrt source file 2024-06-26T05:54:45.2660217Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2024-06-26T05:54:45.2661787Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0, line 2281 <- wrt source file 2024-06-26T05:54:45.2663217Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0 2024-06-26T05:54:45.2664793Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0, line 117 <- wrt source file 2024-06-26T05:54:45.2666465Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0 2024-06-26T05:54:45.2668098Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 546 <- wrt source file 2024-06-26T05:54:45.2840793Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0 2024-06-26T05:54:45.2842588Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 603 <- wrt source file 2024-06-26T05:54:45.2859876Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2024-06-26T05:54:45.2861609Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1223 <- wrt source file 2024-06-26T05:54:45.2979513Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0 2024-06-26T05:54:45.2982875Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1268 <- wrt source file 2024-06-26T05:54:45.2987144Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0 2024-06-26T05:54:45.2988707Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0, line 1341 <- wrt source file 2024-06-26T05:54:45.2994204Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0 2024-06-26T05:54:45.2996197Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1371 <- wrt source file 2024-06-26T05:54:45.2999734Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2024-06-26T05:54:45.3001391Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0, line 1394 <- wrt source file 2024-06-26T05:54:45.3004099Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0 2024-06-26T05:54:45.3005681Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2024-06-26T05:54:45.3023866Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0 2024-06-26T05:54:45.3025492Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0, line 63 <- wrt source file 2024-06-26T05:54:45.3030334Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0 2024-06-26T05:54:45.3032138Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0, line 91 <- wrt source file 2024-06-26T05:54:45.3034246Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0 2024-06-26T05:54:45.3036026Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0, line 178 <- wrt source file 2024-06-26T05:54:45.3046816Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0 2024-06-26T05:54:45.3048320Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0, line 287 <- wrt source file 2024-06-26T05:54:45.3100229Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0 2024-06-26T05:54:45.3101825Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1014 <- wrt source file 2024-06-26T05:54:45.3111761Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0 2024-06-26T05:54:45.3113402Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0, line 1289 <- wrt source file 2024-06-26T05:54:45.3123211Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0 2024-06-26T05:54:45.3124778Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0, line 1373 <- wrt source file 2024-06-26T05:54:45.3129844Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0 2024-06-26T05:54:45.3131421Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0, line 1407 <- wrt source file 2024-06-26T05:54:45.3139286Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0 2024-06-26T05:54:45.3140788Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0, line 1458 <- wrt source file 2024-06-26T05:54:45.3152104Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0 2024-06-26T05:54:45.3153633Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0, line 1499 <- wrt source file 2024-06-26T05:54:45.3167885Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0 2024-06-26T05:54:45.3169522Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0, line 1535 <- wrt source file 2024-06-26T05:54:45.3183709Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0 2024-06-26T05:54:45.3185253Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0, line 1573 <- wrt source file 2024-06-26T05:54:45.3203846Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0 2024-06-26T05:54:45.3205342Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0, line 1746 <- wrt source file 2024-06-26T05:54:45.3234389Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0 2024-06-26T05:54:45.3236168Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0, line 1913 <- wrt source file 2024-06-26T05:54:45.3259213Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0 2024-06-26T05:54:45.3261066Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0, line 2013 <- wrt source file 2024-06-26T05:54:45.3263199Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0 2024-06-26T05:54:45.3265268Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0, line 2113 <- wrt source file 2024-06-26T05:54:45.3266764Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0 2024-06-26T05:54:45.3268200Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0, line 468 <- wrt source file 2024-06-26T05:54:45.3269547Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0 2024-06-26T05:54:45.3270922Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0, line 528 <- wrt source file 2024-06-26T05:54:45.3272274Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0 2024-06-26T05:54:45.3273625Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::load:0, line 619 <- wrt source file 2024-06-26T05:54:45.3275170Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::load:0 2024-06-26T05:54:45.3276593Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0, line 665 <- wrt source file 2024-06-26T05:54:45.3278006Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0 2024-06-26T05:54:45.3279502Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::download_url_to_file:0, line 696 <- wrt source file 2024-06-26T05:54:45.3281105Z * SKIPPED: 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2024-06-26T05:54:45.3334681Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0, line 241 <- wrt source file 2024-06-26T05:54:45.3336752Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0 2024-06-26T05:54:45.3338239Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0, line 392 <- wrt source file 2024-06-26T05:54:45.3348263Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0 2024-06-26T05:54:45.3349706Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0, line 459 <- wrt source file 2024-06-26T05:54:45.3357460Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0 2024-06-26T05:54:45.3359086Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0, line 567 <- wrt source file 2024-06-26T05:54:45.3360623Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0 2024-06-26T05:54:45.3362236Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_fake:0, line 641 <- wrt source file 2024-06-26T05:54:45.5091569Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_fake:0 2024-06-26T05:54:45.5093189Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_autograd:0, line 767 <- wrt source file 2024-06-26T05:54:45.5236622Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_autograd:0 2024-06-26T05:54:45.5238236Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0, line 111 <- wrt source file 2024-06-26T05:54:45.5242875Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0 2024-06-26T05:54:45.5244538Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0, line 418 <- wrt source file 2024-06-26T05:54:45.5277242Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0 2024-06-26T05:54:45.5278969Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0, line 1565 <- wrt source file 2024-06-26T05:54:45.5281221Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0 2024-06-26T05:54:45.5283289Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0, line 1700 <- wrt source file 2024-06-26T05:54:45.5285232Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0 2024-06-26T05:54:45.5286931Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1948 <- wrt source file 2024-06-26T05:54:45.5314780Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2024-06-26T05:54:45.5316506Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0, line 1967 <- wrt source file 2024-06-26T05:54:45.5321487Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0 2024-06-26T05:54:45.5323429Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2024-06-26T05:54:45.5324977Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0 2024-06-26T05:54:45.5327023Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0, line 214 <- wrt source file 2024-06-26T05:54:45.5328743Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0 2024-06-26T05:54:45.5331156Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0, line 277 <- wrt source file 2024-06-26T05:54:45.5333590Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0 2024-06-26T05:54:45.5335678Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0, line 738 <- wrt source file 2024-06-26T05:54:45.5337169Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0 2024-06-26T05:54:45.5338686Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::load:0, line 1089 <- wrt source file 2024-06-26T05:54:45.5340162Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::load:0 2024-06-26T05:54:45.5341718Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0, line 20 <- wrt source file 2024-06-26T05:54:45.5343291Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0 2024-06-26T05:54:45.5345014Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file 2024-06-26T05:54:45.5346763Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0 2024-06-26T05:54:45.5348553Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file 2024-06-26T05:54:45.5350352Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0 2024-06-26T05:54:45.5352009Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0, line 54 <- wrt source file 2024-06-26T05:54:45.5353496Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0 2024-06-26T05:54:45.5355190Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0, line 136 <- wrt source file 2024-06-26T05:54:45.5356642Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0 2024-06-26T05:54:45.5358162Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0, line 205 <- wrt source file 2024-06-26T05:54:45.5418822Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0 2024-06-26T05:54:45.5420797Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 124 <- wrt source file 2024-06-26T05:54:45.5422601Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2024-06-26T05:54:45.5424672Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0, line 154 <- wrt source file 2024-06-26T05:54:45.5426351Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0 2024-06-26T05:54:45.5428683Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1746 <- wrt source file 2024-06-26T05:54:45.5430991Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0 2024-06-26T05:54:45.5433070Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 94 <- wrt source file 2024-06-26T05:54:45.5434986Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2024-06-26T05:54:45.5437368Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 194 <- wrt source file 2024-06-26T05:54:45.5440119Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2024-06-26T05:54:45.5443033Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0, line 222 <- wrt source file 2024-06-26T05:54:45.5445703Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0 2024-06-26T05:54:45.5447383Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 242 <- wrt source file 2024-06-26T05:54:45.5449082Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2024-06-26T05:54:45.5450696Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0, line 215 <- wrt source file 2024-06-26T05:54:45.5452173Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0 2024-06-26T05:54:45.5453686Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0, line 279 <- wrt source file 2024-06-26T05:54:45.5455167Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0 2024-06-26T05:54:45.5456761Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0, line 320 <- wrt source file 2024-06-26T05:54:45.5458401Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0 2024-06-26T05:54:45.5460620Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 196 <- wrt source file 2024-06-26T05:54:45.5462444Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2024-06-26T05:54:45.5464524Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 258 <- wrt source file 2024-06-26T05:54:45.5467263Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0 2024-06-26T05:54:45.5470293Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0, line 289 <- wrt source file 2024-06-26T05:54:45.5473166Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0 2024-06-26T05:54:45.5476245Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0, line 147 <- wrt source file 2024-06-26T05:54:45.5479020Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0 2024-06-26T05:54:45.5482084Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 21 <- wrt source file 2024-06-26T05:54:45.5485283Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0 2024-06-26T05:54:45.5488372Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::as_nested_tensor:0, line 56 <- wrt source file 2024-06-26T05:54:45.5491413Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::as_nested_tensor:0 2024-06-26T05:54:45.5494382Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0, line 210 <- wrt source file 2024-06-26T05:54:45.5497265Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0 2024-06-26T05:54:45.5500129Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0, line 272 <- wrt source file 2024-06-26T05:54:45.5530640Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0 2024-06-26T05:54:45.5533644Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0, line 348 <- wrt source file 2024-06-26T05:54:45.5549262Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2024-06-26T05:54:45.5552572Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0, line 437 <- wrt source file 2024-06-26T05:54:45.5558525Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2024-06-26T05:54:45.5561739Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0, line 522 <- wrt source file 2024-06-26T05:54:45.5601883Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0 2024-06-26T05:54:45.5605101Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_op/impl.py::custom_op:0, line 83 <- wrt source file 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2024-06-26T05:54:45.7348944Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1221 <- wrt source file 2024-06-26T05:54:45.7405913Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2024-06-26T05:54:45.7409025Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1386 <- wrt source file 2024-06-26T05:54:45.7423292Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2024-06-26T05:54:45.7426457Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1550 <- wrt source file 2024-06-26T05:54:45.7429700Z * SKIPPED: 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2024-06-26T05:54:45.7754149Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0 2024-06-26T05:54:45.7756795Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0, line 93 <- wrt source file 2024-06-26T05:54:45.7759219Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0 2024-06-26T05:54:45.7761644Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0, line 74 <- wrt source file 2024-06-26T05:54:45.7933719Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0 2024-06-26T05:54:45.7935685Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 193 <- wrt source file 2024-06-26T05:54:45.7937533Z * SKIPPED: 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SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2024-06-26T05:54:45.8118958Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 159 <- wrt source file 2024-06-26T05:54:45.8161989Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2024-06-26T05:54:45.8163826Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0, line 406 <- wrt source file 2024-06-26T05:54:45.8165411Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0 2024-06-26T05:54:45.8167232Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 169 <- wrt source file 2024-06-26T05:54:45.8199962Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2024-06-26T05:54:45.8201725Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0, line 304 <- wrt source file 2024-06-26T05:54:45.8203456Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0 2024-06-26T05:54:45.8205195Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 995 <- wrt source file 2024-06-26T05:54:45.8250493Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2024-06-26T05:54:45.8252312Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1060 <- wrt source file 2024-06-26T05:54:45.8254054Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2024-06-26T05:54:45.8255793Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1281 <- wrt source file 2024-06-26T05:54:45.8267797Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2024-06-26T05:54:45.8269745Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1347 <- wrt source file 2024-06-26T05:54:45.8272081Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2024-06-26T05:54:45.8274049Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1410 <- wrt source file 2024-06-26T05:54:45.8276855Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2024-06-26T05:54:45.8278568Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1455 <- wrt source file 2024-06-26T05:54:45.8280181Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2024-06-26T05:54:45.8282515Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1565 <- wrt source file 2024-06-26T05:54:45.8284559Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2024-06-26T05:54:45.8286596Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 88 <- wrt source file 2024-06-26T05:54:45.8288560Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0 2024-06-26T05:54:45.8290175Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 60 <- wrt source file 2024-06-26T05:54:45.8291754Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2024-06-26T05:54:45.8293566Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file 2024-06-26T05:54:45.8295521Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2024-06-26T05:54:45.8297745Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file 2024-06-26T05:54:45.8300039Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2024-06-26T05:54:45.8302182Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 23 <- wrt source file 2024-06-26T05:54:45.8304249Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2024-06-26T05:54:45.8306496Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 60 <- wrt source file 2024-06-26T05:54:45.8309130Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2024-06-26T05:54:45.8311261Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 127 <- wrt source file 2024-06-26T05:54:45.8313332Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2024-06-26T05:54:45.8315366Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 25 <- wrt source file 2024-06-26T05:54:45.8317146Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2024-06-26T05:54:45.8318914Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 277 <- wrt source file 2024-06-26T05:54:45.8338409Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2024-06-26T05:54:45.8340982Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 167 <- wrt source file 2024-06-26T05:54:45.8343167Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2024-06-26T05:54:45.8344908Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 227 <- wrt source file 2024-06-26T05:54:45.8346626Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2024-06-26T05:54:45.8348611Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 288 <- wrt source file 2024-06-26T05:54:45.8350331Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2024-06-26T05:54:45.8352217Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 75 <- wrt source file 2024-06-26T05:54:45.8354893Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2024-06-26T05:54:45.8357735Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 115 <- wrt source file 2024-06-26T05:54:45.8360972Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2024-06-26T05:54:45.8363887Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 34 <- wrt source file 2024-06-26T05:54:45.8365992Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2024-06-26T05:54:45.8369003Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 103 <- wrt source file 2024-06-26T05:54:45.8372081Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2024-06-26T05:54:45.8373982Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 167 <- wrt source file 2024-06-26T05:54:45.8376655Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2024-06-26T05:54:45.8380583Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 231 <- wrt source file 2024-06-26T05:54:45.8384785Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2024-06-26T05:54:45.8389059Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 290 <- wrt source file 2024-06-26T05:54:45.8393273Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2024-06-26T05:54:45.8397459Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 349 <- wrt source file 2024-06-26T05:54:45.8401177Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2024-06-26T05:54:45.8404775Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 29 <- wrt source file 2024-06-26T05:54:45.8408235Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2024-06-26T05:54:45.8411651Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 392 <- wrt source file 2024-06-26T05:54:45.8414960Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2024-06-26T05:54:45.8418353Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 639 <- wrt source file 2024-06-26T05:54:45.8421637Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2024-06-26T05:54:45.8425027Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 975 <- wrt source file 2024-06-26T05:54:45.8428417Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2024-06-26T05:54:45.8431868Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1028 <- wrt source file 2024-06-26T05:54:45.8435344Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2024-06-26T05:54:45.8438816Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1071 <- wrt source file 2024-06-26T05:54:45.8442353Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2024-06-26T05:54:45.8445778Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 32 <- wrt source file 2024-06-26T05:54:45.8449079Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2024-06-26T05:54:45.8452311Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 405 <- wrt source file 2024-06-26T05:54:45.8455445Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2024-06-26T05:54:45.8458694Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 506 <- wrt source file 2024-06-26T05:54:45.8461823Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2024-06-26T05:54:45.8465122Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 691 <- wrt source file 2024-06-26T05:54:45.8468499Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2024-06-26T05:54:45.8471893Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 781 <- wrt source file 2024-06-26T05:54:45.8475339Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2024-06-26T05:54:45.8478768Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 875 <- wrt source file 2024-06-26T05:54:45.8482218Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2024-06-26T05:54:45.8485664Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 85 <- wrt source file 2024-06-26T05:54:45.8489144Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2024-06-26T05:54:45.8492693Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 209 <- wrt source file 2024-06-26T05:54:45.8496266Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2024-06-26T05:54:45.8499930Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 22 <- wrt source file 2024-06-26T05:54:45.8503679Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2024-06-26T05:54:45.8507413Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 153 <- wrt source file 2024-06-26T05:54:45.8511071Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2024-06-26T05:54:45.8514523Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 118 <- wrt source file 2024-06-26T05:54:45.8517801Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2024-06-26T05:54:45.8521698Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0, line 60 <- wrt source file 2024-06-26T05:54:45.8536799Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0 2024-06-26T05:54:45.8541409Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0, line 92 <- wrt source file 2024-06-26T05:54:45.8546066Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0 2024-06-26T05:54:45.8550353Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 54 <- wrt source file 2024-06-26T05:54:45.8554457Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2024-06-26T05:54:45.8558398Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 20 <- wrt source file 2024-06-26T05:54:45.8561914Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2024-06-26T05:54:45.8565419Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 48 <- wrt source file 2024-06-26T05:54:45.8568995Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2024-06-26T05:54:45.8572438Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 145 <- wrt source file 2024-06-26T05:54:45.8575663Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2024-06-26T05:54:45.8579031Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 29 <- wrt source file 2024-06-26T05:54:45.8582479Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2024-06-26T05:54:45.8586024Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 68 <- wrt source file 2024-06-26T05:54:45.8589568Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2024-06-26T05:54:45.8593109Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 116 <- wrt source file 2024-06-26T05:54:45.8596732Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2024-06-26T05:54:45.8600340Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 145 <- wrt source file 2024-06-26T05:54:45.8604048Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2024-06-26T05:54:45.8607518Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 86 <- wrt source file 2024-06-26T05:54:45.8610646Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0 2024-06-26T05:54:45.8613885Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 107 <- wrt source file 2024-06-26T05:54:45.8617205Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2024-06-26T05:54:45.8620443Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 219 <- wrt source file 2024-06-26T05:54:45.8623630Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2024-06-26T05:54:45.8626802Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 282 <- wrt source file 2024-06-26T05:54:45.8629988Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2024-06-26T05:54:45.8633234Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 420 <- wrt source file 2024-06-26T05:54:45.8636597Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2024-06-26T05:54:45.8639877Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 588 <- wrt source file 2024-06-26T05:54:45.8643080Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2024-06-26T05:54:45.8646397Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 647 <- wrt source file 2024-06-26T05:54:45.8649891Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2024-06-26T05:54:45.8653528Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 698 <- wrt source file 2024-06-26T05:54:45.8657229Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2024-06-26T05:54:45.8660780Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 56 <- wrt source file 2024-06-26T05:54:45.8664064Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2024-06-26T05:54:45.8667440Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 129 <- wrt source file 2024-06-26T05:54:45.8670818Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2024-06-26T05:54:45.8674176Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 217 <- wrt source file 2024-06-26T05:54:45.8677553Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2024-06-26T05:54:45.8680794Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 139 <- wrt source file 2024-06-26T05:54:45.8684099Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2024-06-26T05:54:45.8687360Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 472 <- wrt source file 2024-06-26T05:54:45.8690563Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2024-06-26T05:54:45.8693850Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 493 <- wrt source file 2024-06-26T05:54:45.8697106Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2024-06-26T05:54:45.8700476Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 506 <- wrt source file 2024-06-26T05:54:45.8703700Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2024-06-26T05:54:45.8706936Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 527 <- wrt source file 2024-06-26T05:54:45.8710127Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2024-06-26T05:54:45.8713251Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 646 <- wrt source file 2024-06-26T05:54:45.8716508Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2024-06-26T05:54:45.8719985Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0, line 83 <- wrt source file 2024-06-26T05:54:45.8723805Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0 2024-06-26T05:54:45.8727503Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0, line 80 <- wrt source file 2024-06-26T05:54:45.8731092Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0 2024-06-26T05:54:45.8734739Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0, line 160 <- wrt source file 2024-06-26T05:54:45.8738387Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0 2024-06-26T05:54:45.8742203Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0, line 385 <- wrt source file 2024-06-26T05:54:45.8746040Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2024-06-26T05:54:45.8749511Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 26 <- wrt source file 2024-06-26T05:54:45.8752575Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0 2024-06-26T05:54:45.8755799Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 82 <- wrt source file 2024-06-26T05:54:45.8758799Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0 2024-06-26T05:54:45.8761876Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::unpack_dual:0, line 154 <- wrt source file 2024-06-26T05:54:45.8764853Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::unpack_dual:0 2024-06-26T05:54:45.8767843Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::dual_level:0, line 190 <- wrt source file 2024-06-26T05:54:45.8770751Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::dual_level:0 2024-06-26T05:54:45.8774066Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0, line 65 <- wrt source file 2024-06-26T05:54:45.8777434Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0 2024-06-26T05:54:45.8801221Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 235 <- wrt source file 2024-06-26T05:54:45.8804676Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2024-06-26T05:54:45.8807867Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0, line 478 <- wrt source file 2024-06-26T05:54:45.8810705Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0 2024-06-26T05:54:45.8813585Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0, line 292 <- wrt source file 2024-06-26T05:54:45.8816390Z * SKIPPED: 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* DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0, line 999 <- wrt source file 2024-06-26T05:54:45.8839564Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0 2024-06-26T05:54:45.8842445Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0, line 1098 <- wrt source file 2024-06-26T05:54:45.8845239Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0 2024-06-26T05:54:45.8848163Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0, line 50 <- wrt source file 2024-06-26T05:54:45.8851003Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0 2024-06-26T05:54:45.8853894Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0, line 108 <- wrt source file 2024-06-26T05:54:45.8856838Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2024-06-26T05:54:45.8859845Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0, line 166 <- wrt source file 2024-06-26T05:54:45.8862880Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0 2024-06-26T05:54:45.8865933Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 232 <- wrt source file 2024-06-26T05:54:45.8868930Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::inference_mode:0 2024-06-26T05:54:45.8871880Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.name:0, line 51 <- wrt source file 2024-06-26T05:54:45.8874951Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.name:0 2024-06-26T05:54:45.8877891Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0, line 96 <- wrt source file 2024-06-26T05:54:45.8880896Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0 2024-06-26T05:54:45.8884018Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0, line 133 <- wrt source file 2024-06-26T05:54:45.8887225Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2024-06-26T05:54:45.8890798Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 240 <- wrt source file 2024-06-26T05:54:45.8894347Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::batch_isend_irecv:0, line 2331 <- wrt source file 2024-06-26T05:54:45.9013674Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::batch_isend_irecv:0 2024-06-26T05:54:45.9016996Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_reduce:0, line 2440 <- wrt source file 2024-06-26T05:54:45.9020179Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_reduce:0 2024-06-26T05:54:45.9023467Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 2687 <- wrt source file 2024-06-26T05:54:45.9026816Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0 2024-06-26T05:54:45.9030179Z * DOCTEST : 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DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3191 <- wrt source file 2024-06-26T05:54:45.9054046Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0 2024-06-26T05:54:45.9057444Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather:0, line 3286 <- wrt source file 2024-06-26T05:54:45.9060708Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather:0 2024-06-26T05:54:45.9064045Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3371 <- wrt source file 2024-06-26T05:54:45.9067506Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0 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* DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0, line 39 <- wrt source file 2024-06-26T05:54:45.9133671Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0 2024-06-26T05:54:45.9137097Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0, line 47 <- wrt source file 2024-06-26T05:54:45.9140656Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0 2024-06-26T05:54:45.9144072Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0, line 40 <- wrt source file 2024-06-26T05:54:45.9147286Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0 2024-06-26T05:54:45.9150653Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0, line 186 <- wrt source file 2024-06-26T05:54:45.9153940Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0 2024-06-26T05:54:45.9157594Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0, line 30 <- wrt source file 2024-06-26T05:54:45.9161451Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0 2024-06-26T05:54:45.9165267Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0, line 457 <- wrt source file 2024-06-26T05:54:45.9168951Z * SKIPPED: 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2024-06-26T05:54:45.9389451Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0, line 236 <- wrt source file 2024-06-26T05:54:45.9392521Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0 2024-06-26T05:54:45.9395799Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0, line 134 <- wrt source file 2024-06-26T05:54:45.9399074Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0 2024-06-26T05:54:45.9402778Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0, line 42 <- wrt source file 2024-06-26T05:54:45.9406715Z * SKIPPED: 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2024-06-26T05:54:45.9601789Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 28 <- wrt source file 2024-06-26T05:54:45.9604816Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2024-06-26T05:54:45.9607759Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0, line 18 <- wrt source file 2024-06-26T05:54:45.9610658Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0 2024-06-26T05:54:45.9613599Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0, line 27 <- wrt source file 2024-06-26T05:54:45.9616568Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0 2024-06-26T05:54:45.9619680Z * DOCTEST : 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22 <- wrt source file 2024-06-26T05:54:45.9698280Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0 2024-06-26T05:54:45.9701415Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 22 <- wrt source file 2024-06-26T05:54:45.9704494Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0 2024-06-26T05:54:45.9707665Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0, line 21 <- wrt source file 2024-06-26T05:54:45.9710803Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0 2024-06-26T05:54:45.9714018Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 21 <- wrt source file 2024-06-26T05:54:45.9717325Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0 2024-06-26T05:54:45.9720533Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 27 <- wrt source file 2024-06-26T05:54:45.9723700Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0 2024-06-26T05:54:45.9726773Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0, line 17 <- wrt source file 2024-06-26T05:54:45.9729710Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0 2024-06-26T05:54:45.9732782Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 40 <- wrt source file 2024-06-26T05:54:45.9735901Z * SUCCESS: 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2024-06-26T05:54:46.0624824Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0 2024-06-26T05:54:46.0628120Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0, line 3451 <- wrt source file 2024-06-26T05:54:46.0634070Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0 2024-06-26T05:54:46.0637797Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3523 <- wrt source file 2024-06-26T05:54:46.0640966Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2024-06-26T05:54:46.0644607Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3600 <- wrt source file 2024-06-26T05:54:46.0648438Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2024-06-26T05:54:46.0651728Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0, line 5063 <- wrt source file 2024-06-26T05:54:46.0656966Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0 2024-06-26T05:54:46.0665829Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2024-06-26T05:54:46.0668556Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0 2024-06-26T05:54:46.0671327Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2024-06-26T05:54:46.0674048Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0 2024-06-26T05:54:46.0676953Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2024-06-26T05:54:46.0679645Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0 2024-06-26T05:54:46.0682468Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2024-06-26T05:54:46.0685201Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0 2024-06-26T05:54:46.0687956Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2024-06-26T05:54:46.0716692Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0 2024-06-26T05:54:46.0720177Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2024-06-26T05:54:46.0736669Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0 2024-06-26T05:54:46.0740032Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0, line 102 <- wrt source file 2024-06-26T05:54:46.0742781Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0 2024-06-26T05:54:46.0745520Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0, line 159 <- wrt source file 2024-06-26T05:54:46.0748305Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0 2024-06-26T05:54:46.0750960Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0, line 186 <- wrt source file 2024-06-26T05:54:46.0753611Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0 2024-06-26T05:54:46.0756474Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0, line 221 <- wrt source file 2024-06-26T05:54:46.0759200Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0 2024-06-26T05:54:46.0761996Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0, line 235 <- wrt source file 2024-06-26T05:54:46.0764748Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0 2024-06-26T05:54:46.0767384Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0, line 252 <- wrt source file 2024-06-26T05:54:46.0769934Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0 2024-06-26T05:54:46.0772536Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0, line 265 <- wrt source file 2024-06-26T05:54:46.0775088Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0 2024-06-26T05:54:46.0777683Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0, line 281 <- wrt source file 2024-06-26T05:54:46.0780199Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0 2024-06-26T05:54:46.0782793Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0, line 303 <- wrt source file 2024-06-26T05:54:46.0785348Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0 2024-06-26T05:54:46.0788073Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0, line 389 <- wrt source file 2024-06-26T05:54:46.0790821Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0 2024-06-26T05:54:46.0793614Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0, line 421 <- wrt source file 2024-06-26T05:54:46.0796454Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0 2024-06-26T05:54:46.0799369Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 472 <- wrt source file 2024-06-26T05:54:46.0802227Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0 2024-06-26T05:54:46.0805051Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0, line 529 <- wrt source file 2024-06-26T05:54:46.0807804Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0 2024-06-26T05:54:46.0810581Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0, line 560 <- wrt source file 2024-06-26T05:54:46.0813269Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0 2024-06-26T05:54:46.0815954Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0, line 613 <- wrt source file 2024-06-26T05:54:46.0818628Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0 2024-06-26T05:54:46.0821530Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 81 <- wrt source file 2024-06-26T05:54:46.0828203Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2024-06-26T05:54:46.0831234Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 92 <- wrt source file 2024-06-26T05:54:46.0834174Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0 2024-06-26T05:54:46.0837305Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0, line 70 <- wrt source file 2024-06-26T05:54:46.0840396Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0 2024-06-26T05:54:46.0843476Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0, line 112 <- wrt source file 2024-06-26T05:54:46.0846393Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0 2024-06-26T05:54:46.0850857Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0, line 171 <- wrt source file 2024-06-26T05:54:46.0853816Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0 2024-06-26T05:54:46.0856791Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 227 <- wrt source file 2024-06-26T05:54:46.0859792Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2024-06-26T05:54:46.0862809Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 292 <- wrt source file 2024-06-26T05:54:46.0865725Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0 2024-06-26T05:54:46.0868684Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 320 <- wrt source file 2024-06-26T05:54:46.0871653Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2024-06-26T05:54:46.0874849Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 352 <- wrt source file 2024-06-26T05:54:46.0877945Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2024-06-26T05:54:46.0880968Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0, line 385 <- wrt source file 2024-06-26T05:54:46.0883940Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0 2024-06-26T05:54:46.0886894Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0, line 418 <- wrt source file 2024-06-26T05:54:46.0889770Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0 2024-06-26T05:54:46.0892804Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0, line 457 <- wrt source file 2024-06-26T05:54:46.0896188Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0 2024-06-26T05:54:46.0899730Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 502 <- wrt source file 2024-06-26T05:54:46.0902890Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0 2024-06-26T05:54:46.0906036Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0, line 545 <- wrt source file 2024-06-26T05:54:46.0908920Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0 2024-06-26T05:54:46.0911852Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0, line 587 <- wrt source file 2024-06-26T05:54:46.0914812Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0 2024-06-26T05:54:46.0917832Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0, line 640 <- wrt source file 2024-06-26T05:54:46.0920731Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0 2024-06-26T05:54:46.0923967Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0, line 678 <- wrt source file 2024-06-26T05:54:46.0927292Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0 2024-06-26T05:54:46.0930673Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0, line 720 <- wrt source file 2024-06-26T05:54:46.0934004Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0 2024-06-26T05:54:46.0937499Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 763 <- wrt source file 2024-06-26T05:54:46.0941018Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2024-06-26T05:54:46.0944683Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 812 <- wrt source file 2024-06-26T05:54:46.0948101Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2024-06-26T05:54:46.0951658Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 848 <- wrt source file 2024-06-26T05:54:46.0955057Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2024-06-26T05:54:46.0958140Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0, line 881 <- wrt source file 2024-06-26T05:54:46.0961231Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0 2024-06-26T05:54:46.0964315Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 924 <- wrt source file 2024-06-26T05:54:46.0967370Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0 2024-06-26T05:54:46.0970596Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1026 <- wrt source file 2024-06-26T05:54:46.0973868Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2024-06-26T05:54:46.0977032Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1489 <- wrt source file 2024-06-26T05:54:46.0980025Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0 2024-06-26T05:54:46.0983118Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1531 <- wrt source file 2024-06-26T05:54:46.0986209Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0 2024-06-26T05:54:46.0989279Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1554 <- wrt source file 2024-06-26T05:54:46.0992320Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2024-06-26T05:54:46.0995504Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1589 <- wrt source file 2024-06-26T05:54:46.0998494Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0 2024-06-26T05:54:46.1001552Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1647 <- wrt source file 2024-06-26T05:54:46.1004528Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0 2024-06-26T05:54:46.1007573Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1688 <- wrt source file 2024-06-26T05:54:46.1010592Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2024-06-26T05:54:46.1013659Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1724 <- wrt source file 2024-06-26T05:54:46.1016700Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2024-06-26T05:54:46.1019805Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file 2024-06-26T05:54:46.1022854Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2024-06-26T05:54:46.1025945Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file 2024-06-26T05:54:46.1188713Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2024-06-26T05:54:46.1192047Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file 2024-06-26T05:54:46.3746535Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2024-06-26T05:54:46.3859224Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2024-06-26T05:54:46.3880759Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2024-06-26T05:54:46.3884065Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0, line 85 <- wrt source file 2024-06-26T05:54:46.3887063Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0 2024-06-26T05:54:46.3890109Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0, line 291 <- wrt source file 2024-06-26T05:54:46.3893107Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0 2024-06-26T05:54:46.3896426Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 465 <- wrt source file 2024-06-26T05:54:46.3899597Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0 2024-06-26T05:54:46.3902683Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0, line 597 <- wrt source file 2024-06-26T05:54:46.3905757Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0 2024-06-26T05:54:46.3908889Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 749 <- wrt source file 2024-06-26T05:54:46.3912031Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0 2024-06-26T05:54:46.3915341Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2024-06-26T05:54:46.3918487Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2024-06-26T05:54:46.3921716Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 77 <- wrt source file 2024-06-26T05:54:46.3924846Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2024-06-26T05:54:46.3927877Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2024-06-26T05:54:46.3930723Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0 2024-06-26T05:54:46.3933642Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file 2024-06-26T05:54:46.3936838Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2024-06-26T05:54:46.3940501Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file 2024-06-26T05:54:46.3943760Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2024-06-26T05:54:46.3946714Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file 2024-06-26T05:54:46.4014996Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2024-06-26T05:54:46.4018530Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file 2024-06-26T05:54:46.4021542Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2024-06-26T05:54:46.4024668Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file 2024-06-26T05:54:46.4096341Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2024-06-26T05:54:46.4099932Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2024-06-26T05:54:46.4102775Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0 2024-06-26T05:54:46.4105804Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file 2024-06-26T05:54:46.4108796Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0 2024-06-26T05:54:46.4111656Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0, line 261 <- wrt source file 2024-06-26T05:54:46.4124171Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0 2024-06-26T05:54:46.4127209Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2024-06-26T05:54:46.4138276Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2024-06-26T05:54:46.4141965Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2024-06-26T05:54:46.4330574Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2024-06-26T05:54:46.4334169Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2024-06-26T05:54:46.6872395Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2024-06-26T05:54:46.6984690Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 87 <- wrt source file 2024-06-26T05:54:46.6987728Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2024-06-26T05:54:46.6990688Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2024-06-26T05:54:46.6993544Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0 2024-06-26T05:54:46.6996557Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file 2024-06-26T05:54:46.7002150Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0 2024-06-26T05:54:46.7005028Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file 2024-06-26T05:54:46.7022285Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0 2024-06-26T05:54:46.7025156Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2024-06-26T05:54:46.7030196Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0 2024-06-26T05:54:46.7032995Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 211 <- wrt source file 2024-06-26T05:54:46.7056236Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2024-06-26T05:54:46.7059603Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 321 <- wrt source file 2024-06-26T05:54:46.7063212Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2024-06-26T05:54:46.7066269Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 406 <- wrt source file 2024-06-26T05:54:46.7078540Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2024-06-26T05:54:46.7081896Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 517 <- wrt source file 2024-06-26T05:54:46.7087868Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2024-06-26T05:54:46.7090715Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 595 <- wrt source file 2024-06-26T05:54:46.7094399Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0 2024-06-26T05:54:46.7097316Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 677 <- wrt source file 2024-06-26T05:54:46.7100412Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0 2024-06-26T05:54:46.7103352Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 748 <- wrt source file 2024-06-26T05:54:46.7112791Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2024-06-26T05:54:46.7116562Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 941 <- wrt source file 2024-06-26T05:54:46.7121328Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2024-06-26T05:54:46.7124854Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1261 <- wrt source file 2024-06-26T05:54:46.7129872Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2024-06-26T05:54:46.7133083Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1401 <- wrt source file 2024-06-26T05:54:46.7139833Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2024-06-26T05:54:46.7142944Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1466 <- wrt source file 2024-06-26T05:54:46.7147333Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2024-06-26T05:54:46.7150397Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1545 <- wrt source file 2024-06-26T05:54:46.7155847Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2024-06-26T05:54:46.7158913Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1645 <- wrt source file 2024-06-26T05:54:46.7168236Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2024-06-26T05:54:46.7171215Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1886 <- wrt source file 2024-06-26T05:54:46.7198838Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0 2024-06-26T05:54:46.7203173Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 544 <- wrt source file 2024-06-26T05:54:46.7206633Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2024-06-26T05:54:46.7209821Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0, line 945 <- wrt source file 2024-06-26T05:54:46.7213546Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0 2024-06-26T05:54:46.7216521Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0, line 1180 <- wrt source file 2024-06-26T05:54:46.7220481Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0 2024-06-26T05:54:46.7223589Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2032 <- wrt source file 2024-06-26T05:54:46.7226692Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2024-06-26T05:54:46.7229822Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2461 <- wrt source file 2024-06-26T05:54:46.7232913Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0 2024-06-26T05:54:46.7236221Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2489 <- wrt source file 2024-06-26T05:54:46.7239461Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2024-06-26T05:54:46.7242706Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2516 <- wrt source file 2024-06-26T05:54:46.7245713Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0 2024-06-26T05:54:46.7248843Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2543 <- wrt source file 2024-06-26T05:54:46.7251985Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2024-06-26T05:54:46.7255186Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2574 <- wrt source file 2024-06-26T05:54:46.7258361Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0 2024-06-26T05:54:46.7261511Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2598 <- wrt source file 2024-06-26T05:54:46.7264513Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0 2024-06-26T05:54:46.7267651Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2636 <- wrt source file 2024-06-26T05:54:46.7270696Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2024-06-26T05:54:46.7274214Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2024-06-26T05:54:46.7292920Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2024-06-26T05:54:46.7296692Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file 2024-06-26T05:54:46.7302442Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2024-06-26T05:54:46.7306386Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file 2024-06-26T05:54:46.7311331Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2024-06-26T05:54:46.7315145Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file 2024-06-26T05:54:46.7318202Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2024-06-26T05:54:46.7321377Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 69 <- wrt source file 2024-06-26T05:54:46.7324511Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2024-06-26T05:54:46.7327576Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 120 <- wrt source file 2024-06-26T05:54:46.7343051Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2024-06-26T05:54:46.7346145Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 184 <- wrt source file 2024-06-26T05:54:47.3747974Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2024-06-26T05:54:47.3962836Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 238 <- wrt source file 2024-06-26T05:54:47.3973094Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2024-06-26T05:54:47.3976251Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 291 <- wrt source file 2024-06-26T05:54:47.3979289Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2024-06-26T05:54:47.3982359Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 347 <- wrt source file 2024-06-26T05:54:47.4003008Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2024-06-26T05:54:47.4006137Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 391 <- wrt source file 2024-06-26T05:54:47.4009551Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2024-06-26T05:54:47.4012678Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 435 <- wrt source file 2024-06-26T05:54:47.4015757Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2024-06-26T05:54:47.4018863Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 492 <- wrt source file 2024-06-26T05:54:47.4021931Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2024-06-26T05:54:47.4025037Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 550 <- wrt source file 2024-06-26T05:54:47.4028403Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2024-06-26T05:54:47.4031724Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 593 <- wrt source file 2024-06-26T05:54:47.4035108Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2024-06-26T05:54:47.4038242Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 650 <- wrt source file 2024-06-26T05:54:47.9232925Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2024-06-26T05:54:47.9443097Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 684 <- wrt source file 2024-06-26T05:54:47.9453059Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2024-06-26T05:54:47.9456091Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 739 <- wrt source file 2024-06-26T05:54:47.9459451Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2024-06-26T05:54:47.9462422Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 798 <- wrt source file 2024-06-26T05:54:47.9484940Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2024-06-26T05:54:47.9487977Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2024-06-26T05:54:47.9491455Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2024-06-26T05:54:47.9494658Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file 2024-06-26T05:54:47.9497845Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2024-06-26T05:54:47.9500923Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 118 <- wrt source file 2024-06-26T05:54:47.9503838Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2024-06-26T05:54:47.9506784Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 195 <- wrt source file 2024-06-26T05:54:47.9558233Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2024-06-26T05:54:47.9561272Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 278 <- wrt source file 2024-06-26T05:54:48.1824162Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2024-06-26T05:54:48.1870324Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 352 <- wrt source file 2024-06-26T05:54:48.1882407Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2024-06-26T05:54:48.1885524Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 534 <- wrt source file 2024-06-26T05:54:48.2632564Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2024-06-26T05:54:48.2668601Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 622 <- wrt source file 2024-06-26T05:54:48.2679863Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2024-06-26T05:54:48.2682905Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 714 <- wrt source file 2024-06-26T05:54:48.2720826Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2024-06-26T05:54:48.2723860Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 827 <- wrt source file 2024-06-26T05:54:48.4454467Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2024-06-26T05:54:48.4498716Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 917 <- wrt source file 2024-06-26T05:54:48.4551169Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2024-06-26T05:54:48.4554240Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1003 <- wrt source file 2024-06-26T05:54:48.5424278Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2024-06-26T05:54:48.5427358Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1117 <- wrt source file 2024-06-26T05:54:48.5435301Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2024-06-26T05:54:48.5438621Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1168 <- wrt source file 2024-06-26T05:54:48.5490958Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2024-06-26T05:54:48.5494623Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1227 <- wrt source file 2024-06-26T05:54:48.7729219Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2024-06-26T05:54:48.7818262Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1282 <- wrt source file 2024-06-26T05:54:48.7824894Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2024-06-26T05:54:48.7828107Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1316 <- wrt source file 2024-06-26T05:54:48.7835164Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2024-06-26T05:54:48.7838366Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1359 <- wrt source file 2024-06-26T05:54:48.7866469Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2024-06-26T05:54:48.7869694Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1406 <- wrt source file 2024-06-26T05:54:48.7872843Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2024-06-26T05:54:48.7876480Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1437 <- wrt source file 2024-06-26T05:54:48.7879813Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2024-06-26T05:54:48.7883086Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1476 <- wrt source file 2024-06-26T05:54:48.7904431Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2024-06-26T05:54:48.7907310Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0, line 588 <- wrt source file 2024-06-26T05:54:48.7918480Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0 2024-06-26T05:54:48.7921944Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 945 <- wrt source file 2024-06-26T05:54:48.8234979Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0 2024-06-26T05:54:48.8237645Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1283 <- wrt source file 2024-06-26T05:54:48.8253453Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0 2024-06-26T05:54:48.8256867Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1534 <- wrt source file 2024-06-26T05:54:48.8266664Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2024-06-26T05:54:48.8270102Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1656 <- wrt source file 2024-06-26T05:54:48.8279265Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2024-06-26T05:54:48.8282892Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1770 <- wrt source file 2024-06-26T05:54:48.8294919Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2024-06-26T05:54:48.8298516Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 69 <- wrt source file 2024-06-26T05:54:48.8309466Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0 2024-06-26T05:54:48.8312878Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 241 <- wrt source file 2024-06-26T05:54:48.8316423Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2024-06-26T05:54:48.8320036Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 519 <- wrt source file 2024-06-26T05:54:48.8323487Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2024-06-26T05:54:48.8326882Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file 2024-06-26T05:54:49.4399740Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0 2024-06-26T05:54:49.4416949Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 258 <- wrt source file 2024-06-26T05:54:49.4420725Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0 2024-06-26T05:54:49.4424212Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 323 <- wrt source file 2024-06-26T05:54:49.5021817Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2024-06-26T05:54:49.5026937Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 536 <- wrt source file 2024-06-26T05:54:49.6257915Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2024-06-26T05:54:49.6265224Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 657 <- wrt source file 2024-06-26T05:54:49.6482463Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2024-06-26T05:54:49.6485844Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 961 <- wrt source file 2024-06-26T05:54:49.6845580Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2024-06-26T05:54:49.6848872Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2024-06-26T05:54:49.6870881Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2024-06-26T05:54:49.6874131Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file 2024-06-26T05:54:49.6883387Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2024-06-26T05:54:49.6886763Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file 2024-06-26T05:54:49.6892669Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2024-06-26T05:54:49.6895985Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 127 <- wrt source file 2024-06-26T05:54:49.6899174Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2024-06-26T05:54:49.6902545Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 619 <- wrt source file 2024-06-26T05:54:49.6906029Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2024-06-26T05:54:49.6909664Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1413 <- wrt source file 2024-06-26T05:54:49.6913357Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2024-06-26T05:54:49.6917364Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1976 <- wrt source file 2024-06-26T05:54:49.6921643Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2024-06-26T05:54:49.6925808Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 1986 <- wrt source file 2024-06-26T05:54:49.6929773Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2024-06-26T05:54:49.6933823Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2021 <- wrt source file 2024-06-26T05:54:49.6938003Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2024-06-26T05:54:49.6941743Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 35 <- wrt source file 2024-06-26T05:54:49.6945141Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0 2024-06-26T05:54:49.6948283Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0, line 33 <- wrt source file 2024-06-26T05:54:49.6951047Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0 2024-06-26T05:54:49.6954062Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2024-06-26T05:54:49.6957345Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2024-06-26T05:54:49.6960569Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0, line 360 <- wrt source file 2024-06-26T05:54:49.6963823Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2024-06-26T05:54:49.6967057Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 591 <- wrt source file 2024-06-26T05:54:49.6970311Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2024-06-26T05:54:49.6973600Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0, line 505 <- wrt source file 2024-06-26T05:54:49.6976968Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0 2024-06-26T05:54:49.6980067Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0, line 846 <- wrt source file 2024-06-26T05:54:49.6982853Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0 2024-06-26T05:54:49.6985819Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 882 <- wrt source file 2024-06-26T05:54:49.6988869Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2024-06-26T05:54:49.6991894Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 925 <- wrt source file 2024-06-26T05:54:49.6994930Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2024-06-26T05:54:49.6997943Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0, line 1192 <- wrt source file 2024-06-26T05:54:49.7000826Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0 2024-06-26T05:54:49.7003681Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1220 <- wrt source file 2024-06-26T05:54:49.7006495Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0 2024-06-26T05:54:49.7009424Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 309 <- wrt source file 2024-06-26T05:54:49.7012439Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0 2024-06-26T05:54:49.7015386Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 390 <- wrt source file 2024-06-26T05:54:49.7018204Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2024-06-26T05:54:49.7021099Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 444 <- wrt source file 2024-06-26T05:54:49.7023941Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2024-06-26T05:54:49.7026791Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 501 <- wrt source file 2024-06-26T05:54:49.7029641Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2024-06-26T05:54:49.7032541Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 531 <- wrt source file 2024-06-26T05:54:49.7035500Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2024-06-26T05:54:49.7038527Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 313 <- wrt source file 2024-06-26T05:54:49.7041734Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2024-06-26T05:54:49.7044949Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 345 <- wrt source file 2024-06-26T05:54:49.7048214Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2024-06-26T05:54:49.7051421Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 187 <- wrt source file 2024-06-26T05:54:49.7054490Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0 2024-06-26T05:54:49.7057551Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 133 <- wrt source file 2024-06-26T05:54:49.7060542Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2024-06-26T05:54:49.7063637Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 155 <- wrt source file 2024-06-26T05:54:49.7066829Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2024-06-26T05:54:49.7070191Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 317 <- wrt source file 2024-06-26T05:54:49.7073602Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2024-06-26T05:54:49.7077421Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0, line 178 <- wrt source file 2024-06-26T05:54:49.7081533Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0 2024-06-26T05:54:49.7085098Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 306 <- wrt source file 2024-06-26T05:54:49.7087968Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2024-06-26T05:54:49.7090975Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 408 <- wrt source file 2024-06-26T05:54:49.7094034Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2024-06-26T05:54:49.7097021Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 508 <- wrt source file 2024-06-26T05:54:49.7099813Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2024-06-26T05:54:49.7102710Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 568 <- wrt source file 2024-06-26T05:54:49.7105678Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2024-06-26T05:54:49.7108647Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 633 <- wrt source file 2024-06-26T05:54:49.7111545Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2024-06-26T05:54:49.7114457Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 711 <- wrt source file 2024-06-26T05:54:49.7117379Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2024-06-26T05:54:49.7120323Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 840 <- wrt source file 2024-06-26T05:54:49.7123373Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0 2024-06-26T05:54:49.7126359Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 977 <- wrt source file 2024-06-26T05:54:49.7129316Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2024-06-26T05:54:49.7132381Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1133 <- wrt source file 2024-06-26T05:54:49.7135466Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2024-06-26T05:54:49.7138602Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 1276 <- wrt source file 2024-06-26T05:54:49.7141692Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0 2024-06-26T05:54:49.7144783Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1508 <- wrt source file 2024-06-26T05:54:49.7147747Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0 2024-06-26T05:54:49.7150985Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1778 <- wrt source file 2024-06-26T05:54:49.7154470Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2024-06-26T05:54:49.7158233Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1794 <- wrt source file 2024-06-26T05:54:49.7161806Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2024-06-26T05:54:49.7165055Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1939 <- wrt source file 2024-06-26T05:54:49.7167955Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0 2024-06-26T05:54:49.7170850Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0, line 316 <- wrt source file 2024-06-26T05:54:49.7173666Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0 2024-06-26T05:54:49.7176547Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0, line 20 <- wrt source file 2024-06-26T05:54:49.7179425Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0 2024-06-26T05:54:49.7182378Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0, line 514 <- wrt source file 2024-06-26T05:54:49.7185270Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0 2024-06-26T05:54:49.7188389Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0, line 334 <- wrt source file 2024-06-26T05:54:49.7191715Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0 2024-06-26T05:54:49.7194956Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0, line 112 <- wrt source file 2024-06-26T05:54:49.7197836Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0 2024-06-26T05:54:49.7200950Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 508 <- wrt source file 2024-06-26T05:54:49.7204207Z * SKIPPED: 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2024-06-26T05:54:49.7224893Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2024-06-26T05:54:49.7228471Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4197 <- wrt source file 2024-06-26T05:54:49.7231970Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0 2024-06-26T05:54:49.7235520Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0, line 192 <- wrt source file 2024-06-26T05:54:49.7238833Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0 2024-06-26T05:54:49.7242531Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0, line 284 <- wrt source file 2024-06-26T05:54:49.7246434Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0 2024-06-26T05:54:49.7250413Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0, line 29 <- wrt source file 2024-06-26T05:54:49.7254417Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0 2024-06-26T05:54:49.7257854Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 261 <- wrt source file 2024-06-26T05:54:49.7260768Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2024-06-26T05:54:49.7263716Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 303 <- wrt source file 2024-06-26T05:54:49.7266657Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2024-06-26T05:54:49.7269588Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 333 <- wrt source file 2024-06-26T05:54:49.7272403Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2024-06-26T05:54:49.7275364Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 368 <- wrt source file 2024-06-26T05:54:49.7278257Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2024-06-26T05:54:49.7281267Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 403 <- wrt source file 2024-06-26T05:54:49.7284209Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2024-06-26T05:54:49.7287123Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 440 <- wrt source file 2024-06-26T05:54:49.7289927Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2024-06-26T05:54:49.7292958Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 816 <- wrt source file 2024-06-26T05:54:49.7296082Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2024-06-26T05:54:49.7299001Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0, line 917 <- wrt source file 2024-06-26T05:54:49.7301718Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0 2024-06-26T05:54:49.7304859Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 69 <- wrt source file 2024-06-26T05:54:49.7308382Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2024-06-26T05:54:49.7312031Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 322 <- wrt source file 2024-06-26T05:54:49.7315883Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0 2024-06-26T05:54:49.7319474Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0, line 354 <- wrt source file 2024-06-26T05:54:49.7322870Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0 2024-06-26T05:54:49.7326145Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 534 <- wrt source file 2024-06-26T05:54:49.7329310Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0 2024-06-26T05:54:49.7332551Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 736 <- wrt source file 2024-06-26T05:54:49.7335767Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0 2024-06-26T05:54:49.7338786Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 72 <- wrt source file 2024-06-26T05:54:49.7341562Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0 2024-06-26T05:54:49.7344540Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 98 <- wrt source file 2024-06-26T05:54:49.7347621Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0 2024-06-26T05:54:49.7350701Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 223 <- wrt source file 2024-06-26T05:54:49.7353713Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0 2024-06-26T05:54:49.7356826Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::random_split:0, line 445 <- wrt source file 2024-06-26T05:54:49.7359800Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::random_split:0 2024-06-26T05:54:49.7362819Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0, line 30 <- wrt source file 2024-06-26T05:54:49.7365748Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0 2024-06-26T05:54:49.7368895Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0, line 208 <- wrt source file 2024-06-26T05:54:49.7372195Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0 2024-06-26T05:54:49.7375353Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 255 <- wrt source file 2024-06-26T05:54:49.7378349Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0 2024-06-26T05:54:49.7381513Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 38 <- wrt source file 2024-06-26T05:54:49.7384724Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2024-06-26T05:54:49.7387856Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 124 <- wrt source file 2024-06-26T05:54:49.7390870Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0 2024-06-26T05:54:49.7394004Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 283 <- wrt source file 2024-06-26T05:54:49.7397326Z * SKIPPED: 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36 <- wrt source file 2024-06-26T05:54:49.7440652Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2024-06-26T05:54:49.7444527Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 86 <- wrt source file 2024-06-26T05:54:49.7448308Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0 2024-06-26T05:54:49.7451955Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 289 <- wrt source file 2024-06-26T05:54:49.7455522Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0 2024-06-26T05:54:49.7459325Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0, line 369 <- wrt source file 2024-06-26T05:54:49.7463199Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0 2024-06-26T05:54:49.7467096Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0, line 550 <- wrt source file 2024-06-26T05:54:49.7470910Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0 2024-06-26T05:54:49.7474871Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 616 <- wrt source file 2024-06-26T05:54:49.7478559Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0 2024-06-26T05:54:49.7482413Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 31 <- wrt source file 2024-06-26T05:54:49.7486256Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0 2024-06-26T05:54:49.7490115Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0, line 33 <- wrt source file 2024-06-26T05:54:49.7493923Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0 2024-06-26T05:54:49.7497704Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 46 <- wrt source file 2024-06-26T05:54:49.7501361Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0 2024-06-26T05:54:49.7505097Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 105 <- wrt source file 2024-06-26T05:54:49.7508831Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0 2024-06-26T05:54:49.7512582Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0, line 172 <- wrt source file 2024-06-26T05:54:49.7516303Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0 2024-06-26T05:54:49.7520012Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0, line 35 <- wrt source file 2024-06-26T05:54:49.7523779Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0 2024-06-26T05:54:49.7527720Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 22 <- wrt source file 2024-06-26T05:54:49.7531670Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2024-06-26T05:54:49.7535551Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 24 <- wrt source file 2024-06-26T05:54:49.7539388Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2024-06-26T05:54:49.7543131Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0, line 32 <- wrt source file 2024-06-26T05:54:49.7546718Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2024-06-26T05:54:49.7550463Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 32 <- wrt source file 2024-06-26T05:54:49.7554278Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2024-06-26T05:54:49.7558139Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 26 <- wrt source file 2024-06-26T05:54:49.7561869Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2024-06-26T05:54:49.7565535Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 70 <- wrt source file 2024-06-26T05:54:49.7569160Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2024-06-26T05:54:49.7572790Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 25 <- wrt source file 2024-06-26T05:54:49.7576396Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2024-06-26T05:54:49.7580082Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 24 <- wrt source file 2024-06-26T05:54:49.7583818Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2024-06-26T05:54:49.7587512Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 37 <- wrt source file 2024-06-26T05:54:49.7591085Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2024-06-26T05:54:49.7594708Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0, line 47 <- wrt source file 2024-06-26T05:54:49.7598210Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2024-06-26T05:54:49.7601694Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 433 <- wrt source file 2024-06-26T05:54:50.1007145Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2024-06-26T05:54:50.1010248Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 529 <- wrt source file 2024-06-26T05:54:50.1013128Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2024-06-26T05:54:50.1015816Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 216 <- wrt source file 2024-06-26T05:54:50.1018538Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2024-06-26T05:54:50.1021038Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 314 <- wrt source file 2024-06-26T05:54:50.1023334Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2024-06-26T05:54:50.1026078Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 362 <- wrt source file 2024-06-26T05:54:50.1028005Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2024-06-26T05:54:50.1029942Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 394 <- wrt source file 2024-06-26T05:54:50.1031858Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2024-06-26T05:54:50.1033780Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0, line 441 <- wrt source file 2024-06-26T05:54:50.1035917Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2024-06-26T05:54:50.1038118Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 480 <- wrt source file 2024-06-26T05:54:50.1041323Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2024-06-26T05:54:50.1044758Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 533 <- wrt source file 2024-06-26T05:54:50.1048115Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2024-06-26T05:54:50.1051083Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 599 <- wrt source file 2024-06-26T05:54:50.1054196Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2024-06-26T05:54:50.1058109Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 648 <- wrt source file 2024-06-26T05:54:50.1061404Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2024-06-26T05:54:50.1064715Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 811 <- wrt source file 2024-06-26T05:54:50.1067907Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2024-06-26T05:54:50.1071491Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 878 <- wrt source file 2024-06-26T05:54:50.1074949Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2024-06-26T05:54:50.1078411Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 989 <- wrt source file 2024-06-26T05:54:50.1081805Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2024-06-26T05:54:50.1085207Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1063 <- wrt source file 2024-06-26T05:54:50.1088980Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2024-06-26T05:54:50.1092659Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1084 <- wrt source file 2024-06-26T05:54:50.1096175Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2024-06-26T05:54:50.1099797Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1108 <- wrt source file 2024-06-26T05:54:50.1103257Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2024-06-26T05:54:50.1106506Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1154 <- wrt source file 2024-06-26T05:54:50.1109630Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2024-06-26T05:54:50.1111291Z ============ 2024-06-26T05:54:50.1111809Z Finished doctests 2024-06-26T05:54:50.1112162Z 331 / 696 passed 2024-06-26T05:54:50.1112577Z  2024-06-26T05:54:50.1113153Z === Found 90 parse-time warnings === 2024-06-26T05:54:50.1114031Z --- Parse Warning: 1 / 90 --- 2024-06-26T05:54:50.1116761Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=426. 2024-06-26T05:54:50.1119425Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1120497Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-06-26T05:54:50.1121353Z 2024-06-26T05:54:50.1121956Z This is helpful when you want to visualize data over some 2024-06-26T05:54:50.1122831Z range of inputs. See below for a plotting example. 2024-06-26T05:54:50.1123485Z 2024-06-26T05:54:50.1124084Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-06-26T05:54:50.1125098Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-06-26T05:54:50.1126285Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-06-26T05:54:50.1127515Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-06-26T05:54:50.1128806Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-06-26T05:54:50.1129713Z to the result shape. 2024-06-26T05:54:50.1130369Z 2024-06-26T05:54:50.1130760Z .. note:: 2024-06-26T05:54:50.1131409Z 0D inputs are treated equivalently to 1D inputs of a 2024-06-26T05:54:50.1132286Z single element. 2024-06-26T05:54:50.1132800Z 2024-06-26T05:54:50.1133171Z .. warning:: 2024-06-26T05:54:50.1133797Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-06-26T05:54:50.1134914Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-06-26T05:54:50.1135661Z 2024-06-26T05:54:50.1136231Z In the future `torch.meshgrid` will transition to 2024-06-26T05:54:50.1137169Z `indexing='xy'` as the default. 2024-06-26T05:54:50.1137764Z 2024-06-26T05:54:50.1138236Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-06-26T05:54:50.1138965Z this issue with the goal of migrating to NumPy's behavior. 2024-06-26T05:54:50.1139488Z 2024-06-26T05:54:50.1139833Z .. seealso:: 2024-06-26T05:54:50.1140285Z 2024-06-26T05:54:50.1140773Z :func:`torch.cartesian_prod` has the same effect but it 2024-06-26T05:54:50.1141423Z collects the data in a tensor of vectors. 2024-06-26T05:54:50.1142075Z 2024-06-26T05:54:50.1142440Z Args: 2024-06-26T05:54:50.1143006Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-06-26T05:54:50.1143984Z treated as tensors of size :math:`(1,)` automatically 2024-06-26T05:54:50.1144469Z 2024-06-26T05:54:50.1144825Z indexing: (str, optional): the indexing mode, either "xy" 2024-06-26T05:54:50.1145581Z or "ij", defaults to "ij". See warning for future changes. 2024-06-26T05:54:50.1146348Z 2024-06-26T05:54:50.1147001Z If "xy" is selected, the first dimension corresponds 2024-06-26T05:54:50.1147954Z to the cardinality of the second input and the second 2024-06-26T05:54:50.1148712Z dimension corresponds to the cardinality of the first 2024-06-26T05:54:50.1149206Z input. 2024-06-26T05:54:50.1149506Z 2024-06-26T05:54:50.1149854Z If "ij" is selected, the dimensions are in the same 2024-06-26T05:54:50.1150401Z order as the cardinality of the inputs. 2024-06-26T05:54:50.1150831Z 2024-06-26T05:54:50.1151062Z Returns: 2024-06-26T05:54:50.1151460Z seq (sequence of Tensors): If the input has :math:`N` 2024-06-26T05:54:50.1152156Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-06-26T05:54:50.1152791Z output will also have :math:`N` tensors, where each tensor 2024-06-26T05:54:50.1153421Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-06-26T05:54:50.1153845Z 2024-06-26T05:54:50.1154082Z Example:: 2024-06-26T05:54:50.1154355Z 2024-06-26T05:54:50.1154771Z >>> x = torch.tensor([1, 2, 3]) 2024-06-26T05:54:50.1155270Z >>> y = torch.tensor([4, 5, 6]) 2024-06-26T05:54:50.1155653Z 2024-06-26T05:54:50.1156117Z Observe the element-wise pairings across the grid, (1, 4), 2024-06-26T05:54:50.1156729Z (1, 5), ..., (3, 6). This is the same thing as the 2024-06-26T05:54:50.1157193Z cartesian product. 2024-06-26T05:54:50.1157738Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-06-26T05:54:50.1158228Z >>> grid_x 2024-06-26T05:54:50.1158538Z tensor([[1, 1, 1], 2024-06-26T05:54:50.1158894Z [2, 2, 2], 2024-06-26T05:54:50.1159245Z [3, 3, 3]]) 2024-06-26T05:54:50.1159592Z >>> grid_y 2024-06-26T05:54:50.1160023Z tensor([[4, 5, 6], 2024-06-26T05:54:50.1160378Z [4, 5, 6], 2024-06-26T05:54:50.1160764Z [4, 5, 6]]) 2024-06-26T05:54:50.1161170Z 2024-06-26T05:54:50.1161537Z This correspondence can be seen when these grids are 2024-06-26T05:54:50.1162083Z stacked properly. 2024-06-26T05:54:50.1162611Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-06-26T05:54:50.1163229Z ... torch.cartesian_prod(x, y)) 2024-06-26T05:54:50.1163651Z True 2024-06-26T05:54:50.1163924Z 2024-06-26T05:54:50.1164297Z `torch.meshgrid` is commonly used to produce a grid for 2024-06-26T05:54:50.1164794Z plotting. 2024-06-26T05:54:50.1165187Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-06-26T05:54:50.1165753Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-06-26T05:54:50.1166263Z >>> import matplotlib.pyplot as plt 2024-06-26T05:54:50.1166814Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-06-26T05:54:50.1167370Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-06-26T05:54:50.1167951Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-06-26T05:54:50.1168470Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-06-26T05:54:50.1169018Z >>> ax = plt.axes(projection='3d') 2024-06-26T05:54:50.1169548Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-06-26T05:54:50.1170018Z >>> plt.show() 2024-06-26T05:54:50.1170343Z 2024-06-26T05:54:50.1170644Z .. image:: ../_static/img/meshgrid.png 2024-06-26T05:54:50.1171062Z :width: 512 2024-06-26T05:54:50.1171375Z 2024-06-26T05:54:50.1171602Z 2024-06-26T05:54:50.1172150Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1172740Z 2024-06-26T05:54:50.1172982Z warnings.warn(msg) 2024-06-26T05:54:50.1173269Z 2024-06-26T05:54:50.1173602Z --- Parse Warning: 2 / 90 --- 2024-06-26T05:54:50.1175229Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=815. 2024-06-26T05:54:50.1176932Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1178040Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-06-26T05:54:50.1178800Z 2024-06-26T05:54:50.1179121Z Returns the unique elements of the input tensor. 2024-06-26T05:54:50.1179569Z 2024-06-26T05:54:50.1180113Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-06-26T05:54:50.1181039Z this function also eliminates non-consecutive duplicate values. 2024-06-26T05:54:50.1181558Z 2024-06-26T05:54:50.1182008Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-06-26T05:54:50.1182903Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-06-26T05:54:50.1183871Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-06-26T05:54:50.1184686Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-06-26T05:54:50.1185176Z 2024-06-26T05:54:50.1185391Z Args: 2024-06-26T05:54:50.1185700Z input (Tensor): the input tensor 2024-06-26T05:54:50.1186302Z sorted (bool): Whether to sort the unique elements in ascending order 2024-06-26T05:54:50.1186890Z before returning as output. 2024-06-26T05:54:50.1187471Z return_inverse (bool): Whether to also return the indices for where 2024-06-26T05:54:50.1188383Z elements in the original input ended up in the returned unique list. 2024-06-26T05:54:50.1189237Z return_counts (bool): Whether to also return the counts for each unique 2024-06-26T05:54:50.1189851Z element. 2024-06-26T05:54:50.1190377Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-06-26T05:54:50.1191126Z unique of the flattened input is returned. Otherwise, each of the 2024-06-26T05:54:50.1191843Z tensors indexed by the given dimension is treated as one of the 2024-06-26T05:54:50.1192584Z elements to apply the unique operation upon. See examples for more 2024-06-26T05:54:50.1193177Z details. Default: ``None`` 2024-06-26T05:54:50.1193548Z 2024-06-26T05:54:50.1193776Z Returns: 2024-06-26T05:54:50.1194383Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-06-26T05:54:50.1235485Z 2024-06-26T05:54:50.1236054Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-06-26T05:54:50.1236754Z - **inverse_indices** (*Tensor*): (optional) if 2024-06-26T05:54:50.1237346Z :attr:`return_inverse` is True, there will be an additional 2024-06-26T05:54:50.1238044Z returned tensor (same shape as input) representing the indices 2024-06-26T05:54:50.1238764Z for where elements in the original input map to in the output; 2024-06-26T05:54:50.1239463Z otherwise, this function will only return a single tensor. 2024-06-26T05:54:50.1240081Z - **counts** (*Tensor*): (optional) if 2024-06-26T05:54:50.1240651Z :attr:`return_counts` is True, there will be an additional 2024-06-26T05:54:50.1241368Z returned tensor (same shape as output or output.size(dim), 2024-06-26T05:54:50.1242041Z if dim was specified) representing the number of occurrences 2024-06-26T05:54:50.1242628Z for each unique value or tensor. 2024-06-26T05:54:50.1243040Z 2024-06-26T05:54:50.1243275Z Example:: 2024-06-26T05:54:50.1243538Z 2024-06-26T05:54:50.1243961Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-06-26T05:54:50.1244511Z >>> output 2024-06-26T05:54:50.1244812Z tensor([1, 2, 3]) 2024-06-26T05:54:50.1245129Z 2024-06-26T05:54:50.1245421Z >>> output, inverse_indices = torch.unique( 2024-06-26T05:54:50.1246095Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-06-26T05:54:50.1246697Z >>> output 2024-06-26T05:54:50.1246988Z tensor([1, 2, 3]) 2024-06-26T05:54:50.1247327Z >>> inverse_indices 2024-06-26T05:54:50.1247681Z tensor([0, 2, 1, 2]) 2024-06-26T05:54:50.1247998Z 2024-06-26T05:54:50.1248306Z >>> output, inverse_indices = torch.unique( 2024-06-26T05:54:50.1248989Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-06-26T05:54:50.1249586Z >>> output 2024-06-26T05:54:50.1249891Z tensor([1, 2, 3]) 2024-06-26T05:54:50.1250229Z >>> inverse_indices 2024-06-26T05:54:50.1250567Z tensor([[0, 2], 2024-06-26T05:54:50.1250887Z [1, 2]]) 2024-06-26T05:54:50.1251192Z 2024-06-26T05:54:50.1251430Z >>> a = torch.tensor([ 2024-06-26T05:54:50.1251782Z ... [ 2024-06-26T05:54:50.1252083Z ... [1, 1, 0, 0], 2024-06-26T05:54:50.1252440Z ... [1, 1, 0, 0], 2024-06-26T05:54:50.1252804Z ... [0, 0, 1, 1], 2024-06-26T05:54:50.1253154Z ... ], 2024-06-26T05:54:50.1253427Z ... [ 2024-06-26T05:54:50.1253724Z ... [0, 0, 1, 1], 2024-06-26T05:54:50.1254089Z ... [0, 0, 1, 1], 2024-06-26T05:54:50.1254604Z ... [1, 1, 1, 1], 2024-06-26T05:54:50.1254963Z ... ], 2024-06-26T05:54:50.1255315Z ... [ 2024-06-26T05:54:50.1255603Z ... [1, 1, 0, 0], 2024-06-26T05:54:50.1255970Z ... [1, 1, 0, 0], 2024-06-26T05:54:50.1256389Z ... [0, 0, 1, 1], 2024-06-26T05:54:50.1256737Z ... ], 2024-06-26T05:54:50.1257028Z ... ]) 2024-06-26T05:54:50.1257298Z 2024-06-26T05:54:50.1257739Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-06-26T05:54:50.1258528Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-06-26T05:54:50.1259193Z >>> # each other, so one of them will be removed. 2024-06-26T05:54:50.1259678Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-06-26T05:54:50.1260093Z tensor(True) 2024-06-26T05:54:50.1260516Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-06-26T05:54:50.1260952Z >>> a_unique_dim0 2024-06-26T05:54:50.1261299Z tensor([[[0, 0, 1, 1], 2024-06-26T05:54:50.1261661Z [0, 0, 1, 1], 2024-06-26T05:54:50.1262004Z [1, 1, 1, 1]], 2024-06-26T05:54:50.1262367Z [[1, 1, 0, 0], 2024-06-26T05:54:50.1262719Z [1, 1, 0, 0], 2024-06-26T05:54:50.1263058Z [0, 0, 1, 1]]]) 2024-06-26T05:54:50.1263400Z 2024-06-26T05:54:50.1263913Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-06-26T05:54:50.1264477Z >>> # `a_unique_dim0`: 2024-06-26T05:54:50.1264903Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-06-26T05:54:50.1265345Z tensor(True) 2024-06-26T05:54:50.1265708Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-06-26T05:54:50.1266147Z tensor(True) 2024-06-26T05:54:50.1266442Z 2024-06-26T05:54:50.1266860Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-06-26T05:54:50.1267591Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-06-26T05:54:50.1268149Z >>> # them will be removed. 2024-06-26T05:54:50.1268556Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-06-26T05:54:50.1268964Z tensor(True) 2024-06-26T05:54:50.1269295Z >>> torch.unique(a, dim=1) 2024-06-26T05:54:50.1269685Z tensor([[[0, 0, 1, 1], 2024-06-26T05:54:50.1270030Z [1, 1, 0, 0]], 2024-06-26T05:54:50.1270392Z [[1, 1, 1, 1], 2024-06-26T05:54:50.1270742Z [0, 0, 1, 1]], 2024-06-26T05:54:50.1271086Z [[0, 0, 1, 1], 2024-06-26T05:54:50.1271438Z [1, 1, 0, 0]]]) 2024-06-26T05:54:50.1271781Z 2024-06-26T05:54:50.1272212Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-06-26T05:54:50.1272932Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-06-26T05:54:50.1273596Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-06-26T05:54:50.1274203Z >>> # sub-tensors will be removed. 2024-06-26T05:54:50.1274855Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-06-26T05:54:50.1275268Z tensor(True) 2024-06-26T05:54:50.1275597Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-06-26T05:54:50.1276003Z tensor(True) 2024-06-26T05:54:50.1276337Z >>> torch.unique(a, dim=2) 2024-06-26T05:54:50.1276713Z tensor([[[0, 1], 2024-06-26T05:54:50.1277037Z [0, 1], 2024-06-26T05:54:50.1277357Z [1, 0]], 2024-06-26T05:54:50.1277667Z [[1, 0], 2024-06-26T05:54:50.1277979Z [1, 0], 2024-06-26T05:54:50.1278296Z [1, 1]], 2024-06-26T05:54:50.1278677Z [[0, 1], 2024-06-26T05:54:50.1278992Z [0, 1], 2024-06-26T05:54:50.1279310Z [1, 0]]]) 2024-06-26T05:54:50.1279667Z 2024-06-26T05:54:50.1280220Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1280806Z 2024-06-26T05:54:50.1281167Z warnings.warn(msg) 2024-06-26T05:54:50.1281479Z 2024-06-26T05:54:50.1281823Z --- Parse Warning: 3 / 90 --- 2024-06-26T05:54:50.1283393Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=931. 2024-06-26T05:54:50.1285080Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1285894Z Given an operator and some sample arguments, tests if the operator is 2024-06-26T05:54:50.1286511Z registered correctly. 2024-06-26T05:54:50.1286844Z 2024-06-26T05:54:50.1287278Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-06-26T05:54:50.1288077Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-06-26T05:54:50.1288895Z and these APIs require that the functions you pass them satisfy certain 2024-06-26T05:54:50.1289692Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-06-26T05:54:50.1290367Z ``opcheck`` tests these metadata and properties. 2024-06-26T05:54:50.1290801Z 2024-06-26T05:54:50.1291078Z Concretely, we test the following: 2024-06-26T05:54:50.1291638Z - test_schema: if the operator's schema is correct. 2024-06-26T05:54:50.1292325Z - test_autograd_registration: if autograd was registered correctly. 2024-06-26T05:54:50.1293057Z - test_faketensor: If the operator has a FakeTensor kernel 2024-06-26T05:54:50.1293706Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-06-26T05:54:50.1294411Z but not sufficient) for the operator to work with PyTorch compilation 2024-06-26T05:54:50.1295014Z APIs (torch.compile/export/FX). 2024-06-26T05:54:50.1295633Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-06-26T05:54:50.1296294Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-06-26T05:54:50.1296977Z This checks that the outputs (and gradients, if applicable) are the 2024-06-26T05:54:50.1297684Z same under eager-mode PyTorch and torch.compile. 2024-06-26T05:54:50.1298221Z This test is a superset of ``test_faketensor``. 2024-06-26T05:54:50.1298659Z 2024-06-26T05:54:50.1299057Z For best results, please call ``opcheck`` multiple times with a 2024-06-26T05:54:50.1299704Z representative set of inputs. If your operator supports 2024-06-26T05:54:50.1300418Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-06-26T05:54:50.1301209Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-06-26T05:54:50.1301892Z use ``opcheck`` with inputs on all supported devices. 2024-06-26T05:54:50.1302349Z 2024-06-26T05:54:50.1302583Z Args: 2024-06-26T05:54:50.1302995Z op: The operator. Must either be a function decorated with 2024-06-26T05:54:50.1303691Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-06-26T05:54:50.1304462Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-06-26T05:54:50.1305079Z args: The args to the operator 2024-06-26T05:54:50.1305521Z kwargs: The kwargs to the operator 2024-06-26T05:54:50.1306079Z test_utils: Tests that we should run. Default: all of them. 2024-06-26T05:54:50.1306682Z Example: ("test_schema", "test_faketensor") 2024-06-26T05:54:50.1307281Z raise_exception: If we should raise an exception on the first 2024-06-26T05:54:50.1307980Z error. If False, we will return a dict with information 2024-06-26T05:54:50.1308536Z on if each test passed or not. 2024-06-26T05:54:50.1308960Z 2024-06-26T05:54:50.1309208Z .. warning:: 2024-06-26T05:54:50.1309493Z 2024-06-26T05:54:50.1309950Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-06-26T05:54:50.1310719Z opcheck tests if your usage of torch.library APIs is correct while 2024-06-26T05:54:50.1311471Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-06-26T05:54:50.1312201Z mathematically correct. Use both to test custom ops that support 2024-06-26T05:54:50.1312769Z gradient computation. 2024-06-26T05:54:50.1313118Z 2024-06-26T05:54:50.1313340Z Example: 2024-06-26T05:54:50.1313600Z 2024-06-26T05:54:50.1313953Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:50.1314581Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-06-26T05:54:50.1315400Z >>> def numpy_add(x: Tensor, y: float) -> Tensor: 2024-06-26T05:54:50.1315903Z >>> x_np = x.numpy(force=True) 2024-06-26T05:54:50.1316326Z >>> z_np = x_np + y 2024-06-26T05:54:50.1316760Z >>> return torch.from_numpy(z_np).to(x.device) 2024-06-26T05:54:50.1317207Z >>> 2024-06-26T05:54:50.1317506Z >>> @numpy_sin.register_fake 2024-06-26T05:54:50.1317887Z >>> def _(x, y): 2024-06-26T05:54:50.1318255Z >>> return torch.empty_like(x) 2024-06-26T05:54:50.1318650Z >>> 2024-06-26T05:54:50.1318972Z >>> def setup_context(ctx, inputs, output): 2024-06-26T05:54:50.1319415Z >>> y, = inputs 2024-06-26T05:54:50.1319759Z >>> ctx.y = y 2024-06-26T05:54:50.1320067Z >>> 2024-06-26T05:54:50.1320364Z >>> def backward(ctx, grad): 2024-06-26T05:54:50.1320794Z >>> return grad * ctx.y, None 2024-06-26T05:54:50.1321256Z >>> 2024-06-26T05:54:50.1321715Z >>> numpy_sin.register_autograd(backward, setup_context=setup_context) 2024-06-26T05:54:50.1322275Z >>> 2024-06-26T05:54:50.1322543Z >>> sample_inputs = [ 2024-06-26T05:54:50.1322934Z >>> (torch.randn(3), 3.14), 2024-06-26T05:54:50.1323473Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-06-26T05:54:50.1324003Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-06-26T05:54:50.1324713Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-06-26T05:54:50.1325249Z >>> ] 2024-06-26T05:54:50.1325501Z >>> 2024-06-26T05:54:50.1325804Z >>> for args in sample_inputs: 2024-06-26T05:54:50.1326268Z >>> torch.library.opcheck(foo, args) 2024-06-26T05:54:50.1326682Z 2024-06-26T05:54:50.1326911Z 2024-06-26T05:54:50.1327451Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1328021Z 2024-06-26T05:54:50.1352070Z warnings.warn(msg) 2024-06-26T05:54:50.1352415Z 2024-06-26T05:54:50.1352786Z --- Parse Warning: 4 / 90 --- 2024-06-26T05:54:50.1354402Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=340. 2024-06-26T05:54:50.1356224Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-06-26T05:54:50.1356859Z Retrieves the CUDA runtime API module. 2024-06-26T05:54:50.1357269Z 2024-06-26T05:54:50.1357496Z 2024-06-26T05:54:50.1357956Z This function initializes the CUDA runtime environment if it is not already 2024-06-26T05:54:50.1358784Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-06-26T05:54:50.1359816Z runtime API module provides access to various CUDA runtime functions. 2024-06-26T05:54:50.1360369Z 2024-06-26T05:54:50.1360658Z Args: 2024-06-26T05:54:50.1360920Z ``None`` 2024-06-26T05:54:50.1361263Z 2024-06-26T05:54:50.1361495Z Returns: 2024-06-26T05:54:50.1361920Z module: The CUDA runtime API module (_cudart). 2024-06-26T05:54:50.1362352Z 2024-06-26T05:54:50.1362582Z Raises: 2024-06-26T05:54:50.1363140Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-06-26T05:54:50.1364110Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-06-26T05:54:50.1364858Z 2024-06-26T05:54:50.1365169Z Example of CUDA operations with profiling: 2024-06-26T05:54:50.1365610Z >>> import torch 2024-06-26T05:54:50.1366074Z >>> from torch.cuda import cudart, check_error 2024-06-26T05:54:50.1366537Z >>> import os 2024-06-26T05:54:50.1366833Z >>> 2024-06-26T05:54:50.1367201Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-06-26T05:54:50.1367618Z >>> 2024-06-26T05:54:50.1367958Z >>> def perform_cuda_operations_with_streams(): 2024-06-26T05:54:50.1368467Z >>> stream = torch.cuda.Stream() 2024-06-26T05:54:50.1368947Z >>> with torch.cuda.stream(stream): 2024-06-26T05:54:50.1369498Z >>> x = torch.randn(100, 100, device='cuda') 2024-06-26T05:54:50.1370080Z >>> y = torch.randn(100, 100, device='cuda') 2024-06-26T05:54:50.1370558Z >>> z = torch.mul(x, y) 2024-06-26T05:54:50.1370950Z >>> return z 2024-06-26T05:54:50.1371270Z >>> 2024-06-26T05:54:50.1371582Z >>> torch.cuda.synchronize() 2024-06-26T05:54:50.1372043Z >>> print("====== Start nsys profiling ======") 2024-06-26T05:54:50.1372580Z >>> check_error(cudart().cudaProfilerStart()) 2024-06-26T05:54:50.1373123Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-06-26T05:54:50.1373686Z >>> result = perform_cuda_operations_with_streams() 2024-06-26T05:54:50.1374225Z >>> print("CUDA operations completed.") 2024-06-26T05:54:50.1374781Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-06-26T05:54:50.1375346Z >>> print("====== End nsys profiling ======") 2024-06-26T05:54:50.1375764Z 2024-06-26T05:54:50.1376177Z To run this example and save the profiling information, execute: 2024-06-26T05:54:50.1377183Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-06-26T05:54:50.1377896Z 2024-06-26T05:54:50.1378371Z This command profiles the CUDA operations in the provided script and saves 2024-06-26T05:54:50.1379136Z the profiling information to a file named `trace_name.prof`. 2024-06-26T05:54:50.1379931Z The `--profile-from-start off` option ensures that profiling starts only 2024-06-26T05:54:50.1380624Z after the `cudaProfilerStart` call in the script. 2024-06-26T05:54:50.1381368Z The `--csv` and `--print-summary` options format the profiling output as a 2024-06-26T05:54:50.1382015Z CSV file and print a summary, respectively. 2024-06-26T05:54:50.1382767Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-06-26T05:54:50.1383478Z overwrite of the output file if it already exists. 2024-06-26T05:54:50.1383940Z 2024-06-26T05:54:50.1385079Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2024-06-26T05:54:50.1386194Z 2024-06-26T05:54:50.1386875Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-06-26T05:54:50.1387635Z ^ 2024-06-26T05:54:50.1387891Z warnings.warn(msg) 2024-06-26T05:54:50.1388201Z 2024-06-26T05:54:50.1388525Z --- Parse Warning: 5 / 90 --- 2024-06-26T05:54:50.1390268Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101. 2024-06-26T05:54:50.1392041Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1392649Z 2024-06-26T05:54:50.1393087Z Append the given callback function to this ``Future``, which will be run 2024-06-26T05:54:50.1393854Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-06-26T05:54:50.1394753Z the same ``Future``, but the order in which they will be executed cannot 2024-06-26T05:54:50.1395554Z be guaranteed (to enforce a certain order consider chaining: 2024-06-26T05:54:50.1396269Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-06-26T05:54:50.1397024Z is the reference to this ``Future``. The callback function can use the 2024-06-26T05:54:50.1397770Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-06-26T05:54:50.1398531Z already completed, the given callback will be run immediately inline. 2024-06-26T05:54:50.1399090Z 2024-06-26T05:54:50.1399551Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-06-26T05:54:50.1400305Z callback might be invoked while the async kernels that are populating 2024-06-26T05:54:50.1401211Z those tensors haven't yet finished executing on the device. However, the 2024-06-26T05:54:50.1401977Z callback will be invoked with some dedicated streams set as current 2024-06-26T05:54:50.1402700Z (fetched from a global pool) which will be synchronized with those 2024-06-26T05:54:50.1403460Z kernels. Hence any operation performed by the callback on these tensors 2024-06-26T05:54:50.1404235Z will be scheduled on the device after the kernels complete. In other 2024-06-26T05:54:50.1405024Z words, as long as the callback doesn't switch streams, it can safely 2024-06-26T05:54:50.1405783Z manipulate the result without any additional synchronization. This is 2024-06-26T05:54:50.1406511Z similar to the non-blocking behavior of :meth:`wait`. 2024-06-26T05:54:50.1406963Z 2024-06-26T05:54:50.1407393Z Similarly, if the callback returns a value that contains tensors that 2024-06-26T05:54:50.1408141Z reside on a GPU, it can do so even if the kernels that are producing 2024-06-26T05:54:50.1408882Z these tensors are still running on the device, as long as the callback 2024-06-26T05:54:50.1409682Z didn't change streams during its execution. If one wants to change 2024-06-26T05:54:50.1410480Z streams, one must be careful to re-synchronize them with the original 2024-06-26T05:54:50.1411249Z streams, that is, those that were current when the callback was invoked. 2024-06-26T05:54:50.1411799Z 2024-06-26T05:54:50.1412027Z Args: 2024-06-26T05:54:50.1412450Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-06-26T05:54:50.1413026Z the only argument. 2024-06-26T05:54:50.1413436Z 2024-06-26T05:54:50.1413668Z Returns: 2024-06-26T05:54:50.1414051Z A new ``Future`` object that holds the return value of the 2024-06-26T05:54:50.1414694Z ``callback`` and will be marked as completed when the given 2024-06-26T05:54:50.1415216Z ``callback`` finishes. 2024-06-26T05:54:50.1415535Z 2024-06-26T05:54:50.1415913Z .. note:: Note that if the callback function throws, either 2024-06-26T05:54:50.1416592Z through the original future being completed with an exception and 2024-06-26T05:54:50.1417300Z calling ``fut.wait()``, or through other code in the callback, the 2024-06-26T05:54:50.1418012Z future returned by ``then`` will be marked appropriately with the 2024-06-26T05:54:50.1418777Z encountered error. However, if this callback later completes 2024-06-26T05:54:50.1419522Z additional futures, those futures are not marked as completed with 2024-06-26T05:54:50.1420279Z an error and the user is responsible for handling completion/waiting 2024-06-26T05:54:50.1420917Z on those futures independently. 2024-06-26T05:54:50.1421291Z 2024-06-26T05:54:50.1421530Z Example:: 2024-06-26T05:54:50.1421894Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-06-26T05:54:50.1422355Z >>> def callback(fut): 2024-06-26T05:54:50.1422788Z ... print(f"RPC return value is {fut.wait()}.") 2024-06-26T05:54:50.1423278Z >>> fut = torch.futures.Future() 2024-06-26T05:54:50.1423793Z >>> # The inserted callback will print the return value when 2024-06-26T05:54:50.1424354Z >>> # receiving the response from "worker1" 2024-06-26T05:54:50.1424849Z >>> cb_fut = fut.then(callback) 2024-06-26T05:54:50.1425256Z >>> chain_cb_fut = cb_fut.then( 2024-06-26T05:54:50.1425743Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-06-26T05:54:50.1426204Z ... ) 2024-06-26T05:54:50.1426464Z >>> fut.set_result(5) 2024-06-26T05:54:50.1426819Z RPC return value is 5. 2024-06-26T05:54:50.1427183Z Chained cb done. None 2024-06-26T05:54:50.1427495Z 2024-06-26T05:54:50.1428035Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1428622Z 2024-06-26T05:54:50.1428860Z warnings.warn(msg) 2024-06-26T05:54:50.1429161Z 2024-06-26T05:54:50.1429502Z --- Parse Warning: 6 / 90 --- 2024-06-26T05:54:50.1431179Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=209. 2024-06-26T05:54:50.1432980Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1433592Z 2024-06-26T05:54:50.1434028Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-06-26T05:54:50.1434914Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-06-26T05:54:50.1435520Z cannot be marked completed twice. 2024-06-26T05:54:50.1435899Z 2024-06-26T05:54:50.1436326Z If the result contains tensors that reside on GPUs, this method can be 2024-06-26T05:54:50.1437075Z called even if the asynchronous kernels that are populating those 2024-06-26T05:54:50.1437886Z tensors haven't yet completed running on the device, provided that the 2024-06-26T05:54:50.1438650Z streams on which those kernels were enqueued are set as the current ones 2024-06-26T05:54:50.1439486Z when this method is called. Put simply, it's safe to call this method 2024-06-26T05:54:50.1440230Z immediately after launching those kernels, without any additional 2024-06-26T05:54:50.1441124Z synchronization, as long as one doesn't change streams in between. This 2024-06-26T05:54:50.1441900Z method will record events on all the relevant current streams and will 2024-06-26T05:54:50.1442647Z use them to ensure proper scheduling for all the consumers of this 2024-06-26T05:54:50.1443197Z ``Future``. 2024-06-26T05:54:50.1443446Z 2024-06-26T05:54:50.1443678Z Args: 2024-06-26T05:54:50.1444041Z result (object): the result object of this ``Future``. 2024-06-26T05:54:50.1444498Z 2024-06-26T05:54:50.1444740Z Example:: 2024-06-26T05:54:50.1445100Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-06-26T05:54:50.1445560Z >>> import threading 2024-06-26T05:54:50.1445904Z >>> import time 2024-06-26T05:54:50.1446258Z >>> def slow_set_future(fut, value): 2024-06-26T05:54:50.1446664Z ... time.sleep(0.5) 2024-06-26T05:54:50.1447037Z ... fut.set_result(value) 2024-06-26T05:54:50.1447519Z >>> fut = torch.futures.Future() 2024-06-26T05:54:50.1447928Z >>> t = threading.Thread( 2024-06-26T05:54:50.1448365Z ... target=slow_set_future, 2024-06-26T05:54:50.1448794Z ... args=(fut, torch.ones(2) * 3) 2024-06-26T05:54:50.1449178Z ... ) 2024-06-26T05:54:50.1449473Z >>> t.start() 2024-06-26T05:54:50.1449785Z >>> print(fut.wait()) 2024-06-26T05:54:50.1450112Z tensor([3., 3.]) 2024-06-26T05:54:50.1450414Z >>> t.join() 2024-06-26T05:54:50.1450678Z 2024-06-26T05:54:50.1451220Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1451810Z 2024-06-26T05:54:50.1452042Z warnings.warn(msg) 2024-06-26T05:54:50.1452346Z 2024-06-26T05:54:50.1452681Z --- Parse Warning: 7 / 90 --- 2024-06-26T05:54:50.1454323Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=191. 2024-06-26T05:54:50.1456016Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1456757Z Return the sum of each row of the given sparse tensor. 2024-06-26T05:54:50.1457227Z 2024-06-26T05:54:50.1457682Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-06-26T05:54:50.1458449Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-06-26T05:54:50.1459180Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-06-26T05:54:50.1459882Z returns a dense tensor instead of a sparse tensor. 2024-06-26T05:54:50.1460380Z 2024-06-26T05:54:50.1460880Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-06-26T05:54:50.1461663Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-06-26T05:54:50.1462181Z 2024-06-26T05:54:50.1462613Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-06-26T05:54:50.1463399Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-06-26T05:54:50.1463994Z 2024-06-26T05:54:50.1464226Z Args: 2024-06-26T05:54:50.1464545Z input (Tensor): the input sparse tensor 2024-06-26T05:54:50.1465278Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-06-26T05:54:50.1465957Z over all dims. 2024-06-26T05:54:50.1466548Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-06-26T05:54:50.1467211Z Default: dtype of :attr:`input`. 2024-06-26T05:54:50.1467615Z 2024-06-26T05:54:50.1467862Z Example:: 2024-06-26T05:54:50.1468117Z 2024-06-26T05:54:50.1468351Z >>> nnz = 3 2024-06-26T05:54:50.1468671Z >>> dims = [5, 5, 2, 3] 2024-06-26T05:54:50.1469146Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-06-26T05:54:50.1469797Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-06-26T05:54:50.1470396Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-06-26T05:54:50.1470843Z >>> size = torch.Size(dims) 2024-06-26T05:54:50.1471389Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:50.1471921Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-06-26T05:54:50.1472336Z >>> S 2024-06-26T05:54:50.1472653Z tensor(indices=tensor([[2, 0, 3], 2024-06-26T05:54:50.1473092Z [2, 4, 1]]), 2024-06-26T05:54:50.1473612Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-06-26T05:54:50.1474174Z [ 0.3411, 0.0918, -0.2312]], 2024-06-26T05:54:50.1474591Z 2024-06-26T05:54:50.1475056Z [[ 0.5348, 0.0634, -2.0494], 2024-06-26T05:54:50.1475689Z [-0.7125, -1.0646, 2.1844]], 2024-06-26T05:54:50.1476149Z 2024-06-26T05:54:50.1476487Z [[ 0.1276, 0.1874, -0.6334], 2024-06-26T05:54:50.1477034Z [-1.9682, -0.5340, 0.7483]]]), 2024-06-26T05:54:50.1477612Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-06-26T05:54:50.1478063Z 2024-06-26T05:54:50.1478475Z # when sum over only part of sparse_dims, return a sparse tensor 2024-06-26T05:54:50.1479057Z >>> torch.sparse.sum(S, [1, 3]) 2024-06-26T05:54:50.1479495Z tensor(indices=tensor([[0, 2, 3]]), 2024-06-26T05:54:50.1480017Z values=tensor([[-1.4512, 0.4073], 2024-06-26T05:54:50.1480533Z [-0.8901, 0.2017], 2024-06-26T05:54:50.1481145Z [-0.3183, -1.7539]]), 2024-06-26T05:54:50.1481633Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-06-26T05:54:50.1482072Z 2024-06-26T05:54:50.1482426Z # when sum over all sparse dim, return a dense tensor 2024-06-26T05:54:50.1482927Z # with summed dims squeezed 2024-06-26T05:54:50.1483362Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-06-26T05:54:50.1483844Z tensor([-2.6596, -1.1450]) 2024-06-26T05:54:50.1484195Z 2024-06-26T05:54:50.1484741Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1485327Z 2024-06-26T05:54:50.1485561Z warnings.warn(msg) 2024-06-26T05:54:50.1485863Z 2024-06-26T05:54:50.1486199Z --- Parse Warning: 8 / 90 --- 2024-06-26T05:54:50.1487786Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=38. 2024-06-26T05:54:50.1489488Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1490092Z 2024-06-26T05:54:50.1490522Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-06-26T05:54:50.1491276Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-06-26T05:54:50.1492020Z pushes the map into PyTorch operations called by ``func``, effectively 2024-06-26T05:54:50.1492608Z vectorizing those operations. 2024-06-26T05:54:50.1492955Z 2024-06-26T05:54:50.1493402Z vmap is useful for handling batch dimensions: one can write a function 2024-06-26T05:54:50.1494145Z ``func`` that runs on examples and then lift it to a function that can 2024-06-26T05:54:50.1494893Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-06-26T05:54:50.1495569Z compute batched gradients when composed with autograd. 2024-06-26T05:54:50.1496037Z 2024-06-26T05:54:50.1496268Z .. note:: 2024-06-26T05:54:50.1496687Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-06-26T05:54:50.1497326Z convenience. Use whichever one you'd like. 2024-06-26T05:54:50.1497743Z 2024-06-26T05:54:50.1497975Z Args: 2024-06-26T05:54:50.1498416Z func (function): A Python function that takes one or more arguments. 2024-06-26T05:54:50.1499009Z Must return one or more Tensors. 2024-06-26T05:54:50.1499601Z in_dims (int or nested structure): Specifies which dimension of the 2024-06-26T05:54:50.1500282Z inputs should be mapped over. ``in_dims`` should have a 2024-06-26T05:54:50.1500926Z structure like the inputs. If the ``in_dim`` for a particular 2024-06-26T05:54:50.1501613Z input is None, then that indicates there is no map dimension. 2024-06-26T05:54:50.1502147Z Default: 0. 2024-06-26T05:54:50.1502619Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-06-26T05:54:50.1503333Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-06-26T05:54:50.1504033Z it should have one element per output. Default: 0. 2024-06-26T05:54:50.1504670Z randomness (str): Specifies whether the randomness in this 2024-06-26T05:54:50.1505454Z vmap should be the same or different across batches. If 'different', 2024-06-26T05:54:50.1506289Z the randomness for each batch will be different. If 'same', the 2024-06-26T05:54:50.1507093Z randomness will be the same across batches. If 'error', any calls to 2024-06-26T05:54:50.1507890Z random functions will error. Default: 'error'. WARNING: this flag 2024-06-26T05:54:50.1508618Z only applies to random PyTorch operations and does not apply to 2024-06-26T05:54:50.1509291Z Python's random module or numpy randomness. 2024-06-26T05:54:50.1509955Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-06-26T05:54:50.1510799Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-06-26T05:54:50.1511729Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-06-26T05:54:50.1512705Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-06-26T05:54:50.1513340Z 2024-06-26T05:54:50.1513580Z Returns: 2024-06-26T05:54:50.1513988Z Returns a new "batched" function. It takes the same inputs as 2024-06-26T05:54:50.1514761Z ``func``, except each input has an extra dimension at the index 2024-06-26T05:54:50.1515447Z specified by ``in_dims``. It takes returns the same outputs as 2024-06-26T05:54:50.1516110Z ``func``, except each output has an extra dimension at the index 2024-06-26T05:54:50.1516658Z specified by ``out_dims``. 2024-06-26T05:54:50.1517018Z 2024-06-26T05:54:50.1517241Z .. warning: 2024-06-26T05:54:50.1517758Z :func:`vmap` works best with functional-style code. Please do not 2024-06-26T05:54:50.1518509Z perform any side-effects in ``func``, with the exception of 2024-06-26T05:54:50.1519303Z in-place PyTorch operations. Examples of side-effects include mutating 2024-06-26T05:54:50.1520082Z Python data structures and assigning values to variables not captured 2024-06-26T05:54:50.1520658Z in ``func``. 2024-06-26T05:54:50.1520936Z 2024-06-26T05:54:50.1521463Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-06-26T05:54:50.1522334Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-06-26T05:54:50.1523102Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-06-26T05:54:50.1523648Z 2024-06-26T05:54:50.1524048Z >>> torch.dot # [D], [D] -> [] 2024-06-26T05:54:50.1524756Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-06-26T05:54:50.1525368Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-06-26T05:54:50.1525827Z >>> batched_dot(x, y) 2024-06-26T05:54:50.1526150Z 2024-06-26T05:54:50.1526600Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-06-26T05:54:50.1527218Z model authoring experience. 2024-06-26T05:54:50.1527569Z 2024-06-26T05:54:50.1527836Z >>> batch_size, feature_size = 3, 5 2024-06-26T05:54:50.1528366Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-06-26T05:54:50.1528855Z >>> 2024-06-26T05:54:50.1529117Z >>> def model(feature_vec): 2024-06-26T05:54:50.1529576Z >>> # Very simple linear model with activation 2024-06-26T05:54:50.1530091Z >>> return feature_vec.dot(weights).relu() 2024-06-26T05:54:50.1530509Z >>> 2024-06-26T05:54:50.1530872Z >>> examples = torch.randn(batch_size, feature_size) 2024-06-26T05:54:50.1531388Z >>> result = torch.vmap(model)(examples) 2024-06-26T05:54:50.1531873Z 2024-06-26T05:54:50.1532363Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-06-26T05:54:50.1533263Z or impossible to batch. One example is higher-order gradient computation. 2024-06-26T05:54:50.1534165Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-06-26T05:54:50.1535046Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-06-26T05:54:50.1535855Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-06-26T05:54:50.1536693Z we can vectorize the whole computation, computing the Jacobian in a single 2024-06-26T05:54:50.1537306Z call to ``autograd.grad``. 2024-06-26T05:54:50.1537627Z 2024-06-26T05:54:50.1537859Z >>> # Setup 2024-06-26T05:54:50.1538136Z >>> N = 5 2024-06-26T05:54:50.1538413Z >>> f = lambda x: x ** 2 2024-06-26T05:54:50.1538821Z >>> x = torch.randn(N, requires_grad=True) 2024-06-26T05:54:50.1539285Z >>> y = f(x) 2024-06-26T05:54:50.1539583Z >>> I_N = torch.eye(N) 2024-06-26T05:54:50.1539913Z >>> 2024-06-26T05:54:50.1540184Z >>> # Sequential approach 2024-06-26T05:54:50.1540722Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-06-26T05:54:50.1541330Z >>> for v in I_N.unbind()] 2024-06-26T05:54:50.1541818Z >>> jacobian = torch.stack(jacobian_rows) 2024-06-26T05:54:50.1542225Z >>> 2024-06-26T05:54:50.1542527Z >>> # vectorized gradient computation 2024-06-26T05:54:50.1542949Z >>> def get_vjp(v): 2024-06-26T05:54:50.1543323Z >>> return torch.autograd.grad(y, x, v) 2024-06-26T05:54:50.1543808Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-06-26T05:54:50.1544209Z 2024-06-26T05:54:50.1544703Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-06-26T05:54:50.1545337Z 2024-06-26T05:54:50.1545729Z >>> torch.dot # [D], [D] -> [] 2024-06-26T05:54:50.1546548Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-06-26T05:54:50.1547298Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-06-26T05:54:50.1547820Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-06-26T05:54:50.1548234Z 2024-06-26T05:54:50.1548696Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-06-26T05:54:50.1549403Z the dimension that each inputs are batched along as 2024-06-26T05:54:50.1549855Z 2024-06-26T05:54:50.1550231Z >>> torch.dot # [N], [N] -> [] 2024-06-26T05:54:50.1550969Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-06-26T05:54:50.1551619Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-06-26T05:54:50.1552301Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-06-26T05:54:50.1552919Z 2024-06-26T05:54:50.1553421Z If there are multiple inputs each of which is batched along different dimensions, 2024-06-26T05:54:50.1554227Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-06-26T05:54:50.1554895Z 2024-06-26T05:54:50.1555291Z >>> torch.dot # [D], [D] -> [] 2024-06-26T05:54:50.1556043Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-06-26T05:54:50.1556712Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-06-26T05:54:50.1557476Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-06-26T05:54:50.1558076Z 2024-06-26T05:54:50.1558554Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-06-26T05:54:50.1559199Z matching the shape of the input: 2024-06-26T05:54:50.1559574Z 2024-06-26T05:54:50.1559946Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-06-26T05:54:50.1560530Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-06-26T05:54:50.1561082Z >>> input = {'x': x, 'y': y} 2024-06-26T05:54:50.1561701Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-06-26T05:54:50.1562229Z >>> batched_dot(input) 2024-06-26T05:54:50.1562557Z 2024-06-26T05:54:50.1563116Z By default, the output is batched along the first dimension. However, it can be batched 2024-06-26T05:54:50.1563828Z along any dimension by using ``out_dims`` 2024-06-26T05:54:50.1564226Z 2024-06-26T05:54:50.1564455Z >>> f = lambda x: x ** 2 2024-06-26T05:54:50.1564814Z >>> x = torch.randn(2, 5) 2024-06-26T05:54:50.1565222Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-06-26T05:54:50.1565657Z >>> batched_pow(x) # [5, 2] 2024-06-26T05:54:50.1565999Z 2024-06-26T05:54:50.1566570Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-06-26T05:54:50.1567237Z accept kwargs 2024-06-26T05:54:50.1567496Z 2024-06-26T05:54:50.1567737Z >>> x = torch.randn([2, 5]) 2024-06-26T05:54:50.1568100Z >>> def fn(x, scale=4.): 2024-06-26T05:54:50.1568442Z >>> return x * scale 2024-06-26T05:54:50.1568767Z >>> 2024-06-26T05:54:50.1569031Z >>> batched_pow = torch.vmap(fn) 2024-06-26T05:54:50.1569499Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-06-26T05:54:50.1570152Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-06-26T05:54:50.1570712Z 2024-06-26T05:54:50.1570942Z .. note:: 2024-06-26T05:54:50.1571462Z vmap does not provide general autobatching or handle variable-length 2024-06-26T05:54:50.1572040Z sequences out of the box. 2024-06-26T05:54:50.1572377Z 2024-06-26T05:54:50.1572899Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1573468Z 2024-06-26T05:54:50.1573705Z warnings.warn(msg) 2024-06-26T05:54:50.1573992Z 2024-06-26T05:54:50.1574304Z --- Parse Warning: 9 / 90 --- 2024-06-26T05:54:50.1576037Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=329. 2024-06-26T05:54:50.1577845Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1578427Z 2024-06-26T05:54:50.1578826Z Raises an AssertionError if two items are not equal up to desired 2024-06-26T05:54:50.1579366Z precision. 2024-06-26T05:54:50.1579606Z 2024-06-26T05:54:50.1579965Z .. note:: It is recommended to use one of `assert_allclose`, 2024-06-26T05:54:50.1580587Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-06-26T05:54:50.1581226Z instead of this function for more consistent floating point 2024-06-26T05:54:50.1581755Z comparisons. 2024-06-26T05:54:50.1582050Z 2024-06-26T05:54:50.1582460Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-06-26T05:54:50.1583002Z 2024-06-26T05:54:50.1583395Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-06-26T05:54:50.1583838Z 2024-06-26T05:54:50.1584283Z That is a looser test than originally documented, but agrees with what the 2024-06-26T05:54:50.1585069Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-06-26T05:54:50.1585840Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-06-26T05:54:50.1586449Z delegates to assert_array_almost_equal 2024-06-26T05:54:50.1586832Z 2024-06-26T05:54:50.1587054Z Parameters 2024-06-26T05:54:50.1587321Z ---------- 2024-06-26T05:54:50.1587588Z actual : array_like 2024-06-26T05:54:50.1587906Z The object to check. 2024-06-26T05:54:50.1588234Z desired : array_like 2024-06-26T05:54:50.1588604Z The expected object. 2024-06-26T05:54:50.1588939Z decimal : int, optional 2024-06-26T05:54:50.1589329Z Desired precision, default is 7. 2024-06-26T05:54:50.1589734Z err_msg : str, optional 2024-06-26T05:54:50.1590153Z The error message to be printed in case of failure. 2024-06-26T05:54:50.1590654Z verbose : bool, optional 2024-06-26T05:54:50.1591160Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:50.1591690Z 2024-06-26T05:54:50.1591901Z Raises 2024-06-26T05:54:50.1592165Z ------ 2024-06-26T05:54:50.1592401Z AssertionError 2024-06-26T05:54:50.1592836Z If actual and desired are not equal up to specified precision. 2024-06-26T05:54:50.1593337Z 2024-06-26T05:54:50.1593544Z See Also 2024-06-26T05:54:50.1593815Z -------- 2024-06-26T05:54:50.1594278Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:50.1595113Z relative and/or absolute precision. 2024-06-26T05:54:50.1595718Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:50.1596242Z 2024-06-26T05:54:50.1596473Z Examples 2024-06-26T05:54:50.1596742Z -------- 2024-06-26T05:54:50.1597116Z >>> from torch._numpy.testing import assert_almost_equal 2024-06-26T05:54:50.1597656Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-06-26T05:54:50.1598211Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-06-26T05:54:50.1598739Z Traceback (most recent call last): 2024-06-26T05:54:50.1599112Z ... 2024-06-26T05:54:50.1599358Z AssertionError: 2024-06-26T05:54:50.1599705Z Arrays are not almost equal to 10 decimals 2024-06-26T05:54:50.1600132Z ACTUAL: 2.3333333333333 2024-06-26T05:54:50.1600445Z DESIRED: 2.33333334 2024-06-26T05:54:50.1600742Z 2024-06-26T05:54:50.1601125Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-06-26T05:54:50.1601755Z ... np.array([1.0,2.33333334]), decimal=9) 2024-06-26T05:54:50.1602227Z Traceback (most recent call last): 2024-06-26T05:54:50.1602611Z ... 2024-06-26T05:54:50.1602853Z AssertionError: 2024-06-26T05:54:50.1603270Z Arrays are not almost equal to 9 decimals 2024-06-26T05:54:50.1603693Z 2024-06-26T05:54:50.1603983Z Mismatched elements: 1 / 2 (50%) 2024-06-26T05:54:50.1604486Z Max absolute difference: 6.666699636781459e-09 2024-06-26T05:54:50.1605037Z Max relative difference: 2.8571569790287484e-09 2024-06-26T05:54:50.1605532Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-06-26T05:54:50.1606047Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-06-26T05:54:50.1606468Z 2024-06-26T05:54:50.1606666Z 2024-06-26T05:54:50.1607186Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1607765Z 2024-06-26T05:54:50.1607995Z warnings.warn(msg) 2024-06-26T05:54:50.1608290Z 2024-06-26T05:54:50.1608622Z --- Parse Warning: 10 / 90 --- 2024-06-26T05:54:50.1610344Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=454. 2024-06-26T05:54:50.1612169Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1612769Z 2024-06-26T05:54:50.1613181Z Raises an AssertionError if two items are not equal up to significant 2024-06-26T05:54:50.1613734Z digits. 2024-06-26T05:54:50.1613967Z 2024-06-26T05:54:50.1614330Z .. note:: It is recommended to use one of `assert_allclose`, 2024-06-26T05:54:50.1614956Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-06-26T05:54:50.1615611Z instead of this function for more consistent floating point 2024-06-26T05:54:50.1616205Z comparisons. 2024-06-26T05:54:50.1616510Z 2024-06-26T05:54:50.1616890Z Given two numbers, check that they are approximately equal. 2024-06-26T05:54:50.1617630Z Approximately equal is defined as the number of significant digits 2024-06-26T05:54:50.1618173Z that agree. 2024-06-26T05:54:50.1618437Z 2024-06-26T05:54:50.1618696Z Parameters 2024-06-26T05:54:50.1618991Z ---------- 2024-06-26T05:54:50.1619253Z actual : scalar 2024-06-26T05:54:50.1619540Z The object to check. 2024-06-26T05:54:50.1619877Z desired : scalar 2024-06-26T05:54:50.1620183Z The expected object. 2024-06-26T05:54:50.1620524Z significant : int, optional 2024-06-26T05:54:50.1620917Z Desired precision, default is 7. 2024-06-26T05:54:50.1621321Z err_msg : str, optional 2024-06-26T05:54:50.1621737Z The error message to be printed in case of failure. 2024-06-26T05:54:50.1622222Z verbose : bool, optional 2024-06-26T05:54:50.1622771Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:50.1623301Z 2024-06-26T05:54:50.1623524Z Raises 2024-06-26T05:54:50.1623790Z ------ 2024-06-26T05:54:50.1624028Z AssertionError 2024-06-26T05:54:50.1624476Z If actual and desired are not equal up to specified precision. 2024-06-26T05:54:50.1624993Z 2024-06-26T05:54:50.1625215Z See Also 2024-06-26T05:54:50.1625490Z -------- 2024-06-26T05:54:50.1625956Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:50.1626595Z relative and/or absolute precision. 2024-06-26T05:54:50.1627195Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:50.1627717Z 2024-06-26T05:54:50.1627932Z Examples 2024-06-26T05:54:50.1628208Z -------- 2024-06-26T05:54:50.1628815Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-06-26T05:54:50.1629752Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-06-26T05:54:50.1630416Z ... significant=8) 2024-06-26T05:54:50.1631151Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-06-26T05:54:50.1631800Z ... significant=8) 2024-06-26T05:54:50.1632265Z Traceback (most recent call last): 2024-06-26T05:54:50.1632645Z ... 2024-06-26T05:54:50.1632890Z AssertionError: 2024-06-26T05:54:50.1633253Z Items are not equal to 8 significant digits: 2024-06-26T05:54:50.1633728Z ACTUAL: 1.234567e-21 2024-06-26T05:54:50.1634076Z DESIRED: 1.2345672e-21 2024-06-26T05:54:50.1634385Z 2024-06-26T05:54:50.1634853Z the evaluated condition that raises the exception is 2024-06-26T05:54:50.1635300Z 2024-06-26T05:54:50.1635744Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-06-26T05:54:50.1636245Z True 2024-06-26T05:54:50.1636473Z 2024-06-26T05:54:50.1636696Z 2024-06-26T05:54:50.1637220Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1637798Z 2024-06-26T05:54:50.1638044Z warnings.warn(msg) 2024-06-26T05:54:50.1638346Z 2024-06-26T05:54:50.1638671Z --- Parse Warning: 11 / 90 --- 2024-06-26T05:54:50.1640403Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=733. 2024-06-26T05:54:50.1642285Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1642890Z 2024-06-26T05:54:50.1643297Z Raises an AssertionError if two array_like objects are not equal. 2024-06-26T05:54:50.1643833Z 2024-06-26T05:54:50.1644255Z Given two array_like objects, check that the shape is equal and all 2024-06-26T05:54:50.1645067Z elements of these objects are equal (but see the Notes for the special 2024-06-26T05:54:50.1645821Z handling of a scalar). An exception is raised at shape mismatch or 2024-06-26T05:54:50.1646618Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-06-26T05:54:50.1647371Z are compared like numbers, no assertion is raised if both objects have 2024-06-26T05:54:50.1648002Z NaNs in the same positions. 2024-06-26T05:54:50.1648355Z 2024-06-26T05:54:50.1648786Z The usual caution for verifying equality with floating point numbers is 2024-06-26T05:54:50.1649364Z advised. 2024-06-26T05:54:50.1649617Z 2024-06-26T05:54:50.1649836Z Parameters 2024-06-26T05:54:50.1650130Z ---------- 2024-06-26T05:54:50.1650401Z x : array_like 2024-06-26T05:54:50.1650700Z The actual object to check. 2024-06-26T05:54:50.1651078Z y : array_like 2024-06-26T05:54:50.1651394Z The desired, expected object. 2024-06-26T05:54:50.1651817Z err_msg : str, optional 2024-06-26T05:54:50.1652259Z The error message to be printed in case of failure. 2024-06-26T05:54:50.1652743Z verbose : bool, optional 2024-06-26T05:54:50.1653252Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:50.1653814Z strict : bool, optional 2024-06-26T05:54:50.1654321Z If True, raise an AssertionError when either the shape or the data 2024-06-26T05:54:50.1654997Z type of the array_like objects does not match. The special 2024-06-26T05:54:50.1655677Z handling for scalars mentioned in the Notes section is disabled. 2024-06-26T05:54:50.1656208Z 2024-06-26T05:54:50.1656423Z Raises 2024-06-26T05:54:50.1656692Z ------ 2024-06-26T05:54:50.1656945Z AssertionError 2024-06-26T05:54:50.1657293Z If actual and desired objects are not equal. 2024-06-26T05:54:50.1657719Z 2024-06-26T05:54:50.1657946Z See Also 2024-06-26T05:54:50.1658208Z -------- 2024-06-26T05:54:50.1658673Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:50.1659326Z relative and/or absolute precision. 2024-06-26T05:54:50.1659917Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:50.1660436Z 2024-06-26T05:54:50.1660659Z Notes 2024-06-26T05:54:50.1660903Z ----- 2024-06-26T05:54:50.1661327Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-06-26T05:54:50.1662073Z function checks that each element of the array_like object is equal to 2024-06-26T05:54:50.1662836Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-06-26T05:54:50.1663391Z 2024-06-26T05:54:50.1663620Z Examples 2024-06-26T05:54:50.1663887Z -------- 2024-06-26T05:54:50.1664215Z The first assert does not raise an exception: 2024-06-26T05:54:50.1664637Z 2024-06-26T05:54:50.1664962Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-06-26T05:54:50.1665499Z ... [np.exp(0),2.33333, np.nan]) 2024-06-26T05:54:50.1665927Z 2024-06-26T05:54:50.1666367Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-06-26T05:54:50.1666979Z functions for these cases instead: 2024-06-26T05:54:50.1667349Z 2024-06-26T05:54:50.1667663Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-06-26T05:54:50.1668189Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-06-26T05:54:50.1668737Z ... rtol=1e-10, atol=0) 2024-06-26T05:54:50.1669130Z 2024-06-26T05:54:50.1669543Z As mentioned in the Notes section, `assert_array_equal` has special 2024-06-26T05:54:50.1670296Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-06-26T05:54:50.1670845Z 2024-06-26T05:54:50.1671111Z >>> x = np.full((2, 5), fill_value=3) 2024-06-26T05:54:50.1671543Z >>> np.testing.assert_array_equal(x, 3) 2024-06-26T05:54:50.1671934Z 2024-06-26T05:54:50.1672431Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-06-26T05:54:50.1672996Z array: 2024-06-26T05:54:50.1673257Z 2024-06-26T05:54:50.1673588Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-06-26T05:54:50.1674083Z Traceback (most recent call last): 2024-06-26T05:54:50.1674449Z ... 2024-06-26T05:54:50.1674883Z AssertionError: 2024-06-26T05:54:50.1675193Z Arrays are not equal 2024-06-26T05:54:50.1675495Z 2024-06-26T05:54:50.1675784Z (shapes (2, 5), () mismatch) 2024-06-26T05:54:50.1676166Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-06-26T05:54:50.1676547Z [3, 3, 3, 3, 3]]) 2024-06-26T05:54:50.1676887Z y: torch.ndarray(3) 2024-06-26T05:54:50.1677193Z 2024-06-26T05:54:50.1677605Z The `strict` parameter also ensures that the array data types match: 2024-06-26T05:54:50.1678142Z 2024-06-26T05:54:50.1678392Z >>> x = np.array([2, 2, 2]) 2024-06-26T05:54:50.1678838Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-06-26T05:54:50.1679367Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-06-26T05:54:50.1679865Z Traceback (most recent call last): 2024-06-26T05:54:50.1680232Z ... 2024-06-26T05:54:50.1680488Z AssertionError: 2024-06-26T05:54:50.1680792Z Arrays are not equal 2024-06-26T05:54:50.1681162Z 2024-06-26T05:54:50.1681528Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-06-26T05:54:50.1682004Z x: torch.ndarray([2, 2, 2]) 2024-06-26T05:54:50.1682369Z y: torch.ndarray([2., 2., 2.]) 2024-06-26T05:54:50.1682726Z 2024-06-26T05:54:50.1683273Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1683848Z 2024-06-26T05:54:50.1684093Z warnings.warn(msg) 2024-06-26T05:54:50.1684390Z 2024-06-26T05:54:50.1684720Z --- Parse Warning: 12 / 90 --- 2024-06-26T05:54:50.1686490Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=839. 2024-06-26T05:54:50.1688350Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1688954Z 2024-06-26T05:54:50.1689370Z Raises an AssertionError if two objects are not equal up to desired 2024-06-26T05:54:50.1689927Z precision. 2024-06-26T05:54:50.1690185Z 2024-06-26T05:54:50.1690550Z .. note:: It is recommended to use one of `assert_allclose`, 2024-06-26T05:54:50.1691184Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-06-26T05:54:50.1691850Z instead of this function for more consistent floating point 2024-06-26T05:54:50.1692372Z comparisons. 2024-06-26T05:54:50.1692679Z 2024-06-26T05:54:50.1693146Z The test verifies identical shapes and that the elements of ``actual`` and 2024-06-26T05:54:50.1693738Z ``desired`` satisfy. 2024-06-26T05:54:50.1694046Z 2024-06-26T05:54:50.1694403Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-06-26T05:54:50.1694820Z 2024-06-26T05:54:50.1695278Z That is a looser test than originally documented, but agrees with what the 2024-06-26T05:54:50.1696110Z actual implementation did up to rounding vagaries. An exception is raised 2024-06-26T05:54:50.1696904Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-06-26T05:54:50.1697694Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-06-26T05:54:50.1697851Z objects have NaNs in the same positions. 2024-06-26T05:54:50.1697950Z 2024-06-26T05:54:50.1698049Z Parameters 2024-06-26T05:54:50.1698165Z ---------- 2024-06-26T05:54:50.1698276Z x : array_like 2024-06-26T05:54:50.1698398Z The actual object to check. 2024-06-26T05:54:50.1698495Z y : array_like 2024-06-26T05:54:50.1698637Z The desired, expected object. 2024-06-26T05:54:50.1698820Z decimal : int, optional 2024-06-26T05:54:50.1698958Z Desired precision, default is 6. 2024-06-26T05:54:50.1699123Z err_msg : str, optional 2024-06-26T05:54:50.1699330Z The error message to be printed in case of failure. 2024-06-26T05:54:50.1699457Z verbose : bool, optional 2024-06-26T05:54:50.1699769Z If True, the conflicting values are appended to the error message. 2024-06-26T05:54:50.1699857Z 2024-06-26T05:54:50.1699958Z Raises 2024-06-26T05:54:50.1700070Z ------ 2024-06-26T05:54:50.1700173Z AssertionError 2024-06-26T05:54:50.1700460Z If actual and desired are not equal up to specified precision. 2024-06-26T05:54:50.1700545Z 2024-06-26T05:54:50.1700638Z See Also 2024-06-26T05:54:50.1700763Z -------- 2024-06-26T05:54:50.1701070Z assert_allclose: Compare two array_like objects for equality with desired 2024-06-26T05:54:50.1701270Z relative and/or absolute precision. 2024-06-26T05:54:50.1701549Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-06-26T05:54:50.1701633Z 2024-06-26T05:54:50.1701728Z Examples 2024-06-26T05:54:50.1701853Z -------- 2024-06-26T05:54:50.1702075Z the first assert does not raise an exception 2024-06-26T05:54:50.1702200Z 2024-06-26T05:54:50.1702443Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-06-26T05:54:50.1702606Z ... [1.0,2.333,np.nan]) 2024-06-26T05:54:50.1702692Z 2024-06-26T05:54:50.1702937Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-06-26T05:54:50.1703116Z ... [1.0,2.33339,np.nan], decimal=5) 2024-06-26T05:54:50.1703262Z Traceback (most recent call last): 2024-06-26T05:54:50.1703354Z ... 2024-06-26T05:54:50.1703460Z AssertionError: 2024-06-26T05:54:50.1703631Z Arrays are not almost equal to 5 decimals 2024-06-26T05:54:50.1703732Z 2024-06-26T05:54:50.1703860Z Mismatched elements: 1 / 3 (33.3%) 2024-06-26T05:54:50.1704095Z Max absolute difference: 5.999999999994898e-05 2024-06-26T05:54:50.1704317Z Max relative difference: 2.5713661239633743e-05 2024-06-26T05:54:50.1704530Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-06-26T05:54:50.1704752Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-06-26T05:54:50.1704837Z 2024-06-26T05:54:50.1705065Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-06-26T05:54:50.1705248Z ... [1.0,2.33333, 5], decimal=5) 2024-06-26T05:54:50.1705382Z Traceback (most recent call last): 2024-06-26T05:54:50.1705472Z ... 2024-06-26T05:54:50.1705589Z AssertionError: 2024-06-26T05:54:50.1705743Z Arrays are not almost equal to 5 decimals 2024-06-26T05:54:50.1705851Z 2024-06-26T05:54:50.1705978Z x and y nan location mismatch: 2024-06-26T05:54:50.1706191Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-06-26T05:54:50.1706411Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-06-26T05:54:50.1706499Z 2024-06-26T05:54:50.1706584Z 2024-06-26T05:54:50.1706990Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1707075Z 2024-06-26T05:54:50.1707184Z warnings.warn(msg) 2024-06-26T05:54:50.1707284Z 2024-06-26T05:54:50.1707484Z --- Parse Warning: 13 / 90 --- 2024-06-26T05:54:50.1708927Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1789. 2024-06-26T05:54:50.1709347Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1709626Z Context manager that resets warning registry for catching warnings 2024-06-26T05:54:50.1709774Z 2024-06-26T05:54:50.1710103Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-06-26T05:54:50.1710437Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-06-26T05:54:50.1710772Z it impossible to retrigger the warning in this module, whatever you put in 2024-06-26T05:54:50.1711128Z the warnings filters. This context manager accepts a sequence of `modules` 2024-06-26T05:54:50.1711313Z as a keyword argument to its constructor and: 2024-06-26T05:54:50.1711413Z 2024-06-26T05:54:50.1711724Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-06-26T05:54:50.1711822Z on entry; 2024-06-26T05:54:50.1712076Z * resets ``__warningregistry__`` to its previous state on exit. 2024-06-26T05:54:50.1712164Z 2024-06-26T05:54:50.1712500Z This makes it possible to trigger any warning afresh inside the context 2024-06-26T05:54:50.1712750Z manager without disturbing the state of warnings outside. 2024-06-26T05:54:50.1712835Z 2024-06-26T05:54:50.1713156Z For compatibility with Python 3.0, please consider all arguments to be 2024-06-26T05:54:50.1713295Z keyword-only. 2024-06-26T05:54:50.1713380Z 2024-06-26T05:54:50.1713488Z Parameters 2024-06-26T05:54:50.1713608Z ---------- 2024-06-26T05:54:50.1713725Z record : bool, optional 2024-06-26T05:54:50.1713980Z Specifies whether warnings should be captured by a custom 2024-06-26T05:54:50.1714290Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-06-26T05:54:50.1716909Z returned by the context manager. Otherwise None is returned by the 2024-06-26T05:54:50.1717226Z context manager. The objects appended to the list are arguments whose 2024-06-26T05:54:50.1717444Z attributes mirror the arguments to ``showwarning()``. 2024-06-26T05:54:50.1717584Z modules : sequence, optional 2024-06-26T05:54:50.1717896Z Sequence of modules for which to reset warnings registry on entry and 2024-06-26T05:54:50.1718239Z restore on exit. To work correctly, all 'ignore' filters should 2024-06-26T05:54:50.1718389Z filter by one of these modules. 2024-06-26T05:54:50.1718475Z 2024-06-26T05:54:50.1718574Z Examples 2024-06-26T05:54:50.1718707Z -------- 2024-06-26T05:54:50.1718816Z >>> import warnings 2024-06-26T05:54:50.1719068Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-06-26T05:54:50.1719244Z ... modules=[np.core.fromnumeric]): 2024-06-26T05:54:50.1719444Z ... warnings.simplefilter('always') 2024-06-26T05:54:50.1719797Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-06-26T05:54:50.1720046Z ... # do something that raises a warning but ignore those in 2024-06-26T05:54:50.1720173Z ... # np.core.fromnumeric 2024-06-26T05:54:50.1720264Z 2024-06-26T05:54:50.1720671Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1720759Z 2024-06-26T05:54:50.1720867Z warnings.warn(msg) 2024-06-26T05:54:50.1720966Z 2024-06-26T05:54:50.1721244Z --- Parse Warning: 14 / 90 --- 2024-06-26T05:54:50.1722659Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=273. 2024-06-26T05:54:50.1723065Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1723347Z Applies a 1D convolution over a quantized input signal composed of 2024-06-26T05:54:50.1723496Z several quantized input planes. 2024-06-26T05:54:50.1723582Z 2024-06-26T05:54:50.1723875Z For details on input arguments, parameters, and implementation see 2024-06-26T05:54:50.1724098Z :class:`~torch.nn.Conv1d`. 2024-06-26T05:54:50.1724184Z 2024-06-26T05:54:50.1724288Z .. note:: 2024-06-26T05:54:50.1724620Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-06-26T05:54:50.1724705Z 2024-06-26T05:54:50.1724800Z .. note:: 2024-06-26T05:54:50.1725097Z Only `torch.quint8` is supported for the input data type. 2024-06-26T05:54:50.1725186Z 2024-06-26T05:54:50.1725285Z 2024-06-26T05:54:50.1725385Z Attributes: 2024-06-26T05:54:50.1725667Z weight (Tensor): packed tensor derived from the learnable weight 2024-06-26T05:54:50.1725806Z parameter. 2024-06-26T05:54:50.1725997Z scale (Tensor): scalar for the output scale 2024-06-26T05:54:50.1726211Z zero_point (Tensor): scalar for the output zero point 2024-06-26T05:54:50.1726310Z 2024-06-26T05:54:50.1726553Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-06-26T05:54:50.1726642Z 2024-06-26T05:54:50.1726756Z Examples:: 2024-06-26T05:54:50.1726843Z 2024-06-26T05:54:50.1727040Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-06-26T05:54:50.1727233Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-06-26T05:54:50.1727383Z >>> input = torch.randn(20, 16, 100) 2024-06-26T05:54:50.1727519Z >>> # quantize input to quint8 2024-06-26T05:54:50.1727650Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.1727936Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-06-26T05:54:50.1728132Z ... dtype=torch.quint8) 2024-06-26T05:54:50.1728250Z >>> output = m(q_input) 2024-06-26T05:54:50.1728336Z 2024-06-26T05:54:50.1728440Z 2024-06-26T05:54:50.1728841Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1728929Z 2024-06-26T05:54:50.1729053Z warnings.warn(msg) 2024-06-26T05:54:50.1729141Z 2024-06-26T05:54:50.1729345Z --- Parse Warning: 15 / 90 --- 2024-06-26T05:54:50.1730729Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=9. 2024-06-26T05:54:50.1731137Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1731343Z A quantized long short-term memory (LSTM). 2024-06-26T05:54:50.1731446Z 2024-06-26T05:54:50.1731827Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-06-26T05:54:50.1731914Z 2024-06-26T05:54:50.1732026Z Attributes: 2024-06-26T05:54:50.1732187Z layers : instances of the `_LSTMLayer` 2024-06-26T05:54:50.1732286Z 2024-06-26T05:54:50.1732388Z .. note:: 2024-06-26T05:54:50.1732691Z To access the weights and biases, you need to access them per layer. 2024-06-26T05:54:50.1732935Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-06-26T05:54:50.1733027Z 2024-06-26T05:54:50.1733126Z Examples:: 2024-06-26T05:54:50.1733256Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.1733387Z >>> custom_module_config = { 2024-06-26T05:54:50.1733629Z ... 'float_to_observed_custom_module_class': { 2024-06-26T05:54:50.1733809Z ... nn.LSTM: nn.quantizable.LSTM, 2024-06-26T05:54:50.1733907Z ... }, 2024-06-26T05:54:50.1734162Z ... 'observed_to_quantized_custom_module_class': { 2024-06-26T05:54:50.1734370Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-06-26T05:54:50.1734467Z ... } 2024-06-26T05:54:50.1734561Z ... } 2024-06-26T05:54:50.1734870Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-06-26T05:54:50.1735182Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-06-26T05:54:50.1735314Z 2024-06-26T05:54:50.1735706Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1735792Z 2024-06-26T05:54:50.1735912Z warnings.warn(msg) 2024-06-26T05:54:50.1736022Z 2024-06-26T05:54:50.1736225Z --- Parse Warning: 16 / 90 --- 2024-06-26T05:54:50.1737834Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=230. 2024-06-26T05:54:50.1738247Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1738467Z Squashes the sparse masks into the appropriate tensors. 2024-06-26T05:54:50.1738591Z 2024-06-26T05:54:50.1738883Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-06-26T05:54:50.1739143Z the module will have a `sparse_params` dict attached to it. 2024-06-26T05:54:50.1739234Z 2024-06-26T05:54:50.1739327Z Args: 2024-06-26T05:54:50.1739598Z params_to_keep: List of keys to save in the module or a dict 2024-06-26T05:54:50.1739817Z representing the modules and keys that will have 2024-06-26T05:54:50.1739983Z sparsity parameters saved 2024-06-26T05:54:50.1740281Z params_to_keep_per_layer: Dict to specify the params that should be 2024-06-26T05:54:50.1740496Z saved for specific layers. The keys in the dict 2024-06-26T05:54:50.1740713Z should be the module fqn, while the values should 2024-06-26T05:54:50.1740958Z be a list of strings with the names of the variables 2024-06-26T05:54:50.1741127Z to save in the `sparse_params` 2024-06-26T05:54:50.1741213Z 2024-06-26T05:54:50.1741325Z Examples: 2024-06-26T05:54:50.1741506Z >>> # xdoctest: +SKIP("locals are undefined") 2024-06-26T05:54:50.1741722Z >>> # Don't save any sparse params 2024-06-26T05:54:50.1741867Z >>> sparsifier.squash_mask() 2024-06-26T05:54:50.1742112Z >>> hasattr(model.submodule1, 'sparse_params') 2024-06-26T05:54:50.1742218Z False 2024-06-26T05:54:50.1742303Z 2024-06-26T05:54:50.1742455Z >>> # Keep sparse params per layer 2024-06-26T05:54:50.1742603Z >>> sparsifier.squash_mask( 2024-06-26T05:54:50.1742747Z ... params_to_keep_per_layer={ 2024-06-26T05:54:50.1742987Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-06-26T05:54:50.1743225Z ... 'submodule2.linear42': ('baz',) 2024-06-26T05:54:50.1743325Z ... }) 2024-06-26T05:54:50.1743537Z >>> print(model.submodule1.linear1.sparse_params) 2024-06-26T05:54:50.1743713Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:50.1743925Z >>> print(model.submodule2.linear42.sparse_params) 2024-06-26T05:54:50.1744063Z {'baz': 0.1} 2024-06-26T05:54:50.1744164Z 2024-06-26T05:54:50.1744330Z >>> # Keep sparse params for all layers 2024-06-26T05:54:50.1744628Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-06-26T05:54:50.1744833Z >>> print(model.submodule1.linear1.sparse_params) 2024-06-26T05:54:50.1744990Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:50.1745215Z >>> print(model.submodule2.linear42.sparse_params) 2024-06-26T05:54:50.1745371Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:50.1745456Z 2024-06-26T05:54:50.1745743Z >>> # Keep some sparse params for all layers, and specific ones for 2024-06-26T05:54:50.1745895Z >>> # some other layers 2024-06-26T05:54:50.1746033Z >>> sparsifier.squash_mask( 2024-06-26T05:54:50.1746277Z ... params_to_keep=('foo', 'bar'), 2024-06-26T05:54:50.1746420Z ... params_to_keep_per_layer={ 2024-06-26T05:54:50.1746672Z ... 'submodule2.linear42': ('baz',) 2024-06-26T05:54:50.1746786Z ... }) 2024-06-26T05:54:50.1746996Z >>> print(model.submodule1.linear1.sparse_params) 2024-06-26T05:54:50.1747169Z {'foo': 42, 'bar': 24} 2024-06-26T05:54:50.1747379Z >>> print(model.submodule2.linear42.sparse_params) 2024-06-26T05:54:50.1747570Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-06-26T05:54:50.1747674Z 2024-06-26T05:54:50.1748068Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1748181Z 2024-06-26T05:54:50.1748304Z warnings.warn(msg) 2024-06-26T05:54:50.1748389Z 2024-06-26T05:54:50.1748587Z --- Parse Warning: 17 / 90 --- 2024-06-26T05:54:50.1750158Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=178. 2024-06-26T05:54:50.1750564Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1750651Z 2024-06-26T05:54:50.1751006Z Config object that specifies the supported data types passed as arguments to 2024-06-26T05:54:50.1751331Z quantize ops in the reference model spec, for input and output activations, 2024-06-26T05:54:50.1751451Z weights, and biases. 2024-06-26T05:54:50.1751538Z 2024-06-26T05:54:50.1751741Z For example, consider the following reference model: 2024-06-26T05:54:50.1751840Z 2024-06-26T05:54:50.1752098Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-06-26T05:54:50.1752185Z 2024-06-26T05:54:50.1752492Z The pattern in the square brackets refers to the reference pattern of 2024-06-26T05:54:50.1752800Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-06-26T05:54:50.1753110Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-06-26T05:54:50.1753434Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-06-26T05:54:50.1753728Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-06-26T05:54:50.1753858Z the second quantize op (quant2). 2024-06-26T05:54:50.1753957Z 2024-06-26T05:54:50.1754256Z Note that the dtype here does not refer to the interface dtypes of the 2024-06-26T05:54:50.1754553Z op. For example, the "input dtype" here is not the dtype of the input 2024-06-26T05:54:50.1754978Z tensor passed to the quantized linear op. Though it can still be the 2024-06-26T05:54:50.1755260Z same as the interface dtype, this is not always the case, e.g. the 2024-06-26T05:54:50.1755569Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-06-26T05:54:50.1755860Z specified in the DTypeConfig would still be quint8. The semantics of 2024-06-26T05:54:50.1756240Z dtypes here are the same as the semantics of the dtypes specified in 2024-06-26T05:54:50.1756411Z the observers. 2024-06-26T05:54:50.1756619Z 2024-06-26T05:54:50.1757000Z These dtypes are matched against the ones specified in the user's 2024-06-26T05:54:50.1757330Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-06-26T05:54:50.1757656Z specified in the DTypeConfig (if any), then we will quantize the given 2024-06-26T05:54:50.1758033Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-06-26T05:54:50.1758201Z the pattern will not be quantized. 2024-06-26T05:54:50.1758401Z 2024-06-26T05:54:50.1758581Z Example usage:: 2024-06-26T05:54:50.1758695Z 2024-06-26T05:54:50.1758871Z >>> # xdoctest: +SKIP(failing) 2024-06-26T05:54:50.1759122Z >>> dtype_config1 = DTypeConfig( 2024-06-26T05:54:50.1759282Z ... input_dtype=torch.quint8, 2024-06-26T05:54:50.1759465Z ... output_dtype=torch.quint8, 2024-06-26T05:54:50.1759703Z ... weight_dtype=torch.qint8, 2024-06-26T05:54:50.1759864Z ... bias_dtype=torch.float) 2024-06-26T05:54:50.1759978Z 2024-06-26T05:54:50.1760180Z >>> dtype_config2 = DTypeConfig( 2024-06-26T05:54:50.1760374Z ... input_dtype=DTypeWithConstraints( 2024-06-26T05:54:50.1760582Z ... dtype=torch.quint8, 2024-06-26T05:54:50.1760750Z ... quant_min_lower_bound=0, 2024-06-26T05:54:50.1760932Z ... quant_max_upper_bound=255, 2024-06-26T05:54:50.1761192Z ... ), 2024-06-26T05:54:50.1761435Z ... output_dtype=DTypeWithConstraints( 2024-06-26T05:54:50.1761589Z ... dtype=torch.quint8, 2024-06-26T05:54:50.1761815Z ... quant_min_lower_bound=0, 2024-06-26T05:54:50.1761987Z ... quant_max_upper_bound=255, 2024-06-26T05:54:50.1762111Z ... ), 2024-06-26T05:54:50.1762341Z ... weight_dtype=DTypeWithConstraints( 2024-06-26T05:54:50.1762490Z ... dtype=torch.qint8, 2024-06-26T05:54:50.1762724Z ... quant_min_lower_bound=-128, 2024-06-26T05:54:50.1762951Z ... quant_max_upper_bound=127, 2024-06-26T05:54:50.1763085Z ... ), 2024-06-26T05:54:50.1763235Z ... bias_dtype=torch.float) 2024-06-26T05:54:50.1763386Z 2024-06-26T05:54:50.1763547Z >>> dtype_config1.input_dtype 2024-06-26T05:54:50.1763675Z torch.quint8 2024-06-26T05:54:50.1763847Z 2024-06-26T05:54:50.1764003Z >>> dtype_config2.input_dtype 2024-06-26T05:54:50.1764136Z torch.quint8 2024-06-26T05:54:50.1764299Z 2024-06-26T05:54:50.1764504Z >>> dtype_config2.input_dtype_with_constraints 2024-06-26T05:54:50.1765297Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-06-26T05:54:50.1765428Z 2024-06-26T05:54:50.1765856Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1766015Z 2024-06-26T05:54:50.1766154Z warnings.warn(msg) 2024-06-26T05:54:50.1766267Z 2024-06-26T05:54:50.1766553Z --- Parse Warning: 18 / 90 --- 2024-06-26T05:54:50.1768418Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=287. 2024-06-26T05:54:50.1768856Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1769019Z 2024-06-26T05:54:50.1769411Z Takes in optional filter values and generates two tables with desired information. 2024-06-26T05:54:50.1769566Z 2024-06-26T05:54:50.1769953Z The generated tables are presented in both a list-of-lists format 2024-06-26T05:54:50.1770082Z 2024-06-26T05:54:50.1770443Z The reason for the two tables are that they handle different things: 2024-06-26T05:54:50.1770689Z 1.) the first table handles all tensor level information 2024-06-26T05:54:50.1771017Z 2.) the second table handles and displays all channel based information 2024-06-26T05:54:50.1771169Z 2024-06-26T05:54:50.1771700Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-06-26T05:54:50.1772275Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-06-26T05:54:50.1772837Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-06-26T05:54:50.1772987Z 2024-06-26T05:54:50.1773129Z Tensor table columns: 2024-06-26T05:54:50.1773511Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:50.1773810Z ---- --------- --------- --------- --------- --------- 2024-06-26T05:54:50.1773991Z 2024-06-26T05:54:50.1774180Z Per-Channel table columns: 2024-06-26T05:54:50.1774511Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:50.1774855Z ---- --------- ------- --------- --------- --------- --------- 2024-06-26T05:54:50.1774969Z 2024-06-26T05:54:50.1775107Z Args: 2024-06-26T05:54:50.1775528Z feature_filter (str, optional): Filters the features presented to only those that 2024-06-26T05:54:50.1775693Z contain this filter substring 2024-06-26T05:54:50.1775981Z Default = "", results in all the features being printed 2024-06-26T05:54:50.1776412Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:50.1776792Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:50.1776969Z 2024-06-26T05:54:50.1777136Z Returns a dictionary with two keys: 2024-06-26T05:54:50.1777404Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-06-26T05:54:50.1777627Z "tensor_level_info", "channel_level_info" 2024-06-26T05:54:50.1777795Z Each key maps to a tuple with: 2024-06-26T05:54:50.1777978Z A list of the headers of each table 2024-06-26T05:54:50.1778319Z A list of lists containing the table information row by row 2024-06-26T05:54:50.1778595Z The 0th index row will contain the headers of the columns 2024-06-26T05:54:50.1778789Z The rest of the rows will contain data 2024-06-26T05:54:50.1778958Z 2024-06-26T05:54:50.1779088Z Example Use: 2024-06-26T05:54:50.1779285Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:50.1779582Z >>> mod_report_visualizer.generate_filtered_tables( 2024-06-26T05:54:50.1779770Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:50.1779937Z ... module_fqn_filter = "block1" 2024-06-26T05:54:50.1780377Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-06-26T05:54:50.1780494Z 2024-06-26T05:54:50.1780985Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1781098Z 2024-06-26T05:54:50.1781240Z warnings.warn(msg) 2024-06-26T05:54:50.1781392Z 2024-06-26T05:54:50.1781634Z --- Parse Warning: 19 / 90 --- 2024-06-26T05:54:50.1783519Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=380. 2024-06-26T05:54:50.1784022Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1784135Z 2024-06-26T05:54:50.1784536Z Takes in optional filter values and prints out formatted tables of the information. 2024-06-26T05:54:50.1784689Z 2024-06-26T05:54:50.1785206Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-06-26T05:54:50.1785500Z 1.) the first table handles all tensor level information 2024-06-26T05:54:50.1785843Z 2.) the second table handles and displays all channel based information 2024-06-26T05:54:50.1785959Z 2024-06-26T05:54:50.1786496Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-06-26T05:54:50.1806707Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-06-26T05:54:50.1807460Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-06-26T05:54:50.1807621Z 2024-06-26T05:54:50.1807739Z Tensor table columns: 2024-06-26T05:54:50.1808063Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:50.1808352Z ---- --------- --------- --------- --------- --------- 2024-06-26T05:54:50.1808454Z 2024-06-26T05:54:50.1808615Z Per-Channel table columns: 2024-06-26T05:54:50.1808700Z 2024-06-26T05:54:50.1809014Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-06-26T05:54:50.1809283Z ---- --------- ------- --------- --------- --------- --------- 2024-06-26T05:54:50.1809370Z 2024-06-26T05:54:50.1809480Z Args: 2024-06-26T05:54:50.1809884Z feature_filter (str, optional): Filters the features presented to only those that 2024-06-26T05:54:50.1810027Z contain this filter substring 2024-06-26T05:54:50.1810261Z Default = "", results in all the features being printed 2024-06-26T05:54:50.1810614Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:50.1810963Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:50.1811070Z 2024-06-26T05:54:50.1811173Z Example Use: 2024-06-26T05:54:50.1811360Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:50.1811579Z >>> mod_report_visualizer.generate_table_visualization( 2024-06-26T05:54:50.1811742Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:50.1811893Z ... module_fqn_filter = "block1" 2024-06-26T05:54:50.1811985Z ... ) 2024-06-26T05:54:50.1812240Z >>> # prints out neatly formatted table with per_channel_min info 2024-06-26T05:54:50.1812424Z >>> # for all modules in block 1 of the model 2024-06-26T05:54:50.1812513Z 2024-06-26T05:54:50.1812912Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1813014Z 2024-06-26T05:54:50.1813126Z warnings.warn(msg) 2024-06-26T05:54:50.1813214Z 2024-06-26T05:54:50.1813434Z --- Parse Warning: 20 / 90 --- 2024-06-26T05:54:50.1815294Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=533. 2024-06-26T05:54:50.1815717Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1815804Z 2024-06-26T05:54:50.1816137Z Takes in a feature and optional module_filter and plots of the desired data. 2024-06-26T05:54:50.1816236Z 2024-06-26T05:54:50.1816612Z For per channel features, it averages the value across the channels and plots a point 2024-06-26T05:54:50.1816976Z per module. The reason for this is that for models with hundreds of channels, it can 2024-06-26T05:54:50.1817381Z be hard to differentiate one channel line from another, and so the point of generating 2024-06-26T05:54:50.1817761Z a single average point per module is to give a sense of general trends that encourage 2024-06-26T05:54:50.1817872Z further deep dives. 2024-06-26T05:54:50.1817971Z 2024-06-26T05:54:50.1818062Z Note: 2024-06-26T05:54:50.1818434Z Only features in the report that have tensor value data are plottable by this class 2024-06-26T05:54:50.1818663Z When the tensor information is plotted, it will plot: 2024-06-26T05:54:50.1818841Z idx as the x val, feature value as the y_val 2024-06-26T05:54:50.1819067Z When the channel information is plotted, it will plot: 2024-06-26T05:54:50.1819464Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-06-26T05:54:50.1819830Z The reason for this is that we want to be able to compare values across the 2024-06-26T05:54:50.1820202Z channels for same layer, and it will be hard if values are staggered by idx 2024-06-26T05:54:50.1820468Z This means each module is represented by only 1 x value 2024-06-26T05:54:50.1820564Z Args: 2024-06-26T05:54:50.1820876Z feature_filter (str): Filters the features presented to only those that 2024-06-26T05:54:50.1821008Z contain this filter substring 2024-06-26T05:54:50.1821356Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:50.1821715Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:50.1821800Z 2024-06-26T05:54:50.1821904Z Example Use: 2024-06-26T05:54:50.1822096Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:50.1822308Z >>> mod_report_visualizer.generate_plot_visualization( 2024-06-26T05:54:50.1822474Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:50.1822611Z ... module_fqn_filter = "block1" 2024-06-26T05:54:50.1822700Z ... ) 2024-06-26T05:54:50.1822943Z >>> # outputs line plot of per_channel_min information for all 2024-06-26T05:54:50.1823255Z >>> # modules in block1 of model each channel gets it's own line, 2024-06-26T05:54:50.1823555Z >>> # and it's plotted across the in-order modules on the x-axis 2024-06-26T05:54:50.1823646Z 2024-06-26T05:54:50.1824034Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1824113Z 2024-06-26T05:54:50.1824226Z warnings.warn(msg) 2024-06-26T05:54:50.1824309Z 2024-06-26T05:54:50.1824503Z --- Parse Warning: 21 / 90 --- 2024-06-26T05:54:50.1826392Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=601. 2024-06-26T05:54:50.1826804Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1826896Z 2024-06-26T05:54:50.1827278Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-06-26T05:54:50.1827360Z 2024-06-26T05:54:50.1827454Z Note: 2024-06-26T05:54:50.1827824Z Only features in the report that have tensor value data can be viewed as a histogram 2024-06-26T05:54:50.1828203Z If you want to plot a histogram from all the channel values of a specific feature for 2024-06-26T05:54:50.1828557Z a specific model, make sure to specify both the model and the feature properly 2024-06-26T05:54:50.1828904Z in the filters and you should be able to see a distribution of the channel data 2024-06-26T05:54:50.1828997Z 2024-06-26T05:54:50.1829084Z Args: 2024-06-26T05:54:50.1829439Z feature_filter (str, optional): Filters the features presented to only those that 2024-06-26T05:54:50.1829579Z contain this filter substring 2024-06-26T05:54:50.1829795Z Default = "", results in all the features being printed 2024-06-26T05:54:50.1830139Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-06-26T05:54:50.1830489Z Default = "", results in all the modules in the reports to be visible in the table 2024-06-26T05:54:50.1830789Z num_bins (int, optional): The number of bins to create the histogram with 2024-06-26T05:54:50.1831040Z Default = 10, the values will be split into 10 equal sized bins 2024-06-26T05:54:50.1831128Z 2024-06-26T05:54:50.1831222Z Example Use: 2024-06-26T05:54:50.1831340Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.1831754Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-06-26T05:54:50.1831941Z ... feature_filter = "per_channel_min", 2024-06-26T05:54:50.1832080Z ... module_fqn_filter = "block1" 2024-06-26T05:54:50.1832168Z ... ) 2024-06-26T05:54:50.1832564Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-06-26T05:54:50.1832916Z information is gathered across all channels for all modules in block 1 for the 2024-06-26T05:54:50.1833206Z per_channel_min and is displayed in a histogram of equally sized bins 2024-06-26T05:54:50.1833294Z 2024-06-26T05:54:50.1833688Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1833767Z 2024-06-26T05:54:50.1833871Z warnings.warn(msg) 2024-06-26T05:54:50.1833956Z 2024-06-26T05:54:50.1834175Z --- Parse Warning: 22 / 90 --- 2024-06-26T05:54:50.1835770Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2736. 2024-06-26T05:54:50.1836187Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1836271Z 2024-06-26T05:54:50.1836559Z Gathers picklable objects from the whole group in a single process. 2024-06-26T05:54:50.1836645Z 2024-06-26T05:54:50.1836965Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-06-26T05:54:50.1837173Z object must be picklable in order to be gathered. 2024-06-26T05:54:50.1837252Z 2024-06-26T05:54:50.1837336Z Args: 2024-06-26T05:54:50.1837503Z obj (Any): Input object. Must be picklable. 2024-06-26T05:54:50.1837778Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-06-26T05:54:50.1838023Z should be correctly sized as the size of the group for this 2024-06-26T05:54:50.1838376Z collective and will contain the output. Must be ``None`` on non-dst 2024-06-26T05:54:50.1838502Z ranks. (default is ``None``) 2024-06-26T05:54:50.1839027Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0) 2024-06-26T05:54:50.1839322Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-06-26T05:54:50.1839570Z the default process group will be used. Default is ``None``. 2024-06-26T05:54:50.1839657Z 2024-06-26T05:54:50.1839748Z Returns: 2024-06-26T05:54:50.1840010Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-06-26T05:54:50.1840145Z output of the collective. 2024-06-26T05:54:50.1840227Z 2024-06-26T05:54:50.1840532Z .. note:: Note that this API differs slightly from the gather collective 2024-06-26T05:54:50.1840844Z since it does not provide an async_op handle and thus will be a blocking 2024-06-26T05:54:50.1840928Z call. 2024-06-26T05:54:50.1841007Z 2024-06-26T05:54:50.1841461Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-06-26T05:54:50.1841754Z of objects must be moved to the GPU device before communication takes 2024-06-26T05:54:50.1841945Z place. In this case, the device used is given by 2024-06-26T05:54:50.1842294Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-06-26T05:54:50.1842577Z ensure that this is set so that each rank has an individual GPU, via 2024-06-26T05:54:50.1842699Z ``torch.cuda.set_device()``. 2024-06-26T05:54:50.1842778Z 2024-06-26T05:54:50.1842869Z .. warning:: 2024-06-26T05:54:50.1843134Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-06-26T05:54:50.1843439Z known to be insecure. It is possible to construct malicious pickle data 2024-06-26T05:54:50.1843773Z which will execute arbitrary code during unpickling. Only call this 2024-06-26T05:54:50.1843941Z function with data you trust. 2024-06-26T05:54:50.1844021Z 2024-06-26T05:54:50.1844112Z .. warning:: 2024-06-26T05:54:50.1844431Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-06-26T05:54:50.1844802Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-06-26T05:54:50.1845022Z pickled. Please consider using :func:`gather` instead. 2024-06-26T05:54:50.1845102Z 2024-06-26T05:54:50.1845193Z Example:: 2024-06-26T05:54:50.1845372Z >>> # xdoctest: +SKIP("need process group init") 2024-06-26T05:54:50.1845603Z >>> # Note: Process group initialization omitted on each rank. 2024-06-26T05:54:50.1845746Z >>> import torch.distributed as dist 2024-06-26T05:54:50.1845868Z >>> # Assumes world_size of 3. 2024-06-26T05:54:50.1846138Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-06-26T05:54:50.1846295Z >>> output = [None for _ in gather_objects] 2024-06-26T05:54:50.1846408Z >>> dist.gather_object( 2024-06-26T05:54:50.1846553Z ... gather_objects[dist.get_rank()], 2024-06-26T05:54:50.1846724Z ... output if dist.get_rank() == 0 else None, 2024-06-26T05:54:50.1846822Z ... dst=0 2024-06-26T05:54:50.1846906Z ... ) 2024-06-26T05:54:50.1847006Z >>> # On rank 0 2024-06-26T05:54:50.1847093Z >>> output 2024-06-26T05:54:50.1847228Z ['foo', 12, {1: 2}] 2024-06-26T05:54:50.1847311Z 2024-06-26T05:54:50.1847700Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1847780Z 2024-06-26T05:54:50.1847885Z warnings.warn(msg) 2024-06-26T05:54:50.1847964Z 2024-06-26T05:54:50.1848154Z --- Parse Warning: 23 / 90 --- 2024-06-26T05:54:50.1849461Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2024-06-26T05:54:50.1849864Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1849946Z 2024-06-26T05:54:50.1850091Z Module ``torch.distributed.launch``. 2024-06-26T05:54:50.1850168Z 2024-06-26T05:54:50.1850492Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-06-26T05:54:50.1850684Z training processes on each of the training nodes. 2024-06-26T05:54:50.1850763Z 2024-06-26T05:54:50.1850860Z .. warning:: 2024-06-26T05:54:50.1850939Z 2024-06-26T05:54:50.1851369Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-06-26T05:54:50.1851456Z 2024-06-26T05:54:50.1851858Z The utility can be used for single-node distributed training, in which one or 2024-06-26T05:54:50.1852194Z more processes per node will be spawned. The utility can be used for either 2024-06-26T05:54:50.1852509Z CPU training or GPU training. If the utility is used for GPU training, 2024-06-26T05:54:50.1852849Z each distributed process will be operating on a single GPU. This can achieve 2024-06-26T05:54:50.1853217Z well-improved single-node training performance. It can also be used in 2024-06-26T05:54:50.1853651Z multi-node distributed training, by spawning up multiple processes on each node 2024-06-26T05:54:50.1854020Z for well-improved multi-node distributed training performance as well. 2024-06-26T05:54:50.1854325Z This will especially be beneficial for systems with multiple Infiniband 2024-06-26T05:54:50.1854750Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-06-26T05:54:50.1854889Z aggregated communication bandwidth. 2024-06-26T05:54:50.1854991Z 2024-06-26T05:54:50.1855389Z In both cases of single-node distributed training or multi-node distributed 2024-06-26T05:54:50.1855734Z training, this utility will launch the given number of processes per node 2024-06-26T05:54:50.1856175Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-06-26T05:54:50.1856494Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-06-26T05:54:50.1856797Z and each process will be operating on a single GPU from *GPU 0 to 2024-06-26T05:54:50.1856970Z GPU (nproc_per_node - 1)*. 2024-06-26T05:54:50.1857057Z 2024-06-26T05:54:50.1857175Z **How to use this module:** 2024-06-26T05:54:50.1857274Z 2024-06-26T05:54:50.1857519Z 1. Single-Node multi-process distributed training 2024-06-26T05:54:50.1857606Z 2024-06-26T05:54:50.1857711Z :: 2024-06-26T05:54:50.1857796Z 2024-06-26T05:54:50.1858170Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-06-26T05:54:50.1858525Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-06-26T05:54:50.1858693Z arguments of your training script) 2024-06-26T05:54:50.1858793Z 2024-06-26T05:54:50.1859140Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-06-26T05:54:50.1859229Z 2024-06-26T05:54:50.1859328Z 2024-06-26T05:54:50.1859526Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-06-26T05:54:50.1859614Z 2024-06-26T05:54:50.1859717Z :: 2024-06-26T05:54:50.1859802Z 2024-06-26T05:54:50.1860173Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-06-26T05:54:50.1860457Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-06-26T05:54:50.1860798Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-06-26T05:54:50.1861004Z and all other arguments of your training script) 2024-06-26T05:54:50.1861105Z 2024-06-26T05:54:50.1861200Z Node 2: 2024-06-26T05:54:50.1861288Z 2024-06-26T05:54:50.1861393Z :: 2024-06-26T05:54:50.1861482Z 2024-06-26T05:54:50.1861850Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-06-26T05:54:50.1862190Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-06-26T05:54:50.1862589Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-06-26T05:54:50.1862808Z and all other arguments of your training script) 2024-06-26T05:54:50.1862895Z 2024-06-26T05:54:50.1863126Z 3. To look up what optional arguments this module offers: 2024-06-26T05:54:50.1863227Z 2024-06-26T05:54:50.1863317Z :: 2024-06-26T05:54:50.1863404Z 2024-06-26T05:54:50.1863638Z python -m torch.distributed.launch --help 2024-06-26T05:54:50.1863728Z 2024-06-26T05:54:50.1863815Z 2024-06-26T05:54:50.1863941Z **Important Notices:** 2024-06-26T05:54:50.1864027Z 2024-06-26T05:54:50.1864335Z 1. This utility and multi-process distributed (single-node or 2024-06-26T05:54:50.1864745Z multi-node) GPU training currently only achieves the best performance using 2024-06-26T05:54:50.1865087Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-06-26T05:54:50.1865201Z use for GPU training. 2024-06-26T05:54:50.1865300Z 2024-06-26T05:54:50.1865660Z 2. In your training program, you must parse the command-line argument: 2024-06-26T05:54:50.1866029Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-06-26T05:54:50.1866361Z If your training program uses GPUs, you should ensure that your code only 2024-06-26T05:54:50.1866638Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-06-26T05:54:50.1866736Z 2024-06-26T05:54:50.1866863Z Parsing the local_rank argument 2024-06-26T05:54:50.1866952Z 2024-06-26T05:54:50.1867056Z :: 2024-06-26T05:54:50.1867143Z 2024-06-26T05:54:50.1867260Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.1867425Z >>> import argparse 2024-06-26T05:54:50.1867592Z >>> parser = argparse.ArgumentParser() 2024-06-26T05:54:50.1867941Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-06-26T05:54:50.1868086Z >>> args = parser.parse_args() 2024-06-26T05:54:50.1868173Z 2024-06-26T05:54:50.1868365Z Set your device to local rank using either 2024-06-26T05:54:50.1868466Z 2024-06-26T05:54:50.1868557Z :: 2024-06-26T05:54:50.1868642Z 2024-06-26T05:54:50.1868925Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-06-26T05:54:50.1869012Z 2024-06-26T05:54:50.1869100Z or 2024-06-26T05:54:50.1869197Z 2024-06-26T05:54:50.1869286Z :: 2024-06-26T05:54:50.1869371Z 2024-06-26T05:54:50.1869558Z >>> with torch.cuda.device(args.local_rank): 2024-06-26T05:54:50.1869674Z >>> # your code to run 2024-06-26T05:54:50.1869798Z >>> ... 2024-06-26T05:54:50.1869901Z 2024-06-26T05:54:50.1870022Z .. versionchanged:: 2.0.0 2024-06-26T05:54:50.1870108Z 2024-06-26T05:54:50.1870524Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-06-26T05:54:50.1870926Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-06-26T05:54:50.1871169Z previously used underscored ``--local_rank``. 2024-06-26T05:54:50.1871256Z 2024-06-26T05:54:50.1871577Z For backward compatibility, it may be necessary for users to handle both 2024-06-26T05:54:50.1872025Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-06-26T05:54:50.1872389Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-06-26T05:54:50.1872730Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-06-26T05:54:50.1873123Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-06-26T05:54:50.1873369Z including ``"--local-rank"`` should be sufficient. 2024-06-26T05:54:50.1873456Z 2024-06-26T05:54:50.1873796Z 3. In your training program, you are supposed to call the following function 2024-06-26T05:54:50.1874137Z at the beginning to start the distributed backend. It is strongly recommended 2024-06-26T05:54:50.1874453Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-06-26T05:54:50.1874857Z but ``env://`` is the one that is officially supported by this module. 2024-06-26T05:54:50.1874947Z 2024-06-26T05:54:50.1875051Z :: 2024-06-26T05:54:50.1875137Z 2024-06-26T05:54:50.1875471Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-06-26T05:54:50.1875739Z >>> init_method='env://') 2024-06-26T05:54:50.1875826Z 2024-06-26T05:54:50.1876161Z 4. In your training program, you can either use regular distributed functions 2024-06-26T05:54:50.1876516Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-06-26T05:54:50.1876791Z training program uses GPUs for training and you would like to use 2024-06-26T05:54:50.1877047Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-06-26T05:54:50.1877187Z here is how to configure it. 2024-06-26T05:54:50.1877275Z 2024-06-26T05:54:50.1877381Z :: 2024-06-26T05:54:50.1877466Z 2024-06-26T05:54:50.1877734Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-06-26T05:54:50.1877953Z >>> device_ids=[args.local_rank], 2024-06-26T05:54:50.1878155Z >>> output_device=args.local_rank) 2024-06-26T05:54:50.1878243Z 2024-06-26T05:54:50.1878589Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-06-26T05:54:50.1878924Z that your code will be operating on. This is generally the local rank of the 2024-06-26T05:54:50.1879309Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-06-26T05:54:50.1879620Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-06-26T05:54:50.1879750Z utility 2024-06-26T05:54:50.1879836Z 2024-06-26T05:54:50.1880245Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-06-26T05:54:50.1880539Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-06-26T05:54:50.1880926Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-06-26T05:54:50.1881322Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-06-26T05:54:50.1881608Z will not pass ``--local-rank`` when you specify this flag. 2024-06-26T05:54:50.1881709Z 2024-06-26T05:54:50.1881813Z .. warning:: 2024-06-26T05:54:50.1881899Z 2024-06-26T05:54:50.1882234Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-06-26T05:54:50.1882566Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-06-26T05:54:50.1882722Z write to a networked filesystem. See 2024-06-26T05:54:50.1883027Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-06-26T05:54:50.1883305Z how things can go wrong if you don't do this correctly. 2024-06-26T05:54:50.1883393Z 2024-06-26T05:54:50.1883493Z 2024-06-26T05:54:50.1883580Z 2024-06-26T05:54:50.1883670Z 2024-06-26T05:54:50.1884077Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1884162Z 2024-06-26T05:54:50.1884287Z warnings.warn(msg) 2024-06-26T05:54:50.1884376Z 2024-06-26T05:54:50.1884577Z --- Parse Warning: 24 / 90 --- 2024-06-26T05:54:50.1886170Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2024-06-26T05:54:50.1886587Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1886677Z 2024-06-26T05:54:50.1887018Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-06-26T05:54:50.1887218Z Needs to be called on all ranks in an SPMD fashion. 2024-06-26T05:54:50.1887305Z 2024-06-26T05:54:50.1887411Z Args: 2024-06-26T05:54:50.1887778Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-06-26T05:54:50.1888006Z of shards that represent the local shards on this rank. 2024-06-26T05:54:50.1888337Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-06-26T05:54:50.1888492Z shape of the overall sharded tensor. 2024-06-26T05:54:50.1888593Z 2024-06-26T05:54:50.1888696Z Keyword args: 2024-06-26T05:54:50.1889042Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-06-26T05:54:50.1889223Z the default process group will be used. 2024-06-26T05:54:50.1889452Z init_rrefs (bool, optional): Whether or not to initialize 2024-06-26T05:54:50.1889733Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-06-26T05:54:50.1890014Z Need to initialize the RPC Framework if specified as ``True``. 2024-06-26T05:54:50.1890127Z Default: ``False``. 2024-06-26T05:54:50.1890215Z 2024-06-26T05:54:50.1890326Z Returns: 2024-06-26T05:54:50.1890528Z A :class:`ShardedTensor` object handle on this rank 2024-06-26T05:54:50.1890616Z 2024-06-26T05:54:50.1890716Z 2024-06-26T05:54:50.1890814Z Examples: 2024-06-26T05:54:50.1891165Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-06-26T05:54:50.1891441Z each shard have a (5, 5) local tensor, we can do it like below: 2024-06-26T05:54:50.1891559Z 2024-06-26T05:54:50.1891671Z on rank 0: 2024-06-26T05:54:50.1891830Z >>> # xdoctest: +SKIP("not distributed") 2024-06-26T05:54:50.1892028Z >>> local_shard_metadata = ShardMetadata( 2024-06-26T05:54:50.1892165Z >>> shard_offsets=[0, 0], 2024-06-26T05:54:50.1892313Z >>> shard_lengths=[5, 5], 2024-06-26T05:54:50.1892449Z >>> placement="rank:0/cuda:0" 2024-06-26T05:54:50.1892555Z >>> ) 2024-06-26T05:54:50.1892815Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-06-26T05:54:50.1893068Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-06-26T05:54:50.1893169Z 2024-06-26T05:54:50.1893266Z on rank 1: 2024-06-26T05:54:50.1893423Z >>> # xdoctest: +SKIP("not distributed") 2024-06-26T05:54:50.1893593Z >>> local_shard_metadata = ShardMetadata( 2024-06-26T05:54:50.1893756Z >>> shard_offsets=[5, 0], 2024-06-26T05:54:50.1893877Z >>> shard_lengths=[5, 5], 2024-06-26T05:54:50.1894020Z >>> placement="rank:1/cuda:1" 2024-06-26T05:54:50.1894115Z >>> ) 2024-06-26T05:54:50.1894386Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-06-26T05:54:50.1894640Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-06-26T05:54:50.1894728Z 2024-06-26T05:54:50.1895137Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1895224Z 2024-06-26T05:54:50.1895334Z warnings.warn(msg) 2024-06-26T05:54:50.1895434Z 2024-06-26T05:54:50.1895634Z --- Parse Warning: 25 / 90 --- 2024-06-26T05:54:50.1897264Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784. 2024-06-26T05:54:50.1897694Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1897782Z 2024-06-26T05:54:50.1898123Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-06-26T05:54:50.1898280Z size and sharding spec on each rank. 2024-06-26T05:54:50.1898369Z 2024-06-26T05:54:50.1898476Z Args: 2024-06-26T05:54:50.1898779Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-06-26T05:54:50.1899123Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-06-26T05:54:50.1899357Z The specification describing how to shard the Tensor. 2024-06-26T05:54:50.1899580Z global_size (Sequence[int]): Size of the sharded tensor. 2024-06-26T05:54:50.1899904Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-06-26T05:54:50.1900028Z Default: None 2024-06-26T05:54:50.1900256Z init_rrefs (bool, optional): Whether or not to initialize 2024-06-26T05:54:50.1900538Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-06-26T05:54:50.1900818Z Need to initialize the RPC Framework if specified as ``True``. 2024-06-26T05:54:50.1900931Z Default: ``False``. 2024-06-26T05:54:50.1901018Z 2024-06-26T05:54:50.1901128Z Returns: 2024-06-26T05:54:50.1901461Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-06-26T05:54:50.1901626Z tensor stored in the current rank. 2024-06-26T05:54:50.1901713Z 2024-06-26T05:54:50.1901808Z Examples: 2024-06-26T05:54:50.1901935Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.1902119Z >>> # All tensors below are of torch.int64 type. 2024-06-26T05:54:50.1902276Z >>> # We have 2 process groups, 2 ranks. 2024-06-26T05:54:50.1902533Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-06-26T05:54:50.1902848Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-06-26T05:54:50.1902954Z >>> local_tensor 2024-06-26T05:54:50.1903120Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-06-26T05:54:50.1903240Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-06-26T05:54:50.1903350Z >>> sharding_dim = 0 2024-06-26T05:54:50.1903549Z >>> sharding_spec = ChunkShardingSpec( 2024-06-26T05:54:50.1903665Z dim=sharding_dim, 2024-06-26T05:54:50.1903774Z placements=[ 2024-06-26T05:54:50.1903902Z "rank:0/cuda:0", 2024-06-26T05:54:50.1904011Z "rank:1/cuda:1", 2024-06-26T05:54:50.1904105Z ], 2024-06-26T05:54:50.1904214Z ) 2024-06-26T05:54:50.1904560Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-06-26T05:54:50.1904667Z >>> st 2024-06-26T05:54:50.1904773Z ShardedTensor( 2024-06-26T05:54:50.1904929Z ShardedTensorMetadata( 2024-06-26T05:54:50.1905059Z shards_metadata=[ 2024-06-26T05:54:50.1905417Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-06-26T05:54:50.1905764Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-06-26T05:54:50.1905874Z ], 2024-06-26T05:54:50.1905998Z size=torch.Size([2, 4]) 2024-06-26T05:54:50.1906089Z ) 2024-06-26T05:54:50.1906211Z >>> st.local_tensor() 2024-06-26T05:54:50.1906331Z tensor([1, 2, 3, 4]) # Rank 0 2024-06-26T05:54:50.1906446Z tensor([3, 4, 5, 6]) # Rank 1 2024-06-26T05:54:50.1906549Z 2024-06-26T05:54:50.1906922Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-06-26T05:54:50.1907255Z rank validations, and we only validate the local shard on the current rank. 2024-06-26T05:54:50.1907591Z We fully rely on the user to ensure local tensor is sharded based on the 2024-06-26T05:54:50.1907702Z sharding spec. 2024-06-26T05:54:50.1907799Z 2024-06-26T05:54:50.1908202Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1908289Z 2024-06-26T05:54:50.1908414Z warnings.warn(msg) 2024-06-26T05:54:50.1908502Z 2024-06-26T05:54:50.1908708Z --- Parse Warning: 26 / 90 --- 2024-06-26T05:54:50.1910285Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023. 2024-06-26T05:54:50.1910692Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1910778Z 2024-06-26T05:54:50.1911145Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-06-26T05:54:50.1911260Z single local shard. 2024-06-26T05:54:50.1911359Z 2024-06-26T05:54:50.1911725Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-06-26T05:54:50.1912059Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-06-26T05:54:50.1912201Z we swap local shards directly. 2024-06-26T05:54:50.1912562Z For more generic cases, we merge different shards across different ranks and split 2024-06-26T05:54:50.1912916Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-06-26T05:54:50.1913016Z 2024-06-26T05:54:50.1913109Z Args: 2024-06-26T05:54:50.1913489Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-06-26T05:54:50.1913720Z specification describing how the tensor is sharded. 2024-06-26T05:54:50.1913807Z 2024-06-26T05:54:50.1913904Z Returns: 2024-06-26T05:54:50.1914194Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-06-26T05:54:50.1914313Z 2024-06-26T05:54:50.1914408Z Examples: 2024-06-26T05:54:50.1914534Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.1914856Z >>> # We have 2 process groups, 2 ranks. 2024-06-26T05:54:50.1915114Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-06-26T05:54:50.1915318Z >>> tensor = torch.stack([tensor, tensor]) 2024-06-26T05:54:50.1915416Z >>> tensor 2024-06-26T05:54:50.1915588Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-06-26T05:54:50.1915745Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-06-26T05:54:50.1915898Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-06-26T05:54:50.1916077Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-06-26T05:54:50.1916189Z >>> sharding_dim = 0 2024-06-26T05:54:50.1916325Z >>> spec = ChunkShardingSpec( 2024-06-26T05:54:50.1916454Z dim=sharding_dim, 2024-06-26T05:54:50.1916596Z placements=[ 2024-06-26T05:54:50.1916717Z "rank:0/cuda:0", 2024-06-26T05:54:50.1916839Z "rank:1/cuda:1", 2024-06-26T05:54:50.1916948Z "rank:2/cuda:2", 2024-06-26T05:54:50.1917053Z "rank:3/cuda:3", 2024-06-26T05:54:50.1917162Z ], 2024-06-26T05:54:50.1917253Z ) 2024-06-26T05:54:50.1917378Z >>> current_offsets = [0] * 2 2024-06-26T05:54:50.1917522Z >>> current_offsets[0] = rank * 2 2024-06-26T05:54:50.1917665Z >>> shard_metadata = ShardMetadata( 2024-06-26T05:54:50.1917874Z shard_offsets=copy.deepcopy(current_offsets), 2024-06-26T05:54:50.1918007Z shard_sizes=tensor.size(), 2024-06-26T05:54:50.1918162Z placement=spec.placements[rank], 2024-06-26T05:54:50.1918271Z ) 2024-06-26T05:54:50.1918382Z >>> local_shards = [ 2024-06-26T05:54:50.1918479Z Shard( 2024-06-26T05:54:50.1918607Z tensor=tensor, 2024-06-26T05:54:50.1918749Z metadata=shard_metadata, 2024-06-26T05:54:50.1918844Z ) 2024-06-26T05:54:50.1918953Z ] 2024-06-26T05:54:50.1919259Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-06-26T05:54:50.1919371Z >>> sharding_dim = 1 2024-06-26T05:54:50.1919551Z >>> resharding_spec = ChunkShardingSpec( 2024-06-26T05:54:50.1919664Z dim=sharding_dim, 2024-06-26T05:54:50.1919771Z placements=[ 2024-06-26T05:54:50.1919896Z "rank:0/cuda:0", 2024-06-26T05:54:50.1920004Z "rank:1/cuda:1", 2024-06-26T05:54:50.1920111Z "rank:2/cuda:2", 2024-06-26T05:54:50.1920232Z "rank:3/cuda:3", 2024-06-26T05:54:50.1920325Z ], 2024-06-26T05:54:50.1920429Z ) 2024-06-26T05:54:50.1920561Z >>> st.reshard(resharding_spec) 2024-06-26T05:54:50.1920712Z >>> tensor = st.local_shards()[0].tensor 2024-06-26T05:54:50.1920822Z >>> tensor 2024-06-26T05:54:50.1921077Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-06-26T05:54:50.1921283Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-06-26T05:54:50.1921483Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-06-26T05:54:50.1921683Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-06-26T05:54:50.1921773Z 2024-06-26T05:54:50.1922203Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1922290Z 2024-06-26T05:54:50.1922400Z warnings.warn(msg) 2024-06-26T05:54:50.1922503Z 2024-06-26T05:54:50.1922704Z --- Parse Warning: 27 / 90 --- 2024-06-26T05:54:50.1924190Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2024-06-26T05:54:50.1924658Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1924783Z 2024-06-26T05:54:50.1925081Z Representation of a sharding plan, describes how to shard a module 2024-06-26T05:54:50.1925497Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-06-26T05:54:50.1925880Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-06-26T05:54:50.1926253Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-06-26T05:54:50.1926341Z 2024-06-26T05:54:50.1926434Z Args: 2024-06-26T05:54:50.1926816Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-06-26T05:54:50.1927037Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-06-26T05:54:50.1927538Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-06-26T05:54:50.1927910Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-06-26T05:54:50.1928071Z a parameter to a `ShardingSpec`. 2024-06-26T05:54:50.1928451Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-06-26T05:54:50.1928581Z to a `Sharder` object. 2024-06-26T05:54:50.1929023Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-06-26T05:54:50.1929473Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-06-26T05:54:50.1929817Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-06-26T05:54:50.1929927Z Default: `None` 2024-06-26T05:54:50.1930292Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-06-26T05:54:50.1930708Z a module's sharded output to be returned as a Tensor from its local shards to 2024-06-26T05:54:50.1931056Z ensure further processing in a data parallel fashion. ("" in list means the 2024-06-26T05:54:50.1931165Z root module). 2024-06-26T05:54:50.1931271Z Default: None 2024-06-26T05:54:50.1931380Z Example: 2024-06-26T05:54:50.1931791Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-06-26T05:54:50.1932206Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-06-26T05:54:50.1932307Z 2024-06-26T05:54:50.1932532Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-06-26T05:54:50.1932662Z >>> class MyModule(nn.Module): 2024-06-26T05:54:50.1932791Z >>> def __init__(self): 2024-06-26T05:54:50.1932911Z >>> super().__init__() 2024-06-26T05:54:50.1933042Z >>> self.fc1 = nn.Linear() 2024-06-26T05:54:50.1933184Z >>> self.gelu = nn.GELU() 2024-06-26T05:54:50.1933311Z >>> self.fc2 = nn.Linear() 2024-06-26T05:54:50.1933455Z >>> self.relu = nn.Linear() 2024-06-26T05:54:50.1933547Z >>> 2024-06-26T05:54:50.1933677Z >>> def forward(self, input): 2024-06-26T05:54:50.1933921Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-06-26T05:54:50.1934009Z 2024-06-26T05:54:50.1934095Z 2024-06-26T05:54:50.1934282Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-06-26T05:54:50.1934420Z >>> sharding_plan = ShardingPlan( 2024-06-26T05:54:50.1934521Z >>> plan={ 2024-06-26T05:54:50.1934657Z >>> "fc1.weight": spec1, 2024-06-26T05:54:50.1934776Z >>> "fc2.weight": spec2 2024-06-26T05:54:50.1934869Z >>> }, 2024-06-26T05:54:50.1934992Z >>> output_plan={ 2024-06-26T05:54:50.1935114Z >>> "fc2": output_spec 2024-06-26T05:54:50.1935240Z >>> }, 2024-06-26T05:54:50.1935385Z >>> return_local_tensor=["fc2"] 2024-06-26T05:54:50.1935476Z >>> ) 2024-06-26T05:54:50.1935601Z 2024-06-26T05:54:50.1936009Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1936096Z 2024-06-26T05:54:50.1936205Z warnings.warn(msg) 2024-06-26T05:54:50.1936333Z 2024-06-26T05:54:50.1936538Z --- Parse Warning: 28 / 90 --- 2024-06-26T05:54:50.1938047Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/experimental/local_map.py line=30. 2024-06-26T05:54:50.1938457Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1938546Z 2024-06-26T05:54:50.1938946Z ``local_map`` is an experimental API that allows users to apply on :class:`DTensors` 2024-06-26T05:54:50.1939241Z a function that is written to be applied on :class:`~torch.Tensors`. 2024-06-26T05:54:50.1939329Z 2024-06-26T05:54:50.1939439Z Args: 2024-06-26T05:54:50.1939719Z func (Callable): the function to be applied on each local shard of 2024-06-26T05:54:50.1939831Z :class:`DTensor`s. 2024-06-26T05:54:50.1940139Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-06-26T05:54:50.1940551Z the desired placements of the :class:`DTensor`s in `func`'s flattened output. 2024-06-26T05:54:50.1940888Z If the flattened `output` is a single value, the `out_placements` should be 2024-06-26T05:54:50.1941208Z of type `PlacementType`. Otherwise if the flattened `output` has multiple 2024-06-26T05:54:50.1941537Z values, the `out_placements` should be a tuple of `PlacementType` values 1:1 2024-06-26T05:54:50.1941708Z mapping to the flattened `output`. 2024-06-26T05:54:50.1941987Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-06-26T05:54:50.1942364Z placements (a `Tuple[Placement]` value). For non-:class:`Tensor` output, 2024-06-26T05:54:50.1942538Z the `PlacementType` should be `None`. 2024-06-26T05:54:50.1942872Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-06-26T05:54:50.1943199Z in. In this case, even if `out_placements` is not `None`, the result function 2024-06-26T05:54:50.1943527Z should ignore the desired placements because the application is not on 2024-06-26T05:54:50.1943639Z :class:`DTensors`. 2024-06-26T05:54:50.1943868Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-06-26T05:54:50.1944277Z the required placements of the :class:`DTensor`s in `func`'s flattened input. 2024-06-26T05:54:50.1944572Z If `in_placements` is specified, `local_map` would examine whether the 2024-06-26T05:54:50.1944905Z placements of each :class:`DTensor` argument is the same as the required 2024-06-26T05:54:50.1945146Z placements or not. If the placements are not the same and 2024-06-26T05:54:50.1945471Z `redistribute_inputs` is `False`, an exception will be raised. Otherwise if 2024-06-26T05:54:50.1945814Z `redistribute_inputs` is `True`, the argument will be first redistributed to 2024-06-26T05:54:50.1946147Z the required sharding placements before passing its local tensor to `func`. 2024-06-26T05:54:50.1946460Z The only exception is when required placements are not `None` and the 2024-06-26T05:54:50.1946798Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-06-26T05:54:50.1947093Z will be skipped and the argument will be directly passed to `func`. 2024-06-26T05:54:50.1947420Z If `in_placements` is `None`, no placements examination will be performed. 2024-06-26T05:54:50.1947559Z Default: `None` 2024-06-26T05:54:50.1947735Z device_mesh (:class:`DeviceMesh`, optional): 2024-06-26T05:54:50.1948046Z the device mesh that all the :class:`DTensor`s are placed on. If not 2024-06-26T05:54:50.1948460Z specified, this will be inferred from the input :class:`DTensor`s' device 2024-06-26T05:54:50.1948805Z mesh. `local_map` requires every :class:`DTensor`s to be placed on the same 2024-06-26T05:54:50.1948957Z device mesh. Default: `None`. 2024-06-26T05:54:50.1949113Z redistribute_inputs (bool, optional): 2024-06-26T05:54:50.1949470Z the bool value indicating whether to reshard the input :class:`DTensor`s when 2024-06-26T05:54:50.1949804Z their placements are different from the required input placements. If this 2024-06-26T05:54:50.1950125Z value is `False` and some :class:`DTensor` input has a different placement, 2024-06-26T05:54:50.1950362Z an exception will be raised. Default: `False`. 2024-06-26T05:54:50.1950455Z 2024-06-26T05:54:50.1950549Z Returns: 2024-06-26T05:54:50.1950916Z A `Callable` that applies `func` to each local shard of the input :class:`DTensor` 2024-06-26T05:54:50.1951240Z and returns a :class:`DTensor` constructed from the return value of `func`. 2024-06-26T05:54:50.1951328Z 2024-06-26T05:54:50.1951436Z Raises: 2024-06-26T05:54:50.1951792Z AssertionError: If the input :class:`DTensor`s are not placed on the same device 2024-06-26T05:54:50.1952138Z mesh, or if they are placed on a different device mesh than the `device_mesh` 2024-06-26T05:54:50.1952252Z argument passed in. 2024-06-26T05:54:50.1952339Z 2024-06-26T05:54:50.1952781Z AssertionError: For any non-:class:`DTensor` output, we require its corresponding 2024-06-26T05:54:50.1953133Z output placement in `out_placements` be `None`. An AssertionError will be raised 2024-06-26T05:54:50.1953253Z if this is not the case. 2024-06-26T05:54:50.1953354Z 2024-06-26T05:54:50.1953701Z ValueError: If `redistribute_inputs=False` but the input :class:`DTensor` needs 2024-06-26T05:54:50.1953892Z a redistribution according to `in_placements`. 2024-06-26T05:54:50.1953992Z 2024-06-26T05:54:50.1954088Z Example: 2024-06-26T05:54:50.1954239Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:50.1954437Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-06-26T05:54:50.1954721Z >>> partial_sum_tensor = torch.mm(W, X) 2024-06-26T05:54:50.1955045Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-06-26T05:54:50.1955183Z >>> return reduced_tensor 2024-06-26T05:54:50.1955277Z >>> 2024-06-26T05:54:50.1955461Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-06-26T05:54:50.1955632Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-06-26T05:54:50.1955750Z >>> Y = torch.mm(W, X) 2024-06-26T05:54:50.1956096Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-06-26T05:54:50.1956421Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-06-26T05:54:50.1956516Z >>> 2024-06-26T05:54:50.1956896Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-06-26T05:54:50.1957059Z >>> local_mm_allreduce_forward = local_map( 2024-06-26T05:54:50.1957184Z >>> mm_allreduce_forward, 2024-06-26T05:54:50.1957341Z >>> out_placements=[Replicate()], 2024-06-26T05:54:50.1957494Z >>> in_placements=[col_wise, row_wise], 2024-06-26T05:54:50.1957620Z >>> device_mesh=device_mesh, 2024-06-26T05:54:50.1957725Z >>> ) 2024-06-26T05:54:50.1957817Z >>> 2024-06-26T05:54:50.1958250Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-06-26T05:54:50.1958692Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-06-26T05:54:50.1959211Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-06-26T05:54:50.1959352Z 2024-06-26T05:54:50.1959615Z NOTE: This API is currently experimental and subject to change 2024-06-26T05:54:50.1959704Z 2024-06-26T05:54:50.1960145Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1960232Z 2024-06-26T05:54:50.1960345Z warnings.warn(msg) 2024-06-26T05:54:50.1960445Z 2024-06-26T05:54:50.1960650Z --- Parse Warning: 29 / 90 --- 2024-06-26T05:54:50.1962365Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2024-06-26T05:54:50.1962861Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1962999Z 2024-06-26T05:54:50.1963198Z Run post-localSGD algorithm. 2024-06-26T05:54:50.1963287Z 2024-06-26T05:54:50.1963668Z This DDP communication hook is used for running post-localSGD algorithm, 2024-06-26T05:54:50.1963891Z by combining with a model averaging component (e.g., 2024-06-26T05:54:50.1964339Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-06-26T05:54:50.1964480Z that runs after the optimizer step. 2024-06-26T05:54:50.1964582Z 2024-06-26T05:54:50.1964674Z Args: 2024-06-26T05:54:50.1965027Z state (PostLocalSGDState): State information to run post-localSGD. 2024-06-26T05:54:50.1965422Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-06-26T05:54:50.1966100Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-06-26T05:54:50.1966467Z Note that since DDP comm hook only supports single process single device mode, 2024-06-26T05:54:50.1966668Z only exactly one tensor is stored in this bucket. 2024-06-26T05:54:50.1966758Z 2024-06-26T05:54:50.1966867Z Returns: 2024-06-26T05:54:50.1967198Z Future handler of the communication, which updates the gradients in place. 2024-06-26T05:54:50.1967287Z 2024-06-26T05:54:50.1967407Z Example:: 2024-06-26T05:54:50.1967522Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.1967844Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-06-26T05:54:50.1968016Z start_localSGD_iter=10) 2024-06-26T05:54:50.1968247Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:50.1968722Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-06-26T05:54:50.1969225Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-06-26T05:54:50.1969314Z 2024-06-26T05:54:50.1969726Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1969812Z 2024-06-26T05:54:50.1969921Z warnings.warn(msg) 2024-06-26T05:54:50.1970022Z 2024-06-26T05:54:50.1970228Z --- Parse Warning: 30 / 90 --- 2024-06-26T05:54:50.1971834Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2024-06-26T05:54:50.1972255Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1972343Z 2024-06-26T05:54:50.1972473Z Implement PowerSGD algorithm. 2024-06-26T05:54:50.1972575Z 2024-06-26T05:54:50.1972873Z This DDP communication hook implements PowerSGD gradient compression 2024-06-26T05:54:50.1973231Z algorithm described in the `paper `_. 2024-06-26T05:54:50.1973595Z Once gradient tensors are aggregated across all workers, this hook applies 2024-06-26T05:54:50.1973714Z compression as follows: 2024-06-26T05:54:50.1973815Z 2024-06-26T05:54:50.1974572Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-06-26T05:54:50.1974663Z 2024-06-26T05:54:50.1975262Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-06-26T05:54:50.1975350Z 2024-06-26T05:54:50.1975927Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-06-26T05:54:50.1976027Z 2024-06-26T05:54:50.1976194Z 2. Handles uncompressed tensors: 2024-06-26T05:54:50.1976283Z 2024-06-26T05:54:50.1976990Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-06-26T05:54:50.1977079Z 2024-06-26T05:54:50.1977547Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-06-26T05:54:50.1977650Z 2024-06-26T05:54:50.1977970Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-06-26T05:54:50.1978071Z 2024-06-26T05:54:50.1978491Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-06-26T05:54:50.1978937Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-06-26T05:54:50.1979040Z 2024-06-26T05:54:50.1979236Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-06-26T05:54:50.1979323Z 2024-06-26T05:54:50.1979471Z 3.3. Allreduces Ps as a batch; 2024-06-26T05:54:50.1979561Z 2024-06-26T05:54:50.1979704Z 3.4. Orthogonalizes each P in Ps; 2024-06-26T05:54:50.1979804Z 2024-06-26T05:54:50.1980082Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-06-26T05:54:50.1980169Z 2024-06-26T05:54:50.1980308Z 3.6. Allreduces Qs as a batch; 2024-06-26T05:54:50.1980397Z 2024-06-26T05:54:50.1980821Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-06-26T05:54:50.1980921Z 2024-06-26T05:54:50.1981485Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-06-26T05:54:50.1981903Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-06-26T05:54:50.1982503Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-06-26T05:54:50.1982593Z 2024-06-26T05:54:50.1982700Z Args: 2024-06-26T05:54:50.1983273Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-06-26T05:54:50.1983771Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-06-26T05:54:50.1983920Z and ``min_compression_rate``. 2024-06-26T05:54:50.1984593Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-06-26T05:54:50.1984961Z Note that since DDP comm hook only supports single process single device mode, 2024-06-26T05:54:50.1985163Z only exactly one tensor is stored in this bucket. 2024-06-26T05:54:50.1985249Z 2024-06-26T05:54:50.1985355Z Returns: 2024-06-26T05:54:50.1985685Z Future handler of the communication, which updates the gradients in place. 2024-06-26T05:54:50.1985771Z 2024-06-26T05:54:50.1985917Z Example:: 2024-06-26T05:54:50.1986031Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.1986385Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-06-26T05:54:50.1986650Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-06-26T05:54:50.1986888Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-06-26T05:54:50.1986976Z 2024-06-26T05:54:50.1987382Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.1987469Z 2024-06-26T05:54:50.1987580Z warnings.warn(msg) 2024-06-26T05:54:50.1987678Z 2024-06-26T05:54:50.1987881Z --- Parse Warning: 31 / 90 --- 2024-06-26T05:54:50.1989601Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=36. 2024-06-26T05:54:50.1990017Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.1990103Z 2024-06-26T05:54:50.1990410Z Averages parameters periodically after the warm-up stage. 2024-06-26T05:54:50.1990497Z 2024-06-26T05:54:50.1990935Z This can be used for running `post-local SGD `_, 2024-06-26T05:54:50.1991205Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-06-26T05:54:50.1991523Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-06-26T05:54:50.1991611Z 2024-06-26T05:54:50.1991719Z Args: 2024-06-26T05:54:50.1991937Z period (int): The number of steps per model averaging. 2024-06-26T05:54:50.1992325Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-06-26T05:54:50.1992500Z Otherwise, only DDP needs to be used. 2024-06-26T05:54:50.1992845Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-06-26T05:54:50.1993020Z model averaging is skipped. 2024-06-26T05:54:50.1993324Z process_group: The process group to be used for all-reduce. 2024-06-26T05:54:50.1993519Z If ``None``, the default process group, which 2024-06-26T05:54:50.1993796Z is created by :func:`torch.distributed.init_process_group`, 2024-06-26T05:54:50.1993960Z will be used. (default: ``None``) 2024-06-26T05:54:50.1994047Z 2024-06-26T05:54:50.1994168Z Example:: 2024-06-26T05:54:50.1994253Z 2024-06-26T05:54:50.1994422Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:50.1994538Z >>> import torch 2024-06-26T05:54:50.1994825Z >>> import torch.distributed as dist 2024-06-26T05:54:50.1995242Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-06-26T05:54:50.1995616Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-06-26T05:54:50.1995738Z >>> import torch.nn as nn 2024-06-26T05:54:50.1995833Z >>> 2024-06-26T05:54:50.1996079Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-06-26T05:54:50.1996208Z >>> torch.cuda.set_device(rank) 2024-06-26T05:54:50.1996399Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-06-26T05:54:50.1996604Z >>> model = nn.parallel.DistributedDataParallel( 2024-06-26T05:54:50.1996790Z >>> module, device_ids=[rank], output_device=rank 2024-06-26T05:54:50.1996896Z >>> ) 2024-06-26T05:54:50.1997141Z >>> # Register a post-localSGD communication hook. 2024-06-26T05:54:50.1997525Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-06-26T05:54:50.1997745Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:50.1997838Z >>> 2024-06-26T05:54:50.1998276Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-06-26T05:54:50.1998501Z >>> # After 100 steps, run model averaging every 4 steps. 2024-06-26T05:54:50.1998987Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-06-26T05:54:50.1999356Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-06-26T05:54:50.1999485Z >>> for step in range(0, 200): 2024-06-26T05:54:50.1999608Z >>> optimizer.zero_grad() 2024-06-26T05:54:50.1999763Z >>> loss = loss_fn(output, labels) 2024-06-26T05:54:50.1999875Z >>> loss.backward() 2024-06-26T05:54:50.1999990Z >>> optimizer.step() 2024-06-26T05:54:50.2000263Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-06-26T05:54:50.2000630Z >>> # inter-node communication only occurs every 4 iterations after 2024-06-26T05:54:50.2000797Z >>> # the initial ``warmup_steps`` period. 2024-06-26T05:54:50.2001016Z >>> averager.average_parameters(model.parameters()) 2024-06-26T05:54:50.2001181Z 2024-06-26T05:54:50.2001581Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2001682Z 2024-06-26T05:54:50.2001797Z warnings.warn(msg) 2024-06-26T05:54:50.2001884Z 2024-06-26T05:54:50.2002100Z --- Parse Warning: 32 / 90 --- 2024-06-26T05:54:50.2003882Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18. 2024-06-26T05:54:50.2004306Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2004393Z 2024-06-26T05:54:50.2004831Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-06-26T05:54:50.2004935Z 2024-06-26T05:54:50.2005352Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-06-26T05:54:50.2005684Z by using different periods concurrently after the warm-up stage. 2024-06-26T05:54:50.2006266Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-06-26T05:54:50.2006799Z that supports `post-local SGD `_, which essentially only supports 2024-06-26T05:54:50.2007310Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-06-26T05:54:50.2007792Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-06-26T05:54:50.2008265Z Similarly, the process groups within this class do not have such an intra-machine process 2024-06-26T05:54:50.2008722Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-06-26T05:54:50.2008811Z 2024-06-26T05:54:50.2008903Z Args: 2024-06-26T05:54:50.2009275Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-06-26T05:54:50.2009552Z process group size, used for initializing process groups of 2024-06-26T05:54:50.2009873Z different sizes in a hierarchy to average parameters concurrently. 2024-06-26T05:54:50.2010162Z Particularly, at each iteration, there will be at most a single 2024-06-26T05:54:50.2010554Z process group that runs averaging -- the period of such group should 2024-06-26T05:54:50.2010866Z have the largest period which the current step can be divided by. 2024-06-26T05:54:50.2011097Z For example, if the dict has three keys: 2, 4, and 8, 2024-06-26T05:54:50.2011418Z then this means totally three process groups will be created to 2024-06-26T05:54:50.2011719Z average parameters every 2, 4, and 8 iterations, respectively. 2024-06-26T05:54:50.2012022Z At the 4th iteration, only the second process group will run 2024-06-26T05:54:50.2012309Z averaging, because the first process group should be a 2024-06-26T05:54:50.2012624Z subset of the second process group, and no need to execute the first 2024-06-26T05:54:50.2012789Z process group redundantly. 2024-06-26T05:54:50.2013095Z On the other hand, the third process group can only be triggered 2024-06-26T05:54:50.2013413Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-06-26T05:54:50.2013946Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-06-26T05:54:50.2014548Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-06-26T05:54:50.2014805Z If ``None``, the default process group, which is created 2024-06-26T05:54:50.2015115Z by :func:`torch.distributed.init_process_group`, will be used. 2024-06-26T05:54:50.2015280Z (default: ``None``) 2024-06-26T05:54:50.2015367Z 2024-06-26T05:54:50.2015486Z Example:: 2024-06-26T05:54:50.2015689Z >>> # xdoctest: +SKIP('undefined rank') 2024-06-26T05:54:50.2015844Z >>> from collections import OrderedDict 2024-06-26T05:54:50.2015965Z >>> import torch 2024-06-26T05:54:50.2016115Z >>> import torch.distributed as dist 2024-06-26T05:54:50.2016483Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-06-26T05:54:50.2016627Z >>> PostLocalSGDState, 2024-06-26T05:54:50.2016747Z >>> post_localSGD_hook, 2024-06-26T05:54:50.2016843Z >>> ) 2024-06-26T05:54:50.2017352Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-06-26T05:54:50.2017473Z >>> import torch.nn as nn 2024-06-26T05:54:50.2017580Z >>> 2024-06-26T05:54:50.2017815Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-06-26T05:54:50.2017944Z >>> torch.cuda.set_device(rank) 2024-06-26T05:54:50.2018137Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-06-26T05:54:50.2018345Z >>> model = nn.parallel.DistributedDataParallel( 2024-06-26T05:54:50.2018531Z >>> module, device_ids=[rank], output_device=rank 2024-06-26T05:54:50.2018640Z >>> ) 2024-06-26T05:54:50.2018887Z >>> # Register a post-localSGD communication hook. 2024-06-26T05:54:50.2019364Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-06-26T05:54:50.2019538Z >>> subgroup, _ = dist.new_subgroups() 2024-06-26T05:54:50.2019947Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-06-26T05:54:50.2020175Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:50.2020269Z >>> 2024-06-26T05:54:50.2020662Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-06-26T05:54:50.2020836Z >>> # the 16 processes every 16 iterations. 2024-06-26T05:54:50.2021095Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-06-26T05:54:50.2021404Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-06-26T05:54:50.2021870Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-06-26T05:54:50.2022280Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-06-26T05:54:50.2022527Z >>> # After 100 steps, run model averaging at two levels. 2024-06-26T05:54:50.2022669Z >>> for step in range(0, 200): 2024-06-26T05:54:50.2022794Z >>> optimizer.zero_grad() 2024-06-26T05:54:50.2022980Z >>> loss = loss_fn(output, labels) 2024-06-26T05:54:50.2023095Z >>> loss.backward() 2024-06-26T05:54:50.2023213Z >>> optimizer.step() 2024-06-26T05:54:50.2023431Z >>> # Average parameters after ``optimizer.step()``. 2024-06-26T05:54:50.2023899Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-06-26T05:54:50.2024107Z >>> averager.average_parameters(model.parameters()) 2024-06-26T05:54:50.2024209Z 2024-06-26T05:54:50.2024313Z .. warning :: 2024-06-26T05:54:50.2024711Z The last group size in the dict must be the size of the provided ``process_group``, 2024-06-26T05:54:50.2025035Z which indicates model averaging at the highest level of the hierarchy. 2024-06-26T05:54:50.2025464Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-06-26T05:54:50.2025554Z 2024-06-26T05:54:50.2025669Z .. warning :: 2024-06-26T05:54:50.2025963Z `HierarchicalModelAverager` is experimental and subject to change. 2024-06-26T05:54:50.2026061Z 2024-06-26T05:54:50.2026457Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2026543Z 2024-06-26T05:54:50.2026664Z warnings.warn(msg) 2024-06-26T05:54:50.2026750Z 2024-06-26T05:54:50.2026950Z --- Parse Warning: 33 / 90 --- 2024-06-26T05:54:50.2028554Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2024-06-26T05:54:50.2028962Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2029050Z 2024-06-26T05:54:50.2029463Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-06-26T05:54:50.2029809Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-06-26T05:54:50.2029910Z 2024-06-26T05:54:50.2030123Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-06-26T05:54:50.2030209Z 2024-06-26T05:54:50.2030319Z .. warning:: 2024-06-26T05:54:50.2030539Z Current implementation only supports loading Tensors. 2024-06-26T05:54:50.2030625Z 2024-06-26T05:54:50.2030780Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:50.2030888Z >>> sd = {"mode": model} 2024-06-26T05:54:50.2030985Z >>> dcp.load( 2024-06-26T05:54:50.2031092Z >>> sd, 2024-06-26T05:54:50.2031295Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-06-26T05:54:50.2031454Z >>> planner=DynamicMetaLoadPlanner(), 2024-06-26T05:54:50.2031607Z >>> checkpoint_id="path_to_model.pt" 2024-06-26T05:54:50.2031697Z >>> ) 2024-06-26T05:54:50.2031782Z 2024-06-26T05:54:50.2032187Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2032273Z 2024-06-26T05:54:50.2032382Z warnings.warn(msg) 2024-06-26T05:54:50.2032478Z 2024-06-26T05:54:50.2032678Z --- Parse Warning: 34 / 90 --- 2024-06-26T05:54:50.2034236Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=148. 2024-06-26T05:54:50.2034791Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2034932Z 2024-06-26T05:54:50.2035438Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-06-26T05:54:50.2036023Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-06-26T05:54:50.2036167Z metadata file, like Torch Save files. 2024-06-26T05:54:50.2036304Z 2024-06-26T05:54:50.2036556Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-06-26T05:54:50.2036642Z 2024-06-26T05:54:50.2036754Z .. warning:: 2024-06-26T05:54:50.2036975Z Current implementation only supports loading Tensors. 2024-06-26T05:54:50.2037061Z 2024-06-26T05:54:50.2037213Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:50.2037322Z >>> sd = {"mode": model} 2024-06-26T05:54:50.2037421Z >>> dcp.load( 2024-06-26T05:54:50.2037529Z >>> sd, 2024-06-26T05:54:50.2037761Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-06-26T05:54:50.2037934Z >>> planner=DynamicMetaLoadPlanner(), 2024-06-26T05:54:50.2038074Z >>> checkpoint_id="path_to_model.pt" 2024-06-26T05:54:50.2038166Z >>> ) 2024-06-26T05:54:50.2038264Z 2024-06-26T05:54:50.2038658Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2038743Z 2024-06-26T05:54:50.2038866Z warnings.warn(msg) 2024-06-26T05:54:50.2038952Z 2024-06-26T05:54:50.2039152Z --- Parse Warning: 35 / 90 --- 2024-06-26T05:54:50.2040758Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=213. 2024-06-26T05:54:50.2041242Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2041330Z 2024-06-26T05:54:50.2041631Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-06-26T05:54:50.2041719Z 2024-06-26T05:54:50.2041938Z This is the current recommended way to checkpoint FSDP. 2024-06-26T05:54:50.2042068Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2042266Z >>> import torch.distributed.checkpoint as dist_cp 2024-06-26T05:54:50.2042378Z >>> # Save 2024-06-26T05:54:50.2042495Z >>> model: torch.nn.Model 2024-06-26T05:54:50.2042638Z >>> optim_params = model.parameters() 2024-06-26T05:54:50.2042833Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-06-26T05:54:50.2042926Z >>> # Save 2024-06-26T05:54:50.2043216Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-06-26T05:54:50.2043335Z >>> state_dict = { 2024-06-26T05:54:50.2043537Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-06-26T05:54:50.2043671Z >>> "model": model.state_dict() 2024-06-26T05:54:50.2043774Z >>> } 2024-06-26T05:54:50.2043894Z >>> dist_cp.save_state_dict( 2024-06-26T05:54:50.2044021Z >>> state_dict=optim_state, 2024-06-26T05:54:50.2044263Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-06-26T05:54:50.2044438Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-06-26T05:54:50.2044530Z >>> ) 2024-06-26T05:54:50.2044631Z >>> 2024-06-26T05:54:50.2044724Z >>> # Load 2024-06-26T05:54:50.2045032Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-06-26T05:54:50.2045209Z >>> model_state_dict = model_tp.state_dict() 2024-06-26T05:54:50.2045318Z >>> checkpoint = { 2024-06-26T05:54:50.2045456Z >>> "model": model_state_dict 2024-06-26T05:54:50.2045548Z >>> } 2024-06-26T05:54:50.2045665Z >>> dist_cp.load_state_dict( 2024-06-26T05:54:50.2045802Z >>> state_dict=checkpoint, 2024-06-26T05:54:50.2046045Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-06-26T05:54:50.2046217Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-06-26T05:54:50.2046359Z >>> ) 2024-06-26T05:54:50.2046554Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-06-26T05:54:50.2046680Z >>> 2024-06-26T05:54:50.2046920Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-06-26T05:54:50.2047033Z >>> model_state_dict, 2024-06-26T05:54:50.2047196Z >>> optimizer_key="optimizer", 2024-06-26T05:54:50.2047444Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-06-26T05:54:50.2047538Z >>> ) 2024-06-26T05:54:50.2047626Z >>> 2024-06-26T05:54:50.2047830Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-06-26T05:54:50.2047997Z >>> model, optim, optim_state["optimizer"] 2024-06-26T05:54:50.2048104Z >>> ) 2024-06-26T05:54:50.2048191Z >>> 2024-06-26T05:54:50.2048342Z >>> optim.load_state_dict(flattened_osd) 2024-06-26T05:54:50.2048443Z 2024-06-26T05:54:50.2048874Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2048963Z 2024-06-26T05:54:50.2049086Z warnings.warn(msg) 2024-06-26T05:54:50.2049173Z 2024-06-26T05:54:50.2049374Z --- Parse Warning: 36 / 90 --- 2024-06-26T05:54:50.2050847Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=110. 2024-06-26T05:54:50.2051257Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2051344Z 2024-06-26T05:54:50.2051746Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-06-26T05:54:50.2051833Z 2024-06-26T05:54:50.2052242Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-06-26T05:54:50.2052330Z 2024-06-26T05:54:50.2052724Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-06-26T05:54:50.2052879Z will be visible to the whole process. 2024-06-26T05:54:50.2052967Z 2024-06-26T05:54:50.2053347Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-06-26T05:54:50.2053446Z 2024-06-26T05:54:50.2053645Z 1) set_up_planner - called on all ranks. 2024-06-26T05:54:50.2053808Z Signals the start of a checkpoint save. 2024-06-26T05:54:50.2053905Z 2024-06-26T05:54:50.2054108Z 2) create_local_plan - called on all ranks. 2024-06-26T05:54:50.2054503Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-06-26T05:54:50.2054601Z 2024-06-26T05:54:50.2054891Z 3) create_global_plan - called on the coordinator rank only. 2024-06-26T05:54:50.2055160Z Takes the SavePlan from all ranks and make any global decision. 2024-06-26T05:54:50.2055259Z 2024-06-26T05:54:50.2055439Z 4) finish_plan - called on all ranks. 2024-06-26T05:54:50.2055750Z This gives each rank a chance to adjust to global planning decisions. 2024-06-26T05:54:50.2055838Z 2024-06-26T05:54:50.2056084Z 5) resolve_data - called multiple times on each rank 2024-06-26T05:54:50.2056381Z Lookups a value on the `state_dict` for the storage layer to write. 2024-06-26T05:54:50.2056466Z 2024-06-26T05:54:50.2056875Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-06-26T05:54:50.2057134Z most changes can be expressed by changes in a single method. 2024-06-26T05:54:50.2057219Z 2024-06-26T05:54:50.2057374Z There are 3 usual patterns of extension: 2024-06-26T05:54:50.2057477Z 2024-06-26T05:54:50.2057818Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-06-26T05:54:50.2058179Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-06-26T05:54:50.2058284Z 2024-06-26T05:54:50.2058464Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:50.2058635Z >>> class RenamePlanner(DefaultSavePlanner): 2024-06-26T05:54:50.2058796Z >>> def set_up_planner( 2024-06-26T05:54:50.2058896Z >>> self, 2024-06-26T05:54:50.2059038Z >>> state_dict: STATE_DICT_TYPE, 2024-06-26T05:54:50.2059241Z >>> storage_meta: Optional[StorageMeta], 2024-06-26T05:54:50.2059370Z >>> is_coordinator: bool, 2024-06-26T05:54:50.2059513Z >>> ) -> None: 2024-06-26T05:54:50.2059661Z >>> # prefix all keys with `foo_`` 2024-06-26T05:54:50.2060087Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-06-26T05:54:50.2060190Z 2024-06-26T05:54:50.2060655Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-06-26T05:54:50.2060743Z 2024-06-26T05:54:50.2060937Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:50.2061104Z >>> class FP16Planner(DefaultSavePlanner): 2024-06-26T05:54:50.2061238Z >>> def create_local_plan(self): 2024-06-26T05:54:50.2061408Z >>> plan = super().create_local_plan() 2024-06-26T05:54:50.2061522Z >>> for p in plan: 2024-06-26T05:54:50.2061674Z >>> if p.tensor_data is not None: 2024-06-26T05:54:50.2061906Z >>> p.tensor_data.properties.dtype = torch.float16 2024-06-26T05:54:50.2062015Z >>> return plan 2024-06-26T05:54:50.2062107Z >>> 2024-06-26T05:54:50.2062275Z >>> def resolve_data(self, write_item): 2024-06-26T05:54:50.2062439Z >>> item = super().resolve_data(write_item) 2024-06-26T05:54:50.2062835Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-06-26T05:54:50.2062923Z 2024-06-26T05:54:50.2063482Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-06-26T05:54:50.2063588Z 2024-06-26T05:54:50.2063733Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:50.2063861Z >>> from itertools import islice 2024-06-26T05:54:50.2064014Z >>> from dataclasses import replace 2024-06-26T05:54:50.2064238Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-06-26T05:54:50.2064710Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-06-26T05:54:50.2064953Z >>> # This sample doesn't handle ShardedTensors 2024-06-26T05:54:50.2065119Z >>> def create_global_plan(self, all_plans): 2024-06-26T05:54:50.2065236Z >>> def chunk(it, size): 2024-06-26T05:54:50.2065359Z >>> it = iter(it) 2024-06-26T05:54:50.2065580Z >>> return list(iter(lambda: tuple(islice(it, size)), ())) 2024-06-26T05:54:50.2065699Z >>> all_plans = [ 2024-06-26T05:54:50.2065896Z >>> replace(plan, items=items) for plan, items in 2024-06-26T05:54:50.2066139Z >>> zip(all_plans, chunk(all_plans[0].items, len(all_plans))) 2024-06-26T05:54:50.2066244Z >>> ] 2024-06-26T05:54:50.2066428Z >>> return super().create_global_plan(all_plans) 2024-06-26T05:54:50.2066517Z 2024-06-26T05:54:50.2066893Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-06-26T05:54:50.2067259Z accomplished by having each rank contribute their data items in the local plan and 2024-06-26T05:54:50.2067393Z the global planner aggregate them: 2024-06-26T05:54:50.2067490Z 2024-06-26T05:54:50.2067631Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:50.2067832Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-06-26T05:54:50.2068058Z >>> def create_local_plan(self) -> SavePlan: 2024-06-26T05:54:50.2068211Z >>> plan = super().create_local_plan() 2024-06-26T05:54:50.2068495Z >>> return replace(plan, planner_data="per-rank-data") 2024-06-26T05:54:50.2068617Z >>> 2024-06-26T05:54:50.2069098Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-06-26T05:54:50.2069397Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-06-26T05:54:50.2069610Z >>> merged_data = [p.planner_data for p in global_plan] 2024-06-26T05:54:50.2069863Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-06-26T05:54:50.2070017Z >>> return global_plan, metadata 2024-06-26T05:54:50.2070105Z 2024-06-26T05:54:50.2070495Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2070595Z 2024-06-26T05:54:50.2070704Z warnings.warn(msg) 2024-06-26T05:54:50.2070790Z 2024-06-26T05:54:50.2071001Z --- Parse Warning: 37 / 90 --- 2024-06-26T05:54:50.2072484Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=275. 2024-06-26T05:54:50.2072911Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2072998Z 2024-06-26T05:54:50.2073385Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-06-26T05:54:50.2073485Z 2024-06-26T05:54:50.2073869Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-06-26T05:54:50.2073957Z 2024-06-26T05:54:50.2074358Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-06-26T05:54:50.2074500Z will be visible to the whole process. 2024-06-26T05:54:50.2074586Z 2024-06-26T05:54:50.2075113Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-06-26T05:54:50.2075202Z 2024-06-26T05:54:50.2075402Z 1) set_up_planner - called on all ranks. 2024-06-26T05:54:50.2075588Z Signals the start of loading a checkpoint. 2024-06-26T05:54:50.2075674Z 2024-06-26T05:54:50.2075893Z 2) create_local_plan - called on all ranks. 2024-06-26T05:54:50.2076291Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-06-26T05:54:50.2076377Z 2024-06-26T05:54:50.2076686Z 3) create_global_plan - called on the coordinator rank only. 2024-06-26T05:54:50.2076951Z Takes the LoadPlan from all ranks and make any global decision. 2024-06-26T05:54:50.2077037Z 2024-06-26T05:54:50.2077283Z 4) load_bytes - called multiple times on each rank 2024-06-26T05:54:50.2077557Z This is called once per non-tensor value in state_dict. 2024-06-26T05:54:50.2077643Z 2024-06-26T05:54:50.2078012Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-06-26T05:54:50.2078265Z They are called in pair for each Tensor value in state_dict. 2024-06-26T05:54:50.2078352Z 2024-06-26T05:54:50.2078770Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-06-26T05:54:50.2079014Z most changes can be expressed by changes in a single method. 2024-06-26T05:54:50.2079099Z 2024-06-26T05:54:50.2079275Z There are two usual patterns of extension: 2024-06-26T05:54:50.2079362Z 2024-06-26T05:54:50.2079720Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-06-26T05:54:50.2080124Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-06-26T05:54:50.2080444Z to keep a reference to the original state_dict as load happens in place so 2024-06-26T05:54:50.2080616Z we need to be able to perform it in place 2024-06-26T05:54:50.2080702Z 2024-06-26T05:54:50.2080843Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:50.2081097Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-06-26T05:54:50.2081219Z >>> def set_up_planner( 2024-06-26T05:54:50.2081376Z >>> self, 2024-06-26T05:54:50.2081532Z >>> state_dict: STATE_DICT_TYPE, 2024-06-26T05:54:50.2081691Z >>> metadata: Metadata, 2024-06-26T05:54:50.2081818Z >>> is_coordinator: bool, 2024-06-26T05:54:50.2081967Z >>> ) -> None: 2024-06-26T05:54:50.2082185Z >>> self.original_state_dict = state_dict 2024-06-26T05:54:50.2082424Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-06-26T05:54:50.2082525Z >>> 2024-06-26T05:54:50.2082674Z >>> if self.flatten_sharded_tensors: 2024-06-26T05:54:50.2082891Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-06-26T05:54:50.2082980Z >>> 2024-06-26T05:54:50.2083117Z >>> if self.flatten_state_dict: 2024-06-26T05:54:50.2083378Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-06-26T05:54:50.2083468Z >>> 2024-06-26T05:54:50.2083641Z >>> self.state_dict = state_dict 2024-06-26T05:54:50.2083786Z >>> self.metadata = metadata 2024-06-26T05:54:50.2083945Z >>> self.is_coordinator = is_coordinator 2024-06-26T05:54:50.2084036Z >>> 2024-06-26T05:54:50.2084207Z >>> def load_bytes(self, read_item, value): 2024-06-26T05:54:50.2084339Z >>> # Remove the "foo_" prefix 2024-06-26T05:54:50.2084664Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value) 2024-06-26T05:54:50.2084761Z 2024-06-26T05:54:50.2084847Z 2024-06-26T05:54:50.2085186Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-06-26T05:54:50.2085281Z 2024-06-26T05:54:50.2085422Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:50.2085624Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-06-26T05:54:50.2085788Z >>> def resolve_tensor(self, read_item): 2024-06-26T05:54:50.2085964Z >>> tensor = super().resolve_tensor(read_item) 2024-06-26T05:54:50.2086165Z >>> return torch.empty_like(tensor, device="cpu") 2024-06-26T05:54:50.2086255Z >>> 2024-06-26T05:54:50.2086429Z >>> def commit_tensor(self, read_item, tensor): 2024-06-26T05:54:50.2086646Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-06-26T05:54:50.2086734Z 2024-06-26T05:54:50.2087132Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2087232Z 2024-06-26T05:54:50.2087343Z warnings.warn(msg) 2024-06-26T05:54:50.2087428Z 2024-06-26T05:54:50.2087640Z --- Parse Warning: 38 / 90 --- 2024-06-26T05:54:50.2089103Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=60. 2024-06-26T05:54:50.2089523Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2089610Z 2024-06-26T05:54:50.2089797Z Load a distributed ``state_dict`` in SPMD style. 2024-06-26T05:54:50.2089895Z 2024-06-26T05:54:50.2090147Z Each rank will try to read the least amount of data necessary 2024-06-26T05:54:50.2090464Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-06-26T05:54:50.2090814Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-06-26T05:54:50.2090899Z 2024-06-26T05:54:50.2091256Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-06-26T05:54:50.2091619Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-06-26T05:54:50.2091837Z ``load_state_dict`` once the deserialization is complete. 2024-06-26T05:54:50.2091922Z 2024-06-26T05:54:50.2092039Z .. warning:: 2024-06-26T05:54:50.2092264Z All tensors in ``state_dict`` must be allocated on their 2024-06-26T05:54:50.2092514Z destination device *prior to* calling this function. 2024-06-26T05:54:50.2092617Z 2024-06-26T05:54:50.2093000Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-06-26T05:54:50.2093163Z on state_dict. 2024-06-26T05:54:50.2093250Z 2024-06-26T05:54:50.2093348Z .. warning:: 2024-06-26T05:54:50.2093671Z Users must call `load_state_dict` on the root module to ensure load 2024-06-26T05:54:50.2093961Z pos-processing and non-tensor data properly propagates. 2024-06-26T05:54:50.2094049Z 2024-06-26T05:54:50.2094156Z .. note: 2024-06-26T05:54:50.2094470Z If no process group is initialized, this function will assume the intent 2024-06-26T05:54:50.2094786Z is to load a checkpoint into the local process. This can be useful in the 2024-06-26T05:54:50.2095148Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-06-26T05:54:50.2095290Z or ShardedTensor) 2024-06-26T05:54:50.2095381Z 2024-06-26T05:54:50.2095486Z .. note: 2024-06-26T05:54:50.2095666Z Rank 0 is assumed to be the coordinator rank. 2024-06-26T05:54:50.2095754Z 2024-06-26T05:54:50.2095855Z Args: 2024-06-26T05:54:50.2096054Z state_dict (Dict[str, Any]): The state_dict to save. 2024-06-26T05:54:50.2096238Z checkpoint_id (Union[str, os.PathLike, None]): 2024-06-26T05:54:50.2096546Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-06-26T05:54:50.2096833Z depends on the storage. It can be a path to a folder or to a file. 2024-06-26T05:54:50.2097137Z It can also be a key if the storage is a key-value store. 2024-06-26T05:54:50.2097249Z (Default: ``None``) 2024-06-26T05:54:50.2097414Z storage_reader (Optional[StorageReader]): 2024-06-26T05:54:50.2097704Z Instance of StorageWriter used to perform reads. If this is not 2024-06-26T05:54:50.2097974Z specified, DCP will automatically infer the reader based on the 2024-06-26T05:54:50.2098242Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-06-26T05:54:50.2098394Z be raised. (Default: ``None``) 2024-06-26T05:54:50.2098538Z planner (Optional[LoadPlanner]): 2024-06-26T05:54:50.2098812Z Instance of LoadPlanner. If this is not specificed, the default 2024-06-26T05:54:50.2098995Z planner will be used. (Default: ``None``) 2024-06-26T05:54:50.2099159Z process_group (Optional[ProcessGroup]): 2024-06-26T05:54:50.2099471Z ProcessGroup to be used for cross-rank synchronization. 2024-06-26T05:54:50.2099583Z (Default: ``None``) 2024-06-26T05:54:50.2099671Z 2024-06-26T05:54:50.2099783Z Returns: 2024-06-26T05:54:50.2099875Z None. 2024-06-26T05:54:50.2099959Z 2024-06-26T05:54:50.2100065Z Examples 2024-06-26T05:54:50.2100181Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2100302Z >>> my_model = MyModule() 2024-06-26T05:54:50.2100501Z >>> optimizer = Adagrad(my_model.parameters()) 2024-06-26T05:54:50.2100667Z >>> model_state_dict = my_model.state_dict() 2024-06-26T05:54:50.2101075Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-06-26T05:54:50.2101177Z 2024-06-26T05:54:50.2101387Z >>> torch.distributed.checkpoint.load_state_dict( 2024-06-26T05:54:50.2101532Z >>> state_dict=model_state_dict, 2024-06-26T05:54:50.2101698Z >>> storage_reader=fs_storage_reader, 2024-06-26T05:54:50.2101791Z >>> ) 2024-06-26T05:54:50.2101878Z 2024-06-26T05:54:50.2102157Z >>> # module.load_state_dict() function might have customized steps 2024-06-26T05:54:50.2102327Z >>> # to flush the state_dict, must call it to 2024-06-26T05:54:50.2102471Z >>> # ensure correct behavior. 2024-06-26T05:54:50.2102644Z >>> my_model.load_state_dict(model_state_dict) 2024-06-26T05:54:50.2102766Z 2024-06-26T05:54:50.2102877Z .. note:: 2024-06-26T05:54:50.2103156Z load_state_dict uses collectives to coordinate reads across ranks. 2024-06-26T05:54:50.2103548Z For NCCL-based process groups, internal tensor representations of 2024-06-26T05:54:50.2103884Z objects must be moved to the GPU device before communication takes place. 2024-06-26T05:54:50.2104223Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-06-26T05:54:50.2104617Z and it is the user's responsibility to ensure that this is set so that each 2024-06-26T05:54:50.2104882Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-06-26T05:54:50.2104967Z 2024-06-26T05:54:50.2105361Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2105460Z 2024-06-26T05:54:50.2105568Z warnings.warn(msg) 2024-06-26T05:54:50.2105670Z 2024-06-26T05:54:50.2105903Z --- Parse Warning: 39 / 90 --- 2024-06-26T05:54:50.2107360Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=66. 2024-06-26T05:54:50.2107793Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2107882Z 2024-06-26T05:54:50.2108031Z Save a distributed model in SPMD style. 2024-06-26T05:54:50.2108131Z 2024-06-26T05:54:50.2108384Z This function is different from ``torch.save()`` as it handles 2024-06-26T05:54:50.2108731Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-06-26T05:54:50.2108831Z 2024-06-26T05:54:50.2109187Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-06-26T05:54:50.2109381Z save will call ``state_dict`` before serialization. 2024-06-26T05:54:50.2109480Z 2024-06-26T05:54:50.2109586Z .. warning:: 2024-06-26T05:54:50.2109925Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-06-26T05:54:50.2110042Z for saved state_dicts. 2024-06-26T05:54:50.2110128Z 2024-06-26T05:54:50.2110240Z .. warning:: 2024-06-26T05:54:50.2110532Z If using the `process_group` argument, make sure that only its ranks 2024-06-26T05:54:50.2110815Z call `save_state_dict` and that all data in state_dict belong to it. 2024-06-26T05:54:50.2110913Z 2024-06-26T05:54:50.2111009Z .. note:: 2024-06-26T05:54:50.2111435Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-06-26T05:54:50.2111803Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-06-26T05:54:50.2111931Z group needs to be passed in. 2024-06-26T05:54:50.2112017Z 2024-06-26T05:54:50.2112124Z .. note:: 2024-06-26T05:54:50.2112504Z If no process group is available, this function assumes the intention is to save the 2024-06-26T05:54:50.2112643Z state_dict in the local process. 2024-06-26T05:54:50.2112741Z 2024-06-26T05:54:50.2112837Z .. note: 2024-06-26T05:54:50.2113030Z Rank 0 is assumed to be the coordinator rank. 2024-06-26T05:54:50.2113117Z 2024-06-26T05:54:50.2113203Z 2024-06-26T05:54:50.2113309Z Args: 2024-06-26T05:54:50.2113509Z state_dict (Dict[str, Any]): The state_dict to save. 2024-06-26T05:54:50.2113692Z checkpoint_id (Union[str, os.PathLike, None]): 2024-06-26T05:54:50.2113998Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-06-26T05:54:50.2114286Z depends on the storage. It can be a path to a folder or to a file. 2024-06-26T05:54:50.2114582Z It can also be a key if the storage is a key-value store. 2024-06-26T05:54:50.2114836Z (Default: ``None``) 2024-06-26T05:54:50.2115005Z storage_writer (Optional[StorageWriter]): 2024-06-26T05:54:50.2115337Z Instance of StorageWriter used to perform writes. If this is not 2024-06-26T05:54:50.2115622Z specified, DCP will automatically infer the writer based on the 2024-06-26T05:54:50.2115933Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-06-26T05:54:50.2116121Z be raised. (Default: ``None``) 2024-06-26T05:54:50.2116261Z planner (Optional[SavePlanner]): 2024-06-26T05:54:50.2116533Z Instance of SavePlanner. If this is not specificed, the default 2024-06-26T05:54:50.2116713Z planner will be used. (Default: ``None``) 2024-06-26T05:54:50.2116874Z process_group (Optional[ProcessGroup]): 2024-06-26T05:54:50.2117169Z ProcessGroup to be used for cross-rank synchronization. 2024-06-26T05:54:50.2117291Z (Default: ``None``) 2024-06-26T05:54:50.2117377Z 2024-06-26T05:54:50.2117470Z Returns: 2024-06-26T05:54:50.2117728Z Metadata: Metadata object for the saved checkpoint. 2024-06-26T05:54:50.2117817Z 2024-06-26T05:54:50.2117911Z Example: 2024-06-26T05:54:50.2118036Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2118154Z >>> my_model = MyModule() 2024-06-26T05:54:50.2118239Z 2024-06-26T05:54:50.2118392Z >>> state_dict = {"model": my_model} 2024-06-26T05:54:50.2118478Z 2024-06-26T05:54:50.2118887Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-06-26T05:54:50.2119072Z >>> torch.distributed.checkpoint.save( 2024-06-26T05:54:50.2119194Z >>> state_dict=state_dict, 2024-06-26T05:54:50.2119358Z >>> storage_writer=fs_storage_writer, 2024-06-26T05:54:50.2119449Z >>> ) 2024-06-26T05:54:50.2119535Z 2024-06-26T05:54:50.2119647Z .. note:: 2024-06-26T05:54:50.2119930Z save_state_dict uses collectives to coordinate writes across ranks. 2024-06-26T05:54:50.2120278Z For NCCL-based process groups, internal tensor representations of 2024-06-26T05:54:50.2120614Z objects must be moved to the GPU device before communication takes place. 2024-06-26T05:54:50.2120929Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-06-26T05:54:50.2121369Z and it is the user's responsibility to ensure that this is set so that 2024-06-26T05:54:50.2121666Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-06-26T05:54:50.2121756Z 2024-06-26T05:54:50.2122151Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2122251Z 2024-06-26T05:54:50.2122362Z warnings.warn(msg) 2024-06-26T05:54:50.2122448Z 2024-06-26T05:54:50.2122662Z --- Parse Warning: 40 / 90 --- 2024-06-26T05:54:50.2124167Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=169. 2024-06-26T05:54:50.2124593Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2125019Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-06-26T05:54:50.2125417Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-06-26T05:54:50.2125515Z 2024-06-26T05:54:50.2125620Z .. warning:: 2024-06-26T05:54:50.2125831Z This feature is experimental and subject to change. 2024-06-26T05:54:50.2125930Z 2024-06-26T05:54:50.2126022Z Args: 2024-06-26T05:54:50.2126228Z state_dict (Dict[str, Any]): The state_dict to save. 2024-06-26T05:54:50.2126432Z checkpoint_id (Union[str, os.PathLike, None]): 2024-06-26T05:54:50.2126732Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-06-26T05:54:50.2127036Z depends on the storage. It can be a path to a folder or to a file. 2024-06-26T05:54:50.2127417Z It can also be a key if the storage is a key-value store. 2024-06-26T05:54:50.2127564Z (Default: ``None``) 2024-06-26T05:54:50.2127753Z storage_writer (Optional[StorageWriter]): 2024-06-26T05:54:50.2128134Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-06-26T05:54:50.2128473Z this is not specified, DCP will automatically infer the writer based on the 2024-06-26T05:54:50.2128760Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-06-26T05:54:50.2128905Z be raised. (Default: ``None``) 2024-06-26T05:54:50.2129056Z planner (Optional[SavePlanner]): 2024-06-26T05:54:50.2129348Z Instance of SavePlanner. If this is not specificed, the default 2024-06-26T05:54:50.2129518Z planner will be used. (Default: ``None``) 2024-06-26T05:54:50.2129727Z process_group (Optional[ProcessGroup]): 2024-06-26T05:54:50.2130032Z ProcessGroup to be used for cross-rank synchronization. 2024-06-26T05:54:50.2130147Z (Default: ``None``) 2024-06-26T05:54:50.2130249Z 2024-06-26T05:54:50.2130343Z Returns: 2024-06-26T05:54:50.2130631Z Future: A future holding the resultant Metadata object from `save`. 2024-06-26T05:54:50.2130728Z 2024-06-26T05:54:50.2130823Z Example: 2024-06-26T05:54:50.2130938Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2131071Z >>> my_model = MyModule() 2024-06-26T05:54:50.2131155Z 2024-06-26T05:54:50.2131301Z >>> state_dict = {"model": my_model} 2024-06-26T05:54:50.2131398Z 2024-06-26T05:54:50.2131806Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-06-26T05:54:50.2132084Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-06-26T05:54:50.2132227Z >>> state_dict=state_dict, 2024-06-26T05:54:50.2132386Z >>> storage_writer=fs_storage_writer, 2024-06-26T05:54:50.2132492Z >>> ) 2024-06-26T05:54:50.2132588Z >>> 2024-06-26T05:54:50.2132708Z >>> # ... do some work ... 2024-06-26T05:54:50.2132813Z >>> 2024-06-26T05:54:50.2132950Z >>> checkpoint_future.result() 2024-06-26T05:54:50.2133036Z 2024-06-26T05:54:50.2133138Z 2024-06-26T05:54:50.2133531Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2133620Z 2024-06-26T05:54:50.2133741Z warnings.warn(msg) 2024-06-26T05:54:50.2133826Z 2024-06-26T05:54:50.2134026Z --- Parse Warning: 41 / 90 --- 2024-06-26T05:54:50.2135617Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2024-06-26T05:54:50.2136028Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2136116Z 2024-06-26T05:54:50.2136380Z Initialize rendezvous event object and record its operations. 2024-06-26T05:54:50.2136465Z 2024-06-26T05:54:50.2136556Z Args: 2024-06-26T05:54:50.2136739Z run_id (str): The run id of the rendezvous. 2024-06-26T05:54:50.2136931Z message (str): The message describing the event. 2024-06-26T05:54:50.2137290Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-06-26T05:54:50.2137542Z name (str): Event name. (E.g. Current action being performed). 2024-06-26T05:54:50.2137693Z hostname (str): Hostname of the node. 2024-06-26T05:54:50.2137896Z pid (Optional[int]): The process id of the node. 2024-06-26T05:54:50.2138227Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-06-26T05:54:50.2138640Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-06-26T05:54:50.2138860Z rank (Optional[int]): The rank of the node, if known. 2024-06-26T05:54:50.2138984Z Returns: 2024-06-26T05:54:50.2139075Z None 2024-06-26T05:54:50.2139183Z Example: 2024-06-26T05:54:50.2139379Z >>> # See DynamicRendezvousHandler class 2024-06-26T05:54:50.2139486Z >>> def _record( 2024-06-26T05:54:50.2139598Z ... self, 2024-06-26T05:54:50.2139708Z ... message: str, 2024-06-26T05:54:50.2139909Z ... node_state: NodeState = NodeState.RUNNING, 2024-06-26T05:54:50.2140046Z ... rank: Optional[int] = None, 2024-06-26T05:54:50.2140177Z ... ) -> None: 2024-06-26T05:54:50.2140343Z ... construct_and_record_rdzv_event( 2024-06-26T05:54:50.2140564Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-06-26T05:54:50.2140752Z ... run_id=self._settings.run_id, 2024-06-26T05:54:50.2140885Z ... message=message, 2024-06-26T05:54:50.2141013Z ... node_state=node_state, 2024-06-26T05:54:50.2141165Z ... hostname=self._this_node.addr, 2024-06-26T05:54:50.2141316Z ... pid=self._this_node.pid, 2024-06-26T05:54:50.2141481Z ... local_id=self._this_node.local_id, 2024-06-26T05:54:50.2141588Z ... rank=rank, 2024-06-26T05:54:50.2141698Z ... ) 2024-06-26T05:54:50.2141791Z 2024-06-26T05:54:50.2142188Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2142290Z 2024-06-26T05:54:50.2142403Z warnings.warn(msg) 2024-06-26T05:54:50.2142492Z 2024-06-26T05:54:50.2142707Z --- Parse Warning: 42 / 90 --- 2024-06-26T05:54:50.2144105Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=113. 2024-06-26T05:54:50.2144530Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2144625Z 2024-06-26T05:54:50.2144888Z This configures FSDP-native mixed precision training. 2024-06-26T05:54:50.2144991Z 2024-06-26T05:54:50.2145094Z Attributes: 2024-06-26T05:54:50.2145406Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-06-26T05:54:50.2145686Z parameters during forward and backward and thus the dtype for 2024-06-26T05:54:50.2145982Z forward and backward computation. Outside forward and backward, the 2024-06-26T05:54:50.2146238Z *sharded* parameters are kept in full precision (e.g. for the 2024-06-26T05:54:50.2146530Z optimizer step), and for model checkpointing, the parameters are 2024-06-26T05:54:50.2146740Z always saved in full precision. (Default: ``None``) 2024-06-26T05:54:50.2147039Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-06-26T05:54:50.2147385Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-06-26T05:54:50.2147637Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-06-26T05:54:50.2147920Z the ``param_dtype`` value, still running gradient reduction in low 2024-06-26T05:54:50.2148201Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-06-26T05:54:50.2148470Z to force gradient reduction to run in full precision. (Default: 2024-06-26T05:54:50.2148578Z ``None``) 2024-06-26T05:54:50.2148857Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-06-26T05:54:50.2149142Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-06-26T05:54:50.2149404Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-06-26T05:54:50.2149713Z dtype thereafter. For model checkpointing, the buffers are saved 2024-06-26T05:54:50.2149976Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-06-26T05:54:50.2150101Z ``None``) 2024-06-26T05:54:50.2150360Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-06-26T05:54:50.2150683Z gradients to full precision after the backward pass in preparation 2024-06-26T05:54:50.2150966Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-06-26T05:54:50.2151246Z in the dtype used for gradient reduction, which can save memory if 2024-06-26T05:54:50.2151532Z using a custom optimizer that supports running in low precision. 2024-06-26T05:54:50.2151644Z (Default: ``False``) 2024-06-26T05:54:50.2151933Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-06-26T05:54:50.2152234Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-06-26T05:54:50.2152520Z that parameter and input dtypes match for forward computation, as 2024-06-26T05:54:50.2152818Z required by many ops. This may need to be set to ``True`` when only 2024-06-26T05:54:50.2153113Z applying mixed precision to some but not all FSDP modules, in which 2024-06-26T05:54:50.2153465Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-06-26T05:54:50.2153589Z (Default: ``False``) 2024-06-26T05:54:50.2153878Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-06-26T05:54:50.2154152Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-06-26T05:54:50.2154489Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-06-26T05:54:50.2154803Z this does not do anything. (Default: ``True``) 2024-06-26T05:54:50.2155106Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-06-26T05:54:50.2155355Z module classes to ignore for mixed precision when using an 2024-06-26T05:54:50.2155599Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-06-26T05:54:50.2155896Z applied to them separately with mixed precision disabled (meaning 2024-06-26T05:54:50.2156178Z that the final FSDP construction would deviate from the specified 2024-06-26T05:54:50.2156438Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-06-26T05:54:50.2156729Z not do anything. This API is experimental and subject to change. 2024-06-26T05:54:50.2156860Z (Default: ``(_BatchNorm,)``) 2024-06-26T05:54:50.2156948Z 2024-06-26T05:54:50.2157186Z .. note:: This API is experimental and subject to change. 2024-06-26T05:54:50.2157272Z 2024-06-26T05:54:50.2157582Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-06-26T05:54:50.2157672Z 2024-06-26T05:54:50.2157924Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-06-26T05:54:50.2158071Z precision, but buffers are not. 2024-06-26T05:54:50.2158157Z 2024-06-26T05:54:50.2158441Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-06-26T05:54:50.2158748Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-06-26T05:54:50.2159122Z Disabling FSDP's mixed precision for those norm modules only means that 2024-06-26T05:54:50.2159405Z the affine parameters are kept in ``float32``. However, this incurs 2024-06-26T05:54:50.2159795Z separate all-gathers and reduce-scatters for those norm modules, which 2024-06-26T05:54:50.2160098Z may be inefficient, so if the workload permits, the user should prefer 2024-06-26T05:54:50.2160303Z to still apply mixed precision to those modules. 2024-06-26T05:54:50.2160390Z 2024-06-26T05:54:50.2160683Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-06-26T05:54:50.2161095Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-06-26T05:54:50.2161446Z modules will have FSDP applied to them separately with mixed precision 2024-06-26T05:54:50.2161675Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-06-26T05:54:50.2161777Z 2024-06-26T05:54:50.2162087Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-06-26T05:54:50.2162370Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-06-26T05:54:50.2162612Z its ``cast_root_forward_inputs`` takes precedence over its 2024-06-26T05:54:50.2162905Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-06-26T05:54:50.2163205Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-06-26T05:54:50.2163502Z sufficient for the typical case where each FSDP instance has the same 2024-06-26T05:54:50.2163828Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-06-26T05:54:50.2164144Z ``param_dtype`` at the beginning of the model's forward pass. 2024-06-26T05:54:50.2164233Z 2024-06-26T05:54:50.2164514Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-06-26T05:54:50.2164832Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-06-26T05:54:50.2165135Z values to configure casting inputs or not before each instance's 2024-06-26T05:54:50.2165404Z forward. In such a case, since the casts happen before each FSDP 2024-06-26T05:54:50.2165762Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-06-26T05:54:50.2166072Z submodules run before its FSDP submodules to avoid the activation dtype 2024-06-26T05:54:50.2166373Z being changed due to a different ``MixedPrecision`` configuration. 2024-06-26T05:54:50.2166458Z 2024-06-26T05:54:50.2166561Z Example:: 2024-06-26T05:54:50.2166661Z 2024-06-26T05:54:50.2166835Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:50.2167061Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-06-26T05:54:50.2167185Z >>> model[1] = FSDP( 2024-06-26T05:54:50.2167288Z >>> model[1], 2024-06-26T05:54:50.2167678Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-06-26T05:54:50.2167782Z >>> ) 2024-06-26T05:54:50.2167888Z >>> model = FSDP( 2024-06-26T05:54:50.2167986Z >>> model, 2024-06-26T05:54:50.2168390Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-06-26T05:54:50.2168483Z >>> ) 2024-06-26T05:54:50.2168580Z 2024-06-26T05:54:50.2168875Z The above shows a working example. On the other hand, if ``model[1]`` 2024-06-26T05:54:50.2169145Z were replaced with ``model[0]``, meaning that the submodule using 2024-06-26T05:54:50.2169449Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-06-26T05:54:50.2169741Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-06-26T05:54:50.2169834Z ones. 2024-06-26T05:54:50.2169929Z 2024-06-26T05:54:50.2170013Z 2024-06-26T05:54:50.2170407Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2170500Z 2024-06-26T05:54:50.2170606Z warnings.warn(msg) 2024-06-26T05:54:50.2170690Z 2024-06-26T05:54:50.2170904Z --- Parse Warning: 43 / 90 --- 2024-06-26T05:54:50.2172671Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=648. 2024-06-26T05:54:50.2173094Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2173479Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-06-26T05:54:50.2173614Z 2024-06-26T05:54:50.2174052Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-06-26T05:54:50.2174369Z The target module does not have to be a FSDP module. If the target 2024-06-26T05:54:50.2174665Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-06-26T05:54:50.2174764Z 2024-06-26T05:54:50.2175094Z .. note:: This API should be called for only the top-level (root) 2024-06-26T05:54:50.2175190Z module. 2024-06-26T05:54:50.2175292Z 2024-06-26T05:54:50.2175589Z .. note:: This API enables users to transparently use the conventional 2024-06-26T05:54:50.2175895Z ``state_dict`` API to take model checkpoints in cases where the 2024-06-26T05:54:50.2176185Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-06-26T05:54:50.2176529Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-06-26T05:54:50.2176849Z instances, while dispatching into `sharded_state_dict` implementation 2024-06-26T05:54:50.2176955Z for FSDP: 2024-06-26T05:54:50.2177042Z 2024-06-26T05:54:50.2177157Z Example:: 2024-06-26T05:54:50.2177242Z 2024-06-26T05:54:50.2177421Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:50.2177566Z >>> model = DDP(FSDP(...)) 2024-06-26T05:54:50.2177706Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:50.2177808Z >>> model, 2024-06-26T05:54:50.2177998Z >>> StateDictType.SHARDED_STATE_DICT, 2024-06-26T05:54:50.2178286Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-06-26T05:54:50.2178608Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-06-26T05:54:50.2178702Z >>> ) 2024-06-26T05:54:50.2178872Z >>> param_state_dict = model.state_dict() 2024-06-26T05:54:50.2179109Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-06-26T05:54:50.2179198Z 2024-06-26T05:54:50.2179291Z Args: 2024-06-26T05:54:50.2179470Z module (torch.nn.Module): Root module. 2024-06-26T05:54:50.2179777Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-06-26T05:54:50.2180090Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-06-26T05:54:50.2180245Z target ``state_dict_type``. 2024-06-26T05:54:50.2180576Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-06-26T05:54:50.2180738Z for the optimizer state dict. 2024-06-26T05:54:50.2180827Z 2024-06-26T05:54:50.2180921Z Returns: 2024-06-26T05:54:50.2181224Z A StateDictSettings that include the previous state_dict type and 2024-06-26T05:54:50.2181369Z configuration for the module. 2024-06-26T05:54:50.2181461Z 2024-06-26T05:54:50.2181866Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2181951Z 2024-06-26T05:54:50.2182061Z warnings.warn(msg) 2024-06-26T05:54:50.2182159Z 2024-06-26T05:54:50.2182357Z --- Parse Warning: 44 / 90 --- 2024-06-26T05:54:50.2184088Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=804. 2024-06-26T05:54:50.2184509Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2184897Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-06-26T05:54:50.2185034Z 2024-06-26T05:54:50.2185480Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-06-26T05:54:50.2185680Z :meth:`set_state_dict_type` for the detail. 2024-06-26T05:54:50.2185785Z 2024-06-26T05:54:50.2185890Z Example:: 2024-06-26T05:54:50.2185978Z 2024-06-26T05:54:50.2186171Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:50.2186305Z >>> model = DDP(FSDP(...)) 2024-06-26T05:54:50.2186449Z >>> with FSDP.state_dict_type( 2024-06-26T05:54:50.2186567Z >>> model, 2024-06-26T05:54:50.2186746Z >>> StateDictType.SHARDED_STATE_DICT, 2024-06-26T05:54:50.2186843Z >>> ): 2024-06-26T05:54:50.2187047Z >>> checkpoint = model.state_dict() 2024-06-26T05:54:50.2187138Z 2024-06-26T05:54:50.2187233Z Args: 2024-06-26T05:54:50.2187419Z module (torch.nn.Module): Root module. 2024-06-26T05:54:50.2187734Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-06-26T05:54:50.2188060Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-06-26T05:54:50.2188272Z configuration for the target ``state_dict_type``. 2024-06-26T05:54:50.2188579Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-06-26T05:54:50.2188858Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-06-26T05:54:50.2188953Z 2024-06-26T05:54:50.2189350Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2189455Z 2024-06-26T05:54:50.2189566Z warnings.warn(msg) 2024-06-26T05:54:50.2189655Z 2024-06-26T05:54:50.2189873Z --- Parse Warning: 45 / 90 --- 2024-06-26T05:54:50.2191626Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1801. 2024-06-26T05:54:50.2192049Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2192137Z 2024-06-26T05:54:50.2192528Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-06-26T05:54:50.2192629Z 2024-06-26T05:54:50.2192945Z The given state-dict can be transformed to one of three types: 2024-06-26T05:54:50.2193350Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-06-26T05:54:50.2193451Z 2024-06-26T05:54:50.2193768Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-06-26T05:54:50.2194072Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-06-26T05:54:50.2194182Z avoid OOM. 2024-06-26T05:54:50.2194269Z 2024-06-26T05:54:50.2194581Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-06-26T05:54:50.2195015Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-06-26T05:54:50.2195110Z memory. 2024-06-26T05:54:50.2195210Z 2024-06-26T05:54:50.2195507Z For local state_dict, no transformation will be performed. But a state 2024-06-26T05:54:50.2195836Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-06-26T05:54:50.2195988Z nature (this is not supported yet). 2024-06-26T05:54:50.2196077Z 2024-06-26T05:54:50.2196176Z Example:: 2024-06-26T05:54:50.2196278Z 2024-06-26T05:54:50.2196449Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:50.2196767Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-06-26T05:54:50.2206192Z >>> from torch.distributed.fsdp import StateDictType 2024-06-26T05:54:50.2206605Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-06-26T05:54:50.2206879Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-06-26T05:54:50.2207052Z >>> # Save a checkpoint 2024-06-26T05:54:50.2207170Z >>> model, optim = ... 2024-06-26T05:54:50.2207313Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:50.2207411Z >>> model, 2024-06-26T05:54:50.2207568Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:50.2207760Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:50.2207958Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:50.2208051Z >>> ) 2024-06-26T05:54:50.2208205Z >>> state_dict = model.state_dict() 2024-06-26T05:54:50.2208471Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-06-26T05:54:50.2208666Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-06-26T05:54:50.2208794Z >>> # Load a checkpoint 2024-06-26T05:54:50.2208910Z >>> model, optim = ... 2024-06-26T05:54:50.2209110Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-06-26T05:54:50.2209250Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:50.2209348Z >>> model, 2024-06-26T05:54:50.2209502Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:50.2209686Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:50.2209879Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:50.2209987Z >>> ) 2024-06-26T05:54:50.2210132Z >>> model.load_state_dict(state_dict) 2024-06-26T05:54:50.2210330Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-06-26T05:54:50.2210490Z >>> model, optim, optim_state_dict 2024-06-26T05:54:50.2210586Z >>> ) 2024-06-26T05:54:50.2210746Z >>> optim.load_state_dict(optim_state_dict) 2024-06-26T05:54:50.2210847Z 2024-06-26T05:54:50.2210939Z Args: 2024-06-26T05:54:50.2211212Z model (torch.nn.Module): Root module (which may or may not be a 2024-06-26T05:54:50.2211496Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-06-26T05:54:50.2211674Z were passed into the optimizer ``optim``. 2024-06-26T05:54:50.2211982Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-06-26T05:54:50.2212102Z parameters. 2024-06-26T05:54:50.2212386Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-06-26T05:54:50.2212687Z transform. If the value is None, optim.state_dict() will be used. ( 2024-06-26T05:54:50.2212798Z Default: ``None``) 2024-06-26T05:54:50.2213186Z group (dist.ProcessGroup): Model's process group across which parameters 2024-06-26T05:54:50.2213458Z are sharded or ``None`` if using the default process group. ( 2024-06-26T05:54:50.2213569Z Default: ``None``) 2024-06-26T05:54:50.2213656Z 2024-06-26T05:54:50.2213768Z Returns: 2024-06-26T05:54:50.2214038Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-06-26T05:54:50.2214269Z ``model``. The sharding of the optimizer state is based on 2024-06-26T05:54:50.2214396Z ``state_dict_type``. 2024-06-26T05:54:50.2214483Z 2024-06-26T05:54:50.2214882Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2214983Z 2024-06-26T05:54:50.2215092Z warnings.warn(msg) 2024-06-26T05:54:50.2215179Z 2024-06-26T05:54:50.2215395Z --- Parse Warning: 46 / 90 --- 2024-06-26T05:54:50.2217176Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1899. 2024-06-26T05:54:50.2217641Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2217758Z 2024-06-26T05:54:50.2218352Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-06-26T05:54:50.2218479Z 2024-06-26T05:54:50.2218703Z Given a ``optim_state_dict`` that is transformed through 2024-06-26T05:54:50.2218992Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-06-26T05:54:50.2219298Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-06-26T05:54:50.2219551Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-06-26T05:54:50.2219638Z 2024-06-26T05:54:50.2219824Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:50.2220169Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-06-26T05:54:50.2220398Z >>> from torch.distributed.fsdp import StateDictType 2024-06-26T05:54:50.2220636Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-06-26T05:54:50.2220907Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-06-26T05:54:50.2221035Z >>> # Save a checkpoint 2024-06-26T05:54:50.2221151Z >>> model, optim = ... 2024-06-26T05:54:50.2221278Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:50.2221386Z >>> model, 2024-06-26T05:54:50.2221540Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:50.2221713Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:50.2221922Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:50.2222015Z >>> ) 2024-06-26T05:54:50.2222155Z >>> state_dict = model.state_dict() 2024-06-26T05:54:50.2222317Z >>> original_osd = optim.state_dict() 2024-06-26T05:54:50.2222491Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-06-26T05:54:50.2222590Z >>> model, 2024-06-26T05:54:50.2222700Z >>> optim, 2024-06-26T05:54:50.2222840Z >>> optim_state_dict=original_osd 2024-06-26T05:54:50.2222946Z >>> ) 2024-06-26T05:54:50.2223135Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-06-26T05:54:50.2223251Z >>> # Load a checkpoint 2024-06-26T05:54:50.2223378Z >>> model, optim = ... 2024-06-26T05:54:50.2223579Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-06-26T05:54:50.2223704Z >>> FSDP.set_state_dict_type( 2024-06-26T05:54:50.2223811Z >>> model, 2024-06-26T05:54:50.2223963Z >>> StateDictType.FULL_STATE_DICT, 2024-06-26T05:54:50.2224136Z >>> FullStateDictConfig(rank0_only=False), 2024-06-26T05:54:50.2224343Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-06-26T05:54:50.2224434Z >>> ) 2024-06-26T05:54:50.2224581Z >>> model.load_state_dict(state_dict) 2024-06-26T05:54:50.2224791Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-06-26T05:54:50.2224935Z >>> model, optim, optim_state_dict 2024-06-26T05:54:50.2225029Z >>> ) 2024-06-26T05:54:50.2225198Z >>> optim.load_state_dict(optim_state_dict) 2024-06-26T05:54:50.2225289Z 2024-06-26T05:54:50.2225384Z Args: 2024-06-26T05:54:50.2225663Z model (torch.nn.Module): Root module (which may or may not be a 2024-06-26T05:54:50.2225928Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-06-26T05:54:50.2226118Z were passed into the optimizer ``optim``. 2024-06-26T05:54:50.2226389Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-06-26T05:54:50.2226493Z parameters. 2024-06-26T05:54:50.2226789Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-06-26T05:54:50.2227057Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-06-26T05:54:50.2227373Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-06-26T05:54:50.2227670Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-06-26T05:54:50.2227962Z load_directly (bool): If this is set to True, this API will also 2024-06-26T05:54:50.2228255Z call optim.load_state_dict(result) before returning the result. 2024-06-26T05:54:50.2228560Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-06-26T05:54:50.2228674Z (Default: ``False``) 2024-06-26T05:54:50.2229069Z group (dist.ProcessGroup): Model's process group across which parameters 2024-06-26T05:54:50.2229327Z are sharded or ``None`` if using the default process group. ( 2024-06-26T05:54:50.2229438Z Default: ``None``) 2024-06-26T05:54:50.2229538Z 2024-06-26T05:54:50.2229929Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2230055Z 2024-06-26T05:54:50.2230181Z warnings.warn(msg) 2024-06-26T05:54:50.2230266Z 2024-06-26T05:54:50.2230467Z --- Parse Warning: 47 / 90 --- 2024-06-26T05:54:50.2231998Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=137. 2024-06-26T05:54:50.2232407Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2232504Z 2024-06-26T05:54:50.2232796Z RemoteModule instance can only be created after RPC initialization. 2024-06-26T05:54:50.2232884Z 2024-06-26T05:54:50.2233217Z It creates a user-specified module on a specified remote node. 2024-06-26T05:54:50.2233544Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-06-26T05:54:50.2233668Z executed on the remote node. 2024-06-26T05:54:50.2234006Z It takes care of autograd recording to ensure the backward pass propagates 2024-06-26T05:54:50.2234209Z gradients back to the corresponding remote module. 2024-06-26T05:54:50.2234840Z It can be shared across processors using `RPC framework `__, 2024-06-26T05:54:50.2235115Z without incurring any overheads of copying the actual module, 2024-06-26T05:54:50.2235381Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-06-26T05:54:50.2235508Z pointing to the remote module. 2024-06-26T05:54:50.2235608Z 2024-06-26T05:54:50.2235874Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-06-26T05:54:50.2236160Z the ``forward`` method of the module returned by the ``module_cls``. 2024-06-26T05:54:50.2236246Z 2024-06-26T05:54:50.2236679Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-06-26T05:54:50.2236783Z 2024-06-26T05:54:50.2237124Z Particularly, to create a hybrid model, typically the local modules should be 2024-06-26T05:54:50.2237641Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-06-26T05:54:50.2237760Z Hybrid Example: 2024-06-26T05:54:50.2237907Z >>> class HybridModel(nn.Module): 2024-06-26T05:54:50.2238029Z >>> def __init__(self): 2024-06-26T05:54:50.2238187Z >>> nn.Module.__init__(self) 2024-06-26T05:54:50.2238379Z >>> self.remote_embedding = RemoteModule(...) 2024-06-26T05:54:50.2238549Z >>> self.local_linear = nn.Linear(...) 2024-06-26T05:54:50.2238648Z 2024-06-26T05:54:50.2238922Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-06-26T05:54:50.2239351Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-06-26T05:54:50.2239630Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-06-26T05:54:50.2239898Z ``def forward(input: Tensor) -> Tensor:`` and 2024-06-26T05:54:50.2240175Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-06-26T05:54:50.2240301Z 2024-06-26T05:54:50.2240408Z .. note:: 2024-06-26T05:54:50.2240616Z If the remote module is placed on a cuda device, 2024-06-26T05:54:50.2240977Z any input CPU tensors will be automatically moved to the same cuda device, 2024-06-26T05:54:50.2241628Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-06-26T05:54:50.2241732Z 2024-06-26T05:54:50.2241825Z Args: 2024-06-26T05:54:50.2242316Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-06-26T05:54:50.2242755Z The device can be a local device or a remote device specified by one of the following remote 2024-06-26T05:54:50.2242854Z formats: 2024-06-26T05:54:50.2243007Z 2024-06-26T05:54:50.2243203Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-06-26T05:54:50.2243402Z 2. "/" (ex: "trainer0/cuda:0"). 2024-06-26T05:54:50.2243505Z 2024-06-26T05:54:50.2243848Z In addition, the device field can be optional and the default value is "cpu". 2024-06-26T05:54:50.2243998Z module_cls (nn.Module): For example, 2024-06-26T05:54:50.2244152Z >>> class MyModule(nn.Module): 2024-06-26T05:54:50.2244275Z >>> def forward(input): 2024-06-26T05:54:50.2244401Z >>> return input + 1 2024-06-26T05:54:50.2244506Z >>> 2024-06-26T05:54:50.2244626Z >>> module_cls = MyModule 2024-06-26T05:54:50.2244884Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-06-26T05:54:50.2245154Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-06-26T05:54:50.2245519Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-06-26T05:54:50.2245867Z to be created. The type object should be decorated by @torch.jit.interface. 2024-06-26T05:54:50.2246239Z If not provided, the generated RemoteModule is not torchscript-able. 2024-06-26T05:54:50.2246563Z Warning, this is an experimental API and susceptible to frequent changes. 2024-06-26T05:54:50.2246666Z 2024-06-26T05:54:50.2246760Z Returns: 2024-06-26T05:54:50.2247085Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-06-26T05:54:50.2247467Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-06-26T05:54:50.2247825Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-06-26T05:54:50.2248063Z on the user-provided module on the remote side. 2024-06-26T05:54:50.2248167Z 2024-06-26T05:54:50.2248270Z Example:: 2024-06-26T05:54:50.2248477Z Run the following code in two different processes: 2024-06-26T05:54:50.2248580Z 2024-06-26T05:54:50.2248731Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:50.2248851Z >>> # On worker 0: 2024-06-26T05:54:50.2248958Z >>> import torch 2024-06-26T05:54:50.2249121Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:50.2249268Z >>> from torch import nn, Tensor 2024-06-26T05:54:50.2249563Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-06-26T05:54:50.2249656Z >>> 2024-06-26T05:54:50.2249848Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-06-26T05:54:50.2250005Z >>> remote_linear_module = RemoteModule( 2024-06-26T05:54:50.2250172Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-06-26T05:54:50.2250277Z >>> ) 2024-06-26T05:54:50.2250408Z >>> input = torch.randn(128, 20) 2024-06-26T05:54:50.2250611Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-06-26T05:54:50.2250740Z >>> ret = ret_fut.wait() 2024-06-26T05:54:50.2250881Z >>> rpc.shutdown() 2024-06-26T05:54:50.2250970Z 2024-06-26T05:54:50.2251087Z >>> # On worker 1: 2024-06-26T05:54:50.2251225Z >>> import torch 2024-06-26T05:54:50.2251388Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:50.2251493Z >>> 2024-06-26T05:54:50.2251701Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-06-26T05:54:50.2251826Z >>> rpc.shutdown() 2024-06-26T05:54:50.2251912Z 2024-06-26T05:54:50.2252308Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2252407Z 2024-06-26T05:54:50.2252517Z warnings.warn(msg) 2024-06-26T05:54:50.2252603Z 2024-06-26T05:54:50.2252818Z --- Parse Warning: 48 / 90 --- 2024-06-26T05:54:50.2254432Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=514. 2024-06-26T05:54:50.2254844Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2254947Z 2024-06-26T05:54:50.2255375Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-06-26T05:54:50.2255464Z 2024-06-26T05:54:50.2255907Z This alternate initialization method can be particularly useful if we want to create multiple 2024-06-26T05:54:50.2256326Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-06-26T05:54:50.2256426Z 2024-06-26T05:54:50.2256796Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-06-26T05:54:50.2257033Z which is not supported. The recommended way is as follows: 2024-06-26T05:54:50.2257133Z 2024-06-26T05:54:50.2257285Z 1. the sender creates a RemoteModule; 2024-06-26T05:54:50.2257478Z 2. the sender sends its ``module_rref`` over RPC; 2024-06-26T05:54:50.2257952Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-06-26T05:54:50.2258041Z 2024-06-26T05:54:50.2258145Z Example:: 2024-06-26T05:54:50.2258361Z Run the following code in two different processes: 2024-06-26T05:54:50.2258449Z 2024-06-26T05:54:50.2258599Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:50.2258720Z >>> # On worker 0: 2024-06-26T05:54:50.2258825Z >>> import torch 2024-06-26T05:54:50.2258989Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:50.2259138Z >>> from torch import nn, Tensor 2024-06-26T05:54:50.2259434Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-06-26T05:54:50.2259540Z >>> 2024-06-26T05:54:50.2259717Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-06-26T05:54:50.2259855Z >>> remote_module = RemoteModule( 2024-06-26T05:54:50.2260045Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-06-26T05:54:50.2260137Z >>> ) 2024-06-26T05:54:50.2260232Z >>> 2024-06-26T05:54:50.2260381Z >>> remote_module1 = rpc.rpc_sync( 2024-06-26T05:54:50.2260492Z >>> "worker1/cpu", 2024-06-26T05:54:50.2260655Z >>> RemoteModule.init_from_module_rref, 2024-06-26T05:54:50.2260872Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-06-26T05:54:50.2260964Z >>> ) 2024-06-26T05:54:50.2261074Z >>> rpc.shutdown() 2024-06-26T05:54:50.2261176Z 2024-06-26T05:54:50.2261281Z >>> # On worker 1: 2024-06-26T05:54:50.2261386Z >>> import torch 2024-06-26T05:54:50.2261559Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:50.2261648Z >>> 2024-06-26T05:54:50.2261827Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-06-26T05:54:50.2261948Z >>> rpc.shutdown() 2024-06-26T05:54:50.2262062Z 2024-06-26T05:54:50.2262155Z Args: 2024-06-26T05:54:50.2262654Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-06-26T05:54:50.2263112Z The device can be a local device or a remote device specified by one of the following remote 2024-06-26T05:54:50.2263221Z formats: 2024-06-26T05:54:50.2263342Z 2024-06-26T05:54:50.2263531Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-06-26T05:54:50.2263740Z 2. "/" (ex: "trainer0/cuda:0"). 2024-06-26T05:54:50.2263828Z 2024-06-26T05:54:50.2264169Z In addition, the device field can be optional and the default value is "cpu". 2024-06-26T05:54:50.2264531Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-06-26T05:54:50.2264657Z the created remote module. 2024-06-26T05:54:50.2265049Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-06-26T05:54:50.2265404Z to be created. The type object should be decorated by @torch.jit.interface. 2024-06-26T05:54:50.2265773Z If not provided, the generated RemoteModule is not torchscript-able. 2024-06-26T05:54:50.2266113Z Warning, this is an experimental API and susceptible to frequent changes. 2024-06-26T05:54:50.2266203Z 2024-06-26T05:54:50.2266296Z Returns: 2024-06-26T05:54:50.2266634Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-06-26T05:54:50.2267015Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-06-26T05:54:50.2267372Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-06-26T05:54:50.2267621Z on the user-provided module on the remote side. 2024-06-26T05:54:50.2267710Z 2024-06-26T05:54:50.2268105Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2268206Z 2024-06-26T05:54:50.2268315Z warnings.warn(msg) 2024-06-26T05:54:50.2268401Z 2024-06-26T05:54:50.2268617Z --- Parse Warning: 49 / 90 --- 2024-06-26T05:54:50.2270094Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=606. 2024-06-26T05:54:50.2270516Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2270604Z 2024-06-26T05:54:50.2270911Z A RemoteModule instance can only be created after RPC initialization. 2024-06-26T05:54:50.2271012Z 2024-06-26T05:54:50.2271337Z It creates a user-specified module on a specified remote node. 2024-06-26T05:54:50.2271667Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-06-26T05:54:50.2271812Z executed on the remote node. 2024-06-26T05:54:50.2272142Z It takes care of autograd recording to ensure the backward pass propagates 2024-06-26T05:54:50.2272345Z gradients back to the corresponding remote module. 2024-06-26T05:54:50.2272450Z 2024-06-26T05:54:50.2272747Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-06-26T05:54:50.2273060Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-06-26T05:54:50.2273398Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-06-26T05:54:50.2273673Z and ``forward`` are the same as the ``forward`` method of the module 2024-06-26T05:54:50.2273817Z returned by the ``module_cls``. 2024-06-26T05:54:50.2273905Z 2024-06-26T05:54:50.2274185Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-06-26T05:54:50.2274747Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-06-26T05:54:50.2275123Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-06-26T05:54:50.2275210Z 2024-06-26T05:54:50.2275488Z | ``def forward(input: Tensor) -> Tensor:`` 2024-06-26T05:54:50.2275757Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-06-26T05:54:50.2275844Z 2024-06-26T05:54:50.2275986Z Args: 2024-06-26T05:54:50.2276475Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-06-26T05:54:50.2276959Z The format should be "/", where the device field can be parsed as torch.device type. 2024-06-26T05:54:50.2277147Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-06-26T05:54:50.2277492Z In addition, the device field can be optional and the default value is "cpu". 2024-06-26T05:54:50.2277856Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-06-26T05:54:50.2277976Z 2024-06-26T05:54:50.2278117Z >>> class MyModule(nn.Module): 2024-06-26T05:54:50.2278257Z >>> def forward(input): 2024-06-26T05:54:50.2278387Z >>> return input + 1 2024-06-26T05:54:50.2278479Z >>> 2024-06-26T05:54:50.2278613Z >>> module_cls = MyModule 2024-06-26T05:54:50.2278701Z 2024-06-26T05:54:50.2278965Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-06-26T05:54:50.2279232Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-06-26T05:54:50.2279318Z 2024-06-26T05:54:50.2279415Z Returns: 2024-06-26T05:54:50.2279754Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-06-26T05:54:50.2280130Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-06-26T05:54:50.2280501Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-06-26T05:54:50.2280740Z on the user-provided module on the remote side. 2024-06-26T05:54:50.2280830Z 2024-06-26T05:54:50.2280946Z Example:: 2024-06-26T05:54:50.2281225Z Run the following code in two different processes: 2024-06-26T05:54:50.2281317Z 2024-06-26T05:54:50.2281480Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:50.2281589Z >>> # On worker 0: 2024-06-26T05:54:50.2281696Z >>> import torch 2024-06-26T05:54:50.2281874Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:50.2282008Z >>> from torch import nn, Tensor 2024-06-26T05:54:50.2282303Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-06-26T05:54:50.2282409Z >>> 2024-06-26T05:54:50.2282589Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-06-26T05:54:50.2282759Z >>> remote_linear_module = RemoteModule( 2024-06-26T05:54:50.2282927Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-06-26T05:54:50.2283022Z >>> ) 2024-06-26T05:54:50.2283170Z >>> input = torch.randn(128, 20) 2024-06-26T05:54:50.2283375Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-06-26T05:54:50.2283493Z >>> ret = ret_fut.wait() 2024-06-26T05:54:50.2283633Z >>> rpc.shutdown() 2024-06-26T05:54:50.2283761Z 2024-06-26T05:54:50.2283936Z >>> # On worker 1: 2024-06-26T05:54:50.2284163Z >>> import torch 2024-06-26T05:54:50.2284475Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:50.2284629Z >>> 2024-06-26T05:54:50.2284839Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-06-26T05:54:50.2284949Z >>> rpc.shutdown() 2024-06-26T05:54:50.2285036Z 2024-06-26T05:54:50.2285297Z Furthermore, a more practical example that is combined with 2024-06-26T05:54:50.2285925Z `DistributedDataParallel `__ (DDP) 2024-06-26T05:54:50.2286383Z can be found in this `tutorial `__. 2024-06-26T05:54:50.2286529Z 2024-06-26T05:54:50.2286937Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2287064Z 2024-06-26T05:54:50.2287174Z warnings.warn(msg) 2024-06-26T05:54:50.2287261Z 2024-06-26T05:54:50.2287505Z --- Parse Warning: 50 / 90 --- 2024-06-26T05:54:50.2288990Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=129. 2024-06-26T05:54:50.2289399Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2289500Z 2024-06-26T05:54:50.2289801Z DistributedOptimizer takes remote references to parameters scattered 2024-06-26T05:54:50.2290158Z across workers and applies the given optimizer locally for each parameter. 2024-06-26T05:54:50.2290260Z 2024-06-26T05:54:50.2290588Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-06-26T05:54:50.2290799Z to retrieve the gradients for specific parameters. 2024-06-26T05:54:50.2290890Z 2024-06-26T05:54:50.2291000Z Concurrent calls to 2024-06-26T05:54:50.2291299Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-06-26T05:54:50.2291478Z either from the same or different clients, will 2024-06-26T05:54:50.2291849Z be serialized on each worker -- as each worker's optimizer can only work 2024-06-26T05:54:50.2292156Z on one set of gradients at a time. However, there is no guarantee that 2024-06-26T05:54:50.2292544Z the full forward-backward-optimizer sequence will execute for one client 2024-06-26T05:54:50.2292856Z at a time. This means that the gradients being applied may not correspond 2024-06-26T05:54:50.2293176Z to the latest forward pass executed on a given worker. Also, there is no 2024-06-26T05:54:50.2293317Z guaranteed ordering across workers. 2024-06-26T05:54:50.2293404Z 2024-06-26T05:54:50.2293747Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-06-26T05:54:50.2294071Z by default, so that optimizer updates are not blocked by the Python Global 2024-06-26T05:54:50.2294423Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-06-26T05:54:50.2294743Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-06-26T05:54:50.2295095Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-06-26T05:54:50.2295237Z for your own custom optimizers. 2024-06-26T05:54:50.2295326Z 2024-06-26T05:54:50.2295418Z Args: 2024-06-26T05:54:50.2295686Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-06-26T05:54:50.2295815Z instantiate on each worker. 2024-06-26T05:54:50.2296104Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-06-26T05:54:50.2296223Z to optimize. 2024-06-26T05:54:50.2296514Z args: arguments to pass to the optimizer constructor on each worker. 2024-06-26T05:54:50.2296817Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-06-26T05:54:50.2296919Z 2024-06-26T05:54:50.2297026Z Example:: 2024-06-26T05:54:50.2297187Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:50.2297408Z >>> import torch.distributed.autograd as dist_autograd 2024-06-26T05:54:50.2297574Z >>> import torch.distributed.rpc as rpc 2024-06-26T05:54:50.2297711Z >>> from torch import optim 2024-06-26T05:54:50.2297965Z >>> from torch.distributed.optim import DistributedOptimizer 2024-06-26T05:54:50.2298058Z >>> 2024-06-26T05:54:50.2298248Z >>> with dist_autograd.context() as context_id: 2024-06-26T05:54:50.2298361Z >>> # Forward pass. 2024-06-26T05:54:50.2298633Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-06-26T05:54:50.2298944Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-06-26T05:54:50.2299140Z >>> loss = rref1.to_here() + rref2.to_here() 2024-06-26T05:54:50.2299232Z >>> 2024-06-26T05:54:50.2299357Z >>> # Backward pass. 2024-06-26T05:54:50.2299582Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-06-26T05:54:50.2299674Z >>> 2024-06-26T05:54:50.2299798Z >>> # Optimizer. 2024-06-26T05:54:50.2299961Z >>> dist_optim = DistributedOptimizer( 2024-06-26T05:54:50.2300078Z >>> optim.SGD, 2024-06-26T05:54:50.2300189Z >>> [rref1, rref2], 2024-06-26T05:54:50.2300290Z >>> lr=0.05, 2024-06-26T05:54:50.2300394Z >>> ) 2024-06-26T05:54:50.2300526Z >>> dist_optim.step(context_id) 2024-06-26T05:54:50.2300614Z 2024-06-26T05:54:50.2300850Z __ https://github.com/pytorch/tutorials/pull/1465 2024-06-26T05:54:50.2300940Z 2024-06-26T05:54:50.2301338Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2301440Z 2024-06-26T05:54:50.2301550Z warnings.warn(msg) 2024-06-26T05:54:50.2301636Z 2024-06-26T05:54:50.2301856Z --- Parse Warning: 51 / 90 --- 2024-06-26T05:54:50.2303436Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2024-06-26T05:54:50.2303846Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2303948Z 2024-06-26T05:54:50.2304564Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-06-26T05:54:50.2304781Z This optimizer runs local optimizer at every step. 2024-06-26T05:54:50.2305309Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-06-26T05:54:50.2305399Z 2024-06-26T05:54:50.2305502Z Args: 2024-06-26T05:54:50.2305629Z optim: The local optimizer. 2024-06-26T05:54:50.2305979Z averager: A model averager instance to run post-localSGD algorithm. 2024-06-26T05:54:50.2306083Z 2024-06-26T05:54:50.2306185Z Example:: 2024-06-26T05:54:50.2306273Z 2024-06-26T05:54:50.2306454Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:50.2306559Z >>> import torch 2024-06-26T05:54:50.2306710Z >>> import torch.distributed as dist 2024-06-26T05:54:50.2307083Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-06-26T05:54:50.2307201Z >>> import torch.nn as nn 2024-06-26T05:54:50.2307457Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-06-26T05:54:50.2307834Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-06-26T05:54:50.2307958Z >>> PostLocalSGDState, 2024-06-26T05:54:50.2308090Z >>> post_localSGD_hook, 2024-06-26T05:54:50.2308180Z >>> ) 2024-06-26T05:54:50.2308271Z >>> 2024-06-26T05:54:50.2308495Z >>> model = nn.parallel.DistributedDataParallel( 2024-06-26T05:54:50.2308683Z >>> module, device_ids=[rank], output_device=rank 2024-06-26T05:54:50.2308776Z >>> ) 2024-06-26T05:54:50.2308881Z >>> 2024-06-26T05:54:50.2309126Z >>> # Register a post-localSGD communication hook. 2024-06-26T05:54:50.2309511Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-06-26T05:54:50.2309734Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-06-26T05:54:50.2309827Z >>> 2024-06-26T05:54:50.2310168Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-06-26T05:54:50.2310569Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-06-26T05:54:50.2310787Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-06-26T05:54:50.2311111Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-06-26T05:54:50.2311257Z >>> opt = PostLocalSGDOptimizer( 2024-06-26T05:54:50.2311405Z >>> optim=local_optim, 2024-06-26T05:54:50.2311744Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-06-26T05:54:50.2311835Z >>> ) 2024-06-26T05:54:50.2311928Z >>> 2024-06-26T05:54:50.2312262Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-06-26T05:54:50.2312763Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-06-26T05:54:50.2313403Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-06-26T05:54:50.2313549Z >>> for step in range(0, 200): 2024-06-26T05:54:50.2313663Z >>> opt.zero_grad() 2024-06-26T05:54:50.2313810Z >>> loss = loss_fn(output, labels) 2024-06-26T05:54:50.2313936Z >>> loss.backward() 2024-06-26T05:54:50.2314046Z >>> opt.step() 2024-06-26T05:54:50.2314144Z 2024-06-26T05:54:50.2314539Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2314748Z 2024-06-26T05:54:50.2314878Z warnings.warn(msg) 2024-06-26T05:54:50.2314965Z 2024-06-26T05:54:50.2315168Z --- Parse Warning: 52 / 90 --- 2024-06-26T05:54:50.2316807Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282. 2024-06-26T05:54:50.2317216Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2317305Z 2024-06-26T05:54:50.2317871Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-06-26T05:54:50.2317963Z 2024-06-26T05:54:50.2318128Z The sharing is done as described by ZeRO_. 2024-06-26T05:54:50.2318226Z 2024-06-26T05:54:50.2318421Z The local optimizer instance in each rank is only 2024-06-26T05:54:50.2318750Z responsible for updating approximately ``1 / world_size`` parameters and 2024-06-26T05:54:50.2319027Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-06-26T05:54:50.2319357Z parameters are updated locally, each rank will broadcast its parameters to 2024-06-26T05:54:50.2319626Z all other peers to keep all model replicas in the same state. 2024-06-26T05:54:50.2319873Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-06-26T05:54:50.2320297Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-06-26T05:54:50.2320424Z memory consumption. 2024-06-26T05:54:50.2320512Z 2024-06-26T05:54:50.2320910Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-06-26T05:54:50.2321312Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-06-26T05:54:50.2321649Z not divided among ranks. The partition is arbitrary and might not match the 2024-06-26T05:54:50.2321828Z the parameter registration or usage order. 2024-06-26T05:54:50.2321915Z 2024-06-26T05:54:50.2322012Z Arguments: 2024-06-26T05:54:50.2322283Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-06-26T05:54:50.2322543Z or :class:`dict` s giving all parameters, which will be sharded 2024-06-26T05:54:50.2322649Z across ranks. 2024-06-26T05:54:50.2322753Z 2024-06-26T05:54:50.2322853Z Keyword Args: 2024-06-26T05:54:50.2323149Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-06-26T05:54:50.2323324Z optimizer. 2024-06-26T05:54:50.2323601Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-06-26T05:54:50.2323894Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-06-26T05:54:50.2324134Z :meth:`torch.distributed.init_process_group`). 2024-06-26T05:54:50.2324432Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-06-26T05:54:50.2324721Z packed into buckets to speed up communication, and ``param.data`` 2024-06-26T05:54:50.2325014Z fields point to bucket views at different offsets; if ``False``, 2024-06-26T05:54:50.2325285Z each individual parameter is communicated separately, and each 2024-06-26T05:54:50.2325501Z ``params.data`` stays intact (default: ``False``). 2024-06-26T05:54:50.2325790Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-06-26T05:54:50.2326127Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-06-26T05:54:50.2326418Z synchronization; this requires (1) either a functional optimizer 2024-06-26T05:54:50.2326671Z for the ``optimizer_class`` argument or one with a functional 2024-06-26T05:54:50.2326905Z equivalent and (2) registering a DDP communication hook 2024-06-26T05:54:50.2327183Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-06-26T05:54:50.2327399Z parameters are packed into buckets matching those in 2024-06-26T05:54:50.2327631Z :class:`DistributedDataParallel`, meaning that the 2024-06-26T05:54:50.2327828Z ``parameters_as_bucket_view`` argument is ignored. 2024-06-26T05:54:50.2328090Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-06-26T05:54:50.2328212Z (per normal). 2024-06-26T05:54:50.2328330Z (default: ``False``) 2024-06-26T05:54:50.2328617Z **defaults: any trailing arguments, which are forwarded to the local 2024-06-26T05:54:50.2328731Z optimizer. 2024-06-26T05:54:50.2328822Z 2024-06-26T05:54:50.2328925Z Example:: 2024-06-26T05:54:50.2329025Z 2024-06-26T05:54:50.2329139Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2329258Z >>> import torch.nn as nn 2024-06-26T05:54:50.2329542Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-06-26T05:54:50.2329815Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-06-26T05:54:50.2330126Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-06-26T05:54:50.2330293Z >>> ddp = DDP(model, device_ids=[rank]) 2024-06-26T05:54:50.2330446Z >>> opt = ZeroRedundancyOptimizer( 2024-06-26T05:54:50.2330580Z >>> ddp.parameters(), 2024-06-26T05:54:50.2330737Z >>> optimizer_class=torch.optim.Adam, 2024-06-26T05:54:50.2330838Z >>> lr=0.01 2024-06-26T05:54:50.2330945Z >>> ) 2024-06-26T05:54:50.2331077Z >>> ddp(inputs).sum().backward() 2024-06-26T05:54:50.2331180Z >>> opt.step() 2024-06-26T05:54:50.2331284Z 2024-06-26T05:54:50.2331384Z .. warning:: 2024-06-26T05:54:50.2331662Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-06-26T05:54:50.2331913Z passed-in parameters are the same dense type. 2024-06-26T05:54:50.2332000Z 2024-06-26T05:54:50.2332098Z .. warning:: 2024-06-26T05:54:50.2332402Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-06-26T05:54:50.2332673Z the way that overlapping :class:`DistributedDataParallel` with 2024-06-26T05:54:50.2332974Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-06-26T05:54:50.2333282Z two or three training iterations do not perform parameter updates in 2024-06-26T05:54:50.2333536Z the optimizer step, depending on if ``static_graph=False`` or 2024-06-26T05:54:50.2333819Z ``static_graph=True``, respectively. This is because it needs 2024-06-26T05:54:50.2334054Z information about the gradient bucketing strategy used by 2024-06-26T05:54:50.2334368Z :class:`DistributedDataParallel`, which is not finalized until the 2024-06-26T05:54:50.2334678Z second forward pass if ``static_graph=False`` or until the third 2024-06-26T05:54:50.2334962Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-06-26T05:54:50.2335091Z is to prepend dummy inputs. 2024-06-26T05:54:50.2335190Z 2024-06-26T05:54:50.2335516Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-06-26T05:54:50.2335603Z 2024-06-26T05:54:50.2335780Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-06-26T05:54:50.2335867Z 2024-06-26T05:54:50.2335951Z 2024-06-26T05:54:50.2336391Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2336481Z 2024-06-26T05:54:50.2336592Z warnings.warn(msg) 2024-06-26T05:54:50.2336689Z 2024-06-26T05:54:50.2336891Z --- Parse Warning: 53 / 90 --- 2024-06-26T05:54:50.2338387Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2024-06-26T05:54:50.2338800Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2338886Z 2024-06-26T05:54:50.2339214Z Custom reducer class that can be used to specify a custom operation that 2024-06-26T05:54:50.2339437Z reduces losses of multiple microbatches into one value. 2024-06-26T05:54:50.2339523Z 2024-06-26T05:54:50.2339632Z Example: 2024-06-26T05:54:50.2339742Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2339881Z >>> sum_reducer = _CustomReducer( 2024-06-26T05:54:50.2340010Z >>> torch.tensor(0.0), 2024-06-26T05:54:50.2340120Z >>> lambda a, b: a + b 2024-06-26T05:54:50.2340209Z >>> ) 2024-06-26T05:54:50.2340311Z 2024-06-26T05:54:50.2340705Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2340805Z 2024-06-26T05:54:50.2340915Z warnings.warn(msg) 2024-06-26T05:54:50.2341003Z 2024-06-26T05:54:50.2341215Z --- Parse Warning: 54 / 90 --- 2024-06-26T05:54:50.2342625Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6. 2024-06-26T05:54:50.2343034Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2343134Z 2024-06-26T05:54:50.2343463Z A decorator for a function indicating that the return value of the function 2024-06-26T05:54:50.2343756Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-06-26T05:54:50.2344094Z function can run asynchronously on the RPC callee. More specifically, the 2024-06-26T05:54:50.2344416Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-06-26T05:54:50.2344743Z function and installs subsequent processing steps as a callback to that 2024-06-26T05:54:50.2345065Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-06-26T05:54:50.2345350Z from the :class:`~torch.futures.Future` when completed and send the 2024-06-26T05:54:50.2345608Z value back as the RPC response. That also means the returned 2024-06-26T05:54:50.2345926Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-06-26T05:54:50.2346260Z sent through RPC. This decorator is useful when the wrapped function's 2024-06-26T05:54:50.2346546Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-06-26T05:54:50.2346877Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-06-26T05:54:50.2346965Z 2024-06-26T05:54:50.2347270Z .. note:: To enable asynchronous execution, applications must pass the 2024-06-26T05:54:50.2347616Z function object returned by this decorator to RPC APIs. If RPC detected 2024-06-26T05:54:50.2347943Z attributes installed by this decorator, it knows that this function 2024-06-26T05:54:50.2348196Z returns a ``Future`` object and will handle that accordingly. 2024-06-26T05:54:50.2348488Z However, this does not mean this decorator has to be outmost one when 2024-06-26T05:54:50.2348807Z defining a function. For example, when combined with ``@staticmethod`` 2024-06-26T05:54:50.2349095Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-06-26T05:54:50.2349396Z inner decorator to allow the target function be recognized as a static 2024-06-26T05:54:50.2349751Z or class function. This target function can still execute asynchronously 2024-06-26T05:54:50.2350060Z because, when accessed, the static or class method preserves attributes 2024-06-26T05:54:50.2350271Z installed by ``@rpc.functions.async_execution``. 2024-06-26T05:54:50.2350358Z 2024-06-26T05:54:50.2350444Z 2024-06-26T05:54:50.2350554Z Example:: 2024-06-26T05:54:50.2350834Z The returned :class:`~torch.futures.Future` object can come from 2024-06-26T05:54:50.2351002Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-06-26T05:54:50.2351312Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-06-26T05:54:50.2351541Z constructor. The example below shows directly using the 2024-06-26T05:54:50.2351710Z :class:`~torch.futures.Future` returned by 2024-06-26T05:54:50.2351874Z :meth:`~torch.futures.Future.then`. 2024-06-26T05:54:50.2351962Z 2024-06-26T05:54:50.2352120Z >>> from torch.distributed import rpc 2024-06-26T05:54:50.2352227Z >>> 2024-06-26T05:54:50.2352377Z >>> # omitting setup and shutdown RPC 2024-06-26T05:54:50.2352469Z >>> 2024-06-26T05:54:50.2352595Z >>> # On all workers 2024-06-26T05:54:50.2352735Z >>> @rpc.functions.async_execution 2024-06-26T05:54:50.2352888Z >>> def async_add_chained(to, x, y, z): 2024-06-26T05:54:50.2353165Z >>> # This function runs on "worker1" and returns immediately when 2024-06-26T05:54:50.2353426Z >>> # the callback is installed through the `then(cb)` API. In the 2024-06-26T05:54:50.2353701Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-06-26T05:54:50.2353922Z >>> # When the return value of that `rpc_async` arrives at 2024-06-26T05:54:50.2354171Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-06-26T05:54:50.2354453Z >>> # and set the value for the previously returned `Future`, which 2024-06-26T05:54:50.2354918Z >>> # will then trigger RPC to send the result back to "worker0". 2024-06-26T05:54:50.2355146Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:50.2355312Z >>> lambda fut: fut.wait() + z 2024-06-26T05:54:50.2355406Z >>> ) 2024-06-26T05:54:50.2355498Z >>> 2024-06-26T05:54:50.2355618Z >>> # On worker0 2024-06-26T05:54:50.2355732Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2355846Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:50.2355963Z >>> "worker1", 2024-06-26T05:54:50.2356076Z >>> async_add_chained, 2024-06-26T05:54:50.2356247Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-06-26T05:54:50.2356340Z >>> ) 2024-06-26T05:54:50.2356494Z >>> print(ret) # prints tensor([3., 3.]) 2024-06-26T05:54:50.2356596Z 2024-06-26T05:54:50.2356899Z When combined with TorchScript decorators, this decorator must be the 2024-06-26T05:54:50.2357003Z outmost one. 2024-06-26T05:54:50.2357152Z 2024-06-26T05:54:50.2357276Z >>> from torch import Tensor 2024-06-26T05:54:50.2357427Z >>> from torch.futures import Future 2024-06-26T05:54:50.2357641Z >>> from torch.distributed import rpc 2024-06-26T05:54:50.2357732Z >>> 2024-06-26T05:54:50.2357881Z >>> # omitting setup and shutdown RPC 2024-06-26T05:54:50.2358033Z >>> 2024-06-26T05:54:50.2358146Z >>> # On all workers 2024-06-26T05:54:50.2358261Z >>> @torch.jit.script 2024-06-26T05:54:50.2358534Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-06-26T05:54:50.2358642Z >>> return x + y 2024-06-26T05:54:50.2358734Z >>> 2024-06-26T05:54:50.2358893Z >>> @rpc.functions.async_execution 2024-06-26T05:54:50.2359005Z >>> @torch.jit.script 2024-06-26T05:54:50.2359330Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-06-26T05:54:50.2359597Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-06-26T05:54:50.2359694Z >>> 2024-06-26T05:54:50.2359811Z >>> # On worker0 2024-06-26T05:54:50.2359925Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:50.2360031Z >>> "worker1", 2024-06-26T05:54:50.2360148Z >>> async_add, 2024-06-26T05:54:50.2360302Z >>> args=("worker2", torch.ones(2), 1) 2024-06-26T05:54:50.2360399Z >>> ) 2024-06-26T05:54:50.2360567Z >>> print(ret) # prints tensor([2., 2.]) 2024-06-26T05:54:50.2360656Z 2024-06-26T05:54:50.2360957Z When combined with static or class method, this decorator must be the 2024-06-26T05:54:50.2361168Z inner one. 2024-06-26T05:54:50.2361257Z 2024-06-26T05:54:50.2361413Z >>> from torch.distributed import rpc 2024-06-26T05:54:50.2361520Z >>> 2024-06-26T05:54:50.2361667Z >>> # omitting setup and shutdown RPC 2024-06-26T05:54:50.2361759Z >>> 2024-06-26T05:54:50.2361885Z >>> # On all workers 2024-06-26T05:54:50.2362024Z >>> class AsyncExecutionClass: 2024-06-26T05:54:50.2362115Z >>> 2024-06-26T05:54:50.2362239Z >>> @staticmethod 2024-06-26T05:54:50.2362392Z >>> @rpc.functions.async_execution 2024-06-26T05:54:50.2362546Z >>> def static_async_add(to, x, y, z): 2024-06-26T05:54:50.2362791Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:50.2362938Z >>> lambda fut: fut.wait() + z 2024-06-26T05:54:50.2363049Z >>> ) 2024-06-26T05:54:50.2363141Z >>> 2024-06-26T05:54:50.2363246Z >>> @classmethod 2024-06-26T05:54:50.2363410Z >>> @rpc.functions.async_execution 2024-06-26T05:54:50.2363573Z >>> def class_async_add(cls, to, x, y, z): 2024-06-26T05:54:50.2363737Z >>> ret_fut = torch.futures.Future() 2024-06-26T05:54:50.2363951Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:50.2364156Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-06-26T05:54:50.2364256Z >>> ) 2024-06-26T05:54:50.2364385Z >>> return ret_fut 2024-06-26T05:54:50.2364479Z >>> 2024-06-26T05:54:50.2364628Z >>> @rpc.functions.async_execution 2024-06-26T05:54:50.2364810Z >>> def bound_async_add(self, to, x, y, z): 2024-06-26T05:54:50.2365038Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-06-26T05:54:50.2365185Z >>> lambda fut: fut.wait() + z 2024-06-26T05:54:50.2365295Z >>> ) 2024-06-26T05:54:50.2365386Z >>> 2024-06-26T05:54:50.2365501Z >>> # On worker0 2024-06-26T05:54:50.2365616Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:50.2365720Z >>> "worker1", 2024-06-26T05:54:50.2365914Z >>> AsyncExecutionClass.static_async_add, 2024-06-26T05:54:50.2366071Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-06-26T05:54:50.2366166Z >>> ) 2024-06-26T05:54:50.2366375Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:50.2366497Z >>> 2024-06-26T05:54:50.2366665Z >>> ret = rpc.rpc_sync( 2024-06-26T05:54:50.2366835Z >>> "worker1", 2024-06-26T05:54:50.2367011Z >>> AsyncExecutionClass.class_async_add, 2024-06-26T05:54:50.2367197Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-06-26T05:54:50.2367306Z >>> ) 2024-06-26T05:54:50.2367457Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:50.2367548Z 2024-06-26T05:54:50.2367777Z This decorator also works with RRef helpers, i.e., . 2024-06-26T05:54:50.2367962Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-06-26T05:54:50.2368171Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-06-26T05:54:50.2368370Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-06-26T05:54:50.2368455Z 2024-06-26T05:54:50.2368652Z >>> from torch.distributed import rpc 2024-06-26T05:54:50.2368764Z >>> 2024-06-26T05:54:50.2368944Z >>> # reuse the AsyncExecutionClass class above 2024-06-26T05:54:50.2369166Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-06-26T05:54:50.2369454Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-06-26T05:54:50.2369608Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:50.2369713Z >>> 2024-06-26T05:54:50.2369919Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-06-26T05:54:50.2370241Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-06-26T05:54:50.2370408Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:50.2370498Z >>> 2024-06-26T05:54:50.2370700Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-06-26T05:54:50.2371035Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-06-26T05:54:50.2371189Z >>> print(ret) # prints tensor([4., 4.]) 2024-06-26T05:54:50.2371277Z 2024-06-26T05:54:50.2371700Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2371788Z 2024-06-26T05:54:50.2371912Z warnings.warn(msg) 2024-06-26T05:54:50.2371998Z 2024-06-26T05:54:50.2372200Z --- Parse Warning: 55 / 90 --- 2024-06-26T05:54:50.2373819Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=108. 2024-06-26T05:54:50.2374228Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2374315Z 2024-06-26T05:54:50.2374603Z Set device mapping between each RPC caller and callee pair. This 2024-06-26T05:54:50.2374843Z function can be called multiple times to incrementally add 2024-06-26T05:54:50.2374980Z device placement configurations. 2024-06-26T05:54:50.2375080Z 2024-06-26T05:54:50.2375173Z Args: 2024-06-26T05:54:50.2375288Z to (str): Callee name. 2024-06-26T05:54:50.2375573Z device_map (Dict of int, str, or torch.device): Device placement 2024-06-26T05:54:50.2375815Z mappings from this worker to the callee. This map must be 2024-06-26T05:54:50.2375932Z invertible. 2024-06-26T05:54:50.2376020Z 2024-06-26T05:54:50.2376118Z Example: 2024-06-26T05:54:50.2376280Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:50.2376388Z >>> # both workers 2024-06-26T05:54:50.2376496Z >>> def add(x, y): 2024-06-26T05:54:50.2376748Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-06-26T05:54:50.2376883Z >>> return x + y, (x + y).to(2) 2024-06-26T05:54:50.2376976Z >>> 2024-06-26T05:54:50.2377095Z >>> # on worker 0 2024-06-26T05:54:50.2377276Z >>> options = TensorPipeRpcBackendOptions( 2024-06-26T05:54:50.2377435Z >>> num_worker_threads=8, 2024-06-26T05:54:50.2377593Z >>> device_maps={"worker1": {0: 1}} 2024-06-26T05:54:50.2377858Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-06-26T05:54:50.2377951Z >>> ) 2024-06-26T05:54:50.2378134Z >>> options.set_device_map("worker1", {1: 2}) 2024-06-26T05:54:50.2378384Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-06-26T05:54:50.2378477Z >>> 2024-06-26T05:54:50.2378601Z >>> rpc.init_rpc( 2024-06-26T05:54:50.2378704Z >>> "worker0", 2024-06-26T05:54:50.2378815Z >>> rank=0, 2024-06-26T05:54:50.2378926Z >>> world_size=2, 2024-06-26T05:54:50.2379096Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-06-26T05:54:50.2379245Z >>> rpc_backend_options=options 2024-06-26T05:54:50.2379344Z >>> ) 2024-06-26T05:54:50.2379433Z >>> 2024-06-26T05:54:50.2379582Z >>> x = torch.ones(2) 2024-06-26T05:54:50.2379800Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-06-26T05:54:50.2380051Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-06-26T05:54:50.2380321Z >>> # sending the return value back, it will follow the invert of 2024-06-26T05:54:50.2380569Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-06-26T05:54:50.2380682Z >>> # cuda:1 on worker0 2024-06-26T05:54:50.2380947Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-06-26T05:54:50.2381194Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-06-26T05:54:50.2381280Z 2024-06-26T05:54:50.2381685Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2381773Z 2024-06-26T05:54:50.2381886Z warnings.warn(msg) 2024-06-26T05:54:50.2381988Z 2024-06-26T05:54:50.2382190Z --- Parse Warning: 56 / 90 --- 2024-06-26T05:54:50.2383731Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=366. 2024-06-26T05:54:50.2384142Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2384230Z 2024-06-26T05:54:50.2384867Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-06-26T05:54:50.2385289Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-06-26T05:54:50.2385375Z 2024-06-26T05:54:50.2385487Z Keyword Args: 2024-06-26T05:54:50.2385739Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-06-26T05:54:50.2386209Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-06-26T05:54:50.2386744Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-06-26T05:54:50.2386894Z as a placeholder. default: None. 2024-06-26T05:54:50.2387199Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-06-26T05:54:50.2387742Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-06-26T05:54:50.2388297Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-06-26T05:54:50.2388476Z input_kwarg_layouts (Dict[str, Placement]): 2024-06-26T05:54:50.2389026Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-06-26T05:54:50.2389133Z default: None 2024-06-26T05:54:50.2389351Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-06-26T05:54:50.2389883Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-06-26T05:54:50.2390134Z have the desired DTensor layouts. default: None. 2024-06-26T05:54:50.2390301Z use_local_output (bool, optional): 2024-06-26T05:54:50.2390837Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-06-26T05:54:50.2390944Z Returns: 2024-06-26T05:54:50.2391451Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-06-26T05:54:50.2391539Z 2024-06-26T05:54:50.2391661Z Example:: 2024-06-26T05:54:50.2391788Z >>> # xdoctest: +SKIP(failing) 2024-06-26T05:54:50.2392205Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-06-26T05:54:50.2392471Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-06-26T05:54:50.2392601Z >>> ... 2024-06-26T05:54:50.2393045Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-06-26T05:54:50.2393209Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-06-26T05:54:50.2393300Z >>> 2024-06-26T05:54:50.2393789Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-06-26T05:54:50.2393984Z >>> # and then redistributed to Replicated DTensor. 2024-06-26T05:54:50.2394100Z >>> parallelize_module( 2024-06-26T05:54:50.2394294Z >>> block, # this can be a submodule or module 2024-06-26T05:54:50.2394394Z >>> tp_mesh, 2024-06-26T05:54:50.2394512Z >>> parallelize_plan={ 2024-06-26T05:54:50.2394806Z >>> "attn": PrepareModuleInput( 2024-06-26T05:54:50.2394994Z >>> input_layouts=(Shard(0), None, None, ...), 2024-06-26T05:54:50.2395219Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-06-26T05:54:50.2395334Z >>> ), 2024-06-26T05:54:50.2395428Z >>> } 2024-06-26T05:54:50.2395519Z >>> ) 2024-06-26T05:54:50.2395624Z 2024-06-26T05:54:50.2396026Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2396127Z 2024-06-26T05:54:50.2396240Z warnings.warn(msg) 2024-06-26T05:54:50.2396328Z 2024-06-26T05:54:50.2396540Z --- Parse Warning: 57 / 90 --- 2024-06-26T05:54:50.2398070Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=521. 2024-06-26T05:54:50.2398480Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2398576Z 2024-06-26T05:54:50.2399221Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-06-26T05:54:50.2399659Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-06-26T05:54:50.2399759Z 2024-06-26T05:54:50.2399859Z Keyword Args: 2024-06-26T05:54:50.2400083Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-06-26T05:54:50.2400567Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-06-26T05:54:50.2401185Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-06-26T05:54:50.2401392Z ``None`` need to be specified as a placeholder. 2024-06-26T05:54:50.2401643Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-06-26T05:54:50.2402204Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-06-26T05:54:50.2402426Z have the desired DTensor layouts. 2024-06-26T05:54:50.2402568Z use_local_output (bool, optional): 2024-06-26T05:54:50.2403074Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-06-26T05:54:50.2403222Z Returns: 2024-06-26T05:54:50.2403732Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-06-26T05:54:50.2403837Z 2024-06-26T05:54:50.2403939Z Example:: 2024-06-26T05:54:50.2404065Z >>> # xdoctest: +SKIP(failing) 2024-06-26T05:54:50.2404503Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-06-26T05:54:50.2404758Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-06-26T05:54:50.2404853Z >>> ... 2024-06-26T05:54:50.2405335Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-06-26T05:54:50.2405504Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-06-26T05:54:50.2405596Z >>> 2024-06-26T05:54:50.2406170Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-06-26T05:54:50.2406355Z >>> # and then redistributed to Sharded DTensor. 2024-06-26T05:54:50.2406492Z >>> parallelize_module( 2024-06-26T05:54:50.2406674Z >>> block, # this can be a submodule or module 2024-06-26T05:54:50.2406778Z >>> tp_mesh, 2024-06-26T05:54:50.2406975Z >>> parallelize_plan = PrepareModuleOutput( 2024-06-26T05:54:50.2407125Z >>> output_layouts=Replicate(), 2024-06-26T05:54:50.2407276Z >>> desired_output_layouts=Shard(0) 2024-06-26T05:54:50.2407385Z >>> ) 2024-06-26T05:54:50.2407480Z >>> ) 2024-06-26T05:54:50.2407566Z 2024-06-26T05:54:50.2407981Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2408072Z 2024-06-26T05:54:50.2408183Z warnings.warn(msg) 2024-06-26T05:54:50.2408284Z 2024-06-26T05:54:50.2408487Z --- Parse Warning: 58 / 90 --- 2024-06-26T05:54:50.2409983Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=109. 2024-06-26T05:54:50.2410402Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2410488Z 2024-06-26T05:54:50.2410735Z Creates a RelaxedBernoulli distribution, parametrized by 2024-06-26T05:54:50.2410983Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-06-26T05:54:50.2411292Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-06-26T05:54:50.2411561Z so the values are in (0, 1), and has reparametrizable samples. 2024-06-26T05:54:50.2411649Z 2024-06-26T05:54:50.2411750Z Example:: 2024-06-26T05:54:50.2411844Z 2024-06-26T05:54:50.2412082Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:50.2412256Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-06-26T05:54:50.2412444Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-06-26T05:54:50.2412546Z >>> m.sample() 2024-06-26T05:54:50.2412699Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-06-26T05:54:50.2412795Z 2024-06-26T05:54:50.2412890Z Args: 2024-06-26T05:54:50.2413068Z temperature (Tensor): relaxation temperature 2024-06-26T05:54:50.2413303Z probs (Number, Tensor): the probability of sampling `1` 2024-06-26T05:54:50.2413562Z logits (Number, Tensor): the log-odds of sampling `1` 2024-06-26T05:54:50.2413662Z 2024-06-26T05:54:50.2414056Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2414143Z 2024-06-26T05:54:50.2414305Z warnings.warn(msg) 2024-06-26T05:54:50.2414392Z 2024-06-26T05:54:50.2414592Z --- Parse Warning: 59 / 90 --- 2024-06-26T05:54:50.2416208Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=97. 2024-06-26T05:54:50.2416619Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2416705Z 2024-06-26T05:54:50.2417004Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-06-26T05:54:50.2417258Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-06-26T05:54:50.2417605Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-06-26T05:54:50.2417819Z its samples are on simplex, and are reparametrizable. 2024-06-26T05:54:50.2417932Z 2024-06-26T05:54:50.2418048Z Example:: 2024-06-26T05:54:50.2418133Z 2024-06-26T05:54:50.2418371Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:50.2418593Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-06-26T05:54:50.2418777Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-06-26T05:54:50.2418879Z >>> m.sample() 2024-06-26T05:54:50.2419044Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-06-26T05:54:50.2419129Z 2024-06-26T05:54:50.2419220Z Args: 2024-06-26T05:54:50.2419408Z temperature (Tensor): relaxation temperature 2024-06-26T05:54:50.2419551Z probs (Tensor): event probabilities 2024-06-26T05:54:50.2419801Z logits (Tensor): unnormalized log probability for each event 2024-06-26T05:54:50.2419898Z 2024-06-26T05:54:50.2420290Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2420378Z 2024-06-26T05:54:50.2420501Z warnings.warn(msg) 2024-06-26T05:54:50.2420588Z 2024-06-26T05:54:50.2420800Z --- Parse Warning: 60 / 90 --- 2024-06-26T05:54:50.2422302Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=12. 2024-06-26T05:54:50.2422715Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2422818Z 2024-06-26T05:54:50.2423106Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-06-26T05:54:50.2423423Z distribution where all component are from different parameterizations of 2024-06-26T05:54:50.2423720Z the same distribution type. It is parameterized by a `Categorical` 2024-06-26T05:54:50.2423968Z "selecting distribution" (over `k` component) and a component 2024-06-26T05:54:50.2424244Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-06-26T05:54:50.2424474Z (equal to `[k]`) which indexes each (batch of) component. 2024-06-26T05:54:50.2424560Z 2024-06-26T05:54:50.2424677Z Examples:: 2024-06-26T05:54:50.2424762Z 2024-06-26T05:54:50.2424915Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:50.2425201Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-06-26T05:54:50.2425342Z >>> # weighted normal distributions 2024-06-26T05:54:50.2425495Z >>> mix = D.Categorical(torch.ones(5,)) 2024-06-26T05:54:50.2425701Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-06-26T05:54:50.2425856Z >>> gmm = MixtureSameFamily(mix, comp) 2024-06-26T05:54:50.2425942Z 2024-06-26T05:54:50.2426220Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-06-26T05:54:50.2426392Z >>> # weighted bivariate normal distributions 2024-06-26T05:54:50.2426544Z >>> mix = D.Categorical(torch.ones(5,)) 2024-06-26T05:54:50.2426728Z >>> comp = D.Independent(D.Normal( 2024-06-26T05:54:50.2426903Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-06-26T05:54:50.2427084Z >>> gmm = MixtureSameFamily(mix, comp) 2024-06-26T05:54:50.2427184Z 2024-06-26T05:54:50.2427426Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-06-26T05:54:50.2427733Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-06-26T05:54:50.2427886Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-06-26T05:54:50.2428024Z >>> comp = D.Independent(D.Normal( 2024-06-26T05:54:50.2428218Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-06-26T05:54:50.2428372Z >>> gmm = MixtureSameFamily(mix, comp) 2024-06-26T05:54:50.2428458Z 2024-06-26T05:54:50.2428560Z Args: 2024-06-26T05:54:50.2428884Z mixture_distribution: `torch.distributions.Categorical`-like 2024-06-26T05:54:50.2429157Z instance. Manages the probability of selecting component. 2024-06-26T05:54:50.2429401Z The number of categories must match the rightmost batch 2024-06-26T05:54:50.2429650Z dimension of the `component_distribution`. Must have either 2024-06-26T05:54:50.2429834Z scalar `batch_shape` or `batch_shape` matching 2024-06-26T05:54:50.2430076Z `component_distribution.batch_shape[:-1]` 2024-06-26T05:54:50.2430416Z component_distribution: `torch.distributions.Distribution`-like 2024-06-26T05:54:50.2430711Z instance. Right-most batch dimension indexes component. 2024-06-26T05:54:50.2430799Z 2024-06-26T05:54:50.2431191Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2431288Z 2024-06-26T05:54:50.2431397Z warnings.warn(msg) 2024-06-26T05:54:50.2431483Z 2024-06-26T05:54:50.2431692Z --- Parse Warning: 61 / 90 --- 2024-06-26T05:54:50.2433235Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=230. 2024-06-26T05:54:50.2433646Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2433917Z Return a new dict with new, potentially nested, key value pair 2024-06-26T05:54:50.2434003Z 2024-06-26T05:54:50.2434186Z >>> purchase = {'name': 'Alice', 2024-06-26T05:54:50.2434416Z ... 'order': {'items': ['Apple', 'Orange'], 2024-06-26T05:54:50.2434757Z ... 'costs': [0.50, 1.25]}, 2024-06-26T05:54:50.2435001Z ... 'credit card': '5555-1234-1234-1234'} 2024-06-26T05:54:50.2435348Z >>> assoc_in(purchase, ['order', 'costs'], [0.25, 1.00]) # doctest: +SKIP 2024-06-26T05:54:50.2435531Z {'credit card': '5555-1234-1234-1234', 2024-06-26T05:54:50.2435682Z 'name': 'Alice', 2024-06-26T05:54:50.2435974Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-06-26T05:54:50.2436066Z 2024-06-26T05:54:50.2436478Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2436564Z 2024-06-26T05:54:50.2436673Z warnings.warn(msg) 2024-06-26T05:54:50.2436777Z 2024-06-26T05:54:50.2436981Z --- Parse Warning: 62 / 90 --- 2024-06-26T05:54:50.2438536Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2024-06-26T05:54:50.2438955Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2439147Z Update value in a (potentially) nested dictionary 2024-06-26T05:54:50.2439245Z 2024-06-26T05:54:50.2439340Z inputs: 2024-06-26T05:54:50.2439578Z d - dictionary on which to operate 2024-06-26T05:54:50.2439961Z keys - list or tuple giving the location of the value to be changed in d 2024-06-26T05:54:50.2440211Z func - function to operate on that value 2024-06-26T05:54:50.2440297Z 2024-06-26T05:54:50.2440634Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-06-26T05:54:50.2440947Z original dictionary with v replaced by func(v), but does not mutate the 2024-06-26T05:54:50.2441128Z original dictionary. 2024-06-26T05:54:50.2441231Z 2024-06-26T05:54:50.2441551Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-06-26T05:54:50.2441860Z specified by the keys, with the innermost value set to func(default). 2024-06-26T05:54:50.2441948Z 2024-06-26T05:54:50.2442067Z >>> inc = lambda x: x + 1 2024-06-26T05:54:50.2442260Z >>> update_in({'a': 0}, ['a'], inc) 2024-06-26T05:54:50.2442416Z {'a': 1} 2024-06-26T05:54:50.2442507Z 2024-06-26T05:54:50.2442703Z >>> transaction = {'name': 'Alice', 2024-06-26T05:54:50.2442950Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-06-26T05:54:50.2443174Z ... 'costs': [0.50, 1.25]}, 2024-06-26T05:54:50.2443413Z ... 'credit card': '5555-1234-1234-1234'} 2024-06-26T05:54:50.2443760Z >>> update_in(transaction, ['purchase', 'costs'], sum) # doctest: +SKIP 2024-06-26T05:54:50.2443942Z {'credit card': '5555-1234-1234-1234', 2024-06-26T05:54:50.2444087Z 'name': 'Alice', 2024-06-26T05:54:50.2444360Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-06-26T05:54:50.2444446Z 2024-06-26T05:54:50.2444619Z >>> # updating a value when k0 is not in d 2024-06-26T05:54:50.2444788Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-06-26T05:54:50.2444928Z {1: {2: {3: 'bar'}}} 2024-06-26T05:54:50.2445142Z >>> update_in({1: 'foo'}, [2, 3, 4], inc, 0) 2024-06-26T05:54:50.2445292Z {1: 'foo', 2: {3: {4: 1}}} 2024-06-26T05:54:50.2445393Z 2024-06-26T05:54:50.2445789Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2445878Z 2024-06-26T05:54:50.2446003Z warnings.warn(msg) 2024-06-26T05:54:50.2446093Z 2024-06-26T05:54:50.2446296Z --- Parse Warning: 63 / 90 --- 2024-06-26T05:54:50.2447838Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=303. 2024-06-26T05:54:50.2448246Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2448464Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-06-26T05:54:50.2448566Z 2024-06-26T05:54:50.2448834Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-06-26T05:54:50.2449121Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-06-26T05:54:50.2449212Z 2024-06-26T05:54:50.2449502Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-06-26T05:54:50.2449685Z structures such as dictionaries and lists. 2024-06-26T05:54:50.2449773Z 2024-06-26T05:54:50.2449952Z >>> transaction = {'name': 'Alice', 2024-06-26T05:54:50.2450217Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-06-26T05:54:50.2450436Z ... 'costs': [0.50, 1.25]}, 2024-06-26T05:54:50.2450661Z ... 'credit card': '5555-1234-1234-1234'} 2024-06-26T05:54:50.2450911Z >>> get_in(['purchase', 'items', 0], transaction) 2024-06-26T05:54:50.2451033Z 'Apple' 2024-06-26T05:54:50.2451203Z >>> get_in(['name'], transaction) 2024-06-26T05:54:50.2451337Z 'Alice' 2024-06-26T05:54:50.2451595Z >>> get_in(['purchase', 'total'], transaction) 2024-06-26T05:54:50.2451848Z >>> get_in(['purchase', 'items', 'apple'], transaction) 2024-06-26T05:54:50.2452123Z >>> get_in(['purchase', 'items', 10], transaction) 2024-06-26T05:54:50.2452352Z >>> get_in(['purchase', 'total'], transaction, 0) 2024-06-26T05:54:50.2452460Z 0 2024-06-26T05:54:50.2452691Z >>> get_in(['y'], {}, no_default=True) 2024-06-26T05:54:50.2452835Z Traceback (most recent call last): 2024-06-26T05:54:50.2452941Z ... 2024-06-26T05:54:50.2453073Z KeyError: 'y' 2024-06-26T05:54:50.2453162Z 2024-06-26T05:54:50.2453277Z See Also: 2024-06-26T05:54:50.2453384Z itertoolz.get 2024-06-26T05:54:50.2453497Z operator.getitem 2024-06-26T05:54:50.2453602Z 2024-06-26T05:54:50.2453999Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2454084Z 2024-06-26T05:54:50.2454235Z warnings.warn(msg) 2024-06-26T05:54:50.2454326Z 2024-06-26T05:54:50.2454524Z --- Parse Warning: 64 / 90 --- 2024-06-26T05:54:50.2456083Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=355. 2024-06-26T05:54:50.2456492Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2456646Z Group a collection by a key function 2024-06-26T05:54:50.2456731Z 2024-06-26T05:54:50.2457025Z >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank'] 2024-06-26T05:54:50.2457194Z >>> groupby(len, names) # doctest: +SKIP 2024-06-26T05:54:50.2457488Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-06-26T05:54:50.2457575Z 2024-06-26T05:54:50.2457718Z >>> iseven = lambda x: x % 2 == 0 2024-06-26T05:54:50.2457955Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-06-26T05:54:50.2458102Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-06-26T05:54:50.2458204Z 2024-06-26T05:54:50.2458431Z Non-callable keys imply grouping on a member. 2024-06-26T05:54:50.2458518Z 2024-06-26T05:54:50.2458791Z >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'}, 2024-06-26T05:54:50.2459012Z ... {'name': 'Bob', 'gender': 'M'}, 2024-06-26T05:54:50.2459294Z ... {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP 2024-06-26T05:54:50.2459500Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-06-26T05:54:50.2459683Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-06-26T05:54:50.2459890Z {'gender': 'M', 'name': 'Charlie'}]} 2024-06-26T05:54:50.2459977Z 2024-06-26T05:54:50.2460159Z Not to be confused with ``itertools.groupby`` 2024-06-26T05:54:50.2460257Z 2024-06-26T05:54:50.2460353Z See Also: 2024-06-26T05:54:50.2460448Z countby 2024-06-26T05:54:50.2460549Z 2024-06-26T05:54:50.2460940Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2461026Z 2024-06-26T05:54:50.2461148Z warnings.warn(msg) 2024-06-26T05:54:50.2461233Z 2024-06-26T05:54:50.2461433Z --- Parse Warning: 65 / 90 --- 2024-06-26T05:54:50.2462830Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601. 2024-06-26T05:54:50.2463234Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2463509Z Applies Batch Normalization over a N-Dimensional input. 2024-06-26T05:54:50.2463608Z 2024-06-26T05:54:50.2464206Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-06-26T05:54:50.2464548Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-06-26T05:54:50.2464823Z Internal Covariate Shift `__ . 2024-06-26T05:54:50.2464941Z 2024-06-26T05:54:50.2465060Z .. math:: 2024-06-26T05:54:50.2465146Z 2024-06-26T05:54:50.2465584Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-06-26T05:54:50.2465684Z 2024-06-26T05:54:50.2466058Z The mean and standard-deviation are calculated per-dimension over all 2024-06-26T05:54:50.2466441Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-06-26T05:54:50.2466775Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-06-26T05:54:50.2467016Z By default, the elements of :math:`\gamma` are sampled from 2024-06-26T05:54:50.2467431Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-06-26T05:54:50.2467876Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-06-26T05:54:50.2468017Z `torch.var(input, unbiased=False)`. 2024-06-26T05:54:50.2468124Z 2024-06-26T05:54:50.2468446Z Also by default, during training this layer keeps running estimates of its 2024-06-26T05:54:50.2468763Z computed mean and variance, which are then used for normalization during 2024-06-26T05:54:50.2469095Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-06-26T05:54:50.2469188Z of 0.1. 2024-06-26T05:54:50.2469273Z 2024-06-26T05:54:50.2469602Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-06-26T05:54:50.2469897Z keep running estimates, and batch statistics are instead used during 2024-06-26T05:54:50.2470017Z evaluation time as well. 2024-06-26T05:54:50.2470119Z 2024-06-26T05:54:50.2470220Z .. note:: 2024-06-26T05:54:50.2470531Z This :attr:`momentum` argument is different from one used in optimizer 2024-06-26T05:54:50.2470848Z classes and the conventional notion of momentum. Mathematically, the 2024-06-26T05:54:50.2471024Z update rule for running statistics here is 2024-06-26T05:54:50.2471522Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-06-26T05:54:50.2471816Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-06-26T05:54:50.2471931Z new observed value. 2024-06-26T05:54:50.2472029Z 2024-06-26T05:54:50.2472442Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-06-26T05:54:50.2472866Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-06-26T05:54:50.2473156Z Normalization or Spatio-temporal Batch Normalization. 2024-06-26T05:54:50.2473244Z 2024-06-26T05:54:50.2473429Z Currently :class:`SyncBatchNorm` only supports 2024-06-26T05:54:50.2473817Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-06-26T05:54:50.2474106Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-06-26T05:54:50.2474394Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-06-26T05:54:50.2474504Z Network with DDP. 2024-06-26T05:54:50.2474590Z 2024-06-26T05:54:50.2474850Z Args: 2024-06-26T05:54:50.2475068Z num_features: :math:`C` from an expected input of size 2024-06-26T05:54:50.2475180Z :math:`(N, C, +)` 2024-06-26T05:54:50.2475459Z eps: a value added to the denominator for numerical stability. 2024-06-26T05:54:50.2475612Z Default: ``1e-5`` 2024-06-26T05:54:50.2475867Z momentum: the value used for the running_mean and running_var 2024-06-26T05:54:50.2476165Z computation. Can be set to ``None`` for cumulative moving average 2024-06-26T05:54:50.2476378Z (i.e. simple average). Default: 0.1 2024-06-26T05:54:50.2476652Z affine: a boolean value that when set to ``True``, this module has 2024-06-26T05:54:50.2476925Z learnable affine parameters. Default: ``True`` 2024-06-26T05:54:50.2477245Z track_running_stats: a boolean value that when set to ``True``, this 2024-06-26T05:54:50.2477576Z module tracks the running mean and variance, and when set to ``False``, 2024-06-26T05:54:50.2477883Z this module does not track such statistics, and initializes statistics 2024-06-26T05:54:50.2478154Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-06-26T05:54:50.2478494Z When these buffers are ``None``, this module always uses batch statistics. 2024-06-26T05:54:50.2478697Z in both training and eval modes. Default: ``True`` 2024-06-26T05:54:50.2479060Z process_group: synchronization of stats happen within each process group 2024-06-26T05:54:50.2479366Z individually. Default behavior is synchronization across the whole 2024-06-26T05:54:50.2479466Z world 2024-06-26T05:54:50.2479564Z 2024-06-26T05:54:50.2479658Z Shape: 2024-06-26T05:54:50.2479825Z - Input: :math:`(N, C, +)` 2024-06-26T05:54:50.2480081Z - Output: :math:`(N, C, +)` (same shape as input) 2024-06-26T05:54:50.2480166Z 2024-06-26T05:54:50.2480267Z .. note:: 2024-06-26T05:54:50.2480607Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-06-26T05:54:50.2480876Z synchronization is disabled when ``model.eval()`` is set or if 2024-06-26T05:54:50.2481118Z ``self.training`` is otherwise ``False``. 2024-06-26T05:54:50.2481225Z 2024-06-26T05:54:50.2481330Z Examples:: 2024-06-26T05:54:50.2481415Z 2024-06-26T05:54:50.2481549Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2481692Z >>> # With Learnable Parameters 2024-06-26T05:54:50.2481826Z >>> m = nn.SyncBatchNorm(100) 2024-06-26T05:54:50.2482003Z >>> # creating process group (optional) 2024-06-26T05:54:50.2482193Z >>> # ranks is a list of int identifying rank ids. 2024-06-26T05:54:50.2482316Z >>> ranks = list(range(8)) 2024-06-26T05:54:50.2482469Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-06-26T05:54:50.2482666Z >>> # Note: every rank calls into new_group for every 2024-06-26T05:54:50.2482886Z >>> # process group created, even if that rank is not 2024-06-26T05:54:50.2483006Z >>> # part of the group. 2024-06-26T05:54:50.2483334Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-06-26T05:54:50.2483613Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-06-26T05:54:50.2483764Z >>> # Without Learnable Parameters 2024-06-26T05:54:50.2484047Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-06-26T05:54:50.2484222Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-06-26T05:54:50.2484339Z >>> output = m(input) 2024-06-26T05:54:50.2484425Z 2024-06-26T05:54:50.2484588Z >>> # network is nn.BatchNorm layer 2024-06-26T05:54:50.2484955Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-06-26T05:54:50.2485187Z >>> # only single gpu per process is currently supported 2024-06-26T05:54:50.2485487Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-06-26T05:54:50.2485639Z >>> sync_bn_network, 2024-06-26T05:54:50.2485827Z >>> device_ids=[args.local_rank], 2024-06-26T05:54:50.2486007Z >>> output_device=args.local_rank) 2024-06-26T05:54:50.2486134Z 2024-06-26T05:54:50.2486548Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2486633Z 2024-06-26T05:54:50.2486772Z warnings.warn(msg) 2024-06-26T05:54:50.2486870Z 2024-06-26T05:54:50.2487070Z --- Parse Warning: 66 / 90 --- 2024-06-26T05:54:50.2488620Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=824. 2024-06-26T05:54:50.2489047Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2489472Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-06-26T05:54:50.2489571Z 2024-06-26T05:54:50.2489665Z Args: 2024-06-26T05:54:50.2490025Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-06-26T05:54:50.2490327Z process_group (optional): process group to scope synchronization, 2024-06-26T05:54:50.2490474Z default is the whole world 2024-06-26T05:54:50.2490558Z 2024-06-26T05:54:50.2490668Z Returns: 2024-06-26T05:54:50.2491022Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-06-26T05:54:50.2491322Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-06-26T05:54:50.2491636Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-06-26T05:54:50.2491735Z instead. 2024-06-26T05:54:50.2491836Z 2024-06-26T05:54:50.2491940Z Example:: 2024-06-26T05:54:50.2492029Z 2024-06-26T05:54:50.2492211Z >>> # Network with nn.BatchNorm layer 2024-06-26T05:54:50.2492404Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:50.2492564Z >>> module = torch.nn.Sequential( 2024-06-26T05:54:50.2492736Z >>> torch.nn.Linear(20, 100), 2024-06-26T05:54:50.2492900Z >>> torch.nn.BatchNorm1d(100), 2024-06-26T05:54:50.2493011Z >>> ).cuda() 2024-06-26T05:54:50.2493198Z >>> # creating process group (optional) 2024-06-26T05:54:50.2493395Z >>> # ranks is a list of int identifying rank ids. 2024-06-26T05:54:50.2493524Z >>> ranks = list(range(8)) 2024-06-26T05:54:50.2493681Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-06-26T05:54:50.2493886Z >>> # Note: every rank calls into new_group for every 2024-06-26T05:54:50.2494091Z >>> # process group created, even if that rank is not 2024-06-26T05:54:50.2494237Z >>> # part of the group. 2024-06-26T05:54:50.2494398Z >>> # xdoctest: +SKIP("distributed") 2024-06-26T05:54:50.2494744Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-06-26T05:54:50.2495017Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-06-26T05:54:50.2495423Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-06-26T05:54:50.2495527Z 2024-06-26T05:54:50.2495624Z 2024-06-26T05:54:50.2496020Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2496122Z 2024-06-26T05:54:50.2496232Z warnings.warn(msg) 2024-06-26T05:54:50.2496320Z 2024-06-26T05:54:50.2496534Z --- Parse Warning: 67 / 90 --- 2024-06-26T05:54:50.2497866Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60. 2024-06-26T05:54:50.2498292Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2498416Z 2024-06-26T05:54:50.2498846Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-06-26T05:54:50.2498971Z 2024-06-26T05:54:50.2499345Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-06-26T05:54:50.2499696Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-06-26T05:54:50.2499797Z 2024-06-26T05:54:50.2500237Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-06-26T05:54:50.2500617Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-06-26T05:54:50.2500847Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-06-26T05:54:50.2500932Z 2024-06-26T05:54:50.2501023Z Shape: 2024-06-26T05:54:50.2501462Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-06-26T05:54:50.2501808Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-06-26T05:54:50.2502204Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-06-26T05:54:50.2502370Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-06-26T05:54:50.2502457Z 2024-06-26T05:54:50.2502562Z Args: 2024-06-26T05:54:50.2502758Z dim (Union[int, str]): Dimension to be unflattened 2024-06-26T05:54:50.2503215Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-06-26T05:54:50.2503313Z 2024-06-26T05:54:50.2503410Z Examples: 2024-06-26T05:54:50.2503538Z >>> input = torch.randn(2, 50) 2024-06-26T05:54:50.2503668Z >>> # With tuple of ints 2024-06-26T05:54:50.2503779Z >>> m = nn.Sequential( 2024-06-26T05:54:50.2503894Z >>> nn.Linear(50, 50), 2024-06-26T05:54:50.2504042Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-06-26T05:54:50.2504132Z >>> ) 2024-06-26T05:54:50.2504244Z >>> output = m(input) 2024-06-26T05:54:50.2504365Z >>> output.size() 2024-06-26T05:54:50.2504477Z torch.Size([2, 2, 5, 5]) 2024-06-26T05:54:50.2504598Z >>> # With torch.Size 2024-06-26T05:54:50.2504711Z >>> m = nn.Sequential( 2024-06-26T05:54:50.2504823Z >>> nn.Linear(50, 50), 2024-06-26T05:54:50.2504998Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-06-26T05:54:50.2505088Z >>> ) 2024-06-26T05:54:50.2505199Z >>> output = m(input) 2024-06-26T05:54:50.2505317Z >>> output.size() 2024-06-26T05:54:50.2505428Z torch.Size([2, 2, 5, 5]) 2024-06-26T05:54:50.2505584Z >>> # With namedshape (tuple of tuples) 2024-06-26T05:54:50.2505855Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-06-26T05:54:50.2506200Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-06-26T05:54:50.2506330Z >>> output = unflatten(input) 2024-06-26T05:54:50.2506450Z >>> output.size() 2024-06-26T05:54:50.2506562Z torch.Size([2, 2, 5, 5]) 2024-06-26T05:54:50.2506646Z 2024-06-26T05:54:50.2507052Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2507138Z 2024-06-26T05:54:50.2507249Z warnings.warn(msg) 2024-06-26T05:54:50.2507346Z 2024-06-26T05:54:50.2507544Z --- Parse Warning: 68 / 90 --- 2024-06-26T05:54:50.2509026Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1696. 2024-06-26T05:54:50.2509431Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2509690Z Creates a criterion that measures the triplet loss given input 2024-06-26T05:54:50.2509995Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-06-26T05:54:50.2510278Z positive, and negative examples, respectively), and a nonnegative, 2024-06-26T05:54:50.2510702Z real-valued function ("distance function") used to compute the relationship 2024-06-26T05:54:50.2511038Z between the anchor and positive example ("positive distance") and the 2024-06-26T05:54:50.2511239Z anchor and negative example ("negative distance"). 2024-06-26T05:54:50.2511339Z 2024-06-26T05:54:50.2511686Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-06-26T05:54:50.2511800Z can be described as: 2024-06-26T05:54:50.2511900Z 2024-06-26T05:54:50.2511998Z .. math:: 2024-06-26T05:54:50.2512193Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-06-26T05:54:50.2512489Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-06-26T05:54:50.2512605Z 2024-06-26T05:54:50.2513032Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-06-26T05:54:50.2513436Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-06-26T05:54:50.2513773Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-06-26T05:54:50.2514112Z between the positive and negative distances that is required for the loss to 2024-06-26T05:54:50.2514446Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-06-26T05:54:50.2514813Z that the distance function can handle. 2024-06-26T05:54:50.2514918Z 2024-06-26T05:54:50.2515115Z If :attr:`reduction` is not ``'none'`` 2024-06-26T05:54:50.2515273Z (default ``'mean'``), then: 2024-06-26T05:54:50.2515374Z 2024-06-26T05:54:50.2515474Z .. math:: 2024-06-26T05:54:50.2515576Z \ell(x, y) = 2024-06-26T05:54:50.2515697Z \begin{cases} 2024-06-26T05:54:50.2516047Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-06-26T05:54:50.2516369Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-06-26T05:54:50.2516487Z \end{cases} 2024-06-26T05:54:50.2516572Z 2024-06-26T05:54:50.2516897Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-06-26T05:54:50.2517247Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-06-26T05:54:50.2517333Z 2024-06-26T05:54:50.2517424Z Args: 2024-06-26T05:54:50.2517863Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-06-26T05:54:50.2518114Z quantifies the closeness of two tensors. If not specified, 2024-06-26T05:54:50.2518353Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-06-26T05:54:50.2518715Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-06-26T05:54:50.2519092Z between the positive and negative distances required for the loss to be 0. Larger 2024-06-26T05:54:50.2519485Z margins penalize cases where the negative examples are not distant enough from the 2024-06-26T05:54:50.2519716Z anchors, relative to the positives. Default: :math:`1`. 2024-06-26T05:54:50.2520055Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-06-26T05:54:50.2520409Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-06-26T05:54:50.2520758Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-06-26T05:54:50.2521213Z negative example than the anchor is, swaps the positive example and the anchor in 2024-06-26T05:54:50.2521393Z the loss computation. Default: ``False``. 2024-06-26T05:54:50.2521768Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-06-26T05:54:50.2522188Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-06-26T05:54:50.2522566Z ``'mean'``: the sum of the output will be divided by the number of 2024-06-26T05:54:50.2523014Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-06-26T05:54:50.2523116Z 2024-06-26T05:54:50.2523203Z 2024-06-26T05:54:50.2523298Z Shape: 2024-06-26T05:54:50.2523730Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-06-26T05:54:50.2523897Z as supported by the distance function. 2024-06-26T05:54:50.2524347Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-06-26T05:54:50.2524450Z otherwise. 2024-06-26T05:54:50.2524536Z 2024-06-26T05:54:50.2524690Z Examples:: 2024-06-26T05:54:50.2524778Z 2024-06-26T05:54:50.2524899Z >>> # Initialize embeddings 2024-06-26T05:54:50.2525064Z >>> embedding = nn.Embedding(1000, 128) 2024-06-26T05:54:50.2525234Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-06-26T05:54:50.2525405Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-06-26T05:54:50.2525588Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-06-26T05:54:50.2525726Z >>> anchor = embedding(anchor_ids) 2024-06-26T05:54:50.2525877Z >>> positive = embedding(positive_ids) 2024-06-26T05:54:50.2526036Z >>> negative = embedding(negative_ids) 2024-06-26T05:54:50.2526127Z >>> 2024-06-26T05:54:50.2526302Z >>> # Built-in Distance Function 2024-06-26T05:54:50.2526424Z >>> triplet_loss = \ 2024-06-26T05:54:50.2526786Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-06-26T05:54:50.2527003Z >>> output = triplet_loss(anchor, positive, negative) 2024-06-26T05:54:50.2527120Z >>> output.backward() 2024-06-26T05:54:50.2527210Z >>> 2024-06-26T05:54:50.2527351Z >>> # Custom Distance Function 2024-06-26T05:54:50.2527473Z >>> def l_infinity(x1, x2): 2024-06-26T05:54:50.2527739Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-06-26T05:54:50.2527844Z >>> 2024-06-26T05:54:50.2528097Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-06-26T05:54:50.2528208Z >>> triplet_loss = ( 2024-06-26T05:54:50.2528581Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-06-26T05:54:50.2528778Z >>> output = triplet_loss(anchor, positive, negative) 2024-06-26T05:54:50.2528893Z >>> output.backward() 2024-06-26T05:54:50.2528994Z >>> 2024-06-26T05:54:50.2529146Z >>> # Custom Distance Function (Lambda) 2024-06-26T05:54:50.2529255Z >>> triplet_loss = ( 2024-06-26T05:54:50.2529444Z >>> nn.TripletMarginWithDistanceLoss( 2024-06-26T05:54:50.2529792Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-06-26T05:54:50.2530003Z >>> output = triplet_loss(anchor, positive, negative) 2024-06-26T05:54:50.2530115Z >>> output.backward() 2024-06-26T05:54:50.2530201Z 2024-06-26T05:54:50.2530313Z Reference: 2024-06-26T05:54:50.2530729Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-06-26T05:54:50.2530971Z http://www.bmva.org/bmvc/2016/papers/paper119/index.html 2024-06-26T05:54:50.2531074Z 2024-06-26T05:54:50.2531471Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-06-26T05:54:50.2531556Z 2024-06-26T05:54:50.2531678Z warnings.warn(msg) 2024-06-26T05:54:50.2531763Z 2024-06-26T05:54:50.2531962Z --- Parse Warning: 69 / 90 --- 2024-06-26T05:54:50.2533343Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=395. 2024-06-26T05:54:50.2533811Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2534042Z Computes a partial inverse of :class:`MaxPool2d`. 2024-06-26T05:54:50.2534135Z 2024-06-26T05:54:50.2534564Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-06-26T05:54:50.2534667Z 2024-06-26T05:54:50.2534969Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-06-26T05:54:50.2535298Z including the indices of the maximal values and computes a partial inverse 2024-06-26T05:54:50.2535553Z in which all non-maximal values are set to zero. 2024-06-26T05:54:50.2535643Z 2024-06-26T05:54:50.2535737Z Note: 2024-06-26T05:54:50.2536198Z This operation may behave nondeterministically when the input indices has repeat values. 2024-06-26T05:54:50.2536703Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-06-26T05:54:50.2536792Z 2024-06-26T05:54:50.2537114Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-06-26T05:54:50.2537348Z sizes. Hence, the inversion process can get ambiguous. 2024-06-26T05:54:50.2537620Z To accommodate this, you can provide the needed output size 2024-06-26T05:54:50.2537908Z as an additional argument :attr:`output_size` in the forward call. 2024-06-26T05:54:50.2538068Z See the Inputs and Example below. 2024-06-26T05:54:50.2538171Z 2024-06-26T05:54:50.2538266Z Args: 2024-06-26T05:54:50.2538508Z kernel_size (int or tuple): Size of the max pooling window. 2024-06-26T05:54:50.2538755Z stride (int or tuple): Stride of the max pooling window. 2024-06-26T05:54:50.2538937Z It is set to :attr:`kernel_size` by default. 2024-06-26T05:54:50.2539191Z padding (int or tuple): Padding that was added to the input 2024-06-26T05:54:50.2539296Z 2024-06-26T05:54:50.2539392Z Inputs: 2024-06-26T05:54:50.2539596Z - `input`: the input Tensor to invert 2024-06-26T05:54:50.2539950Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-06-26T05:54:50.2540210Z - `output_size` (optional): the targeted output size 2024-06-26T05:54:50.2540311Z 2024-06-26T05:54:50.2540406Z Shape: 2024-06-26T05:54:50.2540736Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-06-26T05:54:50.2541127Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-06-26T05:54:50.2541213Z 2024-06-26T05:54:50.2541315Z .. math:: 2024-06-26T05:54:50.2541840Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-06-26T05:54:50.2541927Z 2024-06-26T05:54:50.2542030Z .. math:: 2024-06-26T05:54:50.2542544Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-06-26T05:54:50.2542630Z 2024-06-26T05:54:50.2542856Z or as given by :attr:`output_size` in the call operator 2024-06-26T05:54:50.2542952Z 2024-06-26T05:54:50.2543052Z Example:: 2024-06-26T05:54:50.2543137Z 2024-06-26T05:54:50.2543367Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-06-26T05:54:50.2543532Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-06-26T05:54:50.2543718Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-06-26T05:54:50.2543868Z [ 5., 6., 7., 8.], 2024-06-26T05:54:50.2544016Z [ 9., 10., 11., 12.], 2024-06-26T05:54:50.2544213Z [13., 14., 15., 16.]]]]) 2024-06-26T05:54:50.2544358Z >>> output, indices = pool(input) 2024-06-26T05:54:50.2544533Z >>> unpool(output, indices) 2024-06-26T05:54:50.2544681Z tensor([[[[ 0., 0., 0., 0.], 2024-06-26T05:54:50.2544840Z [ 0., 6., 0., 8.], 2024-06-26T05:54:50.2544970Z [ 0., 0., 0., 0.], 2024-06-26T05:54:50.2545113Z [ 0., 14., 0., 16.]]]]) 2024-06-26T05:54:50.2545407Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-06-26T05:54:50.2545593Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-06-26T05:54:50.2545763Z [ 6., 7., 8., 9., 10.], 2024-06-26T05:54:50.2545922Z [11., 12., 13., 14., 15.], 2024-06-26T05:54:50.2546120Z [16., 17., 18., 19., 20.]]]]) 2024-06-26T05:54:50.2546267Z >>> output, indices = pool(input) 2024-06-26T05:54:50.2546497Z >>> # This call will not work without specifying output_size 2024-06-26T05:54:50.2546713Z >>> unpool(output, indices, output_size=input.size()) 2024-06-26T05:54:50.2546851Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-06-26T05:54:50.2546981Z [ 0., 7., 0., 9., 0.], 2024-06-26T05:54:50.2547120Z [ 0., 0., 0., 0., 0.], 2024-06-26T05:54:50.2547258Z [ 0., 17., 0., 19., 0.]]]]) 2024-06-26T05:54:50.2547344Z 2024-06-26T05:54:50.2547441Z 2024-06-26T05:54:50.2547530Z 2024-06-26T05:54:50.2547926Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2548023Z 2024-06-26T05:54:50.2548132Z warnings.warn(msg) 2024-06-26T05:54:50.2548218Z 2024-06-26T05:54:50.2548432Z --- Parse Warning: 70 / 90 --- 2024-06-26T05:54:50.2549789Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=270. 2024-06-26T05:54:50.2550214Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2550733Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-06-26T05:54:50.2550819Z 2024-06-26T05:54:50.2551281Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-06-26T05:54:50.2551410Z and with 2D inputs, this class 2024-06-26T05:54:50.2551495Z 2024-06-26T05:54:50.2551943Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-06-26T05:54:50.2552386Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-06-26T05:54:50.2552829Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-06-26T05:54:50.2552917Z 2024-06-26T05:54:50.2553413Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-06-26T05:54:50.2553527Z operations. 2024-06-26T05:54:50.2553613Z 2024-06-26T05:54:50.2554019Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-06-26T05:54:50.2554356Z pass. This scales the output of the Embedding before performing a weighted 2024-06-26T05:54:50.2554816Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-06-26T05:54:50.2555144Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-06-26T05:54:50.2555283Z :attr:`per_sample_weights`. 2024-06-26T05:54:50.2555372Z 2024-06-26T05:54:50.2555528Z Args: 2024-06-26T05:54:50.2555773Z num_embeddings (int): size of the dictionary of embeddings 2024-06-26T05:54:50.2556029Z embedding_dim (int): the size of each embedding vector 2024-06-26T05:54:50.2556479Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-06-26T05:54:50.2556731Z is renormalized to have norm :attr:`max_norm`. 2024-06-26T05:54:50.2557317Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-06-26T05:54:50.2557795Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-06-26T05:54:50.2558109Z the words in the mini-batch. Default ``False``. 2024-06-26T05:54:50.2558427Z Note: this option is not supported when ``mode="max"``. 2024-06-26T05:54:50.2558793Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-06-26T05:54:50.2559099Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-06-26T05:54:50.2559414Z into consideration. ``"mean"`` computes the average of the values 2024-06-26T05:54:50.2559675Z in the bag, ``"max"`` computes the max value over each bag. 2024-06-26T05:54:50.2559828Z Default: ``"mean"`` 2024-06-26T05:54:50.2560298Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-06-26T05:54:50.2560653Z Notes for more details regarding sparse gradients. Note: this option is not 2024-06-26T05:54:50.2560838Z supported when ``mode="max"``. 2024-06-26T05:54:50.2561432Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-06-26T05:54:50.2561757Z is equivalent to the size of `indices`. This matches the CSR format. 2024-06-26T05:54:50.2562234Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-06-26T05:54:50.2562607Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-06-26T05:54:50.2562951Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-06-26T05:54:50.2563331Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-06-26T05:54:50.2563691Z zeros, but can be updated to another value to be used as the padding vector. 2024-06-26T05:54:50.2564055Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-06-26T05:54:50.2564194Z reduction. 2024-06-26T05:54:50.2564282Z 2024-06-26T05:54:50.2564392Z Attributes: 2024-06-26T05:54:50.2564829Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-06-26T05:54:50.2565023Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-06-26T05:54:50.2565121Z 2024-06-26T05:54:50.2565225Z Examples:: 2024-06-26T05:54:50.2565310Z 2024-06-26T05:54:50.2565556Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-06-26T05:54:50.2565832Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-06-26T05:54:50.2566015Z >>> # a batch of 2 samples of 4 indices each 2024-06-26T05:54:50.2566279Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-06-26T05:54:50.2566507Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-06-26T05:54:50.2566792Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-06-26T05:54:50.2566934Z >>> embedding_sum(input, offsets) 2024-06-26T05:54:50.2567147Z tensor([[-0.8861, -5.4350, -0.0523], 2024-06-26T05:54:50.2567344Z [ 1.1306, -2.5798, -1.0044]]) 2024-06-26T05:54:50.2567430Z 2024-06-26T05:54:50.2567566Z >>> # Example with padding_idx 2024-06-26T05:54:50.2567926Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-06-26T05:54:50.2568185Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-06-26T05:54:50.2568391Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-06-26T05:54:50.2568532Z >>> embedding_sum(input, offsets) 2024-06-26T05:54:50.2568694Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-06-26T05:54:50.2568890Z [-0.7082, 3.2145, -2.6251]]) 2024-06-26T05:54:50.2568977Z 2024-06-26T05:54:50.2569216Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-06-26T05:54:50.2569425Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-06-26T05:54:50.2569633Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-06-26T05:54:50.2569755Z embedding.weight, 2024-06-26T05:54:50.2569934Z padding_idx=embedding.padding_idx, 2024-06-26T05:54:50.2570075Z mode='sum') 2024-06-26T05:54:50.2570165Z 2024-06-26T05:54:50.2570568Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2570654Z 2024-06-26T05:54:50.2570762Z warnings.warn(msg) 2024-06-26T05:54:50.2570860Z 2024-06-26T05:54:50.2571059Z --- Parse Warning: 71 / 90 --- 2024-06-26T05:54:50.2572593Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1743. 2024-06-26T05:54:50.2573004Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2573090Z 2024-06-26T05:54:50.2573418Z Context manager for training with uneven inputs across processes in DDP. 2024-06-26T05:54:50.2573503Z 2024-06-26T05:54:50.2573860Z This context manager will keep track of already-joined DDP processes, 2024-06-26T05:54:50.2574161Z and "shadow" the forward and backward passes by inserting collective 2024-06-26T05:54:50.2574522Z communication operations to match with the ones created by non-joined 2024-06-26T05:54:50.2574831Z DDP processes. This will ensure each collective call has a corresponding 2024-06-26T05:54:50.2575202Z call by already-joined DDP processes, preventing hangs or errors that 2024-06-26T05:54:50.2575463Z would otherwise happen when training with uneven inputs across 2024-06-26T05:54:50.2575778Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-06-26T05:54:50.2576073Z specified to be ``True``, all trainers will throw an error once one rank 2024-06-26T05:54:50.2576349Z runs out of inputs, allowing these errors to be caught and handled 2024-06-26T05:54:50.2576492Z according to application logic. 2024-06-26T05:54:50.2576578Z 2024-06-26T05:54:50.2576877Z Once all DDP processes have joined, the context manager will broadcast 2024-06-26T05:54:50.2577191Z the model corresponding to the last joined process to all processes to 2024-06-26T05:54:50.2577380Z ensure the model is the same across all processes 2024-06-26T05:54:50.2577504Z (which is guaranteed by DDP). 2024-06-26T05:54:50.2577601Z 2024-06-26T05:54:50.2577886Z To use this to enable training with uneven inputs across processes, 2024-06-26T05:54:50.2578222Z simply wrap this context manager around your training loop. No further 2024-06-26T05:54:50.2578452Z modifications to the model or data loading is required. 2024-06-26T05:54:50.2578564Z 2024-06-26T05:54:50.2578679Z .. warning:: 2024-06-26T05:54:50.2578975Z If the model or training loop this context manager is wrapped around 2024-06-26T05:54:50.2579248Z has additional distributed collective operations, such as 2024-06-26T05:54:50.2579569Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-06-26T05:54:50.2579841Z ``throw_on_early_termination`` must be enabled. This is because this 2024-06-26T05:54:50.2580182Z context manager is not aware of non-DDP collective communication. 2024-06-26T05:54:50.2580429Z This flag will cause all ranks to throw when any one rank 2024-06-26T05:54:50.2580702Z exhausts inputs, allowing these errors to be caught and recovered 2024-06-26T05:54:50.2580850Z from across all ranks. 2024-06-26T05:54:50.2580953Z 2024-06-26T05:54:50.2581047Z Args: 2024-06-26T05:54:50.2581291Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-06-26T05:54:50.2581587Z gradients by the initial ``world_size`` DDP training was launched 2024-06-26T05:54:50.2581817Z with. If ``False``, will compute the effective world size 2024-06-26T05:54:50.2582089Z (number of ranks that have not depleted their inputs yet) and 2024-06-26T05:54:50.2582282Z divide gradients by that during allreduce. Set 2024-06-26T05:54:50.2582516Z ``divide_by_initial_world_size=True`` to ensure every input 2024-06-26T05:54:50.2582809Z sample including the uneven inputs have equal weight in terms of 2024-06-26T05:54:50.2583041Z how much they contribute to the global gradient. This is 2024-06-26T05:54:50.2583270Z achieved by always dividing the gradient by the initial 2024-06-26T05:54:50.2583540Z ``world_size`` even when we encounter uneven inputs. If you set 2024-06-26T05:54:50.2583776Z this to ``False``, we divide the gradient by the remaining 2024-06-26T05:54:50.2584048Z number of nodes. This ensures parity with training on a smaller 2024-06-26T05:54:50.2584301Z ``world_size`` although it also means the uneven inputs would 2024-06-26T05:54:50.2584560Z contribute more towards the global gradient. Typically, you 2024-06-26T05:54:50.2584834Z would want to set this to ``True`` for cases where the last few 2024-06-26T05:54:50.2585101Z inputs of your training job are uneven. In extreme cases, where 2024-06-26T05:54:50.2585359Z there is a large discrepancy in the number of inputs, setting 2024-06-26T05:54:50.2585562Z this to ``False`` might provide better results. 2024-06-26T05:54:50.2585850Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-06-26T05:54:50.2586089Z in ``enable=False`` to disable in cases where you know that 2024-06-26T05:54:50.2586356Z inputs are even across participating processes. Default is 2024-06-26T05:54:50.2586457Z ``True``. 2024-06-26T05:54:50.2586716Z throw_on_early_termination (bool): Whether to throw an error 2024-06-26T05:54:50.2586957Z or continue training when at least one rank has exhausted 2024-06-26T05:54:50.2587217Z inputs. If ``True``, will throw upon the first rank reaching end 2024-06-26T05:54:50.2587475Z of data. If ``False``, will continue training with a smaller 2024-06-26T05:54:50.2587736Z effective world size until all ranks are joined. Note that if 2024-06-26T05:54:50.2587892Z this flag is specified, then the flag 2024-06-26T05:54:50.2588129Z ``divide_by_initial_world_size`` would be ignored. Default 2024-06-26T05:54:50.2588232Z is ``False``. 2024-06-26T05:54:50.2588320Z 2024-06-26T05:54:50.2588417Z 2024-06-26T05:54:50.2588554Z Example:: 2024-06-26T05:54:50.2588640Z 2024-06-26T05:54:50.2588799Z >>> # xdoctest: +SKIP("Distributed") 2024-06-26T05:54:50.2588904Z >>> import torch 2024-06-26T05:54:50.2589081Z >>> import torch.distributed as dist 2024-06-26T05:54:50.2589192Z >>> import os 2024-06-26T05:54:50.2589379Z >>> import torch.multiprocessing as mp 2024-06-26T05:54:50.2589498Z >>> import torch.nn as nn 2024-06-26T05:54:50.2589632Z >>> # On each spawned worker 2024-06-26T05:54:50.2589742Z >>> def worker(rank): 2024-06-26T05:54:50.2589986Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-06-26T05:54:50.2590123Z >>> torch.cuda.set_device(rank) 2024-06-26T05:54:50.2590302Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-06-26T05:54:50.2596117Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-06-26T05:54:50.2596443Z >>> model, device_ids=[rank], output_device=rank 2024-06-26T05:54:50.2596547Z >>> ) 2024-06-26T05:54:50.2596739Z >>> # Rank 1 gets one more input than rank 0. 2024-06-26T05:54:50.2597000Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-06-26T05:54:50.2597118Z >>> with model.join(): 2024-06-26T05:54:50.2597256Z >>> for _ in range(5): 2024-06-26T05:54:50.2597392Z >>> for inp in inputs: 2024-06-26T05:54:50.2597553Z >>> loss = model(inp).sum() 2024-06-26T05:54:50.2597683Z >>> loss.backward() 2024-06-26T05:54:50.2597949Z >>> # Without the join() API, the below synchronization will hang 2024-06-26T05:54:50.2598247Z >>> # blocking for rank 1's allreduce to complete. 2024-06-26T05:54:50.2598409Z >>> torch.cuda.synchronize(device=rank) 2024-06-26T05:54:50.2598496Z 2024-06-26T05:54:50.2598908Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2598997Z 2024-06-26T05:54:50.2599107Z warnings.warn(msg) 2024-06-26T05:54:50.2599207Z 2024-06-26T05:54:50.2599412Z --- Parse Warning: 72 / 90 --- 2024-06-26T05:54:50.2601105Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2034. 2024-06-26T05:54:50.2601651Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2601738Z 2024-06-26T05:54:50.2602178Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-06-26T05:54:50.2602264Z 2024-06-26T05:54:50.2602539Z Registers an optimizer with DDP such that the optimization for a 2024-06-26T05:54:50.2602887Z parameter will run immediately when that parameter's gradient is 2024-06-26T05:54:50.2603156Z finished with reduction, instead of waiting for all parameters' 2024-06-26T05:54:50.2603447Z gradients to finish reduction. This can result in a training speedup 2024-06-26T05:54:50.2603760Z depending on your workload since the optimizer can run while gradient 2024-06-26T05:54:50.2604075Z reduction for other parameters are still ongoing. In addition, this has 2024-06-26T05:54:50.2604380Z the potential to reduce peak memory consumption during training, as it 2024-06-26T05:54:50.2604731Z only needs to load the per-parameter optimizer states of a single 2024-06-26T05:54:50.2605077Z parameter at a time, instead of loading all per-parameter optimizer 2024-06-26T05:54:50.2605194Z states at once. 2024-06-26T05:54:50.2605281Z 2024-06-26T05:54:50.2605372Z Args: 2024-06-26T05:54:50.2605651Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-06-26T05:54:50.2605767Z as a fused optimizer. 2024-06-26T05:54:50.2605985Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-06-26T05:54:50.2606348Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-06-26T05:54:50.2606700Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-06-26T05:54:50.2606974Z Optimizers. If this is omitted, all DDP model parameters will be 2024-06-26T05:54:50.2607123Z optimized. 2024-06-26T05:54:50.2607399Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-06-26T05:54:50.2607491Z 2024-06-26T05:54:50.2607614Z .. warning :: 2024-06-26T05:54:50.2607893Z _register_fused_optim should only be called once on a DDP instance, 2024-06-26T05:54:50.2608180Z and registering multiple fused optimizers for the same DDP model 2024-06-26T05:54:50.2608344Z is not currently supported. Please ping 2024-06-26T05:54:50.2608649Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-06-26T05:54:50.2608802Z for your use case. 2024-06-26T05:54:50.2608894Z 2024-06-26T05:54:50.2608995Z .. warning :: 2024-06-26T05:54:50.2609254Z _register_fused_optim and register_comm_hook currently do not 2024-06-26T05:54:50.2609535Z compose together, meaning that custom DDP communication hooks are 2024-06-26T05:54:50.2609761Z not supported with overlapped optimizers. Please ping 2024-06-26T05:54:50.2610109Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-06-26T05:54:50.2610279Z for your use case. 2024-06-26T05:54:50.2610383Z 2024-06-26T05:54:50.2610498Z .. warning :: 2024-06-26T05:54:50.2610792Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-06-26T05:54:50.2610969Z with overlapped optimizer. Please ping 2024-06-26T05:54:50.2611265Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-06-26T05:54:50.2611375Z for your use case. 2024-06-26T05:54:50.2611479Z 2024-06-26T05:54:50.2611580Z Example:: 2024-06-26T05:54:50.2611667Z 2024-06-26T05:54:50.2611860Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-06-26T05:54:50.2612339Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-06-26T05:54:50.2612616Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-06-26T05:54:50.2612748Z >>> lr = 1e-2 2024-06-26T05:54:50.2612858Z >>> betas = (0.9, 0.99) 2024-06-26T05:54:50.2612974Z >>> eps = 1e-6 2024-06-26T05:54:50.2613283Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-06-26T05:54:50.2613438Z >>> # Example with subset of parameters 2024-06-26T05:54:50.2613616Z >>> params_to_opt = [list(net.parameters())[0]] 2024-06-26T05:54:50.2613757Z >>> net._register_fused_optim( 2024-06-26T05:54:50.2614077Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-06-26T05:54:50.2614186Z ... ) 2024-06-26T05:54:50.2614273Z 2024-06-26T05:54:50.2614669Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2614773Z 2024-06-26T05:54:50.2614882Z warnings.warn(msg) 2024-06-26T05:54:50.2614967Z 2024-06-26T05:54:50.2615183Z --- Parse Warning: 73 / 90 --- 2024-06-26T05:54:50.2616690Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=6. 2024-06-26T05:54:50.2617102Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2617400Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-06-26T05:54:50.2617488Z 2024-06-26T05:54:50.2617855Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-06-26T05:54:50.2618298Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-06-26T05:54:50.2618658Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-06-26T05:54:50.2619130Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-06-26T05:54:50.2619241Z 2024-06-26T05:54:50.2619342Z .. note:: 2024-06-26T05:54:50.2619674Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-06-26T05:54:50.2619974Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-06-26T05:54:50.2620271Z layer with 4d weight will be affected by ``model.to``, which does not 2024-06-26T05:54:50.2620577Z necessarily benefit from conversion to specified ``memory_format``. 2024-06-26T05:54:50.2620889Z One place we are confident in is that NHWC(channels_last) conversion for 2024-06-26T05:54:50.2621234Z convolution in cuDNN, As it is beneficial to run convolution in NHWC, 2024-06-26T05:54:50.2621522Z even in cases where we have to apply permutation to input tensors. 2024-06-26T05:54:50.2621611Z 2024-06-26T05:54:50.2621939Z Hence our strategy here is to convert only the weight of convolution to 2024-06-26T05:54:50.2622088Z channels_last. This ensures that; 2024-06-26T05:54:50.2622380Z 1. Fast convolution kernels will be used, the benefit of which could 2024-06-26T05:54:50.2622696Z outweigh overhead of permutation (if input is not in the same format) 2024-06-26T05:54:50.2623013Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-06-26T05:54:50.2623151Z from memory_format conversion. 2024-06-26T05:54:50.2623251Z 2024-06-26T05:54:50.2623565Z The optimal case is that, layers between convolution layers are channels 2024-06-26T05:54:50.2623885Z last compatible. Input tensor would be permuted to channels last when it 2024-06-26T05:54:50.2624210Z encounters the first convolution layer and stay in that memory format. 2024-06-26T05:54:50.2624528Z Hence following convolutions will not need to permute its input tensor. 2024-06-26T05:54:50.2624632Z 2024-06-26T05:54:50.2624942Z In case where a channels last incompatible layer is between convolution 2024-06-26T05:54:50.2625241Z layers, we need to permute the input tensor back to contiguous format 2024-06-26T05:54:50.2625571Z for that layer. The input tensor will go through the remaining layers in 2024-06-26T05:54:50.2625876Z contiguous format and be permuted to channels last when it encounters 2024-06-26T05:54:50.2626218Z another convolution layer. There's no point in propagating that 2024-06-26T05:54:50.2626530Z permutation to an earlier layer, as most layers are quite agnostic to 2024-06-26T05:54:50.2626643Z ``memory_format``. 2024-06-26T05:54:50.2626733Z 2024-06-26T05:54:50.2627064Z This claim might change when PyTorch supports fusion of permutation, as 2024-06-26T05:54:50.2627375Z there might have been a better spot to fuse the permutation other than 2024-06-26T05:54:50.2627541Z immediately before a convolution. 2024-06-26T05:54:50.2627630Z 2024-06-26T05:54:50.2627724Z Args: 2024-06-26T05:54:50.2628032Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-06-26T05:54:50.2628161Z ``nn.Module`` 2024-06-26T05:54:50.2628355Z memory_format: user specified ``memory_format``, 2024-06-26T05:54:50.2628613Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-06-26T05:54:50.2628701Z 2024-06-26T05:54:50.2628796Z Returns: 2024-06-26T05:54:50.2628996Z The original module with updated ``nn.Conv2d`` 2024-06-26T05:54:50.2629086Z 2024-06-26T05:54:50.2629213Z Example: 2024-06-26T05:54:50.2629415Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:50.2629617Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-06-26T05:54:50.2629984Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-06-26T05:54:50.2630141Z >>> model = nn.Sequential( 2024-06-26T05:54:50.2630299Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-06-26T05:54:50.2630440Z >>> # This is identical to: 2024-06-26T05:54:50.2630763Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:50.2631121Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:50.2631251Z >>> out = model(input) 2024-06-26T05:54:50.2631342Z 2024-06-26T05:54:50.2631764Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2631867Z 2024-06-26T05:54:50.2631977Z warnings.warn(msg) 2024-06-26T05:54:50.2632063Z 2024-06-26T05:54:50.2632277Z --- Parse Warning: 74 / 90 --- 2024-06-26T05:54:50.2633795Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=81. 2024-06-26T05:54:50.2634215Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2634493Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-06-26T05:54:50.2634981Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-06-26T05:54:50.2635383Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-06-26T05:54:50.2635744Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-06-26T05:54:50.2636173Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-06-26T05:54:50.2636277Z 2024-06-26T05:54:50.2636378Z .. note:: 2024-06-26T05:54:50.2636710Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-06-26T05:54:50.2637015Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-06-26T05:54:50.2637315Z layer with 4d weight will be affected by ``model.to``, which does not 2024-06-26T05:54:50.2637622Z necessarily benefit from conversion to specified ``memory_format``. 2024-06-26T05:54:50.2637937Z One place we are confident in is that NHWC(channels_last) conversion for 2024-06-26T05:54:50.2638239Z convolution in cuDNN, As it is beneficial to run convolution in NHWC, 2024-06-26T05:54:50.2638541Z even in cases where we have to apply permutation to input tensors. 2024-06-26T05:54:50.2638631Z 2024-06-26T05:54:50.2638943Z Hence our strategy here is to convert only the weight of convolution to 2024-06-26T05:54:50.2639107Z channels_last. This ensures that; 2024-06-26T05:54:50.2639403Z 1. Fast convolution kernels will be used, the benefit of which could 2024-06-26T05:54:50.2639722Z outweigh overhead of permutation (if input is not in the same format) 2024-06-26T05:54:50.2640038Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-06-26T05:54:50.2640175Z from memory_format conversion. 2024-06-26T05:54:50.2640274Z 2024-06-26T05:54:50.2640587Z The optimal case is that, layers between convolution layers are channels 2024-06-26T05:54:50.2640900Z last compatible. Input tensor would be permuted to channels last when it 2024-06-26T05:54:50.2641311Z encounters the first convolution layer and stay in that memory format. 2024-06-26T05:54:50.2641709Z Hence following convolutions will not need to permute its input tensor. 2024-06-26T05:54:50.2641794Z 2024-06-26T05:54:50.2642117Z In case where a channels last incompatible layer is between convolution 2024-06-26T05:54:50.2642450Z layers, we need to permute the input tensor back to contiguous format 2024-06-26T05:54:50.2642812Z for that layer. The input tensor will go through the remaining layers in 2024-06-26T05:54:50.2643116Z contiguous format and be permuted to channels last when it encounters 2024-06-26T05:54:50.2643460Z another convolution layer. There's no point in propagating that 2024-06-26T05:54:50.2643774Z permutation to an earlier layer, as most layers are quite agnostic to 2024-06-26T05:54:50.2643884Z ``memory_format``. 2024-06-26T05:54:50.2643971Z 2024-06-26T05:54:50.2644342Z This claim might change when PyTorch supports fusion of permutation, as 2024-06-26T05:54:50.2644653Z there might have been a better spot to fuse the permutation other than 2024-06-26T05:54:50.2644805Z immediately before a convolution. 2024-06-26T05:54:50.2644907Z 2024-06-26T05:54:50.2645002Z Args: 2024-06-26T05:54:50.2645298Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-06-26T05:54:50.2645446Z ``nn.Module`` 2024-06-26T05:54:50.2645639Z memory_format: user specified ``memory_format``, 2024-06-26T05:54:50.2645897Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-06-26T05:54:50.2645985Z 2024-06-26T05:54:50.2646080Z Returns: 2024-06-26T05:54:50.2646281Z The original module with updated ``nn.Conv3d`` 2024-06-26T05:54:50.2646369Z 2024-06-26T05:54:50.2646465Z Example: 2024-06-26T05:54:50.2646664Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-06-26T05:54:50.2646869Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-06-26T05:54:50.2647210Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-06-26T05:54:50.2647353Z >>> model = nn.Sequential( 2024-06-26T05:54:50.2647508Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-06-26T05:54:50.2647634Z >>> # This is identical to: 2024-06-26T05:54:50.2647974Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:50.2648331Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last) 2024-06-26T05:54:50.2648460Z >>> out = model(input) 2024-06-26T05:54:50.2648551Z 2024-06-26T05:54:50.2648944Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2649046Z 2024-06-26T05:54:50.2649156Z warnings.warn(msg) 2024-06-26T05:54:50.2649244Z 2024-06-26T05:54:50.2649462Z --- Parse Warning: 75 / 90 --- 2024-06-26T05:54:50.2650819Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=937. 2024-06-26T05:54:50.2651231Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2651553Z Prune tensor by removing random channels along the specified dimension. 2024-06-26T05:54:50.2651640Z 2024-06-26T05:54:50.2651951Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-06-26T05:54:50.2652259Z by removing the specified ``amount`` of (currently unpruned) channels 2024-06-26T05:54:50.2652444Z along the specified ``dim`` selected at random. 2024-06-26T05:54:50.2652720Z Modifies module in place (and also return the modified module) 2024-06-26T05:54:50.2652814Z by: 2024-06-26T05:54:50.2652903Z 2024-06-26T05:54:50.2653305Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:50.2653600Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:50.2653920Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:50.2654229Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:50.2654371Z ``name+'_orig'``. 2024-06-26T05:54:50.2654458Z 2024-06-26T05:54:50.2654567Z Args: 2024-06-26T05:54:50.2654804Z module (nn.Module): module containing the tensor to prune 2024-06-26T05:54:50.2655053Z name (str): parameter name within ``module`` on which pruning 2024-06-26T05:54:50.2655172Z will act. 2024-06-26T05:54:50.2655399Z amount (int or float): quantity of parameters to prune. 2024-06-26T05:54:50.2655686Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-06-26T05:54:50.2655962Z fraction of parameters to prune. If ``int``, it represents the 2024-06-26T05:54:50.2656135Z absolute number of parameters to prune. 2024-06-26T05:54:50.2656439Z dim (int): index of the dim along which we define channels to prune. 2024-06-26T05:54:50.2656526Z 2024-06-26T05:54:50.2656624Z Returns: 2024-06-26T05:54:50.2656945Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-06-26T05:54:50.2657035Z 2024-06-26T05:54:50.2657134Z Examples: 2024-06-26T05:54:50.2657262Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2657408Z >>> m = prune.random_structured( 2024-06-26T05:54:50.2657650Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-06-26T05:54:50.2657754Z ... ) 2024-06-26T05:54:50.2657998Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-06-26T05:54:50.2658144Z >>> print(columns_pruned) 2024-06-26T05:54:50.2658239Z 3 2024-06-26T05:54:50.2658329Z 2024-06-26T05:54:50.2658738Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2658828Z 2024-06-26T05:54:50.2658943Z warnings.warn(msg) 2024-06-26T05:54:50.2659042Z 2024-06-26T05:54:50.2659240Z --- Parse Warning: 76 / 90 --- 2024-06-26T05:54:50.2660579Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=978. 2024-06-26T05:54:50.2661000Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2661508Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-06-26T05:54:50.2661600Z 2024-06-26T05:54:50.2661920Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-06-26T05:54:50.2662218Z by removing the specified ``amount`` of (currently unpruned) channels 2024-06-26T05:54:50.2662521Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-06-26T05:54:50.2662787Z Modifies module in place (and also return the modified module) 2024-06-26T05:54:50.2662883Z by: 2024-06-26T05:54:50.2662982Z 2024-06-26T05:54:50.2663338Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:50.2663633Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:50.2663934Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:50.2664205Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:50.2664347Z ``name+'_orig'``. 2024-06-26T05:54:50.2664445Z 2024-06-26T05:54:50.2664539Z Args: 2024-06-26T05:54:50.2664777Z module (nn.Module): module containing the tensor to prune 2024-06-26T05:54:50.2665071Z name (str): parameter name within ``module`` on which pruning 2024-06-26T05:54:50.2665176Z will act. 2024-06-26T05:54:50.2665442Z amount (int or float): quantity of parameters to prune. 2024-06-26T05:54:50.2665686Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-06-26T05:54:50.2665979Z fraction of parameters to prune. If ``int``, it represents the 2024-06-26T05:54:50.2666170Z absolute number of parameters to prune. 2024-06-26T05:54:50.2666505Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-06-26T05:54:50.2666711Z entries for argument ``p`` in :func:`torch.norm`. 2024-06-26T05:54:50.2667011Z dim (int): index of the dim along which we define channels to prune. 2024-06-26T05:54:50.2667316Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-06-26T05:54:50.2667605Z shape as module parameter) used to compute mask for pruning. 2024-06-26T05:54:50.2667936Z The values in this tensor indicate the importance of the corresponding 2024-06-26T05:54:50.2668111Z elements in the parameter being pruned. 2024-06-26T05:54:50.2668441Z If unspecified or None, the module parameter will be used in its place. 2024-06-26T05:54:50.2668532Z 2024-06-26T05:54:50.2668628Z Returns: 2024-06-26T05:54:50.2668939Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-06-26T05:54:50.2669027Z 2024-06-26T05:54:50.2669124Z Examples: 2024-06-26T05:54:50.2669296Z >>> from torch.nn.utils import prune 2024-06-26T05:54:50.2669428Z >>> m = prune.ln_structured( 2024-06-26T05:54:50.2669763Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-06-26T05:54:50.2669872Z ... ) 2024-06-26T05:54:50.2669966Z 2024-06-26T05:54:50.2670359Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2670459Z 2024-06-26T05:54:50.2670568Z warnings.warn(msg) 2024-06-26T05:54:50.2670656Z 2024-06-26T05:54:50.2670871Z --- Parse Warning: 77 / 90 --- 2024-06-26T05:54:50.2672262Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1025. 2024-06-26T05:54:50.2672684Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2672771Z 2024-06-26T05:54:50.2673345Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-06-26T05:54:50.2673448Z 2024-06-26T05:54:50.2673576Z Modifies modules in place by: 2024-06-26T05:54:50.2673663Z 2024-06-26T05:54:50.2674027Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:50.2674326Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:50.2674726Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:50.2675012Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:50.2675151Z ``name+'_orig'``. 2024-06-26T05:54:50.2675237Z 2024-06-26T05:54:50.2675344Z Args: 2024-06-26T05:54:50.2675604Z parameters (Iterable of (module, name) tuples): parameters of 2024-06-26T05:54:50.2675888Z the model to prune in a global fashion, i.e. by aggregating all 2024-06-26T05:54:50.2676207Z weights prior to deciding which ones to prune. module must be of 2024-06-26T05:54:50.2676417Z type :class:`nn.Module`, and name must be a string. 2024-06-26T05:54:50.2676722Z pruning_method (function): a valid pruning function from this module, 2024-06-26T05:54:50.2677022Z or a custom one implemented by the user that satisfies the 2024-06-26T05:54:50.2677382Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-06-26T05:54:50.2677740Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-06-26T05:54:50.2678130Z the corresponding parameter's importance scores tensor. The tensor 2024-06-26T05:54:50.2678432Z should be the same shape as the parameter, and is used for computing 2024-06-26T05:54:50.2678561Z mask for pruning. 2024-06-26T05:54:50.2678848Z If unspecified or None, the parameter will be used in place of its 2024-06-26T05:54:50.2678971Z importance scores. 2024-06-26T05:54:50.2679132Z kwargs: other keyword arguments such as: 2024-06-26T05:54:50.2679410Z amount (int or float): quantity of parameters to prune across the 2024-06-26T05:54:50.2679546Z specified parameters. 2024-06-26T05:54:50.2679819Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-06-26T05:54:50.2680088Z fraction of parameters to prune. If ``int``, it represents the 2024-06-26T05:54:50.2680269Z absolute number of parameters to prune. 2024-06-26T05:54:50.2680358Z 2024-06-26T05:54:50.2680453Z Raises: 2024-06-26T05:54:50.2680718Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-06-26T05:54:50.2680806Z 2024-06-26T05:54:50.2680899Z Note: 2024-06-26T05:54:50.2681343Z Since global structured pruning doesn't make much sense unless the 2024-06-26T05:54:50.2681621Z norm is normalized by the size of the parameter, we now limit the 2024-06-26T05:54:50.2681831Z scope of global pruning to unstructured methods. 2024-06-26T05:54:50.2681920Z 2024-06-26T05:54:50.2682016Z Examples: 2024-06-26T05:54:50.2682179Z >>> from torch.nn.utils import prune 2024-06-26T05:54:50.2682341Z >>> from collections import OrderedDict 2024-06-26T05:54:50.2682495Z >>> net = nn.Sequential(OrderedDict([ 2024-06-26T05:54:50.2682692Z ... ('first', nn.Linear(10, 4)), 2024-06-26T05:54:50.2682872Z ... ('second', nn.Linear(4, 1)), 2024-06-26T05:54:50.2682965Z ... ])) 2024-06-26T05:54:50.2683100Z >>> parameters_to_prune = ( 2024-06-26T05:54:50.2683261Z ... (net.first, 'weight'), 2024-06-26T05:54:50.2683422Z ... (net.second, 'weight'), 2024-06-26T05:54:50.2683529Z ... ) 2024-06-26T05:54:50.2683658Z >>> prune.global_unstructured( 2024-06-26T05:54:50.2683778Z ... parameters_to_prune, 2024-06-26T05:54:50.2683963Z ... pruning_method=prune.L1Unstructured, 2024-06-26T05:54:50.2684066Z ... amount=10, 2024-06-26T05:54:50.2684157Z ... ) 2024-06-26T05:54:50.2684469Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-06-26T05:54:50.2684566Z tensor(10) 2024-06-26T05:54:50.2684656Z 2024-06-26T05:54:50.2684757Z 2024-06-26T05:54:50.2685152Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2685255Z 2024-06-26T05:54:50.2685365Z warnings.warn(msg) 2024-06-26T05:54:50.2685451Z 2024-06-26T05:54:50.2685664Z --- Parse Warning: 78 / 90 --- 2024-06-26T05:54:50.2687024Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1144. 2024-06-26T05:54:50.2687429Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2688070Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-06-26T05:54:50.2688157Z 2024-06-26T05:54:50.2688443Z Modifies module in place (and also return the modified module) by: 2024-06-26T05:54:50.2688543Z 2024-06-26T05:54:50.2688938Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-06-26T05:54:50.2689247Z binary mask applied to the parameter ``name`` by the pruning method. 2024-06-26T05:54:50.2689582Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-06-26T05:54:50.2689882Z original (unpruned) parameter is stored in a new parameter named 2024-06-26T05:54:50.2690037Z ``name+'_orig'``. 2024-06-26T05:54:50.2690123Z 2024-06-26T05:54:50.2690216Z Args: 2024-06-26T05:54:50.2690472Z module (nn.Module): module containing the tensor to prune 2024-06-26T05:54:50.2690723Z name (str): parameter name within ``module`` on which pruning 2024-06-26T05:54:50.2690825Z will act. 2024-06-26T05:54:50.2691077Z mask (Tensor): binary mask to be applied to the parameter. 2024-06-26T05:54:50.2691163Z 2024-06-26T05:54:50.2691258Z Returns: 2024-06-26T05:54:50.2691598Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-06-26T05:54:50.2691687Z 2024-06-26T05:54:50.2691788Z Examples: 2024-06-26T05:54:50.2691959Z >>> from torch.nn.utils import prune 2024-06-26T05:54:50.2692098Z >>> m = prune.custom_from_mask( 2024-06-26T05:54:50.2692426Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-06-26T05:54:50.2692519Z ... ) 2024-06-26T05:54:50.2692634Z >>> print(m.bias_mask) 2024-06-26T05:54:50.2692758Z tensor([0., 1., 0.]) 2024-06-26T05:54:50.2692843Z 2024-06-26T05:54:50.2692934Z 2024-06-26T05:54:50.2693337Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2693423Z 2024-06-26T05:54:50.2693533Z warnings.warn(msg) 2024-06-26T05:54:50.2693633Z 2024-06-26T05:54:50.2693831Z --- Parse Warning: 79 / 90 --- 2024-06-26T05:54:50.2695189Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=103. 2024-06-26T05:54:50.2695614Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2696097Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-06-26T05:54:50.2696188Z 2024-06-26T05:54:50.2696521Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-06-26T05:54:50.2696816Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-06-26T05:54:50.2697113Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-06-26T05:54:50.2697213Z (UAI 2018). 2024-06-26T05:54:50.2697300Z 2024-06-26T05:54:50.2697596Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-06-26T05:54:50.2697921Z but using exponential weights instead of equal weights across iterations. 2024-06-26T05:54:50.2698011Z 2024-06-26T05:54:50.2698337Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-06-26T05:54:50.2698655Z on the device :attr:`device` and allows to compute running averages of the 2024-06-26T05:54:50.2698797Z parameters of the :attr:`model`. 2024-06-26T05:54:50.2698897Z 2024-06-26T05:54:50.2698989Z Args: 2024-06-26T05:54:50.2699196Z model (torch.nn.Module): model to use with SWA/EMA 2024-06-26T05:54:50.2699529Z device (torch.device, optional): if provided, the averaged model will be 2024-06-26T05:54:50.2699668Z stored on the :attr:`device` 2024-06-26T05:54:50.2699960Z avg_fn (function, optional): the averaging function used to update 2024-06-26T05:54:50.2700228Z parameters; the function must take in the current value of the 2024-06-26T05:54:50.2700519Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-06-26T05:54:50.2700838Z parameter, and the number of models already averaged; if None, 2024-06-26T05:54:50.2701081Z an equally weighted average is used (default: None) 2024-06-26T05:54:50.2701386Z multi_avg_fn (function, optional): the averaging function used to update 2024-06-26T05:54:50.2701742Z parameters inplace; the function must take in the current values of the 2024-06-26T05:54:50.2702183Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-06-26T05:54:50.2702542Z parameters as a list, and the number of models already averaged; if None, 2024-06-26T05:54:50.2702754Z an equally weighted average is used (default: None) 2024-06-26T05:54:50.2703040Z use_buffers (bool): if ``True``, it will compute running averages for 2024-06-26T05:54:50.2703403Z both the parameters and the buffers of the model. (default: ``False``) 2024-06-26T05:54:50.2703492Z 2024-06-26T05:54:50.2703589Z Example: 2024-06-26T05:54:50.2703778Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:50.2703949Z >>> loader, optimizer, model, loss_fn = ... 2024-06-26T05:54:50.2704188Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-06-26T05:54:50.2704498Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-06-26T05:54:50.2704655Z >>> T_max=300) 2024-06-26T05:54:50.2704768Z >>> swa_start = 160 2024-06-26T05:54:50.2704967Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-06-26T05:54:50.2705085Z >>> for i in range(300): 2024-06-26T05:54:50.2705251Z >>> for input, target in loader: 2024-06-26T05:54:50.2705394Z >>> optimizer.zero_grad() 2024-06-26T05:54:50.2705575Z >>> loss_fn(model(input), target).backward() 2024-06-26T05:54:50.2705722Z >>> optimizer.step() 2024-06-26T05:54:50.2705843Z >>> if i > swa_start: 2024-06-26T05:54:50.2706016Z >>> swa_model.update_parameters(model) 2024-06-26T05:54:50.2706168Z >>> swa_scheduler.step() 2024-06-26T05:54:50.2706272Z >>> else: 2024-06-26T05:54:50.2706398Z >>> scheduler.step() 2024-06-26T05:54:50.2706504Z >>> 2024-06-26T05:54:50.2706713Z >>> # Update bn statistics for the swa_model at the end 2024-06-26T05:54:50.2706926Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-06-26T05:54:50.2707027Z 2024-06-26T05:54:50.2707443Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-06-26T05:54:50.2707724Z If no averaging function is provided, the default is to compute 2024-06-26T05:54:50.2707973Z equally-weighted average of the weights (SWA). 2024-06-26T05:54:50.2708062Z 2024-06-26T05:54:50.2708171Z Example: 2024-06-26T05:54:50.2708344Z >>> # xdoctest: +SKIP("undefined variables") 2024-06-26T05:54:50.2708619Z >>> # Compute exponential moving averages of the weights and buffers 2024-06-26T05:54:50.2708866Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-06-26T05:54:50.2709161Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-06-26T05:54:50.2709249Z 2024-06-26T05:54:50.2709368Z .. note:: 2024-06-26T05:54:50.2709673Z When using SWA/EMA with models containing Batch Normalization you may 2024-06-26T05:54:50.2709958Z need to update the activation statistics for Batch Normalization. 2024-06-26T05:54:50.2710309Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-06-26T05:54:50.2710633Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-06-26T05:54:50.2711082Z statistics in a post-training step by passing data through the model. The 2024-06-26T05:54:50.2711447Z second does it during the parameter update phase by averaging all buffers. 2024-06-26T05:54:50.2711806Z Empirical evidence has shown that updating the statistics in normalization 2024-06-26T05:54:50.2712125Z layers increases accuracy, but you may wish to empirically test which 2024-06-26T05:54:50.2712327Z approach yields the best results in your problem. 2024-06-26T05:54:50.2712414Z 2024-06-26T05:54:50.2712528Z .. note:: 2024-06-26T05:54:50.2712895Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-06-26T05:54:50.2712982Z 2024-06-26T05:54:50.2713095Z .. note:: 2024-06-26T05:54:50.2713364Z When :meth:`update_parameters` is called for the first time (i.e. 2024-06-26T05:54:50.2713656Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-06-26T05:54:50.2713940Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-06-26T05:54:50.2714199Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-06-26T05:54:50.2714343Z to update the parameters. 2024-06-26T05:54:50.2714432Z 2024-06-26T05:54:50.2714864Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-06-26T05:54:50.2715032Z https://arxiv.org/abs/1803.05407 2024-06-26T05:54:50.2715360Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-06-26T05:54:50.2715459Z Average: 2024-06-26T05:54:50.2715620Z https://arxiv.org/abs/1806.05594 2024-06-26T05:54:50.2715953Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-06-26T05:54:50.2716096Z https://arxiv.org/abs/1904.11943 2024-06-26T05:54:50.2716475Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-06-26T05:54:50.2716590Z Generalizes Well: 2024-06-26T05:54:50.2716748Z https://arxiv.org/abs/2001.02312 2024-06-26T05:54:50.2716863Z .. _Polyak averaging: 2024-06-26T05:54:50.2717141Z https://paperswithcode.com/method/polyak-averaging 2024-06-26T05:54:50.2717250Z 2024-06-26T05:54:50.2717639Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2717726Z 2024-06-26T05:54:50.2717850Z warnings.warn(msg) 2024-06-26T05:54:50.2717935Z 2024-06-26T05:54:50.2718137Z --- Parse Warning: 80 / 90 --- 2024-06-26T05:54:50.2719456Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=354. 2024-06-26T05:54:50.2719864Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2720156Z Anneals the learning rate in each parameter group to a fixed value. 2024-06-26T05:54:50.2720259Z 2024-06-26T05:54:50.2720568Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-06-26T05:54:50.2720866Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-06-26T05:54:50.2720955Z 2024-06-26T05:54:50.2721119Z Args: 2024-06-26T05:54:50.2721358Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-06-26T05:54:50.2721649Z swa_lrs (float or list): the learning rate value for all param groups 2024-06-26T05:54:50.2721821Z together or separately for each group. 2024-06-26T05:54:50.2722099Z annealing_epochs (int): number of epochs in the annealing phase 2024-06-26T05:54:50.2722206Z (default: 10) 2024-06-26T05:54:50.2722492Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-06-26T05:54:50.2722848Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-06-26T05:54:50.2722960Z (default: "cos") 2024-06-26T05:54:50.2723302Z last_epoch (int): the index of the last epoch (default: -1) 2024-06-26T05:54:50.2723401Z 2024-06-26T05:54:50.2723676Z The :class:`SWALR` scheduler can be used together with other 2024-06-26T05:54:50.2723991Z schedulers to switch to a constant learning rate late in the training 2024-06-26T05:54:50.2724110Z as in the example below. 2024-06-26T05:54:50.2724195Z 2024-06-26T05:54:50.2724305Z Example: 2024-06-26T05:54:50.2724478Z >>> # xdoctest: +SKIP("Undefined variables") 2024-06-26T05:54:50.2724627Z >>> loader, optimizer, model = ... 2024-06-26T05:54:50.2724786Z >>> lr_lambda = lambda epoch: 0.9 2024-06-26T05:54:50.2725136Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-06-26T05:54:50.2725270Z >>> lr_lambda=lr_lambda) 2024-06-26T05:54:50.2725511Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-06-26T05:54:50.2725747Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-06-26T05:54:50.2725855Z >>> swa_start = 160 2024-06-26T05:54:50.2725990Z >>> for i in range(300): 2024-06-26T05:54:50.2726142Z >>> for input, target in loader: 2024-06-26T05:54:50.2726300Z >>> optimizer.zero_grad() 2024-06-26T05:54:50.2726487Z >>> loss_fn(model(input), target).backward() 2024-06-26T05:54:50.2726616Z >>> optimizer.step() 2024-06-26T05:54:50.2726752Z >>> if i > swa_start: 2024-06-26T05:54:50.2726890Z >>> swa_scheduler.step() 2024-06-26T05:54:50.2726988Z >>> else: 2024-06-26T05:54:50.2727127Z >>> scheduler.step() 2024-06-26T05:54:50.2727216Z 2024-06-26T05:54:50.2727513Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-06-26T05:54:50.2727674Z https://arxiv.org/abs/1803.05407 2024-06-26T05:54:50.2727767Z 2024-06-26T05:54:50.2728160Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2728259Z 2024-06-26T05:54:50.2728371Z warnings.warn(msg) 2024-06-26T05:54:50.2728456Z 2024-06-26T05:54:50.2728668Z --- Parse Warning: 81 / 90 --- 2024-06-26T05:54:50.2730042Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1268. 2024-06-26T05:54:50.2730464Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2730655Z Asserts that ``actual`` and ``expected`` are close. 2024-06-26T05:54:50.2730743Z 2024-06-26T05:54:50.2731361Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-06-26T05:54:50.2731450Z 2024-06-26T05:54:50.2731553Z .. math:: 2024-06-26T05:54:50.2731651Z 2024-06-26T05:54:50.2732291Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-06-26T05:54:50.2732379Z 2024-06-26T05:54:50.2732966Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-06-26T05:54:50.2733236Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-06-26T05:54:50.2733339Z 2024-06-26T05:54:50.2733612Z In addition, they are only considered close if they have the same 2024-06-26T05:54:50.2733698Z 2024-06-26T05:54:50.2734029Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-06-26T05:54:50.2734244Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-06-26T05:54:50.2734512Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-06-26T05:54:50.2734732Z - stride (if ``check_stride`` is ``True``). 2024-06-26T05:54:50.2734847Z 2024-06-26T05:54:50.2735278Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-06-26T05:54:50.2735408Z 2024-06-26T05:54:50.2735936Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-06-26T05:54:50.2736443Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-06-26T05:54:50.2736771Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-06-26T05:54:50.2737312Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-06-26T05:54:50.2737444Z 2024-06-26T05:54:50.2737846Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-06-26T05:54:50.2738338Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-06-26T05:54:50.2738464Z definition above. 2024-06-26T05:54:50.2738550Z 2024-06-26T05:54:50.2739068Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-06-26T05:54:50.2739699Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-06-26T05:54:50.2740266Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-06-26T05:54:50.2740908Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-06-26T05:54:50.2741219Z their elements are considered close according to the above definition. 2024-06-26T05:54:50.2741310Z 2024-06-26T05:54:50.2741422Z .. note:: 2024-06-26T05:54:50.2741508Z 2024-06-26T05:54:50.2741971Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-06-26T05:54:50.2742527Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-06-26T05:54:50.2742909Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-06-26T05:54:50.2742997Z 2024-06-26T05:54:50.2743104Z Args: 2024-06-26T05:54:50.2743232Z actual (Any): Actual input. 2024-06-26T05:54:50.2743374Z expected (Any): Expected input. 2024-06-26T05:54:50.2743876Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-06-26T05:54:50.2744089Z are allowed. Otherwise type equality is required. 2024-06-26T05:54:50.2744608Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-06-26T05:54:50.2744985Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-06-26T05:54:50.2745479Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-06-26T05:54:50.2745862Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-06-26T05:54:50.2746219Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-06-26T05:54:50.2746636Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-06-26T05:54:50.2746978Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-06-26T05:54:50.2747377Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-06-26T05:54:50.2747906Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-06-26T05:54:50.2748501Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-06-26T05:54:50.2748737Z :func:`torch.promote_types`) before being compared. 2024-06-26T05:54:50.2749246Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-06-26T05:54:50.2749793Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-06-26T05:54:50.2749909Z compared. 2024-06-26T05:54:50.2750411Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-06-26T05:54:50.2750938Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-06-26T05:54:50.2751462Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-06-26T05:54:50.2751611Z should return the new message. 2024-06-26T05:54:50.2751699Z 2024-06-26T05:54:50.2751806Z Raises: 2024-06-26T05:54:50.2752126Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-06-26T05:54:50.2752357Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-06-26T05:54:50.2752787Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-06-26T05:54:50.2753276Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-06-26T05:54:50.2753403Z different types. 2024-06-26T05:54:50.2753990Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-06-26T05:54:50.2754585Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-06-26T05:54:50.2755145Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-06-26T05:54:50.2755559Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-06-26T05:54:50.2755722Z :attr:`~torch.Tensor.layout`. 2024-06-26T05:54:50.2756010Z AssertionError: If only one of corresponding tensors is quantized. 2024-06-26T05:54:50.2756619Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-06-26T05:54:50.2757036Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-06-26T05:54:50.2757185Z :attr:`~torch.Tensor.device`. 2024-06-26T05:54:50.2757645Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-06-26T05:54:50.2758153Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-06-26T05:54:50.2758640Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-06-26T05:54:50.2758738Z 2024-06-26T05:54:50.2759335Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-06-26T05:54:50.2759589Z ``dtype``'s, the maximum of both tolerances is used. 2024-06-26T05:54:50.2759688Z 2024-06-26T05:54:50.2759901Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2760080Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-06-26T05:54:50.2760243Z +===========================+============+==========+ 2024-06-26T05:54:50.2760533Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-06-26T05:54:50.2760736Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2761117Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-06-26T05:54:50.2761325Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2761613Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:50.2761820Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2762054Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-06-26T05:54:50.2762271Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2762511Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-06-26T05:54:50.2762710Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2762961Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:50.2763199Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2763441Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-06-26T05:54:50.2763658Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2763892Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:50.2764096Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2764345Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:50.2764545Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2764794Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:50.2764995Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2765226Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:50.2765445Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2765682Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-06-26T05:54:50.2765887Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2766066Z | other | ``0.0`` | ``0.0`` | 2024-06-26T05:54:50.2766271Z +---------------------------+------------+----------+ 2024-06-26T05:54:50.2766360Z 2024-06-26T05:54:50.2766478Z .. note:: 2024-06-26T05:54:50.2766565Z 2024-06-26T05:54:50.2767098Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-06-26T05:54:50.2767611Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-06-26T05:54:50.2767973Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-06-26T05:54:50.2768074Z 2024-06-26T05:54:50.2768189Z >>> import functools 2024-06-26T05:54:50.2768532Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-06-26T05:54:50.2768722Z >>> assert_equal(1e-9, 1e-10) 2024-06-26T05:54:50.2768874Z Traceback (most recent call last): 2024-06-26T05:54:50.2768970Z ... 2024-06-26T05:54:50.2769148Z AssertionError: Scalars are not equal! 2024-06-26T05:54:50.2769251Z 2024-06-26T05:54:50.2769430Z Expected 1e-10 but got 1e-09. 2024-06-26T05:54:50.2769660Z Absolute difference: 9.000000000000001e-10 2024-06-26T05:54:50.2769785Z Relative difference: 9.0 2024-06-26T05:54:50.2769884Z 2024-06-26T05:54:50.2769981Z Examples: 2024-06-26T05:54:50.2770131Z >>> # tensor to tensor comparison 2024-06-26T05:54:50.2770376Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-06-26T05:54:50.2770546Z >>> actual = torch.acos(torch.cos(expected)) 2024-06-26T05:54:50.2770739Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:50.2770875Z 2024-06-26T05:54:50.2771022Z >>> # scalar to scalar comparison 2024-06-26T05:54:50.2771127Z >>> import math 2024-06-26T05:54:50.2771309Z >>> expected = math.sqrt(2.0) 2024-06-26T05:54:50.2771447Z >>> actual = 2.0 / math.sqrt(2.0) 2024-06-26T05:54:50.2771667Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:50.2771767Z 2024-06-26T05:54:50.2771941Z >>> # numpy array to numpy array comparison 2024-06-26T05:54:50.2772060Z >>> import numpy as np 2024-06-26T05:54:50.2772288Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-06-26T05:54:50.2772447Z >>> actual = np.arccos(np.cos(expected)) 2024-06-26T05:54:50.2772637Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:50.2772741Z 2024-06-26T05:54:50.2772892Z >>> # sequence to sequence comparison 2024-06-26T05:54:50.2773052Z >>> import numpy as np 2024-06-26T05:54:50.2773423Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-06-26T05:54:50.2773600Z >>> # length and their elements have to match. 2024-06-26T05:54:50.2773824Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-06-26T05:54:50.2773952Z >>> actual = tuple(expected) 2024-06-26T05:54:50.2774146Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:50.2774247Z 2024-06-26T05:54:50.2774399Z >>> # mapping to mapping comparison 2024-06-26T05:54:50.2774560Z >>> from collections import OrderedDict 2024-06-26T05:54:50.2774691Z >>> import numpy as np 2024-06-26T05:54:50.2774814Z >>> foo = torch.tensor(1.0) 2024-06-26T05:54:50.2774916Z >>> bar = 2.0 2024-06-26T05:54:50.2775045Z >>> baz = np.array(3.0) 2024-06-26T05:54:50.2775402Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-06-26T05:54:50.2775714Z >>> # have to have the same set of keys and their elements have to match. 2024-06-26T05:54:50.2775988Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-06-26T05:54:50.2776171Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-06-26T05:54:50.2776374Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:50.2776461Z 2024-06-26T05:54:50.2776627Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-06-26T05:54:50.2776773Z >>> actual = expected.clone() 2024-06-26T05:54:50.2777007Z >>> # By default, directly related instances can be compared 2024-06-26T05:54:50.2777297Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-06-26T05:54:50.2777580Z >>> # This check can be made more strict with allow_subclasses=False 2024-06-26T05:54:50.2777725Z >>> torch.testing.assert_close( 2024-06-26T05:54:50.2777997Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-06-26T05:54:50.2778104Z ... ) 2024-06-26T05:54:50.2778259Z Traceback (most recent call last): 2024-06-26T05:54:50.2778364Z ... 2024-06-26T05:54:50.2778635Z TypeError: No comparison pair was able to handle inputs of type 2024-06-26T05:54:50.2778995Z and . 2024-06-26T05:54:50.2779328Z >>> # If the inputs are not directly related, they are never considered close 2024-06-26T05:54:50.2779553Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-06-26T05:54:50.2779701Z Traceback (most recent call last): 2024-06-26T05:54:50.2779808Z ... 2024-06-26T05:54:50.2780284Z TypeError: No comparison pair was able to handle inputs of type 2024-06-26T05:54:50.2780453Z and . 2024-06-26T05:54:50.2780862Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-06-26T05:54:50.2781017Z >>> # their type if check_dtype=False. 2024-06-26T05:54:50.2781278Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-06-26T05:54:50.2781367Z 2024-06-26T05:54:50.2781522Z >>> # NaN != NaN by default. 2024-06-26T05:54:50.2781702Z >>> expected = torch.tensor(float("Nan")) 2024-06-26T05:54:50.2781837Z >>> actual = expected.clone() 2024-06-26T05:54:50.2782027Z >>> torch.testing.assert_close(actual, expected) 2024-06-26T05:54:50.2782189Z Traceback (most recent call last): 2024-06-26T05:54:50.2782282Z ... 2024-06-26T05:54:50.2782445Z AssertionError: Scalars are not close! 2024-06-26T05:54:50.2782562Z 2024-06-26T05:54:50.2782689Z Expected nan but got nan. 2024-06-26T05:54:50.2782960Z Absolute difference: nan (up to 1e-05 allowed) 2024-06-26T05:54:50.2783228Z Relative difference: nan (up to 1.3e-06 allowed) 2024-06-26T05:54:50.2783493Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-06-26T05:54:50.2783583Z 2024-06-26T05:54:50.2783764Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-06-26T05:54:50.2783926Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-06-26T05:54:50.2784134Z >>> # The default error message can be overwritten. 2024-06-26T05:54:50.2784521Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-06-26T05:54:50.2784672Z Traceback (most recent call last): 2024-06-26T05:54:50.2784776Z ... 2024-06-26T05:54:50.2784978Z AssertionError: Argh, the tensors are not close! 2024-06-26T05:54:50.2785310Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-06-26T05:54:50.2785444Z >>> # extra information 2024-06-26T05:54:50.2785587Z >>> torch.testing.assert_close( 2024-06-26T05:54:50.2785853Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-06-26T05:54:50.2785961Z ... ) 2024-06-26T05:54:50.2786114Z Traceback (most recent call last): 2024-06-26T05:54:50.2786209Z ... 2024-06-26T05:54:50.2786344Z AssertionError: Header 2024-06-26T05:54:50.2786447Z 2024-06-26T05:54:50.2786619Z Tensor-likes are not close! 2024-06-26T05:54:50.2786730Z 2024-06-26T05:54:50.2786876Z Mismatched elements: 2 / 3 (66.7%) 2024-06-26T05:54:50.2787255Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-06-26T05:54:50.2787630Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-06-26T05:54:50.2787730Z 2024-06-26T05:54:50.2787837Z Footer 2024-06-26T05:54:50.2787933Z 2024-06-26T05:54:50.2788323Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2788426Z 2024-06-26T05:54:50.2788535Z warnings.warn(msg) 2024-06-26T05:54:50.2788620Z 2024-06-26T05:54:50.2788832Z --- Parse Warning: 82 / 90 --- 2024-06-26T05:54:50.2790293Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61. 2024-06-26T05:54:50.2790698Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2790805Z 2024-06-26T05:54:50.2791216Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-06-26T05:54:50.2791298Z 2024-06-26T05:54:50.2791704Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-06-26T05:54:50.2792075Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-06-26T05:54:50.2792421Z server like load. It can emulate multiple calling threads to a single module 2024-06-26T05:54:50.2792790Z provided. In the future we plan to enhance this component to support inter and 2024-06-26T05:54:50.2793212Z intra-op parallelism as well as multiple models running in a single process. 2024-06-26T05:54:50.2793309Z 2024-06-26T05:54:50.2793656Z Please note that even though nn.Module is supported, it might incur an overhead 2024-06-26T05:54:50.2793973Z from the need to hold GIL every time we execute Python code or pass around 2024-06-26T05:54:50.2794315Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-06-26T05:54:50.2794760Z model for inference deployment it is better to switch to using it in this 2024-06-26T05:54:50.2794860Z benchmark. 2024-06-26T05:54:50.2794963Z 2024-06-26T05:54:50.2795111Z Example:: 2024-06-26T05:54:50.2795196Z 2024-06-26T05:54:50.2795367Z >>> # xdoctest: +SKIP("undefined vars") 2024-06-26T05:54:50.2795554Z >>> from torch.utils import ThroughputBenchmark 2024-06-26T05:54:50.2795735Z >>> bench = ThroughputBenchmark(my_module) 2024-06-26T05:54:50.2796008Z >>> # Pre-populate benchmark's data set with the inputs 2024-06-26T05:54:50.2796125Z >>> for input in inputs: 2024-06-26T05:54:50.2796451Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-06-26T05:54:50.2796619Z ... bench.add_input(input[0], x2=input[1]) 2024-06-26T05:54:50.2796876Z >>> # Inputs supplied above are randomly used during the execution 2024-06-26T05:54:50.2797014Z >>> stats = bench.benchmark( 2024-06-26T05:54:50.2797135Z ... num_calling_threads=4, 2024-06-26T05:54:50.2797259Z ... num_warmup_iters = 100, 2024-06-26T05:54:50.2797387Z ... num_iters = 1000, 2024-06-26T05:54:50.2797479Z ... ) 2024-06-26T05:54:50.2797717Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-06-26T05:54:50.2797967Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-06-26T05:54:50.2798052Z 2024-06-26T05:54:50.2798457Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2798545Z 2024-06-26T05:54:50.2798652Z warnings.warn(msg) 2024-06-26T05:54:50.2798749Z 2024-06-26T05:54:50.2798946Z --- Parse Warning: 83 / 90 --- 2024-06-26T05:54:50.2800332Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=115. 2024-06-26T05:54:50.2800752Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2800977Z Register a container-like type as pytree node. 2024-06-26T05:54:50.2801135Z 2024-06-26T05:54:50.2801245Z Args: 2024-06-26T05:54:50.2801507Z cls (type): A Python type to treat as an internal pytree node. 2024-06-26T05:54:50.2801883Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-06-26T05:54:50.2802259Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-06-26T05:54:50.2802654Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-06-26T05:54:50.2802821Z passed to the ``unflatten_fn``. 2024-06-26T05:54:50.2803196Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-06-26T05:54:50.2803567Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-06-26T05:54:50.2803792Z The function should return an instance of ``cls``. 2024-06-26T05:54:50.2804194Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-06-26T05:54:50.2804412Z qualified name used when serializing the tree spec. 2024-06-26T05:54:50.2804870Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-06-26T05:54:50.2805298Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-06-26T05:54:50.2805687Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-06-26T05:54:50.2806086Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-06-26T05:54:50.2806458Z how to convert the custom json dumpable representation of the context back to the 2024-06-26T05:54:50.2806831Z original context. This is used for json deserialization, which is being used in 2024-06-26T05:54:50.2807021Z :mod:`torch.export` right now. 2024-06-26T05:54:50.2807107Z 2024-06-26T05:54:50.2807221Z Example:: 2024-06-26T05:54:50.2807307Z 2024-06-26T05:54:50.2807424Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2807629Z >>> # Registry a Python type with lambda functions 2024-06-26T05:54:50.2807753Z >>> register_pytree_node( 2024-06-26T05:54:50.2807864Z ... set, 2024-06-26T05:54:50.2808027Z ... lambda s: (sorted(s), None, None), 2024-06-26T05:54:50.2808188Z ... lambda children, _: set(children), 2024-06-26T05:54:50.2808292Z ... ) 2024-06-26T05:54:50.2808384Z 2024-06-26T05:54:50.2808781Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2808883Z 2024-06-26T05:54:50.2808992Z warnings.warn(msg) 2024-06-26T05:54:50.2809077Z 2024-06-26T05:54:50.2809287Z --- Parse Warning: 84 / 90 --- 2024-06-26T05:54:50.2810743Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1186. 2024-06-26T05:54:50.2811158Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2811258Z 2024-06-26T05:54:50.2811538Z Context passed to policy function during selective checkpointing. 2024-06-26T05:54:50.2811638Z 2024-06-26T05:54:50.2811958Z This class is used to pass relevant metadata to the policy function during 2024-06-26T05:54:50.2812299Z selective checkpointing. The metadata includes whether the current invocation 2024-06-26T05:54:50.2812529Z of the policy function is during recomputation or not. 2024-06-26T05:54:50.2812617Z 2024-06-26T05:54:50.2812712Z Example: 2024-06-26T05:54:50.2812846Z >>> # xdoctest: +SKIP(stub) 2024-06-26T05:54:50.2812942Z >>> 2024-06-26T05:54:50.2813110Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-06-26T05:54:50.2813253Z >>> print(ctx.is_recompute) 2024-06-26T05:54:50.2813348Z >>> 2024-06-26T05:54:50.2813699Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-06-26T05:54:50.2813805Z >>> 2024-06-26T05:54:50.2813995Z >>> out = torch.utils.checkpoint.checkpoint( 2024-06-26T05:54:50.2814099Z >>> fn, x, y, 2024-06-26T05:54:50.2814233Z >>> use_reentrant=False, 2024-06-26T05:54:50.2814355Z >>> context_fn=context_fn, 2024-06-26T05:54:50.2814451Z >>> ) 2024-06-26T05:54:50.2814592Z 2024-06-26T05:54:50.2815037Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2815126Z 2024-06-26T05:54:50.2815253Z warnings.warn(msg) 2024-06-26T05:54:50.2815339Z 2024-06-26T05:54:50.2815554Z --- Parse Warning: 85 / 90 --- 2024-06-26T05:54:50.2817044Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1319. 2024-06-26T05:54:50.2817524Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2817684Z 2024-06-26T05:54:50.2818006Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-06-26T05:54:50.2818091Z 2024-06-26T05:54:50.2818405Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-06-26T05:54:50.2818607Z operations are recomputed during the backward pass. 2024-06-26T05:54:50.2818693Z 2024-06-26T05:54:50.2818800Z Args: 2024-06-26T05:54:50.2818950Z policy_fn_or_list (Callable or List): 2024-06-26T05:54:50.2819221Z - If a policy function is provided, it should accept a 2024-06-26T05:54:50.2819579Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-06-26T05:54:50.2819879Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-06-26T05:54:50.2820208Z indicating whether the execution of the op should be recomputed or not. 2024-06-26T05:54:50.2820555Z - If a list of operations is provided, it is equivalent to a policy 2024-06-26T05:54:50.2820794Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-06-26T05:54:50.2821095Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-06-26T05:54:50.2821199Z operations. 2024-06-26T05:54:50.2821480Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-06-26T05:54:50.2821787Z raised if any tensors cached by selective activation checkpoint are 2024-06-26T05:54:50.2822078Z mutated in order to ensure correctness. If set to `True`, this check 2024-06-26T05:54:50.2822193Z is disabled. 2024-06-26T05:54:50.2822290Z Returns: 2024-06-26T05:54:50.2822422Z A tuple of two context managers. 2024-06-26T05:54:50.2822520Z 2024-06-26T05:54:50.2822617Z Example: 2024-06-26T05:54:50.2822746Z >>> # xdoctest: +REQUIRES(LINUX) 2024-06-26T05:54:50.2822869Z >>> import functools 2024-06-26T05:54:50.2822958Z >>> 2024-06-26T05:54:50.2823126Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-06-26T05:54:50.2823300Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-06-26T05:54:50.2823389Z >>> 2024-06-26T05:54:50.2823494Z >>> ops_to_save = [ 2024-06-26T05:54:50.2823647Z >>> torch.ops.aten.mm.default, 2024-06-26T05:54:50.2823736Z >>> ] 2024-06-26T05:54:50.2823828Z >>> 2024-06-26T05:54:50.2824004Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-06-26T05:54:50.2824123Z >>> if op in ops_to_save: 2024-06-26T05:54:50.2824292Z >>> return CheckpointPolicy.MUST_SAVE 2024-06-26T05:54:50.2824399Z >>> else: 2024-06-26T05:54:50.2824586Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-06-26T05:54:50.2824677Z >>> 2024-06-26T05:54:50.2825043Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-06-26T05:54:50.2825133Z >>> 2024-06-26T05:54:50.2825243Z >>> # or equivalently 2024-06-26T05:54:50.2825620Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-06-26T05:54:50.2825708Z >>> 2024-06-26T05:54:50.2825825Z >>> def fn(x, y): 2024-06-26T05:54:50.2826087Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-06-26T05:54:50.2826175Z >>> 2024-06-26T05:54:50.2826378Z >>> out = torch.utils.checkpoint.checkpoint( 2024-06-26T05:54:50.2826481Z >>> fn, x, y, 2024-06-26T05:54:50.2826595Z >>> use_reentrant=False, 2024-06-26T05:54:50.2826731Z >>> context_fn=context_fn, 2024-06-26T05:54:50.2826853Z >>> ) 2024-06-26T05:54:50.2826937Z 2024-06-26T05:54:50.2827344Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2827460Z 2024-06-26T05:54:50.2827567Z warnings.warn(msg) 2024-06-26T05:54:50.2827663Z 2024-06-26T05:54:50.2827861Z --- Parse Warning: 86 / 90 --- 2024-06-26T05:54:50.2829258Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=923. 2024-06-26T05:54:50.2829679Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2829765Z 2024-06-26T05:54:50.2829961Z Create a :class:`setuptools.Extension` for C++. 2024-06-26T05:54:50.2830046Z 2024-06-26T05:54:50.2830385Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-06-26T05:54:50.2830698Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-06-26T05:54:50.2830786Z 2024-06-26T05:54:50.2831057Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-06-26T05:54:50.2831262Z constructor. Full list arguments can be found at 2024-06-26T05:54:50.2831764Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-06-26T05:54:50.2831849Z 2024-06-26T05:54:50.2831954Z Example: 2024-06-26T05:54:50.2832065Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2832255Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:50.2832405Z >>> from setuptools import setup 2024-06-26T05:54:50.2832699Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-06-26T05:54:50.2832808Z >>> setup( 2024-06-26T05:54:50.2832958Z ... name='extension', 2024-06-26T05:54:50.2833066Z ... ext_modules=[ 2024-06-26T05:54:50.2833193Z ... CppExtension( 2024-06-26T05:54:50.2833359Z ... name='extension', 2024-06-26T05:54:50.2833557Z ... sources=['extension.cpp'], 2024-06-26T05:54:50.2833765Z ... extra_compile_args=['-g'], 2024-06-26T05:54:50.2834023Z ... extra_link_flags=['-Wl,--no-as-needed', '-lm']) 2024-06-26T05:54:50.2834118Z ... ], 2024-06-26T05:54:50.2834233Z ... cmdclass={ 2024-06-26T05:54:50.2834421Z ... 'build_ext': BuildExtension 2024-06-26T05:54:50.2834514Z ... }) 2024-06-26T05:54:50.2834722Z 2024-06-26T05:54:50.2835121Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2835207Z 2024-06-26T05:54:50.2835331Z warnings.warn(msg) 2024-06-26T05:54:50.2835416Z 2024-06-26T05:54:50.2835614Z --- Parse Warning: 87 / 90 --- 2024-06-26T05:54:50.2837012Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=974. 2024-06-26T05:54:50.2837424Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2837529Z 2024-06-26T05:54:50.2837740Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-06-26T05:54:50.2837825Z 2024-06-26T05:54:50.2838152Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-06-26T05:54:50.2838421Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-06-26T05:54:50.2838730Z extension. This includes the CUDA include path, library path and runtime 2024-06-26T05:54:50.2838837Z library. 2024-06-26T05:54:50.2838921Z 2024-06-26T05:54:50.2839193Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-06-26T05:54:50.2839394Z constructor. Full list arguments can be found at 2024-06-26T05:54:50.2839948Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-06-26T05:54:50.2840033Z 2024-06-26T05:54:50.2840180Z Example: 2024-06-26T05:54:50.2840294Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2840503Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:50.2840674Z >>> from setuptools import setup 2024-06-26T05:54:50.2840975Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-06-26T05:54:50.2841167Z >>> setup( 2024-06-26T05:54:50.2841335Z ... name='cuda_extension', 2024-06-26T05:54:50.2841440Z ... ext_modules=[ 2024-06-26T05:54:50.2841570Z ... CUDAExtension( 2024-06-26T05:54:50.2841766Z ... name='cuda_extension', 2024-06-26T05:54:50.2842049Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-06-26T05:54:50.2842325Z ... extra_compile_args={'cxx': ['-g'], 2024-06-26T05:54:50.2842549Z ... 'nvcc': ['-O2']}, 2024-06-26T05:54:50.2842826Z ... extra_link_flags=['-Wl,--no-as-needed', '-lcuda']) 2024-06-26T05:54:50.2842931Z ... ], 2024-06-26T05:54:50.2843035Z ... cmdclass={ 2024-06-26T05:54:50.2843224Z ... 'build_ext': BuildExtension 2024-06-26T05:54:50.2843332Z ... }) 2024-06-26T05:54:50.2843418Z 2024-06-26T05:54:50.2843547Z Compute capabilities: 2024-06-26T05:54:50.2843627Z 2024-06-26T05:54:50.2844051Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-06-26T05:54:50.2844466Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-06-26T05:54:50.2844913Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-06-26T05:54:50.2845411Z newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch 2024-06-26T05:54:50.2845831Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-06-26T05:54:50.2845982Z support (see below for details on PTX). 2024-06-26T05:54:50.2846066Z 2024-06-26T05:54:50.2846513Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-06-26T05:54:50.2846657Z CCs you want the extension to support: 2024-06-26T05:54:50.2846741Z 2024-06-26T05:54:50.2846998Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-06-26T05:54:50.2847346Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-06-26T05:54:50.2847444Z 2024-06-26T05:54:50.2847878Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-06-26T05:54:50.2848389Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-06-26T05:54:50.2848907Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-06-26T05:54:50.2849397Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-06-26T05:54:50.2849906Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-06-26T05:54:50.2850412Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-06-26T05:54:50.2850851Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-06-26T05:54:50.2851384Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-06-26T05:54:50.2851497Z "8.0 8.6" would be better. 2024-06-26T05:54:50.2851582Z 2024-06-26T05:54:50.2852090Z Note that while it's possible to include all supported archs, the more archs get included the 2024-06-26T05:54:50.2852505Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-06-26T05:54:50.2852618Z 2024-06-26T05:54:50.2853179Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-06-26T05:54:50.2853516Z To workaround the issue, move python binding logic to pure C++ file. 2024-06-26T05:54:50.2853602Z 2024-06-26T05:54:50.2853744Z Example use: 2024-06-26T05:54:50.2853860Z #include 2024-06-26T05:54:50.2854064Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-06-26T05:54:50.2854166Z 2024-06-26T05:54:50.2854266Z Instead of: 2024-06-26T05:54:50.2854403Z #include 2024-06-26T05:54:50.2854607Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-06-26T05:54:50.2854692Z 2024-06-26T05:54:50.2855074Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-06-26T05:54:50.2855765Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-06-26T05:54:50.2855854Z 2024-06-26T05:54:50.2855995Z Relocatable device code linking: 2024-06-26T05:54:50.2856082Z 2024-06-26T05:54:50.2856471Z If you want to reference device symbols across compilation units (across object files), 2024-06-26T05:54:50.2856932Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-06-26T05:54:50.2857433Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-06-26T05:54:50.2857912Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-06-26T05:54:50.2858429Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-06-26T05:54:50.2858715Z help reduce the protentional perf degradation of `-rdc`. 2024-06-26T05:54:50.2858959Z Note that it needs to be used at both steps to be useful. 2024-06-26T05:54:50.2859050Z 2024-06-26T05:54:50.2859683Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-06-26T05:54:50.2859993Z There is also a case where `-dlink` is used without `-rdc`: 2024-06-26T05:54:50.2860416Z when an extension is linked against a static lib containing rdc-compiled objects 2024-06-26T05:54:50.2860698Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-06-26T05:54:50.2860799Z 2024-06-26T05:54:50.2861081Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-06-26T05:54:50.2861180Z 2024-06-26T05:54:50.2861277Z Example: 2024-06-26T05:54:50.2861391Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2861602Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:50.2861714Z >>> CUDAExtension( 2024-06-26T05:54:50.2861884Z ... name='cuda_extension', 2024-06-26T05:54:50.2862166Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-06-26T05:54:50.2862274Z ... dlink=True, 2024-06-26T05:54:50.2862427Z ... dlink_libraries=["dlink_lib"], 2024-06-26T05:54:50.2862650Z ... extra_compile_args={'cxx': ['-g'], 2024-06-26T05:54:50.2862879Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-06-26T05:54:50.2862966Z 2024-06-26T05:54:50.2863375Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2863461Z 2024-06-26T05:54:50.2863569Z warnings.warn(msg) 2024-06-26T05:54:50.2863666Z 2024-06-26T05:54:50.2863864Z --- Parse Warning: 88 / 90 --- 2024-06-26T05:54:50.2865192Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1232. 2024-06-26T05:54:50.2865614Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2865730Z 2024-06-26T05:54:50.2865978Z Load a PyTorch C++ extension just-in-time (JIT). 2024-06-26T05:54:50.2866095Z 2024-06-26T05:54:50.2866381Z To load an extension, a Ninja build file is emitted, which is used to 2024-06-26T05:54:50.2866694Z compile the given sources into a dynamic library. This library is 2024-06-26T05:54:50.2866983Z subsequently loaded into the current Python process as a module and 2024-06-26T05:54:50.2867144Z returned from this function, ready for use. 2024-06-26T05:54:50.2867240Z 2024-06-26T05:54:50.2867523Z By default, the directory to which the build file is emitted and the 2024-06-26T05:54:50.2867831Z resulting library compiled to is ``/torch_extensions/``, where 2024-06-26T05:54:50.2868124Z ```` is the temporary folder on the current platform and ```` 2024-06-26T05:54:50.2868450Z the name of the extension. This location can be overridden in two ways. 2024-06-26T05:54:50.2868750Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-06-26T05:54:50.2869040Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-06-26T05:54:50.2869332Z into subfolders of this directory. Second, if the ``build_directory`` 2024-06-26T05:54:50.2869658Z argument to this function is supplied, it overrides the entire path, i.e. 2024-06-26T05:54:50.2869879Z the library will be compiled into that folder directly. 2024-06-26T05:54:50.2869963Z 2024-06-26T05:54:50.2870262Z To compile the sources, the default system compiler (``c++``) is used, 2024-06-26T05:54:50.2870595Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-06-26T05:54:50.2870883Z additional arguments to the compilation process, ``extra_cflags`` or 2024-06-26T05:54:50.2871196Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-06-26T05:54:50.2871533Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-06-26T05:54:50.2871742Z ``extra_cflags`` to pass further include directories. 2024-06-26T05:54:50.2871832Z 2024-06-26T05:54:50.2872145Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-06-26T05:54:50.2872420Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-06-26T05:54:50.2872752Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-06-26T05:54:50.2873039Z passing the CUDA lib64 directory as a library directory, and linking 2024-06-26T05:54:50.2873251Z ``cudart``. You can pass additional flags to nvcc via 2024-06-26T05:54:50.2873528Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-06-26T05:54:50.2873840Z heuristics for finding the CUDA install directory are used, which usually 2024-06-26T05:54:50.2874150Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-06-26T05:54:50.2874255Z safest option. 2024-06-26T05:54:50.2874351Z 2024-06-26T05:54:50.2874442Z Args: 2024-06-26T05:54:50.2874876Z name: The name of the extension to build. This MUST be the same as the 2024-06-26T05:54:50.2875022Z name of the pybind11 module! 2024-06-26T05:54:50.2875304Z sources: A list of relative or absolute paths to C++ source files. 2024-06-26T05:54:50.2875600Z extra_cflags: optional list of compiler flags to forward to the build. 2024-06-26T05:54:50.2875904Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-06-26T05:54:50.2876032Z when building CUDA sources. 2024-06-26T05:54:50.2876321Z extra_ldflags: optional list of linker flags to forward to the build. 2024-06-26T05:54:50.2876616Z extra_include_paths: optional list of include directories to forward 2024-06-26T05:54:50.2876722Z to the build. 2024-06-26T05:54:50.2876954Z build_directory: optional path to use as build workspace. 2024-06-26T05:54:50.2877254Z verbose: If ``True``, turns on verbose logging of load steps. 2024-06-26T05:54:50.2877549Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-06-26T05:54:50.2877816Z the build. If set to ``None`` (default), this value is 2024-06-26T05:54:50.2878112Z automatically determined based on the existence of ``.cu`` or 2024-06-26T05:54:50.2878354Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-06-26T05:54:50.2878502Z and libraries to be included. 2024-06-26T05:54:50.2878781Z is_python_module: If ``True`` (default), imports the produced shared 2024-06-26T05:54:50.2879041Z library as a Python module. If ``False``, behavior depends on 2024-06-26T05:54:50.2879164Z ``is_standalone``. 2024-06-26T05:54:50.2879447Z is_standalone: If ``False`` (default) loads the constructed extension 2024-06-26T05:54:50.2879755Z into the process as a plain dynamic library. If ``True``, build a 2024-06-26T05:54:50.2879890Z standalone executable. 2024-06-26T05:54:50.2879979Z 2024-06-26T05:54:50.2880081Z Returns: 2024-06-26T05:54:50.2880223Z If ``is_python_module`` is ``True``: 2024-06-26T05:54:50.2880463Z Returns the loaded PyTorch extension as a Python module. 2024-06-26T05:54:50.2880560Z 2024-06-26T05:54:50.2880839Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-06-26T05:54:50.2881187Z Returns nothing. (The shared library is loaded into the process as 2024-06-26T05:54:50.2881313Z a side effect.) 2024-06-26T05:54:50.2881398Z 2024-06-26T05:54:50.2881531Z If ``is_standalone`` is ``True``. 2024-06-26T05:54:50.2881825Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-06-26T05:54:50.2882070Z added to the PATH environment variable as a side effect.) 2024-06-26T05:54:50.2882155Z 2024-06-26T05:54:50.2882263Z Example: 2024-06-26T05:54:50.2882377Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2882554Z >>> from torch.utils.cpp_extension import load 2024-06-26T05:54:50.2882675Z >>> module = load( 2024-06-26T05:54:50.2882834Z ... name='extension', 2024-06-26T05:54:50.2883099Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-06-26T05:54:50.2883270Z ... extra_cflags=['-O2'], 2024-06-26T05:54:50.2883375Z ... verbose=True) 2024-06-26T05:54:50.2883472Z 2024-06-26T05:54:50.2883863Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2883943Z 2024-06-26T05:54:50.2884060Z warnings.warn(msg) 2024-06-26T05:54:50.2884145Z 2024-06-26T05:54:50.2884344Z --- Parse Warning: 89 / 90 --- 2024-06-26T05:54:50.2885720Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1524. 2024-06-26T05:54:50.2886130Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2886216Z 2024-06-26T05:54:50.2886571Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-06-26T05:54:50.2886659Z 2024-06-26T05:54:50.2886971Z This function behaves exactly like :func:`load`, but takes its sources as 2024-06-26T05:54:50.2887291Z strings rather than filenames. These strings are stored to files in the 2024-06-26T05:54:50.2887572Z build directory, after which the behavior of :func:`load_inline` is 2024-06-26T05:54:50.2887705Z identical to :func:`load`. 2024-06-26T05:54:50.2887789Z 2024-06-26T05:54:50.2887882Z See `the 2024-06-26T05:54:50.2888322Z tests `_ 2024-06-26T05:54:50.2888485Z for good examples of using this function. 2024-06-26T05:54:50.2888606Z 2024-06-26T05:54:50.2889010Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-06-26T05:54:50.2889366Z the necessary header includes, as well as the (pybind11) binding code. More 2024-06-26T05:54:50.2889680Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-06-26T05:54:50.2889976Z single ``.cpp`` file. This file is then prepended with ``#include 2024-06-26T05:54:50.2890090Z ``. 2024-06-26T05:54:50.2890175Z 2024-06-26T05:54:50.2890490Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-06-26T05:54:50.2890795Z automatically generated for each function specified. ``functions`` can 2024-06-26T05:54:50.2891114Z either be a list of function names, or a dictionary mapping from function 2024-06-26T05:54:50.2891427Z names to docstrings. If a list is given, the name of each function is used 2024-06-26T05:54:50.2891569Z as its docstring. 2024-06-26T05:54:50.2891669Z 2024-06-26T05:54:50.2891968Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-06-26T05:54:50.2892199Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-06-26T05:54:50.2892495Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-06-26T05:54:50.2892785Z separately, but ultimately linked into a single library. Note that no 2024-06-26T05:54:50.2893099Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-06-26T05:54:50.2893422Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-06-26T05:54:50.2893714Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-06-26T05:54:50.2893859Z include its name in ``functions``). 2024-06-26T05:54:50.2893945Z 2024-06-26T05:54:50.2894193Z See :func:`load` for a description of arguments omitted below. 2024-06-26T05:54:50.2894293Z 2024-06-26T05:54:50.2894386Z Args: 2024-06-26T05:54:50.2894675Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-06-26T05:54:50.2894988Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-06-26T05:54:50.2895265Z functions: A list of function names for which to generate function 2024-06-26T05:54:50.2895553Z bindings. If a dictionary is given, it should map function names to 2024-06-26T05:54:50.2895798Z docstrings (which are otherwise just the function names). 2024-06-26T05:54:50.2896093Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-06-26T05:54:50.2896309Z the build. If set to ``None`` (default), this value is 2024-06-26T05:54:50.2896582Z automatically determined based on whether ``cuda_sources`` is 2024-06-26T05:54:50.2896783Z provided. Set it to ``True`` to force CUDA headers 2024-06-26T05:54:50.2896928Z and libraries to be included. 2024-06-26T05:54:50.2897202Z with_pytorch_error_handling: Determines whether pytorch error and 2024-06-26T05:54:50.2897464Z warning macros are handled by pytorch instead of pybind. To do 2024-06-26T05:54:50.2897776Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-06-26T05:54:50.2898040Z function. This redirection might cause issues in obscure cases 2024-06-26T05:54:50.2898301Z of cpp. This flag should be set to ``False`` when this redirect 2024-06-26T05:54:50.2898418Z causes issues. 2024-06-26T05:54:50.2898506Z 2024-06-26T05:54:50.2898599Z Example: 2024-06-26T05:54:50.2898807Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-06-26T05:54:50.2899012Z >>> from torch.utils.cpp_extension import load_inline 2024-06-26T05:54:50.2899117Z >>> source = """ 2024-06-26T05:54:50.2899325Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-06-26T05:54:50.2899448Z return x.sin() + y.sin(); 2024-06-26T05:54:50.2899579Z } 2024-06-26T05:54:50.2899671Z """ 2024-06-26T05:54:50.2899907Z >>> module = load_inline(name='inline_extension', 2024-06-26T05:54:50.2900110Z ... cpp_sources=[source], 2024-06-26T05:54:50.2900316Z ... functions=['sin_add']) 2024-06-26T05:54:50.2900431Z 2024-06-26T05:54:50.2900541Z .. note:: 2024-06-26T05:54:50.2900814Z By default, the Ninja backend uses #CPUS + 2 workers to build the 2024-06-26T05:54:50.2901096Z extension. This may use up too many resources on some systems. One 2024-06-26T05:54:50.2901412Z can control the number of workers by setting the `MAX_JOBS` environment 2024-06-26T05:54:50.2901597Z variable to a non-negative number. 2024-06-26T05:54:50.2901684Z 2024-06-26T05:54:50.2902091Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2902207Z 2024-06-26T05:54:50.2902320Z warnings.warn(msg) 2024-06-26T05:54:50.2902420Z 2024-06-26T05:54:50.2902618Z --- Parse Warning: 90 / 90 --- 2024-06-26T05:54:50.2904069Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=14. 2024-06-26T05:54:50.2904478Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-06-26T05:54:50.2904740Z Sampler that restricts data loading to a subset of the dataset. 2024-06-26T05:54:50.2904845Z 2024-06-26T05:54:50.2905019Z It is especially useful in conjunction with 2024-06-26T05:54:50.2905363Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-06-26T05:54:50.2905744Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-06-26T05:54:50.2906058Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-06-26T05:54:50.2906238Z original dataset that is exclusive to it. 2024-06-26T05:54:50.2906324Z 2024-06-26T05:54:50.2906425Z .. note:: 2024-06-26T05:54:50.2906773Z Dataset is assumed to be of constant size and that any instance of it always 2024-06-26T05:54:50.2906955Z returns the same elements in the same order. 2024-06-26T05:54:50.2907038Z 2024-06-26T05:54:50.2907143Z Args: 2024-06-26T05:54:50.2907293Z dataset: Dataset used for sampling. 2024-06-26T05:54:50.2907574Z num_replicas (int, optional): Number of processes participating in 2024-06-26T05:54:50.2907911Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-06-26T05:54:50.2908045Z current distributed group. 2024-06-26T05:54:50.2908372Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-06-26T05:54:50.2908662Z By default, :attr:`rank` is retrieved from the current distributed 2024-06-26T05:54:50.2908759Z group. 2024-06-26T05:54:50.2909063Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-06-26T05:54:50.2909176Z indices. 2024-06-26T05:54:50.2909442Z seed (int, optional): random seed used to shuffle the sampler if 2024-06-26T05:54:50.2909713Z :attr:`shuffle=True`. This number should be identical across all 2024-06-26T05:54:50.2909929Z processes in the distributed group. Default: ``0``. 2024-06-26T05:54:50.2910226Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-06-26T05:54:50.2910517Z tail of the data to make it evenly divisible across the number of 2024-06-26T05:54:50.2910791Z replicas. If ``False``, the sampler will add extra indices to make 2024-06-26T05:54:50.2911073Z the data evenly divisible across the replicas. Default: ``False``. 2024-06-26T05:54:50.2911201Z 2024-06-26T05:54:50.2911304Z .. warning:: 2024-06-26T05:54:50.2911555Z In distributed mode, calling the :meth:`set_epoch` method at 2024-06-26T05:54:50.2911970Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-06-26T05:54:50.2912347Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-06-26T05:54:50.2912518Z the same ordering will be always used. 2024-06-26T05:54:50.2912604Z 2024-06-26T05:54:50.2912703Z Example:: 2024-06-26T05:54:50.2912797Z 2024-06-26T05:54:50.2912912Z >>> # xdoctest: +SKIP 2024-06-26T05:54:50.2913196Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-06-26T05:54:50.2913432Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-06-26T05:54:50.2913613Z ... sampler=sampler) 2024-06-26T05:54:50.2913792Z >>> for epoch in range(start_epoch, n_epochs): 2024-06-26T05:54:50.2913923Z ... if is_distributed: 2024-06-26T05:54:50.2914070Z ... sampler.set_epoch(epoch) 2024-06-26T05:54:50.2914178Z ... train(loader) 2024-06-26T05:54:50.2914280Z 2024-06-26T05:54:50.2914786Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-06-26T05:54:50.2914887Z 2024-06-26T05:54:50.2914997Z warnings.warn(msg) 2024-06-26T05:54:50.2915084Z 2024-06-26T05:54:50.2915243Z  2024-06-26T05:54:50.2915489Z === Found 9 run-time warnings === 2024-06-26T05:54:50.2915688Z --- Runtime Warning: 1 / 9 --- 2024-06-26T05:54:50.2916042Z example = 2024-06-26T05:54:50.2918114Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py:1241: 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:1928.) 2024-06-26T05:54:50.2918257Z return super().refine_names(names) 2024-06-26T05:54:50.2918361Z 2024-06-26T05:54:50.2918562Z --- Runtime Warning: 2 / 9 --- 2024-06-26T05:54:50.2918973Z example = 2024-06-26T05:54:50.2919945Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py:225: UserWarning: Warning only once for all operators, other operators may also be overrided. 2024-06-26T05:54:50.2920353Z Overriding a previously registered kernel for the same operator and the same dispatch key 2024-06-26T05:54:50.2920685Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2024-06-26T05:54:50.2921153Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2024-06-26T05:54:50.2921270Z dispatch key: CPU 2024-06-26T05:54:50.2921856Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2024-06-26T05:54:50.2922616Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:160.) 2024-06-26T05:54:50.2922883Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2024-06-26T05:54:50.2922970Z 2024-06-26T05:54:50.2923168Z --- Runtime Warning: 3 / 9 --- 2024-06-26T05:54:50.2923492Z example = 2024-06-26T05:54:50.2925180Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py:106: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:180.) 2024-06-26T05:54:50.2925558Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2024-06-26T05:54:50.2925654Z 2024-06-26T05:54:50.2925896Z --- Runtime Warning: 4 / 9 --- 2024-06-26T05:54:50.2926239Z example = 2024-06-26T05:54:50.2928785Z :1: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) 2024-06-26T05:54:50.2928884Z 2024-06-26T05:54:50.2929080Z --- Runtime Warning: 5 / 9 --- 2024-06-26T05:54:50.2929509Z example = 2024-06-26T05:54:50.2931815Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/const_fold.py:251: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer 2024-06-26T05:54:50.2932032Z new_node = root_const_gm.graph.get_attr(in_node.target) 2024-06-26T05:54:50.2932117Z 2024-06-26T05:54:50.2932324Z --- Runtime Warning: 6 / 9 --- 2024-06-26T05:54:50.2932710Z example = 2024-06-26T05:54:50.2934373Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py:379: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2024-06-26T05:54:50.2934479Z warnings.warn( 2024-06-26T05:54:50.2934561Z 2024-06-26T05:54:50.2934770Z --- Runtime Warning: 7 / 9 --- 2024-06-26T05:54:50.2935207Z example = 2024-06-26T05:54:50.2936860Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py:379: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2024-06-26T05:54:50.2936976Z warnings.warn( 2024-06-26T05:54:50.2937060Z 2024-06-26T05:54:50.2937267Z --- Runtime Warning: 8 / 9 --- 2024-06-26T05:54:50.2937633Z example = 2024-06-26T05:54:50.2938885Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2024-06-26T05:54:50.2939043Z WeightNorm.apply(module, name, dim) 2024-06-26T05:54:50.2939128Z 2024-06-26T05:54:50.2939326Z --- Runtime Warning: 9 / 9 --- 2024-06-26T05:54:50.2939743Z example = 2024-06-26T05:54:50.2940984Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2024-06-26T05:54:50.2941140Z WeightNorm.apply(module, name, dim) 2024-06-26T05:54:50.2941225Z 2024-06-26T05:54:50.2941580Z === 331 passed, 365 skipped, 99 warnings in 11.16 seconds === 2024-06-26T05:54:50.2941961Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2024-06-26 05:54:50.118760] 2024-06-26T05:54:52.3380497Z running install 2024-06-26T05:54:52.3382712Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-06-26T05:54:52.3385082Z !! 2024-06-26T05:54:52.3385325Z 2024-06-26T05:54:52.3385649Z ******************************************************************************** 2024-06-26T05:54:52.3386698Z Please avoid running ``setup.py`` directly. 2024-06-26T05:54:52.3387704Z Instead, use pypa/build, pypa/installer or other 2024-06-26T05:54:52.3388686Z standards-based tools. 2024-06-26T05:54:52.3389155Z 2024-06-26T05:54:52.3390053Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-06-26T05:54:52.3391428Z ******************************************************************************** 2024-06-26T05:54:52.3392090Z 2024-06-26T05:54:52.3392253Z !! 2024-06-26T05:54:52.3392716Z self.initialize_options() 2024-06-26T05:54:52.3500402Z running build 2024-06-26T05:54:52.3501129Z running build_py 2024-06-26T05:54:52.3562055Z creating build 2024-06-26T05:54:52.3562807Z creating build/lib.linux-x86_64-cpython-312 2024-06-26T05:54:52.3563992Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-06-26T05:54:52.3565758Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-06-26T05:54:52.3567124Z running build_ext 2024-06-26T05:54:52.3581777Z building 'torch_test_cpp_extension.cpp' extension 2024-06-26T05:54:52.3582811Z creating build/temp.linux-x86_64-cpython-312 2024-06-26T05:54:52.3590788Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c extension.cpp -o build/temp.linux-x86_64-cpython-312/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:54:53.4512254Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-06-26T05:54:53.4513725Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-06-26T05:54:53.4515312Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9, 2024-06-26T05:54:53.4516055Z from extension.cpp:1: 2024-06-26T05:54:53.4519283Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-06-26T05:54:53.4520465Z extension.cpp:45:53: required from here 2024-06-26T05:54:53.4522401Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1555:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-06-26T05:54:53.4524029Z 1555 | class class_ : public detail::generic_type { 2024-06-26T05:54:53.4524485Z | ^~~~~~ 2024-06-26T05:54:53.4527999Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so 2024-06-26T05:54:53.8563261Z building 'torch_test_cpp_extension.maia' extension 2024-06-26T05:54:53.8569451Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-312/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:54:54.8662034Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so 2024-06-26T05:54:55.2300097Z building 'torch_test_cpp_extension.rng' extension 2024-06-26T05:54:55.2306004Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-312/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:54:56.3971473Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-06-26T05:54:56.3972906Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-06-26T05:54:56.3974122Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-06-26T05:54:56.3975472Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-06-26T05:54:56.3976620Z from rng_extension.cpp:6: 2024-06-26T05:54:56.3978283Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1106: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-06-26T05:54:56.3979351Z 1106 | # pragma unroll 2024-06-26T05:54:56.3979657Z | 2024-06-26T05:54:56.3980478Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1141, 2024-06-26T05:54:56.3981814Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-06-26T05:54:56.3983016Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-06-26T05:54:56.3984200Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-06-26T05:54:56.3985718Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-06-26T05:54:56.3986667Z from rng_extension.cpp:6: 2024-06-26T05:54:56.3987898Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-06-26T05:54:56.3988938Z 59 | #pragma unroll 2024-06-26T05:54:56.3989221Z | 2024-06-26T05:54:56.3990235Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-06-26T05:54:56.3991271Z 72 | #pragma unroll 2024-06-26T05:54:56.3991553Z | 2024-06-26T05:54:56.3992366Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1142, 2024-06-26T05:54:56.3993774Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-06-26T05:54:56.3995151Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6, 2024-06-26T05:54:56.3996329Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-06-26T05:54:56.3997675Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-06-26T05:54:56.3998583Z from rng_extension.cpp:6: 2024-06-26T05:54:56.3999768Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:131: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-06-26T05:54:56.4000832Z 131 | #pragma unroll 2024-06-26T05:54:56.4001198Z | 2024-06-26T05:54:56.4004701Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so 2024-06-26T05:54:56.7895161Z running install_lib 2024-06-26T05:54:56.7958474Z creating install 2024-06-26T05:54:56.7959028Z creating install/opt 2024-06-26T05:54:56.7959594Z creating install/opt/conda 2024-06-26T05:54:56.7960210Z creating install/opt/conda/envs 2024-06-26T05:54:56.7960880Z creating install/opt/conda/envs/py_3.12 2024-06-26T05:54:56.7961736Z creating install/opt/conda/envs/py_3.12/lib 2024-06-26T05:54:56.7962626Z creating install/opt/conda/envs/py_3.12/lib/python3.12 2024-06-26T05:54:56.7964021Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-06-26T05:54:56.7965753Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:54:56.7968461Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/__init__.py -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:54:56.7972186Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:54:56.8056893Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:54:56.8142870Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:54:56.8235822Z byte-compiling ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension/__init__.py to __init__.cpython-312.pyc 2024-06-26T05:54:56.8237710Z running install_egg_info 2024-06-26T05:54:56.8374912Z running egg_info 2024-06-26T05:54:56.8375674Z creating torch_test_cpp_extension.egg-info 2024-06-26T05:54:56.8429418Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-06-26T05:54:56.8433134Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-06-26T05:54:56.8435122Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-06-26T05:54:56.8436617Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-06-26T05:54:56.8495031Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-06-26T05:54:56.8500802Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-06-26T05:54:56.8503442Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info 2024-06-26T05:54:56.8506802Z running install_scripts 2024-06-26T05:54:58.7025081Z running install 2024-06-26T05:54:58.7026540Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-06-26T05:54:58.7027649Z !! 2024-06-26T05:54:58.7027821Z 2024-06-26T05:54:58.7028050Z ******************************************************************************** 2024-06-26T05:54:58.7028610Z Please avoid running ``setup.py`` directly. 2024-06-26T05:54:58.7029189Z Instead, use pypa/build, pypa/installer or other 2024-06-26T05:54:58.7029791Z standards-based tools. 2024-06-26T05:54:58.7030030Z 2024-06-26T05:54:58.7030573Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-06-26T05:54:58.7031393Z ******************************************************************************** 2024-06-26T05:54:58.7031805Z 2024-06-26T05:54:58.7031920Z !! 2024-06-26T05:54:58.7032162Z self.initialize_options() 2024-06-26T05:54:58.7138794Z running build 2024-06-26T05:54:58.7139093Z running build_ext 2024-06-26T05:54:58.7446822Z building 'no_python_abi_suffix_test' extension 2024-06-26T05:54:58.7448085Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build 2024-06-26T05:54:58.7449380Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312 2024-06-26T05:54:58.7737243Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-06-26T05:54:58.7738320Z Compiling objects... 2024-06-26T05:54:58.7738714Z Using envvar MAX_JOBS (6) as the number of workers... 2024-06-26T05:54:58.8777612Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/no_python_abi_suffix_test.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=no_python_abi_suffix_test -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:54:58.8814513Z creating build/lib.linux-x86_64-cpython-312 2024-06-26T05:54:58.8819177Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so 2024-06-26T05:54:58.9403571Z running install_lib 2024-06-26T05:54:58.9464138Z creating install 2024-06-26T05:54:58.9464617Z creating install/opt 2024-06-26T05:54:58.9465464Z creating install/opt/conda 2024-06-26T05:54:58.9466024Z creating install/opt/conda/envs 2024-06-26T05:54:58.9466791Z creating install/opt/conda/envs/py_3.12 2024-06-26T05:54:58.9467469Z creating install/opt/conda/envs/py_3.12/lib 2024-06-26T05:54:58.9468305Z creating install/opt/conda/envs/py_3.12/lib/python3.12 2024-06-26T05:54:58.9469146Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-06-26T05:54:58.9470393Z copying build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-06-26T05:54:58.9474219Z running install_egg_info 2024-06-26T05:54:58.9619962Z running egg_info 2024-06-26T05:54:58.9620443Z creating no_python_abi_suffix_test.egg-info 2024-06-26T05:54:58.9674308Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2024-06-26T05:54:58.9677985Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2024-06-26T05:54:58.9680164Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2024-06-26T05:54:58.9681308Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-06-26T05:54:58.9738528Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-06-26T05:54:58.9743564Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-06-26T05:54:58.9745101Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info 2024-06-26T05:54:58.9748732Z running install_scripts 2024-06-26T05:54:59.3326115Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_aot_no_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:54:59.332160] 2024-06-26T05:55:03.3432970Z 2024-06-26T05:55:03.3435088Z test_cpp_extensions_aot_no_ninja 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_aot_no_ninja_1.1_019c297ecb81bfb5_.log 2024-06-26T05:55:03.3444070Z Running 17 items in this shard: test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_no_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_no_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_no_ninja.py::TestTorchLibrary::test_torch_library 2024-06-26T05:55:03.3451937Z 2024-06-26T05:55:03.3452330Z Running test_cpp_extensions_aot_ninja 1/1 ... [2024-06-26 05:55:03.343656] 2024-06-26T05:55:05.5999490Z running install 2024-06-26T05:55:05.6001486Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-06-26T05:55:05.6003058Z !! 2024-06-26T05:55:05.6003227Z 2024-06-26T05:55:05.6003411Z ******************************************************************************** 2024-06-26T05:55:05.6004158Z Please avoid running ``setup.py`` directly. 2024-06-26T05:55:05.6004698Z Instead, use pypa/build, pypa/installer or other 2024-06-26T05:55:05.6005247Z standards-based tools. 2024-06-26T05:55:05.6005500Z 2024-06-26T05:55:05.6005996Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-06-26T05:55:05.6006732Z ******************************************************************************** 2024-06-26T05:55:05.6007100Z 2024-06-26T05:55:05.6007186Z !! 2024-06-26T05:55:05.6007441Z self.initialize_options() 2024-06-26T05:55:05.6119906Z running build 2024-06-26T05:55:05.6120199Z running build_py 2024-06-26T05:55:05.6180467Z creating build 2024-06-26T05:55:05.6181431Z creating build/lib.linux-x86_64-cpython-312 2024-06-26T05:55:05.6182438Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-06-26T05:55:05.6183481Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2024-06-26T05:55:05.6185910Z running build_ext 2024-06-26T05:55:05.6496680Z building 'torch_test_cpp_extension.cpp' extension 2024-06-26T05:55:05.6497905Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312 2024-06-26T05:55:05.6784733Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-06-26T05:55:05.6785762Z Compiling objects... 2024-06-26T05:55:05.6786180Z Using envvar MAX_JOBS (6) as the number of workers... 2024-06-26T05:55:06.3771624Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:55:06.3864522Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so 2024-06-26T05:55:06.6343785Z building 'torch_test_cpp_extension.maia' extension 2024-06-26T05:55:06.6638993Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-06-26T05:55:06.6645418Z Compiling objects... 2024-06-26T05:55:06.6646172Z Using envvar MAX_JOBS (6) as the number of workers... 2024-06-26T05:55:07.3361604Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/maia_extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/maia_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:55:07.3409106Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so 2024-06-26T05:55:07.5714929Z building 'torch_test_cpp_extension.rng' extension 2024-06-26T05:55:07.6008569Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-06-26T05:55:07.6009589Z Compiling objects... 2024-06-26T05:55:07.6010057Z Using envvar MAX_JOBS (6) as the number of workers... 2024-06-26T05:55:08.4370981Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/rng_extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/rng_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-06-26T05:55:08.4417671Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so 2024-06-26T05:55:08.7006240Z running install_lib 2024-06-26T05:55:08.7070332Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:55:08.7117876Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:55:08.7166833Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2024-06-26T05:55:08.7220123Z running install_egg_info 2024-06-26T05:55:08.7358389Z running egg_info 2024-06-26T05:55:08.7411792Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-06-26T05:55:08.7415111Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-06-26T05:55:08.7425392Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-06-26T05:55:08.7501257Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-06-26T05:55:08.7506681Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-06-26T05:55:08.7533155Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info' (and everything under it) 2024-06-26T05:55:08.7535164Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info 2024-06-26T05:55:08.7538281Z running install_scripts 2024-06-26T05:55:10.6085572Z running install 2024-06-26T05:55:10.6087691Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-06-26T05:55:10.6088737Z !! 2024-06-26T05:55:10.6088883Z 2024-06-26T05:55:10.6089075Z ******************************************************************************** 2024-06-26T05:55:10.6089614Z Please avoid running ``setup.py`` directly. 2024-06-26T05:55:10.6090163Z Instead, use pypa/build, pypa/installer or other 2024-06-26T05:55:10.6090682Z standards-based tools. 2024-06-26T05:55:10.6090940Z 2024-06-26T05:55:10.6091423Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-06-26T05:55:10.6092171Z ******************************************************************************** 2024-06-26T05:55:10.6092537Z 2024-06-26T05:55:10.6092626Z !! 2024-06-26T05:55:10.6092881Z self.initialize_options() 2024-06-26T05:55:10.6200096Z running build 2024-06-26T05:55:10.6200380Z running build_ext 2024-06-26T05:55:10.6505932Z building 'no_python_abi_suffix_test' extension 2024-06-26T05:55:10.6788089Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/build.ninja... 2024-06-26T05:55:10.6789158Z Compiling objects... 2024-06-26T05:55:10.6789559Z Using envvar MAX_JOBS (6) as the number of workers... 2024-06-26T05:55:10.7038193Z ninja: no work to do. 2024-06-26T05:55:10.7079590Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so 2024-06-26T05:55:10.7613004Z running install_lib 2024-06-26T05:55:10.7673131Z copying build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2024-06-26T05:55:10.7677556Z running install_egg_info 2024-06-26T05:55:10.7825342Z running egg_info 2024-06-26T05:55:10.7879337Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2024-06-26T05:55:10.7882881Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2024-06-26T05:55:10.7896439Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2024-06-26T05:55:10.7959220Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-06-26T05:55:10.7964591Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-06-26T05:55:10.7973927Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info' (and everything under it) 2024-06-26T05:55:10.7976213Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info 2024-06-26T05:55:10.7979402Z running install_scripts 2024-06-26T05:55:11.1425740Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_aot_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:11.142025] 2024-06-26T05:55:15.1062736Z 2024-06-26T05:55:15.1064582Z test_cpp_extensions_aot_ninja 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_aot_ninja_1.1_ef0eace08d8a9219_.log 2024-06-26T05:55:15.1074200Z Running 17 items in this shard: test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_ninja.py::TestTorchLibrary::test_torch_library 2024-06-26T05:55:15.1082080Z 2024-06-26T05:55:15.1082483Z Running dynamo/test_dynamic_shapes 1/1 ... [2024-06-26 05:55:15.106534] 2024-06-26T05:55:15.1084422Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_dynamic_shapes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:15.106827] 2024-06-26T05:55:19.1230442Z 2024-06-26T05:55:19.1232293Z dynamo/test_dynamic_shapes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dynamic_shapes_1.1_deb48f70aea060a2_.log 2024-06-26T05:55:19.1233793Z 2024-06-26T05:55:19.1234414Z Running dynamo/test_fx_passes_pre_grad 1/1 ... [2024-06-26 05:55:19.123189] 2024-06-26T05:55:19.1240235Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_fx_passes_pre_grad.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:19.123544] 2024-06-26T05:55:21.3170704Z 2024-06-26T05:55:21.3172352Z dynamo/test_fx_passes_pre_grad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_fx_passes_pre_grad_1.1_0baca9615704822d_.log 2024-06-26T05:55:21.3173715Z 2024-06-26T05:55:21.3174192Z Running dynamo/test_frame_init 1/1 ... [2024-06-26 05:55:21.317192] 2024-06-26T05:55:21.3178533Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_frame_init.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:21.317518] 2024-06-26T05:55:23.4668572Z 2024-06-26T05:55:23.4670350Z dynamo/test_frame_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_frame_init_1.1_f661af449d802aa9_.log 2024-06-26T05:55:23.4671442Z 2024-06-26T05:55:23.4671835Z Running dynamo/test_sdpa 1/1 ... [2024-06-26 05:55:23.466975] 2024-06-26T05:55:23.4675555Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_sdpa.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:23.467257] 2024-06-26T05:55:25.6004572Z 2024-06-26T05:55:25.6006221Z dynamo/test_sdpa 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sdpa_1.1_36f06883218e2740_.log 2024-06-26T05:55:25.6007342Z 2024-06-26T05:55:25.6008112Z Running dynamo/test_exceptions 1/1 ... [2024-06-26 05:55:25.600614] 2024-06-26T05:55:25.6012375Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exceptions.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:25.600921] 2024-06-26T05:55:27.7778153Z 2024-06-26T05:55:27.7779641Z dynamo/test_exceptions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exceptions_1.1_cbb293d60126d750_.log 2024-06-26T05:55:27.7780849Z 2024-06-26T05:55:27.7782004Z Running dynamo/test_repros 1/1 ... [2024-06-26 05:55:27.777946] 2024-06-26T05:55:27.7786505Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-06-26 05:55:27.778277] 2024-06-26T05:55:29.9692359Z 2024-06-26T05:55:29.9693793Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_bb2a4f616421a097_.log 2024-06-26T05:55:29.9694991Z 2024-06-26T05:55:29.9695530Z Running dynamo/test_nops 1/1 ... [2024-06-26 05:55:29.969386] 2024-06-26T05:55:29.9699685Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_nops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:29.969696] 2024-06-26T05:55:32.2223127Z 2024-06-26T05:55:32.2224581Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_76702faf5b3f3ca5_.log 2024-06-26T05:55:32.2225534Z 2024-06-26T05:55:32.2225922Z Running dynamo/test_config 1/1 ... [2024-06-26 05:55:32.222384] 2024-06-26T05:55:32.2229494Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-06-26 05:55:32.222673] 2024-06-26T05:55:34.4517710Z 2024-06-26T05:55:34.4519996Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_c3b4549d50937257_.log 2024-06-26T05:55:34.4521372Z 2024-06-26T05:55:34.4521779Z Running test_jiterator 1/1 ... [2024-06-26 05:55:34.451910] 2024-06-26T05:55:34.4525299Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jiterator.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:34.452214] 2024-06-26T05:55:36.7556429Z 2024-06-26T05:55:36.7558288Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_7dc1e778372bfbba_.log 2024-06-26T05:55:36.7559435Z Running 0 items in this shard: 2024-06-26T05:55:36.7559707Z 2024-06-26T05:55:36.7560075Z Running test_matmul_cuda 1/1 ... [2024-06-26 05:55:36.755842] 2024-06-26T05:55:36.7564427Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:36.756169] 2024-06-26T05:55:39.0093238Z 2024-06-26T05:55:39.0094529Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_c5a6f5e866ce0814_.log 2024-06-26T05:55:39.0095543Z Running 0 items in this shard: 2024-06-26T05:55:39.0095797Z 2024-06-26T05:55:39.0097778Z Running dynamo/test_sources 1/1 ... [2024-06-26 05:55:39.009467] 2024-06-26T05:55:39.0100748Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:39.009759] 2024-06-26T05:55:41.1669091Z 2024-06-26T05:55:41.1671114Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_21e1d927f5bf3d9e_.log 2024-06-26T05:55:41.1672738Z 2024-06-26T05:55:41.1673211Z Running xpu/test_conv 1/1 ... [2024-06-26 05:55:41.167101] 2024-06-26T05:55:41.1678753Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'xpu/test_conv.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:41.167513] 2024-06-26T05:55:43.8010688Z 2024-06-26T05:55:43.8012366Z xpu/test_conv 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_conv_1.1_4357fe06d1a03b40_.log 2024-06-26T05:55:43.8013528Z Running 0 items in this shard: 2024-06-26T05:55:43.8013862Z 2024-06-26T05:55:43.8014445Z Running test_cuda 1/1 ... [2024-06-26 05:55:43.801239] 2024-06-26T05:55:43.8018284Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:43.801520] 2024-06-26T05:55:47.0456089Z 2024-06-26T05:55:47.0457366Z test_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_1.1_c173bd91770b1505_.log 2024-06-26T05:55:47.0458310Z Running 0 items in this shard: 2024-06-26T05:55:47.0458656Z 2024-06-26T05:55:47.0460058Z Running dynamo/test_verify_correctness 1/1 ... [2024-06-26 05:55:47.045803] 2024-06-26T05:55:47.0464350Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_verify_correctness.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:47.046146] 2024-06-26T05:55:49.2385478Z 2024-06-26T05:55:49.2387324Z dynamo/test_verify_correctness 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_verify_correctness_1.1_28cffaa8bfd07dcd_.log 2024-06-26T05:55:49.2388647Z 2024-06-26T05:55:49.2388996Z Running dynamo/test_profiler 1/1 ... [2024-06-26 05:55:49.238668] 2024-06-26T05:55:49.2392510Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_profiler.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:49.238959] 2024-06-26T05:55:51.4171121Z 2024-06-26T05:55:51.4172816Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_7c83b582a44e5db2_.log 2024-06-26T05:55:51.4173855Z 2024-06-26T05:55:51.4174216Z Running dynamo/test_reorder_logs 1/1 ... [2024-06-26 05:55:51.417190] 2024-06-26T05:55:51.4195665Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-06-26 05:55:51.417483] 2024-06-26T05:55:53.6469155Z 2024-06-26T05:55:53.6470950Z 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_a492fd7b0f8ebb3d_.log 2024-06-26T05:55:53.6472091Z 2024-06-26T05:55:53.6472356Z Running test_hub 1/1 ... [2024-06-26 05:55:53.647015] 2024-06-26T05:55:53.6475770Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_hub.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:53.647293] 2024-06-26T05:55:55.8745067Z 2024-06-26T05:55:55.8746392Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_6f04e72cd0d033f7_.log 2024-06-26T05:55:55.8747328Z 2024-06-26T05:55:55.8747824Z Running dynamo/test_minifier 1/1 ... [2024-06-26 05:55:55.874603] 2024-06-26T05:55:55.8751864Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:55.874890] 2024-06-26T05:55:58.0781286Z 2024-06-26T05:55:58.0782814Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_177e2037ed084e57_.log 2024-06-26T05:55:58.0783866Z 2024-06-26T05:55:58.0784647Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-06-26 05:55:58.078278] 2024-06-26T05:55:58.0788845Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:55:58.078577] 2024-06-26T05:56:00.3682200Z 2024-06-26T05:56:00.3691224Z dynamo/test_activation_checkpointing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_activation_checkpointing_1.1_051812aaa71f3cf4_.log 2024-06-26T05:56:00.3699167Z 2024-06-26T05:56:00.3712961Z Running dynamo/test_recompile_ux 1/1 ... [2024-06-26 05:56:00.370890] 2024-06-26T05:56:00.3727534Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:00.372151] 2024-06-26T05:56:02.6566395Z 2024-06-26T05:56:02.6568568Z dynamo/test_recompile_ux 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompile_ux_1.1_ec8dfe7ac818af8c_.log 2024-06-26T05:56:02.6569680Z 2024-06-26T05:56:02.6570050Z Running dynamo/test_subclasses 1/1 ... [2024-06-26 05:56:02.656731] 2024-06-26T05:56:02.6573627Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_subclasses.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:02.657030] 2024-06-26T05:56:04.9026652Z 2024-06-26T05:56:04.9035212Z dynamo/test_subclasses 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subclasses_1.1_4741aa6aa5338dd0_.log 2024-06-26T05:56:04.9042764Z 2024-06-26T05:56:04.9055988Z Running lazy/test_extract_compiled_graph 1/1 ... [2024-06-26 05:56:04.905043] 2024-06-26T05:56:04.9071152Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_extract_compiled_graph.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:04.906288] 2024-06-26T05:56:06.3306709Z 2024-06-26T05:56:06.3308581Z lazy/test_extract_compiled_graph 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_extract_compiled_graph_1.1_6d6cf0f05ee80e3f_.log 2024-06-26T05:56:06.3309770Z 2024-06-26T05:56:06.3310242Z Running dynamo/test_aot_autograd_cache 1/1 ... [2024-06-26 05:56:06.330731] 2024-06-26T05:56:06.3314415Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_aot_autograd_cache.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:06.331069] 2024-06-26T05:56:08.6354873Z 2024-06-26T05:56:08.6361965Z dynamo/test_aot_autograd_cache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_cache_1.1_798157b1805b2a73_.log 2024-06-26T05:56:08.6368652Z 2024-06-26T05:56:08.6379445Z Running test_cuda_multigpu 1/1 ... [2024-06-26 05:56:08.637642] 2024-06-26T05:56:08.6391365Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_multigpu.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:08.638642] 2024-06-26T05:56:11.0071530Z 2024-06-26T05:56:11.0073781Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_ca82a9d868759029_.log 2024-06-26T05:56:11.0075212Z Running 0 items in this shard: 2024-06-26T05:56:11.0075467Z 2024-06-26T05:56:11.0075942Z Running torch_np/numpy_tests/lib/test_arraypad 1/1 ... [2024-06-26 05:56:11.007294] 2024-06-26T05:56:11.0079084Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-06-26 05:56:11.007602] 2024-06-26T05:56:13.2758966Z 2024-06-26T05:56:13.2761489Z 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_a18940ded04b79f1_.log 2024-06-26T05:56:13.2763679Z Running 0 items in this shard: 2024-06-26T05:56:13.2764095Z 2024-06-26T05:56:13.2764729Z Running dynamo/test_python_autograd 1/1 ... [2024-06-26 05:56:13.276136] 2024-06-26T05:56:13.2769122Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-06-26 05:56:13.276511] 2024-06-26T05:56:15.4208693Z 2024-06-26T05:56:15.4210571Z 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_51090967dc9f8fd9_.log 2024-06-26T05:56:15.4211848Z 2024-06-26T05:56:15.4212142Z Running test_sparse 1/2 ... [2024-06-26 05:56:15.420998] 2024-06-26T05:56:15.4216282Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-06-26 05:56:15.421308] 2024-06-26T05:56:19.0404223Z 2024-06-26T05:56:19.0405795Z test_sparse 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_sparse_1.2_b56d52774782b41f_.log 2024-06-26T05:56:19.0406788Z Running 0 items in this shard: 2024-06-26T05:56:19.0407049Z 2024-06-26T05:56:19.0468024Z Running dynamo/test_dynamic_shapes 1/1 ... [2024-06-26 05:56:19.046480] 2024-06-26T05:56:19.0472068Z Running dynamo/test_fx_passes_pre_grad 1/1 ... [2024-06-26 05:56:19.046856] 2024-06-26T05:56:19.0475458Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_dynamic_shapes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:19.046931] 2024-06-26T05:56:19.0478775Z Running dynamo/test_frame_init 1/1 ... [2024-06-26 05:56:19.046955] 2024-06-26T05:56:19.0482341Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_fx_passes_pre_grad.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:19.047264] 2024-06-26T05:56:19.0487655Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_frame_init.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:19.047391] 2024-06-26T05:56:21.3548834Z 2024-06-26T05:56:21.3550638Z dynamo/test_frame_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_frame_init_1.1_4456a7bcd9e4c0e3_.log 2024-06-26T05:56:21.3551605Z 2024-06-26T05:56:21.3663914Z 2024-06-26T05:56:21.3665681Z dynamo/test_fx_passes_pre_grad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_fx_passes_pre_grad_1.1_744be301c2584f35_.log 2024-06-26T05:56:21.3666729Z 2024-06-26T05:56:22.9795825Z 2024-06-26T05:56:22.9798133Z dynamo/test_dynamic_shapes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dynamic_shapes_1.1_9e432933fe66ec79_.log 2024-06-26T05:56:22.9799974Z 2024-06-26T05:56:23.8482385Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:23.8637321Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:23.9179963Z Running dynamo/test_sdpa 1/1 ... [2024-06-26 05:56:23.917480] 2024-06-26T05:56:23.9194443Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_sdpa.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:23.918754] 2024-06-26T05:56:23.9594516Z Running dynamo/test_exceptions 1/1 ... [2024-06-26 05:56:23.959012] 2024-06-26T05:56:23.9601837Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exceptions.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:23.959589] 2024-06-26T05:56:25.3731716Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:25.4309784Z Running dynamo/test_repros 1/1 ... [2024-06-26 05:56:25.430555] 2024-06-26T05:56:25.4312683Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-06-26 05:56:25.430868] 2024-06-26T05:56:26.2781528Z 2024-06-26T05:56:26.2783020Z dynamo/test_sdpa 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sdpa_1.1_4e9d12ae5f59f104_.log 2024-06-26T05:56:26.2783932Z 2024-06-26T05:56:26.2936232Z 2024-06-26T05:56:26.2938008Z dynamo/test_exceptions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exceptions_1.1_48a8bd86f350ea4b_.log 2024-06-26T05:56:26.2939075Z 2024-06-26T05:56:27.8232200Z 2024-06-26T05:56:27.8234279Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_d23ae6e7ef1cc10a_.log 2024-06-26T05:56:27.8236151Z 2024-06-26T05:56:28.6075645Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:28.6469883Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:28.6676033Z Running dynamo/test_nops 1/1 ... [2024-06-26 05:56:28.667270] 2024-06-26T05:56:28.6680500Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_nops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:28.667674] 2024-06-26T05:56:28.7404338Z Running dynamo/test_config 1/1 ... [2024-06-26 05:56:28.739882] 2024-06-26T05:56:28.7408146Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-06-26 05:56:28.740290] 2024-06-26T05:56:30.1689531Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:30.2269944Z Running test_jiterator 1/1 ... [2024-06-26 05:56:30.226563] 2024-06-26T05:56:30.2274278Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jiterator.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:30.226875] 2024-06-26T05:56:31.0356144Z 2024-06-26T05:56:31.0357905Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_cd6164de0efa0955_.log 2024-06-26T05:56:31.0358945Z 2024-06-26T05:56:31.1120385Z 2024-06-26T05:56:31.1121802Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_404fcb8f91432dd7_.log 2024-06-26T05:56:31.1122766Z 2024-06-26T05:56:32.6950165Z 2024-06-26T05:56:32.6951944Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_41f335b0da53d8f8_.log 2024-06-26T05:56:32.6953009Z Running 0 items in this shard: 2024-06-26T05:56:32.6953285Z 2024-06-26T05:56:33.4137636Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:33.4722745Z Running test_matmul_cuda 1/1 ... [2024-06-26 05:56:33.471818] 2024-06-26T05:56:33.4726700Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:33.472164] 2024-06-26T05:56:33.5015001Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:33.5599351Z Running dynamo/test_sources 1/1 ... [2024-06-26 05:56:33.559521] 2024-06-26T05:56:33.5602302Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:33.559844] 2024-06-26T05:56:35.1224299Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:35.1808330Z Running xpu/test_conv 1/1 ... [2024-06-26 05:56:35.180342] 2024-06-26T05:56:35.1811653Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'xpu/test_conv.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:35.180731] 2024-06-26T05:56:35.9347916Z 2024-06-26T05:56:35.9350061Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_8be1834d659c92a1_.log 2024-06-26T05:56:35.9351077Z Running 0 items in this shard: 2024-06-26T05:56:35.9351341Z 2024-06-26T05:56:36.0347950Z 2024-06-26T05:56:36.0349466Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_c78a7b272b95cd47_.log 2024-06-26T05:56:38.1157557Z 2024-06-26T05:56:38.1157580Z 2024-06-26T05:56:38.1159648Z xpu/test_conv 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_conv_1.1_e41404c11ec705af_.log 2024-06-26T05:56:38.1160961Z Running 0 items in this shard: 2024-06-26T05:56:38.1161278Z 2024-06-26T05:56:38.3323936Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:38.3913517Z Running test_cuda 1/1 ... [2024-06-26 05:56:38.390948] 2024-06-26T05:56:38.3916839Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:38.391265] 2024-06-26T05:56:38.4405065Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:38.4997148Z Running dynamo/test_verify_correctness 1/1 ... [2024-06-26 05:56:38.499160] 2024-06-26T05:56:38.5000971Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_verify_correctness.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:38.499503] 2024-06-26T05:56:40.4674835Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:40.5485580Z Running dynamo/test_profiler 1/1 ... [2024-06-26 05:56:40.548090] 2024-06-26T05:56:40.5488912Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_profiler.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:40.548461] 2024-06-26T05:56:40.8145181Z 2024-06-26T05:56:40.8146978Z dynamo/test_verify_correctness 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_verify_correctness_1.1_714519e5dc576a5c_.log 2024-06-26T05:56:40.8148223Z 2024-06-26T05:56:41.9431031Z 2024-06-26T05:56:41.9433295Z test_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_1.1_eb02e042248f1d41_.log 2024-06-26T05:56:41.9435232Z Running 0 items in this shard: 2024-06-26T05:56:41.9435662Z 2024-06-26T05:56:42.8853421Z 2024-06-26T05:56:42.8855609Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_0cbb8d7a7a15fda1_.log 2024-06-26T05:56:42.8857340Z 2024-06-26T05:56:43.1904132Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:43.2486349Z Running dynamo/test_reorder_logs 1/1 ... [2024-06-26 05:56:43.248273] 2024-06-26T05:56:43.2490274Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-06-26 05:56:43.248632] 2024-06-26T05:56:44.2983677Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:44.3563499Z Running test_hub 1/1 ... [2024-06-26 05:56:44.355992] 2024-06-26T05:56:44.3567567Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_hub.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:44.356324] 2024-06-26T05:56:45.1983393Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:45.2569430Z Running dynamo/test_minifier 1/1 ... [2024-06-26 05:56:45.256549] 2024-06-26T05:56:45.2572623Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:45.256914] 2024-06-26T05:56:45.5370826Z 2024-06-26T05:56:45.5372529Z 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_a61ac81f27fa9b69_.log 2024-06-26T05:56:45.5373525Z 2024-06-26T05:56:46.6467962Z 2024-06-26T05:56:46.6469969Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_c5a0a502e86b7597_.log 2024-06-26T05:56:46.6471380Z 2024-06-26T05:56:47.6200293Z 2024-06-26T05:56:47.6202522Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_428a00be14aeaa3b_.log 2024-06-26T05:56:47.6204241Z 2024-06-26T05:56:47.9117486Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:47.9709684Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-06-26 05:56:47.970550] 2024-06-26T05:56:47.9712306Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:47.970879] 2024-06-26T05:56:48.9699082Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:49.0275577Z Running dynamo/test_recompile_ux 1/1 ... [2024-06-26 05:56:49.027184] 2024-06-26T05:56:49.0279252Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:49.027513] 2024-06-26T05:56:49.9491346Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:50.0079649Z Running dynamo/test_subclasses 1/1 ... [2024-06-26 05:56:50.007282] 2024-06-26T05:56:50.0082562Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_subclasses.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:50.007662] 2024-06-26T05:56:50.4009501Z 2024-06-26T05:56:50.4013062Z dynamo/test_activation_checkpointing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_activation_checkpointing_1.1_10cae9de456d1e2d_.log 2024-06-26T05:56:50.4016134Z 2024-06-26T05:56:51.3918729Z 2024-06-26T05:56:51.3920594Z dynamo/test_recompile_ux 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompile_ux_1.1_7cd5fd36ed4a251a_.log 2024-06-26T05:56:51.3921657Z 2024-06-26T05:56:52.5670145Z 2024-06-26T05:56:52.5671742Z dynamo/test_subclasses 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subclasses_1.1_0eeb09cb1c50d197_.log 2024-06-26T05:56:52.5672726Z 2024-06-26T05:56:52.9475766Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:53.0069678Z Running lazy/test_extract_compiled_graph 1/1 ... [2024-06-26 05:56:53.006505] 2024-06-26T05:56:53.0073772Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_extract_compiled_graph.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:53.006855] 2024-06-26T05:56:54.0061728Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:54.0657224Z Running dynamo/test_aot_autograd_cache 1/1 ... [2024-06-26 05:56:54.065237] 2024-06-26T05:56:54.0660921Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_aot_autograd_cache.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:54.065583] 2024-06-26T05:56:54.5375605Z 2024-06-26T05:56:54.5382556Z lazy/test_extract_compiled_graph 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_extract_compiled_graph_1.1_5694656d1612c1ee_.log 2024-06-26T05:56:54.5388842Z 2024-06-26T05:56:55.1329759Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:55.1910360Z Running test_cuda_multigpu 1/1 ... [2024-06-26 05:56:55.190622] 2024-06-26T05:56:55.1914038Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_multigpu.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-06-26 05:56:55.190978] 2024-06-26T05:56:56.5275773Z 2024-06-26T05:56:56.5282699Z dynamo/test_aot_autograd_cache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_cache_1.1_fd10e9b0faa932c7_.log 2024-06-26T05:56:56.5288966Z 2024-06-26T05:56:56.9448421Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:57.0042073Z Running torch_np/numpy_tests/lib/test_arraypad 1/1 ... [2024-06-26 05:56:57.003729] 2024-06-26T05:56:57.0045346Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-06-26 05:56:57.004073] 2024-06-26T05:56:57.9866337Z 2024-06-26T05:56:57.9868584Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_2b2bfafd02c22750_.log 2024-06-26T05:56:57.9870265Z Running 0 items in this shard: 2024-06-26T05:56:57.9870689Z 2024-06-26T05:56:59.0540509Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:56:59.1127224Z Running dynamo/test_python_autograd 1/1 ... [2024-06-26 05:56:59.112316] 2024-06-26T05:56:59.1130423Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-06-26 05:56:59.112643] 2024-06-26T05:57:00.3244832Z 2024-06-26T05:57:00.3246816Z 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_4ea2be27ff47df4f_.log 2024-06-26T05:57:00.3252281Z 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 2024-06-26T05:57:00.3256684Z 2024-06-26T05:57:00.4561397Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:57:00.5145329Z Running test_sparse 1/2 ... [2024-06-26 05:57:00.514154] 2024-06-26T05:57:00.5148270Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-06-26 05:57:00.514490] 2024-06-26T05:57:01.5389860Z 2024-06-26T05:57:01.5391669Z 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_0d61ac7205f8b85b_.log 2024-06-26T05:57:01.5392680Z 2024-06-26T05:57:02.8081677Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T05:57:03.8818088Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:05:03.6925522Z 2024-06-26T06:05:03.6926794Z test_sparse 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_sparse_1.2_b3d29de543edffc3_.log 2024-06-26T06:05:03.7630906Z Running 1559 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, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_abs_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asin_cpu_complex64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asin_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asin_cpu_int32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asin_cpu_int8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asinh_cpu_complex128, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asinh_cpu_float64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asinh_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_asinh_cpu_uint8, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atan_cpu_float32, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atan_cpu_int64, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_atan_cpu_int8, 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, test/test_sparse.py::TestSparseUnaryUfuncsCPU::test_inplace_erf_cpu_uint8, 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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 2024-06-26T06:05:03.8309884Z 2024-06-26T06:05:04.6416462Z 2024-06-26T06:05:04.6417011Z real 61m35.000s 2024-06-26T06:05:04.6417551Z user 92m14.755s 2024-06-26T06:05:04.6418423Z sys 6m14.152s 2024-06-26T06:05:04.6418772Z + assert_git_not_dirty 2024-06-26T06:05:04.6419469Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *rocm* ]] 2024-06-26T06:05:04.6420351Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *xla* ]] 2024-06-26T06:05:04.6423104Z ++ git status --porcelain 2024-06-26T06:05:04.6423911Z ++ grep -v '?? third_party' 2024-06-26T06:05:23.4065684Z ++ true 2024-06-26T06:05:23.4066901Z + git_status= 2024-06-26T06:05:23.4068418Z + [[ -n '' ]] 2024-06-26T06:05:23.4068836Z + test_aten 2024-06-26T06:05:23.4069572Z + echo 'Running ATen tests with pytorch lib' 2024-06-26T06:05:23.4070058Z Running ATen tests with pytorch lib 2024-06-26T06:05:23.4071929Z + [[ -n '' ]] 2024-06-26T06:05:23.4072649Z + echo 'Running test with the build folder' 2024-06-26T06:05:23.4073104Z Running test with the build folder 2024-06-26T06:05:23.4073493Z + TEST_BASE_DIR=build/bin 2024-06-26T06:05:23.4074216Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libc10.so build/bin 2024-06-26T06:05:23.4096008Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libcaffe2*' build/bin 2024-06-26T06:05:23.4103839Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libmkldnn*' build/bin 2024-06-26T06:05:23.4111426Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libnccl*' build/bin 2024-06-26T06:05:23.4121559Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorchbind_test.so build/bin 2024-06-26T06:05:23.4126075Z + ls build/bin 2024-06-26T06:05:23.4177755Z CMakeFiles cpu_rng_test 2024-06-26T06:05:23.4178525Z CTestTestfile.cmake dispatch_key_set_test 2024-06-26T06:05:23.4179318Z CppSignature_test dlconvertor_test 2024-06-26T06:05:23.4181658Z Dict_test example_allreduce 2024-06-26T06:05:23.4182474Z Dimname_test extension_backend_test 2024-06-26T06:05:23.4182945Z FileStoreTest half_test 2024-06-26T06:05:23.4183378Z HashStoreTest inline_container_test 2024-06-26T06:05:23.4183817Z IListRef_test ivalue_test 2024-06-26T06:05:23.4184304Z KernelFunction_test kernel_function_legacy_test 2024-06-26T06:05:23.4184851Z List_test kernel_function_test 2024-06-26T06:05:23.4185284Z Makefile kernel_lambda_legacy_test 2024-06-26T06:05:23.4185743Z MaybeOwned_test kernel_lambda_test 2024-06-26T06:05:23.4186225Z NamedTensor_test kernel_stackbased_test 2024-06-26T06:05:23.4186718Z ProcessGroupGlooTest lazy_tensor_test 2024-06-26T06:05:23.4187199Z StorageUtils_test legacy_vmap_test 2024-06-26T06:05:23.4187628Z TCPStoreTest libc10.so 2024-06-26T06:05:23.4188116Z aot_model_compiler_test 'libcaffe2*' 2024-06-26T06:05:23.4188584Z apply_utils_test 'libmkldnn*' 2024-06-26T06:05:23.4189007Z atest 'libnccl*' 2024-06-26T06:05:23.4189371Z backend_fallback_test libtorch.so 2024-06-26T06:05:23.4189783Z basic libtorch_cpu.so 2024-06-26T06:05:23.4190205Z broadcast_test libtorch_global_deps.so 2024-06-26T06:05:23.4190829Z c10_Bitset_test libtorch_python.so 2024-06-26T06:05:23.4191363Z c10_CompileTimeFunctionPointer_test libtorchbind_test.so 2024-06-26T06:05:23.4192079Z c10_ConstexprCrc_test make_boxed_from_unboxed_functor_test 2024-06-26T06:05:23.4192684Z c10_DeadlockDetection_test math_kernel_test 2024-06-26T06:05:23.4193231Z c10_DeviceGuard_test memory_format_test 2024-06-26T06:05:23.4193727Z c10_Device_test memory_overlapping_test 2024-06-26T06:05:23.4194249Z c10_DispatchKeySet_test mobile_memory_cleanup 2024-06-26T06:05:23.4194882Z c10_Half_test native_test 2024-06-26T06:05:23.4195341Z c10_InlineDeviceGuard_test op_allowlist_test 2024-06-26T06:05:23.4195896Z c10_InlineStreamGuard_test op_registration_test 2024-06-26T06:05:23.4196462Z c10_LeftRight_test operator_name_test 2024-06-26T06:05:23.4196950Z c10_Metaprogramming_test operators_test 2024-06-26T06:05:23.4197522Z c10_Scalar_test packedtensoraccessor_test 2024-06-26T06:05:23.4198043Z c10_SizesAndStrides_test parallel_benchmark 2024-06-26T06:05:23.4198520Z c10_StreamGuard_test pow_test 2024-06-26T06:05:23.4198922Z c10_SymInt_test protoc 2024-06-26T06:05:23.4199381Z c10_Synchronized_test protoc-3.13.0.0 2024-06-26T06:05:23.4199858Z c10_ThreadLocal_test quantized_test 2024-06-26T06:05:23.4200314Z c10_TypeIndex_test reduce_ops_test 2024-06-26T06:05:23.4200781Z c10_TypeList_test reportMemoryUsage_test 2024-06-26T06:05:23.4201343Z c10_TypeTraits_test scalar_tensor_test 2024-06-26T06:05:23.4201802Z c10_accumulate_test scalar_test 2024-06-26T06:05:23.4202235Z c10_bfloat16_test static_runtime_bench 2024-06-26T06:05:23.4202710Z c10_bit_cast_test static_runtime_test 2024-06-26T06:05:23.4203214Z c10_complex_math_test stride_properties_test 2024-06-26T06:05:23.4203711Z c10_complex_test tensor_iterator_test 2024-06-26T06:05:23.4204147Z c10_cow_test test_api 2024-06-26T06:05:23.4204528Z c10_exception_test test_cpp_rpc 2024-06-26T06:05:23.4204943Z c10_flags_test test_dist_autograd 2024-06-26T06:05:23.4205449Z c10_generic_math_test test_edge_op_registration 2024-06-26T06:05:23.4206011Z c10_intrusive_ptr_benchmark test_jit 2024-06-26T06:05:23.4206465Z c10_intrusive_ptr_test test_lazy 2024-06-26T06:05:23.4206878Z c10_irange_test test_mobile_nnc 2024-06-26T06:05:23.4207288Z c10_lazy_test test_parallel 2024-06-26T06:05:23.4207693Z c10_logging_test test_tensorexpr 2024-06-26T06:05:23.4208157Z c10_optional_test thread_init_test 2024-06-26T06:05:23.4208668Z c10_ordered_preserving_dict_test torch_shm_manager 2024-06-26T06:05:23.4209196Z c10_registry_test tutorial_tensorexpr 2024-06-26T06:05:23.4209648Z c10_small_vector_test type_ptr_test 2024-06-26T06:05:23.4210078Z c10_ssize_test type_test 2024-06-26T06:05:23.4210511Z c10_string_util_test undefined_tensor_test 2024-06-26T06:05:23.4211023Z c10_string_view_test vec_test_all_types_AVX2 2024-06-26T06:05:23.4211554Z c10_tempfile_test vec_test_all_types_AVX512 2024-06-26T06:05:23.4212071Z c10_typeid_test vec_test_all_types_DEFAULT 2024-06-26T06:05:23.4212577Z cmake_install.cmake verify_api_visibility 2024-06-26T06:05:23.4213059Z cpu_allocator_test weakref_test 2024-06-26T06:05:23.4213605Z cpu_generator_test wrapdim_test 2024-06-26T06:05:23.4214166Z cpu_profiling_allocator_test xla_tensor_test 2024-06-26T06:05:23.4214640Z + aten/tools/run_tests.sh build/bin 2024-06-26T06:05:23.4215088Z + set -e 2024-06-26T06:05:23.4216937Z ++ dirname aten/tools/run_tests.sh 2024-06-26T06:05:23.4223634Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup 2024-06-26T06:05:23.4224566Z + export CPP_TESTS_DIR=build/bin 2024-06-26T06:05:23.4225262Z + CPP_TESTS_DIR=build/bin 2024-06-26T06:05:23.4225668Z + VALGRIND=ON 2024-06-26T06:05:23.4228469Z + python test/run_test.py --cpp --verbose -i cpp/basic cpp/atest cpp/scalar_test cpp/broadcast_test cpp/wrapdim_test cpp/apply_utils_test cpp/dlconvertor_test cpp/native_test cpp/scalar_tensor_test cpp/undefined_tensor_test cpp/extension_backend_test cpp/lazy_tensor_test cpp/tensor_iterator_test cpp/Dimname_test cpp/Dict_test cpp/NamedTensor_test cpp/cpu_generator_test cpp/legacy_vmap_test cpp/operators_test 2024-06-26T06:05:23.5188163Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-06-26T06:05:23.5189337Z import pkg_resources 2024-06-26T06:05:25.2740789Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:05:26.1551951Z Downloading https://ossci-metrics.s3.amazonaws.com/slow-tests.json to /var/lib/jenkins/workspace/test/.pytorch-slow-tests.json 2024-06-26T06:05:26.1555651Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-06-26T06:05:26.1651588Z Found test times from artifacts 2024-06-26T06:05:26.2040180Z Found test times from artifacts 2024-06-26T06:05:26.2055069Z Running all tests 2024-06-26T06:05:26.2059513Z Running parallel tests on 3 processes 2024-06-26T06:05:26.2060935Z Name: tests to run (est. time: 0.0min) 2024-06-26T06:05:26.2061606Z Serial tests (0): 2024-06-26T06:05:26.2062139Z Parallel tests (19): 2024-06-26T06:05:26.2062747Z cpp/Dict_test 1/1 2024-06-26T06:05:26.2063345Z cpp/Dimname_test 1/1 2024-06-26T06:05:26.2063956Z cpp/NamedTensor_test 1/1 2024-06-26T06:05:26.2064363Z cpp/apply_utils_test 1/1 2024-06-26T06:05:26.2064702Z cpp/atest 1/1 2024-06-26T06:05:26.2064973Z cpp/basic 1/1 2024-06-26T06:05:26.2065270Z cpp/broadcast_test 1/1 2024-06-26T06:05:26.2065620Z cpp/cpu_generator_test 1/1 2024-06-26T06:05:26.2065981Z cpp/dlconvertor_test 1/1 2024-06-26T06:05:26.2066350Z cpp/extension_backend_test 1/1 2024-06-26T06:05:26.2066735Z cpp/lazy_tensor_test 1/1 2024-06-26T06:05:26.2067079Z cpp/legacy_vmap_test 1/1 2024-06-26T06:05:26.2067417Z cpp/native_test 1/1 2024-06-26T06:05:26.2067727Z cpp/operators_test 1/1 2024-06-26T06:05:26.2068070Z cpp/scalar_tensor_test 1/1 2024-06-26T06:05:26.2068427Z cpp/scalar_test 1/1 2024-06-26T06:05:26.2068744Z cpp/tensor_iterator_test 1/1 2024-06-26T06:05:26.2069124Z cpp/undefined_tensor_test 1/1 2024-06-26T06:05:26.2069494Z cpp/wrapdim_test 1/1 2024-06-26T06:05:26.2069820Z Name: excluded (est. time: 0.0min) 2024-06-26T06:05:26.2070194Z Serial tests (0): 2024-06-26T06:05:26.2070491Z Parallel tests (0): 2024-06-26T06:05:26.2071141Z Starting test batch 'tests to run' 0.0 seconds after initiating testing 2024-06-26T06:05:26.2143788Z Running cpp/Dict_test 1/1 ... [2024-06-26 06:05:26.214043] 2024-06-26T06:05:26.2149549Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dict_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1457d2b46c640d34.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:26.214520] 2024-06-26T06:05:28.1865646Z 2024-06-26T06:05:28.1868231Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_509a75f3ceb5ddf5_.log 2024-06-26T06:05:28.1869834Z 2024-06-26T06:05:28.1870431Z Running cpp/Dimname_test 1/1 ... [2024-06-26 06:05:28.186494] 2024-06-26T06:05:28.1873984Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dimname_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-11c6a2eef719586e.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:28.186881] 2024-06-26T06:05:28.4653393Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:05:28.7220204Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:05:28.7747804Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:05:29.7539391Z 2024-06-26T06:05:29.7541091Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_4604260a39048b64_.log 2024-06-26T06:05:29.7541992Z 2024-06-26T06:05:29.7542357Z Running cpp/NamedTensor_test 1/1 ... [2024-06-26 06:05:29.753832] 2024-06-26T06:05:29.7544729Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/NamedTensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-2c5e67b381c3b207.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:29.754156] 2024-06-26T06:05:31.2209644Z 2024-06-26T06:05:31.2211911Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_55cab348d17eaff2_.log 2024-06-26T06:05:31.2213397Z 2024-06-26T06:05:31.2213734Z Running cpp/apply_utils_test 1/1 ... [2024-06-26 06:05:31.220743] 2024-06-26T06:05:31.2215697Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/apply_utils_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-5815f10e2d988737.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:31.221062] 2024-06-26T06:05:32.7378853Z 2024-06-26T06:05:32.7380576Z cpp/apply_utils_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.apply_utils_test_1.1_4a204d90670097a7_.log 2024-06-26T06:05:32.7381729Z 2024-06-26T06:05:32.7382063Z Running cpp/atest 1/1 ... [2024-06-26 06:05:32.737743] 2024-06-26T06:05:32.7383922Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/atest', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-6e7a92a6f6db3b4f.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:32.738052] 2024-06-26T06:05:34.2550021Z 2024-06-26T06:05:34.2551615Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_1c7a97528691efb5_.log 2024-06-26T06:05:34.2552471Z 2024-06-26T06:05:34.2552745Z Running cpp/basic 1/1 ... [2024-06-26 06:05:34.254896] 2024-06-26T06:05:34.2555887Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/basic', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-f976147359778eed.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:34.255240] 2024-06-26T06:05:35.7720069Z 2024-06-26T06:05:35.7721811Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_8198ad150363533c_.log 2024-06-26T06:05:35.7722977Z 2024-06-26T06:05:35.7723599Z Running cpp/broadcast_test 1/1 ... [2024-06-26 06:05:35.771833] 2024-06-26T06:05:35.7725714Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/broadcast_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-cbf3dc9afa615acb.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:35.772148] 2024-06-26T06:05:37.2890547Z 2024-06-26T06:05:37.2892310Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_c6ba6ae6be74151c_.log 2024-06-26T06:05:37.2893322Z 2024-06-26T06:05:37.2893700Z Running cpp/cpu_generator_test 1/1 ... [2024-06-26 06:05:37.288903] 2024-06-26T06:05:37.2895867Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/cpu_generator_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-0d7741ef0d9bc1f9.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:37.289247] 2024-06-26T06:05:38.8062335Z 2024-06-26T06:05:38.8063819Z cpp/cpu_generator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.cpu_generator_test_1.1_28514537c78e02ea_.log 2024-06-26T06:05:38.8065572Z 2024-06-26T06:05:38.8065922Z Running cpp/dlconvertor_test 1/1 ... [2024-06-26 06:05:38.805968] 2024-06-26T06:05:38.8067972Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/dlconvertor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-f9ffcd566b2617ae.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:38.806308] 2024-06-26T06:05:40.3231524Z 2024-06-26T06:05:40.3233340Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_fdf13b9ea9c85c3b_.log 2024-06-26T06:05:40.3234365Z 2024-06-26T06:05:40.3234978Z Running cpp/extension_backend_test 1/1 ... [2024-06-26 06:05:40.323016] 2024-06-26T06:05:40.3237855Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/extension_backend_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-cd4280f4e494ecbd.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:40.323371] 2024-06-26T06:05:41.8402871Z 2024-06-26T06:05:41.8404560Z cpp/extension_backend_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.extension_backend_test_1.1_fff6c5a56efc2cdf_.log 2024-06-26T06:05:41.8405571Z 2024-06-26T06:05:41.8406098Z Running cpp/lazy_tensor_test 1/1 ... [2024-06-26 06:05:41.840164] 2024-06-26T06:05:41.8408104Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/lazy_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-ad0cd10b1112d9e5.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:41.840495] 2024-06-26T06:05:43.3573268Z 2024-06-26T06:05:43.3574775Z cpp/lazy_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.lazy_tensor_test_1.1_e8108f62150b4053_.log 2024-06-26T06:05:43.3575892Z 2024-06-26T06:05:43.3576412Z Running cpp/legacy_vmap_test 1/1 ... [2024-06-26 06:05:43.357169] 2024-06-26T06:05:43.3578467Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/legacy_vmap_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-e57980d26700b57b.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:43.357511] 2024-06-26T06:05:44.8744651Z 2024-06-26T06:05:44.8746572Z cpp/legacy_vmap_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.legacy_vmap_test_1.1_71bfab41c29b7db0_.log 2024-06-26T06:05:44.8747714Z 2024-06-26T06:05:44.8748038Z Running cpp/native_test 1/1 ... [2024-06-26 06:05:44.874237] 2024-06-26T06:05:44.8749958Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/native_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-97ab38acef1a858d.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:44.874559] 2024-06-26T06:05:46.3416786Z 2024-06-26T06:05:46.3418620Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_7e14589717260d67_.log 2024-06-26T06:05:46.3419957Z 2024-06-26T06:05:46.3420481Z Running cpp/operators_test 1/1 ... [2024-06-26 06:05:46.341460] 2024-06-26T06:05:46.3422417Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/operators_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-14ee6f34be9b593c.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:46.341766] 2024-06-26T06:05:47.8085666Z 2024-06-26T06:05:47.8087537Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_e88d97e6f84af212_.log 2024-06-26T06:05:47.8089097Z 2024-06-26T06:05:47.8089471Z Running cpp/scalar_tensor_test 1/1 ... [2024-06-26 06:05:47.808394] 2024-06-26T06:05:47.8091642Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-c0fc940b14fc9519.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:47.808682] 2024-06-26T06:05:49.3254093Z 2024-06-26T06:05:49.3255722Z cpp/scalar_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_tensor_test_1.1_c9ecbf9696ecf679_.log 2024-06-26T06:05:49.3256684Z 2024-06-26T06:05:49.3257135Z Running cpp/scalar_test 1/1 ... [2024-06-26 06:05:49.325273] 2024-06-26T06:05:49.3259590Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-a1bbbf6cebabf521.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:49.325645] 2024-06-26T06:05:50.7922399Z 2024-06-26T06:05:50.7923740Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_7336cf6a1f8ff6c1_.log 2024-06-26T06:05:50.7924622Z 2024-06-26T06:05:50.7924985Z Running cpp/tensor_iterator_test 1/1 ... [2024-06-26 06:05:50.792063] 2024-06-26T06:05:50.7927270Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/tensor_iterator_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-7d2136471e3ceb5b.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:50.792402] 2024-06-26T06:05:52.2592569Z 2024-06-26T06:05:52.2594090Z cpp/tensor_iterator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.tensor_iterator_test_1.1_f4d6a3bd4d43cf5b_.log 2024-06-26T06:05:52.2595488Z 2024-06-26T06:05:52.2595871Z Running cpp/undefined_tensor_test 1/1 ... [2024-06-26 06:05:52.259091] 2024-06-26T06:05:52.2598010Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/undefined_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-330ae91fc080e4ac.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:52.259412] 2024-06-26T06:05:53.7763182Z 2024-06-26T06:05:53.7764816Z cpp/undefined_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.undefined_tensor_test_1.1_df85059ef39da3a4_.log 2024-06-26T06:05:53.7765900Z 2024-06-26T06:05:53.7766341Z Running cpp/wrapdim_test 1/1 ... [2024-06-26 06:05:53.776177] 2024-06-26T06:05:53.7769039Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/wrapdim_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-48b08fe11bb89ff8.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:53.776492] 2024-06-26T06:05:55.2932434Z 2024-06-26T06:05:55.2933918Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_7fa9c2bdcf6f34f1_.log 2024-06-26T06:05:55.2934835Z 2024-06-26T06:05:55.2945365Z Running cpp/Dict_test 1/1 ... [2024-06-26 06:05:55.294229] 2024-06-26T06:05:55.2946630Z Running cpp/Dimname_test 1/1 ... [2024-06-26 06:05:55.294407] 2024-06-26T06:05:55.2947759Z Running cpp/NamedTensor_test 1/1 ... [2024-06-26 06:05:55.294466] 2024-06-26T06:05:55.2962763Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dict_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-39dffb809fdf24e7.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:55.294802] 2024-06-26T06:05:55.2968092Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dimname_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-31f2419805b1aea7.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:55.294961] 2024-06-26T06:05:55.2973823Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/NamedTensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-0ac80f79036fc1ec.xml', '-x', '--reruns=2'] ... [2024-06-26 06:05:55.294997] 2024-06-26T06:05:59.2671413Z 2024-06-26T06:05:59.2674079Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_c03bd38d682026b0_.log 2024-06-26T06:05:59.2675303Z 2024-06-26T06:05:59.8680676Z 2024-06-26T06:05:59.8683546Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_9ef79e5019d14f32_.log 2024-06-26T06:05:59.8685241Z 2024-06-26T06:06:01.9568348Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:02.0165953Z Running cpp/apply_utils_test 1/1 ... [2024-06-26 06:06:02.016081] 2024-06-26T06:06:02.0170821Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/apply_utils_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1a3a782511e43c99.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:02.016582] 2024-06-26T06:06:03.1062973Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:03.2112481Z Running cpp/atest 1/1 ... [2024-06-26 06:06:03.210612] 2024-06-26T06:06:03.2119806Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/atest', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-2ad69dc9ca246319.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:03.211177] 2024-06-26T06:06:05.6381695Z 2024-06-26T06:06:05.6383551Z cpp/apply_utils_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.apply_utils_test_1.1_3ba1986cd6ba3f3e_.log 2024-06-26T06:06:05.6384956Z 2024-06-26T06:06:05.9302137Z 2024-06-26T06:06:05.9304177Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_8f9d4ac5d9c93abf_.log 2024-06-26T06:06:05.9305848Z 2024-06-26T06:06:08.1414103Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:08.2246784Z Running cpp/basic 1/1 ... [2024-06-26 06:06:08.224235] 2024-06-26T06:06:08.2252865Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/basic', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-62dc899bca8bdb12.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:08.224775] 2024-06-26T06:06:08.3388779Z 2024-06-26T06:06:08.3390568Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_1c49c0852d1cb9bc_.log 2024-06-26T06:06:08.3391409Z 2024-06-26T06:06:08.9109085Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:09.0163918Z Running cpp/broadcast_test 1/1 ... [2024-06-26 06:06:09.015849] 2024-06-26T06:06:09.0168097Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/broadcast_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-772ece0156a2cbfd.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:09.016410] 2024-06-26T06:06:11.0500864Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:11.1103964Z Running cpp/cpu_generator_test 1/1 ... [2024-06-26 06:06:11.109924] 2024-06-26T06:06:11.1108336Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/cpu_generator_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-3f91ea3f730f7b3c.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:11.110367] 2024-06-26T06:06:11.3955498Z 2024-06-26T06:06:11.3959712Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_448c02796b8f53f5_.log 2024-06-26T06:06:11.3961778Z 2024-06-26T06:06:11.5358005Z 2024-06-26T06:06:11.5361009Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_aea04b6b4f98f476_.log 2024-06-26T06:06:11.5362751Z 2024-06-26T06:06:14.2500474Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:14.3095911Z Running cpp/dlconvertor_test 1/1 ... [2024-06-26 06:06:14.309133] 2024-06-26T06:06:14.3101615Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/dlconvertor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-76126772c29f1ff2.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:14.309612] 2024-06-26T06:06:14.5675931Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:14.6691068Z Running cpp/extension_backend_test 1/1 ... [2024-06-26 06:06:14.668673] 2024-06-26T06:06:14.6695404Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/extension_backend_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-2b5e4014a7cb2d53.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:14.669142] 2024-06-26T06:06:15.6841940Z 2024-06-26T06:06:15.6843888Z cpp/cpu_generator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.cpu_generator_test_1.1_aadcb57c6cc5bce2_.log 2024-06-26T06:06:15.6845306Z 2024-06-26T06:06:16.8800597Z 2024-06-26T06:06:16.8803239Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_e2155aac40c950f3_.log 2024-06-26T06:06:16.8805228Z 2024-06-26T06:06:17.3434550Z 2024-06-26T06:06:17.3436851Z cpp/extension_backend_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.extension_backend_test_1.1_d9278f5c17ae132d_.log 2024-06-26T06:06:17.3438877Z 2024-06-26T06:06:18.4001737Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:18.4603070Z Running cpp/lazy_tensor_test 1/1 ... [2024-06-26 06:06:18.459878] 2024-06-26T06:06:18.4606922Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/lazy_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-b1e8746db7ffe3bc.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:18.460334] 2024-06-26T06:06:19.3537040Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:19.4593999Z Running cpp/legacy_vmap_test 1/1 ... [2024-06-26 06:06:19.458983] 2024-06-26T06:06:19.4599433Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/legacy_vmap_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-47ba595067caf11f.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:19.459555] 2024-06-26T06:06:19.7337680Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:19.8136238Z Running cpp/native_test 1/1 ... [2024-06-26 06:06:19.813150] 2024-06-26T06:06:19.8141667Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/native_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-f949860b441f6eae.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:19.813647] 2024-06-26T06:06:21.2299288Z 2024-06-26T06:06:21.2301895Z cpp/lazy_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.lazy_tensor_test_1.1_33ac3931d93b57e5_.log 2024-06-26T06:06:21.2303963Z 2024-06-26T06:06:22.8336388Z 2024-06-26T06:06:22.8338318Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_483ed622de1bcee2_.log 2024-06-26T06:06:22.8339628Z 2024-06-26T06:06:24.2006345Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:24.3063496Z Running cpp/operators_test 1/1 ... [2024-06-26 06:06:24.305865] 2024-06-26T06:06:24.3069339Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/operators_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-2f491ead6c1788a0.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:24.306415] 2024-06-26T06:06:25.0846172Z 2024-06-26T06:06:25.0848878Z cpp/legacy_vmap_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.legacy_vmap_test_1.1_42a392704f507615_.log 2024-06-26T06:06:25.0850670Z 2024-06-26T06:06:25.6514085Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:25.7106785Z Running cpp/scalar_tensor_test 1/1 ... [2024-06-26 06:06:25.710226] 2024-06-26T06:06:25.7112123Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-067412f10579c89a.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:25.710702] 2024-06-26T06:06:27.2262295Z 2024-06-26T06:06:27.2264469Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_3ff44b882bc7037f_.log 2024-06-26T06:06:27.2266215Z 2024-06-26T06:06:27.9392883Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:28.0011082Z Running cpp/scalar_test 1/1 ... [2024-06-26 06:06:28.000671] 2024-06-26T06:06:28.0018650Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-a91faa9c58edfb6d.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:28.001119] 2024-06-26T06:06:28.3297391Z 2024-06-26T06:06:28.3299264Z cpp/scalar_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_tensor_test_1.1_10189367c5cb63fd_.log 2024-06-26T06:06:28.3300376Z 2024-06-26T06:06:29.9749355Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:30.0343438Z Running cpp/tensor_iterator_test 1/1 ... [2024-06-26 06:06:30.033873] 2024-06-26T06:06:30.0348263Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/tensor_iterator_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-edba351888318baa.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:30.034333] 2024-06-26T06:06:30.7713558Z 2024-06-26T06:06:30.7716275Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_df547fa2b4bc571e_.log 2024-06-26T06:06:30.7718226Z 2024-06-26T06:06:30.8838782Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:30.9488595Z Running cpp/undefined_tensor_test 1/1 ... [2024-06-26 06:06:30.948357] 2024-06-26T06:06:30.9493255Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/undefined_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-f2d998d09b6b249d.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:30.948832] 2024-06-26T06:06:33.5684086Z 2024-06-26T06:06:33.5686605Z cpp/undefined_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.undefined_tensor_test_1.1_29564cf89fa7c644_.log 2024-06-26T06:06:33.5688343Z 2024-06-26T06:06:33.9330729Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:33.9933092Z Running cpp/wrapdim_test 1/1 ... [2024-06-26 06:06:33.992823] 2024-06-26T06:06:33.9937661Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/wrapdim_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-ed8f1b9b6d62d5a0.xml', '-x', '--reruns=2'] ... [2024-06-26 06:06:33.993295] 2024-06-26T06:06:36.7131024Z 2024-06-26T06:06:36.7135957Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_6d022a8daf76ce4e_.log 2024-06-26T06:06:36.7137961Z 2024-06-26T06:06:36.7639166Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:39.0534712Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-06-26T06:06:41.3213330Z 2024-06-26T06:06:41.3214953Z cpp/tensor_iterator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.tensor_iterator_test_1.1_dccd06a3024724f0_.log 2024-06-26T06:06:41.3215928Z 2024-06-26T06:06:42.1088355Z + [[ -x ./tensor_interop_test ]] 2024-06-26T06:06:42.1089215Z + [[ -x ./cudnn_test ]] 2024-06-26T06:06:42.1089843Z + [[ -x ./cuda_generator_test ]] 2024-06-26T06:06:42.1090378Z + [[ -x ./apply_test ]] 2024-06-26T06:06:42.1090857Z + [[ -x ./stream_test ]] 2024-06-26T06:06:42.1091349Z + [[ -x ./cuda_half_test ]] 2024-06-26T06:06:42.1091739Z + [[ -x ./cuda_vectorized_test ]] 2024-06-26T06:06:42.1092151Z + [[ -x ./cuda_distributions_test ]] 2024-06-26T06:06:42.1092596Z + [[ -x ./cuda_optional_test ]] 2024-06-26T06:06:42.1093021Z + [[ -x ./cuda_tensor_interop_test ]] 2024-06-26T06:06:42.1093436Z + [[ -x ./cuda_complex_test ]] 2024-06-26T06:06:42.1094046Z + [[ -x ./cuda_complex_math_test ]] 2024-06-26T06:06:42.1094468Z + [[ -x ./cuda_cub_test ]] 2024-06-26T06:06:42.1094829Z + [[ -x ./cuda_atomic_ops_test ]] 2024-06-26T06:06:42.1095227Z + '[' ON == ON ']' 2024-06-26T06:06:42.1096242Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA' 2024-06-26T06:06:42.1366967Z ==5865== Memcheck, a memory error detector 2024-06-26T06:06:42.1367732Z ==5865== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al. 2024-06-26T06:06:42.1368562Z ==5865== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info 2024-06-26T06:06:42.1369299Z ==5865== Command: build/bin/basic --gtest_filter=-*CUDA 2024-06-26T06:06:42.1369736Z ==5865== 2024-06-26T06:07:10.1272578Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc 2024-06-26T06:07:10.1484938Z Note: Google Test filter = -*CUDA 2024-06-26T06:07:10.1535611Z [==========] Running 4 tests from 1 test suite. 2024-06-26T06:07:10.1561520Z [----------] Global test environment set-up. 2024-06-26T06:07:10.1600021Z [----------] 4 tests from BasicTest 2024-06-26T06:07:10.1621119Z [ RUN ] BasicTest.BasicTestCPU 2024-06-26T06:07:11.5412845Z 345 ms 2024-06-26T06:07:11.6243240Z 52 ms 2024-06-26T06:07:11.6977633Z 65 ms 2024-06-26T06:07:12.2376556Z [ OK ] BasicTest.BasicTestCPU (2072 ms) 2024-06-26T06:07:12.2385207Z [ RUN ] BasicTest.BasicTestHalfCPU 2024-06-26T06:07:12.3660581Z 83 ms 2024-06-26T06:07:12.4152364Z 44 ms 2024-06-26T06:07:12.4812811Z 64 ms 2024-06-26T06:07:12.5349944Z [ OK ] BasicTest.BasicTestHalfCPU (296 ms) 2024-06-26T06:07:12.5351349Z [ RUN ] BasicTest.FactoryMethodsTest 2024-06-26T06:07:12.5664954Z [ OK ] BasicTest.FactoryMethodsTest (31 ms) 2024-06-26T06:07:12.5665594Z [ RUN ] BasicTest.BasicStdTestCPU 2024-06-26T06:07:12.6497281Z Simple example: called once 2024-06-26T06:07:12.7396396Z throw: call_once will retry 2024-06-26T06:07:12.7409019Z Didn't throw, call_once will not attempt again 2024-06-26T06:07:12.7845645Z [ OK ] BasicTest.BasicStdTestCPU (217 ms) 2024-06-26T06:07:12.7867359Z [----------] 4 tests from BasicTest (2623 ms total) 2024-06-26T06:07:12.7867783Z 2024-06-26T06:07:12.7878554Z [----------] Global test environment tear-down 2024-06-26T06:07:12.7910044Z [==========] 4 tests from 1 test suite ran. (2643 ms total) 2024-06-26T06:07:12.7921081Z [ PASSED ] 4 tests. 2024-06-26T06:07:14.5593366Z ==5865== 2024-06-26T06:07:14.5596737Z ==5865== HEAP SUMMARY: 2024-06-26T06:07:14.5597250Z ==5865== in use at exit: 239,520 bytes in 3,995 blocks 2024-06-26T06:07:14.5599618Z ==5865== total heap usage: 730,485 allocs, 726,490 frees, 212,866,016 bytes allocated 2024-06-26T06:07:14.5600279Z ==5865== 2024-06-26T06:07:14.5967938Z ==5865== LEAK SUMMARY: 2024-06-26T06:07:14.5968581Z ==5865== definitely lost: 0 bytes in 0 blocks 2024-06-26T06:07:14.5969347Z ==5865== indirectly lost: 0 bytes in 0 blocks 2024-06-26T06:07:14.5970238Z ==5865== possibly lost: 0 bytes in 0 blocks 2024-06-26T06:07:14.5971114Z ==5865== still reachable: 239,520 bytes in 3,995 blocks 2024-06-26T06:07:14.5971982Z ==5865== suppressed: 0 bytes in 0 blocks 2024-06-26T06:07:14.5972748Z ==5865== Rerun with --leak-check=full to see details of leaked memory 2024-06-26T06:07:14.5973302Z ==5865== 2024-06-26T06:07:14.5973744Z ==5865== For lists of detected and suppressed errors, rerun with: -s 2024-06-26T06:07:14.5974473Z ==5865== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0) 2024-06-26T06:07:14.6265950Z + [[ -x ./tensor_interop_test ]] 2024-06-26T06:07:14.6267700Z + [[ -n '' ]] 2024-06-26T06:07:14.6268017Z + assert_git_not_dirty 2024-06-26T06:07:14.6268619Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *rocm* ]] 2024-06-26T06:07:14.6269464Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *xla* ]] 2024-06-26T06:07:14.6274392Z ++ git status --porcelain 2024-06-26T06:07:14.6275077Z ++ grep -v '?? third_party' 2024-06-26T06:07:14.7939988Z ++ true 2024-06-26T06:07:14.7940930Z + git_status= 2024-06-26T06:07:14.7941554Z + [[ -n '' ]] 2024-06-26T06:07:14.7942189Z + cleanup_workspace 2024-06-26T06:07:14.7943829Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2024-06-26T06:07:14.7945230Z sudo may print the following warning message that can be ignored. The chown command will still run. 2024-06-26T06:07:14.7946196Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2024-06-26T06:07:14.7946846Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2024-06-26T06:07:14.7947675Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2024-06-26T06:07:14.7948570Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2024-06-26T06:07:14.7949274Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2024-06-26T06:07:15.5001277Z ##[group]Run cat test/**/*_toprint.log || true 2024-06-26T06:07:15.5001770Z cat test/**/*_toprint.log || true 2024-06-26T06:07:15.5011952Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:07:15.5012442Z env: 2024-06-26T06:07:15.5012692Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:15.5013302Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:15.5013963Z ##[endgroup] 2024-06-26T06:07:15.5076399Z cat: test/**/*_toprint.log: No such file or directory 2024-06-26T06:07:15.5105624Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2024-06-26T06:07:15.5106079Z kill "$MONITOR_SCRIPT_PID" 2024-06-26T06:07:15.5113029Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:07:15.5113589Z env: 2024-06-26T06:07:15.5113848Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:15.5114457Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:15.5115334Z MONITOR_SCRIPT_PID: 16356 2024-06-26T06:07:15.5115668Z ##[endgroup] 2024-06-26T06:07:15.5315456Z Prepare all required actions 2024-06-26T06:07:15.5315879Z Getting action download info 2024-06-26T06:07:15.6686522Z Download action repository 'actions/upload-artifact@v3' (SHA:a8a3f3ad30e3422c9c7b888a15615d19a852ae32) 2024-06-26T06:07:15.8484882Z ##[group]Run ./.github/actions/upload-test-artifacts 2024-06-26T06:07:15.8485338Z with: 2024-06-26T06:07:15.8485696Z file-suffix: test-dynamo-1-3-linux.2xlarge_26688306089 2024-06-26T06:07:15.8486189Z s3-bucket: gha-artifacts 2024-06-26T06:07:15.8486492Z env: 2024-06-26T06:07:15.8486741Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:15.8487338Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:15.8487985Z ##[endgroup] 2024-06-26T06:07:15.8513625Z ##[group]Run # Remove any previous test jsons if they exist 2024-06-26T06:07:15.8514247Z # Remove any previous test jsons if they exist 2024-06-26T06:07:15.8514857Z rm -f test-jsons-*.zip 2024-06-26T06:07:15.8515404Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test -i '*.json' 2024-06-26T06:07:15.8522848Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:07:15.8523343Z env: 2024-06-26T06:07:15.8523585Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:15.8524191Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:15.8524962Z FILE_SUFFIX: test-dynamo-1-3-linux.2xlarge_26688306089 2024-06-26T06:07:15.8525426Z ##[endgroup] 2024-06-26T06:07:15.8753174Z adding: test/allowlist_for_publicAPI.json (deflated 79%) 2024-06-26T06:07:15.8780661Z adding: test/benchmark_utils/callgrind_artifacts.json (deflated 92%) 2024-06-26T06:07:15.8781487Z adding: test/minioptest_failures_dict.json (deflated 70%) 2024-06-26T06:07:15.8787449Z adding: test/profiler/profiler_utils_mock_events.json (deflated 87%) 2024-06-26T06:07:15.8790006Z adding: test/test-reports/td_exclusions-4c7967db8832ea480de8.json (deflated 81%) 2024-06-26T06:07:15.8791125Z adding: test/test-reports/td_exclusions-d1958d98839ba1e0b819.json (deflated 73%) 2024-06-26T06:07:15.8791933Z adding: test/.pytorch-slow-tests.json (deflated 77%) 2024-06-26T06:07:15.8798814Z adding: test/.pytorch-disabled-tests.json (deflated 84%) 2024-06-26T06:07:15.8824850Z ##[group]Run # Remove any previous test reports if they exist 2024-06-26T06:07:15.8825492Z # Remove any previous test reports if they exist 2024-06-26T06:07:15.8826016Z rm -f test-reports-*.zip 2024-06-26T06:07:15.8826592Z zip -r "test-reports-${FILE_SUFFIX}.zip" test -i '*.xml' -i '*.csv' 2024-06-26T06:07:15.8833790Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:07:15.8834275Z env: 2024-06-26T06:07:15.8834537Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:15.8835309Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:15.8836081Z FILE_SUFFIX: test-dynamo-1-3-linux.2xlarge_26688306089 2024-06-26T06:07:15.8836556Z ##[endgroup] 2024-06-26T06:07:15.9068113Z adding: test/test-reports/python-pytest/test_nn/test_nn-79c4a196f1806519.xml (deflated 96%) 2024-06-26T06:07:15.9124958Z adding: test/test-reports/python-pytest/test_cpp_api_parity/test_cpp_api_parity-a9c5630d12f9f4b4.xml (deflated 99%) 2024-06-26T06:07:15.9158578Z adding: test/test-reports/python-pytest/test_torch/test_torch-78b5b2c7929ef01a.xml (deflated 95%) 2024-06-26T06:07:15.9159833Z adding: test/test-reports/python-pytest/test_show_pickle/test_show_pickle-ceb34740cfb393f1.xml (deflated 37%) 2024-06-26T06:07:15.9161147Z adding: test/test-reports/python-pytest/test_autocast/test_autocast-3f12acb550568561.xml (deflated 86%) 2024-06-26T06:07:15.9268234Z adding: test/test-reports/python-pytest/test_utils/test_utils-4893119002b9bcb0.xml (deflated 98%) 2024-06-26T06:07:15.9269665Z adding: test/test-reports/python-pytest/test_tensorexpr/test_tensorexpr-5d8cfa38140cfb80.xml (deflated 95%) 2024-06-26T06:07:15.9271157Z adding: test/test-reports/python-pytest/test_autograd_fallback/test_autograd_fallback-aaa7b4ff9639f9c0.xml (deflated 88%) 2024-06-26T06:07:15.9275903Z adding: test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-384c1dbf9f6c7644.xml (deflated 92%) 2024-06-26T06:07:15.9277470Z adding: test/test-reports/python-pytest/test_cpp_extensions_stream_and_event/test_cpp_extensions_stream_and_event-0f2ea1b5a58af0ae.xml (deflated 59%) 2024-06-26T06:07:15.9279160Z adding: test/test-reports/python-pytest/test_cpp_extensions_mtia_backend/test_cpp_extensions_mtia_backend-0986ce828922a7ec.xml (deflated 79%) 2024-06-26T06:07:15.9298447Z adding: test/test-reports/python-pytest/test_overrides/test_overrides-954d8f935050e266.xml (deflated 96%) 2024-06-26T06:07:15.9299750Z adding: test/test-reports/python-pytest/test_jit_disabled/test_jit_disabled-500d4db98c2cc65f.xml (deflated 56%) 2024-06-26T06:07:15.9301048Z adding: test/test-reports/python-pytest/test_native_mha/test_native_mha-4d6fca4b797a0b9c.xml (deflated 95%) 2024-06-26T06:07:15.9303198Z adding: test/test-reports/python-pytest/test_cpp_extensions_jit/test_cpp_extensions_jit-6d211922214564a5.xml (deflated 88%) 2024-06-26T06:07:15.9305640Z adding: test/test-reports/python-pytest/test_cpp_extensions_open_device_registration/test_cpp_extensions_open_device_registration-f14981bd188743c0.xml (deflated 84%) 2024-06-26T06:07:15.9308288Z adding: test/test-reports/python-pytest/test_sort_and_select/test_sort_and_select-bc54bb25cae75266.xml (deflated 92%) 2024-06-26T06:07:15.9310220Z adding: test/test-reports/python-pytest/test_multiprocessing/test_multiprocessing-b57c268bffd2b6a5.xml (deflated 88%) 2024-06-26T06:07:15.9311639Z adding: test/test-reports/python-pytest/test_mobile_optimizer/test_mobile_optimizer-c172afa998473a2b.xml (deflated 59%) 2024-06-26T06:07:15.9313073Z adding: test/test-reports/python-pytest/nn.test_pooling/nn.test_pooling-2e9640eda54a92df.xml (deflated 90%) 2024-06-26T06:07:15.9333091Z adding: test/test-reports/python-pytest/test_tensor_creation_ops/test_tensor_creation_ops-e409407011881748.xml (deflated 94%) 2024-06-26T06:07:15.9430747Z adding: test/test-reports/python-pytest/test_reductions/test_reductions-a23ac452854dc533.xml (deflated 98%) 2024-06-26T06:07:15.9432386Z adding: test/test-reports/python-pytest/test_dispatch/test_dispatch-125647a62422020c.xml (deflated 93%) 2024-06-26T06:07:15.9433784Z adding: test/test-reports/python-pytest/test_multiprocessing_spawn/test_multiprocessing_spawn-989f5f658f388c24.xml (deflated 77%) 2024-06-26T06:07:15.9440724Z adding: test/test-reports/python-pytest/test_spectral_ops/test_spectral_ops-5d79d224bb1d9bd2.xml (deflated 95%) 2024-06-26T06:07:15.9443988Z adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-c8141ca17d555069.xml (deflated 94%) 2024-06-26T06:07:15.9446662Z adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-a9409afdcb961e7f.xml (deflated 93%) 2024-06-26T06:07:15.9448347Z adding: test/test-reports/python-pytest/test_cpp_extensions_aot_no_ninja/test_cpp_extensions_aot_no_ninja-285bbac307afdfcb.xml (deflated 85%) 2024-06-26T06:07:15.9449963Z adding: test/test-reports/python-pytest/test_cpp_extensions_aot_ninja/test_cpp_extensions_aot_ninja-ea0ece0557a53621.xml (deflated 85%) 2024-06-26T06:07:15.9451382Z adding: test/test-reports/python-pytest/test_jiterator/test_jiterator-f8f47dd88ee9fe9a.xml (deflated 28%) 2024-06-26T06:07:15.9452652Z adding: test/test-reports/python-pytest/test_jiterator/test_jiterator-5e67beb7adbada6c.xml (deflated 28%) 2024-06-26T06:07:15.9453919Z adding: test/test-reports/python-pytest/test_matmul_cuda/test_matmul_cuda-a408efa8be614d45.xml (deflated 28%) 2024-06-26T06:07:15.9455291Z adding: test/test-reports/python-pytest/test_matmul_cuda/test_matmul_cuda-d8391e960e5ffc32.xml (deflated 28%) 2024-06-26T06:07:15.9456552Z adding: test/test-reports/python-pytest/xpu.test_conv/xpu.test_conv-d10bedd2c0a9f71f.xml (deflated 28%) 2024-06-26T06:07:15.9457915Z adding: test/test-reports/python-pytest/xpu.test_conv/xpu.test_conv-ec1ede6fa7b540d2.xml (deflated 28%) 2024-06-26T06:07:15.9459091Z adding: test/test-reports/python-pytest/test_cuda/test_cuda-f9915ed855aaaee4.xml (deflated 28%) 2024-06-26T06:07:15.9460227Z adding: test/test-reports/python-pytest/test_cuda/test_cuda-703b202a4c156258.xml (deflated 28%) 2024-06-26T06:07:15.9461467Z adding: test/test-reports/python-pytest/test_cuda_multigpu/test_cuda_multigpu-9ab05394879a52d9.xml (deflated 28%) 2024-06-26T06:07:15.9462789Z adding: test/test-reports/python-pytest/test_cuda_multigpu/test_cuda_multigpu-f4e1e34a953f3fc7.xml (deflated 27%) 2024-06-26T06:07:15.9464388Z adding: test/test-reports/python-pytest/torch_np.numpy_tests.lib.test_arraypad/torch_np.numpy_tests.lib.test_arraypad-7a64858e2bfbb226.xml (deflated 28%) 2024-06-26T06:07:15.9466211Z adding: test/test-reports/python-pytest/torch_np.numpy_tests.lib.test_arraypad/torch_np.numpy_tests.lib.test_arraypad-1aaabc902472313a.xml (deflated 77%) 2024-06-26T06:07:15.9467697Z adding: test/test-reports/python-pytest/test_sparse/test_sparse-0b34745ca9d998ea.xml (deflated 27%) 2024-06-26T06:07:15.9490116Z adding: test/test-reports/python-pytest/test_sparse/test_sparse-79613a3b33fd382f.xml (deflated 97%) 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test/test-reports/python-pytest/test.run_test/test.run_test-0d7741ef0d9bc1f9.xml (deflated 29%) 2024-06-26T06:07:15.9501168Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-f9ffcd566b2617ae.xml (deflated 29%) 2024-06-26T06:07:15.9502392Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-cd4280f4e494ecbd.xml (deflated 29%) 2024-06-26T06:07:15.9503599Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-ad0cd10b1112d9e5.xml (deflated 29%) 2024-06-26T06:07:15.9504815Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-e57980d26700b57b.xml (deflated 29%) 2024-06-26T06:07:15.9506770Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-97ab38acef1a858d.xml (deflated 29%) 2024-06-26T06:07:15.9508076Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-14ee6f34be9b593c.xml (deflated 29%) 2024-06-26T06:07:15.9509306Z adding: 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2>&1; then 2024-06-26T06:07:15.9566461Z  zip -r "logs-${FILE_SUFFIX}.zip" test -i '*.log' 2024-06-26T06:07:15.9566944Z fi 2024-06-26T06:07:15.9573768Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:07:15.9574256Z env: 2024-06-26T06:07:15.9574512Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:15.9575105Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:15.9575880Z FILE_SUFFIX: test-dynamo-1-3-linux.2xlarge_26688306089 2024-06-26T06:07:15.9576359Z ##[endgroup] 2024-06-26T06:07:15.9639461Z adding: usage_log.txt (deflated 92%) 2024-06-26T06:07:15.9870065Z adding: test/test-reports/test_hub_1.1_c5a0a502e86b7597_.log (stored 0%) 2024-06-26T06:07:15.9905588Z adding: test/test-reports/test_nn_1.2_32f9faf793a94c9f_.log (deflated 93%) 2024-06-26T06:07:15.9906559Z adding: test/test-reports/test_hub_1.1_6f04e72cd0d033f7_.log (stored 0%) 2024-06-26T06:07:15.9917685Z adding: 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78%) 2024-06-26T06:07:16.0385982Z adding: test/test-reports/cpp.dlconvertor_test_1.1_fdf13b9ea9c85c3b_.log (deflated 48%) 2024-06-26T06:07:16.0387182Z adding: test/test-reports/cpp.dlconvertor_test_1.1_e2155aac40c950f3_.log (deflated 56%) 2024-06-26T06:07:16.0388262Z adding: test/test-reports/cpp.extension_backend_test_1.1_fff6c5a56efc2cdf_.log (deflated 48%) 2024-06-26T06:07:16.0389342Z adding: test/test-reports/cpp.tensor_iterator_test_1.1_dccd06a3024724f0_.log (deflated 88%) 2024-06-26T06:07:16.0390375Z adding: test/test-reports/cpp.lazy_tensor_test_1.1_e8108f62150b4053_.log (deflated 49%) 2024-06-26T06:07:16.0391376Z adding: test/test-reports/cpp.legacy_vmap_test_1.1_71bfab41c29b7db0_.log (deflated 48%) 2024-06-26T06:07:16.0392363Z adding: test/test-reports/cpp.scalar_test_1.1_7336cf6a1f8ff6c1_.log (deflated 49%) 2024-06-26T06:07:16.0393311Z adding: test/test-reports/cpp.Dict_test_1.1_8f9d4ac5d9c93abf_.log (deflated 84%) 2024-06-26T06:07:16.0394268Z adding: test/test-reports/cpp.lazy_tensor_test_1.1_33ac3931d93b57e5_.log (deflated 54%) 2024-06-26T06:07:16.0395442Z adding: test/test-reports/cpp.legacy_vmap_test_1.1_42a392704f507615_.log (deflated 81%) 2024-06-26T06:07:16.0396454Z adding: test/test-reports/cpp.scalar_tensor_test_1.1_10189367c5cb63fd_.log (deflated 60%) 2024-06-26T06:07:16.0397509Z adding: test/test-reports/cpp.undefined_tensor_test_1.1_29564cf89fa7c644_.log (deflated 50%) 2024-06-26T06:07:16.0421213Z ##[group]Run # Remove any previous debugging artifacts if they exist 2024-06-26T06:07:16.0421931Z # Remove any previous debugging artifacts if they exist 2024-06-26T06:07:16.0422477Z rm -f debug-*.zip 2024-06-26T06:07:16.0422837Z if [ -d 'test/debug' ]; then 2024-06-26T06:07:16.0423317Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2024-06-26T06:07:16.0423775Z fi 2024-06-26T06:07:16.0430504Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:07:16.0430992Z env: 2024-06-26T06:07:16.0431254Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:16.0431848Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:16.0432618Z FILE_SUFFIX: test-dynamo-1-3-linux.2xlarge_26688306089 2024-06-26T06:07:16.0433105Z ##[endgroup] 2024-06-26T06:07:16.0509046Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-06-26T06:07:16.0509468Z with: 2024-06-26T06:07:16.0509728Z s3-bucket: gha-artifacts 2024-06-26T06:07:16.0510134Z s3-prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:07:16.0510582Z retention-days: 14 2024-06-26T06:07:16.0510897Z if-no-files-found: warn 2024-06-26T06:07:16.0511235Z path: test-jsons-*.zip 2024-06-26T06:07:16.0511539Z name: artifact 2024-06-26T06:07:16.0511821Z region: us-east-1 2024-06-26T06:07:16.0512100Z env: 2024-06-26T06:07:16.0512334Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:16.0512938Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:16.0513601Z ##[endgroup] 2024-06-26T06:07:16.4201372Z NOTE: s3-prefix specified, ignoring name parameter 2024-06-26T06:07:16.4202063Z With the provided path, there will be 1 file uploaded 2024-06-26T06:07:16.4202693Z Uploading to s3 prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:07:16.5364990Z Starting upload of test-jsons-test-dynamo-1-3-linux.2xlarge_26688306089.zip 2024-06-26T06:07:16.6553603Z Finished upload of test-jsons-test-dynamo-1-3-linux.2xlarge_26688306089.zip 2024-06-26T06:07:16.6690210Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-06-26T06:07:16.6690646Z with: 2024-06-26T06:07:16.6690895Z s3-bucket: gha-artifacts 2024-06-26T06:07:16.6691311Z s3-prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:07:16.6691772Z retention-days: 14 2024-06-26T06:07:16.6692076Z if-no-files-found: error 2024-06-26T06:07:16.6692423Z path: test-reports-*.zip 2024-06-26T06:07:16.6692747Z name: artifact 2024-06-26T06:07:16.6693017Z region: us-east-1 2024-06-26T06:07:16.6693292Z env: 2024-06-26T06:07:16.6693545Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:16.6694140Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:16.6694800Z ##[endgroup] 2024-06-26T06:07:17.0040499Z NOTE: s3-prefix specified, ignoring name parameter 2024-06-26T06:07:17.0041284Z With the provided path, there will be 1 file uploaded 2024-06-26T06:07:17.0041913Z Uploading to s3 prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:07:17.0076706Z Starting upload of test-reports-test-dynamo-1-3-linux.2xlarge_26688306089.zip 2024-06-26T06:07:17.1090062Z Finished upload of test-reports-test-dynamo-1-3-linux.2xlarge_26688306089.zip 2024-06-26T06:07:17.1223755Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-06-26T06:07:17.1224182Z with: 2024-06-26T06:07:17.1224439Z s3-bucket: gha-artifacts 2024-06-26T06:07:17.1224848Z s3-prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:07:17.1225293Z retention-days: 14 2024-06-26T06:07:17.1225607Z if-no-files-found: ignore 2024-06-26T06:07:17.1225941Z path: logs-*.zip 2024-06-26T06:07:17.1226211Z name: artifact 2024-06-26T06:07:17.1226491Z region: us-east-1 2024-06-26T06:07:17.1226767Z env: 2024-06-26T06:07:17.1227002Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:17.1227612Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:17.1228267Z ##[endgroup] 2024-06-26T06:07:17.4561098Z NOTE: s3-prefix specified, ignoring name parameter 2024-06-26T06:07:17.4562224Z With the provided path, there will be 1 file uploaded 2024-06-26T06:07:17.4562850Z Uploading to s3 prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:07:17.4596622Z Starting upload of logs-test-dynamo-1-3-linux.2xlarge_26688306089.zip 2024-06-26T06:07:17.6296653Z Finished upload of logs-test-dynamo-1-3-linux.2xlarge_26688306089.zip 2024-06-26T06:07:17.6431983Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-06-26T06:07:17.6432407Z with: 2024-06-26T06:07:17.6432679Z s3-bucket: gha-artifacts 2024-06-26T06:07:17.6433090Z s3-prefix: pytorch/pytorch/9673645538/1/artifact 2024-06-26T06:07:17.6433531Z retention-days: 14 2024-06-26T06:07:17.6433846Z if-no-files-found: ignore 2024-06-26T06:07:17.6434183Z path: debug-*.zip 2024-06-26T06:07:17.6434461Z name: artifact 2024-06-26T06:07:17.6434888Z region: us-east-1 2024-06-26T06:07:17.6435167Z env: 2024-06-26T06:07:17.6435408Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:17.6436023Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:17.6436698Z ##[endgroup] 2024-06-26T06:07:17.9737726Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2024-06-26T06:07:17.9871902Z ##[group]Run # shellcheck disable=SC2156 2024-06-26T06:07:17.9872362Z # shellcheck disable=SC2156 2024-06-26T06:07:17.9873175Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2024-06-26T06:07:17.9881095Z shell: /usr/bin/bash -e {0} 2024-06-26T06:07:17.9881439Z env: 2024-06-26T06:07:17.9881699Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:17.9882295Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:17.9883035Z ##[endgroup] 2024-06-26T06:07:18.2889230Z GNU gdb (Ubuntu 9.2-0ubuntu1~20.04.2) 9.2 2024-06-26T06:07:18.2890045Z Copyright (C) 2020 Free Software Foundation, Inc. 2024-06-26T06:07:18.2890766Z License GPLv3+: GNU GPL version 3 or later 2024-06-26T06:07:18.2891576Z This is free software: you are free to change and redistribute it. 2024-06-26T06:07:18.2892273Z There is NO WARRANTY, to the extent permitted by law. 2024-06-26T06:07:18.2892830Z Type "show copying" and "show warranty" for details. 2024-06-26T06:07:18.2893422Z This GDB was configured as "x86_64-linux-gnu". 2024-06-26T06:07:18.2893951Z Type "show configuration" for configuration details. 2024-06-26T06:07:18.2894460Z For bug reporting instructions, please see: 2024-06-26T06:07:18.2894931Z . 2024-06-26T06:07:18.2895500Z Find the GDB manual and other documentation resources online at: 2024-06-26T06:07:18.2896120Z . 2024-06-26T06:07:18.2896494Z 2024-06-26T06:07:18.2896607Z For help, type "help". 2024-06-26T06:07:18.2897066Z Type "apropos word" to search for commands related to "word"... 2024-06-26T06:07:18.4142748Z Reading symbols from python... 2024-06-26T06:07:19.4761413Z 2024-06-26T06:07:19.4762303Z warning: core file may not match specified executable file. 2024-06-26T06:07:19.4958460Z [New LWP 1215] 2024-06-26T06:07:19.4958964Z [New LWP 1216] 2024-06-26T06:07:19.4959519Z [New LWP 1218] 2024-06-26T06:07:19.4959779Z [New LWP 1219] 2024-06-26T06:07:19.4960048Z [New LWP 1217] 2024-06-26T06:07:19.4960317Z [New LWP 1221] 2024-06-26T06:07:19.4960573Z [New LWP 1222] 2024-06-26T06:07:19.4960902Z [New LWP 1220] 2024-06-26T06:07:19.5002426Z [Thread debugging using libthread_db enabled] 2024-06-26T06:07:19.5003268Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1". 2024-06-26T06:07:23.2244562Z 50 ../sysdeps/unix/sysv/linux/raise.c: No such file or directory. 2024-06-26T06:07:23.2246013Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load". 2024-06-26T06:07:23.2247761Z Core was generated by `/opt/conda/envs/py_3.12/bin/python -c import os; os.environ["TORCH_CUSTOM_TERMI'. 2024-06-26T06:07:23.2248611Z Program terminated with signal SIGABRT, Aborted. 2024-06-26T06:07:23.2249232Z #0 __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:50 2024-06-26T06:07:23.2249908Z [Current thread is 1 (Thread 0x7fa944fbb280 (LWP 1215))] 2024-06-26T06:07:23.2281713Z To enable execution of this file add 2024-06-26T06:07:23.2282448Z add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit 2024-06-26T06:07:23.2283103Z line to your configuration file "/var/lib/jenkins/.gdbinit". 2024-06-26T06:07:23.2283703Z To completely disable this security protection add 2024-06-26T06:07:23.2284226Z set auto-load safe-path / 2024-06-26T06:07:23.2284680Z line to your configuration file "/var/lib/jenkins/.gdbinit". 2024-06-26T06:07:23.2285326Z For more information about this security protection see the 2024-06-26T06:07:23.2286125Z "Auto-loading safe path" section in the GDB manual. E.g., run from the shell: 2024-06-26T06:07:23.2286788Z info "(gdb)Auto-loading safe path" 2024-06-26T06:07:23.2287345Z #0 __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:50 2024-06-26T06:07:23.2287980Z #1 0x00007fa944fde859 in __GI_abort () at abort.c:79 2024-06-26T06:07:23.2458056Z #2 0x00007fa93a632090 in __gnu_cxx::__verbose_terminate_handler () 2024-06-26T06:07:23.2458806Z at ../../../../libstdc++-v3/libsupc++/vterminate.cc:95 2024-06-26T06:07:23.2464834Z #3 0x00007fa93af3f7ac in c10::detail::terminate_handler() () 2024-06-26T06:07:23.2465812Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:07:23.2490617Z #4 0x00007fa93a63057e in __cxxabiv1::__terminate (handler=) 2024-06-26T06:07:23.2491372Z at ../../../../libstdc++-v3/libsupc++/eh_terminate.cc:48 2024-06-26T06:07:23.2491989Z #5 0x00007fa93a6305d0 in std::terminate () 2024-06-26T06:07:23.2492539Z at ../../../../libstdc++-v3/libsupc++/eh_terminate.cc:58 2024-06-26T06:07:23.2496146Z #6 0x00007fa93af2cfb6 in THPModule_abort(_object*, _object*) () 2024-06-26T06:07:23.2497062Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:07:23.2501344Z #7 0x000000000051e164 in cfunction_vectorcall_NOARGS (func=0x7fa94479c310, 2024-06-26T06:07:23.2502278Z args=, nargsf=, kwnames=) 2024-06-26T06:07:23.2503153Z at /usr/local/src/conda/python-3.12.4/Include/cpython/methodobject.h:50 2024-06-26T06:07:23.2515436Z #8 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:07:23.2516466Z kwnames@entry=, nargsf=9223372036854775808, 2024-06-26T06:07:23.2517809Z nargsf@entry=, args=0x7fa945331078, 2024-06-26T06:07:23.2519128Z args@entry=, callable=0x7fa94479c310, 2024-06-26T06:07:23.2520677Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:23.2522030Z tstate@entry=) 2024-06-26T06:07:23.2523068Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:07:23.2523980Z #9 PyObject_Vectorcall (callable=0x7fa94479c310, args=0x7fa945331078, 2024-06-26T06:07:23.2524650Z nargsf=9223372036854775808, kwnames=0x0) 2024-06-26T06:07:23.2525226Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:07:23.2525927Z #10 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:23.2526636Z frame=0x7fa945331020, throwflag=) 2024-06-26T06:07:23.2527133Z at Python/bytecodes.c:2714 2024-06-26T06:07:23.2527805Z #11 0x00000000005e4dfe in PyEval_EvalCode (co=, 2024-06-26T06:07:23.2528395Z globals=0x7fa944f81bc0, locals=) 2024-06-26T06:07:23.2529012Z at /usr/local/src/conda/python-3.12.4/Python/ceval.c:578 2024-06-26T06:07:23.2531925Z #12 0x000000000060a5d7 in run_eval_code_obj ( 2024-06-26T06:07:23.2532571Z tstate=0x9c11f8 <_PyRuntime+459704>, co=0x7fa944f55460, 2024-06-26T06:07:23.2533120Z globals=0x7fa944f81bc0, locals=0x7fa944f81bc0) 2024-06-26T06:07:23.2533770Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1722 2024-06-26T06:07:23.2555330Z #13 0x0000000000605ca7 in run_mod (mod=, 2024-06-26T06:07:23.2555991Z filename=0x956ca0 <_PyRuntime+24160>, globals=0x7fa944f81bc0, 2024-06-26T06:07:23.2556662Z locals=0x7fa944f81bc0, flags=0x7ffe216f1510, arena=0x7fa944ea3c70) 2024-06-26T06:07:23.2557440Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1743 2024-06-26T06:07:23.2561325Z #14 0x00000000005f58cf in PyRun_StringFlags (str=, start=257, 2024-06-26T06:07:23.2562110Z globals=0x7fa944f81bc0, locals=0x7fa944f81bc0, flags=0x7ffe216f1510) 2024-06-26T06:07:23.2562882Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1618 2024-06-26T06:07:23.2575495Z #15 0x00000000005f57fa in PyRun_SimpleStringFlags ( 2024-06-26T06:07:23.2577430Z command=0x7fa944f623f0 "import os; os.environ[\"TORCH_CUSTOM_TERMINATE\"] ='1';", ' ' , "import torch; import torch._C; torch._C._abort()\n", flags=0x7ffe216f1510) 2024-06-26T06:07:23.2579174Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:480 2024-06-26T06:07:23.2579764Z #16 0x0000000000615c9f in pymain_run_command ( 2024-06-26T06:07:23.2580931Z command=) at /usr/local/src/conda/python-3.12.4/Modules/main.c:255 2024-06-26T06:07:23.2582057Z #17 pymain_run_python (exitcode=0x7ffe216f14e4) 2024-06-26T06:07:23.2582658Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:620 2024-06-26T06:07:23.2583407Z #18 Py_RunMain () at /usr/local/src/conda/python-3.12.4/Modules/main.c:709 2024-06-26T06:07:23.2591776Z #19 0x00000000005cc259 in Py_BytesMain (argc=, 2024-06-26T06:07:23.2592423Z argv=) 2024-06-26T06:07:23.2593014Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:763 2024-06-26T06:07:23.2595844Z #20 0x00007fa944fe0083 in __libc_start_main (main=0x5cc190
, argc=3, 2024-06-26T06:07:23.2596849Z argv=0x7ffe216f1748, init=, fini=, 2024-06-26T06:07:23.2597866Z rtld_fini=, stack_end=0x7ffe216f1738) 2024-06-26T06:07:23.2598670Z at ../csu/libc-start.c:308 2024-06-26T06:07:23.2599033Z #21 0x00000000005cc089 in _start () 2024-06-26T06:07:23.2599592Z at /usr/local/src/conda/python-3.12.4/Parser/parser.c:41555 2024-06-26T06:07:23.4096800Z GNU gdb (Ubuntu 9.2-0ubuntu1~20.04.2) 9.2 2024-06-26T06:07:23.4097715Z Copyright (C) 2020 Free Software Foundation, Inc. 2024-06-26T06:07:23.4098845Z License GPLv3+: GNU GPL version 3 or later 2024-06-26T06:07:23.4100117Z This is free software: you are free to change and redistribute it. 2024-06-26T06:07:23.4101187Z There is NO WARRANTY, to the extent permitted by law. 2024-06-26T06:07:23.4102114Z Type "show copying" and "show warranty" for details. 2024-06-26T06:07:23.4103108Z This GDB was configured as "x86_64-linux-gnu". 2024-06-26T06:07:23.4103926Z Type "show configuration" for configuration details. 2024-06-26T06:07:23.4104769Z For bug reporting instructions, please see: 2024-06-26T06:07:23.4105635Z . 2024-06-26T06:07:23.4106667Z Find the GDB manual and other documentation resources online at: 2024-06-26T06:07:23.4107832Z . 2024-06-26T06:07:23.4108496Z 2024-06-26T06:07:23.4108726Z For help, type "help". 2024-06-26T06:07:23.4109554Z Type "apropos word" to search for commands related to "word"... 2024-06-26T06:07:23.5286556Z Reading symbols from python... 2024-06-26T06:07:24.6485655Z 2024-06-26T06:07:24.6486248Z warning: core file may not match specified executable file. 2024-06-26T06:07:24.6676677Z [New LWP 2944] 2024-06-26T06:07:24.6677124Z [New LWP 2947] 2024-06-26T06:07:24.6677436Z [New LWP 2948] 2024-06-26T06:07:24.6677704Z [New LWP 2949] 2024-06-26T06:07:24.6677963Z [New LWP 2946] 2024-06-26T06:07:24.6678227Z [New LWP 2950] 2024-06-26T06:07:24.6678493Z [New LWP 2951] 2024-06-26T06:07:24.6678742Z [New LWP 2952] 2024-06-26T06:07:24.6704230Z [Thread debugging using libthread_db enabled] 2024-06-26T06:07:24.6705068Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1". 2024-06-26T06:07:28.4082286Z 78 ../sysdeps/unix/syscall-template.S: No such file or directory. 2024-06-26T06:07:28.4083661Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load". 2024-06-26T06:07:28.4085202Z Core was generated by `/opt/conda/envs/py_3.12/bin/python -bb -c from multiprocessing.spawn import spa'. 2024-06-26T06:07:28.4086046Z Program terminated with signal SIGABRT, Aborted. 2024-06-26T06:07:28.4086780Z #0 0x00007fb2599023db in kill () at ../sysdeps/unix/syscall-template.S:78 2024-06-26T06:07:28.4087742Z [Current thread is 1 (Thread 0x7fb2598be280 (LWP 2944))] 2024-06-26T06:07:28.4118042Z To enable execution of this file add 2024-06-26T06:07:28.4118784Z add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit 2024-06-26T06:07:28.4119570Z line to your configuration file "/var/lib/jenkins/.gdbinit". 2024-06-26T06:07:28.4120295Z To completely disable this security protection add 2024-06-26T06:07:28.4121081Z set auto-load safe-path / 2024-06-26T06:07:28.4121898Z line to your configuration file "/var/lib/jenkins/.gdbinit". 2024-06-26T06:07:28.4122865Z For more information about this security protection see the 2024-06-26T06:07:28.4124071Z "Auto-loading safe path" section in the GDB manual. E.g., run from the shell: 2024-06-26T06:07:28.4124757Z info "(gdb)Auto-loading safe path" 2024-06-26T06:07:28.4125406Z #0 0x00007fb2599023db in kill () at ../sysdeps/unix/syscall-template.S:78 2024-06-26T06:07:28.4126003Z #1 0x00000000004f712d in os_kill_impl ( 2024-06-26T06:07:28.4126736Z module=, 2024-06-26T06:07:28.4127770Z signal=, 2024-06-26T06:07:28.4129206Z pid=) 2024-06-26T06:07:28.4130381Z at /usr/local/src/conda/python-3.12.4/Modules/posixmodule.c:8949 2024-06-26T06:07:28.4131048Z #2 os_kill (module=, args=, 2024-06-26T06:07:28.4131556Z nargs=) 2024-06-26T06:07:28.4132173Z at /usr/local/src/conda/python-3.12.4/Modules/clinic/posixmodule.c.h:5035 2024-06-26T06:07:28.4132892Z #3 0x000000000054b25c in cfunction_vectorcall_FASTCALL ( 2024-06-26T06:07:28.4133552Z func=, args=0x7fb259c34430, nargsf=9223372036854775810, 2024-06-26T06:07:28.4134109Z kwnames=) 2024-06-26T06:07:28.4134837Z at /usr/local/src/conda/python-3.12.4/Objects/methodobject.c:424 2024-06-26T06:07:28.4136004Z #4 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:07:28.4137033Z kwnames@entry=, nargsf=9223372036854775810, 2024-06-26T06:07:28.4138372Z nargsf@entry=, args=0x7fb259c34430, 2024-06-26T06:07:28.4139829Z args@entry=, callable=0x7fb25984e930, 2024-06-26T06:07:28.4141690Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:28.4143087Z tstate@entry=) 2024-06-26T06:07:28.4144676Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:07:28.4145591Z #5 PyObject_Vectorcall (callable=0x7fb25984e930, args=0x7fb259c34430, 2024-06-26T06:07:28.4146188Z nargsf=9223372036854775810, kwnames=0x0) 2024-06-26T06:07:28.4146888Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:07:28.4148005Z #6 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:28.4148673Z frame=0x7fb259c343d0, throwflag=) 2024-06-26T06:07:28.4149131Z at Python/bytecodes.c:2714 2024-06-26T06:07:28.4149626Z #7 0x00000000005e4dfe in PyEval_EvalCode (co=, 2024-06-26T06:07:28.4150211Z globals=0x7fb259885cc0, locals=) 2024-06-26T06:07:28.4150822Z at /usr/local/src/conda/python-3.12.4/Python/ceval.c:578 2024-06-26T06:07:28.4152449Z #8 0x000000000060a5d7 in run_eval_code_obj ( 2024-06-26T06:07:28.4153060Z tstate=0x9c11f8 <_PyRuntime+459704>, co=0x7fb2597d12f0, 2024-06-26T06:07:28.4153615Z globals=0x7fb259885cc0, locals=0x7fb259885cc0) 2024-06-26T06:07:28.4154415Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1722 2024-06-26T06:07:28.4176132Z #9 0x0000000000605ca7 in run_mod (mod=, 2024-06-26T06:07:28.4176755Z filename=0x956ca0 <_PyRuntime+24160>, globals=0x7fb259885cc0, 2024-06-26T06:07:28.4177426Z locals=0x7fb259885cc0, flags=0x7ffd73923d20, arena=0x7fb2597a7cb0) 2024-06-26T06:07:28.4178201Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1743 2024-06-26T06:07:28.4181816Z #10 0x00000000005f58cf in PyRun_StringFlags (str=, start=257, 2024-06-26T06:07:28.4182658Z globals=0x7fb259885cc0, locals=0x7fb259885cc0, flags=0x7ffd73923d20) 2024-06-26T06:07:28.4183450Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1618 2024-06-26T06:07:28.4187282Z #11 0x00000000005f57fa in PyRun_SimpleStringFlags ( 2024-06-26T06:07:28.4189019Z command=0x7fb25983dfd0 "from multiprocessing.spawn import spawn_main; spawn_main(tracker_fd=7, pipe_handle=9)\n", flags=0x7ffd73923d20) 2024-06-26T06:07:28.4190482Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:480 2024-06-26T06:07:28.4191442Z #12 0x0000000000615c9f in pymain_run_command ( 2024-06-26T06:07:28.4192609Z command=) at /usr/local/src/conda/python-3.12.4/Modules/main.c:255 2024-06-26T06:07:28.4193674Z #13 pymain_run_python (exitcode=0x7ffd73923cf4) 2024-06-26T06:07:28.4194273Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:620 2024-06-26T06:07:28.4195150Z #14 Py_RunMain () at /usr/local/src/conda/python-3.12.4/Modules/main.c:709 2024-06-26T06:07:28.4311279Z #15 0x00000000005cc259 in Py_BytesMain (argc=, 2024-06-26T06:07:28.4311939Z argv=) 2024-06-26T06:07:28.4312521Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:763 2024-06-26T06:07:28.4315996Z #16 0x00007fb2598e3083 in __libc_start_main (main=0x5cc190
, argc=5, 2024-06-26T06:07:28.4317216Z argv=0x7ffd73923f58, init=, fini=, 2024-06-26T06:07:28.4317869Z rtld_fini=, stack_end=0x7ffd73923f48) 2024-06-26T06:07:28.4318444Z at ../csu/libc-start.c:308 2024-06-26T06:07:28.4318797Z #17 0x00000000005cc089 in _start () 2024-06-26T06:07:28.4319378Z at /usr/local/src/conda/python-3.12.4/Parser/parser.c:41555 2024-06-26T06:07:28.5647045Z GNU gdb (Ubuntu 9.2-0ubuntu1~20.04.2) 9.2 2024-06-26T06:07:28.5648078Z Copyright (C) 2020 Free Software Foundation, Inc. 2024-06-26T06:07:28.5649152Z License GPLv3+: GNU GPL version 3 or later 2024-06-26T06:07:28.5649969Z This is free software: you are free to change and redistribute it. 2024-06-26T06:07:28.5650615Z There is NO WARRANTY, to the extent permitted by law. 2024-06-26T06:07:28.5651348Z Type "show copying" and "show warranty" for details. 2024-06-26T06:07:28.5651938Z This GDB was configured as "x86_64-linux-gnu". 2024-06-26T06:07:28.5652468Z Type "show configuration" for configuration details. 2024-06-26T06:07:28.5652991Z For bug reporting instructions, please see: 2024-06-26T06:07:28.5653462Z . 2024-06-26T06:07:28.5654030Z Find the GDB manual and other documentation resources online at: 2024-06-26T06:07:28.5654652Z . 2024-06-26T06:07:28.5655022Z 2024-06-26T06:07:28.5655132Z For help, type "help". 2024-06-26T06:07:28.5655591Z Type "apropos word" to search for commands related to "word"... 2024-06-26T06:07:28.6818150Z Reading symbols from python... 2024-06-26T06:07:29.7436440Z 2024-06-26T06:07:29.7437357Z warning: core file may not match specified executable file. 2024-06-26T06:07:29.7635107Z [New LWP 2978] 2024-06-26T06:07:29.7661037Z [Thread debugging using libthread_db enabled] 2024-06-26T06:07:29.7662144Z Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1". 2024-06-26T06:07:33.5934264Z 50 ../sysdeps/unix/sysv/linux/raise.c: No such file or directory. 2024-06-26T06:07:33.5936014Z warning: File "/var/lib/jenkins/workspace/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load". 2024-06-26T06:07:33.5937663Z Core was generated by `/opt/conda/envs/py_3.12/bin/python -bb test_multiprocessing_spawn.py --shard-id'. 2024-06-26T06:07:33.5938516Z Program terminated with signal SIGABRT, Aborted. 2024-06-26T06:07:33.5939150Z #0 raise (sig=) at ../sysdeps/unix/sysv/linux/raise.c:50 2024-06-26T06:07:33.5972113Z To enable execution of this file add 2024-06-26T06:07:33.5973359Z add-auto-load-safe-path /var/lib/jenkins/workspace/.gdbinit 2024-06-26T06:07:33.5974779Z line to your configuration file "/var/lib/jenkins/.gdbinit". 2024-06-26T06:07:33.5975392Z To completely disable this security protection add 2024-06-26T06:07:33.5975909Z set auto-load safe-path / 2024-06-26T06:07:33.5976378Z line to your configuration file "/var/lib/jenkins/.gdbinit". 2024-06-26T06:07:33.5977020Z For more information about this security protection see the 2024-06-26T06:07:33.5977815Z "Auto-loading safe path" section in the GDB manual. E.g., run from the shell: 2024-06-26T06:07:33.5978489Z info "(gdb)Auto-loading safe path" 2024-06-26T06:07:33.5979048Z #0 raise (sig=) at ../sysdeps/unix/sysv/linux/raise.c:50 2024-06-26T06:07:33.5980671Z #1 2024-06-26T06:07:33.5981643Z #2 0x00007f5a362593db in kill () at ../sysdeps/unix/syscall-template.S:78 2024-06-26T06:07:33.5983775Z #3 0x00000000004f712d in os_kill_impl ( 2024-06-26T06:07:33.5984610Z module=, 2024-06-26T06:07:33.5985892Z signal=, 2024-06-26T06:07:33.5986913Z pid=) 2024-06-26T06:07:33.5987885Z at /usr/local/src/conda/python-3.12.4/Modules/posixmodule.c:8949 2024-06-26T06:07:33.5988547Z #4 os_kill (module=, args=, 2024-06-26T06:07:33.5989045Z nargs=) 2024-06-26T06:07:33.5989685Z at /usr/local/src/conda/python-3.12.4/Modules/clinic/posixmodule.c.h:5035 2024-06-26T06:07:33.5991218Z #5 0x000000000054b25c in cfunction_vectorcall_FASTCALL ( 2024-06-26T06:07:33.5991899Z func=, args=0x7f5a3658d100, nargsf=9223372036854775810, 2024-06-26T06:07:33.5992481Z kwnames=) 2024-06-26T06:07:33.5993067Z at /usr/local/src/conda/python-3.12.4/Objects/methodobject.c:424 2024-06-26T06:07:33.5998389Z #6 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:07:33.5999478Z kwnames@entry=, nargsf=9223372036854775810, 2024-06-26T06:07:33.6001065Z nargsf@entry=, args=0x7f5a3658d100, 2024-06-26T06:07:33.6002393Z args@entry=, callable=0x7f5a361aa9d0, 2024-06-26T06:07:33.6003914Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6005211Z tstate@entry=) 2024-06-26T06:07:33.6006239Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:07:33.6007003Z #7 PyObject_Vectorcall (callable=0x7f5a361aa9d0, args=0x7f5a3658d100, 2024-06-26T06:07:33.6007596Z nargsf=9223372036854775810, kwnames=0x0) 2024-06-26T06:07:33.6008181Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:07:33.6135862Z #8 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6136769Z frame=0x7f5a3658d0a0, throwflag=) 2024-06-26T06:07:33.6137569Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6138234Z #9 0x00007f5a2c27484c in custom_eval_frame_shim () 2024-06-26T06:07:33.6139055Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:07:33.6140252Z #10 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:07:33.6141350Z kwargs=, 2024-06-26T06:07:33.6142365Z args=, 2024-06-26T06:07:33.6143816Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:07:33.6149722Z #11 _PyEval_EvalFrameDefault (tstate=, frame=0x7f5a3658cff0, 2024-06-26T06:07:33.6150570Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:07:33.6151547Z #12 0x00007f5a2c27484c in custom_eval_frame_shim () 2024-06-26T06:07:33.6152392Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:07:33.6153624Z #13 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:07:33.6154928Z kwargs=, 2024-06-26T06:07:33.6171374Z args=, 2024-06-26T06:07:33.6173529Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:07:33.6174764Z #14 _PyEval_EvalFrameDefault (tstate=, frame=0x7f5a3658cf78, 2024-06-26T06:07:33.6175535Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:07:33.6176363Z #15 0x00007f5a2c27484c in custom_eval_frame_shim () 2024-06-26T06:07:33.6177318Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:07:33.6178172Z #16 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:07:33.6179199Z kwnames@entry=, nargsf=9223372036854775809, 2024-06-26T06:07:33.6180534Z nargsf@entry=, args=0x7f5a3658cf50, 2024-06-26T06:07:33.6181851Z args@entry=, callable=0x7f59d64e3560, 2024-06-26T06:07:33.6183268Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6184687Z tstate@entry=) 2024-06-26T06:07:33.6186264Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:07:33.6187275Z #17 PyObject_Vectorcall (callable=0x7f59d64e3560, args=0x7f5a3658cf50, 2024-06-26T06:07:33.6187870Z nargsf=9223372036854775809, kwnames=0x0) 2024-06-26T06:07:33.6188444Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:07:33.6189142Z #18 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6189805Z frame=0x7f5a3658cec0, throwflag=) 2024-06-26T06:07:33.6190266Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6190692Z #19 0x00007f5a2c27484c in custom_eval_frame_shim () 2024-06-26T06:07:33.6191511Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:07:33.6194126Z #20 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x7f59cf805810, 2024-06-26T06:07:33.6195623Z kwnames@entry=, nargsf=9223372036854775809, 2024-06-26T06:07:33.6197100Z nargsf@entry=, args=0x7f5a3658ce98, 2024-06-26T06:07:33.6198512Z args@entry=, callable=0x7f59d64ec040, 2024-06-26T06:07:33.6199942Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6201342Z tstate@entry=) 2024-06-26T06:07:33.6202518Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:07:33.6203372Z #21 PyObject_Vectorcall (callable=0x7f59d64ec040, args=0x7f5a3658ce98, 2024-06-26T06:07:33.6203992Z nargsf=9223372036854775809, kwnames=0x7f59cf805810) 2024-06-26T06:07:33.6204619Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:07:33.6205317Z #22 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6205963Z frame=0x7f5a3658ce10, throwflag=) 2024-06-26T06:07:33.6206479Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6207243Z #23 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:07:33.6208378Z kwnames@entry=, nargsf=9223372036854775810, 2024-06-26T06:07:33.6209712Z nargsf@entry=, args=0x7f5a3658ce00, 2024-06-26T06:07:33.6211026Z args@entry=, callable=0x7f59cf8379c0, 2024-06-26T06:07:33.6212724Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6214001Z tstate@entry=) 2024-06-26T06:07:33.6215239Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:07:33.6216465Z #24 PyObject_Vectorcall (callable=0x7f59cf8379c0, args=0x7f5a3658ce00, 2024-06-26T06:07:33.6217057Z nargsf=9223372036854775810, kwnames=0x0) 2024-06-26T06:07:33.6217636Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:07:33.6218330Z #25 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6218988Z frame=0x7f5a3658cda0, throwflag=) 2024-06-26T06:07:33.6219457Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6220539Z #26 0x000000000042cb39 in _PyEval_Vector (kwnames=0x0, 2024-06-26T06:07:33.6221442Z argcount=, args=0x7ffe1d85ba60, locals=0x0, 2024-06-26T06:07:33.6222147Z func=0x7f59cf837560, tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:07:33.6222941Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_ceval.h:91 2024-06-26T06:07:33.6225014Z #27 _PyFunction_Vectorcall (kwnames=0x0, nargsf=, 2024-06-26T06:07:33.6225796Z stack=0x7ffe1d85ba60, func=0x7f59cf837560) 2024-06-26T06:07:33.6226414Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:419 2024-06-26T06:07:33.6227598Z #28 _PyObject_FastCallDictTstate (tstate=, 2024-06-26T06:07:33.6228851Z callable=, args=0x7ffe1d85ba60, 2024-06-26T06:07:33.6229991Z nargsf=, 2024-06-26T06:07:33.6231471Z kwargs=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:133 2024-06-26T06:07:33.6233161Z #29 0x0000000000558334 in _PyObject_Call_Prepend (kwargs=0x0, 2024-06-26T06:07:33.6233834Z args=0x7f59cf46b6d0, obj=0x7f59cf373f20, callable=0x7f59cf837560, 2024-06-26T06:07:33.6234499Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:07:33.6235274Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:508 2024-06-26T06:07:33.6237241Z #30 slot_tp_init (self=0x7f59cf373f20, args=0x7f59cf46b6d0, kwds=0x0) 2024-06-26T06:07:33.6238013Z at /usr/local/src/conda/python-3.12.4/Objects/typeobject.c:9020 2024-06-26T06:07:33.6240286Z #31 0x000000000051b2db in type_call (kwds=0x0, args=0x7f59cf46b6d0, 2024-06-26T06:07:33.6241149Z type=) 2024-06-26T06:07:33.6241652Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:250 2024-06-26T06:07:33.6244523Z #32 _PyObject_MakeTpCall (tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6245476Z callable=0x7d2a3f0, args=, nargs=, 2024-06-26T06:07:33.6246293Z keywords=0x0) at /usr/local/src/conda/python-3.12.4/Objects/call.c:240 2024-06-26T06:07:33.6248229Z #33 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6249095Z frame=0x7f5a3658cd30, throwflag=) 2024-06-26T06:07:33.6249578Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6253195Z #34 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:07:33.6254514Z kwnames@entry=, nargsf=9223372036854775809, 2024-06-26T06:07:33.6255852Z nargsf@entry=, args=0x7f5a3658cd28, 2024-06-26T06:07:33.6257449Z args@entry=, callable=0x7f59d64eec00, 2024-06-26T06:07:33.6258918Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6260365Z tstate@entry=) 2024-06-26T06:07:33.6261629Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:07:33.6262419Z #35 PyObject_Vectorcall (callable=0x7f59d64eec00, args=0x7f5a3658cd28, 2024-06-26T06:07:33.6262996Z nargsf=9223372036854775809, kwnames=0x0) 2024-06-26T06:07:33.6263579Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:07:33.6264273Z #36 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6264922Z frame=0x7f5a3658ccc8, throwflag=) 2024-06-26T06:07:33.6265661Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6266345Z #37 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:07:33.6267689Z kwnames@entry=, nargsf=9223372036854775809, 2024-06-26T06:07:33.6269127Z nargsf@entry=, args=0x7f5a3658cc98, 2024-06-26T06:07:33.6270734Z args@entry=, callable=0x7f59d64e3600, 2024-06-26T06:07:33.6272213Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6273625Z tstate@entry=) 2024-06-26T06:07:33.6275032Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:07:33.6275829Z #38 PyObject_Vectorcall (callable=0x7f59d64e3600, args=0x7f5a3658cc98, 2024-06-26T06:07:33.6276418Z nargsf=9223372036854775809, kwnames=0x0) 2024-06-26T06:07:33.6276990Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:07:33.6277690Z #39 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6278349Z frame=0x7f5a3658cbd8, throwflag=) 2024-06-26T06:07:33.6278985Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6280168Z #40 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x7f5a36045dc0, 2024-06-26T06:07:33.6281551Z kwnames@entry=, nargsf=9223372036854775809, 2024-06-26T06:07:33.6282898Z nargsf@entry=, args=0x7f5a3658cbc0, 2024-06-26T06:07:33.6284493Z args@entry=, callable=0x7f59d62822a0, 2024-06-26T06:07:33.6286045Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6287345Z tstate@entry=) 2024-06-26T06:07:33.6288368Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:07:33.6289158Z #41 PyObject_Vectorcall (callable=0x7f59d62822a0, args=0x7f5a3658cbc0, 2024-06-26T06:07:33.6289787Z nargsf=9223372036854775809, kwnames=0x7f5a36045dc0) 2024-06-26T06:07:33.6290411Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:07:33.6294285Z #42 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6295175Z frame=0x7f5a3658cb48, throwflag=) 2024-06-26T06:07:33.6295761Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6296201Z #43 0x00007f5a2c27484c in custom_eval_frame_shim () 2024-06-26T06:07:33.6297037Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:07:33.6302549Z #44 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:07:33.6303894Z kwnames@entry=, nargsf=9223372036854775810, 2024-06-26T06:07:33.6305233Z nargsf@entry=, args=0x7f5a3658cb38, 2024-06-26T06:07:33.6306709Z args@entry=, callable=0x7f59cf6b9800, 2024-06-26T06:07:33.6308213Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6309494Z tstate@entry=) 2024-06-26T06:07:33.6310513Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:07:33.6311299Z #45 PyObject_Vectorcall (callable=0x7f59cf6b9800, args=0x7f5a3658cb38, 2024-06-26T06:07:33.6311979Z nargsf=9223372036854775810, kwnames=0x0) 2024-06-26T06:07:33.6312550Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:07:33.6317128Z #46 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6317805Z frame=0x7f5a3658cac8, throwflag=) 2024-06-26T06:07:33.6318389Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6318848Z #47 0x00007f5a2c274a4b in eval_custom_code () 2024-06-26T06:07:33.6319657Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:07:33.6324380Z #48 0x00007f5a2c2746c4 in custom_eval_frame_shim () 2024-06-26T06:07:33.6325221Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:07:33.6328076Z #49 0x000000000045985e in _PyEval_EvalFrame ( 2024-06-26T06:07:33.6329024Z throwflag=, frame=0x7f5a3658ca40, 2024-06-26T06:07:33.6329890Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:07:33.6330506Z at /usr/local/src/conda/python-3.12.4/Python/compile.c:2838 2024-06-26T06:07:33.6332848Z #50 _PyEval_Vector (kwnames=, argcount=, 2024-06-26T06:07:33.6333553Z args=0x7ffe1d85c638, locals=0x0, func=0x7f59d1d7f240, 2024-06-26T06:07:33.6334052Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:07:33.6334651Z at /usr/local/src/conda/python-3.12.4/Python/ceval.c:1683 2024-06-26T06:07:33.6335727Z #51 _PyFunction_Vectorcall (kwnames=, nargsf=, 2024-06-26T06:07:33.6336568Z stack=0x7ffe1d85c638, func=0x7f59d1d7f240) 2024-06-26T06:07:33.6337177Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:419 2024-06-26T06:07:33.6338262Z #52 _PyObject_VectorcallTstate (tstate=, 2024-06-26T06:07:33.6339554Z callable=, args=0x7ffe1d85c638, 2024-06-26T06:07:33.6340820Z nargsf=, kwnames=) 2024-06-26T06:07:33.6342133Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:92 2024-06-26T06:07:33.6343191Z #53 0x0000000000575e80 in method_vectorcall (method=, 2024-06-26T06:07:33.6344169Z args=0x963870 <_PyRuntime+76336>, nargsf=0, kwnames=0x0) 2024-06-26T06:07:33.6344877Z at /usr/local/src/conda/python-3.12.4/Objects/classobject.c:69 2024-06-26T06:07:33.6345608Z #54 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:07:33.6346825Z kwargs=, 2024-06-26T06:07:33.6347829Z args=, 2024-06-26T06:07:33.6349275Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:07:33.6350536Z #55 _PyEval_EvalFrameDefault (tstate=, frame=0x7f5a3658c9a0, 2024-06-26T06:07:33.6351425Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:07:33.6352091Z #56 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:07:33.6353128Z kwnames@entry=, nargsf=9223372036854775809, 2024-06-26T06:07:33.6354461Z nargsf@entry=, args=0x7f5a3658c980, 2024-06-26T06:07:33.6356092Z args@entry=, callable=0x7f59cf836700, 2024-06-26T06:07:33.6357509Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6359033Z tstate@entry=) 2024-06-26T06:07:33.6360497Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:07:33.6361344Z #57 PyObject_Vectorcall (callable=0x7f59cf836700, args=0x7f5a3658c980, 2024-06-26T06:07:33.6361937Z nargsf=9223372036854775809, kwnames=0x0) 2024-06-26T06:07:33.6362519Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:07:33.6363199Z #58 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6363858Z frame=0x7f5a3658c920, throwflag=) 2024-06-26T06:07:33.6364592Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6365466Z #59 0x000000000053ee61 in _PyObject_VectorcallTstate (kwnames=0x0, 2024-06-26T06:07:33.6366508Z kwnames@entry=, nargsf=9223372036854775810, 2024-06-26T06:07:33.6367842Z nargsf@entry=, args=0x7f5a3658c900, 2024-06-26T06:07:33.6369538Z args@entry=, callable=0x7f5a35dcc720, 2024-06-26T06:07:33.6371221Z callable@entry=, tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6372801Z tstate@entry=) 2024-06-26T06:07:33.6374014Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:77 2024-06-26T06:07:33.6375175Z #60 PyObject_Vectorcall (callable=0x7f5a35dcc720, args=0x7f5a3658c900, 2024-06-26T06:07:33.6375887Z nargsf=9223372036854775810, kwnames=0x0) 2024-06-26T06:07:33.6376528Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:325 2024-06-26T06:07:33.6377221Z #61 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6377861Z frame=0x7f5a3658c860, throwflag=) 2024-06-26T06:07:33.6378443Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6378941Z #62 0x000000000045985e in _PyEval_EvalFrame ( 2024-06-26T06:07:33.6379910Z throwflag=, frame=0x7f5a3658c860, 2024-06-26T06:07:33.6380777Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:07:33.6381575Z at /usr/local/src/conda/python-3.12.4/Python/compile.c:2838 2024-06-26T06:07:33.6382366Z #63 _PyEval_Vector (kwnames=, argcount=, 2024-06-26T06:07:33.6383024Z args=0x7f59cf861360, locals=0x0, func=0x7f5a35dcc900, 2024-06-26T06:07:33.6383624Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:07:33.6384369Z at /usr/local/src/conda/python-3.12.4/Python/ceval.c:1683 2024-06-26T06:07:33.6385199Z #64 _PyFunction_Vectorcall (kwnames=, nargsf=, 2024-06-26T06:07:33.6385845Z stack=0x7f59cf861360, func=0x7f5a35dcc900) 2024-06-26T06:07:33.6386440Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:419 2024-06-26T06:07:33.6387026Z #65 _PyObject_VectorcallTstate (tstate=, 2024-06-26T06:07:33.6387952Z callable=, args=0x7f59cf861360, 2024-06-26T06:07:33.6389449Z nargsf=, kwnames=) 2024-06-26T06:07:33.6390654Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:92 2024-06-26T06:07:33.6391309Z #66 0x0000000000575e4d in method_vectorcall ( 2024-06-26T06:07:33.6392060Z method=method@entry=0x7f59cf87a300, args=args@entry=0x7f59cf861368, 2024-06-26T06:07:33.6392789Z nargsf=, kwnames=0x7f59cf3733d0) 2024-06-26T06:07:33.6393525Z at /usr/local/src/conda/python-3.12.4/Objects/classobject.c:61 2024-06-26T06:07:33.6394518Z #67 0x000000000055b492 in _PyVectorcall_Call (kwargs=, 2024-06-26T06:07:33.6395460Z tuple=, callable=0x7f59cf87a300, 2024-06-26T06:07:33.6396183Z func=0x575b60 , tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:07:33.6397081Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:283 2024-06-26T06:07:33.6397689Z #68 _PyObject_Call (tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6398344Z callable=0x7f59cf87a300, args=, kwargs=) 2024-06-26T06:07:33.6399082Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:354 2024-06-26T06:07:33.6399618Z #69 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:07:33.6400353Z kwargs=, 2024-06-26T06:07:33.6401445Z args=, 2024-06-26T06:07:33.6402888Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:07:33.6406124Z #70 _PyEval_EvalFrameDefault (tstate=, frame=0x7f5a3658c7c8, 2024-06-26T06:07:33.6406992Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:07:33.6407679Z #71 0x00007f5a2c274a4b in eval_custom_code () 2024-06-26T06:07:33.6408530Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:07:33.6413855Z #72 0x00007f5a2c2746c4 in custom_eval_frame_shim () 2024-06-26T06:07:33.6414738Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so 2024-06-26T06:07:33.6418822Z #73 0x000000000055b492 in _PyVectorcall_Call (kwargs=, 2024-06-26T06:07:33.6419836Z tuple=, callable=0x7f59cfb2eb60, 2024-06-26T06:07:33.6420478Z func=0x553210 <_PyFunction_Vectorcall>, 2024-06-26T06:07:33.6421065Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:07:33.6422004Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:283 2024-06-26T06:07:33.6422636Z #74 _PyObject_Call (tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6423321Z callable=0x7f59cfb2eb60, args=, kwargs=) 2024-06-26T06:07:33.6424068Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:354 2024-06-26T06:07:33.6424607Z #75 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:07:33.6425490Z kwargs=, 2024-06-26T06:07:33.6426630Z args=, 2024-06-26T06:07:33.6428442Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:07:33.6429653Z #76 _PyEval_EvalFrameDefault (tstate=, frame=0x7f5a3658c670, 2024-06-26T06:07:33.6430352Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:07:33.6430882Z #77 0x000000000057633d in _PyEval_EvalFrame ( 2024-06-26T06:07:33.6431749Z throwflag=, frame=0x7f5a3658c488, 2024-06-26T06:07:33.6432864Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:07:33.6433597Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_ceval.h:85 2024-06-26T06:07:33.6434393Z #78 _PyEval_Vector (kwnames=, argcount=, 2024-06-26T06:07:33.6435257Z args=0x7f59d2646eb0, locals=0x0, func=0x7f59d1d75c60, 2024-06-26T06:07:33.6435970Z tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:07:33.6436560Z at /usr/local/src/conda/python-3.12.4/Python/ceval.c:1683 2024-06-26T06:07:33.6437262Z #79 _PyFunction_Vectorcall (kwnames=, nargsf=, 2024-06-26T06:07:33.6437902Z stack=0x7f59d2646eb0, func=0x7f59d1d75c60) 2024-06-26T06:07:33.6438601Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:419 2024-06-26T06:07:33.6439723Z #80 _PyObject_VectorcallTstate (tstate=, 2024-06-26T06:07:33.6440570Z callable=0x7f59d1d75c60, args=0x7f59d2646eb0, nargsf=, 2024-06-26T06:07:33.6441215Z kwnames=) 2024-06-26T06:07:33.6441882Z at /usr/local/src/conda/python-3.12.4/Include/internal/pycore_call.h:92 2024-06-26T06:07:33.6445084Z #81 0x0000000000575e4d in method_vectorcall ( 2024-06-26T06:07:33.6446025Z method=method@entry=0x7f59cf879900, args=args@entry=0x7f59d2646eb8, 2024-06-26T06:07:33.6446657Z nargsf=, kwnames=0x7f59cf3713f0) 2024-06-26T06:07:33.6447535Z at /usr/local/src/conda/python-3.12.4/Objects/classobject.c:61 2024-06-26T06:07:33.6448759Z #82 0x000000000055b492 in _PyVectorcall_Call (kwargs=, 2024-06-26T06:07:33.6449580Z tuple=, callable=0x7f59cf879900, 2024-06-26T06:07:33.6450704Z func=0x575b60 , tstate=0x9c11f8 <_PyRuntime+459704>) 2024-06-26T06:07:33.6451837Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:283 2024-06-26T06:07:33.6452602Z #83 _PyObject_Call (tstate=0x9c11f8 <_PyRuntime+459704>, 2024-06-26T06:07:33.6453416Z callable=0x7f59cf879900, args=, kwargs=) 2024-06-26T06:07:33.6454280Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:354 2024-06-26T06:07:33.6455233Z #84 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:07:33.6456023Z kwargs=, 2024-06-26T06:07:33.6457038Z args=, 2024-06-26T06:07:33.6458496Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:07:33.6460287Z #85 _PyEval_EvalFrameDefault (tstate=, frame=0x7f5a3658c400, 2024-06-26T06:07:33.6460985Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:07:33.6461575Z #86 0x000000000051df35 in _PyObject_FastCallDictTstate ( 2024-06-26T06:07:33.6462188Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f5a35dccae0, 2024-06-26T06:07:33.6462882Z args=0x7ffe1d85d5f0, nargsf=, kwargs=) 2024-06-26T06:07:33.6463622Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:144 2024-06-26T06:07:33.6464664Z #87 0x0000000000558836 in _PyObject_Call_Prepend ( 2024-06-26T06:07:33.6465512Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f5a35dccae0, 2024-06-26T06:07:33.6466168Z obj=0x7f59cf4691c0, args=, kwargs=0x7f59cf6995c0) 2024-06-26T06:07:33.6466890Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:508 2024-06-26T06:07:33.6468801Z #88 0x000000000062f5e6 in slot_tp_call (self=0x7f59cf4691c0, 2024-06-26T06:07:33.6469644Z args=0x963858 <_PyRuntime+76312>, kwds=0x7f59cf6995c0) 2024-06-26T06:07:33.6470354Z at /usr/local/src/conda/python-3.12.4/Objects/typeobject.c:8776 2024-06-26T06:07:33.6473296Z #89 0x000000000051b30b in _PyObject_MakeTpCall ( 2024-06-26T06:07:33.6474199Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f59cf4691c0, 2024-06-26T06:07:33.6475057Z args=, nargs=, keywords=0x7f59d1dbe5f0) 2024-06-26T06:07:33.6475817Z at /usr/local/src/conda/python-3.12.4/Include/object.h:704 2024-06-26T06:07:33.6477125Z #90 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6477781Z frame=0x7f5a3658c370, throwflag=) 2024-06-26T06:07:33.6478253Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6481702Z #91 0x000000000051df35 in _PyObject_FastCallDictTstate ( 2024-06-26T06:07:33.6482349Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f59d425a980, 2024-06-26T06:07:33.6483047Z args=0x7ffe1d85d950, nargsf=, kwargs=) 2024-06-26T06:07:33.6483916Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:144 2024-06-26T06:07:33.6486988Z #92 0x0000000000558836 in _PyObject_Call_Prepend ( 2024-06-26T06:07:33.6487601Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f59d425a980, 2024-06-26T06:07:33.6488279Z obj=0x7f59d1f47600, args=, kwargs=0x7f59cf68d3c0) 2024-06-26T06:07:33.6488993Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:508 2024-06-26T06:07:33.6491448Z #93 0x000000000062f5e6 in slot_tp_call (self=0x7f59d1f47600, 2024-06-26T06:07:33.6492435Z args=0x963858 <_PyRuntime+76312>, kwds=0x7f59cf68d3c0) 2024-06-26T06:07:33.6493147Z at /usr/local/src/conda/python-3.12.4/Objects/typeobject.c:8776 2024-06-26T06:07:33.6495431Z #94 0x000000000055b425 in _PyObject_Call ( 2024-06-26T06:07:33.6496254Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f59d1f47600, 2024-06-26T06:07:33.6497027Z args=0x963858 <_PyRuntime+76312>, kwargs=) 2024-06-26T06:07:33.6497708Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:367 2024-06-26T06:07:33.6498239Z #95 0x000000000052b0a2 in PyCFunction_Call ( 2024-06-26T06:07:33.6499232Z kwargs=, 2024-06-26T06:07:33.6500391Z args=, 2024-06-26T06:07:33.6501837Z callable=) at /usr/local/src/conda/python-3.12.4/Objects/call.c:387 2024-06-26T06:07:33.6503146Z #96 _PyEval_EvalFrameDefault (tstate=, frame=0x7f5a3658c008, 2024-06-26T06:07:33.6504056Z throwflag=) at Python/bytecodes.c:3262 2024-06-26T06:07:33.6504659Z #97 0x000000000051df35 in _PyObject_FastCallDictTstate ( 2024-06-26T06:07:33.6505332Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f59d425a980, 2024-06-26T06:07:33.6506027Z args=0x7ffe1d85dcb0, nargsf=, kwargs=) 2024-06-26T06:07:33.6506771Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:144 2024-06-26T06:07:33.6509248Z #98 0x0000000000558836 in _PyObject_Call_Prepend ( 2024-06-26T06:07:33.6509879Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f59d425a980, 2024-06-26T06:07:33.6510559Z obj=0x7f59d1f47790, args=, kwargs=0x7f59d17ef640) 2024-06-26T06:07:33.6511269Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:508 2024-06-26T06:07:33.6513229Z #99 0x000000000062f5e6 in slot_tp_call (self=0x7f59d1f47790, 2024-06-26T06:07:33.6513870Z args=0x963858 <_PyRuntime+76312>, kwds=0x7f59d17ef640) 2024-06-26T06:07:33.6514583Z at /usr/local/src/conda/python-3.12.4/Objects/typeobject.c:8776 2024-06-26T06:07:33.6518316Z #100 0x000000000051b30b in _PyObject_MakeTpCall ( 2024-06-26T06:07:33.6519168Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f59d1f47790, 2024-06-26T06:07:33.6519868Z args=, nargs=, keywords=0x7f59d3ec2e00) 2024-06-26T06:07:33.6520632Z at /usr/local/src/conda/python-3.12.4/Include/object.h:704 2024-06-26T06:07:33.6521975Z #101 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6522924Z frame=0x7f5a3658b968, throwflag=) 2024-06-26T06:07:33.6523404Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6526620Z #102 0x000000000051df35 in _PyObject_FastCallDictTstate ( 2024-06-26T06:07:33.6527521Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f59d425a980, 2024-06-26T06:07:33.6528220Z args=0x7ffe1d85e010, nargsf=, kwargs=) 2024-06-26T06:07:33.6528955Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:144 2024-06-26T06:07:33.6532029Z #103 0x0000000000558836 in _PyObject_Call_Prepend ( 2024-06-26T06:07:33.6533011Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f59d425a980, 2024-06-26T06:07:33.6533685Z obj=0x7f59d1f47880, args=, kwargs=0x7f59d1daeac0) 2024-06-26T06:07:33.6534488Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:508 2024-06-26T06:07:33.6536527Z #104 0x000000000062f5e6 in slot_tp_call (self=0x7f59d1f47880, 2024-06-26T06:07:33.6537402Z args=0x963858 <_PyRuntime+76312>, kwds=0x7f59d1daeac0) 2024-06-26T06:07:33.6538112Z at /usr/local/src/conda/python-3.12.4/Objects/typeobject.c:8776 2024-06-26T06:07:33.6541027Z #105 0x000000000051b30b in _PyObject_MakeTpCall ( 2024-06-26T06:07:33.6541963Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f59d1f47880, 2024-06-26T06:07:33.6542651Z args=, nargs=, keywords=0x7f59d3e76e30) 2024-06-26T06:07:33.6543410Z at /usr/local/src/conda/python-3.12.4/Include/object.h:704 2024-06-26T06:07:33.6544822Z #106 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6545671Z frame=0x7f5a3658b690, throwflag=) 2024-06-26T06:07:33.6546136Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6549325Z #107 0x000000000051df35 in _PyObject_FastCallDictTstate ( 2024-06-26T06:07:33.6550254Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f59d425a980, 2024-06-26T06:07:33.6550941Z args=0x7ffe1d85e370, nargsf=, kwargs=) 2024-06-26T06:07:33.6551817Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:144 2024-06-26T06:07:33.6554504Z #108 0x0000000000558836 in _PyObject_Call_Prepend ( 2024-06-26T06:07:33.6555533Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f59d425a980, 2024-06-26T06:07:33.6556192Z obj=0x7f59d1f46c50, args=, kwargs=0x7f59d1b84480) 2024-06-26T06:07:33.6556919Z at /usr/local/src/conda/python-3.12.4/Objects/call.c:508 2024-06-26T06:07:33.6558756Z #109 0x000000000062f5e6 in slot_tp_call (self=0x7f59d1f46c50, 2024-06-26T06:07:33.6559610Z args=0x963858 <_PyRuntime+76312>, kwds=0x7f59d1b84480) 2024-06-26T06:07:33.6560410Z at /usr/local/src/conda/python-3.12.4/Objects/typeobject.c:8776 2024-06-26T06:07:33.6563147Z #110 0x000000000051b30b in _PyObject_MakeTpCall ( 2024-06-26T06:07:33.6563997Z tstate=0x9c11f8 <_PyRuntime+459704>, callable=0x7f59d1f46c50, 2024-06-26T06:07:33.6564693Z args=, nargs=, keywords=0x7f59d3e774f0) 2024-06-26T06:07:33.6565512Z at /usr/local/src/conda/python-3.12.4/Include/object.h:704 2024-06-26T06:07:33.6566785Z #111 0x0000000000525e75 in _PyEval_EvalFrameDefault (tstate=, 2024-06-26T06:07:33.6567579Z frame=0x7f5a3658b218, throwflag=) 2024-06-26T06:07:33.6568055Z at Python/bytecodes.c:2714 2024-06-26T06:07:33.6569831Z #112 0x00000000005e4dfe in PyEval_EvalCode (co=, 2024-06-26T06:07:33.6570653Z globals=0x7f5a361ddf40, locals=) 2024-06-26T06:07:33.6571298Z at /usr/local/src/conda/python-3.12.4/Python/ceval.c:578 2024-06-26T06:07:33.6574329Z #113 0x000000000060a5d7 in run_eval_code_obj ( 2024-06-26T06:07:33.6575162Z tstate=0x9c11f8 <_PyRuntime+459704>, co=0x1cb32e0, 2024-06-26T06:07:33.6575676Z globals=0x7f5a361ddf40, locals=0x7f5a361ddf40) 2024-06-26T06:07:33.6576346Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1722 2024-06-26T06:07:33.6598324Z #114 0x0000000000605ca7 in run_mod (mod=, 2024-06-26T06:07:33.6599304Z filename=0x7f5a3612f590, globals=0x7f5a361ddf40, locals=0x7f5a361ddf40, 2024-06-26T06:07:33.6599934Z flags=0x7ffe1d85e890, arena=0x7f5a360ffcb0) 2024-06-26T06:07:33.6600594Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1743 2024-06-26T06:07:33.6605776Z #115 0x000000000061db12 in pyrun_file (fp=fp@entry=0x1bf6490, 2024-06-26T06:07:33.6606709Z filename=filename@entry=0x7f5a3612f590, start=start@entry=257, 2024-06-26T06:07:33.6607445Z globals=globals@entry=0x7f5a361ddf40, locals=locals@entry=0x7f5a361ddf40, 2024-06-26T06:07:33.6608105Z closeit=closeit@entry=1, flags=0x7ffe1d85e890) 2024-06-26T06:07:33.6608773Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:1643 2024-06-26T06:07:33.6611141Z #116 0x000000000061d3b0 in _PyRun_SimpleFileObject (fp=0x1bf6490, 2024-06-26T06:07:33.6612090Z filename=0x7f5a3612f590, closeit=1, flags=0x7ffe1d85e890) 2024-06-26T06:07:33.6612795Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:433 2024-06-26T06:07:33.6616333Z #117 0x000000000061d123 in _PyRun_AnyFileObject (fp=0x1bf6490, 2024-06-26T06:07:33.6617221Z filename=0x7f5a3612f590, closeit=1, flags=0x7ffe1d85e890) 2024-06-26T06:07:33.6617925Z at /usr/local/src/conda/python-3.12.4/Python/pythonrun.c:78 2024-06-26T06:07:33.6620502Z #118 0x0000000000615b43 in pymain_run_file_obj (skip_source_first_line=0, 2024-06-26T06:07:33.6621696Z filename=0x7f5a3612f590, program_name=0x7f5a35f96ec0) 2024-06-26T06:07:33.6622825Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:360 2024-06-26T06:07:33.6623879Z #119 pymain_run_file (config=0x963dd8 <_PyRuntime+77720>) 2024-06-26T06:07:33.6625005Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:379 2024-06-26T06:07:33.6626008Z #120 pymain_run_python (exitcode=0x7ffe1d85e864) 2024-06-26T06:07:33.6627121Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:629 2024-06-26T06:07:33.6628372Z #121 Py_RunMain () at /usr/local/src/conda/python-3.12.4/Modules/main.c:709 2024-06-26T06:07:33.6742912Z #122 0x00000000005cc259 in Py_BytesMain (argc=, 2024-06-26T06:07:33.6743901Z argv=) 2024-06-26T06:07:33.6744810Z at /usr/local/src/conda/python-3.12.4/Modules/main.c:763 2024-06-26T06:07:33.6747853Z #123 0x00007f5a3623a083 in __libc_start_main (main=0x5cc190
, argc=17, 2024-06-26T06:07:33.6748930Z argv=0x7ffe1d85eac8, init=, fini=, 2024-06-26T06:07:33.6749798Z rtld_fini=, stack_end=0x7ffe1d85eab8) 2024-06-26T06:07:33.6750687Z at ../csu/libc-start.c:308 2024-06-26T06:07:33.6751276Z #124 0x00000000005cc089 in _start () 2024-06-26T06:07:33.6752334Z at /usr/local/src/conda/python-3.12.4/Parser/parser.c:41555 2024-06-26T06:07:33.8835595Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2024-06-26T06:07:33.8836168Z with: 2024-06-26T06:07:33.8836406Z env: 2024-06-26T06:07:33.8836652Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:33.8837257Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:33.8837916Z ##[endgroup] 2024-06-26T06:07:33.8858740Z ##[group]Run set -eou pipefail 2024-06-26T06:07:33.8859136Z set -eou pipefail 2024-06-26T06:07:33.8859472Z  2024-06-26T06:07:33.8860162Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2024-06-26T06:07:33.8860795Z for _ in $(seq 1440); do 2024-06-26T06:07:33.8861252Z  # Break if no ssh session exists anymore 2024-06-26T06:07:33.8861739Z  if [ "$(who)" = "" ]; then 2024-06-26T06:07:33.8862116Z  break 2024-06-26T06:07:33.8862417Z  fi 2024-06-26T06:07:33.8862726Z  echo "." 2024-06-26T06:07:33.8863034Z  sleep 5 2024-06-26T06:07:33.8863330Z done 2024-06-26T06:07:33.8870469Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:07:33.8870960Z env: 2024-06-26T06:07:33.8871215Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:33.8871812Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:33.8872473Z ##[endgroup] 2024-06-26T06:07:33.8893551Z Holding runner for 2 hours until all ssh sessions have logged out 2024-06-26T06:07:33.8951020Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-06-26T06:07:33.8951798Z # ignore expansion of "docker ps -q" since it could be empty 2024-06-26T06:07:33.8952391Z # shellcheck disable=SC2046 2024-06-26T06:07:33.8952834Z docker stop $(docker ps -q) || true 2024-06-26T06:07:33.8953287Z # Prune all of the docker images 2024-06-26T06:07:33.8953730Z docker system prune -af 2024-06-26T06:07:33.8960727Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:07:33.8961374Z env: 2024-06-26T06:07:33.8961633Z GIT_DEFAULT_BRANCH: main 2024-06-26T06:07:33.8962247Z DOCKER_CONTAINER_ID: b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:33.8962903Z ##[endgroup] 2024-06-26T06:07:34.2212484Z b1cbc8a302fe 2024-06-26T06:07:34.6535256Z Deleted Containers: 2024-06-26T06:07:34.6535985Z b1cbc8a302fe4017c090dc6c790d42e0b4165539a443236a806c21a787b9abbf 2024-06-26T06:07:34.6536443Z 2024-06-26T06:07:36.6774545Z Deleted Images: 2024-06-26T06:07:36.6776663Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:91382da70d5719cd7007b6b80b71d2f48398f6b7 2024-06-26T06:07:36.6778666Z untagged: 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2024-06-26T06:07:36.8183975Z Adding repository directory to the temporary git global config as a safe directory 2024-06-26T06:07:36.8187198Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-06-26T06:07:36.8229362Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-06-26T06:07:36.8258574Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2024-06-26T06:07:36.8615775Z Entering 'android/libs/fbjni' 2024-06-26T06:07:36.8667187Z Entering 'third_party/FP16' 2024-06-26T06:07:36.8716160Z Entering 'third_party/FXdiv' 2024-06-26T06:07:36.8767352Z Entering 'third_party/NNPACK' 2024-06-26T06:07:36.8817718Z Entering 'third_party/VulkanMemoryAllocator' 2024-06-26T06:07:36.8859156Z Entering 'third_party/XNNPACK' 2024-06-26T06:07:36.8988315Z Entering 'third_party/benchmark' 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'third_party/gemmlowp/gemmlowp' 2024-06-26T06:07:36.9878375Z Entering 'third_party/gloo' 2024-06-26T06:07:36.9931563Z Entering 'third_party/googletest' 2024-06-26T06:07:36.9980681Z Entering 'third_party/ideep' 2024-06-26T06:07:37.0042984Z Entering 'third_party/ideep/mkl-dnn' 2024-06-26T06:07:37.0124585Z Entering 'third_party/ittapi' 2024-06-26T06:07:37.0166099Z Entering 'third_party/kineto' 2024-06-26T06:07:37.0205517Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-06-26T06:07:37.0244851Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-06-26T06:07:37.0285973Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-06-26T06:07:37.0325602Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-06-26T06:07:37.0365393Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-06-26T06:07:37.0403894Z Entering 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2024-06-26T06:07:37.0986221Z Entering 'third_party/onnx/third_party/pybind11' 2024-06-26T06:07:37.1041986Z Entering 'third_party/opentelemetry-cpp' 2024-06-26T06:07:37.1083032Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-06-26T06:07:37.1121994Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-06-26T06:07:37.1161240Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-06-26T06:07:37.1200112Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-06-26T06:07:37.1239934Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-06-26T06:07:37.1278353Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-06-26T06:07:37.1318017Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-06-26T06:07:37.1356118Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-06-26T06:07:37.1395979Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-06-26T06:07:37.1435417Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-06-26T06:07:37.1493640Z Entering 'third_party/pocketfft' 2024-06-26T06:07:37.1534082Z Entering 'third_party/protobuf' 2024-06-26T06:07:37.1623433Z Entering 'third_party/protobuf/third_party/benchmark' 2024-06-26T06:07:37.1677554Z Entering 'third_party/protobuf/third_party/googletest' 2024-06-26T06:07:37.1734751Z Entering 'third_party/psimd' 2024-06-26T06:07:37.1781752Z Entering 'third_party/pthreadpool' 2024-06-26T06:07:37.1830410Z Entering 'third_party/pybind11' 2024-06-26T06:07:37.1884680Z Entering 'third_party/python-peachpy' 2024-06-26T06:07:37.1932504Z Entering 'third_party/sleef' 2024-06-26T06:07:37.1986563Z Entering 'third_party/tensorpipe' 2024-06-26T06:07:37.2047175Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-06-26T06:07:37.2108594Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-06-26T06:07:37.2161982Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-06-26T06:07:37.2217049Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-06-26T06:07:37.2275534Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-06-26T06:07:37.2338735Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-06-26T06:07:37.2364654Z http.https://github.com/.extraheader 2024-06-26T06:07:37.2373326Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2024-06-26T06:07:37.2403534Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2024-06-26T06:07:37.2641245Z Entering 'android/libs/fbjni' 2024-06-26T06:07:37.2666550Z http.https://github.com/.extraheader 2024-06-26T06:07:37.2692858Z Entering 'third_party/FP16' 2024-06-26T06:07:37.2719365Z http.https://github.com/.extraheader 2024-06-26T06:07:37.2744732Z Entering 'third_party/FXdiv' 2024-06-26T06:07:37.2771392Z http.https://github.com/.extraheader 2024-06-26T06:07:37.2797562Z Entering 'third_party/NNPACK' 2024-06-26T06:07:37.2823444Z http.https://github.com/.extraheader 2024-06-26T06:07:37.2849852Z Entering 'third_party/VulkanMemoryAllocator' 2024-06-26T06:07:37.2876342Z http.https://github.com/.extraheader 2024-06-26T06:07:37.2902792Z Entering 'third_party/XNNPACK' 2024-06-26T06:07:37.2928876Z http.https://github.com/.extraheader 2024-06-26T06:07:37.2972433Z Entering 'third_party/benchmark' 2024-06-26T06:07:37.2999217Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3025496Z Entering 'third_party/cpp-httplib' 2024-06-26T06:07:37.3051650Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3077772Z Entering 'third_party/cpuinfo' 2024-06-26T06:07:37.3104503Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3131940Z Entering 'third_party/cudnn_frontend' 2024-06-26T06:07:37.3158303Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3184938Z Entering 'third_party/cutlass' 2024-06-26T06:07:37.3210627Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3244065Z Entering 'third_party/eigen' 2024-06-26T06:07:37.3269540Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3298753Z Entering 'third_party/fbgemm' 2024-06-26T06:07:37.3324374Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3351160Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-06-26T06:07:37.3376602Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3402806Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-06-26T06:07:37.3427361Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3455630Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-06-26T06:07:37.3480482Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3512651Z Entering 'third_party/fbgemm/third_party/googletest' 2024-06-26T06:07:37.3537786Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3563713Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-06-26T06:07:37.3588520Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3616456Z Entering 'third_party/flatbuffers' 2024-06-26T06:07:37.3642077Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3671290Z Entering 'third_party/fmt' 2024-06-26T06:07:37.3697117Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3723825Z Entering 'third_party/foxi' 2024-06-26T06:07:37.3748628Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3774908Z Entering 'third_party/gemmlowp/gemmlowp' 2024-06-26T06:07:37.3800213Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3826215Z Entering 'third_party/gloo' 2024-06-26T06:07:37.3851683Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3877865Z Entering 'third_party/googletest' 2024-06-26T06:07:37.3903988Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3929546Z Entering 'third_party/ideep' 2024-06-26T06:07:37.3955578Z http.https://github.com/.extraheader 2024-06-26T06:07:37.3980308Z Entering 'third_party/ideep/mkl-dnn' 2024-06-26T06:07:37.4005717Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4037990Z Entering 'third_party/ittapi' 2024-06-26T06:07:37.4064049Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4090669Z Entering 'third_party/kineto' 2024-06-26T06:07:37.4117019Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4143202Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-06-26T06:07:37.4169093Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4195825Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-06-26T06:07:37.4221705Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4249342Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-06-26T06:07:37.4275299Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4301410Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-06-26T06:07:37.4326378Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4352919Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-06-26T06:07:37.4378097Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4405024Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-06-26T06:07:37.4430555Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4458089Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-06-26T06:07:37.4483268Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4509919Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-06-26T06:07:37.4536393Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4563331Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-06-26T06:07:37.4588183Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4617072Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-06-26T06:07:37.4642410Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4669873Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-06-26T06:07:37.4694710Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4721507Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-06-26T06:07:37.4746121Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4773267Z Entering 'third_party/mimalloc' 2024-06-26T06:07:37.4799183Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4825557Z Entering 'third_party/nccl/nccl' 2024-06-26T06:07:37.4851578Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4878362Z Entering 'third_party/nlohmann' 2024-06-26T06:07:37.4903365Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4930821Z Entering 'third_party/onnx' 2024-06-26T06:07:37.4957128Z http.https://github.com/.extraheader 2024-06-26T06:07:37.4998052Z Entering 'third_party/onnx/third_party/benchmark' 2024-06-26T06:07:37.5023732Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5049765Z Entering 'third_party/onnx/third_party/pybind11' 2024-06-26T06:07:37.5077240Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5104776Z Entering 'third_party/opentelemetry-cpp' 2024-06-26T06:07:37.5129791Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5157651Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-06-26T06:07:37.5182051Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5208375Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-06-26T06:07:37.5233216Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5259703Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-06-26T06:07:37.5284817Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5310301Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-06-26T06:07:37.5335184Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5362301Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-06-26T06:07:37.5387329Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5413380Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-06-26T06:07:37.5438144Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5464001Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-06-26T06:07:37.5488726Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5513829Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-06-26T06:07:37.5539250Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5567260Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-06-26T06:07:37.5591836Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5621165Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-06-26T06:07:37.5646089Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5690690Z Entering 'third_party/pocketfft' 2024-06-26T06:07:37.5717418Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5743798Z Entering 'third_party/protobuf' 2024-06-26T06:07:37.5769614Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5799103Z Entering 'third_party/protobuf/third_party/benchmark' 2024-06-26T06:07:37.5823606Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5850135Z Entering 'third_party/protobuf/third_party/googletest' 2024-06-26T06:07:37.5875830Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5903292Z Entering 'third_party/psimd' 2024-06-26T06:07:37.5928909Z http.https://github.com/.extraheader 2024-06-26T06:07:37.5954420Z Entering 'third_party/pthreadpool' 2024-06-26T06:07:37.5979440Z http.https://github.com/.extraheader 2024-06-26T06:07:37.6006325Z Entering 'third_party/pybind11' 2024-06-26T06:07:37.6031194Z http.https://github.com/.extraheader 2024-06-26T06:07:37.6057382Z Entering 'third_party/python-peachpy' 2024-06-26T06:07:37.6084171Z http.https://github.com/.extraheader 2024-06-26T06:07:37.6110350Z Entering 'third_party/sleef' 2024-06-26T06:07:37.6135287Z http.https://github.com/.extraheader 2024-06-26T06:07:37.6161555Z Entering 'third_party/tensorpipe' 2024-06-26T06:07:37.6186849Z http.https://github.com/.extraheader 2024-06-26T06:07:37.6213293Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-06-26T06:07:37.6238664Z http.https://github.com/.extraheader 2024-06-26T06:07:37.6264193Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-06-26T06:07:37.6289411Z http.https://github.com/.extraheader 2024-06-26T06:07:37.6314872Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-06-26T06:07:37.6339906Z http.https://github.com/.extraheader 2024-06-26T06:07:37.6365934Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-06-26T06:07:37.6391102Z http.https://github.com/.extraheader 2024-06-26T06:07:37.6416372Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-06-26T06:07:37.6443068Z http.https://github.com/.extraheader 2024-06-26T06:07:37.6545448Z A job completed hook has been configured by the self-hosted runner administrator 2024-06-26T06:07:37.6565167Z ##[group]Run '/home/ec2-user/runner-scripts/cleanup.sh' 2024-06-26T06:07:37.6572094Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-06-26T06:07:37.6572602Z ##[endgroup] 2024-06-26T06:07:38.3810097Z Cleaning up orphan processes