2024-08-20T21:32:57.7487661Z Current runner version: '2.319.1' 2024-08-20T21:32:57.7494882Z Runner name: 'i-0d639d84405661b71' 2024-08-20T21:32:57.7495673Z Runner group name: 'Default' 2024-08-20T21:32:57.7496524Z Machine name: 'ip-10-0-43-193' 2024-08-20T21:32:57.7514308Z Testing runner upgrade compatibility 2024-08-20T21:32:58.0048155Z ##[group]GITHUB_TOKEN Permissions 2024-08-20T21:32:58.0050484Z Actions: read 2024-08-20T21:32:58.0051055Z Attestations: read 2024-08-20T21:32:58.0051526Z Checks: read 2024-08-20T21:32:58.0051984Z Contents: read 2024-08-20T21:32:58.0052473Z Deployments: read 2024-08-20T21:32:58.0052942Z Discussions: read 2024-08-20T21:32:58.0053350Z Issues: read 2024-08-20T21:32:58.0053812Z Metadata: read 2024-08-20T21:32:58.0054256Z Packages: read 2024-08-20T21:32:58.0054701Z Pages: read 2024-08-20T21:32:58.0055179Z PullRequests: read 2024-08-20T21:32:58.0055676Z RepositoryProjects: read 2024-08-20T21:32:58.0056165Z SecurityEvents: read 2024-08-20T21:32:58.0056678Z Statuses: read 2024-08-20T21:32:58.0057365Z ##[endgroup] 2024-08-20T21:32:58.0060829Z Secret source: Actions 2024-08-20T21:32:58.0061476Z Prepare workflow directory 2024-08-20T21:32:58.3583394Z Prepare all required actions 2024-08-20T21:32:58.3746902Z Getting action download info 2024-08-20T21:32:58.5851223Z Download action repository 'pytorch/test-infra@main' (SHA:0c3a2634aaa2f638c8f640e743f03d696ce1191f) 2024-08-20T21:32:58.8962309Z Download action repository 'pytorch/pytorch@main' (SHA:15b5a0b67fc3f34fb0bf1afa6f91e0c4c2b7fd8d) 2024-08-20T21:33:01.9661524Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0) 2024-08-20T21:33:02.1138894Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2024-08-20T21:33:02.4182621Z Getting action download info 2024-08-20T21:33:02.5056688Z Download action repository 'malfet/checkout@silent-checkout' (SHA:e07af140b3ccefc05679e3755b9db68f4ee4589c) 2024-08-20T21:33:02.7066165Z Getting action download info 2024-08-20T21:33:02.7960245Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2024-08-20T21:33:02.9485848Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/pull/133712/merge (f2fb9405c2fa9f9502a76363091cce6fd8179736) 2024-08-20T21:33:02.9488160Z ##[group] Inputs 2024-08-20T21:33:02.9488736Z build-environment: linux-focal-py3.12-clang10-experimental-split-build 2024-08-20T21:33:02.9492288Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}]} 2024-08-20T21:33:02.9495443Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:33:02.9496452Z sync-tag: 2024-08-20T21:33:02.9497369Z timeout-minutes: 600 2024-08-20T21:33:02.9497690Z use-gha: 2024-08-20T21:33:02.9497948Z dashboard-tag: 2024-08-20T21:33:02.9498253Z s3-bucket: gha-artifacts 2024-08-20T21:33:02.9498591Z aws-role-to-assume: 2024-08-20T21:33:02.9498896Z ##[endgroup] 2024-08-20T21:33:02.9499895Z Complete job name: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:33:03.0058511Z A job started hook has been configured by the self-hosted runner administrator 2024-08-20T21:33:03.0200006Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2024-08-20T21:33:03.0209752Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:33:03.0210265Z ##[endgroup] 2024-08-20T21:33:04.5817442Z Runner Type: amz2023.linux.2xlarge 2024-08-20T21:33:04.5818345Z Instance Type: c5.2xlarge 2024-08-20T21:33:04.5819028Z AMI Name: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64 2024-08-20T21:33:04.5819593Z AMI ID: ami-06c68f701d8090592 2024-08-20T21:33:10.3808808Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2024-08-20T21:33:10.3809398Z with: 2024-08-20T21:33:10.3810268Z github-secret: *** 2024-08-20T21:33:10.3811300Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2024-08-20T21:33:10.3812370Z activate-with-label: false 2024-08-20T21:33:10.3812715Z label: with-ssh 2024-08-20T21:33:10.3813034Z remove-existing-keys: true 2024-08-20T21:33:10.3813402Z fail-silently: true 2024-08-20T21:33:10.3813699Z env: 2024-08-20T21:33:10.3813970Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:33:10.3814316Z ##[endgroup] 2024-08-20T21:33:10.4666066Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2024-08-20T21:33:10.7249804Z Grabbing public ssh keys from https://github.com/pytorchmergebot.keys 2024-08-20T21:33:10.7910331Z No SSH keys found for user pytorchmergebot 2024-08-20T21:33:10.7911049Z Grabbing public ssh keys from https://github.com/XuehaiPan.keys 2024-08-20T21:33:10.8599474Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh 2024-08-20T21:33:10.8613611Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys 2024-08-20T21:33:10.8637363Z Login using: ssh ec2-user@ec2-3-95-223-225.compute-1.amazonaws.com 2024-08-20T21:33:10.8638237Z All testing is done inside the container, to start an interactive session run: 2024-08-20T21:33:10.8639080Z docker exec -it $(docker container ps --format '{{.ID}}') bash 2024-08-20T21:33:10.8799229Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2024-08-20T21:33:10.8799809Z with: 2024-08-20T21:33:10.8800081Z submodules: recursive 2024-08-20T21:33:10.8800413Z fetch-depth: 0 2024-08-20T21:33:10.8800691Z env: 2024-08-20T21:33:10.8800953Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:33:10.8801272Z ##[endgroup] 2024-08-20T21:33:10.8987008Z ##[group]Run retry () { 2024-08-20T21:33:10.8987370Z retry () { 2024-08-20T21:33:10.8987975Z  $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) 2024-08-20T21:33:10.8988544Z } 2024-08-20T21:33:10.8988823Z echo "${GITHUB_WORKSPACE}" 2024-08-20T21:33:10.8989238Z if [ -z "${NO_SUDO}" ]; then 2024-08-20T21:33:10.8989708Z  retry sudo rm -rf "${GITHUB_WORKSPACE}" 2024-08-20T21:33:10.8990148Z else 2024-08-20T21:33:10.8990684Z  retry rm -rf "${GITHUB_WORKSPACE}" 2024-08-20T21:33:10.8991110Z fi 2024-08-20T21:33:10.8991446Z mkdir "${GITHUB_WORKSPACE}" 2024-08-20T21:33:10.9001029Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:33:10.9001541Z env: 2024-08-20T21:33:10.9001823Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:33:10.9002138Z NO_SUDO: 2024-08-20T21:33:10.9002395Z ##[endgroup] 2024-08-20T21:33:10.9028331Z /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:33:12.7877562Z ##[group]Run malfet/checkout@silent-checkout 2024-08-20T21:33:12.7877998Z with: 2024-08-20T21:33:12.7878300Z ref: 40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:33:12.7878743Z fetch-depth: 0 2024-08-20T21:33:12.7879046Z submodules: recursive 2024-08-20T21:33:12.7879354Z quiet-checkout: true 2024-08-20T21:33:12.7879686Z repository: pytorch/pytorch 2024-08-20T21:33:12.7880164Z token: *** 2024-08-20T21:33:12.7880418Z ssh-strict: true 2024-08-20T21:33:12.7880733Z persist-credentials: true 2024-08-20T21:33:12.7881075Z clean: true 2024-08-20T21:33:12.7881379Z sparse-checkout-cone-mode: true 2024-08-20T21:33:12.7881756Z lfs: false 2024-08-20T21:33:12.7882036Z set-safe-directory: true 2024-08-20T21:33:12.7882339Z env: 2024-08-20T21:33:12.7882593Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:33:12.7883094Z ##[endgroup] 2024-08-20T21:33:12.8980983Z Syncing repository: pytorch/pytorch 2024-08-20T21:33:12.8983792Z ##[group]Getting Git version info 2024-08-20T21:33:12.8984880Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-08-20T21:33:12.8986384Z [command]/usr/bin/git version 2024-08-20T21:33:12.8986948Z git version 2.40.1 2024-08-20T21:33:12.8989236Z ##[endgroup] 2024-08-20T21:33:12.9005983Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/96ffbbef-c353-45d2-a113-1fc7d44f8407' before making global git config changes 2024-08-20T21:33:12.9008061Z Adding repository directory to the temporary git global config as a safe directory 2024-08-20T21:33:12.9009429Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:33:12.9038359Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-08-20T21:33:12.9042818Z ##[group]Initializing the repository 2024-08-20T21:33:12.9046476Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:33:12.9301481Z hint: Using 'master' as the name for the initial branch. This default branch name 2024-08-20T21:33:12.9302376Z hint: is subject to change. To configure the initial branch name to use in all 2024-08-20T21:33:12.9303211Z hint: of your new repositories, which will suppress this warning, call: 2024-08-20T21:33:12.9303881Z hint: 2024-08-20T21:33:12.9304335Z hint: git config --global init.defaultBranch 2024-08-20T21:33:12.9304863Z hint: 2024-08-20T21:33:12.9305706Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2024-08-20T21:33:12.9307236Z hint: 'development'. The just-created branch can be renamed via this command: 2024-08-20T21:33:12.9308585Z hint: 2024-08-20T21:33:12.9309183Z hint: git branch -m 2024-08-20T21:33:12.9310467Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2024-08-20T21:33:12.9315443Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2024-08-20T21:33:12.9353047Z ##[endgroup] 2024-08-20T21:33:12.9354005Z ##[group]Disabling automatic garbage collection 2024-08-20T21:33:12.9357000Z [command]/usr/bin/git config --local gc.auto 0 2024-08-20T21:33:12.9386596Z ##[endgroup] 2024-08-20T21:33:12.9387455Z ##[group]Setting up auth 2024-08-20T21:33:12.9393986Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-08-20T21:33:12.9427183Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2024-08-20T21:33:12.9679787Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-08-20T21:33:12.9711714Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2024-08-20T21:33:12.9963792Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-08-20T21:33:13.0010654Z ##[endgroup] 2024-08-20T21:33:13.0011195Z ##[group]Fetching the repository 2024-08-20T21:33:13.0017282Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --progress --no-recurse-submodules --quiet origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2024-08-20T21:33:16.0835204Z remote: Enumerating objects: 1008512 2024-08-20T21:33:16.0836043Z remote: Enumerating objects: 1010096, done. 2024-08-20T21:33:16.0837034Z remote: 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2024-08-20T21:33:16.0887358Z remote: Counting objects: 85% (1347/1584) 2024-08-20T21:33:16.0887865Z remote: Counting objects: 86% (1363/1584) 2024-08-20T21:33:16.0888375Z remote: Counting objects: 87% (1379/1584) 2024-08-20T21:33:16.0888867Z remote: Counting objects: 88% (1394/1584) 2024-08-20T21:33:16.0889374Z remote: Counting objects: 89% (1410/1584) 2024-08-20T21:33:16.0889884Z remote: Counting objects: 90% (1426/1584) 2024-08-20T21:33:16.0890666Z remote: Counting objects: 91% (1442/1584) 2024-08-20T21:33:16.0891183Z remote: Counting objects: 92% (1458/1584) 2024-08-20T21:33:16.0891695Z remote: Counting objects: 93% (1474/1584) 2024-08-20T21:33:16.0892187Z remote: Counting objects: 94% (1489/1584) 2024-08-20T21:33:16.0892700Z remote: Counting objects: 95% (1505/1584) 2024-08-20T21:33:16.0893209Z remote: Counting objects: 96% (1521/1584) 2024-08-20T21:33:16.0893698Z remote: Counting objects: 97% (1537/1584) 2024-08-20T21:33:16.0894206Z remote: Counting objects: 98% (1553/1584) 2024-08-20T21:33:16.0894722Z remote: Counting objects: 99% (1569/1584) 2024-08-20T21:33:16.0895240Z remote: Counting objects: 100% (1584/1584) 2024-08-20T21:33:16.0895772Z remote: Counting objects: 100% (1584/1584), done. 2024-08-20T21:33:16.1072647Z remote: Compressing objects: 0% (1/809) 2024-08-20T21:33:16.1275804Z remote: Compressing objects: 1% (9/809) 2024-08-20T21:33:16.1504805Z remote: Compressing objects: 2% (17/809) 2024-08-20T21:33:16.1825777Z remote: Compressing objects: 3% (25/809) 2024-08-20T21:33:16.2427205Z remote: Compressing objects: 4% (33/809) 2024-08-20T21:33:16.3191641Z remote: Compressing objects: 5% (41/809) 2024-08-20T21:33:16.3669744Z remote: Compressing objects: 6% (49/809) 2024-08-20T21:33:16.4136569Z remote: Compressing objects: 7% (57/809) 2024-08-20T21:33:16.4503037Z remote: Compressing objects: 8% (65/809) 2024-08-20T21:33:16.4804308Z remote: Compressing objects: 9% (73/809) 2024-08-20T21:33:16.5058877Z remote: Compressing objects: 10% (81/809) 2024-08-20T21:33:16.5336084Z remote: Compressing objects: 11% (89/809) 2024-08-20T21:33:16.5527195Z remote: Compressing objects: 12% (98/809) 2024-08-20T21:33:16.5663316Z remote: Compressing objects: 13% (106/809) 2024-08-20T21:33:16.5787255Z remote: Compressing objects: 14% (114/809) 2024-08-20T21:33:16.5870156Z remote: Compressing objects: 15% (122/809) 2024-08-20T21:33:16.5951561Z remote: Compressing objects: 16% (130/809) 2024-08-20T21:33:16.6025693Z remote: Compressing objects: 17% (138/809) 2024-08-20T21:33:16.6075208Z remote: Compressing objects: 18% (146/809) 2024-08-20T21:33:16.6114098Z remote: Compressing objects: 19% (154/809) 2024-08-20T21:33:16.6146619Z remote: Compressing objects: 20% (162/809) 2024-08-20T21:33:16.6171565Z remote: Compressing objects: 21% (170/809) 2024-08-20T21:33:16.6184529Z remote: Compressing objects: 22% (178/809) 2024-08-20T21:33:16.6186387Z remote: Compressing objects: 23% (187/809) 2024-08-20T21:33:16.6194718Z remote: Compressing objects: 24% (195/809) 2024-08-20T21:33:16.6206050Z remote: Compressing objects: 25% (203/809) 2024-08-20T21:33:16.6212548Z remote: Compressing objects: 26% (211/809) 2024-08-20T21:33:16.6216939Z remote: Compressing objects: 27% (219/809) 2024-08-20T21:33:16.6227322Z remote: Compressing objects: 28% (227/809) 2024-08-20T21:33:16.6236899Z remote: Compressing objects: 29% (235/809) 2024-08-20T21:33:16.6243791Z remote: Compressing objects: 30% (243/809) 2024-08-20T21:33:16.6262950Z remote: Compressing objects: 31% (251/809) 2024-08-20T21:33:16.6265747Z remote: Compressing objects: 32% (259/809) 2024-08-20T21:33:16.6283342Z remote: Compressing objects: 33% (267/809) 2024-08-20T21:33:16.6289470Z remote: Compressing objects: 34% (276/809) 2024-08-20T21:33:16.6302221Z remote: Compressing objects: 35% (284/809) 2024-08-20T21:33:16.6308876Z remote: Compressing objects: 36% (292/809) 2024-08-20T21:33:16.6316268Z remote: Compressing objects: 37% (300/809) 2024-08-20T21:33:16.6322010Z remote: Compressing objects: 38% (308/809) 2024-08-20T21:33:16.6331030Z remote: Compressing objects: 39% (316/809) 2024-08-20T21:33:16.6341755Z remote: Compressing objects: 40% (324/809) 2024-08-20T21:33:16.6349316Z remote: Compressing objects: 41% (332/809) 2024-08-20T21:33:16.6355598Z remote: Compressing objects: 42% (340/809) 2024-08-20T21:33:16.6363402Z remote: Compressing objects: 43% (348/809) 2024-08-20T21:33:16.6375511Z remote: Compressing objects: 44% (356/809) 2024-08-20T21:33:16.6379232Z remote: Compressing objects: 45% (365/809) 2024-08-20T21:33:16.6385630Z remote: Compressing objects: 46% (373/809) 2024-08-20T21:33:16.6391713Z remote: Compressing objects: 47% (381/809) 2024-08-20T21:33:16.6397744Z remote: Compressing objects: 48% (389/809) 2024-08-20T21:33:16.6403187Z remote: Compressing objects: 49% (397/809) 2024-08-20T21:33:16.6407587Z remote: Compressing objects: 50% (405/809) 2024-08-20T21:33:16.6412778Z remote: Compressing objects: 51% (413/809) 2024-08-20T21:33:16.6415267Z remote: Compressing objects: 52% (421/809) 2024-08-20T21:33:16.6419357Z remote: Compressing objects: 53% (429/809) 2024-08-20T21:33:16.6422025Z remote: Compressing objects: 54% (437/809) 2024-08-20T21:33:16.6425205Z remote: Compressing objects: 55% (445/809) 2024-08-20T21:33:16.6427824Z remote: Compressing objects: 56% (454/809) 2024-08-20T21:33:16.6430487Z remote: Compressing objects: 57% (462/809) 2024-08-20T21:33:16.6431955Z remote: Compressing objects: 58% (470/809) 2024-08-20T21:33:16.6433129Z remote: Compressing objects: 59% (478/809) 2024-08-20T21:33:16.6434621Z remote: Compressing objects: 60% (486/809) 2024-08-20T21:33:16.6435637Z remote: Compressing objects: 61% (494/809) 2024-08-20T21:33:16.6436534Z remote: Compressing objects: 62% (502/809) 2024-08-20T21:33:16.6437332Z remote: Compressing objects: 63% (510/809) 2024-08-20T21:33:16.6438347Z remote: Compressing objects: 64% (518/809) 2024-08-20T21:33:16.6441968Z remote: Compressing objects: 65% (526/809) 2024-08-20T21:33:16.6450815Z remote: Compressing objects: 66% (534/809) 2024-08-20T21:33:16.6455759Z remote: Compressing objects: 67% (543/809) 2024-08-20T21:33:16.6460035Z remote: Compressing objects: 68% (551/809) 2024-08-20T21:33:16.6463859Z remote: Compressing objects: 69% (559/809) 2024-08-20T21:33:16.6467594Z remote: Compressing objects: 70% (567/809) 2024-08-20T21:33:16.6472259Z remote: Compressing objects: 71% (575/809) 2024-08-20T21:33:16.6475958Z remote: Compressing objects: 72% (583/809) 2024-08-20T21:33:16.6480291Z remote: Compressing objects: 73% (591/809) 2024-08-20T21:33:16.6485218Z remote: Compressing objects: 74% (599/809) 2024-08-20T21:33:16.6488797Z remote: Compressing objects: 75% (607/809) 2024-08-20T21:33:16.6492131Z remote: Compressing objects: 76% (615/809) 2024-08-20T21:33:16.6497154Z remote: Compressing objects: 77% (623/809) 2024-08-20T21:33:16.6500626Z remote: Compressing objects: 78% (632/809) 2024-08-20T21:33:16.6502558Z remote: Compressing objects: 79% (640/809) 2024-08-20T21:33:16.6505619Z remote: Compressing objects: 80% (648/809) 2024-08-20T21:33:16.6508750Z remote: Compressing objects: 81% (656/809) 2024-08-20T21:33:16.6511414Z remote: Compressing objects: 82% (664/809) 2024-08-20T21:33:16.6519502Z remote: Compressing objects: 83% (672/809) 2024-08-20T21:33:16.6521755Z remote: Compressing objects: 84% (680/809) 2024-08-20T21:33:16.6524004Z remote: Compressing objects: 85% (688/809) 2024-08-20T21:33:16.6526492Z remote: Compressing objects: 86% (696/809) 2024-08-20T21:33:16.6528805Z remote: Compressing objects: 87% (704/809) 2024-08-20T21:33:16.6532050Z remote: Compressing objects: 88% (712/809) 2024-08-20T21:33:16.6534360Z remote: Compressing objects: 89% (721/809) 2024-08-20T21:33:16.6537864Z remote: Compressing objects: 90% (729/809) 2024-08-20T21:33:16.6539959Z remote: Compressing objects: 91% (737/809) 2024-08-20T21:33:16.6541259Z remote: Compressing objects: 92% (745/809) 2024-08-20T21:33:16.6542617Z remote: Compressing objects: 93% (753/809) 2024-08-20T21:33:16.6544233Z remote: Compressing objects: 94% (761/809) 2024-08-20T21:33:16.6545772Z remote: Compressing objects: 95% (769/809) 2024-08-20T21:33:16.6547266Z remote: Compressing objects: 96% (777/809) 2024-08-20T21:33:16.6548257Z remote: Compressing objects: 97% (785/809) 2024-08-20T21:33:16.6551587Z remote: Compressing objects: 98% (793/809) 2024-08-20T21:33:16.6553045Z remote: Compressing objects: 99% (801/809) 2024-08-20T21:33:16.6554060Z remote: Compressing objects: 100% (809/809) 2024-08-20T21:33:16.6554625Z remote: Compressing objects: 100% (809/809), done. 2024-08-20T21:33:37.8605740Z remote: Total 1010096 (delta 1022), reused 1211 (delta 772), pack-reused 1008512 (from 1) 2024-08-20T21:34:04.5807019Z [command]/usr/bin/git rev-parse --verify --quiet 40ec5f6ddd9787aca0449b24128343ff4c4a88b3^{object} 2024-08-20T21:34:04.5835580Z 40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:34:04.5839953Z ##[endgroup] 2024-08-20T21:34:04.5840472Z ##[group]Determining the checkout info 2024-08-20T21:34:04.5841915Z ##[endgroup] 2024-08-20T21:34:04.5842794Z ##[group]Checking out the ref 2024-08-20T21:34:04.5845109Z [command]/usr/bin/git checkout --quiet --force 40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:34:05.9544623Z ##[endgroup] 2024-08-20T21:34:05.9545455Z ##[group]Setting up auth for fetching submodules 2024-08-20T21:34:05.9550027Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-08-20T21:34:05.9601244Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2024-08-20T21:34:05.9630322Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2024-08-20T21:34:05.9658445Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2024-08-20T21:34:05.9684280Z ##[endgroup] 2024-08-20T21:34:05.9684767Z ##[group]Fetching submodules 2024-08-20T21:34:05.9687687Z [command]/usr/bin/git submodule sync --recursive 2024-08-20T21:34:05.9966868Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2024-08-20T21:34:06.0239892Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2024-08-20T21:34:06.0241416Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2024-08-20T21:34:06.0243327Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2024-08-20T21:34:06.0245900Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2024-08-20T21:34:06.0249137Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2024-08-20T21:34:06.0251784Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2024-08-20T21:34:06.0254657Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2024-08-20T21:34:06.0257830Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2024-08-20T21:34:06.0260720Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2024-08-20T21:34:06.0263970Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2024-08-20T21:34:06.0267032Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2024-08-20T21:34:06.0270285Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen' 2024-08-20T21:34:06.0273761Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2024-08-20T21:34:06.0277318Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2024-08-20T21:34:06.0280687Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2024-08-20T21:34:06.0284563Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2024-08-20T21:34:06.0289566Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo' 2024-08-20T21:34:06.0295490Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2024-08-20T21:34:06.0299262Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2024-08-20T21:34:06.0303497Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2024-08-20T21:34:06.0307631Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2024-08-20T21:34:06.0311884Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2024-08-20T21:34:06.0316112Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl' 2024-08-20T21:34:06.0320538Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2024-08-20T21:34:06.0324908Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2024-08-20T21:34:06.0330267Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2024-08-20T21:34:06.0334653Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2024-08-20T21:34:06.0339463Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2024-08-20T21:34:06.0344288Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2024-08-20T21:34:06.0349378Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2024-08-20T21:34:06.0354320Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2024-08-20T21:34:06.0359732Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2024-08-20T21:34:06.0364731Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2024-08-20T21:34:06.0371706Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2024-08-20T21:34:06.0399960Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2024-08-20T21:34:06.3125665Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2024-08-20T21:34:06.4898021Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2024-08-20T21:34:06.8656032Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2024-08-20T21:34:07.0939609Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2024-08-20T21:34:09.2122371Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2024-08-20T21:34:23.1530725Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2024-08-20T21:34:23.5583029Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2024-08-20T21:34:24.0790930Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2024-08-20T21:34:24.6640599Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2024-08-20T21:34:25.8150643Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2024-08-20T21:34:27.8184698Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'... 2024-08-20T21:34:33.3323398Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2024-08-20T21:34:34.8253473Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2024-08-20T21:34:36.5464274Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2024-08-20T21:34:37.8924112Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2024-08-20T21:34:38.2886338Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2024-08-20T21:34:38.5972923Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2024-08-20T21:34:39.8712512Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2024-08-20T21:34:40.2548139Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2024-08-20T21:34:40.5005908Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2024-08-20T21:34:42.0682816Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2024-08-20T21:34:42.9455826Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nccl/nccl'... 2024-08-20T21:34:43.5951482Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2024-08-20T21:34:49.9360270Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2024-08-20T21:34:52.1597726Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2024-08-20T21:34:58.3875030Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2024-08-20T21:34:58.6365636Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2024-08-20T21:35:08.6288474Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2024-08-20T21:35:08.8076868Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2024-08-20T21:35:08.9920186Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2024-08-20T21:35:10.0006633Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2024-08-20T21:35:10.2947999Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2024-08-20T21:35:10.9219986Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2024-08-20T21:35:11.3024598Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2024-08-20T21:35:11.3133806Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2024-08-20T21:35:11.3216956Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2024-08-20T21:35:11.3445465Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2024-08-20T21:35:11.3811522Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191' 2024-08-20T21:35:12.3149073Z Submodule path 'third_party/XNNPACK': checked out 'fcbf55af6cf28a4627bcd1f703ab7ad843f0f3a2' 2024-08-20T21:35:12.3371470Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415' 2024-08-20T21:35:12.3820533Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006' 2024-08-20T21:35:12.4812689Z Submodule path 'third_party/cpuinfo': checked out '3c8b1533ac03dd6531ab6e7b9245d488f13a82a5' 2024-08-20T21:35:12.5145876Z Submodule path 'third_party/cudnn_frontend': checked out '23511ba176243f27b3b275da1fb3814ea805a171' 2024-08-20T21:35:13.0019464Z Submodule path 'third_party/cutlass': checked out 'bbe579a9e3beb6ea6626d9227ec32d0dae119a49' 2024-08-20T21:35:13.2488206Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c' 2024-08-20T21:35:13.3285135Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e' 2024-08-20T21:35:13.3301482Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit' 2024-08-20T21:35:13.3304224Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo' 2024-08-20T21:35:13.3306576Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass' 2024-08-20T21:35:13.3309059Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest' 2024-08-20T21:35:13.3311656Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch' 2024-08-20T21:35:13.3336603Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'... 2024-08-20T21:35:14.3139838Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'... 2024-08-20T21:35:15.2280003Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'... 2024-08-20T21:35:17.2033678Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'... 2024-08-20T21:35:18.3480259Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'... 2024-08-20T21:35:18.6795464Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81' 2024-08-20T21:35:18.7726935Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3' 2024-08-20T21:35:19.1649440Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6' 2024-08-20T21:35:19.2258201Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796' 2024-08-20T21:35:19.2382785Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1' 2024-08-20T21:35:19.3508438Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b' 2024-08-20T21:35:19.3908889Z Submodule path 'third_party/fmt': checked out '0c9fce2ffefecfdce794e1859584e25877b7b592' 2024-08-20T21:35:19.4305196Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2024-08-20T21:35:19.4553077Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509' 2024-08-20T21:35:19.5006507Z Submodule path 'third_party/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929' 2024-08-20T21:35:19.5129271Z Submodule path 'third_party/ideep': checked out '55ca0191687aaf19aca5cdb7881c791e3bea442b' 2024-08-20T21:35:19.5144878Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2024-08-20T21:35:19.5167813Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2024-08-20T21:35:33.8461028Z Submodule path 'third_party/ideep/mkl-dnn': checked out '1137e04ec0b5251ca2b4400a4fd3c667ce843d67' 2024-08-20T21:35:33.8641538Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42' 2024-08-20T21:35:33.9509757Z Submodule path 'third_party/kineto': checked out 'd9753139d181b9ff42872465aac0e5d3018be415' 2024-08-20T21:35:33.9527519Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2024-08-20T21:35:33.9529812Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2024-08-20T21:35:33.9533243Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2024-08-20T21:35:33.9559480Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2024-08-20T21:35:34.5076891Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2024-08-20T21:35:35.8564105Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2024-08-20T21:35:37.1050819Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2024-08-20T21:35:37.1066897Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-08-20T21:35:37.1069118Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-08-20T21:35:37.1071654Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-20T21:35:37.1074264Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-20T21:35:37.1076713Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-20T21:35:37.1079666Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-20T21:35:37.1082382Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-20T21:35:37.1085280Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-20T21:35:37.1113099Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2024-08-20T21:35:37.9511862Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2024-08-20T21:35:38.3181058Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2024-08-20T21:35:39.6588331Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2024-08-20T21:35:39.9304410Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2024-08-20T21:35:40.4779986Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2024-08-20T21:35:41.6632680Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2024-08-20T21:35:48.1927313Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2024-08-20T21:35:48.5610487Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2024-08-20T21:35:48.5789561Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2024-08-20T21:35:48.6158299Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2024-08-20T21:35:48.6292220Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2024-08-20T21:35:48.6306843Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-20T21:35:48.6334058Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2024-08-20T21:35:48.9298389Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2024-08-20T21:35:48.9479569Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2024-08-20T21:35:48.9884251Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2024-08-20T21:35:49.0862332Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2024-08-20T21:35:49.1023527Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2024-08-20T21:35:49.1426517Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2024-08-20T21:35:49.2005926Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2024-08-20T21:35:49.2369641Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78' 2024-08-20T21:35:49.2608947Z Submodule path 'third_party/nccl/nccl': checked out 'ab2b89c4c339bd7f816fbc114a4b05d386b66290' 2024-08-20T21:35:49.3621984Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806' 2024-08-20T21:35:49.7062210Z Submodule path 'third_party/onnx': checked out '3bf92c03a9f27eba3bda1e5b9e63ea20ec213557' 2024-08-20T21:35:49.7097095Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/onnx/third_party/benchmark' 2024-08-20T21:35:49.7098902Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2024-08-20T21:35:49.7126290Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/benchmark'... 2024-08-20T21:35:50.1738248Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2024-08-20T21:35:51.1261983Z Submodule path 'third_party/onnx/third_party/benchmark': checked out '2dd015dfef425c866d9a43f2c67d8b52d709acb6' 2024-08-20T21:35:51.1596539Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '5b0a6fc2017fcc176545afe3e09c9f9885283242' 2024-08-20T21:35:51.2267730Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2024-08-20T21:35:51.2286995Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-20T21:35:51.2288995Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-20T21:35:51.2291714Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-20T21:35:51.2294479Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T21:35:51.2297048Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T21:35:51.2299639Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T21:35:51.2302432Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 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[command]/usr/bin/git submodule foreach --recursive sh -c "git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url" 2024-08-20T21:36:18.5965529Z Entering 'android/libs/fbjni' 2024-08-20T21:36:18.6014262Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config remote.origin.url 2024-08-20T21:36:18.6028866Z Entering 'third_party/FP16' 2024-08-20T21:36:18.6079584Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config remote.origin.url 2024-08-20T21:36:18.6094844Z Entering 'third_party/FXdiv' 2024-08-20T21:36:18.6141837Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config remote.origin.url 2024-08-20T21:36:18.6157058Z Entering 'third_party/NNPACK' 2024-08-20T21:36:18.6215659Z 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Entering 'third_party/cpuinfo' 2024-08-20T21:36:18.6543000Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config remote.origin.url 2024-08-20T21:36:18.6559026Z Entering 'third_party/cudnn_frontend' 2024-08-20T21:36:18.6607858Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config remote.origin.url 2024-08-20T21:36:18.6622827Z Entering 'third_party/cutlass' 2024-08-20T21:36:18.6669842Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config remote.origin.url 2024-08-20T21:36:18.6693647Z Entering 'third_party/eigen' 2024-08-20T21:36:18.6740518Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/eigen/config remote.origin.url 2024-08-20T21:36:18.6758083Z Entering 'third_party/fbgemm' 2024-08-20T21:36:18.6807911Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config remote.origin.url 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config remote.origin.url 2024-08-20T21:36:18.7407078Z Entering 'third_party/googletest' 2024-08-20T21:36:18.7455526Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config remote.origin.url 2024-08-20T21:36:18.7471231Z Entering 'third_party/ideep' 2024-08-20T21:36:18.7519966Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config remote.origin.url 2024-08-20T21:36:18.7534550Z Entering 'third_party/ideep/mkl-dnn' 2024-08-20T21:36:18.7583526Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config remote.origin.url 2024-08-20T21:36:18.7607514Z Entering 'third_party/ittapi' 2024-08-20T21:36:18.7657150Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config remote.origin.url 2024-08-20T21:36:18.7672715Z 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/cpr/config remote.origin.url 2024-08-20T21:36:18.7929373Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-08-20T21:36:18.7976960Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/fmt/config remote.origin.url 2024-08-20T21:36:18.7990650Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-08-20T21:36:18.8039151Z 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 2024-08-20T21:36:18.8052965Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-08-20T21:36:18.8101614Z 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 2024-08-20T21:36:18.8117999Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-08-20T21:36:18.8166803Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/glog/config remote.origin.url 2024-08-20T21:36:18.8181081Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-08-20T21:36:18.8230146Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/googletest/config remote.origin.url 2024-08-20T21:36:18.8244241Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-20T21:36:18.8293054Z 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config remote.origin.url 2024-08-20T21:36:18.8504770Z Entering 'third_party/mimalloc' 2024-08-20T21:36:18.8553711Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/mimalloc/config remote.origin.url 2024-08-20T21:36:18.8569598Z Entering 'third_party/nccl/nccl' 2024-08-20T21:36:18.8617562Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nccl/nccl/config remote.origin.url 2024-08-20T21:36:18.8634230Z Entering 'third_party/nlohmann' 2024-08-20T21:36:18.8681689Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config remote.origin.url 2024-08-20T21:36:18.8699243Z Entering 'third_party/onnx' 2024-08-20T21:36:18.8747246Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config remote.origin.url 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentelemetry-proto/config remote.origin.url 2024-08-20T21:36:18.9292560Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T21:36:18.9340865Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentracing-cpp/config remote.origin.url 2024-08-20T21:36:18.9355784Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T21:36:18.9405688Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config remote.origin.url 2024-08-20T21:36:18.9419509Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T21:36:18.9468539Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/civetweb/config remote.origin.url 2024-08-20T21:36:18.9485152Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T21:36:18.9532658Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/googletest/config remote.origin.url 2024-08-20T21:36:18.9549399Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T21:36:18.9596974Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config remote.origin.url 2024-08-20T21:36:18.9632800Z Entering 'third_party/pocketfft' 2024-08-20T21:36:18.9681236Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config remote.origin.url 2024-08-20T21:36:18.9697555Z Entering 'third_party/protobuf' 2024-08-20T21:36:18.9745095Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config remote.origin.url 2024-08-20T21:36:18.9763966Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-20T21:36:18.9813025Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config remote.origin.url 2024-08-20T21:36:18.9827572Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-20T21:36:18.9874624Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config remote.origin.url 2024-08-20T21:36:18.9891759Z Entering 'third_party/psimd' 2024-08-20T21:36:18.9940622Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config remote.origin.url 2024-08-20T21:36:18.9955617Z Entering 'third_party/pthreadpool' 2024-08-20T21:36:19.0004220Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config remote.origin.url 2024-08-20T21:36:19.0019355Z Entering 'third_party/pybind11' 2024-08-20T21:36:19.0067721Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config remote.origin.url 2024-08-20T21:36:19.0083035Z Entering 'third_party/python-peachpy' 2024-08-20T21:36:19.0130174Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config remote.origin.url 2024-08-20T21:36:19.0145130Z Entering 'third_party/sleef' 2024-08-20T21:36:19.0191348Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config remote.origin.url 2024-08-20T21:36:19.0206645Z Entering 'third_party/tensorpipe' 2024-08-20T21:36:19.0253607Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config remote.origin.url 2024-08-20T21:36:19.0268360Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T21:36:19.0317185Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config remote.origin.url 2024-08-20T21:36:19.0331527Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T21:36:19.0378086Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config remote.origin.url 2024-08-20T21:36:19.0393113Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T21:36:19.0440514Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config remote.origin.url 2024-08-20T21:36:19.0455327Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T21:36:19.0502204Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config remote.origin.url 2024-08-20T21:36:19.0515687Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T21:36:19.0564028Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config remote.origin.url 2024-08-20T21:36:19.1397188Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2024-08-20T21:36:19.1692428Z Entering 'android/libs/fbjni' 2024-08-20T21:36:19.1733078Z Entering 'third_party/FP16' 2024-08-20T21:36:19.1773086Z Entering 'third_party/FXdiv' 2024-08-20T21:36:19.1813855Z Entering 'third_party/NNPACK' 2024-08-20T21:36:19.1854404Z Entering 'third_party/VulkanMemoryAllocator' 2024-08-20T21:36:19.1893741Z Entering 'third_party/XNNPACK' 2024-08-20T21:36:19.1951555Z Entering 'third_party/benchmark' 2024-08-20T21:36:19.1990748Z Entering 'third_party/cpp-httplib' 2024-08-20T21:36:19.2029702Z Entering 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2024-08-20T21:36:19.6932192Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T21:36:19.6971556Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T21:36:19.7012047Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T21:36:19.7049176Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T21:36:19.7086484Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T21:36:19.7126365Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-08-20T21:36:19.7165972Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-08-20T21:36:19.7224292Z Entering 'third_party/pocketfft' 2024-08-20T21:36:19.7265311Z Entering 'third_party/protobuf' 2024-08-20T21:36:19.7308661Z Entering 'third_party/protobuf/third_party/benchmark' 2024-08-20T21:36:19.7346748Z Entering 'third_party/protobuf/third_party/googletest' 2024-08-20T21:36:19.7386537Z Entering 'third_party/psimd' 2024-08-20T21:36:19.7426092Z Entering 'third_party/pthreadpool' 2024-08-20T21:36:19.7464083Z Entering 'third_party/pybind11' 2024-08-20T21:36:19.7502878Z Entering 'third_party/python-peachpy' 2024-08-20T21:36:19.7541995Z Entering 'third_party/sleef' 2024-08-20T21:36:19.7581812Z Entering 'third_party/tensorpipe' 2024-08-20T21:36:19.7620867Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T21:36:19.7660464Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T21:36:19.7700072Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T21:36:19.7739326Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T21:36:19.7777490Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T21:36:19.7834592Z ##[endgroup] 2024-08-20T21:36:19.7874819Z [command]/usr/bin/git log -1 --format='%H' 2024-08-20T21:36:19.7903652Z '40ec5f6ddd9787aca0449b24128343ff4c4a88b3' 2024-08-20T21:36:19.8107829Z Prepare all required actions 2024-08-20T21:36:19.8108459Z Getting action download info 2024-08-20T21:36:20.1237934Z ##[group]Run ./.github/actions/setup-linux 2024-08-20T21:36:20.1238785Z env: 2024-08-20T21:36:20.1239051Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:20.1239369Z ##[endgroup] 2024-08-20T21:36:20.1346895Z ##[group]Run set -euo pipefail 2024-08-20T21:36:20.1347329Z set -euo pipefail 2024-08-20T21:36:20.1347716Z function get_ec2_metadata() { 2024-08-20T21:36:20.1348392Z  # Pulled from instance metadata endpoint for EC2 2024-08-20T21:36:20.1349300Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2024-08-20T21:36:20.1350094Z  category=$1 2024-08-20T21:36:20.1350597Z  # If it is GCP runner (runner name contains gcp), do not run this 2024-08-20T21:36:20.1351228Z  runner_name_str=i-0d639d84405661b71 2024-08-20T21:36:20.1351734Z  if [[ -f /.inarc ]]; then 2024-08-20T21:36:20.1352197Z  echo "ARC Runner, no info on ec2 metadata" 2024-08-20T21:36:20.1352751Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2024-08-20T21:36:20.1353441Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2024-08-20T21:36:20.1354037Z  else 2024-08-20T21:36:20.1354517Z  curl -fsSL "http://169.254.169.254/latest/meta-data/${category}" 2024-08-20T21:36:20.1355093Z  fi 2024-08-20T21:36:20.1355343Z } 2024-08-20T21:36:20.1355682Z echo "ami-id: $(get_ec2_metadata ami-id)" 2024-08-20T21:36:20.1356315Z echo "instance-id: $(get_ec2_metadata instance-id)" 2024-08-20T21:36:20.1356949Z echo "instance-type: $(get_ec2_metadata instance-type)" 2024-08-20T21:36:20.1357508Z echo "system info $(uname -a)" 2024-08-20T21:36:20.1364128Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:20.1364610Z env: 2024-08-20T21:36:20.1364884Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:20.1365211Z ##[endgroup] 2024-08-20T21:36:20.1456214Z ami-id: ami-06c68f701d8090592 2024-08-20T21:36:20.1507764Z instance-id: i-0d639d84405661b71 2024-08-20T21:36:20.1556517Z instance-type: c5.2xlarge 2024-08-20T21:36:20.1567678Z system info Linux ip-10-0-43-193.ec2.internal 6.1.94-99.176.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 18 14:57:56 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux 2024-08-20T21:36:20.1593049Z ##[group]Run echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> $GITHUB_OUTPUT 2024-08-20T21:36:20.1594019Z echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> $GITHUB_OUTPUT 2024-08-20T21:36:20.1600017Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:20.1600516Z env: 2024-08-20T21:36:20.1600774Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:20.1601108Z ##[endgroup] 2024-08-20T21:36:20.1670309Z ##[group]Run if systemctl is-active --quiet docker; then 2024-08-20T21:36:20.1670918Z if systemctl is-active --quiet docker; then 2024-08-20T21:36:20.1671453Z  echo "Docker daemon is running..."; 2024-08-20T21:36:20.1671894Z else 2024-08-20T21:36:20.1672366Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2024-08-20T21:36:20.1672953Z fi 2024-08-20T21:36:20.1678339Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:20.1678845Z env: 2024-08-20T21:36:20.1679115Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:20.1679448Z ##[endgroup] 2024-08-20T21:36:20.1751936Z Docker daemon is running... 2024-08-20T21:36:20.1801036Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2024-08-20T21:36:20.1801610Z with: 2024-08-20T21:36:20.1801866Z shell: bash 2024-08-20T21:36:20.1802134Z timeout_minutes: 5 2024-08-20T21:36:20.1802443Z max_attempts: 3 2024-08-20T21:36:20.1802744Z retry_wait_seconds: 30 2024-08-20T21:36:20.1806110Z command: AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" # For LF Runners we need to make sure we also login to Meta's ECR docker registry too. META_AWS_ACCOUNT_ID=308535385114 if [ "$AWS_ACCOUNT_ID" != "$META_AWS_ACCOUNT_ID" ] ; then aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$META_AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" fi 2024-08-20T21:36:20.1809491Z polling_interval_seconds: 1 2024-08-20T21:36:20.1809841Z warning_on_retry: true 2024-08-20T21:36:20.1810257Z continue_on_error: false 2024-08-20T21:36:20.1810588Z env: 2024-08-20T21:36:20.1810834Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:20.1811191Z AWS_RETRY_MODE: standard 2024-08-20T21:36:20.1811531Z AWS_MAX_ATTEMPTS: 5 2024-08-20T21:36:20.1811847Z AWS_DEFAULT_REGION: us-east-1 2024-08-20T21:36:20.1812210Z ##[endgroup] 2024-08-20T21:36:21.4028063Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-08-20T21:36:21.4028976Z Configure a credential helper to remove this warning. See 2024-08-20T21:36:21.4029861Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-08-20T21:36:21.4030416Z 2024-08-20T21:36:21.4030521Z Login Succeeded 2024-08-20T21:36:22.2384942Z Command completed after 1 attempt(s). 2024-08-20T21:36:22.2439044Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-08-20T21:36:22.2439831Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-08-20T21:36:22.2440493Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-08-20T21:36:22.2447166Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:22.2447654Z env: 2024-08-20T21:36:22.2447926Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:22.2448265Z ##[endgroup] 2024-08-20T21:36:22.2539403Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-08-20T21:36:22.2540205Z # ignore expansion of "docker ps -q" since it could be empty 2024-08-20T21:36:22.2540805Z # shellcheck disable=SC2046 2024-08-20T21:36:22.2541259Z docker stop $(docker ps -q) || true 2024-08-20T21:36:22.2541713Z # Prune all of the docker images 2024-08-20T21:36:22.2542155Z docker system prune -af 2024-08-20T21:36:22.2547895Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:22.2548398Z env: 2024-08-20T21:36:22.2548664Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:22.2548996Z ##[endgroup] 2024-08-20T21:36:22.2836960Z "docker stop" requires at least 1 argument. 2024-08-20T21:36:22.2837730Z See 'docker stop --help'. 2024-08-20T21:36:22.2837984Z 2024-08-20T21:36:22.2838209Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2024-08-20T21:36:22.2838598Z 2024-08-20T21:36:22.2838740Z Stop one or more running containers 2024-08-20T21:36:22.2997755Z Total reclaimed space: 0B 2024-08-20T21:36:22.3033876Z ##[group]Run set +e 2024-08-20T21:36:22.3034237Z set +e 2024-08-20T21:36:22.3034512Z set -x 2024-08-20T21:36:22.3034798Z  2024-08-20T21:36:22.3035110Z PT_DOMAIN=download.pytorch.org 2024-08-20T21:36:22.3035906Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2024-08-20T21:36:22.3037022Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2024-08-20T21:36:22.3037799Z # one is returned at random 2024-08-20T21:36:22.3038325Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2024-08-20T21:36:22.3038844Z  2024-08-20T21:36:22.3039197Z if [ -z "${RESOLVED_IP}" ]; then 2024-08-20T21:36:22.3039802Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2024-08-20T21:36:22.3040874Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2024-08-20T21:36:22.3041447Z  2024-08-20T21:36:22.3041761Z  if [ -z "${RESOLVED_IP}" ]; then 2024-08-20T21:36:22.3042318Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2024-08-20T21:36:22.3042830Z  exit 1 2024-08-20T21:36:22.3043132Z  fi 2024-08-20T21:36:22.3043402Z fi 2024-08-20T21:36:22.3043670Z  2024-08-20T21:36:22.3044015Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2024-08-20T21:36:22.3044634Z  # Clean up any old records first 2024-08-20T21:36:22.3045141Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2024-08-20T21:36:22.3045591Z fi 2024-08-20T21:36:22.3045841Z  2024-08-20T21:36:22.3046263Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2024-08-20T21:36:22.3046809Z cat /etc/hosts 2024-08-20T21:36:22.3053363Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:22.3053864Z env: 2024-08-20T21:36:22.3054152Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:22.3054477Z ##[endgroup] 2024-08-20T21:36:22.3079123Z + PT_DOMAIN=download.pytorch.org 2024-08-20T21:36:22.3085206Z ++ dig -4 +short download.pytorch.org 2024-08-20T21:36:22.3085737Z ++ tail -n1 2024-08-20T21:36:22.3703836Z + RESOLVED_IP=108.138.64.125 2024-08-20T21:36:22.3704765Z + '[' -z 108.138.64.125 ']' 2024-08-20T21:36:22.3705215Z + grep -r download.pytorch.org /etc/hosts 2024-08-20T21:36:22.3715858Z 108.138.64.125 download.pytorch.org 2024-08-20T21:36:22.3717227Z + sudo sed -i /download.pytorch.org/d /etc/hosts 2024-08-20T21:36:22.5928071Z + echo '108.138.64.125 download.pytorch.org' 2024-08-20T21:36:22.5928643Z + sudo tee -a /etc/hosts 2024-08-20T21:36:22.6337990Z 108.138.64.125 download.pytorch.org 2024-08-20T21:36:22.6353650Z + cat /etc/hosts 2024-08-20T21:36:22.6363136Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2024-08-20T21:36:22.6376208Z ::1 localhost6 localhost6.localdomain6 2024-08-20T21:36:22.6376736Z 108.138.64.125 download.pytorch.org 2024-08-20T21:36:22.6544455Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2024-08-20T21:36:22.6545063Z with: 2024-08-20T21:36:22.6545972Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:22.6547062Z docker-build-dir: .ci/docker 2024-08-20T21:36:22.6547430Z working-directory: . 2024-08-20T21:36:22.6547873Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:36:22.6548417Z force-push: false 2024-08-20T21:36:22.6548702Z env: 2024-08-20T21:36:22.6548945Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:22.6549273Z ##[endgroup] 2024-08-20T21:36:22.6570021Z ##[group]Run set -ex 2024-08-20T21:36:22.6570464Z set -ex 2024-08-20T21:36:22.6570745Z  2024-08-20T21:36:22.6571286Z # If the docker build directory or the build script doesn't exist, the action will 2024-08-20T21:36:22.6572315Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2024-08-20T21:36:22.6573116Z # job could then download the pre-built image as usual 2024-08-20T21:36:22.6573861Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then 2024-08-20T21:36:22.6574535Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:22.6575169Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:22.6575727Z  2024-08-20T21:36:22.6576226Z  echo "There is no Docker build script in ${REPO_NAME} repo, skipping..." 2024-08-20T21:36:22.6576854Z  exit 0 2024-08-20T21:36:22.6577118Z else 2024-08-20T21:36:22.6577461Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:22.6577904Z fi 2024-08-20T21:36:22.6578146Z  2024-08-20T21:36:22.6578602Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2024-08-20T21:36:22.6579477Z  # The docker image name already includes the ECR prefix and tag, so we can just 2024-08-20T21:36:22.6580247Z  # use it as it is, but first let's extract the tag 2024-08-20T21:36:22.6580959Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2024-08-20T21:36:22.6581690Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:22.6600990Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:22.6601837Z else 2024-08-20T21:36:22.6602271Z  DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2024-08-20T21:36:22.6602928Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:22.6603851Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:22.6604618Z fi 2024-08-20T21:36:22.6613893Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:22.6614386Z env: 2024-08-20T21:36:22.6614651Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:22.6614978Z REPO_NAME: pytorch 2024-08-20T21:36:22.6615933Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:22.6616985Z DOCKER_BUILD_DIR: .ci/docker 2024-08-20T21:36:22.6617472Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:36:22.6617980Z ##[endgroup] 2024-08-20T21:36:22.6643823Z + [[ ! -d .ci/docker ]] 2024-08-20T21:36:22.6644376Z + [[ ! -f .ci/docker/build.sh ]] 2024-08-20T21:36:22.6644766Z + echo skip=false 2024-08-20T21:36:22.6646769Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 == *\3\0\8\5\3\5\3\8\5\1\1\4\.\d\k\r\.\e\c\r\.\u\s\-\e\a\s\t\-\1\.\a\m\a\z\o\n\a\w\s\.\c\o\m\/\p\y\t\o\r\c\h* ]] 2024-08-20T21:36:22.6652023Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:22.6653074Z ++ awk -F '[:,]' '{print $2}' 2024-08-20T21:36:22.6841646Z + DOCKER_TAG=f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:22.6842588Z + echo docker-tag=f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:22.6844215Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:22.6875148Z ##[group]Run set +e 2024-08-20T21:36:22.6875507Z set +e 2024-08-20T21:36:22.6875804Z set -x 2024-08-20T21:36:22.6876085Z  2024-08-20T21:36:22.6876335Z login() { 2024-08-20T21:36:22.6876998Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-08-20T21:36:22.6877727Z } 2024-08-20T21:36:22.6877974Z  2024-08-20T21:36:22.6878236Z retry () { 2024-08-20T21:36:22.6878620Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-08-20T21:36:22.6879048Z } 2024-08-20T21:36:22.6879311Z  2024-08-20T21:36:22.6879612Z retry login "${DOCKER_REGISTRY}" 2024-08-20T21:36:22.6880007Z  2024-08-20T21:36:22.6880470Z # Check if image already exists, if it does then skip building it 2024-08-20T21:36:22.6881179Z if docker manifest inspect "${DOCKER_IMAGE}"; then 2024-08-20T21:36:22.6881679Z  exit 0 2024-08-20T21:36:22.6881962Z fi 2024-08-20T21:36:22.6882222Z  2024-08-20T21:36:22.6882699Z # NB: This part requires a full checkout. Otherwise, the merge base will 2024-08-20T21:36:22.6883541Z # be empty. The default action would be to continue rebuild the image 2024-08-20T21:36:22.6884288Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2024-08-20T21:36:22.6884961Z  # if we're on the base branch then use the parent commit 2024-08-20T21:36:22.6885539Z  MERGE_BASE=$(git rev-parse HEAD~) 2024-08-20T21:36:22.6885962Z else 2024-08-20T21:36:22.6886418Z  # otherwise we're on a PR, so use the most recent base commit 2024-08-20T21:36:22.6887099Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2024-08-20T21:36:22.6887599Z fi 2024-08-20T21:36:22.6887861Z  2024-08-20T21:36:22.6888148Z if [[ -z "${MERGE_BASE}" ]]; then 2024-08-20T21:36:22.6888789Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:22.6889244Z  2024-08-20T21:36:22.6889887Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2024-08-20T21:36:22.6891028Z  exit 0 2024-08-20T21:36:22.6891322Z fi 2024-08-20T21:36:22.6891570Z  2024-08-20T21:36:22.6892000Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2024-08-20T21:36:22.6893017Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2024-08-20T21:36:22.6893848Z  exit 1 2024-08-20T21:36:22.6894120Z fi 2024-08-20T21:36:22.6894380Z  2024-08-20T21:36:22.6894859Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2024-08-20T21:36:22.6895818Z # If no image exists but the hash is the same as the previous hash then we should error out here 2024-08-20T21:36:22.6896715Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2024-08-20T21:36:22.6897707Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2024-08-20T21:36:22.6898829Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2024-08-20T21:36:22.6899489Z fi 2024-08-20T21:36:22.6899753Z  2024-08-20T21:36:22.6900225Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-08-20T21:36:22.6906261Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:22.6906763Z env: 2024-08-20T21:36:22.6907030Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:22.6907371Z DOCKER_BUILD_DIR: .ci/docker 2024-08-20T21:36:22.6907827Z BASE_REVISION: 91f3d614142df02f619e44a68e3d9e0dfeba49ec 2024-08-20T21:36:22.6908942Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:22.6910039Z DOCKER_TAG: f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:22.6910649Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:36:22.6911167Z ##[endgroup] 2024-08-20T21:36:22.6936393Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:36:22.6937046Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:36:22.6938974Z + aws ecr get-login-password --region us-east-1 2024-08-20T21:36:22.6940418Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:36:23.2526882Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-08-20T21:36:23.2528001Z Configure a credential helper to remove this warning. See 2024-08-20T21:36:23.2529162Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-08-20T21:36:23.2529708Z 2024-08-20T21:36:23.2529815Z Login Succeeded 2024-08-20T21:36:23.2544528Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:23.5522208Z { 2024-08-20T21:36:23.5522652Z "schemaVersion": 2, 2024-08-20T21:36:23.5523649Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2024-08-20T21:36:23.5524676Z "config": { 2024-08-20T21:36:23.5525371Z "mediaType": "application/vnd.docker.container.image.v1+json", 2024-08-20T21:36:23.5526250Z "size": 43416, 2024-08-20T21:36:23.5527194Z "digest": "sha256:2aaa7058a7e1d063370f2bda46a0fffde19193dd83023b51f0f1d1c55b4a88e4" 2024-08-20T21:36:23.5528221Z }, 2024-08-20T21:36:23.5528552Z "layers": [ 2024-08-20T21:36:23.5528931Z { 2024-08-20T21:36:23.5529616Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:36:23.5530583Z "size": 28584223, 2024-08-20T21:36:23.5531429Z "digest": "sha256:560c024910bebac6b404791af28ebd48a8289303b8377d17b67ffdfe52754f2a" 2024-08-20T21:36:23.5532743Z }, 2024-08-20T21:36:23.5533069Z { 2024-08-20T21:36:23.5533781Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:36:23.5534710Z "size": 1822, 2024-08-20T21:36:23.5535579Z "digest": "sha256:9973d24424a5d77923bf32f3f7cf77d04111177f289c946a51eb77633ac03200" 2024-08-20T21:36:23.5536578Z }, 2024-08-20T21:36:23.5536938Z { 2024-08-20T21:36:23.5537548Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:36:23.5538444Z "size": 313390323, 2024-08-20T21:36:23.5539316Z "digest": "sha256:31cf2448eae342b481298db0563fe7f68f4840ac62385700a1ef3b6824a54e7b" 2024-08-20T21:36:23.5540155Z }, 2024-08-20T21:36:23.5540532Z { 2024-08-20T21:36:23.5541127Z "mediaType": 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"sha256:9841596ece9eebfc9229b66dac9085e17c614f6e2697b9b6787e50cef1fffebe" 2024-08-20T21:36:23.5697030Z }, 2024-08-20T21:36:23.5697245Z { 2024-08-20T21:36:23.5697672Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:36:23.5698226Z "size": 139, 2024-08-20T21:36:23.5698750Z "digest": "sha256:689d9bf507d0f1a37ad9857c17601e3ba4ce677cc7bd6a5ec6778e35b7092eb4" 2024-08-20T21:36:23.5699383Z }, 2024-08-20T21:36:23.5699613Z { 2024-08-20T21:36:23.5700174Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:36:23.5700740Z "size": 32, 2024-08-20T21:36:23.5701289Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:36:23.5701910Z }, 2024-08-20T21:36:23.5702144Z { 2024-08-20T21:36:23.5702586Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:36:23.5703133Z "size": 160, 2024-08-20T21:36:23.5703684Z "digest": 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"sha256:33d589b886753bb2d002c6d637579ac3e17d9c4de498c0d1d8a17bd2cb738313" 2024-08-20T21:36:23.5747476Z }, 2024-08-20T21:36:23.5747692Z { 2024-08-20T21:36:23.5748118Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:36:23.5748675Z "size": 32, 2024-08-20T21:36:23.5749292Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:36:23.5749936Z }, 2024-08-20T21:36:23.5750172Z { 2024-08-20T21:36:23.5750586Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:36:23.5751260Z "size": 108, 2024-08-20T21:36:23.5751781Z "digest": "sha256:005437c751415d116f09202fbaaf1962a998da11c25613e1a0a2420b02fd419d" 2024-08-20T21:36:23.5752376Z }, 2024-08-20T21:36:23.5752610Z { 2024-08-20T21:36:23.5753044Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:36:23.5753594Z "size": 54145661, 2024-08-20T21:36:23.5754176Z "digest": "sha256:3ec2dad3f44186ff7db7ad2623c04eda6ef73bb99fc66d1ed44a42ec0b7a9402" 2024-08-20T21:36:23.5754829Z }, 2024-08-20T21:36:23.5755046Z { 2024-08-20T21:36:23.5755471Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-08-20T21:36:23.5756030Z "size": 32, 2024-08-20T21:36:23.5756563Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-08-20T21:36:23.5757204Z } 2024-08-20T21:36:23.5757434Z ] 2024-08-20T21:36:23.5757644Z } 2024-08-20T21:36:23.5866190Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/} 2024-08-20T21:36:23.5866691Z tag=${ECR_DOCKER_IMAGE##*/} 2024-08-20T21:36:23.5867234Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2024-08-20T21:36:23.5873377Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:23.5873875Z env: 2024-08-20T21:36:23.5874137Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:23.5875108Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:23.5876134Z ##[endgroup] 2024-08-20T21:36:23.5902833Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-py3.12-clang10-f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:23.5982751Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2024-08-20T21:36:23.5983345Z with: 2024-08-20T21:36:23.5984227Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:23.5985394Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:36:23.5985906Z env: 2024-08-20T21:36:23.5986149Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:23.5986478Z ##[endgroup] 2024-08-20T21:36:23.6028136Z ##[group]Run set -x 2024-08-20T21:36:23.6028475Z set -x 2024-08-20T21:36:23.6028766Z set +e 2024-08-20T21:36:23.6029044Z  2024-08-20T21:36:23.6029291Z login() { 2024-08-20T21:36:23.6029958Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-08-20T21:36:23.6030695Z } 2024-08-20T21:36:23.6030939Z  2024-08-20T21:36:23.6031254Z retry () { 2024-08-20T21:36:23.6031618Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-08-20T21:36:23.6032074Z } 2024-08-20T21:36:23.6032335Z  2024-08-20T21:36:23.6032616Z retry login "${DOCKER_REGISTRY}" 2024-08-20T21:36:23.6033017Z  2024-08-20T21:36:23.6033277Z set -e 2024-08-20T21:36:23.6033726Z # ignore output since only exit code is used for conditional 2024-08-20T21:36:23.6034427Z # only pull docker image if it's not available locally 2024-08-20T21:36:23.6035202Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2024-08-20T21:36:23.6035901Z  retry docker pull "${DOCKER_IMAGE}" 2024-08-20T21:36:23.6036317Z fi 2024-08-20T21:36:23.6041850Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:36:23.6042344Z env: 2024-08-20T21:36:23.6042594Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:36:23.6043572Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:23.6044863Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:36:23.6045367Z ##[endgroup] 2024-08-20T21:36:23.6068707Z + set +e 2024-08-20T21:36:23.6069667Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:36:23.6070350Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:36:23.6072957Z + aws ecr get-login-password --region us-east-1 2024-08-20T21:36:23.6073966Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-08-20T21:36:24.1701320Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-08-20T21:36:24.1702685Z Configure a credential helper to remove this warning. See 2024-08-20T21:36:24.1703572Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-08-20T21:36:24.1704133Z 2024-08-20T21:36:24.1704242Z Login Succeeded 2024-08-20T21:36:24.1714826Z + set -e 2024-08-20T21:36:24.1716637Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:24.1844478Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:24.1846326Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:36:24.4520339Z f6d216893d65c7b8ae43df4daaf247db808378e9: Pulling from pytorch/pytorch-linux-focal-py3.12-clang10 2024-08-20T21:36:24.4543742Z 560c024910be: Pulling fs layer 2024-08-20T21:36:24.4544889Z 9973d24424a5: Pulling fs layer 2024-08-20T21:36:24.4545578Z 31cf2448eae3: Pulling fs layer 2024-08-20T21:36:24.4546301Z 2cbc8585686f: Pulling fs layer 2024-08-20T21:36:24.4547014Z 8c4852dbedba: Pulling fs layer 2024-08-20T21:36:24.4548185Z dc477a9bbcc6: Pulling fs layer 2024-08-20T21:36:24.4548927Z 8c153b899f1d: Pulling fs layer 2024-08-20T21:36:24.4549523Z 0b1c4336fa8b: Pulling fs layer 2024-08-20T21:36:24.4550135Z b012c0f517e6: Pulling fs layer 2024-08-20T21:36:24.4550849Z 4fe699dadca1: Pulling fs layer 2024-08-20T21:36:24.4551448Z d67ce3a9cb4b: Pulling fs layer 2024-08-20T21:36:24.4551977Z 9841596ece9e: Pulling fs layer 2024-08-20T21:36:24.4552329Z e72835db4035: Pulling fs layer 2024-08-20T21:36:24.4552832Z 8c4852dbedba: Waiting 2024-08-20T21:36:24.4553352Z 4f4fb700ef54: Pulling fs layer 2024-08-20T21:36:24.4554003Z dc477a9bbcc6: Waiting 2024-08-20T21:36:24.4554376Z 8ef04e16f963: Pulling fs layer 2024-08-20T21:36:24.4554964Z 8c153b899f1d: Waiting 2024-08-20T21:36:24.4555387Z 0b1c4336fa8b: Waiting 2024-08-20T21:36:24.4555916Z 5e6c0035b872: Pulling fs layer 2024-08-20T21:36:24.4556482Z b012c0f517e6: Waiting 2024-08-20T21:36:24.4556968Z 59b7b891e32d: Pulling fs layer 2024-08-20T21:36:24.4557601Z 4fe699dadca1: Waiting 2024-08-20T21:36:24.4558131Z 5f4e7925ebfb: Pulling fs layer 2024-08-20T21:36:24.4558708Z 12a450969960: Pulling fs layer 2024-08-20T21:36:24.4559340Z b8c108e0e581: Pulling fs layer 2024-08-20T21:36:24.4560001Z d3e5b0194b60: Pulling fs layer 2024-08-20T21:36:24.4560625Z d67ce3a9cb4b: Waiting 2024-08-20T21:36:24.4561163Z 2cbc8585686f: Waiting 2024-08-20T21:36:24.4561554Z 9841596ece9e: Waiting 2024-08-20T21:36:24.4562066Z 3e4dba8f4feb: Pulling fs layer 2024-08-20T21:36:24.4562710Z 5e6c0035b872: Waiting 2024-08-20T21:36:24.4563044Z e72835db4035: Waiting 2024-08-20T21:36:24.4563368Z 4dd8f5b900be: Pulling fs layer 2024-08-20T21:36:24.4563894Z 8ef04e16f963: Waiting 2024-08-20T21:36:24.4564407Z 59b7b891e32d: Waiting 2024-08-20T21:36:24.4564896Z 4f4fb700ef54: Waiting 2024-08-20T21:36:24.4565436Z b8c108e0e581: Waiting 2024-08-20T21:36:24.4566013Z f0966db14aac: Pulling fs layer 2024-08-20T21:36:24.4566628Z 12a450969960: Waiting 2024-08-20T21:36:24.4567179Z 5f4e7925ebfb: Waiting 2024-08-20T21:36:24.4567607Z d3e5b0194b60: Waiting 2024-08-20T21:36:24.4567915Z 3ca232406eff: Pulling fs layer 2024-08-20T21:36:24.4568266Z 4dd8f5b900be: Waiting 2024-08-20T21:36:24.4568777Z 93684c5d1426: Pulling fs layer 2024-08-20T21:36:24.4569112Z 3e4dba8f4feb: Waiting 2024-08-20T21:36:24.4569427Z 891f1a671536: Pulling fs layer 2024-08-20T21:36:24.4569776Z 3ca232406eff: Waiting 2024-08-20T21:36:24.4570055Z 93684c5d1426: Waiting 2024-08-20T21:36:24.4570446Z 2d1cde488756: Pulling fs layer 2024-08-20T21:36:24.4570817Z 1b670936c6e7: Pulling fs layer 2024-08-20T21:36:24.4571160Z c89135369bac: Pulling fs layer 2024-08-20T21:36:24.4571522Z e32451be40c1: Pulling fs layer 2024-08-20T21:36:24.4571869Z 2d1cde488756: Waiting 2024-08-20T21:36:24.4572169Z 7647e82c5c3f: Pulling fs layer 2024-08-20T21:36:24.4572528Z 2d334a3c11fd: Pulling fs layer 2024-08-20T21:36:24.4572892Z e9ea16c3c5d4: Pulling fs layer 2024-08-20T21:36:24.4573225Z 1b670936c6e7: Waiting 2024-08-20T21:36:24.4573538Z 66a5808f4ad5: Pulling fs layer 2024-08-20T21:36:24.4573884Z c89135369bac: Waiting 2024-08-20T21:36:24.4574194Z b92de615fef3: Pulling fs layer 2024-08-20T21:36:24.4574553Z 56dd63029e04: Pulling fs layer 2024-08-20T21:36:24.4574922Z 0e19dd6ac611: Pulling fs layer 2024-08-20T21:36:24.4575251Z e32451be40c1: Waiting 2024-08-20T21:36:24.4575549Z 2d334a3c11fd: Waiting 2024-08-20T21:36:24.4575876Z deffd0debe5e: Pulling fs layer 2024-08-20T21:36:24.4576213Z 7647e82c5c3f: Waiting 2024-08-20T21:36:24.4576528Z 823a4c61790a: Pulling fs layer 2024-08-20T21:36:24.4576881Z e9ea16c3c5d4: Waiting 2024-08-20T21:36:24.4577182Z 52c09d8bb114: Pulling fs layer 2024-08-20T21:36:24.4577526Z 56dd63029e04: Waiting 2024-08-20T21:36:24.4577841Z fc113046ff5b: Pulling fs layer 2024-08-20T21:36:24.4578175Z 66a5808f4ad5: Waiting 2024-08-20T21:36:24.4578497Z 58fcfcf8962b: Pulling fs layer 2024-08-20T21:36:24.4578854Z 0e19dd6ac611: Waiting 2024-08-20T21:36:24.4579144Z b92de615fef3: Waiting 2024-08-20T21:36:24.4579467Z a6bbbb7aff78: Pulling fs layer 2024-08-20T21:36:24.4579834Z 58c762741f83: Pulling fs layer 2024-08-20T21:36:24.4580289Z fc113046ff5b: Waiting 2024-08-20T21:36:24.4580617Z 36d6dbd6c3c6: Pulling fs layer 2024-08-20T21:36:24.4580972Z 52c09d8bb114: Waiting 2024-08-20T21:36:24.4581263Z deffd0debe5e: Waiting 2024-08-20T21:36:24.4581569Z 823a4c61790a: Waiting 2024-08-20T21:36:24.4581886Z f6e31afd70a7: Pulling fs layer 2024-08-20T21:36:24.4582222Z 58fcfcf8962b: Waiting 2024-08-20T21:36:24.4582540Z 4076a363f04c: Pulling fs layer 2024-08-20T21:36:24.4582895Z a6bbbb7aff78: Waiting 2024-08-20T21:36:24.4583182Z 58c762741f83: Waiting 2024-08-20T21:36:24.4583495Z da1b55b13c80: Pulling fs layer 2024-08-20T21:36:24.4583847Z 36d6dbd6c3c6: Waiting 2024-08-20T21:36:24.4584133Z f6e31afd70a7: Waiting 2024-08-20T21:36:24.4584487Z 852b7bd929d0: Pulling fs layer 2024-08-20T21:36:24.4584835Z da1b55b13c80: Waiting 2024-08-20T21:36:24.4585136Z 43c374b7d87c: Pulling fs layer 2024-08-20T21:36:24.4585493Z 2ae4d1692345: Pulling fs layer 2024-08-20T21:36:24.4585838Z 852b7bd929d0: Waiting 2024-08-20T21:36:24.4586131Z 02fd8160fb61: Pulling fs layer 2024-08-20T21:36:24.4586477Z 43c374b7d87c: Waiting 2024-08-20T21:36:24.4586789Z 1860f83d1894: Pulling fs layer 2024-08-20T21:36:24.4587124Z 2ae4d1692345: Waiting 2024-08-20T21:36:24.4587439Z 1beebf68aabb: Pulling fs layer 2024-08-20T21:36:24.4587777Z 1860f83d1894: Waiting 2024-08-20T21:36:24.4588072Z 4076a363f04c: Waiting 2024-08-20T21:36:24.4588385Z e0cbebc1b4e2: Pulling fs layer 2024-08-20T21:36:24.4588728Z 1beebf68aabb: Waiting 2024-08-20T21:36:24.4589043Z 3e8f996f54cd: Pulling fs layer 2024-08-20T21:36:24.4589401Z 689d9bf507d0: Pulling fs layer 2024-08-20T21:36:24.4589746Z ca5838b6767d: Pulling fs layer 2024-08-20T21:36:24.4590102Z 2ab493b780e6: Pulling fs layer 2024-08-20T21:36:24.4590774Z aa49767ed126: Pulling fs layer 2024-08-20T21:36:24.4591170Z 689d9bf507d0: Waiting 2024-08-20T21:36:24.4591548Z e0cbebc1b4e2: Waiting 2024-08-20T21:36:24.4591899Z 9c112f6fb43a: Pulling fs layer 2024-08-20T21:36:24.4592310Z ca5838b6767d: Waiting 2024-08-20T21:36:24.4592609Z 056972111ac5: Pulling fs layer 2024-08-20T21:36:24.4593019Z 2ab493b780e6: Waiting 2024-08-20T21:36:24.4593323Z 3e8f996f54cd: Waiting 2024-08-20T21:36:24.4593819Z aa49767ed126: Waiting 2024-08-20T21:36:24.4594132Z 37490d77553a: Pulling fs layer 2024-08-20T21:36:24.4594539Z 9c112f6fb43a: Waiting 2024-08-20T21:36:24.4594841Z 55ffcd4cda01: Pulling fs layer 2024-08-20T21:36:24.4595239Z 37490d77553a: Waiting 2024-08-20T21:36:24.4595540Z 4085287a7508: Pulling fs layer 2024-08-20T21:36:24.4595940Z 55ffcd4cda01: Waiting 2024-08-20T21:36:24.4596257Z 45f64d78b248: Pulling fs layer 2024-08-20T21:36:24.4596634Z c6ce826bc67b: Pulling fs layer 2024-08-20T21:36:24.4597007Z 4085287a7508: Waiting 2024-08-20T21:36:24.4597301Z 45f64d78b248: Waiting 2024-08-20T21:36:24.4597659Z 0c6508f0dedc: Pulling fs layer 2024-08-20T21:36:24.4598030Z af18f4fae28b: Pulling fs layer 2024-08-20T21:36:24.4598452Z e415411fbadd: Pulling fs layer 2024-08-20T21:36:24.4598784Z c6ce826bc67b: Waiting 2024-08-20T21:36:24.4599139Z 0c6508f0dedc: Waiting 2024-08-20T21:36:24.4599450Z 8801c8ab2ed3: Pulling fs layer 2024-08-20T21:36:24.4599844Z e415411fbadd: Waiting 2024-08-20T21:36:24.4600145Z af18f4fae28b: Waiting 2024-08-20T21:36:24.4600500Z 33d589b88675: Pulling fs layer 2024-08-20T21:36:24.4600870Z 005437c75141: Pulling fs layer 2024-08-20T21:36:24.4601248Z 3ec2dad3f441: Pulling fs layer 2024-08-20T21:36:24.4601626Z 005437c75141: Waiting 2024-08-20T21:36:24.4601909Z 3ec2dad3f441: Waiting 2024-08-20T21:36:24.5670970Z 9973d24424a5: Verifying Checksum 2024-08-20T21:36:24.5671677Z 9973d24424a5: Download complete 2024-08-20T21:36:24.6311839Z 2cbc8585686f: Verifying Checksum 2024-08-20T21:36:24.6312333Z 2cbc8585686f: Download complete 2024-08-20T21:36:24.7937604Z 560c024910be: Download complete 2024-08-20T21:36:24.8793391Z dc477a9bbcc6: Verifying Checksum 2024-08-20T21:36:24.8794097Z dc477a9bbcc6: Download complete 2024-08-20T21:36:24.9508138Z 8c153b899f1d: Verifying Checksum 2024-08-20T21:36:24.9508838Z 8c153b899f1d: Download complete 2024-08-20T21:36:25.0209835Z 0b1c4336fa8b: Verifying Checksum 2024-08-20T21:36:25.0210854Z 0b1c4336fa8b: Download complete 2024-08-20T21:36:25.0946460Z b012c0f517e6: Verifying Checksum 2024-08-20T21:36:25.0947459Z b012c0f517e6: Download complete 2024-08-20T21:36:25.1905384Z 4fe699dadca1: Verifying Checksum 2024-08-20T21:36:25.1905884Z 4fe699dadca1: Download complete 2024-08-20T21:36:25.2728276Z d67ce3a9cb4b: Download complete 2024-08-20T21:36:25.3889799Z 9841596ece9e: Download complete 2024-08-20T21:36:25.4746054Z 8c4852dbedba: Verifying Checksum 2024-08-20T21:36:25.4746565Z 8c4852dbedba: Download complete 2024-08-20T21:36:25.4834869Z 4f4fb700ef54: Verifying Checksum 2024-08-20T21:36:25.4835605Z 4f4fb700ef54: Download complete 2024-08-20T21:36:25.5681968Z 8ef04e16f963: Verifying Checksum 2024-08-20T21:36:25.5682777Z 8ef04e16f963: Download complete 2024-08-20T21:36:25.6407799Z 5e6c0035b872: Verifying Checksum 2024-08-20T21:36:25.6408457Z 5e6c0035b872: Download complete 2024-08-20T21:36:25.6971862Z 560c024910be: Pull complete 2024-08-20T21:36:25.7061581Z 59b7b891e32d: Verifying Checksum 2024-08-20T21:36:25.7062295Z 59b7b891e32d: Download complete 2024-08-20T21:36:25.7190505Z 9973d24424a5: Pull complete 2024-08-20T21:36:25.7904575Z 5f4e7925ebfb: Verifying Checksum 2024-08-20T21:36:25.7905040Z 5f4e7925ebfb: Download complete 2024-08-20T21:36:25.8562953Z 12a450969960: Verifying Checksum 2024-08-20T21:36:25.8563437Z 12a450969960: Download complete 2024-08-20T21:36:25.9409364Z b8c108e0e581: Download complete 2024-08-20T21:36:26.0066092Z d3e5b0194b60: Download complete 2024-08-20T21:36:26.0770146Z 3e4dba8f4feb: Verifying Checksum 2024-08-20T21:36:26.0770619Z 3e4dba8f4feb: Download complete 2024-08-20T21:36:26.1474250Z 4dd8f5b900be: Verifying Checksum 2024-08-20T21:36:26.1475034Z 4dd8f5b900be: Download complete 2024-08-20T21:36:27.4199674Z f0966db14aac: Verifying Checksum 2024-08-20T21:36:27.4200196Z f0966db14aac: Download complete 2024-08-20T21:36:27.5009773Z 3ca232406eff: Verifying Checksum 2024-08-20T21:36:27.5010262Z 3ca232406eff: Download complete 2024-08-20T21:36:27.5694999Z 93684c5d1426: Verifying Checksum 2024-08-20T21:36:27.5695699Z 93684c5d1426: Download complete 2024-08-20T21:36:27.6372797Z 891f1a671536: Verifying Checksum 2024-08-20T21:36:27.6373605Z 891f1a671536: Download complete 2024-08-20T21:36:27.6467395Z 31cf2448eae3: Verifying Checksum 2024-08-20T21:36:27.6468010Z 31cf2448eae3: Download complete 2024-08-20T21:36:27.7294633Z 1b670936c6e7: Download complete 2024-08-20T21:36:27.7592239Z 2d1cde488756: Download complete 2024-08-20T21:36:27.8291250Z e32451be40c1: Download complete 2024-08-20T21:36:27.9053149Z 7647e82c5c3f: Verifying Checksum 2024-08-20T21:36:27.9053749Z 7647e82c5c3f: Download complete 2024-08-20T21:36:27.9809554Z 2d334a3c11fd: Verifying Checksum 2024-08-20T21:36:27.9810344Z 2d334a3c11fd: Download complete 2024-08-20T21:36:28.0588180Z e9ea16c3c5d4: Verifying Checksum 2024-08-20T21:36:28.0588983Z e9ea16c3c5d4: Download complete 2024-08-20T21:36:28.1274022Z 66a5808f4ad5: Verifying Checksum 2024-08-20T21:36:28.1274534Z 66a5808f4ad5: Download complete 2024-08-20T21:36:28.1979261Z b92de615fef3: Verifying Checksum 2024-08-20T21:36:28.1979990Z b92de615fef3: Download complete 2024-08-20T21:36:28.2756469Z 56dd63029e04: Verifying Checksum 2024-08-20T21:36:28.2757230Z 56dd63029e04: Download complete 2024-08-20T21:36:28.3539841Z 0e19dd6ac611: Verifying Checksum 2024-08-20T21:36:28.3540640Z 0e19dd6ac611: Download complete 2024-08-20T21:36:28.4304102Z deffd0debe5e: Verifying Checksum 2024-08-20T21:36:28.4304864Z deffd0debe5e: Download complete 2024-08-20T21:36:28.4904723Z 823a4c61790a: Verifying Checksum 2024-08-20T21:36:28.4905301Z 823a4c61790a: Download complete 2024-08-20T21:36:28.5533492Z 52c09d8bb114: Download complete 2024-08-20T21:36:28.6203984Z fc113046ff5b: Verifying Checksum 2024-08-20T21:36:28.6204553Z fc113046ff5b: Download complete 2024-08-20T21:36:29.1043500Z 58fcfcf8962b: Verifying Checksum 2024-08-20T21:36:29.1044229Z 58fcfcf8962b: Download complete 2024-08-20T21:36:29.1774705Z a6bbbb7aff78: Download complete 2024-08-20T21:36:29.2528974Z 58c762741f83: Verifying Checksum 2024-08-20T21:36:29.2529997Z 58c762741f83: Download complete 2024-08-20T21:36:29.3166156Z 36d6dbd6c3c6: Verifying Checksum 2024-08-20T21:36:29.3166837Z 36d6dbd6c3c6: Download complete 2024-08-20T21:36:29.3878296Z f6e31afd70a7: Verifying Checksum 2024-08-20T21:36:29.3878814Z f6e31afd70a7: Download complete 2024-08-20T21:36:29.6415188Z 4076a363f04c: Verifying Checksum 2024-08-20T21:36:29.6415992Z 4076a363f04c: Download complete 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2024-08-20T21:37:55.3127282Z 005437c75141: Pull complete 2024-08-20T21:37:56.9453044Z 3ec2dad3f441: Pull complete 2024-08-20T21:37:57.2410365Z Digest: sha256:53bea9665c81f7bdf502e7eb907e80d01ec46372a842842380b5284cbc52773e 2024-08-20T21:37:57.2625224Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:37:57.3149677Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:37:57.3212191Z ##[group]Run echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT" 2024-08-20T21:37:57.3213340Z echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT" 2024-08-20T21:37:57.3220431Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:37:57.3220933Z env: 2024-08-20T21:37:57.3221194Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:37:57.3221526Z ##[endgroup] 2024-08-20T21:37:57.3327482Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-08-20T21:37:57.3328279Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-08-20T21:37:57.3328977Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 & 2024-08-20T21:37:57.3329623Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2024-08-20T21:37:57.3335963Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:37:57.3336749Z env: 2024-08-20T21:37:57.3337193Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:37:57.3337702Z ##[endgroup] 2024-08-20T21:38:00.0923618Z Defaulting to user installation because normal site-packages is not writeable 2024-08-20T21:38:00.2634960Z Requirement already satisfied: psutil==5.9.1 in /home/ec2-user/.local/lib/python3.9/site-packages (5.9.1) 2024-08-20T21:38:00.2640456Z Requirement already satisfied: nvidia-ml-py==11.525.84 in /home/ec2-user/.local/lib/python3.9/site-packages (11.525.84) 2024-08-20T21:38:00.6520259Z Prepare all required actions 2024-08-20T21:38:00.6520793Z Getting action download info 2024-08-20T21:38:00.7951154Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2024-08-20T21:38:00.9829909Z Download action repository 'actions/download-artifact@v3' (SHA:9bc31d5ccc31df68ecc42ccf4149144866c47d8a) 2024-08-20T21:38:01.1199373Z ##[group]Run ./.github/actions/download-build-artifacts 2024-08-20T21:38:01.1199873Z with: 2024-08-20T21:38:01.1200282Z name: linux-focal-py3.12-clang10-experimental-split-build 2024-08-20T21:38:01.1200839Z s3-bucket: gha-artifacts 2024-08-20T21:38:01.1201173Z env: 2024-08-20T21:38:01.1201423Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:01.1201760Z ##[endgroup] 2024-08-20T21:38:01.1335444Z ##[group]Run seemethere/download-artifact-s3@v4 2024-08-20T21:38:01.1335911Z with: 2024-08-20T21:38:01.1336319Z name: linux-focal-py3.12-clang10-experimental-split-build 2024-08-20T21:38:01.1337099Z s3-bucket: gha-artifacts 2024-08-20T21:38:01.1337438Z region: us-east-1 2024-08-20T21:38:01.1337730Z env: 2024-08-20T21:38:01.1337997Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:01.1338322Z ##[endgroup] 2024-08-20T21:38:01.6445466Z (node:368250) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-08-20T21:38:01.6446221Z 2024-08-20T21:38:01.6446478Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-08-20T21:38:01.6447263Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-08-20T21:38:01.6448531Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-08-20T21:38:01.7237842Z Found 1 objects with prefix pytorch/pytorch/10479309237/linux-focal-py3.12-clang10-experimental-split-build/ 2024-08-20T21:38:01.7239126Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-08-20T21:38:11.3135218Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-08-20T21:38:11.3142022Z Artifact download has finished successfully 2024-08-20T21:38:11.3304858Z ##[group]Run unzip -o artifacts.zip 2024-08-20T21:38:11.3305287Z unzip -o artifacts.zip 2024-08-20T21:38:11.3311134Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:38:11.3311637Z env: 2024-08-20T21:38:11.3311908Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:11.3312232Z ##[endgroup] 2024-08-20T21:38:11.3783889Z Archive: artifacts.zip 2024-08-20T21:38:11.3805766Z creating: dist/ 2024-08-20T21:38:12.2247578Z inflating: dist/torch_no_python-2.5.0a0+git40ec5f6-py3-none-any.whl 2024-08-20T21:38:12.6228345Z inflating: dist/torch-2.5.0a0+git40ec5f6-cp312-cp312-linux_x86_64.whl 2024-08-20T21:38:12.6229265Z creating: build/custom_test_artifacts/ 2024-08-20T21:38:12.6229860Z creating: build/custom_test_artifacts/custom-op-build/ 2024-08-20T21:38:12.6230899Z creating: 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build/lib/torch/ao/nn/sparse/quantized/dynamic/linear.py 2024-08-20T21:38:16.6444304Z creating: build/lib/torch/ao/ns/ 2024-08-20T21:38:16.6444762Z extracting: build/lib/torch/ao/ns/__init__.py 2024-08-20T21:38:16.6445314Z inflating: build/lib/torch/ao/ns/_numeric_suite.py 2024-08-20T21:38:16.6446712Z inflating: build/lib/torch/ao/ns/_numeric_suite_fx.py 2024-08-20T21:38:16.6447358Z creating: build/lib/torch/ao/ns/fx/ 2024-08-20T21:38:16.6447839Z extracting: build/lib/torch/ao/ns/fx/__init__.py 2024-08-20T21:38:16.6449229Z inflating: build/lib/torch/ao/ns/fx/graph_matcher.py 2024-08-20T21:38:16.6452362Z inflating: build/lib/torch/ao/ns/fx/graph_passes.py 2024-08-20T21:38:16.6453903Z inflating: build/lib/torch/ao/ns/fx/mappings.py 2024-08-20T21:38:16.6457787Z inflating: build/lib/torch/ao/ns/fx/n_shadows_utils.py 2024-08-20T21:38:16.6458443Z inflating: build/lib/torch/ao/ns/fx/ns_types.py 2024-08-20T21:38:16.6459398Z inflating: build/lib/torch/ao/ns/fx/pattern_utils.py 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inflating: build/lib/torch/ao/pruning/scheduler/lambda_scheduler.py 2024-08-20T21:38:16.6500068Z creating: build/lib/torch/ao/pruning/sparsifier/ 2024-08-20T21:38:16.6500713Z extracting: build/lib/torch/ao/pruning/sparsifier/__init__.py 2024-08-20T21:38:16.6501660Z inflating: build/lib/torch/ao/pruning/sparsifier/base_sparsifier.py 2024-08-20T21:38:16.6502529Z inflating: build/lib/torch/ao/pruning/sparsifier/nearly_diagonal_sparsifier.py 2024-08-20T21:38:16.6503309Z inflating: build/lib/torch/ao/pruning/sparsifier/utils.py 2024-08-20T21:38:16.6504086Z inflating: build/lib/torch/ao/pruning/sparsifier/weight_norm_sparsifier.py 2024-08-20T21:38:16.6504786Z creating: build/lib/torch/ao/quantization/ 2024-08-20T21:38:16.6505313Z inflating: build/lib/torch/ao/quantization/__init__.py 2024-08-20T21:38:16.6506069Z inflating: build/lib/torch/ao/quantization/_correct_bias.py 2024-08-20T21:38:16.6506722Z inflating: build/lib/torch/ao/quantization/_equalize.py 2024-08-20T21:38:16.6507469Z 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2024-08-20T21:38:17.8537777Z inflating: build/lib/torch/include/ATen/EmptyTensor.h 2024-08-20T21:38:17.8538864Z inflating: build/lib/torch/include/ATen/ExpandBase.h 2024-08-20T21:38:17.8539968Z inflating: build/lib/torch/include/ATen/ExpandUtils.h 2024-08-20T21:38:17.8541054Z extracting: build/lib/torch/include/ATen/Formatting.h 2024-08-20T21:38:17.8542075Z inflating: build/lib/torch/include/ATen/FuncTorchTLS.h 2024-08-20T21:38:17.8543343Z inflating: build/lib/torch/include/ATen/FunctionalStorageImpl.h 2024-08-20T21:38:17.8544801Z inflating: build/lib/torch/include/ATen/FunctionalTensorWrapper.h 2024-08-20T21:38:17.8546093Z extracting: build/lib/torch/include/ATen/Generator.h 2024-08-20T21:38:17.8547152Z inflating: build/lib/torch/include/ATen/InferSize.h 2024-08-20T21:38:17.8548410Z inflating: build/lib/torch/include/ATen/InitialTensorOptions.h 2024-08-20T21:38:17.8549595Z extracting: build/lib/torch/include/ATen/Layout.h 2024-08-20T21:38:17.8551980Z inflating: build/lib/torch/include/ATen/LegacyBatchedFallback.h 2024-08-20T21:38:17.8553439Z inflating: build/lib/torch/include/ATen/LegacyBatchedTensorImpl.h 2024-08-20T21:38:17.8554761Z inflating: build/lib/torch/include/ATen/LegacyVmapMode.h 2024-08-20T21:38:17.8555997Z inflating: build/lib/torch/include/ATen/LegacyVmapTransforms.h 2024-08-20T21:38:17.8558484Z inflating: build/lib/torch/include/ATen/LinalgBackend.h 2024-08-20T21:38:17.8559649Z inflating: build/lib/torch/include/ATen/MapAllocator.h 2024-08-20T21:38:17.8560778Z inflating: build/lib/torch/include/ATen/MatrixRef.h 2024-08-20T21:38:17.8561896Z inflating: build/lib/torch/include/ATen/MemoryOverlap.h 2024-08-20T21:38:17.8563039Z extracting: build/lib/torch/include/ATen/NamedTensor.h 2024-08-20T21:38:17.8564219Z inflating: build/lib/torch/include/ATen/NamedTensorUtils.h 2024-08-20T21:38:17.8565452Z inflating: build/lib/torch/include/ATen/NestedTensorImpl.h 2024-08-20T21:38:17.8566657Z inflating: build/lib/torch/include/ATen/NumericUtils.h 2024-08-20T21:38:17.8567778Z inflating: build/lib/torch/include/ATen/OpMathType.h 2024-08-20T21:38:17.8569369Z inflating: build/lib/torch/include/ATen/OpaqueTensorImpl.h 2024-08-20T21:38:17.8571555Z inflating: build/lib/torch/include/ATen/PTThreadPool.h 2024-08-20T21:38:17.8572616Z inflating: build/lib/torch/include/ATen/PadNd.h 2024-08-20T21:38:17.8573920Z inflating: build/lib/torch/include/ATen/Parallel-inl.h 2024-08-20T21:38:17.8574979Z inflating: build/lib/torch/include/ATen/Parallel.h 2024-08-20T21:38:17.8576030Z inflating: build/lib/torch/include/ATen/ParallelFuture.h 2024-08-20T21:38:17.8577207Z inflating: build/lib/torch/include/ATen/ParallelNative.h 2024-08-20T21:38:17.8578374Z inflating: build/lib/torch/include/ATen/ParallelOpenMP.h 2024-08-20T21:38:17.8579673Z inflating: build/lib/torch/include/ATen/PythonTorchFunctionTLS.h 2024-08-20T21:38:17.8580942Z inflating: build/lib/torch/include/ATen/SavedTensorHooks.h 2024-08-20T21:38:17.8582040Z extracting: build/lib/torch/include/ATen/Scalar.h 2024-08-20T21:38:17.8583076Z inflating: build/lib/torch/include/ATen/ScalarOps.h 2024-08-20T21:38:17.8584263Z inflating: build/lib/torch/include/ATen/ScalarType.h 2024-08-20T21:38:17.8585330Z inflating: build/lib/torch/include/ATen/SequenceNumber.h 2024-08-20T21:38:17.8586471Z extracting: build/lib/torch/include/ATen/SmallVector.h 2024-08-20T21:38:17.8587704Z inflating: build/lib/torch/include/ATen/SparseCsrTensorImpl.h 2024-08-20T21:38:17.8589023Z inflating: build/lib/torch/include/ATen/SparseCsrTensorUtils.h 2024-08-20T21:38:17.8590500Z inflating: build/lib/torch/include/ATen/SparseTensorImpl.h 2024-08-20T21:38:17.8591637Z extracting: build/lib/torch/include/ATen/Storage.h 2024-08-20T21:38:17.8592680Z inflating: build/lib/torch/include/ATen/StorageUtils.h 2024-08-20T21:38:17.8593729Z extracting: build/lib/torch/include/ATen/Tensor.h 2024-08-20T21:38:17.8595440Z inflating: build/lib/torch/include/ATen/TensorAccessor.h 2024-08-20T21:38:17.8596627Z inflating: build/lib/torch/include/ATen/TensorGeometry.h 2024-08-20T21:38:17.8597816Z inflating: build/lib/torch/include/ATen/TensorIndexing.h 2024-08-20T21:38:17.8598980Z inflating: build/lib/torch/include/ATen/TensorIterator.h 2024-08-20T21:38:17.8600260Z inflating: build/lib/torch/include/ATen/TensorIteratorInternal.h 2024-08-20T21:38:17.8601470Z inflating: build/lib/torch/include/ATen/TensorMeta.h 2024-08-20T21:38:17.8602503Z inflating: build/lib/torch/include/ATen/TensorNames.h 2024-08-20T21:38:17.8603659Z inflating: build/lib/torch/include/ATen/TensorOperators.h 2024-08-20T21:38:17.8605941Z extracting: build/lib/torch/include/ATen/TensorOptions.h 2024-08-20T21:38:17.8607288Z inflating: build/lib/torch/include/ATen/TensorSubclassLikeUtils.h 2024-08-20T21:38:17.8608575Z inflating: build/lib/torch/include/ATen/TensorUtils.h 2024-08-20T21:38:17.8609862Z inflating: build/lib/torch/include/ATen/ThreadLocalPythonObjects.h 2024-08-20T21:38:17.8611295Z inflating: build/lib/torch/include/ATen/ThreadLocalState.h 2024-08-20T21:38:17.8612484Z inflating: build/lib/torch/include/ATen/TracerMode.h 2024-08-20T21:38:17.8613591Z inflating: build/lib/torch/include/ATen/TypeDefault.h 2024-08-20T21:38:17.8614550Z inflating: build/lib/torch/include/ATen/Utils.h 2024-08-20T21:38:17.8615526Z inflating: build/lib/torch/include/ATen/Version.h 2024-08-20T21:38:17.8616615Z inflating: build/lib/torch/include/ATen/WrapDimUtils.h 2024-08-20T21:38:17.8617749Z inflating: build/lib/torch/include/ATen/WrapDimUtilsMulti.h 2024-08-20T21:38:17.8618898Z inflating: build/lib/torch/include/ATen/autocast_mode.h 2024-08-20T21:38:17.8619963Z inflating: build/lib/torch/include/ATen/ceil_div.h 2024-08-20T21:38:17.8621002Z inflating: build/lib/torch/include/ATen/code_template.h 2024-08-20T21:38:17.8622193Z inflating: build/lib/torch/include/ATen/cpp_custom_type_hack.h 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build/lib/torch/include/ATen/CompositeExplicitAutogradNonFunctionalFunctions_inl.h 2024-08-20T21:38:17.8640858Z inflating: build/lib/torch/include/ATen/CompositeImplicitAutogradFunctions.h 2024-08-20T21:38:17.8644180Z inflating: build/lib/torch/include/ATen/CompositeImplicitAutogradFunctions_inl.h 2024-08-20T21:38:17.8646221Z inflating: build/lib/torch/include/ATen/CompositeImplicitAutogradNestedTensorFunctions.h 2024-08-20T21:38:17.8648417Z inflating: build/lib/torch/include/ATen/CompositeImplicitAutogradNestedTensorFunctions_inl.h 2024-08-20T21:38:17.8650151Z inflating: build/lib/torch/include/ATen/Functions.h 2024-08-20T21:38:17.8651187Z inflating: build/lib/torch/include/ATen/MetaFunctions.h 2024-08-20T21:38:17.8652323Z inflating: build/lib/torch/include/ATen/MetaFunctions_inl.h 2024-08-20T21:38:17.8653476Z inflating: build/lib/torch/include/ATen/MethodOperators.h 2024-08-20T21:38:17.8654571Z inflating: build/lib/torch/include/ATen/NativeFunctions.h 2024-08-20T21:38:17.8655761Z inflating: build/lib/torch/include/ATen/NativeMetaFunctions.h 2024-08-20T21:38:17.8656880Z inflating: build/lib/torch/include/ATen/Operators.h 2024-08-20T21:38:17.8740707Z inflating: build/lib/torch/include/ATen/RedispatchFunctions.h 2024-08-20T21:38:17.8780398Z inflating: build/lib/torch/include/ATen/RegistrationDeclarations.h 2024-08-20T21:38:17.8848321Z inflating: build/lib/torch/include/ATen/VmapGeneratedPlumbing.h 2024-08-20T21:38:17.8849115Z inflating: build/lib/torch/include/ATen/CUDAFunctions.h 2024-08-20T21:38:17.8850747Z inflating: build/lib/torch/include/ATen/CUDAFunctions_inl.h 2024-08-20T21:38:17.8851493Z creating: build/lib/torch/include/ATen/cpu/ 2024-08-20T21:38:17.8852203Z inflating: build/lib/torch/include/ATen/cpu/FlushDenormal.h 2024-08-20T21:38:17.8852991Z inflating: build/lib/torch/include/ATen/cpu/Utils.h 2024-08-20T21:38:17.8853660Z inflating: build/lib/torch/include/ATen/cpu/vml.h 2024-08-20T21:38:17.8854222Z creating: build/lib/torch/include/ATen/cpu/vec/ 2024-08-20T21:38:17.8854951Z creating: build/lib/torch/include/ATen/cpu/vec/vec256/ 2024-08-20T21:38:17.8855737Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/missing_vld1_neon.h 2024-08-20T21:38:17.8856794Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/missing_vst1_neon.h 2024-08-20T21:38:17.8857607Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256.h 2024-08-20T21:38:17.8859261Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_bfloat16.h 2024-08-20T21:38:17.8860929Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_complex_double.h 2024-08-20T21:38:17.8862660Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_complex_float.h 2024-08-20T21:38:17.8863761Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_convert.h 2024-08-20T21:38:17.8865061Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_double.h 2024-08-20T21:38:17.8866998Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_float.h 2024-08-20T21:38:17.8869230Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_float_neon.h 2024-08-20T21:38:17.8871298Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_half_neon.h 2024-08-20T21:38:17.8875109Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_int.h 2024-08-20T21:38:17.8876053Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_mask.h 2024-08-20T21:38:17.8878757Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vec256_qint.h 2024-08-20T21:38:17.8879624Z creating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/ 2024-08-20T21:38:17.8880530Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_bfloat16_vsx.h 2024-08-20T21:38:17.8881538Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_common_vsx.h 2024-08-20T21:38:17.8882776Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vec256_complex_double_vsx.h 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2024-08-20T21:38:17.8897791Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/vsx/vsx_helpers.h 2024-08-20T21:38:17.8898625Z creating: build/lib/torch/include/ATen/cpu/vec/vec256/zarch/ 2024-08-20T21:38:17.8904222Z inflating: build/lib/torch/include/ATen/cpu/vec/vec256/zarch/vec256_zarch.h 2024-08-20T21:38:17.8905037Z creating: build/lib/torch/include/ATen/cpu/vec/vec512/ 2024-08-20T21:38:17.8905847Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512.h 2024-08-20T21:38:17.8909810Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_bfloat16.h 2024-08-20T21:38:17.8911723Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_complex_double.h 2024-08-20T21:38:17.8914370Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_complex_float.h 2024-08-20T21:38:17.8915416Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_convert.h 2024-08-20T21:38:17.8916495Z inflating: build/lib/torch/include/ATen/cpu/vec/vec512/vec512_double.h 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build/lib/torch/include/ATen/cpu/vec/vec_convert.h 2024-08-20T21:38:17.8935012Z inflating: build/lib/torch/include/ATen/cpu/vec/vec_half.h 2024-08-20T21:38:17.8935869Z inflating: build/lib/torch/include/ATen/cpu/vec/vec_mask.h 2024-08-20T21:38:17.8937229Z inflating: build/lib/torch/include/ATen/cpu/vec/vec_n.h 2024-08-20T21:38:17.8937972Z creating: build/lib/torch/include/ATen/core/ 2024-08-20T21:38:17.8938699Z inflating: build/lib/torch/include/ATen/core/ATenGeneral.h 2024-08-20T21:38:17.8939502Z inflating: build/lib/torch/include/ATen/core/ATenOpList.h 2024-08-20T21:38:17.8940276Z inflating: build/lib/torch/include/ATen/core/ATen_fwd.h 2024-08-20T21:38:17.8940938Z inflating: build/lib/torch/include/ATen/core/ATen_pch.h 2024-08-20T21:38:17.8941542Z inflating: build/lib/torch/include/ATen/core/Array.h 2024-08-20T21:38:17.8942346Z inflating: build/lib/torch/include/ATen/core/Backtrace.h 2024-08-20T21:38:17.8943200Z inflating: build/lib/torch/include/ATen/core/CachingHostAllocator.h 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inflating: build/lib/torch/include/ATen/core/PythonOpRegistrationTrampoline.h 2024-08-20T21:38:17.8964799Z inflating: build/lib/torch/include/ATen/core/QuantizerBase.h 2024-08-20T21:38:17.8965599Z inflating: build/lib/torch/include/ATen/core/Range.h 2024-08-20T21:38:17.8966272Z inflating: build/lib/torch/include/ATen/core/Reduction.h 2024-08-20T21:38:17.8967155Z extracting: build/lib/torch/include/ATen/core/Scalar.h 2024-08-20T21:38:17.8967798Z extracting: build/lib/torch/include/ATen/core/ScalarType.h 2024-08-20T21:38:17.8968415Z inflating: build/lib/torch/include/ATen/core/Tensor.h 2024-08-20T21:38:17.8969069Z inflating: build/lib/torch/include/ATen/core/TensorAccessor.h 2024-08-20T21:38:17.8970285Z inflating: build/lib/torch/include/ATen/core/TensorBase.h 2024-08-20T21:38:17.8971096Z inflating: build/lib/torch/include/ATen/core/TorchDispatchUtils.h 2024-08-20T21:38:17.8972139Z inflating: build/lib/torch/include/ATen/core/TransformationHelper.h 2024-08-20T21:38:17.8973094Z extracting: build/lib/torch/include/ATen/core/UndefinedTensorImpl.h 2024-08-20T21:38:17.8974041Z inflating: build/lib/torch/include/ATen/core/UnsafeFromTH.h 2024-08-20T21:38:17.8974897Z inflating: build/lib/torch/include/ATen/core/VariableHooksInterface.h 2024-08-20T21:38:17.8975905Z inflating: build/lib/torch/include/ATen/core/Variadic.h 2024-08-20T21:38:17.8976664Z inflating: build/lib/torch/include/ATen/core/Vitals.h 2024-08-20T21:38:17.8977401Z inflating: build/lib/torch/include/ATen/core/alias_info.h 2024-08-20T21:38:17.8978018Z inflating: build/lib/torch/include/ATen/core/blob.h 2024-08-20T21:38:17.8978865Z inflating: build/lib/torch/include/ATen/core/builtin_function.h 2024-08-20T21:38:17.8979602Z inflating: build/lib/torch/include/ATen/core/class_type.h 2024-08-20T21:38:17.8980394Z inflating: build/lib/torch/include/ATen/core/custom_class.h 2024-08-20T21:38:17.8981168Z inflating: build/lib/torch/include/ATen/core/dynamic_type.h 2024-08-20T21:38:17.8981967Z inflating: 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build/lib/torch/include/ATen/core/jit_type.h 2024-08-20T21:38:17.9005862Z inflating: build/lib/torch/include/ATen/core/jit_type_base.h 2024-08-20T21:38:17.9006699Z inflating: build/lib/torch/include/ATen/core/operator_name.h 2024-08-20T21:38:17.9007624Z inflating: build/lib/torch/include/ATen/core/qualified_name.h 2024-08-20T21:38:17.9008368Z inflating: build/lib/torch/include/ATen/core/rref_interface.h 2024-08-20T21:38:17.9009163Z inflating: build/lib/torch/include/ATen/core/stack.h 2024-08-20T21:38:17.9009900Z inflating: build/lib/torch/include/ATen/core/symbol.h 2024-08-20T21:38:17.9010763Z inflating: build/lib/torch/include/ATen/core/type_factory.h 2024-08-20T21:38:17.9011590Z inflating: build/lib/torch/include/ATen/core/type_ptr.h 2024-08-20T21:38:17.9012218Z extracting: build/lib/torch/include/ATen/core/typeid.h 2024-08-20T21:38:17.9026612Z inflating: build/lib/torch/include/ATen/core/TensorBody.h 2024-08-20T21:38:17.9030596Z inflating: build/lib/torch/include/ATen/core/aten_interned_strings.h 2024-08-20T21:38:17.9031385Z inflating: build/lib/torch/include/ATen/core/enum_tag.h 2024-08-20T21:38:17.9032022Z creating: build/lib/torch/include/ATen/core/boxing/ 2024-08-20T21:38:17.9033048Z inflating: build/lib/torch/include/ATen/core/boxing/BoxedKernel.h 2024-08-20T21:38:17.9033998Z inflating: build/lib/torch/include/ATen/core/boxing/BoxedKernel_impl.h 2024-08-20T21:38:17.9034980Z inflating: build/lib/torch/include/ATen/core/boxing/KernelFunction.h 2024-08-20T21:38:17.9036033Z inflating: build/lib/torch/include/ATen/core/boxing/KernelFunction_impl.h 2024-08-20T21:38:17.9037027Z inflating: build/lib/torch/include/ATen/core/boxing/OperatorKernel.h 2024-08-20T21:38:17.9037873Z creating: build/lib/torch/include/ATen/core/boxing/impl/ 2024-08-20T21:38:17.9038813Z inflating: build/lib/torch/include/ATen/core/boxing/impl/WrapFunctionIntoFunctor.h 2024-08-20T21:38:17.9040056Z inflating: 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inflating: build/lib/torch/include/ATen/cuda/Atomic.cuh 2024-08-20T21:38:17.9061854Z inflating: build/lib/torch/include/ATen/cuda/CUDAApplyUtils.cuh 2024-08-20T21:38:17.9062820Z inflating: build/lib/torch/include/ATen/cuda/CUDAGraphsUtils.cuh 2024-08-20T21:38:17.9063753Z inflating: build/lib/torch/include/ATen/cuda/CUDATensorMethods.cuh 2024-08-20T21:38:17.9064678Z inflating: build/lib/torch/include/ATen/cuda/DeviceUtils.cuh 2024-08-20T21:38:17.9065494Z inflating: build/lib/torch/include/ATen/cuda/NumericLimits.cuh 2024-08-20T21:38:17.9066241Z inflating: build/lib/torch/include/ATen/cuda/PhiloxUtils.cuh 2024-08-20T21:38:17.9067063Z inflating: build/lib/torch/include/ATen/cuda/ScanUtils.cuh 2024-08-20T21:38:17.9067853Z inflating: build/lib/torch/include/ATen/cuda/cub.cuh 2024-08-20T21:38:17.9068551Z inflating: build/lib/torch/include/ATen/cuda/cub_definitions.cuh 2024-08-20T21:38:17.9069459Z inflating: build/lib/torch/include/ATen/cuda/ATenCUDAGeneral.h 2024-08-20T21:38:17.9070259Z inflating: build/lib/torch/include/ATen/cuda/CUDABlas.h 2024-08-20T21:38:17.9071018Z inflating: build/lib/torch/include/ATen/cuda/CUDAContext.h 2024-08-20T21:38:17.9071831Z inflating: build/lib/torch/include/ATen/cuda/CUDAContextLight.h 2024-08-20T21:38:17.9072767Z inflating: build/lib/torch/include/ATen/cuda/CUDADataType.h 2024-08-20T21:38:17.9073567Z inflating: build/lib/torch/include/ATen/cuda/CUDADevice.h 2024-08-20T21:38:17.9074361Z inflating: build/lib/torch/include/ATen/cuda/CUDAEvent.h 2024-08-20T21:38:17.9075134Z inflating: build/lib/torch/include/ATen/cuda/CUDAGeneratorImpl.h 2024-08-20T21:38:17.9075945Z inflating: build/lib/torch/include/ATen/cuda/CUDAGraph.h 2024-08-20T21:38:17.9076676Z inflating: build/lib/torch/include/ATen/cuda/CUDASparse.h 2024-08-20T21:38:17.9077484Z inflating: build/lib/torch/include/ATen/cuda/CUDASparseBlas.h 2024-08-20T21:38:17.9078497Z inflating: build/lib/torch/include/ATen/cuda/CUDASparseDescriptors.h 2024-08-20T21:38:17.9079283Z inflating: build/lib/torch/include/ATen/cuda/CUDAUtils.h 2024-08-20T21:38:17.9080202Z inflating: build/lib/torch/include/ATen/cuda/CachingHostAllocator.h 2024-08-20T21:38:17.9081110Z inflating: build/lib/torch/include/ATen/cuda/EmptyTensor.h 2024-08-20T21:38:17.9082022Z inflating: build/lib/torch/include/ATen/cuda/Exceptions.h 2024-08-20T21:38:17.9082914Z inflating: build/lib/torch/include/ATen/cuda/PeerToPeerAccess.h 2024-08-20T21:38:17.9083754Z inflating: build/lib/torch/include/ATen/cuda/PhiloxCudaState.h 2024-08-20T21:38:17.9084686Z inflating: build/lib/torch/include/ATen/cuda/PinnedMemoryAllocator.h 2024-08-20T21:38:17.9085506Z inflating: build/lib/torch/include/ATen/cuda/Sleep.h 2024-08-20T21:38:17.9086307Z inflating: build/lib/torch/include/ATen/cuda/ThrustAllocator.h 2024-08-20T21:38:17.9087096Z inflating: build/lib/torch/include/ATen/cuda/cub.h 2024-08-20T21:38:17.9087806Z inflating: build/lib/torch/include/ATen/cuda/jiterator.h 2024-08-20T21:38:17.9088588Z inflating: build/lib/torch/include/ATen/cuda/jiterator_impl.h 2024-08-20T21:38:17.9089411Z inflating: build/lib/torch/include/ATen/cuda/llvm_jit_strings.h 2024-08-20T21:38:17.9090196Z creating: build/lib/torch/include/ATen/cuda/detail/ 2024-08-20T21:38:17.9091284Z inflating: build/lib/torch/include/ATen/cuda/detail/IndexUtils.cuh 2024-08-20T21:38:17.9092260Z inflating: build/lib/torch/include/ATen/cuda/detail/IntegerDivider.cuh 2024-08-20T21:38:17.9093224Z inflating: build/lib/torch/include/ATen/cuda/detail/OffsetCalculator.cuh 2024-08-20T21:38:17.9094228Z inflating: build/lib/torch/include/ATen/cuda/detail/PhiloxCudaStateRaw.cuh 2024-08-20T21:38:17.9095186Z inflating: build/lib/torch/include/ATen/cuda/detail/TensorInfo.cuh 2024-08-20T21:38:17.9096044Z inflating: build/lib/torch/include/ATen/cuda/detail/UnpackRaw.cuh 2024-08-20T21:38:17.9096929Z inflating: build/lib/torch/include/ATen/cuda/detail/CUDAHooks.h 2024-08-20T21:38:17.9097837Z inflating: build/lib/torch/include/ATen/cuda/detail/DeviceThreadHandles.h 2024-08-20T21:38:17.9098785Z inflating: build/lib/torch/include/ATen/cuda/detail/KernelUtils.h 2024-08-20T21:38:17.9099584Z inflating: build/lib/torch/include/ATen/cuda/detail/LazyNVRTC.h 2024-08-20T21:38:17.9100346Z creating: build/lib/torch/include/ATen/cuda/tunable/ 2024-08-20T21:38:17.9101017Z inflating: build/lib/torch/include/ATen/cuda/tunable/GemmCommon.h 2024-08-20T21:38:17.9101968Z inflating: build/lib/torch/include/ATen/cuda/tunable/GemmHipblaslt.h 2024-08-20T21:38:17.9102964Z inflating: build/lib/torch/include/ATen/cuda/tunable/GemmRocblas.h 2024-08-20T21:38:17.9103888Z inflating: build/lib/torch/include/ATen/cuda/tunable/StreamTimer.h 2024-08-20T21:38:17.9104797Z inflating: build/lib/torch/include/ATen/cuda/tunable/Tunable.h 2024-08-20T21:38:17.9105659Z inflating: build/lib/torch/include/ATen/cuda/tunable/TunableGemm.h 2024-08-20T21:38:17.9106410Z inflating: build/lib/torch/include/ATen/cuda/tunable/TunableOp.h 2024-08-20T21:38:17.9107153Z creating: build/lib/torch/include/ATen/cudnn/ 2024-08-20T21:38:17.9107862Z inflating: build/lib/torch/include/ATen/cudnn/Descriptors.h 2024-08-20T21:38:17.9108650Z extracting: build/lib/torch/include/ATen/cudnn/Exceptions.h 2024-08-20T21:38:17.9109458Z inflating: build/lib/torch/include/ATen/cudnn/Handle.h 2024-08-20T21:38:17.9110147Z extracting: build/lib/torch/include/ATen/cudnn/Handles.h 2024-08-20T21:38:17.9110873Z inflating: build/lib/torch/include/ATen/cudnn/Types.h 2024-08-20T21:38:17.9111499Z inflating: build/lib/torch/include/ATen/cudnn/Utils.h 2024-08-20T21:38:17.9112502Z inflating: build/lib/torch/include/ATen/cudnn/cudnn-wrapper.h 2024-08-20T21:38:17.9113259Z creating: build/lib/torch/include/ATen/functorch/ 2024-08-20T21:38:17.9114017Z inflating: build/lib/torch/include/ATen/functorch/ADInterpreters.h 2024-08-20T21:38:17.9114966Z inflating: build/lib/torch/include/ATen/functorch/BatchRulesHelper.h 2024-08-20T21:38:17.9115942Z inflating: 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2024-08-20T21:38:18.4523611Z inflating: build/lib/torch/include/torch/csrc/api/include/torch/serialize/archive.h 2024-08-20T21:38:18.4524503Z inflating: build/lib/torch/include/torch/csrc/api/include/torch/serialize/input-archive.h 2024-08-20T21:38:18.4525139Z inflating: build/lib/torch/include/torch/csrc/api/include/torch/serialize/output-archive.h 2024-08-20T21:38:18.4525906Z inflating: build/lib/torch/include/torch/csrc/api/include/torch/serialize/tensor.h 2024-08-20T21:38:18.4526213Z creating: build/lib/torch/include/torch/csrc/autograd/ 2024-08-20T21:38:18.4528189Z inflating: build/lib/torch/include/torch/csrc/autograd/FunctionsManual.h 2024-08-20T21:38:18.4528779Z inflating: build/lib/torch/include/torch/csrc/autograd/InferenceMode.h 2024-08-20T21:38:18.4530262Z inflating: build/lib/torch/include/torch/csrc/autograd/VariableTypeUtils.h 2024-08-20T21:38:18.4530881Z inflating: build/lib/torch/include/torch/csrc/autograd/anomaly_mode.h 2024-08-20T21:38:18.4531657Z inflating: 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build/lib/torch/include/torch/csrc/autograd/graph_task.h 2024-08-20T21:38:18.4544332Z inflating: build/lib/torch/include/torch/csrc/autograd/input_buffer.h 2024-08-20T21:38:18.4545065Z inflating: build/lib/torch/include/torch/csrc/autograd/input_metadata.h 2024-08-20T21:38:18.4545935Z inflating: build/lib/torch/include/torch/csrc/autograd/jit_decomp_interface.h 2024-08-20T21:38:18.4546487Z inflating: build/lib/torch/include/torch/csrc/autograd/profiler.h 2024-08-20T21:38:18.4547649Z inflating: build/lib/torch/include/torch/csrc/autograd/profiler_kineto.h 2024-08-20T21:38:18.4549061Z inflating: build/lib/torch/include/torch/csrc/autograd/profiler_legacy.h 2024-08-20T21:38:18.4549755Z inflating: build/lib/torch/include/torch/csrc/autograd/profiler_python.h 2024-08-20T21:38:18.4550486Z inflating: build/lib/torch/include/torch/csrc/autograd/python_anomaly_mode.h 2024-08-20T21:38:18.4551072Z inflating: build/lib/torch/include/torch/csrc/autograd/python_autograd.h 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build/lib/torch/include/torch/csrc/distributed/rpc/unpickled_python_call.h 2024-08-20T21:38:18.4694329Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/unpickled_python_remote_call.h 2024-08-20T21:38:18.4695033Z inflating: build/lib/torch/include/torch/csrc/distributed/rpc/utils.h 2024-08-20T21:38:18.4695597Z creating: build/lib/torch/include/torch/csrc/distributed/autograd/ 2024-08-20T21:38:18.4696107Z creating: build/lib/torch/include/torch/csrc/distributed/autograd/context/ 2024-08-20T21:38:18.4697234Z inflating: build/lib/torch/include/torch/csrc/distributed/autograd/context/container.h 2024-08-20T21:38:18.4698439Z inflating: build/lib/torch/include/torch/csrc/distributed/autograd/context/context.h 2024-08-20T21:38:18.4699054Z creating: build/lib/torch/include/torch/csrc/distributed/autograd/functions/ 2024-08-20T21:38:18.4700050Z inflating: build/lib/torch/include/torch/csrc/distributed/autograd/functions/recvrpc_backward.h 2024-08-20T21:38:18.4700859Z inflating: build/lib/torch/include/torch/csrc/distributed/autograd/functions/sendrpc_backward.h 2024-08-20T21:38:18.4701373Z creating: build/lib/torch/include/torch/csrc/distributed/autograd/rpc_messages/ 2024-08-20T21:38:18.4702423Z inflating: build/lib/torch/include/torch/csrc/distributed/autograd/rpc_messages/autograd_metadata.h 2024-08-20T21:38:18.4703622Z inflating: build/lib/torch/include/torch/csrc/distributed/autograd/rpc_messages/cleanup_autograd_context_req.h 2024-08-20T21:38:18.4704664Z inflating: build/lib/torch/include/torch/csrc/distributed/autograd/rpc_messages/cleanup_autograd_context_resp.h 2024-08-20T21:38:18.4705751Z inflating: build/lib/torch/include/torch/csrc/distributed/autograd/rpc_messages/propagate_gradients_req.h 2024-08-20T21:38:18.4706882Z inflating: build/lib/torch/include/torch/csrc/distributed/autograd/rpc_messages/propagate_gradients_resp.h 2024-08-20T21:38:18.4707945Z inflating: 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creating: build/lib/torch/include/torch/csrc/inductor/ 2024-08-20T21:38:18.4719930Z inflating: build/lib/torch/include/torch/csrc/inductor/inductor_ops.h 2024-08-20T21:38:18.4720474Z creating: build/lib/torch/include/torch/csrc/inductor/aoti_runner/ 2024-08-20T21:38:18.4721389Z inflating: build/lib/torch/include/torch/csrc/inductor/aoti_runner/model_container_runner.h 2024-08-20T21:38:18.4722101Z inflating: build/lib/torch/include/torch/csrc/inductor/aoti_runner/model_container_runner_cpu.h 2024-08-20T21:38:18.4722694Z inflating: build/lib/torch/include/torch/csrc/inductor/aoti_runner/model_container_runner_cuda.h 2024-08-20T21:38:18.4723411Z inflating: build/lib/torch/include/torch/csrc/inductor/aoti_runner/pybind.h 2024-08-20T21:38:18.4723921Z creating: build/lib/torch/include/torch/csrc/inductor/aoti_runtime/ 2024-08-20T21:38:18.4724622Z inflating: build/lib/torch/include/torch/csrc/inductor/aoti_runtime/arrayref_tensor.h 2024-08-20T21:38:18.4725195Z inflating: 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build/lib/torch/include/torch/csrc/inductor/aoti_torch/generated/c_shim_cuda.h 2024-08-20T21:38:18.4742510Z creating: build/lib/torch/include/torch/csrc/jit/ 2024-08-20T21:38:18.4742996Z inflating: build/lib/torch/include/torch/csrc/jit/jit_log.h 2024-08-20T21:38:18.4743566Z inflating: build/lib/torch/include/torch/csrc/jit/jit_opt_limit.h 2024-08-20T21:38:18.4744148Z inflating: build/lib/torch/include/torch/csrc/jit/resource_guard.h 2024-08-20T21:38:18.4744559Z creating: build/lib/torch/include/torch/csrc/jit/backends/ 2024-08-20T21:38:18.4745273Z inflating: build/lib/torch/include/torch/csrc/jit/backends/backend.h 2024-08-20T21:38:18.4746390Z inflating: build/lib/torch/include/torch/csrc/jit/backends/backend_debug_handler.h 2024-08-20T21:38:18.4747309Z inflating: build/lib/torch/include/torch/csrc/jit/backends/backend_debug_info.h 2024-08-20T21:38:18.4748003Z inflating: build/lib/torch/include/torch/csrc/jit/backends/backend_detail.h 2024-08-20T21:38:18.4748733Z inflating: 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inflating: build/bin/test_tensorexpr 2024-08-20T21:38:21.9594196Z inflating: build/bin/test_jit 2024-08-20T21:38:21.9632703Z inflating: build/bin/c10_intrusive_ptr_benchmark 2024-08-20T21:38:21.9678385Z inflating: build/bin/c10_Device_test 2024-08-20T21:38:21.9722085Z inflating: build/bin/c10_TypeTraits_test 2024-08-20T21:38:21.9766840Z inflating: build/bin/c10_TypeList_test 2024-08-20T21:38:21.9812650Z inflating: build/bin/c10_TypeIndex_test 2024-08-20T21:38:21.9857418Z inflating: build/bin/c10_Synchronized_test 2024-08-20T21:38:21.9900952Z inflating: build/bin/c10_ConstexprCrc_test 2024-08-20T21:38:21.9950336Z inflating: build/bin/c10_LeftRight_test 2024-08-20T21:38:21.9996163Z inflating: build/bin/c10_ssize_test 2024-08-20T21:38:22.0040547Z inflating: build/bin/c10_DeadlockDetection_test 2024-08-20T21:38:22.0085044Z inflating: build/bin/c10_Half_test 2024-08-20T21:38:22.0133788Z inflating: build/bin/c10_ThreadLocal_test 2024-08-20T21:38:22.0185404Z inflating: build/bin/c10_DispatchKeySet_test 2024-08-20T21:38:22.0229060Z inflating: build/bin/c10_StreamGuard_test 2024-08-20T21:38:22.0283529Z inflating: build/bin/c10_ordered_preserving_dict_test 2024-08-20T21:38:22.0328272Z inflating: build/bin/c10_CompileTimeFunctionPointer_test 2024-08-20T21:38:22.0373478Z inflating: build/bin/c10_tempfile_test 2024-08-20T21:38:22.0418967Z inflating: build/bin/c10_DeviceGuard_test 2024-08-20T21:38:22.0466895Z inflating: build/bin/c10_typeid_test 2024-08-20T21:38:22.0514424Z inflating: build/bin/c10_Scalar_test 2024-08-20T21:38:22.0559540Z inflating: build/bin/c10_SymInt_test 2024-08-20T21:38:22.0605915Z inflating: build/bin/c10_Bitset_test 2024-08-20T21:38:22.0666591Z inflating: build/bin/c10_cow_test 2024-08-20T21:38:22.0716900Z inflating: build/bin/c10_SizesAndStrides_test 2024-08-20T21:38:22.0764285Z inflating: build/bin/c10_InlineDeviceGuard_test 2024-08-20T21:38:22.0814135Z inflating: build/bin/c10_InlineStreamGuard_test 2024-08-20T21:38:22.0859786Z inflating: build/bin/c10_accumulate_test 2024-08-20T21:38:22.0904630Z inflating: build/bin/c10_bit_cast_test 2024-08-20T21:38:22.0952931Z inflating: build/bin/c10_bfloat16_test 2024-08-20T21:38:22.1003265Z inflating: build/bin/c10_string_view_test 2024-08-20T21:38:22.1049713Z inflating: build/bin/c10_exception_test 2024-08-20T21:38:22.1094885Z inflating: build/bin/c10_irange_test 2024-08-20T21:38:22.1140259Z inflating: build/bin/c10_flags_test 2024-08-20T21:38:22.1184479Z inflating: build/bin/c10_generic_math_test 2024-08-20T21:38:22.1233097Z inflating: build/bin/c10_complex_test 2024-08-20T21:38:22.1361007Z inflating: build/bin/c10_intrusive_ptr_test 2024-08-20T21:38:22.1412580Z inflating: build/bin/c10_complex_math_test 2024-08-20T21:38:22.1462368Z inflating: build/bin/c10_logging_test 2024-08-20T21:38:22.1530841Z inflating: build/bin/c10_optional_test 2024-08-20T21:38:22.1578944Z inflating: build/bin/c10_registry_test 2024-08-20T21:38:22.1625927Z inflating: build/bin/c10_lazy_test 2024-08-20T21:38:22.1754855Z inflating: build/bin/c10_small_vector_test 2024-08-20T21:38:22.1800548Z inflating: build/bin/c10_string_util_test 2024-08-20T21:38:22.1847912Z inflating: build/bin/c10_Metaprogramming_test 2024-08-20T21:38:22.1859605Z inflating: build/bin/torch_shm_manager 2024-08-20T21:38:22.1860204Z creating: .additional_ci_files/ 2024-08-20T21:38:22.1909567Z inflating: .additional_ci_files/test-times.json 2024-08-20T21:38:22.2138382Z inflating: .additional_ci_files/test-class-times.json 2024-08-20T21:38:22.2239977Z ##[group]Run rm artifacts.zip 2024-08-20T21:38:22.2240346Z rm artifacts.zip 2024-08-20T21:38:22.2246317Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:38:22.2246834Z env: 2024-08-20T21:38:22.2247100Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:22.2247440Z ##[endgroup] 2024-08-20T21:38:22.2704016Z ##[group]Run df -H 2024-08-20T21:38:22.2704321Z df -H 2024-08-20T21:38:22.2710126Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:38:22.2710634Z env: 2024-08-20T21:38:22.2710892Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:22.2711231Z ##[endgroup] 2024-08-20T21:38:22.2998056Z Filesystem Size Used Avail Use% Mounted on 2024-08-20T21:38:22.2998661Z devtmpfs 4.2M 0 4.2M 0% /dev 2024-08-20T21:38:22.2999163Z tmpfs 8.2G 3.4M 8.2G 1% /dev/shm 2024-08-20T21:38:22.2999625Z tmpfs 3.3G 488k 3.3G 1% /run 2024-08-20T21:38:22.3000084Z /dev/nvme0n1p1 161G 22G 140G 14% / 2024-08-20T21:38:22.3000565Z tmpfs 8.2G 29k 8.2G 1% /tmp 2024-08-20T21:38:22.3001483Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2024-08-20T21:38:22.3083844Z Prepare all required actions 2024-08-20T21:38:22.3084302Z Getting action download info 2024-08-20T21:38:22.4673628Z ##[group]Run ./.github/actions/download-td-artifacts 2024-08-20T21:38:22.4674114Z with: 2024-08-20T21:38:22.4674341Z env: 2024-08-20T21:38:22.4674614Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:22.4674952Z ##[endgroup] 2024-08-20T21:38:22.4756499Z ##[group]Run seemethere/download-artifact-s3@v4 2024-08-20T21:38:22.4756956Z with: 2024-08-20T21:38:22.4757201Z name: td_results 2024-08-20T21:38:22.4757510Z s3-bucket: gha-artifacts 2024-08-20T21:38:22.4757854Z region: us-east-1 2024-08-20T21:38:22.4758126Z env: 2024-08-20T21:38:22.4758389Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:22.4758719Z ##[endgroup] 2024-08-20T21:38:22.9694444Z (node:368275) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-08-20T21:38:22.9695216Z 2024-08-20T21:38:22.9695671Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-08-20T21:38:22.9696770Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-08-20T21:38:22.9698198Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-08-20T21:38:23.0402587Z Found 1 objects with prefix pytorch/pytorch/10479309237/td_results/ 2024-08-20T21:38:23.0403796Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-08-20T21:38:23.1086218Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-08-20T21:38:23.1092745Z Artifact download has finished successfully 2024-08-20T21:38:23.1308150Z ##[group]Run mkdir -p .additional_ci_files 2024-08-20T21:38:23.1308639Z mkdir -p .additional_ci_files 2024-08-20T21:38:23.1309201Z mv td_results.json .additional_ci_files/td_results.json 2024-08-20T21:38:23.1315618Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:38:23.1316102Z env: 2024-08-20T21:38:23.1316385Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:23.1316875Z ##[endgroup] 2024-08-20T21:38:23.1931221Z ##[group]Run .github/scripts/parse_ref.py 2024-08-20T21:38:23.1931702Z .github/scripts/parse_ref.py 2024-08-20T21:38:23.1937465Z shell: /usr/bin/bash -e {0} 2024-08-20T21:38:23.1937803Z env: 2024-08-20T21:38:23.1938066Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:23.1938392Z ##[endgroup] 2024-08-20T21:38:23.2255081Z Prepare all required actions 2024-08-20T21:38:23.2350707Z ##[group]Run ./.github/actions/get-workflow-job-id 2024-08-20T21:38:23.2351176Z with: 2024-08-20T21:38:23.2351832Z github-token: *** 2024-08-20T21:38:23.2352112Z env: 2024-08-20T21:38:23.2352374Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:23.2352706Z ##[endgroup] 2024-08-20T21:38:23.2474450Z ##[group]Run set -eux 2024-08-20T21:38:23.2474793Z set -eux 2024-08-20T21:38:23.2475385Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2024-08-20T21:38:23.2481513Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:38:23.2482018Z env: 2024-08-20T21:38:23.2482284Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:23.2482846Z GITHUB_TOKEN: *** 2024-08-20T21:38:23.2483133Z ##[endgroup] 2024-08-20T21:38:23.2509137Z + python3 .github/scripts/get_workflow_job_id.py 10479309237 i-0d639d84405661b71 2024-08-20T21:38:25.1202085Z setting job-id=29025338681 2024-08-20T21:38:25.1204090Z setting job-name=linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:38:25.1607980Z Prepare all required actions 2024-08-20T21:38:25.1608444Z Getting action download info 2024-08-20T21:38:25.2897092Z ##[group]Run ./.github/actions/filter-test-configs 2024-08-20T21:38:25.2897552Z with: 2024-08-20T21:38:25.2898007Z github-token: *** 2024-08-20T21:38:25.2900426Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}]} 2024-08-20T21:38:25.2903390Z job-name: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:38:25.2904263Z env: 2024-08-20T21:38:25.2904506Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:25.2904835Z ##[endgroup] 2024-08-20T21:38:25.3163776Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2024-08-20T21:38:25.3164350Z with: 2024-08-20T21:38:25.3164610Z shell: bash 2024-08-20T21:38:25.3164883Z timeout_minutes: 10 2024-08-20T21:38:25.3165199Z max_attempts: 5 2024-08-20T21:38:25.3165499Z retry_wait_seconds: 30 2024-08-20T21:38:25.3166632Z command: set -eux # PyYAML 6.0 doesn't work with MacOS x86 anymore # This must run on Python-3.7 (AmazonLinux2) so can't use request=3.32.2 python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-08-20T21:38:25.3167865Z polling_interval_seconds: 1 2024-08-20T21:38:25.3168225Z warning_on_retry: true 2024-08-20T21:38:25.3168559Z continue_on_error: false 2024-08-20T21:38:25.3168865Z env: 2024-08-20T21:38:25.3169227Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:25.3169779Z GITHUB_TOKEN: *** 2024-08-20T21:38:25.3170122Z ##[endgroup] 2024-08-20T21:38:25.4096821Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-08-20T21:38:25.6800346Z Defaulting to user installation because normal site-packages is not writeable 2024-08-20T21:38:25.6976117Z Requirement already satisfied: requests==2.27.1 in /home/ec2-user/.local/lib/python3.9/site-packages (2.27.1) 2024-08-20T21:38:25.6979857Z Requirement already satisfied: pyyaml==6.0.1 in /home/ec2-user/.local/lib/python3.9/site-packages (6.0.1) 2024-08-20T21:38:25.7099184Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2024-08-20T21:38:25.7109424Z Requirement already satisfied: charset-normalizer~=2.0.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2.0.12) 2024-08-20T21:38:25.7113381Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2024.7.4) 2024-08-20T21:38:25.7123842Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2024-08-20T21:38:26.3941378Z Command completed after 1 attempt(s). 2024-08-20T21:38:26.4030959Z ##[group]Run set -x 2024-08-20T21:38:26.4031295Z set -x 2024-08-20T21:38:26.4031587Z  2024-08-20T21:38:26.4032135Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-08-20T21:38:26.4032816Z # in runner workspace 2024-08-20T21:38:26.4033345Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2024-08-20T21:38:26.4040161Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:38:26.4040732Z env: 2024-08-20T21:38:26.4041030Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:26.4041373Z ##[endgroup] 2024-08-20T21:38:26.4066711Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2024-08-20T21:38:26.4373849Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2024-08-20T21:38:26.4374591Z echo "Workflow: ${GITHUB_WORKFLOW}" 2024-08-20T21:38:26.4375041Z echo "Job name: ${JOB_NAME}" 2024-08-20T21:38:26.4375434Z  2024-08-20T21:38:26.4375971Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-08-20T21:38:26.4376643Z # in runner workspace 2024-08-20T21:38:26.4377214Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2024-08-20T21:38:26.4377855Z  --workflow "${GITHUB_WORKFLOW}" \ 2024-08-20T21:38:26.4378296Z  --job-name "${JOB_NAME}" \ 2024-08-20T21:38:26.4380867Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}]}" \ 2024-08-20T21:38:26.4383447Z  --selected-test-configs "" \ 2024-08-20T21:38:26.4383890Z  --pr-number "${PR_NUMBER}" \ 2024-08-20T21:38:26.4384295Z  --tag "${TAG}" \ 2024-08-20T21:38:26.4384654Z  --event-name "${EVENT_NAME}" \ 2024-08-20T21:38:26.4385088Z  --schedule "${SCHEDULE}" \ 2024-08-20T21:38:26.4385508Z  --branch "${HEAD_BRANCH}" 2024-08-20T21:38:26.4391570Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:38:26.4392068Z env: 2024-08-20T21:38:26.4392334Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:26.4392862Z GITHUB_TOKEN: *** 2024-08-20T21:38:26.4393602Z JOB_NAME: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:38:26.4394441Z PR_NUMBER: 133712 2024-08-20T21:38:26.4394726Z TAG: 2024-08-20T21:38:26.4394986Z EVENT_NAME: pull_request 2024-08-20T21:38:26.4395311Z SCHEDULE: 2024-08-20T21:38:26.4395564Z HEAD_BRANCH: 2024-08-20T21:38:26.4395839Z ##[endgroup] 2024-08-20T21:38:26.4420444Z Workflow: pull 2024-08-20T21:38:26.4421671Z Job name: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:38:26.7758086Z INFO:root:Found no test-config label on the PR, so all test configs are included 2024-08-20T21:38:26.9463471Z ##[group]Run echo "Filtered matrix:" 2024-08-20T21:38:26.9463917Z echo "Filtered matrix:" 2024-08-20T21:38:26.9466419Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "default", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 1, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 2, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}, {"config": "dynamo", "shard": 3, "num_shards": 3, "runner": "amz2023.linux.2xlarge"}]}" 2024-08-20T21:38:26.9468913Z  2024-08-20T21:38:26.9469156Z echo 2024-08-20T21:38:26.9469513Z echo "Is the current job unstable? False" 2024-08-20T21:38:26.9469965Z  2024-08-20T21:38:26.9470203Z echo 2024-08-20T21:38:26.9470542Z echo "Is keep-going label set? False" 2024-08-20T21:38:26.9470984Z  2024-08-20T21:38:26.9471225Z echo 2024-08-20T21:38:26.9471520Z echo "Renabled issues? " 2024-08-20T21:38:26.9477366Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:38:26.9477850Z env: 2024-08-20T21:38:26.9478117Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:26.9478452Z ##[endgroup] 2024-08-20T21:38:26.9502027Z Filtered matrix: 2024-08-20T21:38:26.9505547Z {include: [{config: default, shard: 1, num_shards: 3, runner: amz2023.linux.2xlarge}, {config: default, shard: 2, num_shards: 3, runner: amz2023.linux.2xlarge}, {config: default, shard: 3, num_shards: 3, runner: amz2023.linux.2xlarge}, {config: dynamo, shard: 1, num_shards: 3, runner: amz2023.linux.2xlarge}, {config: dynamo, shard: 2, num_shards: 3, runner: amz2023.linux.2xlarge}, {config: dynamo, shard: 3, num_shards: 3, runner: amz2023.linux.2xlarge}]} 2024-08-20T21:38:26.9507849Z 2024-08-20T21:38:26.9507991Z Is the current job unstable? False 2024-08-20T21:38:26.9508286Z 2024-08-20T21:38:26.9508639Z Is keep-going label set? False 2024-08-20T21:38:26.9508885Z 2024-08-20T21:38:26.9509010Z Renabled issues? 2024-08-20T21:38:26.9621365Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-08-20T21:38:26.9622090Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-08-20T21:38:26.9628073Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T21:38:26.9628630Z env: 2024-08-20T21:38:26.9628894Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:26.9629214Z JOB_TIMEOUT: 600 2024-08-20T21:38:26.9629500Z ##[endgroup] 2024-08-20T21:38:26.9741356Z ##[group]Run set -x 2024-08-20T21:38:26.9741757Z set -x 2024-08-20T21:38:26.9742043Z  2024-08-20T21:38:26.9742385Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2024-08-20T21:38:26.9742915Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2024-08-20T21:38:26.9743474Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2024-08-20T21:38:26.9743982Z  TEST_COMMAND=.ci/onnx/test.sh 2024-08-20T21:38:26.9744395Z else 2024-08-20T21:38:26.9744724Z  TEST_COMMAND=.ci/pytorch/test.sh 2024-08-20T21:38:26.9745141Z fi 2024-08-20T21:38:26.9745387Z  2024-08-20T21:38:26.9745847Z # detached container should get cleaned up by teardown_ec2_linux 2024-08-20T21:38:26.9746623Z # TODO: Stop building test binaries as part of the build phase 2024-08-20T21:38:26.9747291Z # Used for GPU_FLAG since that doesn't play nice 2024-08-20T21:38:26.9747897Z # shellcheck disable=SC2086,SC2090 2024-08-20T21:38:26.9748338Z container_name=$(docker run \ 2024-08-20T21:38:26.9748749Z  ${GPU_FLAG:-} \ 2024-08-20T21:38:26.9749111Z  -e BUILD_ENVIRONMENT \ 2024-08-20T21:38:26.9749492Z  -e PR_NUMBER \ 2024-08-20T21:38:26.9749850Z  -e GITHUB_ACTIONS \ 2024-08-20T21:38:26.9750365Z  -e GITHUB_REPOSITORY \ 2024-08-20T21:38:26.9750751Z  -e GITHUB_WORKFLOW \ 2024-08-20T21:38:26.9751275Z  -e GITHUB_JOB \ 2024-08-20T21:38:26.9751687Z  -e GITHUB_RUN_ID \ 2024-08-20T21:38:26.9752084Z  -e GITHUB_RUN_NUMBER \ 2024-08-20T21:38:26.9752468Z  -e GITHUB_RUN_ATTEMPT \ 2024-08-20T21:38:26.9752865Z  -e JOB_ID \ 2024-08-20T21:38:26.9753198Z  -e JOB_NAME \ 2024-08-20T21:38:26.9753521Z  -e BASE_SHA \ 2024-08-20T21:38:26.9753863Z  -e BRANCH \ 2024-08-20T21:38:26.9754184Z  -e SHA1 \ 2024-08-20T21:38:26.9754503Z  -e AWS_DEFAULT_REGION \ 2024-08-20T21:38:26.9754899Z  -e IN_WHEEL_TEST \ 2024-08-20T21:38:26.9755266Z  -e SHARD_NUMBER \ 2024-08-20T21:38:26.9755609Z  -e TEST_CONFIG \ 2024-08-20T21:38:26.9755970Z  -e NUM_TEST_SHARDS \ 2024-08-20T21:38:26.9756353Z  -e REENABLED_ISSUES \ 2024-08-20T21:38:26.9756740Z  -e CONTINUE_THROUGH_ERROR \ 2024-08-20T21:38:26.9757164Z  -e VERBOSE_TEST_LOGS \ 2024-08-20T21:38:26.9757565Z  -e TEST_SHOWLOCALS \ 2024-08-20T21:38:26.9757930Z  -e NO_TEST_TIMEOUT \ 2024-08-20T21:38:26.9758296Z  -e NO_TD \ 2024-08-20T21:38:26.9758626Z  -e TD_DISTRIBUTED \ 2024-08-20T21:38:26.9758981Z  -e PR_LABELS \ 2024-08-20T21:38:26.9759432Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2024-08-20T21:38:26.9759882Z  -e SCCACHE_BUCKET \ 2024-08-20T21:38:26.9760252Z  -e SCCACHE_S3_KEY_PREFIX \ 2024-08-20T21:38:26.9760658Z  -e XLA_CUDA \ 2024-08-20T21:38:26.9761051Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2024-08-20T21:38:26.9761547Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2024-08-20T21:38:26.9762074Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2024-08-20T21:38:26.9762585Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2024-08-20T21:38:26.9763035Z  -e HUGGING_FACE_HUB_TOKEN \ 2024-08-20T21:38:26.9763480Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2024-08-20T21:38:26.9763920Z  -e DASHBOARD_TAG \ 2024-08-20T21:38:26.9764373Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2024-08-20T21:38:26.9764924Z  --security-opt seccomp=unconfined \ 2024-08-20T21:38:26.9765387Z  --cap-add=SYS_PTRACE \ 2024-08-20T21:38:26.9765770Z  --ipc=host \ 2024-08-20T21:38:26.9766099Z  --shm-size="${SHM_SIZE}" \ 2024-08-20T21:38:26.9766485Z  --tty \ 2024-08-20T21:38:26.9766784Z  --detach \ 2024-08-20T21:38:26.9767229Z  --name="${container_name}" \ 2024-08-20T21:38:26.9767651Z  --user jenkins \ 2024-08-20T21:38:26.9768135Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2024-08-20T21:38:26.9768689Z  -w /var/lib/jenkins/workspace \ 2024-08-20T21:38:26.9769125Z  "${DOCKER_IMAGE}" 2024-08-20T21:38:26.9769471Z ) 2024-08-20T21:38:26.9769857Z # Propagate download.pytorch.org IP to container 2024-08-20T21:38:26.9770923Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2024-08-20T21:38:26.9772078Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2024-08-20T21:38:26.9773006Z docker exec -t "${container_name}" sh -c "pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2024-08-20T21:38:26.9778912Z shell: /usr/bin/bash -e {0} 2024-08-20T21:38:26.9779258Z env: 2024-08-20T21:38:26.9779523Z GIT_DEFAULT_BRANCH: main 2024-08-20T21:38:26.9780082Z BUILD_ENVIRONMENT: linux-focal-py3.12-clang10-experimental-split-build 2024-08-20T21:38:26.9780697Z PR_NUMBER: 133712 2024-08-20T21:38:26.9781026Z GITHUB_REPOSITORY: pytorch/pytorch 2024-08-20T21:38:26.9781420Z GITHUB_WORKFLOW: pull 2024-08-20T21:38:26.9781742Z GITHUB_JOB: test 2024-08-20T21:38:26.9782045Z GITHUB_RUN_ID: 10479309237 2024-08-20T21:38:26.9782449Z GITHUB_RUN_NUMBER: 238988 2024-08-20T21:38:26.9783035Z GITHUB_RUN_ATTEMPT: 1 2024-08-20T21:38:26.9783356Z JOB_ID: 29025338681 2024-08-20T21:38:26.9784087Z JOB_NAME: linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:38:26.9784915Z BRANCH: pull/133712 2024-08-20T21:38:26.9785287Z SHA1: 40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:38:26.9785796Z BASE_SHA: 91f3d614142df02f619e44a68e3d9e0dfeba49ec 2024-08-20T21:38:26.9786258Z TEST_CONFIG: dynamo 2024-08-20T21:38:26.9786566Z SHARD_NUMBER: 2 2024-08-20T21:38:26.9786845Z NUM_TEST_SHARDS: 3 2024-08-20T21:38:26.9787161Z REENABLED_ISSUES: 2024-08-20T21:38:26.9787487Z CONTINUE_THROUGH_ERROR: False 2024-08-20T21:38:26.9787854Z VERBOSE_TEST_LOGS: False 2024-08-20T21:38:26.9788200Z TEST_SHOWLOCALS: False 2024-08-20T21:38:26.9788533Z NO_TEST_TIMEOUT: False 2024-08-20T21:38:26.9801601Z NO_TD: False 2024-08-20T21:38:26.9801934Z TD_DISTRIBUTED: False 2024-08-20T21:38:26.9802357Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2024-08-20T21:38:26.9802852Z SCCACHE_S3_KEY_PREFIX: pull 2024-08-20T21:38:26.9803201Z SHM_SIZE: 1g 2024-08-20T21:38:26.9804113Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:38:26.9805108Z XLA_CUDA: 2024-08-20T21:38:26.9805583Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2024-08-20T21:38:26.9806206Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2024-08-20T21:38:26.9806615Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2024-08-20T21:38:26.9807016Z DASHBOARD_TAG: 2024-08-20T21:38:26.9807307Z HUGGING_FACE_HUB_TOKEN: 2024-08-20T21:38:26.9807642Z SCRIBE_GRAPHQL_ACCESS_TOKEN: 2024-08-20T21:38:26.9807999Z ##[endgroup] 2024-08-20T21:38:26.9832637Z + [[ dynamo == \m\u\l\t\i\g\p\u ]] 2024-08-20T21:38:26.9833773Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *onnx* ]] 2024-08-20T21:38:26.9834412Z + TEST_COMMAND=.ci/pytorch/test.sh 2024-08-20T21:38:26.9841666Z +++ nproc --ignore=2 2024-08-20T21:38:27.0099358Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e TEST_SHOWLOCALS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=6 -e SCCACHE_BUCKET -e SCCACHE_S3_KEY_PREFIX -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG --env-file=/tmp/github_env_10479309237 --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:f6d216893d65c7b8ae43df4daaf247db808378e9 2024-08-20T21:38:32.9149405Z + container_name=ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T21:38:32.9152243Z + grep download.pytorch.org /etc/hosts 2024-08-20T21:38:32.9154078Z + docker exec -i ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e sudo bash -c '/bin/cat >> /etc/hosts' 2024-08-20T21:38:33.0665937Z + echo DOCKER_CONTAINER_ID=ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T21:38:33.0669968Z ++ echo dist/torch-2.5.0a0+git40ec5f6-cp312-cp312-linux_x86_64.whl dist/torch_no_python-2.5.0a0+git40ec5f6-py3-none-any.whl 2024-08-20T21:38:33.0672437Z + docker exec -t ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e sh -c 'pip install dist/torch-2.5.0a0+git40ec5f6-cp312-cp312-linux_x86_64.whl dist/torch_no_python-2.5.0a0+git40ec5f6-py3-none-any.whl[opt-einsum] && .ci/pytorch/test.sh' 2024-08-20T21:38:33.5363386Z Processing ./dist/torch-2.5.0a0+git40ec5f6-cp312-cp312-linux_x86_64.whl 2024-08-20T21:38:33.7625705Z Processing ./dist/torch_no_python-2.5.0a0+git40ec5f6-py3-none-any.whl (from torch-no-python==2.5.0a0+git40ec5f6) 2024-08-20T21:38:34.1242529Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6) (3.13.1) 2024-08-20T21:38:34.1245160Z 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+git40ec5f6) (4.12.2) 2024-08-20T21:38:34.1248184Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6) (2.8.8) 2024-08-20T21:38:34.1252010Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6) (3.1.4) 2024-08-20T21:38:34.1254710Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6) (2024.6.1) 2024-08-20T21:38:34.1267148Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6) (72.1.0) 2024-08-20T21:38:34.1273182Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.5.0a0+git40ec5f6) (1.13.1) 2024-08-20T21:38:34.1301482Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy==1.13.1->torch==2.5.0a0+git40ec5f6) (1.3.0) 2024-08-20T21:38:34.1320928Z 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+git40ec5f6->torch-no-python==2.5.0a0+git40ec5f6) (3.3.0) 2024-08-20T21:38:34.1335546Z 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+git40ec5f6->torch-no-python==2.5.0a0+git40ec5f6) (1.26.0) 2024-08-20T21:38:34.1437472Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch==2.5.0a0+git40ec5f6) (2.1.5) 2024-08-20T21:38:34.3582148Z Installing collected packages: torch-no-python, torch 2024-08-20T21:38:44.8584346Z Successfully installed torch-2.5.0a0+git40ec5f6 torch-no-python-2.5.0a0+git40ec5f6 2024-08-20T21:38:44.9457444Z + export TERM=vt100 2024-08-20T21:38:44.9457808Z + TERM=vt100 2024-08-20T21:38:44.9459332Z ++ dirname .ci/pytorch/test.sh 2024-08-20T21:38:44.9492489Z + source .ci/pytorch/common.sh 2024-08-20T21:38:44.9501646Z +++ dirname .ci/pytorch/common.sh 2024-08-20T21:38:44.9508278Z ++ source .ci/pytorch/common_utils.sh 2024-08-20T21:38:44.9515152Z +++ declare -f -t trap_add 2024-08-20T21:38:44.9520479Z ++ set -ex 2024-08-20T21:38:44.9521195Z ++ [[ linux-focal-py3.12-clang10-experimental-split-build == *rocm* ]] 2024-08-20T21:38:44.9521857Z ++ BUILD_TEST_LIBTORCH=0 2024-08-20T21:38:44.9522494Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *rocm* ]] 2024-08-20T21:38:44.9525040Z ++ stat -c %u /var/lib/jenkins/workspace 2024-08-20T21:38:44.9580849Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2024-08-20T21:38:44.9581572Z + trap_add cleanup_workspace EXIT 2024-08-20T21:38:44.9581988Z + trap_add_cmd=cleanup_workspace 2024-08-20T21:38:44.9582347Z + shift 2024-08-20T21:38:44.9582623Z + for trap_add_name in "$@" 2024-08-20T21:38:44.9588020Z +++ trap -p EXIT 2024-08-20T21:38:44.9590809Z ++ eval 'extract_trap_cmd ' 2024-08-20T21:38:44.9591475Z +++ extract_trap_cmd 2024-08-20T21:38:44.9592112Z +++ printf '%s\n' '' 2024-08-20T21:38:44.9592669Z ++ printf '%s\n' cleanup_workspace 2024-08-20T21:38:44.9594528Z + trap -- ' 2024-08-20T21:38:44.9595059Z cleanup_workspace' EXIT 2024-08-20T21:38:44.9595858Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2024-08-20T21:38:45.4582832Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2024-08-20T21:38:45.4811306Z + echo 'Environment variables:' 2024-08-20T21:38:45.4811791Z Environment variables: 2024-08-20T21:38:45.4812103Z + env 2024-08-20T21:38:45.4832647Z INSTALLED_DB=yes 2024-08-20T21:38:45.4833590Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:38:45.4834238Z CONTINUE_THROUGH_ERROR=False 2024-08-20T21:38:45.4835108Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10-experimental-split-build 2024-08-20T21:38:45.4835969Z HOSTNAME=ff189dbb7266 2024-08-20T21:38:45.4836883Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_679ca821-45db-4d50-bd33-b1502ca1c50f 2024-08-20T21:38:45.4837708Z GITHUB_ACTION=__self 2024-08-20T21:38:45.4838131Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-08-20T21:38:45.4838600Z GITHUB_RUN_NUMBER=238988 2024-08-20T21:38:45.4838913Z TEST_CONFIG=dynamo 2024-08-20T21:38:45.4839288Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-08-20T21:38:45.4839763Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-08-20T21:38:45.4840170Z SCRIBE_GRAPHQL_ACCESS_TOKEN= 2024-08-20T21:38:45.4840709Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2024-08-20T21:38:45.4841240Z GITHUB_REF_TYPE=branch 2024-08-20T21:38:45.4841556Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-08-20T21:38:45.4842023Z BASE_SHA=91f3d614142df02f619e44a68e3d9e0dfeba49ec 2024-08-20T21:38:45.4842463Z XLA_CUDA= 2024-08-20T21:38:45.4842717Z HUGGING_FACE_HUB_TOKEN= 2024-08-20T21:38:45.4843472Z *** 2024-08-20T21:38:45.4843741Z GITHUB_REPOSITORY_ID=65600975 2024-08-20T21:38:45.4844150Z GITHUB_ACTIONS=true 2024-08-20T21:38:45.4844511Z SHA1=40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:38:45.4845034Z GITHUB_SHA=f2fb9405c2fa9f9502a76363091cce6fd8179736 2024-08-20T21:38:45.4845746Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/133712/merge 2024-08-20T21:38:45.4846452Z UCC_HOME=/usr 2024-08-20T21:38:45.4846736Z VERBOSE_TEST_LOGS=False 2024-08-20T21:38:45.4847062Z GITHUB_REF=refs/pull/133712/merge 2024-08-20T21:38:45.4847426Z SHARD_NUMBER=2 2024-08-20T21:38:45.4847722Z GITHUB_REF_PROTECTED=false 2024-08-20T21:38:45.4848054Z HOME=/var/lib/jenkins 2024-08-20T21:38:45.4848403Z GITHUB_API_URL=https://api.github.com 2024-08-20T21:38:45.4848837Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-08-20T21:38:45.4849294Z UCX_COMMIT= 2024-08-20T21:38:45.4849578Z SCCACHE_S3_KEY_PREFIX=pull 2024-08-20T21:38:45.4849922Z NUM_TEST_SHARDS=3 2024-08-20T21:38:45.4850273Z UCX_HOME=/usr 2024-08-20T21:38:45.4851183Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_679ca821-45db-4d50-bd33-b1502ca1c50f 2024-08-20T21:38:45.4852869Z JOB_NAME=linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:38:45.4854237Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_679ca821-45db-4d50-bd33-b1502ca1c50f 2024-08-20T21:38:45.4855422Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-08-20T21:38:45.4856101Z GITHUB_EVENT_NAME=pull_request 2024-08-20T21:38:45.4856454Z DASHBOARD_TAG= 2024-08-20T21:38:45.4856735Z GITHUB_RUN_ID=10479309237 2024-08-20T21:38:45.4857697Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_679ca821-45db-4d50-bd33-b1502ca1c50f 2024-08-20T21:38:45.4858608Z GITHUB_ACTOR=pytorchmergebot 2024-08-20T21:38:45.4858941Z PR_NUMBER=133712 2024-08-20T21:38:45.4859252Z DESIRED_CUDA= 2024-08-20T21:38:45.4859530Z GITHUB_RUN_ATTEMPT=1 2024-08-20T21:38:45.4859837Z ANACONDA_PYTHON_VERSION=3.12 2024-08-20T21:38:45.4860269Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-08-20T21:38:45.4860728Z TERM=vt100 2024-08-20T21:38:45.4860980Z INSTALLED_VISION=yes 2024-08-20T21:38:45.4861288Z BRANCH=pull/133712 2024-08-20T21:38:45.4861598Z OPENSSL_ROOT_DIR=/opt/openssl 2024-08-20T21:38:45.4861943Z CUDA_PATH=/usr/local/cuda 2024-08-20T21:38:45.4862722Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-08-20T21:38:45.4863496Z GITHUB_SERVER_URL=https://github.com 2024-08-20T21:38:45.4864150Z UCC_COMMIT= 2024-08-20T21:38:45.4864420Z REENABLED_ISSUES= 2024-08-20T21:38:45.4864704Z DOCS= 2024-08-20T21:38:45.4864942Z INSTALLED_ANDROID= 2024-08-20T21:38:45.4865231Z SHLVL=1 2024-08-20T21:38:45.4865460Z MAX_JOBS=6 2024-08-20T21:38:45.4865728Z GITHUB_ACTOR_ID=97764156 2024-08-20T21:38:45.4866186Z GITHUB_WORKFLOW_SHA=f2fb9405c2fa9f9502a76363091cce6fd8179736 2024-08-20T21:38:45.4866696Z GITHUB_REF_NAME=133712/merge 2024-08-20T21:38:45.4867334Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-08-20T21:38:45.4867912Z GITHUB_JOB=test 2024-08-20T21:38:45.4868196Z NO_TEST_TIMEOUT=False 2024-08-20T21:38:45.4868516Z TD_DISTRIBUTED=False 2024-08-20T21:38:45.4868854Z GITHUB_REPOSITORY=pytorch/pytorch 2024-08-20T21:38:45.4869230Z GITHUB_RETENTION_DAYS=90 2024-08-20T21:38:45.4869565Z OPENSSL_DIR=/opt/openssl 2024-08-20T21:38:45.4869905Z GITHUB_ACTION_REPOSITORY= 2024-08-20T21:38:45.4871016Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-08-20T21:38:45.4872174Z GITHUB_BASE_REF=gh/XuehaiPan/146/base 2024-08-20T21:38:45.4872580Z INSTALLED_ACL= 2024-08-20T21:38:45.4872851Z CI=true 2024-08-20T21:38:45.4873116Z GITHUB_REPOSITORY_OWNER=pytorch 2024-08-20T21:38:45.4873485Z JOB_ID=29025338681 2024-08-20T21:38:45.4873793Z INSTALLED_PROTOBUF=yes 2024-08-20T21:38:45.4874128Z GITHUB_HEAD_REF=gh/XuehaiPan/146/head 2024-08-20T21:38:45.4874525Z GITHUB_ACTION_REF= 2024-08-20T21:38:45.4874976Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-08-20T21:38:45.4875436Z TEST_SHOWLOCALS=False 2024-08-20T21:38:45.4875755Z GITHUB_WORKFLOW=pull 2024-08-20T21:38:45.4876089Z DEBIAN_FRONTEND=noninteractive 2024-08-20T21:38:45.4877029Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_679ca821-45db-4d50-bd33-b1502ca1c50f 2024-08-20T21:38:45.4877883Z NO_TD=False 2024-08-20T21:38:45.4878177Z SKIP_SCCACHE_INITIALIZATION=1 2024-08-20T21:38:45.4878522Z _=/usr/bin/env 2024-08-20T21:38:45.4879007Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2024-08-20T21:38:45.5019667Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch 2024-08-20T21:38:45.5020918Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2024-08-20T21:38:45.5022522Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib 2024-08-20T21:38:45.5023572Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/test 2024-08-20T21:38:45.5024200Z + BUILD_DIR=build 2024-08-20T21:38:45.5024742Z + BUILD_RENAMED_DIR=build_renamed 2024-08-20T21:38:45.5025177Z + BUILD_BIN_DIR=build/bin 2024-08-20T21:38:45.5025543Z + SHARD_NUMBER=2 2024-08-20T21:38:45.5025816Z + NUM_TEST_SHARDS=3 2024-08-20T21:38:45.5026331Z + export VALGRIND=ON 2024-08-20T21:38:45.5026811Z + VALGRIND=ON 2024-08-20T21:38:45.5027405Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *clang9* ]] 2024-08-20T21:38:45.5028280Z + [[ 0 == \1 ]] 2024-08-20T21:38:45.5028677Z + [[ False == \1 ]] 2024-08-20T21:38:45.5029554Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *bazel* ]] 2024-08-20T21:38:45.5030306Z ++ realpath build/custom_test_artifacts 2024-08-20T21:38:45.5056946Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2024-08-20T21:38:45.5057764Z + [[ -n '' ]] 2024-08-20T21:38:45.5058108Z + echo 'Environment variables' 2024-08-20T21:38:45.5058510Z Environment variables 2024-08-20T21:38:45.5058796Z + env 2024-08-20T21:38:45.5063561Z INSTALLED_DB=yes 2024-08-20T21:38:45.5064852Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T21:38:45.5066010Z CONTINUE_THROUGH_ERROR=False 2024-08-20T21:38:45.5066877Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10-experimental-split-build 2024-08-20T21:38:45.5067577Z HOSTNAME=ff189dbb7266 2024-08-20T21:38:45.5068522Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_679ca821-45db-4d50-bd33-b1502ca1c50f 2024-08-20T21:38:45.5069652Z GITHUB_ACTION=__self 2024-08-20T21:38:45.5069997Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-08-20T21:38:45.5070391Z GITHUB_RUN_NUMBER=238988 2024-08-20T21:38:45.5070777Z TEST_CONFIG=dynamo 2024-08-20T21:38:45.5071103Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-08-20T21:38:45.5071582Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-08-20T21:38:45.5072050Z SCRIBE_GRAPHQL_ACCESS_TOKEN= 2024-08-20T21:38:45.5072449Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2024-08-20T21:38:45.5072871Z GITHUB_REF_TYPE=branch 2024-08-20T21:38:45.5073196Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-08-20T21:38:45.5073613Z BASE_SHA=91f3d614142df02f619e44a68e3d9e0dfeba49ec 2024-08-20T21:38:45.5074052Z XLA_CUDA= 2024-08-20T21:38:45.5074303Z HUGGING_FACE_HUB_TOKEN= 2024-08-20T21:38:45.5074739Z *** 2024-08-20T21:38:45.5075005Z GITHUB_REPOSITORY_ID=65600975 2024-08-20T21:38:45.5075346Z GITHUB_ACTIONS=true 2024-08-20T21:38:45.5075705Z SHA1=40ec5f6ddd9787aca0449b24128343ff4c4a88b3 2024-08-20T21:38:45.5076218Z GITHUB_SHA=f2fb9405c2fa9f9502a76363091cce6fd8179736 2024-08-20T21:38:45.5076971Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/133712/merge 2024-08-20T21:38:45.5077621Z UCC_HOME=/usr 2024-08-20T21:38:45.5077903Z VERBOSE_TEST_LOGS=False 2024-08-20T21:38:45.5078221Z GITHUB_REF=refs/pull/133712/merge 2024-08-20T21:38:45.5078587Z SHARD_NUMBER=2 2024-08-20T21:38:45.5078880Z GITHUB_REF_PROTECTED=false 2024-08-20T21:38:45.5079198Z HOME=/var/lib/jenkins 2024-08-20T21:38:45.5079552Z GITHUB_API_URL=https://api.github.com 2024-08-20T21:38:45.5080024Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-08-20T21:38:45.5080387Z UCX_COMMIT= 2024-08-20T21:38:45.5080661Z SCCACHE_S3_KEY_PREFIX=pull 2024-08-20T21:38:45.5080994Z NUM_TEST_SHARDS=3 2024-08-20T21:38:45.5081277Z UCX_HOME=/usr 2024-08-20T21:38:45.5082148Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_679ca821-45db-4d50-bd33-b1502ca1c50f 2024-08-20T21:38:45.5083529Z JOB_NAME=linux-focal-py3.12-clang10-experimental-split-build / test (dynamo, 2, 3, amz2023.linux.2xlarge) 2024-08-20T21:38:45.5084951Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_679ca821-45db-4d50-bd33-b1502ca1c50f 2024-08-20T21:38:45.5086108Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-08-20T21:38:45.5086790Z GITHUB_EVENT_NAME=pull_request 2024-08-20T21:38:45.5087146Z DASHBOARD_TAG= 2024-08-20T21:38:45.5087419Z GITHUB_RUN_ID=10479309237 2024-08-20T21:38:45.5088541Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_679ca821-45db-4d50-bd33-b1502ca1c50f 2024-08-20T21:38:45.5089475Z GITHUB_ACTOR=pytorchmergebot 2024-08-20T21:38:45.5089813Z PR_NUMBER=133712 2024-08-20T21:38:45.5090167Z DESIRED_CUDA= 2024-08-20T21:38:45.5090686Z GITHUB_RUN_ATTEMPT=1 2024-08-20T21:38:45.5090975Z VALGRIND=ON 2024-08-20T21:38:45.5091261Z ANACONDA_PYTHON_VERSION=3.12 2024-08-20T21:38:45.5091699Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-08-20T21:38:45.5092149Z TERM=vt100 2024-08-20T21:38:45.5092417Z INSTALLED_VISION=yes 2024-08-20T21:38:45.5092725Z BRANCH=pull/133712 2024-08-20T21:38:45.5093021Z OPENSSL_ROOT_DIR=/opt/openssl 2024-08-20T21:38:45.5093419Z CUDA_PATH=/usr/local/cuda 2024-08-20T21:38:45.5094223Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-08-20T21:38:45.5094982Z GITHUB_SERVER_URL=https://github.com 2024-08-20T21:38:45.5095370Z UCC_COMMIT= 2024-08-20T21:38:45.5095646Z REENABLED_ISSUES= 2024-08-20T21:38:45.5095915Z DOCS= 2024-08-20T21:38:45.5096173Z INSTALLED_ANDROID= 2024-08-20T21:38:45.5096458Z SHLVL=1 2024-08-20T21:38:45.5096697Z MAX_JOBS=6 2024-08-20T21:38:45.5096979Z GITHUB_ACTOR_ID=97764156 2024-08-20T21:38:45.5097471Z GITHUB_WORKFLOW_SHA=f2fb9405c2fa9f9502a76363091cce6fd8179736 2024-08-20T21:38:45.5098000Z GITHUB_REF_NAME=133712/merge 2024-08-20T21:38:45.5098666Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-08-20T21:38:45.5099253Z GITHUB_JOB=test 2024-08-20T21:38:45.5099680Z NO_TEST_TIMEOUT=False 2024-08-20T21:38:45.5100004Z TD_DISTRIBUTED=False 2024-08-20T21:38:45.5100329Z GITHUB_REPOSITORY=pytorch/pytorch 2024-08-20T21:38:45.5100720Z GITHUB_RETENTION_DAYS=90 2024-08-20T21:38:45.5101060Z OPENSSL_DIR=/opt/openssl 2024-08-20T21:38:45.5101384Z GITHUB_ACTION_REPOSITORY= 2024-08-20T21:38:45.5102469Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-08-20T21:38:45.5103766Z GITHUB_BASE_REF=gh/XuehaiPan/146/base 2024-08-20T21:38:45.5104172Z INSTALLED_ACL= 2024-08-20T21:38:45.5104433Z CI=true 2024-08-20T21:38:45.5104715Z GITHUB_REPOSITORY_OWNER=pytorch 2024-08-20T21:38:45.5105080Z JOB_ID=29025338681 2024-08-20T21:38:45.5105365Z INSTALLED_PROTOBUF=yes 2024-08-20T21:38:45.5105710Z GITHUB_HEAD_REF=gh/XuehaiPan/146/head 2024-08-20T21:38:45.5106106Z GITHUB_ACTION_REF= 2024-08-20T21:38:45.5106550Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-08-20T21:38:45.5107025Z TEST_SHOWLOCALS=False 2024-08-20T21:38:45.5107343Z GITHUB_WORKFLOW=pull 2024-08-20T21:38:45.5107665Z DEBIAN_FRONTEND=noninteractive 2024-08-20T21:38:45.5108624Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_679ca821-45db-4d50-bd33-b1502ca1c50f 2024-08-20T21:38:45.5109472Z NO_TD=False 2024-08-20T21:38:45.5109748Z SKIP_SCCACHE_INITIALIZATION=1 2024-08-20T21:38:45.5110102Z _=/usr/bin/env 2024-08-20T21:38:45.5110444Z + echo 'Testing pytorch' 2024-08-20T21:38:45.5110757Z Testing pytorch 2024-08-20T21:38:45.5111075Z + export LANG=C.UTF-8 2024-08-20T21:38:45.5111402Z + LANG=C.UTF-8 2024-08-20T21:38:45.5138740Z + PR_NUMBER=133712 2024-08-20T21:38:45.5139248Z + [[ dynamo == \d\e\f\a\u\l\t ]] 2024-08-20T21:38:45.5139890Z + [[ dynamo == \d\i\s\t\r\i\b\u\t\e\d ]] 2024-08-20T21:38:45.5140550Z + [[ dynamo == \s\l\o\w ]] 2024-08-20T21:38:45.5141622Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *slow-gradcheck* ]] 2024-08-20T21:38:45.5142839Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *cuda* ]] 2024-08-20T21:38:45.5143699Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *rocm* ]] 2024-08-20T21:38:45.5144569Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *xpu* ]] 2024-08-20T21:38:45.5145177Z + [[ dynamo == *crossref* ]] 2024-08-20T21:38:45.5145792Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *rocm* ]] 2024-08-20T21:38:45.5146653Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *xpu* ]] 2024-08-20T21:38:45.5147695Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *-bazel-* ]] 2024-08-20T21:38:45.5148388Z + pip_install --user ninja==1.10.2 2024-08-20T21:38:45.5148914Z + pip install --progress-bar off --user ninja==1.10.2 2024-08-20T21:38:45.9901661Z Collecting ninja==1.10.2 2024-08-20T21:38:46.0078413Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2024-08-20T21:38:46.0182108Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2024-08-20T21:38:46.1739912Z Installing collected packages: ninja 2024-08-20T21:38:46.1822190Z  WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH. 2024-08-20T21:38:46.1823519Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2024-08-20T21:38:46.1897922Z Successfully installed ninja-1.10.2 2024-08-20T21:38:46.2678666Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-08-20T21:38:46.2681058Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-08-20T21:38:46.2682736Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *aarch64* ]] 2024-08-20T21:38:46.2683636Z + install_tlparse 2024-08-20T21:38:46.2684010Z + pip_install --user tlparse==0.3.25 2024-08-20T21:38:46.2684552Z + pip install --progress-bar off --user tlparse==0.3.25 2024-08-20T21:38:46.6564119Z Collecting tlparse==0.3.25 2024-08-20T21:38:46.6725521Z Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.7 kB) 2024-08-20T21:38:46.6832685Z Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB) 2024-08-20T21:38:46.8628815Z Installing collected packages: tlparse 2024-08-20T21:38:46.9000348Z Successfully installed tlparse-0.3.25 2024-08-20T21:38:46.9777981Z ++ python -m site --user-base 2024-08-20T21:38:46.9959486Z + PATH=/var/lib/jenkins/.local/bin:/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-08-20T21:38:46.9962098Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *asan* ]] 2024-08-20T21:38:46.9963077Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *-debug* ]] 2024-08-20T21:38:46.9964167Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *-bazel-* ]] 2024-08-20T21:38:46.9965563Z + echo 'We are not in debug mode: linux-focal-py3.12-clang10-experimental-split-build. Expect the assertion to pass' 2024-08-20T21:38:46.9967046Z We are not in debug mode: linux-focal-py3.12-clang10-experimental-split-build. Expect the assertion to pass 2024-08-20T21:38:46.9967951Z + cd test 2024-08-20T21:38:46.9968553Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2024-08-20T21:38:48.5594136Z + [[ dynamo == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2024-08-20T21:38:48.5594730Z + [[ dynamo == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2024-08-20T21:38:48.5598976Z + DYNAMO_BENCHMARK_FLAGS=() 2024-08-20T21:38:48.5600137Z + [[ dynamo == *pr_time_benchmarks* ]] 2024-08-20T21:38:48.5600949Z + [[ dynamo == *dynamo_eager* ]] 2024-08-20T21:38:48.5601712Z + [[ dynamo == *aot_eager* ]] 2024-08-20T21:38:48.5602409Z + [[ dynamo == *aot_inductor* ]] 2024-08-20T21:38:48.5603118Z + [[ dynamo == *inductor* ]] 2024-08-20T21:38:48.5603795Z + [[ dynamo == *dynamic* ]] 2024-08-20T21:38:48.5604454Z + [[ dynamo == *cpu* ]] 2024-08-20T21:38:48.5605418Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2024-08-20T21:38:48.5636623Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *libtorch* ]] 2024-08-20T21:38:48.5637615Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *-bazel-* ]] 2024-08-20T21:38:48.5639304Z + cd test 2024-08-20T21:38:48.5640931Z + python -c 'import torch; print(torch.__config__.show())' 2024-08-20T21:38:49.7971573Z PyTorch built with: 2024-08-20T21:38:49.7972410Z - GCC 4.2 2024-08-20T21:38:49.7972946Z - C++ Version: 201703 2024-08-20T21:38:49.7973624Z - clang 10.0.0 2024-08-20T21:38:49.7975038Z - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-08-20T21:38:49.7976293Z - Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) 2024-08-20T21:38:49.7976973Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-08-20T21:38:49.7977505Z - LAPACK is enabled (usually provided by MKL) 2024-08-20T21:38:49.7978004Z - NNPACK is enabled 2024-08-20T21:38:49.7978377Z - CPU capability usage: AVX512 2024-08-20T21:38:49.7986634Z - 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 -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=braced-scalar-init -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wvla-extension -Wnewline-eof -Winconsistent-missing-override -Winconsistent-missing-destructor-override -Wno-pass-failed -Wno-error=old-style-cast -Wconstant-conversion -Wno-missing-braces -Qunused-arguments -fcolor-diagnostics -faligned-new -fno-math-errno -fno-trapping-math -Werror=format, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.5.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 2024-08-20T21:38:49.7997353Z 2024-08-20T21:38:50.0661281Z + cd test 2024-08-20T21:38:50.0662091Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2024-08-20T21:38:51.2894132Z ATen/Parallel: 2024-08-20T21:38:51.2894846Z at::get_num_threads() : 4 2024-08-20T21:38:51.2895575Z at::get_num_interop_threads() : 4 2024-08-20T21:38:51.2896110Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-08-20T21:38:51.2896515Z omp_get_max_threads() : 4 2024-08-20T21:38:51.2897606Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-08-20T21:38:51.2898436Z mkl_get_max_threads() : 4 2024-08-20T21:38:51.2899052Z Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67) 2024-08-20T21:38:51.2899689Z std::thread::hardware_concurrency() : 8 2024-08-20T21:38:51.2900104Z Environment variables: 2024-08-20T21:38:51.2900427Z OMP_NUM_THREADS : [not set] 2024-08-20T21:38:51.2900793Z MKL_NUM_THREADS : [not set] 2024-08-20T21:38:51.2901155Z ATen parallel backend: OpenMP 2024-08-20T21:38:51.2901400Z 2024-08-20T21:38:51.5498168Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *aarch64* ]] 2024-08-20T21:38:51.5498934Z + [[ dynamo == *backward* ]] 2024-08-20T21:38:51.5499279Z + [[ dynamo == *xla* ]] 2024-08-20T21:38:51.5499692Z + [[ dynamo == *executorch* ]] 2024-08-20T21:38:51.5500123Z + [[ dynamo == \j\i\t\_\l\e\g\a\c\y ]] 2024-08-20T21:38:51.5500966Z + [[ linux-focal-py3.12-clang10-experimental-split-build == *libtorch* ]] 2024-08-20T21:38:51.5501659Z + [[ dynamo == distributed ]] 2024-08-20T21:38:51.5502100Z + [[ dynamo == *inductor_distributed* ]] 2024-08-20T21:38:51.5502567Z + [[ dynamo == *inductor-halide* ]] 2024-08-20T21:38:51.5503128Z + [[ dynamo == *inductor-micro-benchmark* ]] 2024-08-20T21:38:51.5503636Z + [[ dynamo == *huggingface* ]] 2024-08-20T21:38:51.5503987Z + [[ dynamo == *timm* ]] 2024-08-20T21:38:51.5504380Z + [[ dynamo == *torchbench* ]] 2024-08-20T21:38:51.5505146Z + [[ dynamo == *inductor_cpp_wrapper_abi_compatible* ]] 2024-08-20T21:38:51.5505630Z + [[ dynamo == *inductor* ]] 2024-08-20T21:38:51.5506041Z + [[ dynamo == *dynamo* ]] 2024-08-20T21:38:51.5506382Z + install_torchvision 2024-08-20T21:38:51.5506738Z + local orig_preload 2024-08-20T21:38:51.5507043Z + local commit 2024-08-20T21:38:51.5507393Z ++ get_pinned_commit vision 2024-08-20T21:38:51.5507750Z ++ cat .github/ci_commit_pins/vision.txt 2024-08-20T21:38:51.5524384Z + commit=d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:38:51.5524914Z + orig_preload= 2024-08-20T21:38:51.5525323Z + '[' -n '' ']' 2024-08-20T21:38:51.5526211Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:38:51.5527728Z + pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:38:51.8968284Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:38:51.8974535Z Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-z6k3bu6g 2024-08-20T21:38:51.9014145Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-z6k3bu6g 2024-08-20T21:38:53.4891226Z Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f' 2024-08-20T21:38:53.4910162Z Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:38:54.8201049Z Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:38:55.1039235Z Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f 2024-08-20T21:38:57.1789227Z Preparing metadata (setup.py) ... [?25l- \ done 2024-08-20T21:38:57.1821799Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (1.26.0) 2024-08-20T21:38:57.1825352Z 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+git40ec5f6) 2024-08-20T21:38:57.1829781Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (10.3.0) 2024-08-20T21:38:57.1878671Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.13.1) 2024-08-20T21:38:57.1883445Z Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2) 2024-08-20T21:38:57.1886772Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8) 2024-08-20T21:38:57.1889473Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.4) 2024-08-20T21:38:57.1892997Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.6.1) 2024-08-20T21:38:57.1897602Z Requirement already satisfied: torch-no-python==2.5.0a0+git40ec5f6 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.5.0a0+git40ec5f6) 2024-08-20T21:38:57.1908072Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (72.1.0) 2024-08-20T21:38:57.1913359Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.1) 2024-08-20T21:38:57.1927319Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy==1.13.1->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0) 2024-08-20T21:38:57.2057939Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (2.1.5) 2024-08-20T21:38:57.2156965Z Building wheels for collected packages: torchvision 2024-08-20T21:40:06.9656908Z Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / done 2024-08-20T21:40:06.9693312Z [?25h Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp312-cp312-linux_x86_64.whl size=1116932 sha256=da66e1f1e6d99205f969fb543f0aeeb75218e1990cd7be259ce061130494e933 2024-08-20T21:40:06.9695067Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/b9/aa/81/39d3509ec629531316195ffac7a7b05ff7603f393064d63ec9 2024-08-20T21:40:06.9728957Z Successfully built torchvision 2024-08-20T21:40:07.1037497Z Installing collected packages: torchvision 2024-08-20T21:40:07.5594538Z Successfully installed torchvision-0.19.0a0+d23a6e1 2024-08-20T21:40:07.6661868Z + '[' -n '' ']' 2024-08-20T21:40:07.6662208Z + test_dynamo_shard 2 2024-08-20T21:40:07.6662574Z + [[ -z 3 ]] 2024-08-20T21:40:07.6663471Z + python tools/dynamo/verify_dynamo.py 2024-08-20T21:40:08.9588036Z Python version: 3.12.4 2024-08-20T21:40:08.9588878Z `torch` version: 2.5.0a0+git40ec5f6 2024-08-20T21:40:08.9589283Z CUDA version: None 2024-08-20T21:40:08.9589605Z ROCM version: None 2024-08-20T21:40:08.9589790Z 2024-08-20T21:40:09.8066699Z CUDA not available -- skipping CUDA check on eager backend 2024-08-20T21:40:09.8067481Z 2024-08-20T21:40:10.7012394Z CUDA not available -- skipping CUDA check on aot_eager backend 2024-08-20T21:40:10.7013219Z 2024-08-20T21:40:18.9629778Z CUDA not available -- skipping CUDA check on inductor backend 2024-08-20T21:40:18.9630252Z 2024-08-20T21:40:18.9630391Z All required checks passed 2024-08-20T21:40:19.6524171Z + python test/run_test.py --dynamo --exclude-inductor-tests --exclude-jit-executor --exclude-distributed-tests --exclude-torch-export-tests --shard 2 3 --verbose 2024-08-20T21:40:19.7573873Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-08-20T21:40:19.7575067Z import pkg_resources 2024-08-20T21:40:21.7391110Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:40:22.7312271Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-08-20T21:40:22.7826848Z Ignoring disabled issues: [''] 2024-08-20T21:40:22.7938920Z Found test times from artifacts 2024-08-20T21:40:22.8376712Z Found test times from artifacts 2024-08-20T21:40:22.8392033Z Running all tests 2024-08-20T21:40:22.8506176Z Running parallel tests on 3 processes 2024-08-20T21:40:22.8510091Z Name: tests to run (est. time: 52.08min) 2024-08-20T21:40:22.8510738Z Serial tests (4): 2024-08-20T21:40:22.8511132Z doctests 1/1 2024-08-20T21:40:22.8511427Z test_nn 1/2 2024-08-20T21:40:22.8511718Z test_cpp_extensions_jit 1/1 2024-08-20T21:40:22.8512101Z test_torch 1/1 2024-08-20T21:40:22.8512577Z Parallel tests (69): 2024-08-20T21:40:22.8513046Z dynamo/test_trace_rules 1/1 2024-08-20T21:40:22.8513758Z dynamo/test_repros 1/1 2024-08-20T21:40:22.8514256Z dynamo/test_higher_order_ops 1/1 2024-08-20T21:40:22.8514833Z dynamo/test_export 1/1 2024-08-20T21:40:22.8519635Z dynamo/test_exc 1/1 2024-08-20T21:40:22.8520285Z dynamo/test_ctx_manager 1/1 2024-08-20T21:40:22.8521057Z dynamo/test_activation_checkpointing 1/1 2024-08-20T21:40:22.8521896Z dynamo/test_optimizers 1/1 2024-08-20T21:40:22.8522483Z dynamo/test_backends 1/1 2024-08-20T21:40:22.8523011Z dynamo/test_skip_non_tensor 1/1 2024-08-20T21:40:22.8523982Z dynamo/test_python_autograd 1/1 2024-08-20T21:40:22.8524772Z dynamo/test_verify_correctness 1/1 2024-08-20T21:40:22.8525412Z dynamo/test_exceptions 1/1 2024-08-20T21:40:22.8526038Z dynamo/test_base_output 1/1 2024-08-20T21:40:22.8526711Z functorch/test_vmap 2/3 2024-08-20T21:40:22.8527341Z dynamo/test_structured_trace 1/1 2024-08-20T21:40:22.8527739Z dynamo/test_hooks 1/1 2024-08-20T21:40:22.8528161Z dynamo/test_profiler 1/1 2024-08-20T21:40:22.8528728Z dynamo/test_recompile_ux 1/1 2024-08-20T21:40:22.8529448Z dynamo/test_deviceguard 1/1 2024-08-20T21:40:22.8529862Z test_linalg 2/4 2024-08-20T21:40:22.8530220Z test_linalg 3/4 2024-08-20T21:40:22.8530517Z test_linalg 4/4 2024-08-20T21:40:22.8530827Z dynamo/test_debug_utils 1/1 2024-08-20T21:40:22.8531184Z test_cuda_multigpu 1/1 2024-08-20T21:40:22.8531535Z test_comparison_utils 1/1 2024-08-20T21:40:22.8531890Z test_mkl_verbose 1/1 2024-08-20T21:40:22.8532214Z test_mkldnn_verbose 1/1 2024-08-20T21:40:22.8532601Z test_custom_ops 1/1 2024-08-20T21:40:22.8532927Z test_ao_sparsity 1/1 2024-08-20T21:40:22.8533283Z functorch/test_eager_transforms 1/1 2024-08-20T21:40:22.8533670Z test_optim 1/1 2024-08-20T21:40:22.8533986Z test_xnnpack_integration 1/1 2024-08-20T21:40:22.8534352Z test_itt 1/1 2024-08-20T21:40:22.8534631Z test_proxy_tensor 1/1 2024-08-20T21:40:22.8534963Z test_masked 1/1 2024-08-20T21:40:22.8535436Z test_view_ops 1/1 2024-08-20T21:40:22.8535750Z test_indexing 1/1 2024-08-20T21:40:22.8536059Z test_monitor 1/1 2024-08-20T21:40:22.8536400Z benchmark_utils/test_benchmark_utils 1/1 2024-08-20T21:40:22.8536840Z test_binary_ufuncs 1/2 2024-08-20T21:40:22.8537184Z test_quantization 1/5 2024-08-20T21:40:22.8537507Z test_quantization 3/5 2024-08-20T21:40:22.8537865Z test_quantization 4/5 2024-08-20T21:40:22.8538203Z test_module_tracker 1/1 2024-08-20T21:40:22.8538538Z torch_np/test_basic 1/1 2024-08-20T21:40:22.8538882Z test_autoload 1/1 2024-08-20T21:40:22.8539213Z torch_np/test_binary_ufuncs 1/1 2024-08-20T21:40:22.8539603Z torch_np/test_unary_ufuncs 1/1 2024-08-20T21:40:22.8540121Z profiler/test_cpp_thread 1/1 2024-08-20T21:40:22.8540499Z test_typing 1/1 2024-08-20T21:40:22.8540793Z torch_np/test_dtype 1/1 2024-08-20T21:40:22.8541157Z torch_np/test_nep50_examples 1/1 2024-08-20T21:40:22.8541582Z distributions/test_constraints 1/1 2024-08-20T21:40:22.8541996Z test_compile_benchmark_util 1/1 2024-08-20T21:40:22.8542399Z test_fx_experimental 1/1 2024-08-20T21:40:22.8542827Z torch_np/numpy_tests/core/test_scalarinherit 1/1 2024-08-20T21:40:22.8543324Z torch_np/numpy_tests/core/test_einsum 1/1 2024-08-20T21:40:22.8543768Z functorch/test_logging 1/1 2024-08-20T21:40:22.8544168Z torch_np/test_ufuncs_basic 1/1 2024-08-20T21:40:22.8544534Z torch_np/test_random 1/1 2024-08-20T21:40:22.8544882Z test_jiterator 1/1 2024-08-20T21:40:22.8545240Z higher_order_ops/test_with_effects 1/1 2024-08-20T21:40:22.8545640Z test_jit 1/1 2024-08-20T21:40:22.8545933Z test_jit_fuser_te 1/1 2024-08-20T21:40:22.8546267Z functorch/test_ac 1/1 2024-08-20T21:40:22.8546638Z test_matmul_cuda 1/1 2024-08-20T21:40:22.8546971Z optim/test_swa_utils 1/1 2024-08-20T21:40:22.8547323Z lazy/test_bindings 1/1 2024-08-20T21:40:22.8547669Z Name: excluded (est. time: 0.0min) 2024-08-20T21:40:22.8548054Z Serial tests (0): 2024-08-20T21:40:22.8548363Z Parallel tests (0): 2024-08-20T21:40:22.8586141Z Running doctests 1/1 ... [2024-08-20 21:40:22.858247] 2024-08-20T21:40:22.9247543Z Start doctest_module('/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch') 2024-08-20T21:40:22.9248742Z Listing tests 2024-08-20T21:40:23.2241239Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=426. 2024-08-20T21:40:23.2243674Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:23.2245626Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-08-20T21:40:23.2246540Z 2024-08-20T21:40:23.2247006Z This is helpful when you want to visualize data over some 2024-08-20T21:40:23.2248131Z range of inputs. See below for a plotting example. 2024-08-20T21:40:23.2248832Z 2024-08-20T21:40:23.2249404Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-08-20T21:40:23.2250823Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-08-20T21:40:23.2252211Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-08-20T21:40:23.2253545Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-08-20T21:40:23.2254750Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-08-20T21:40:23.2255789Z to the result shape. 2024-08-20T21:40:23.2256228Z 2024-08-20T21:40:23.2256439Z .. note:: 2024-08-20T21:40:23.2257176Z 0D inputs are treated equivalently to 1D inputs of a 2024-08-20T21:40:23.2258106Z single element. 2024-08-20T21:40:23.2258575Z 2024-08-20T21:40:23.2258775Z .. warning:: 2024-08-20T21:40:23.2259549Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-08-20T21:40:23.2260868Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-08-20T21:40:23.2261575Z 2024-08-20T21:40:23.2261963Z In the future `torch.meshgrid` will transition to 2024-08-20T21:40:23.2263271Z `indexing='xy'` as the default. 2024-08-20T21:40:23.2263851Z 2024-08-20T21:40:23.2264290Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-08-20T21:40:23.2265531Z this issue with the goal of migrating to NumPy's behavior. 2024-08-20T21:40:23.2266279Z 2024-08-20T21:40:23.2266490Z .. seealso:: 2024-08-20T21:40:23.2266830Z 2024-08-20T21:40:23.2267210Z :func:`torch.cartesian_prod` has the same effect but it 2024-08-20T21:40:23.2268267Z collects the data in a tensor of vectors. 2024-08-20T21:40:23.2268898Z 2024-08-20T21:40:23.2269084Z Args: 2024-08-20T21:40:23.2270058Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-08-20T21:40:23.2271360Z treated as tensors of size :math:`(1,)` automatically 2024-08-20T21:40:23.2273797Z 2024-08-20T21:40:23.2274279Z indexing: (str, optional): the indexing mode, either "xy" 2024-08-20T21:40:23.2275521Z or "ij", defaults to "ij". See warning for future changes. 2024-08-20T21:40:23.2276287Z 2024-08-20T21:40:23.2276664Z If "xy" is selected, the first dimension corresponds 2024-08-20T21:40:23.2277791Z to the cardinality of the second input and the second 2024-08-20T21:40:23.2278909Z dimension corresponds to the cardinality of the first 2024-08-20T21:40:23.2279774Z input. 2024-08-20T21:40:23.2280148Z 2024-08-20T21:40:23.2280553Z If "ij" is selected, the dimensions are in the same 2024-08-20T21:40:23.2281589Z order as the cardinality of the inputs. 2024-08-20T21:40:23.2282209Z 2024-08-20T21:40:23.2282396Z Returns: 2024-08-20T21:40:23.2283072Z seq (sequence of Tensors): If the input has :math:`N` 2024-08-20T21:40:23.2284408Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-08-20T21:40:23.2285533Z output will also have :math:`N` tensors, where each tensor 2024-08-20T21:40:23.2286704Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-08-20T21:40:23.2287196Z 2024-08-20T21:40:23.2287402Z Example:: 2024-08-20T21:40:23.2287708Z 2024-08-20T21:40:23.2287971Z >>> x = torch.tensor([1, 2, 3]) 2024-08-20T21:40:23.2288751Z >>> y = torch.tensor([4, 5, 6]) 2024-08-20T21:40:23.2289297Z 2024-08-20T21:40:23.2289940Z Observe the element-wise pairings across the grid, (1, 4), 2024-08-20T21:40:23.2291636Z (1, 5), ..., (3, 6). This is the same thing as the 2024-08-20T21:40:23.2292503Z cartesian product. 2024-08-20T21:40:23.2293501Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-08-20T21:40:23.2294409Z >>> grid_x 2024-08-20T21:40:23.2294904Z tensor([[1, 1, 1], 2024-08-20T21:40:23.2295507Z [2, 2, 2], 2024-08-20T21:40:23.2296094Z [3, 3, 3]]) 2024-08-20T21:40:23.2296721Z >>> grid_y 2024-08-20T21:40:23.2297282Z tensor([[4, 5, 6], 2024-08-20T21:40:23.2297909Z [4, 5, 6], 2024-08-20T21:40:23.2298515Z [4, 5, 6]]) 2024-08-20T21:40:23.2298971Z 2024-08-20T21:40:23.2299368Z This correspondence can be seen when these grids are 2024-08-20T21:40:23.2300293Z stacked properly. 2024-08-20T21:40:23.2301170Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-08-20T21:40:23.2302298Z ... torch.cartesian_prod(x, y)) 2024-08-20T21:40:23.2303105Z True 2024-08-20T21:40:23.2303415Z 2024-08-20T21:40:23.2303863Z `torch.meshgrid` is commonly used to produce a grid for 2024-08-20T21:40:23.2304776Z plotting. 2024-08-20T21:40:23.2305400Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-08-20T21:40:23.2306341Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-08-20T21:40:23.2307476Z >>> import matplotlib.pyplot as plt 2024-08-20T21:40:23.2308579Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-08-20T21:40:23.2309613Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-08-20T21:40:23.2310648Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-08-20T21:40:23.2311613Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-08-20T21:40:23.2312609Z >>> ax = plt.axes(projection='3d') 2024-08-20T21:40:23.2313502Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-08-20T21:40:23.2314373Z >>> plt.show() 2024-08-20T21:40:23.2314778Z 2024-08-20T21:40:23.2315088Z .. image:: ../_static/img/meshgrid.png 2024-08-20T21:40:23.2315844Z :width: 512 2024-08-20T21:40:23.2316231Z 2024-08-20T21:40:23.2316396Z 2024-08-20T21:40:23.2317400Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:23.2318457Z 2024-08-20T21:40:23.2319936Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=815. 2024-08-20T21:40:23.2322318Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:23.2324379Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-08-20T21:40:23.2325646Z 2024-08-20T21:40:23.2326007Z Returns the unique elements of the input tensor. 2024-08-20T21:40:23.2326695Z 2024-08-20T21:40:23.2327463Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-08-20T21:40:23.2329200Z this function also eliminates non-consecutive duplicate values. 2024-08-20T21:40:23.2330012Z 2024-08-20T21:40:23.2330653Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-08-20T21:40:23.2332315Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-08-20T21:40:23.2334174Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-08-20T21:40:23.2335728Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-08-20T21:40:23.2336483Z 2024-08-20T21:40:23.2336654Z Args: 2024-08-20T21:40:23.2337187Z input (Tensor): the input tensor 2024-08-20T21:40:23.2338285Z sorted (bool): Whether to sort the unique elements in ascending order 2024-08-20T21:40:23.2339318Z before returning as output. 2024-08-20T21:40:23.2340603Z return_inverse (bool): Whether to also return the indices for where 2024-08-20T21:40:23.2341985Z elements in the original input ended up in the returned unique list. 2024-08-20T21:40:23.2343401Z return_counts (bool): Whether to also return the counts for each unique 2024-08-20T21:40:23.2344467Z element. 2024-08-20T21:40:23.2345342Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-08-20T21:40:23.2346696Z unique of the flattened input is returned. Otherwise, each of the 2024-08-20T21:40:23.2348070Z tensors indexed by the given dimension is treated as one of the 2024-08-20T21:40:23.2349402Z elements to apply the unique operation upon. See examples for more 2024-08-20T21:40:23.2350514Z details. Default: ``None`` 2024-08-20T21:40:23.2351042Z 2024-08-20T21:40:23.2351219Z Returns: 2024-08-20T21:40:23.2352188Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-08-20T21:40:23.2353226Z 2024-08-20T21:40:23.2353921Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-08-20T21:40:23.2355176Z - **inverse_indices** (*Tensor*): (optional) if 2024-08-20T21:40:23.2356274Z :attr:`return_inverse` is True, there will be an additional 2024-08-20T21:40:23.2357545Z returned tensor (same shape as input) representing the indices 2024-08-20T21:40:23.2359024Z for where elements in the original input map to in the output; 2024-08-20T21:40:23.2360264Z otherwise, this function will only return a single tensor. 2024-08-20T21:40:23.2361448Z - **counts** (*Tensor*): (optional) if 2024-08-20T21:40:23.2362497Z :attr:`return_counts` is True, there will be an additional 2024-08-20T21:40:23.2363664Z returned tensor (same shape as output or output.size(dim), 2024-08-20T21:40:23.2364906Z if dim was specified) representing the number of occurrences 2024-08-20T21:40:23.2365997Z for each unique value or tensor. 2024-08-20T21:40:23.2366576Z 2024-08-20T21:40:23.2366767Z Example:: 2024-08-20T21:40:23.2367082Z 2024-08-20T21:40:23.2367604Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-08-20T21:40:23.2368632Z >>> output 2024-08-20T21:40:23.2369139Z tensor([1, 2, 3]) 2024-08-20T21:40:23.2369538Z 2024-08-20T21:40:23.2369838Z >>> output, inverse_indices = torch.unique( 2024-08-20T21:40:23.2371151Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-20T21:40:23.2372267Z >>> output 2024-08-20T21:40:23.2372751Z tensor([1, 2, 3]) 2024-08-20T21:40:23.2373320Z >>> inverse_indices 2024-08-20T21:40:23.2373898Z tensor([0, 2, 1, 2]) 2024-08-20T21:40:23.2374326Z 2024-08-20T21:40:23.2374625Z >>> output, inverse_indices = torch.unique( 2024-08-20T21:40:23.2375896Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-20T21:40:23.2376989Z >>> output 2024-08-20T21:40:23.2377496Z tensor([1, 2, 3]) 2024-08-20T21:40:23.2378077Z >>> inverse_indices 2024-08-20T21:40:23.2378690Z tensor([[0, 2], 2024-08-20T21:40:23.2379215Z [1, 2]]) 2024-08-20T21:40:23.2379609Z 2024-08-20T21:40:23.2379829Z >>> a = torch.tensor([ 2024-08-20T21:40:23.2380457Z ... [ 2024-08-20T21:40:23.2380966Z ... [1, 1, 0, 0], 2024-08-20T21:40:23.2381615Z ... [1, 1, 0, 0], 2024-08-20T21:40:23.2382265Z ... [0, 0, 1, 1], 2024-08-20T21:40:23.2382869Z ... ], 2024-08-20T21:40:23.2383375Z ... [ 2024-08-20T21:40:23.2383886Z ... [0, 0, 1, 1], 2024-08-20T21:40:23.2384517Z ... [0, 0, 1, 1], 2024-08-20T21:40:23.2385178Z ... [1, 1, 1, 1], 2024-08-20T21:40:23.2385796Z ... ], 2024-08-20T21:40:23.2386282Z ... [ 2024-08-20T21:40:23.2387009Z ... [1, 1, 0, 0], 2024-08-20T21:40:23.2387678Z ... [1, 1, 0, 0], 2024-08-20T21:40:23.2388287Z ... [0, 0, 1, 1], 2024-08-20T21:40:23.2388934Z ... ], 2024-08-20T21:40:23.2389423Z ... ]) 2024-08-20T21:40:23.2389741Z 2024-08-20T21:40:23.2390478Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-08-20T21:40:23.2391931Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-08-20T21:40:23.2393156Z >>> # each other, so one of them will be removed. 2024-08-20T21:40:23.2394059Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-08-20T21:40:23.2394819Z tensor(True) 2024-08-20T21:40:23.2395444Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-08-20T21:40:23.2396182Z >>> a_unique_dim0 2024-08-20T21:40:23.2396751Z tensor([[[0, 0, 1, 1], 2024-08-20T21:40:23.2397383Z [0, 0, 1, 1], 2024-08-20T21:40:23.2398006Z [1, 1, 1, 1]], 2024-08-20T21:40:23.2398646Z [[1, 1, 0, 0], 2024-08-20T21:40:23.2399265Z [1, 1, 0, 0], 2024-08-20T21:40:23.2399874Z [0, 0, 1, 1]]]) 2024-08-20T21:40:23.2400339Z 2024-08-20T21:40:23.2401075Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-08-20T21:40:23.2402100Z >>> # `a_unique_dim0`: 2024-08-20T21:40:23.2403045Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-08-20T21:40:23.2403861Z tensor(True) 2024-08-20T21:40:23.2404499Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-08-20T21:40:23.2405268Z tensor(True) 2024-08-20T21:40:23.2405632Z 2024-08-20T21:40:23.2406152Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-08-20T21:40:23.2407476Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-08-20T21:40:23.2428935Z >>> # them will be removed. 2024-08-20T21:40:23.2429706Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-08-20T21:40:23.2430431Z tensor(True) 2024-08-20T21:40:23.2431403Z >>> torch.unique(a, dim=1) 2024-08-20T21:40:23.2432117Z tensor([[[0, 0, 1, 1], 2024-08-20T21:40:23.2432718Z [1, 1, 0, 0]], 2024-08-20T21:40:23.2433348Z [[1, 1, 1, 1], 2024-08-20T21:40:23.2433952Z [0, 0, 1, 1]], 2024-08-20T21:40:23.2434573Z [[0, 0, 1, 1], 2024-08-20T21:40:23.2435189Z [1, 1, 0, 0]]]) 2024-08-20T21:40:23.2435650Z 2024-08-20T21:40:23.2436204Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-08-20T21:40:23.2437502Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-08-20T21:40:23.2438684Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-08-20T21:40:23.2440226Z >>> # sub-tensors will be removed. 2024-08-20T21:40:23.2441056Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-08-20T21:40:23.2442328Z tensor(True) 2024-08-20T21:40:23.2442920Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-08-20T21:40:23.2443640Z tensor(True) 2024-08-20T21:40:23.2444201Z >>> torch.unique(a, dim=2) 2024-08-20T21:40:23.2444824Z tensor([[[0, 1], 2024-08-20T21:40:23.2445380Z [0, 1], 2024-08-20T21:40:23.2445938Z [1, 0]], 2024-08-20T21:40:23.2446498Z [[1, 0], 2024-08-20T21:40:23.2447044Z [1, 0], 2024-08-20T21:40:23.2447599Z [1, 1]], 2024-08-20T21:40:23.2448168Z [[0, 1], 2024-08-20T21:40:23.2448693Z [0, 1], 2024-08-20T21:40:23.2449249Z [1, 0]]]) 2024-08-20T21:40:23.2449790Z 2024-08-20T21:40:23.2450959Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:23.2451914Z 2024-08-20T21:40:23.2527094Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=560. 2024-08-20T21:40:23.2530164Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:23.2531263Z 2024-08-20T21:40:23.2531841Z Load a model from a github repo or a local directory. 2024-08-20T21:40:23.2532654Z 2024-08-20T21:40:23.2533813Z Note: Loading a model is the typical use case, but this can also be used to 2024-08-20T21:40:23.2535654Z for loading other objects such as tokenizers, loss functions, etc. 2024-08-20T21:40:23.2536857Z 2024-08-20T21:40:23.2537572Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-08-20T21:40:23.2538837Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-08-20T21:40:23.2540197Z ref (a tag or a branch). 2024-08-20T21:40:23.2540730Z 2024-08-20T21:40:23.2541533Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-08-20T21:40:23.2542842Z path to a local directory. 2024-08-20T21:40:23.2543269Z 2024-08-20T21:40:23.2543817Z Args: 2024-08-20T21:40:23.2544828Z repo_or_dir (str): If ``source`` is 'github', 2024-08-20T21:40:23.2546420Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-08-20T21:40:23.2548889Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-08-20T21:40:23.2551107Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-08-20T21:40:23.2553512Z If ``source`` is 'local' then it should be a path to a local directory. 2024-08-20T21:40:23.2555478Z model (str): the name of a callable (entrypoint) defined in the 2024-08-20T21:40:23.2556952Z repo/dir's ``hubconf.py``. 2024-08-20T21:40:23.2558100Z *args (optional): the corresponding args for callable ``model``. 2024-08-20T21:40:23.2559775Z source (str, optional): 'github' or 'local'. Specifies how 2024-08-20T21:40:23.2561331Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-08-20T21:40:23.2562812Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-08-20T21:40:23.2564321Z This parameter was introduced in v1.12 and helps ensuring that users 2024-08-20T21:40:23.2565647Z only run code from repos that they trust. 2024-08-20T21:40:23.2566272Z 2024-08-20T21:40:23.2566989Z - If ``False``, a prompt will ask the user whether the repo should 2024-08-20T21:40:23.2568000Z be trusted. 2024-08-20T21:40:23.2569169Z - If ``True``, the repo will be added to the trusted list and loaded 2024-08-20T21:40:23.2570363Z without requiring explicit confirmation. 2024-08-20T21:40:23.2571551Z - If ``"check"``, the repo will be checked against the list of 2024-08-20T21:40:23.2572782Z trusted repos in the cache. If it is not present in that list, the 2024-08-20T21:40:23.2574110Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-08-20T21:40:23.2575546Z - If ``None``: this will raise a warning, inviting the user to set 2024-08-20T21:40:23.2577041Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-08-20T21:40:23.2578306Z is only present for backward compatibility and will be removed in 2024-08-20T21:40:23.2579320Z v2.0. 2024-08-20T21:40:23.2579619Z 2024-08-20T21:40:23.2580144Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-08-20T21:40:23.2581490Z force_reload (bool, optional): whether to force a fresh download of 2024-08-20T21:40:23.2582751Z the github repo unconditionally. Does not have any effect if 2024-08-20T21:40:23.2583933Z ``source = 'local'``. Default is ``False``. 2024-08-20T21:40:23.2584973Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-08-20T21:40:23.2586272Z local caches. Note that the message about first download cannot be 2024-08-20T21:40:23.2587525Z muted. Does not have any effect if ``source = 'local'``. 2024-08-20T21:40:23.2588617Z Default is ``True``. 2024-08-20T21:40:23.2589983Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-08-20T21:40:23.2591890Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-08-20T21:40:23.2593815Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-08-20T21:40:23.2595257Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-08-20T21:40:23.2596485Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-08-20T21:40:23.2597295Z 2024-08-20T21:40:23.2597474Z Returns: 2024-08-20T21:40:23.2598164Z The output of the ``model`` callable when called with the given 2024-08-20T21:40:23.2599130Z ``*args`` and ``**kwargs``. 2024-08-20T21:40:23.2599533Z 2024-08-20T21:40:23.2599713Z Example: 2024-08-20T21:40:23.2600269Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:40:23.2601080Z >>> # from a github repo 2024-08-20T21:40:23.2601727Z >>> repo = "pytorch/vision" 2024-08-20T21:40:23.2602357Z >>> model = torch.hub.load( 2024-08-20T21:40:23.2603161Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-08-20T21:40:23.2604099Z ... ) 2024-08-20T21:40:23.2604580Z >>> # from a local directory 2024-08-20T21:40:23.2605327Z >>> path = "/some/local/path/pytorch/vision" 2024-08-20T21:40:23.2607035Z >>> # xdoctest: +SKIP 2024-08-20T21:40:23.2607993Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-08-20T21:40:23.2608832Z 2024-08-20T21:40:23.2609621Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:23.2610646Z 2024-08-20T21:40:23.2612015Z msg = Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=687. 2024-08-20T21:40:23.2614329Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:23.2615621Z Download object at the given URL to a local path. 2024-08-20T21:40:23.2616533Z 2024-08-20T21:40:23.2616712Z Args: 2024-08-20T21:40:23.2617270Z url (str): URL of the object to download 2024-08-20T21:40:23.2618471Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-08-20T21:40:23.2620217Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-08-20T21:40:23.2621609Z Default: None 2024-08-20T21:40:23.2622620Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-08-20T21:40:23.2623716Z Default: True 2024-08-20T21:40:23.2624094Z 2024-08-20T21:40:23.2624260Z Example: 2024-08-20T21:40:23.2624835Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:40:23.2625700Z >>> # xdoctest: +REQUIRES(POSIX) 2024-08-20T21:40:23.2626495Z >>> torch.hub.download_url_to_file( 2024-08-20T21:40:23.2627842Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-08-20T21:40:23.2628938Z ... "/tmp/temporary_file", 2024-08-20T21:40:23.2629636Z ... ) 2024-08-20T21:40:23.2629917Z 2024-08-20T21:40:23.2630063Z 2024-08-20T21:40:23.2630972Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:23.2631901Z 2024-08-20T21:40:23.2633389Z msg = Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=812. 2024-08-20T21:40:23.2635773Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:23.2637111Z Loads the Torch serialized object at the given URL. 2024-08-20T21:40:23.2637784Z 2024-08-20T21:40:23.2638200Z If downloaded file is a zip file, it will be automatically 2024-08-20T21:40:23.2639166Z decompressed. 2024-08-20T21:40:23.2639499Z 2024-08-20T21:40:23.2640405Z If the object is already present in `model_dir`, it's deserialized and 2024-08-20T21:40:23.2641456Z returned. 2024-08-20T21:40:23.2642293Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-08-20T21:40:23.2643917Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-08-20T21:40:23.2644708Z 2024-08-20T21:40:23.2644902Z Args: 2024-08-20T21:40:23.2645456Z url (str): URL of the object to download 2024-08-20T21:40:23.2646534Z model_dir (str, optional): directory in which to save the object 2024-08-20T21:40:23.2648223Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-08-20T21:40:23.2650155Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-08-20T21:40:23.2651327Z Default: True 2024-08-20T21:40:23.2652546Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-08-20T21:40:23.2654423Z ``filename-.ext`` where ```` is the first eight or more 2024-08-20T21:40:23.2655774Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-08-20T21:40:23.2657177Z ensure unique names and to verify the contents of the file. 2024-08-20T21:40:23.2658123Z Default: False 2024-08-20T21:40:23.2659255Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-08-20T21:40:23.2661495Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-08-20T21:40:23.2663300Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-08-20T21:40:23.2664270Z 2024-08-20T21:40:23.2664438Z Example: 2024-08-20T21:40:23.2665047Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:40:23.2666031Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-08-20T21:40:23.2667460Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-08-20T21:40:23.2668987Z ... ) 2024-08-20T21:40:23.2669286Z 2024-08-20T21:40:23.2669470Z 2024-08-20T21:40:23.2670432Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:23.2671331Z 2024-08-20T21:40:23.2672881Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=334. 2024-08-20T21:40:23.2675217Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:40:23.2676756Z Registers the function implementation as the fallback for the given key. 2024-08-20T21:40:23.2677671Z 2024-08-20T21:40:23.2678169Z This function only works for a library with global namespace ("_"). 2024-08-20T21:40:23.2679016Z 2024-08-20T21:40:23.2679199Z Args: 2024-08-20T21:40:23.2680260Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-08-20T21:40:23.2681589Z to register a fallthrough. 2024-08-20T21:40:23.2682996Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-08-20T21:40:23.2684510Z the dispatch key that the library was created with. 2024-08-20T21:40:23.2686197Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-08-20T21:40:23.2688145Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-08-20T21:40:23.2689389Z 2024-08-20T21:40:23.2689600Z Example:: 2024-08-20T21:40:23.2690419Z >>> my_lib = Library("_", "IMPL") 2024-08-20T21:40:23.2691298Z >>> def fallback_kernel(op, *args, **kwargs): 2024-08-20T21:40:23.2692265Z >>> # Handle all autocast ops generically 2024-08-20T21:40:23.2693068Z >>> # ... 2024-08-20T21:40:23.2693951Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-08-20T21:40:23.2694798Z 2024-08-20T21:40:23.2697052Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-08-20T21:40:23.2698980Z 2024-08-20T21:40:23.2699300Z my_lib.fallback(fallback_kernel, "Autocast") 2024-08-20T21:40:23.2700076Z ^ 2024-08-20T21:40:23.2744640Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=692. 2024-08-20T21:40:23.2747788Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:40:23.2749560Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-08-20T21:40:23.2750738Z 2024-08-20T21:40:23.2751176Z Also sometimes known as a "meta kernel", "abstract impl". 2024-08-20T21:40:23.2752228Z 2024-08-20T21:40:23.2752825Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-08-20T21:40:23.2754897Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-08-20T21:40:23.2757287Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-08-20T21:40:23.2759031Z what the properties of the output Tensors are. 2024-08-20T21:40:23.2759937Z 2024-08-20T21:40:23.2760721Z The FakeTensor implementation has the same signature as the operator. 2024-08-20T21:40:23.2762821Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-08-20T21:40:23.2764461Z implementation, assume that all Tensor inputs to the operator are 2024-08-20T21:40:23.2766149Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-08-20T21:40:23.2767811Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-08-20T21:40:23.2769641Z The FakeTensor implementation must consist of only PyTorch operations 2024-08-20T21:40:23.2771470Z (and may not directly access the storage or data of any input or 2024-08-20T21:40:23.2772740Z intermediate Tensors). 2024-08-20T21:40:23.2773161Z 2024-08-20T21:40:23.2773676Z This API may be used as a decorator (see examples). 2024-08-20T21:40:23.2774473Z 2024-08-20T21:40:23.2774800Z For a detailed guide on custom ops, please see 2024-08-20T21:40:23.2776324Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-08-20T21:40:23.2777503Z 2024-08-20T21:40:23.2777703Z Examples: 2024-08-20T21:40:23.2778347Z >>> import torch 2024-08-20T21:40:23.2779106Z >>> import numpy as np 2024-08-20T21:40:23.2779940Z >>> from torch import Tensor 2024-08-20T21:40:23.2780766Z >>> 2024-08-20T21:40:23.2781853Z >>> # Example 1: an operator without data-dependent output shape 2024-08-20T21:40:23.2783342Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-08-20T21:40:23.2785058Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-08-20T21:40:23.2786355Z >>> raise NotImplementedError("Implementation goes here") 2024-08-20T21:40:23.2787153Z >>> 2024-08-20T21:40:23.2787841Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-08-20T21:40:23.2788999Z >>> def _(x, weight, bias): 2024-08-20T21:40:23.2789722Z >>> assert x.dim() == 2 2024-08-20T21:40:23.2790649Z >>> assert weight.dim() == 2 2024-08-20T21:40:23.2791452Z >>> assert bias.dim() == 1 2024-08-20T21:40:23.2792276Z >>> assert x.shape[1] == weight.shape[1] 2024-08-20T21:40:23.2793207Z >>> assert weight.shape[0] == bias.shape[0] 2024-08-20T21:40:23.2794104Z >>> assert x.device == weight.device 2024-08-20T21:40:23.2794867Z >>> 2024-08-20T21:40:23.2795400Z >>> return (x @ weight.t()) + bias 2024-08-20T21:40:23.2796339Z >>> 2024-08-20T21:40:23.2797234Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-08-20T21:40:23.2798215Z >>> x = torch.randn(2, 3) 2024-08-20T21:40:23.2798947Z >>> w = torch.randn(3, 3) 2024-08-20T21:40:23.2799675Z >>> b = torch.randn(3) 2024-08-20T21:40:23.2800496Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-08-20T21:40:23.2801309Z >>> 2024-08-20T21:40:23.2801774Z >>> assert y.shape == (2, 3) 2024-08-20T21:40:23.2802440Z >>> 2024-08-20T21:40:23.2803308Z >>> # Example 2: an operator with data-dependent output shape 2024-08-20T21:40:23.2804811Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-08-20T21:40:23.2806066Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-08-20T21:40:23.2806864Z >>> x_np = x.numpy(force=True) 2024-08-20T21:40:23.2807731Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-08-20T21:40:23.2808689Z >>> return torch.tensor(res, device=x.device) 2024-08-20T21:40:23.2809788Z >>> 2024-08-20T21:40:23.2810543Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-08-20T21:40:23.2811457Z >>> def _(x): 2024-08-20T21:40:23.2812274Z >>> # Number of nonzero-elements is data-dependent. 2024-08-20T21:40:23.2813339Z >>> # Since we cannot peek at the data in an fake impl, 2024-08-20T21:40:23.2814449Z >>> # we use the ctx object to construct a new symint that 2024-08-20T21:40:23.2815794Z >>> # represents the data-dependent size. 2024-08-20T21:40:23.2816687Z >>> ctx = torch.library.get_ctx() 2024-08-20T21:40:23.2817534Z >>> nnz = ctx.new_dynamic_size() 2024-08-20T21:40:23.2818270Z >>> shape = [nnz, x.dim()] 2024-08-20T21:40:23.2819110Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-08-20T21:40:23.2819996Z >>> return result 2024-08-20T21:40:23.2820595Z >>> 2024-08-20T21:40:23.2821320Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-20T21:40:23.2822301Z >>> 2024-08-20T21:40:23.2822840Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-08-20T21:40:23.2824060Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-08-20T21:40:23.2825260Z >>> trace.print_readable() 2024-08-20T21:40:23.2825921Z >>> 2024-08-20T21:40:23.2826745Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-08-20T21:40:23.2827584Z 2024-08-20T21:40:23.2827751Z 2024-08-20T21:40:23.2829663Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-08-20T21:40:23.2831344Z 2024-08-20T21:40:23.2831520Z _._ = None 2024-08-20T21:40:23.2831925Z ^ 2024-08-20T21:40:23.2833781Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=813. 2024-08-20T21:40:23.2836480Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:23.2837764Z Register a backward formula for this custom op. 2024-08-20T21:40:23.2838347Z 2024-08-20T21:40:23.2838812Z In order for an operator to work with autograd, you need to register 2024-08-20T21:40:23.2839740Z a backward formula: 2024-08-20T21:40:23.2840543Z 1. You must tell us how to compute gradients during the backward pass 2024-08-20T21:40:23.2841537Z by providing us a "backward" function. 2024-08-20T21:40:23.2842638Z 2. If you need any values from the forward to compute gradients, you can 2024-08-20T21:40:23.2843837Z use `setup_context` to save values for backward. 2024-08-20T21:40:23.2844472Z 2024-08-20T21:40:23.2845019Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-08-20T21:40:23.2846515Z - ``grads`` is one or more gradients. The number of gradients matches 2024-08-20T21:40:23.2847582Z the number of outputs of the operator. 2024-08-20T21:40:23.2848794Z The ``ctx`` object is `the same ctx object `_ used by 2024-08-20T21:40:23.2850271Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-08-20T21:40:23.2851409Z same as :meth:`torch.autograd.Function.backward`. 2024-08-20T21:40:23.2852053Z 2024-08-20T21:40:23.2852520Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-08-20T21:40:23.2853832Z Please save quantities needed for backward onto the ``ctx`` object via 2024-08-20T21:40:23.2855371Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-08-20T21:40:23.2856709Z or assigning them as attributes of ``ctx``. If your custom op has 2024-08-20T21:40:23.2858114Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-08-20T21:40:23.2859366Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-08-20T21:40:23.2860142Z 2024-08-20T21:40:23.2860699Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-08-20T21:40:23.2862079Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-08-20T21:40:23.2863708Z not depend on or mutate global state. If you need a non-traceable backward, 2024-08-20T21:40:23.2864974Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-08-20T21:40:23.2865814Z 2024-08-20T21:40:23.2865988Z Examples: 2024-08-20T21:40:23.2866502Z >>> import torch 2024-08-20T21:40:23.2867248Z >>> import numpy as np 2024-08-20T21:40:23.2867890Z >>> from torch import Tensor 2024-08-20T21:40:23.2868594Z >>> 2024-08-20T21:40:23.2869332Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-08-20T21:40:23.2870464Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-08-20T21:40:23.2871259Z >>> x_np = x.cpu().numpy() 2024-08-20T21:40:23.2871959Z >>> y_np = np.sin(x_np) 2024-08-20T21:40:23.2872776Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-20T21:40:23.2873630Z >>> 2024-08-20T21:40:23.2874382Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-08-20T21:40:23.2875240Z >>> x, = inputs 2024-08-20T21:40:23.2875858Z >>> ctx.save_for_backward(x) 2024-08-20T21:40:23.2876572Z >>> 2024-08-20T21:40:23.2877064Z >>> def backward(ctx, grad): 2024-08-20T21:40:23.2877778Z >>> x, = ctx.saved_tensors 2024-08-20T21:40:23.2878510Z >>> return grad * x.cos() 2024-08-20T21:40:23.2879158Z >>> 2024-08-20T21:40:23.2879737Z >>> torch.library.register_autograd( 2024-08-20T21:40:23.2880714Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-08-20T21:40:23.2881602Z ... ) 2024-08-20T21:40:23.2881988Z >>> 2024-08-20T21:40:23.2882415Z >>> x = torch.randn(3, requires_grad=True) 2024-08-20T21:40:23.2883193Z >>> y = numpy_sin(x) 2024-08-20T21:40:23.2884005Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-20T21:40:23.2885034Z >>> assert torch.allclose(grad_x, x.cos()) 2024-08-20T21:40:23.2885783Z >>> 2024-08-20T21:40:23.2886416Z >>> # Example with a keyword-only arg 2024-08-20T21:40:23.2887593Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-20T21:40:23.2888871Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-08-20T21:40:23.2889733Z >>> x_np = x.cpu().numpy() 2024-08-20T21:40:23.2890639Z >>> y_np = x_np * val 2024-08-20T21:40:23.2891449Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-20T21:40:23.2892270Z >>> 2024-08-20T21:40:23.2893212Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-08-20T21:40:23.2894342Z >>> ctx.val = keyword_only_inputs["val"] 2024-08-20T21:40:23.2895057Z >>> 2024-08-20T21:40:23.2895529Z >>> def backward(ctx, grad): 2024-08-20T21:40:23.2896424Z >>> return grad * ctx.val 2024-08-20T21:40:23.2897070Z >>> 2024-08-20T21:40:23.2897620Z >>> torch.library.register_autograd( 2024-08-20T21:40:23.2898630Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-08-20T21:40:23.2899529Z ... ) 2024-08-20T21:40:23.2899980Z >>> 2024-08-20T21:40:23.2900529Z >>> x = torch.randn(3, requires_grad=True) 2024-08-20T21:40:23.2901318Z >>> y = numpy_mul(x, val=3.14) 2024-08-20T21:40:23.2902197Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-20T21:40:23.2903273Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-08-20T21:40:23.2903973Z 2024-08-20T21:40:23.2904124Z 2024-08-20T21:40:23.2905146Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:23.2906097Z 2024-08-20T21:40:23.2907548Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1221. 2024-08-20T21:40:23.2909776Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:23.2911257Z Given an operator and some sample arguments, tests if the operator is 2024-08-20T21:40:23.2912333Z registered correctly. 2024-08-20T21:40:23.2912736Z 2024-08-20T21:40:23.2913278Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-08-20T21:40:23.2914756Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-08-20T21:40:23.2916393Z and these APIs require that the functions you pass them satisfy certain 2024-08-20T21:40:23.2917693Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-08-20T21:40:23.2918740Z ``opcheck`` tests these metadata and properties. 2024-08-20T21:40:23.2919376Z 2024-08-20T21:40:23.2919674Z Concretely, we test the following: 2024-08-20T21:40:23.2920735Z - test_schema: if the operator's schema is correct. 2024-08-20T21:40:23.2922033Z - test_autograd_registration: if autograd was registered correctly. 2024-08-20T21:40:23.2923370Z - test_faketensor: If the operator has a FakeTensor kernel 2024-08-20T21:40:23.2924506Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-08-20T21:40:23.2925798Z but not sufficient) for the operator to work with PyTorch compilation 2024-08-20T21:40:23.2926897Z APIs (torch.compile/export/FX). 2024-08-20T21:40:23.2927962Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-08-20T21:40:23.2929220Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-08-20T21:40:23.2930534Z This checks that the outputs (and gradients, if applicable) are the 2024-08-20T21:40:23.2931831Z same under eager-mode PyTorch and torch.compile. 2024-08-20T21:40:23.2932794Z This test is a superset of ``test_faketensor``. 2024-08-20T21:40:23.2933436Z 2024-08-20T21:40:23.2933912Z For best results, please call ``opcheck`` multiple times with a 2024-08-20T21:40:23.2935100Z representative set of inputs. If your operator supports 2024-08-20T21:40:23.2936405Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-08-20T21:40:23.2937844Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-08-20T21:40:23.2939004Z use ``opcheck`` with inputs on all supported devices. 2024-08-20T21:40:23.2939611Z 2024-08-20T21:40:23.2939783Z Args: 2024-08-20T21:40:23.2940506Z op: The operator. Must either be a function decorated with 2024-08-20T21:40:23.2941774Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-08-20T21:40:23.2943463Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-08-20T21:40:23.2944597Z args: The args to the operator 2024-08-20T21:40:23.2945374Z kwargs: The kwargs to the operator 2024-08-20T21:40:23.2946350Z test_utils: Tests that we should run. Default: all of them. 2024-08-20T21:40:23.2947543Z Example: ("test_schema", "test_faketensor") 2024-08-20T21:40:23.2948624Z raise_exception: If we should raise an exception on the first 2024-08-20T21:40:23.2949784Z error. If False, we will return a dict with information 2024-08-20T21:40:23.2950744Z on if each test passed or not. 2024-08-20T21:40:23.2951289Z 2024-08-20T21:40:23.2951508Z .. warning:: 2024-08-20T21:40:23.2951810Z 2024-08-20T21:40:23.2952333Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-08-20T21:40:23.2953639Z opcheck tests if your usage of torch.library APIs is correct while 2024-08-20T21:40:23.2955020Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-08-20T21:40:23.2956368Z mathematically correct. Use both to test custom ops that support 2024-08-20T21:40:23.2957400Z gradient computation. 2024-08-20T21:40:23.2957849Z 2024-08-20T21:40:23.2958020Z Example: 2024-08-20T21:40:23.2958306Z 2024-08-20T21:40:23.2958655Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:40:23.2959766Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-20T21:40:23.2961072Z >>> def numpy_add(x: Tensor, y: float) -> Tensor: 2024-08-20T21:40:23.2961935Z >>> x_np = x.numpy(force=True) 2024-08-20T21:40:23.2962669Z >>> z_np = x_np + y 2024-08-20T21:40:23.2963450Z >>> return torch.from_numpy(z_np).to(x.device) 2024-08-20T21:40:23.2964372Z >>> 2024-08-20T21:40:23.2964861Z >>> @numpy_sin.register_fake 2024-08-20T21:40:23.2965596Z >>> def _(x, y): 2024-08-20T21:40:23.2966206Z >>> return torch.empty_like(x) 2024-08-20T21:40:23.2966877Z >>> 2024-08-20T21:40:23.2967441Z >>> def setup_context(ctx, inputs, output): 2024-08-20T21:40:23.2968242Z >>> y, = inputs 2024-08-20T21:40:23.2968807Z >>> ctx.y = y 2024-08-20T21:40:23.2969405Z >>> 2024-08-20T21:40:23.2969904Z >>> def backward(ctx, grad): 2024-08-20T21:40:23.2970694Z >>> return grad * ctx.y, None 2024-08-20T21:40:23.2971392Z >>> 2024-08-20T21:40:23.2972196Z >>> numpy_sin.register_autograd(backward, setup_context=setup_context) 2024-08-20T21:40:23.2973212Z >>> 2024-08-20T21:40:23.2973700Z >>> sample_inputs = [ 2024-08-20T21:40:23.2974359Z >>> (torch.randn(3), 3.14), 2024-08-20T21:40:23.2975326Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-08-20T21:40:23.2976312Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-08-20T21:40:23.2977607Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-08-20T21:40:23.2978593Z >>> ] 2024-08-20T21:40:23.2979037Z >>> 2024-08-20T21:40:23.2979536Z >>> for args in sample_inputs: 2024-08-20T21:40:23.2980382Z >>> torch.library.opcheck(foo, args) 2024-08-20T21:40:23.2980957Z 2024-08-20T21:40:23.2981123Z 2024-08-20T21:40:23.2982068Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:23.2982883Z 2024-08-20T21:40:23.3343165Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1042. 2024-08-20T21:40:23.3345438Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:23.3347216Z load(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args) 2024-08-20T21:40:23.3348480Z 2024-08-20T21:40:23.3348911Z Loads an object saved with :func:`torch.save` from a file. 2024-08-20T21:40:23.3349670Z 2024-08-20T21:40:23.3350409Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-08-20T21:40:23.3351824Z which underlie tensors, specially. They are first deserialized on the 2024-08-20T21:40:23.3353225Z CPU and are then moved to the device they were saved from. If this fails 2024-08-20T21:40:23.3355209Z (e.g. because the run time system doesn't have certain devices), an exception 2024-08-20T21:40:23.3356748Z is raised. However, storages can be dynamically remapped to an alternative 2024-08-20T21:40:23.3358030Z set of devices using the :attr:`map_location` argument. 2024-08-20T21:40:23.3358692Z 2024-08-20T21:40:23.3359320Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-08-20T21:40:23.3360836Z storage with two arguments: storage and location. The storage argument 2024-08-20T21:40:23.3362288Z will be the initial deserialization of the storage, residing on the CPU. 2024-08-20T21:40:23.3363721Z Each serialized storage has a location tag associated with it which 2024-08-20T21:40:23.3365065Z identifies the device it was saved from, and this tag is the second 2024-08-20T21:40:23.3366728Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-08-20T21:40:23.3368424Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-08-20T21:40:23.3369828Z :attr:`map_location` should return either ``None`` or a storage. If 2024-08-20T21:40:23.3371388Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-08-20T21:40:23.3372950Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-08-20T21:40:23.3374717Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-08-20T21:40:23.3375865Z 2024-08-20T21:40:23.3376483Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-08-20T21:40:23.3378029Z a device tag, it indicates the location where all tensors should be loaded. 2024-08-20T21:40:23.3378935Z 2024-08-20T21:40:23.3379601Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-08-20T21:40:23.3381136Z appearing in the file (keys), to ones that specify where to put the 2024-08-20T21:40:23.3382169Z storages (values). 2024-08-20T21:40:23.3382553Z 2024-08-20T21:40:23.3383111Z User extensions can register their own location tags and tagging and 2024-08-20T21:40:23.3384585Z deserialization methods using :func:`torch.serialization.register_package`. 2024-08-20T21:40:23.3385548Z 2024-08-20T21:40:23.3385741Z Args: 2024-08-20T21:40:23.3386987Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-08-20T21:40:23.3388603Z or a string or os.PathLike object containing a file name 2024-08-20T21:40:23.3390155Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-08-20T21:40:23.3391527Z locations 2024-08-20T21:40:23.3392811Z pickle_module: module used for unpickling metadata and objects (has to 2024-08-20T21:40:23.3393559Z match the :attr:`pickle_module` used to serialize file) 2024-08-20T21:40:23.3394251Z weights_only: Indicates whether unpickler should be restricted to 2024-08-20T21:40:23.3394922Z loading only tensors, primitive types, dictionaries 2024-08-20T21:40:23.3395618Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-08-20T21:40:23.3396567Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-08-20T21:40:23.3397660Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-08-20T21:40:23.3398801Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-08-20T21:40:23.3400135Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-08-20T21:40:23.3401142Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-08-20T21:40:23.3401957Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-08-20T21:40:23.3402973Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-08-20T21:40:23.3403569Z :attr:`errors=...`. 2024-08-20T21:40:23.3403814Z 2024-08-20T21:40:23.3403945Z .. warning:: 2024-08-20T21:40:23.3404421Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-08-20T21:40:23.3405160Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-08-20T21:40:23.3406081Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-08-20T21:40:23.3406950Z during unpickling. Never load data that could have come from an untrusted 2024-08-20T21:40:23.3407876Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-08-20T21:40:23.3408468Z 2024-08-20T21:40:23.3408587Z .. note:: 2024-08-20T21:40:23.3409120Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-08-20T21:40:23.3410213Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-08-20T21:40:23.3411153Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-08-20T21:40:23.3411707Z 2024-08-20T21:40:23.3411823Z .. note:: 2024-08-20T21:40:23.3412424Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-08-20T21:40:23.3413432Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-08-20T21:40:23.3414178Z when loading files saved by Python 2 in Python 3. If this default 2024-08-20T21:40:23.3414988Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-08-20T21:40:23.3415944Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-08-20T21:40:23.3416863Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-08-20T21:40:23.3417683Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-08-20T21:40:23.3418189Z 2024-08-20T21:40:23.3418291Z Example: 2024-08-20T21:40:23.3418642Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-08-20T21:40:23.3419160Z >>> torch.load("tensors.pt", weights_only=True) 2024-08-20T21:40:23.3419646Z # Load all tensors onto the CPU 2024-08-20T21:40:23.3420286Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-08-20T21:40:23.3421004Z # Load all tensors onto the CPU, using a function 2024-08-20T21:40:23.3421464Z >>> torch.load( 2024-08-20T21:40:23.3422004Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-08-20T21:40:23.3422609Z ... ) 2024-08-20T21:40:23.3422903Z # Load all tensors onto GPU 1 2024-08-20T21:40:23.3423301Z >>> torch.load( 2024-08-20T21:40:23.3423628Z ... "tensors.pt", 2024-08-20T21:40:23.3424079Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-08-20T21:40:23.3424590Z ... weights_only=True, 2024-08-20T21:40:23.3425057Z ... ) # type: ignore[attr-defined] 2024-08-20T21:40:23.3425499Z # Map tensors from GPU 1 to GPU 0 2024-08-20T21:40:23.3426144Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-08-20T21:40:23.3426811Z # Load tensor from io.BytesIO object 2024-08-20T21:40:23.3427461Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-08-20T21:40:23.3428138Z >>> with open("tensor.pt", "rb") as f: 2024-08-20T21:40:23.3428613Z ... buffer = io.BytesIO(f.read()) 2024-08-20T21:40:23.3429074Z >>> torch.load(buffer, weights_only=False) 2024-08-20T21:40:23.3429689Z # Load a module with 'ascii' encoding for unpickling 2024-08-20T21:40:23.3430405Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-08-20T21:40:23.3431247Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-08-20T21:40:23.3431787Z 2024-08-20T21:40:23.3432338Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:23.3432829Z 2024-08-20T21:40:23.7105489Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=341. 2024-08-20T21:40:23.7107790Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:40:23.7108823Z Retrieves the CUDA runtime API module. 2024-08-20T21:40:23.7109375Z 2024-08-20T21:40:23.7109404Z 2024-08-20T21:40:23.7109990Z This function initializes the CUDA runtime environment if it is not already 2024-08-20T21:40:23.7111464Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-08-20T21:40:23.7112907Z runtime API module provides access to various CUDA runtime functions. 2024-08-20T21:40:23.7113760Z 2024-08-20T21:40:23.7113952Z Args: 2024-08-20T21:40:23.7114388Z ``None`` 2024-08-20T21:40:23.7114682Z 2024-08-20T21:40:23.7114854Z Returns: 2024-08-20T21:40:23.7115449Z module: The CUDA runtime API module (_cudart). 2024-08-20T21:40:23.7116060Z 2024-08-20T21:40:23.7116249Z Raises: 2024-08-20T21:40:23.7117279Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-08-20T21:40:23.7118919Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-08-20T21:40:23.7120481Z 2024-08-20T21:40:23.7120824Z Example of CUDA operations with profiling: 2024-08-20T21:40:23.7121613Z >>> import torch 2024-08-20T21:40:23.7122279Z >>> from torch.cuda import cudart, check_error 2024-08-20T21:40:23.7123054Z >>> import os 2024-08-20T21:40:23.7123519Z >>> 2024-08-20T21:40:23.7124212Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-08-20T21:40:23.7124942Z >>> 2024-08-20T21:40:23.7125550Z >>> def perform_cuda_operations_with_streams(): 2024-08-20T21:40:23.7126449Z >>> stream = torch.cuda.Stream() 2024-08-20T21:40:23.7127271Z >>> with torch.cuda.stream(stream): 2024-08-20T21:40:23.7128269Z >>> x = torch.randn(100, 100, device='cuda') 2024-08-20T21:40:23.7129298Z >>> y = torch.randn(100, 100, device='cuda') 2024-08-20T21:40:23.7130226Z >>> z = torch.mul(x, y) 2024-08-20T21:40:23.7130941Z >>> return z 2024-08-20T21:40:23.7131460Z >>> 2024-08-20T21:40:23.7131977Z >>> torch.cuda.synchronize() 2024-08-20T21:40:23.7132793Z >>> print("====== Start nsys profiling ======") 2024-08-20T21:40:23.7133699Z >>> check_error(cudart().cudaProfilerStart()) 2024-08-20T21:40:23.7134659Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-08-20T21:40:23.7135654Z >>> result = perform_cuda_operations_with_streams() 2024-08-20T21:40:23.7136604Z >>> print("CUDA operations completed.") 2024-08-20T21:40:23.7137589Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-08-20T21:40:23.7138528Z >>> print("====== End nsys profiling ======") 2024-08-20T21:40:23.7139120Z 2024-08-20T21:40:23.7139606Z To run this example and save the profiling information, execute: 2024-08-20T21:40:23.7141508Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-20T21:40:23.7142674Z 2024-08-20T21:40:23.7143301Z This command profiles the CUDA operations in the provided script and saves 2024-08-20T21:40:23.7144731Z the profiling information to a file named `trace_name.prof`. 2024-08-20T21:40:23.7146159Z The `--profile-from-start off` option ensures that profiling starts only 2024-08-20T21:40:23.7147316Z after the `cudaProfilerStart` call in the script. 2024-08-20T21:40:23.7148671Z The `--csv` and `--print-summary` options format the profiling output as a 2024-08-20T21:40:23.7150048Z CSV file and print a summary, respectively. 2024-08-20T21:40:23.7151474Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-08-20T21:40:23.7152710Z overwrite of the output file if it already exists. 2024-08-20T21:40:23.7153477Z 2024-08-20T21:40:23.7155584Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2024-08-20T21:40:23.7157379Z 2024-08-20T21:40:23.7158456Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-20T21:40:23.7159700Z ^ 2024-08-20T21:40:23.7212533Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101. 2024-08-20T21:40:23.7214862Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:23.7215878Z 2024-08-20T21:40:23.7216442Z Append the given callback function to this ``Future``, which will be run 2024-08-20T21:40:23.7217752Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-08-20T21:40:23.7218992Z the same ``Future``, but the order in which they will be executed cannot 2024-08-20T21:40:23.7220198Z be guaranteed (to enforce a certain order consider chaining: 2024-08-20T21:40:23.7221320Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-08-20T21:40:23.7223047Z is the reference to this ``Future``. The callback function can use the 2024-08-20T21:40:23.7224304Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-08-20T21:40:23.7225608Z already completed, the given callback will be run immediately inline. 2024-08-20T21:40:23.7226399Z 2024-08-20T21:40:23.7227036Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-08-20T21:40:23.7228310Z callback might be invoked while the async kernels that are populating 2024-08-20T21:40:23.7229850Z those tensors haven't yet finished executing on the device. However, the 2024-08-20T21:40:23.7231097Z callback will be invoked with some dedicated streams set as current 2024-08-20T21:40:23.7232244Z (fetched from a global pool) which will be synchronized with those 2024-08-20T21:40:23.7233450Z kernels. Hence any operation performed by the callback on these tensors 2024-08-20T21:40:23.7234722Z will be scheduled on the device after the kernels complete. In other 2024-08-20T21:40:23.7236239Z words, as long as the callback doesn't switch streams, it can safely 2024-08-20T21:40:23.7237445Z manipulate the result without any additional synchronization. This is 2024-08-20T21:40:23.7238674Z similar to the non-blocking behavior of :meth:`wait`. 2024-08-20T21:40:23.7239261Z 2024-08-20T21:40:23.7239790Z Similarly, if the callback returns a value that contains tensors that 2024-08-20T21:40:23.7241021Z reside on a GPU, it can do so even if the kernels that are producing 2024-08-20T21:40:23.7242325Z these tensors are still running on the device, as long as the callback 2024-08-20T21:40:23.7243791Z didn't change streams during its execution. If one wants to change 2024-08-20T21:40:23.7245189Z streams, one must be careful to re-synchronize them with the original 2024-08-20T21:40:23.7246463Z streams, that is, those that were current when the callback was invoked. 2024-08-20T21:40:23.7247228Z 2024-08-20T21:40:23.7247388Z Args: 2024-08-20T21:40:23.7248077Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-08-20T21:40:23.7249089Z the only argument. 2024-08-20T21:40:23.7249596Z 2024-08-20T21:40:23.7249766Z Returns: 2024-08-20T21:40:23.7250492Z A new ``Future`` object that holds the return value of the 2024-08-20T21:40:23.7251600Z ``callback`` and will be marked as completed when the given 2024-08-20T21:40:23.7252444Z ``callback`` finishes. 2024-08-20T21:40:23.7252812Z 2024-08-20T21:40:23.7253213Z .. note:: Note that if the callback function throws, either 2024-08-20T21:40:23.7254628Z through the original future being completed with an exception and 2024-08-20T21:40:23.7255889Z calling ``fut.wait()``, or through other code in the callback, the 2024-08-20T21:40:23.7257095Z future returned by ``then`` will be marked appropriately with the 2024-08-20T21:40:23.7258251Z encountered error. However, if this callback later completes 2024-08-20T21:40:23.7259465Z additional futures, those futures are not marked as completed with 2024-08-20T21:40:23.7260810Z an error and the user is responsible for handling completion/waiting 2024-08-20T21:40:23.7261850Z on those futures independently. 2024-08-20T21:40:23.7262326Z 2024-08-20T21:40:23.7262493Z Example:: 2024-08-20T21:40:23.7263054Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-20T21:40:23.7263814Z >>> def callback(fut): 2024-08-20T21:40:23.7264471Z ... print(f"RPC return value is {fut.wait()}.") 2024-08-20T21:40:23.7265265Z >>> fut = torch.futures.Future() 2024-08-20T21:40:23.7266131Z >>> # The inserted callback will print the return value when 2024-08-20T21:40:23.7267097Z >>> # receiving the response from "worker1" 2024-08-20T21:40:23.7267814Z >>> cb_fut = fut.then(callback) 2024-08-20T21:40:23.7268448Z >>> chain_cb_fut = cb_fut.then( 2024-08-20T21:40:23.7269241Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-08-20T21:40:23.7270019Z ... ) 2024-08-20T21:40:23.7270434Z >>> fut.set_result(5) 2024-08-20T21:40:23.7271182Z RPC return value is 5. 2024-08-20T21:40:23.7271685Z Chained cb done. None 2024-08-20T21:40:23.7272020Z 2024-08-20T21:40:23.7272748Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:23.7273543Z 2024-08-20T21:40:23.7275033Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=209. 2024-08-20T21:40:23.7277294Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:23.7278167Z 2024-08-20T21:40:23.7278691Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-08-20T21:40:23.7279993Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-08-20T21:40:23.7281045Z cannot be marked completed twice. 2024-08-20T21:40:23.7281504Z 2024-08-20T21:40:23.7281983Z If the result contains tensors that reside on GPUs, this method can be 2024-08-20T21:40:23.7283216Z called even if the asynchronous kernels that are populating those 2024-08-20T21:40:23.7284575Z tensors haven't yet completed running on the device, provided that the 2024-08-20T21:40:23.7285938Z streams on which those kernels were enqueued are set as the current ones 2024-08-20T21:40:23.7287389Z when this method is called. Put simply, it's safe to call this method 2024-08-20T21:40:23.7288671Z immediately after launching those kernels, without any additional 2024-08-20T21:40:23.7290469Z synchronization, as long as one doesn't change streams in between. This 2024-08-20T21:40:23.7291737Z method will record events on all the relevant current streams and will 2024-08-20T21:40:23.7293009Z use them to ensure proper scheduling for all the consumers of this 2024-08-20T21:40:23.7293973Z ``Future``. 2024-08-20T21:40:23.7294251Z 2024-08-20T21:40:23.7294432Z Args: 2024-08-20T21:40:23.7294999Z result (object): the result object of this ``Future``. 2024-08-20T21:40:23.7295629Z 2024-08-20T21:40:23.7295805Z Example:: 2024-08-20T21:40:23.7296385Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-20T21:40:23.7297186Z >>> import threading 2024-08-20T21:40:23.7297746Z >>> import time 2024-08-20T21:40:23.7298294Z >>> def slow_set_future(fut, value): 2024-08-20T21:40:23.7298947Z ... time.sleep(0.5) 2024-08-20T21:40:23.7299506Z ... fut.set_result(value) 2024-08-20T21:40:23.7300193Z >>> fut = torch.futures.Future() 2024-08-20T21:40:23.7300851Z >>> t = threading.Thread( 2024-08-20T21:40:23.7301490Z ... target=slow_set_future, 2024-08-20T21:40:23.7302441Z ... args=(fut, torch.ones(2) * 3) 2024-08-20T21:40:23.7303087Z ... ) 2024-08-20T21:40:23.7303511Z >>> t.start() 2024-08-20T21:40:23.7304016Z >>> print(fut.wait()) 2024-08-20T21:40:23.7304569Z tensor([3., 3.]) 2024-08-20T21:40:23.7305105Z >>> t.join() 2024-08-20T21:40:23.7305436Z 2024-08-20T21:40:23.7306153Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:23.7307027Z 2024-08-20T21:40:23.7427707Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=201. 2024-08-20T21:40:23.7429961Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:23.7431242Z Return the sum of each row of the given sparse tensor. 2024-08-20T21:40:23.7431972Z 2024-08-20T21:40:23.7432547Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-08-20T21:40:23.7433940Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-08-20T21:40:23.7435229Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-08-20T21:40:23.7436438Z returns a dense tensor instead of a sparse tensor. 2024-08-20T21:40:23.7437094Z 2024-08-20T21:40:23.7437718Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-08-20T21:40:23.7439103Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-08-20T21:40:23.7440221Z 2024-08-20T21:40:23.7440754Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-08-20T21:40:23.7442184Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-08-20T21:40:23.7443053Z 2024-08-20T21:40:23.7443238Z Args: 2024-08-20T21:40:23.7443758Z input (Tensor): the input sparse tensor 2024-08-20T21:40:23.7445074Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-08-20T21:40:23.7446284Z over all dims. 2024-08-20T21:40:23.7447311Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-08-20T21:40:23.7448532Z Default: dtype of :attr:`input`. 2024-08-20T21:40:23.7449093Z 2024-08-20T21:40:23.7449309Z Example:: 2024-08-20T21:40:23.7449598Z 2024-08-20T21:40:23.7449774Z >>> nnz = 3 2024-08-20T21:40:23.7450392Z >>> dims = [5, 5, 2, 3] 2024-08-20T21:40:23.7451230Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-08-20T21:40:23.7452340Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-08-20T21:40:23.7453366Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-08-20T21:40:23.7454184Z >>> size = torch.Size(dims) 2024-08-20T21:40:23.7455217Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:40:23.7456147Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-08-20T21:40:23.7456900Z >>> S 2024-08-20T21:40:23.7457448Z tensor(indices=tensor([[2, 0, 3], 2024-08-20T21:40:23.7458154Z [2, 4, 1]]), 2024-08-20T21:40:23.7459136Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-08-20T21:40:23.7460135Z [ 0.3411, 0.0918, -0.2312]], 2024-08-20T21:40:23.7460696Z 2024-08-20T21:40:23.7461081Z [[ 0.5348, 0.0634, -2.0494], 2024-08-20T21:40:23.7461997Z [-0.7125, -1.0646, 2.1844]], 2024-08-20T21:40:23.7462594Z 2024-08-20T21:40:23.7462998Z [[ 0.1276, 0.1874, -0.6334], 2024-08-20T21:40:23.7463951Z [-1.9682, -0.5340, 0.7483]]]), 2024-08-20T21:40:23.7464886Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-08-20T21:40:23.7465528Z 2024-08-20T21:40:23.7466010Z # when sum over only part of sparse_dims, return a sparse tensor 2024-08-20T21:40:23.7467005Z >>> torch.sparse.sum(S, [1, 3]) 2024-08-20T21:40:23.7468115Z tensor(indices=tensor([[0, 2, 3]]), 2024-08-20T21:40:23.7469066Z values=tensor([[-1.4512, 0.4073], 2024-08-20T21:40:23.7469960Z [-0.8901, 0.2017], 2024-08-20T21:40:23.7470831Z [-0.3183, -1.7539]]), 2024-08-20T21:40:23.7471664Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-08-20T21:40:23.7472255Z 2024-08-20T21:40:23.7472620Z # when sum over all sparse dim, return a dense tensor 2024-08-20T21:40:23.7473497Z # with summed dims squeezed 2024-08-20T21:40:23.7474218Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-08-20T21:40:23.7475082Z tensor([-2.6596, -1.1450]) 2024-08-20T21:40:23.7475679Z 2024-08-20T21:40:23.7476659Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:23.7477546Z 2024-08-20T21:40:24.8568086Z msg = Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py line=473. 2024-08-20T21:40:24.8570893Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:40:24.8571827Z 2024-08-20T21:40:24.8572583Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2024-08-20T21:40:24.8574275Z The submesh created consists of the dimensions and the communicators indicated by 2024-08-20T21:40:24.8575499Z ``mesh_dim_names`` 2024-08-20T21:40:24.8576221Z 2024-08-20T21:40:24.8576398Z Args: 2024-08-20T21:40:24.8577255Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2024-08-20T21:40:24.8578685Z mesh dimension of the DeviceMesh to create the submesh for. 2024-08-20T21:40:24.8579635Z Returns: 2024-08-20T21:40:24.8580128Z A :class:`DeviceMesh` object 2024-08-20T21:40:24.8580615Z 2024-08-20T21:40:24.8581355Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2024-08-20T21:40:24.8582625Z In the first example: 2024-08-20T21:40:24.8583721Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2024-08-20T21:40:24.8585432Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2024-08-20T21:40:24.8587089Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2024-08-20T21:40:24.8588654Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2024-08-20T21:40:24.8590499Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2024-08-20T21:40:24.8592068Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2024-08-20T21:40:24.8593013Z 2024-08-20T21:40:24.8593229Z In the second example: 2024-08-20T21:40:24.8594372Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2024-08-20T21:40:24.8596243Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2024-08-20T21:40:24.8598118Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2024-08-20T21:40:24.8599970Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2024-08-20T21:40:24.8601069Z 2024-08-20T21:40:24.8601283Z Example:: 2024-08-20T21:40:24.8601797Z >>> # xdoctest: +SKIP("no rank") 2024-08-20T21:40:24.8602741Z >>> from torch.distributed.device_mesh import DeviceMesh 2024-08-20T21:40:24.8603619Z >>> 2024-08-20T21:40:24.8604382Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2024-08-20T21:40:24.8605792Z >>> # of cross-host(dim 0), and within-host (dim 1). 2024-08-20T21:40:24.8607081Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-08-20T21:40:24.8608289Z >>> tp_mesh = mesh_2d["tp"] 2024-08-20T21:40:24.8608965Z >>> dp_mesh = mesh_2d["dp"] 2024-08-20T21:40:24.8609581Z >>> 2024-08-20T21:40:24.8610427Z >>> # Initialize a 3D mesh. 2024-08-20T21:40:24.8611630Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2024-08-20T21:40:24.8613442Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2024-08-20T21:40:24.8614826Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2024-08-20T21:40:24.8615620Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2024-08-20T21:40:24.8616172Z 2024-08-20T21:40:24.8618288Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2024-08-20T21:40:24.8620304Z 2024-08-20T21:40:24.8620931Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-08-20T21:40:24.8622206Z ^ 2024-08-20T21:40:24.8921142Z 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=2745. 2024-08-20T21:40:24.8923763Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:24.8924759Z 2024-08-20T21:40:24.8925310Z Gathers picklable objects from the whole group in a single process. 2024-08-20T21:40:24.8926129Z 2024-08-20T21:40:24.8926710Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-08-20T21:40:24.8928325Z object must be picklable in order to be gathered. 2024-08-20T21:40:24.8928948Z 2024-08-20T21:40:24.8929131Z Args: 2024-08-20T21:40:24.8929661Z obj (Any): Input object. Must be picklable. 2024-08-20T21:40:24.8930837Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-08-20T21:40:24.8932069Z should be correctly sized as the size of the group for this 2024-08-20T21:40:24.8933577Z collective and will contain the output. Must be ``None`` on non-dst 2024-08-20T21:40:24.8934665Z ranks. (default is ``None``) 2024-08-20T21:40:24.8936086Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0) 2024-08-20T21:40:24.8937799Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-08-20T21:40:24.8939001Z the default process group will be used. Default is ``None``. 2024-08-20T21:40:24.8939703Z 2024-08-20T21:40:24.8939859Z Returns: 2024-08-20T21:40:24.8940626Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-08-20T21:40:24.8941554Z output of the collective. 2024-08-20T21:40:24.8942014Z 2024-08-20T21:40:24.8942544Z .. note:: Note that this API differs slightly from the gather collective 2024-08-20T21:40:24.8943781Z since it does not provide an async_op handle and thus will be a blocking 2024-08-20T21:40:24.8944768Z call. 2024-08-20T21:40:24.8945057Z 2024-08-20T21:40:24.8945878Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-08-20T21:40:24.8947237Z of objects must be moved to the GPU device before communication takes 2024-08-20T21:40:24.8948320Z place. In this case, the device used is given by 2024-08-20T21:40:24.8949567Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-08-20T21:40:24.8950841Z ensure that this is set so that each rank has an individual GPU, via 2024-08-20T21:40:24.8951907Z ``torch.cuda.set_device()``. 2024-08-20T21:40:24.8952364Z 2024-08-20T21:40:24.8952543Z .. warning:: 2024-08-20T21:40:24.8953314Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-08-20T21:40:24.8954640Z known to be insecure. It is possible to construct malicious pickle data 2024-08-20T21:40:24.8956001Z which will execute arbitrary code during unpickling. Only call this 2024-08-20T21:40:24.8957055Z function with data you trust. 2024-08-20T21:40:24.8957516Z 2024-08-20T21:40:24.8957710Z .. warning:: 2024-08-20T21:40:24.8958786Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-08-20T21:40:24.8960369Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-08-20T21:40:24.8961652Z pickled. Please consider using :func:`gather` instead. 2024-08-20T21:40:24.8962293Z 2024-08-20T21:40:24.8962473Z Example:: 2024-08-20T21:40:24.8963057Z >>> # xdoctest: +SKIP("need process group init") 2024-08-20T21:40:24.8964106Z >>> # Note: Process group initialization omitted on each rank. 2024-08-20T21:40:24.8965022Z >>> import torch.distributed as dist 2024-08-20T21:40:24.8965752Z >>> # Assumes world_size of 3. 2024-08-20T21:40:24.8966572Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-08-20T21:40:24.8967514Z >>> output = [None for _ in gather_objects] 2024-08-20T21:40:24.8968270Z >>> dist.gather_object( 2024-08-20T21:40:24.8968876Z ... gather_objects[dist.get_rank()], 2024-08-20T21:40:24.8969638Z ... output if dist.get_rank() == 0 else None, 2024-08-20T21:40:24.8970428Z ... dst=0 2024-08-20T21:40:24.8970872Z ... ) 2024-08-20T21:40:24.8971254Z >>> # On rank 0 2024-08-20T21:40:24.8971714Z >>> output 2024-08-20T21:40:24.8972318Z ['foo', 12, {1: 2}] 2024-08-20T21:40:24.8972659Z 2024-08-20T21:40:24.8973337Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:24.8974215Z 2024-08-20T21:40:24.9097078Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2024-08-20T21:40:24.9099651Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:24.9100589Z 2024-08-20T21:40:24.9100999Z Module ``torch.distributed.launch``. 2024-08-20T21:40:24.9101547Z 2024-08-20T21:40:24.9102171Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-08-20T21:40:24.9103440Z training processes on each of the training nodes. 2024-08-20T21:40:24.9104058Z 2024-08-20T21:40:24.9104264Z .. warning:: 2024-08-20T21:40:24.9104558Z 2024-08-20T21:40:24.9105402Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-08-20T21:40:24.9106355Z 2024-08-20T21:40:24.9107107Z The utility can be used for single-node distributed training, in which one or 2024-08-20T21:40:24.9108617Z more processes per node will be spawned. The utility can be used for either 2024-08-20T21:40:24.9110104Z CPU training or GPU training. If the utility is used for GPU training, 2024-08-20T21:40:24.9111531Z each distributed process will be operating on a single GPU. This can achieve 2024-08-20T21:40:24.9113072Z well-improved single-node training performance. It can also be used in 2024-08-20T21:40:24.9114623Z multi-node distributed training, by spawning up multiple processes on each node 2024-08-20T21:40:24.9116171Z for well-improved multi-node distributed training performance as well. 2024-08-20T21:40:24.9117539Z This will especially be beneficial for systems with multiple Infiniband 2024-08-20T21:40:24.9119102Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-08-20T21:40:24.9120268Z aggregated communication bandwidth. 2024-08-20T21:40:24.9120787Z 2024-08-20T21:40:24.9121503Z In both cases of single-node distributed training or multi-node distributed 2024-08-20T21:40:24.9122954Z training, this utility will launch the given number of processes per node 2024-08-20T21:40:24.9124554Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-08-20T21:40:24.9126005Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-08-20T21:40:24.9127387Z and each process will be operating on a single GPU from *GPU 0 to 2024-08-20T21:40:24.9128484Z GPU (nproc_per_node - 1)*. 2024-08-20T21:40:24.9128905Z 2024-08-20T21:40:24.9129115Z **How to use this module:** 2024-08-20T21:40:24.9129528Z 2024-08-20T21:40:24.9130006Z 1. Single-Node multi-process distributed training 2024-08-20T21:40:24.9130731Z 2024-08-20T21:40:24.9131157Z :: 2024-08-20T21:40:24.9131428Z 2024-08-20T21:40:24.9132135Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:40:24.9133599Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-08-20T21:40:24.9134654Z arguments of your training script) 2024-08-20T21:40:24.9135229Z 2024-08-20T21:40:24.9135861Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-08-20T21:40:24.9136694Z 2024-08-20T21:40:24.9136703Z 2024-08-20T21:40:24.9137034Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-08-20T21:40:24.9137667Z 2024-08-20T21:40:24.9137855Z :: 2024-08-20T21:40:24.9138096Z 2024-08-20T21:40:24.9138808Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:40:24.9140125Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-08-20T21:40:24.9141337Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-20T21:40:24.9142464Z and all other arguments of your training script) 2024-08-20T21:40:24.9143122Z 2024-08-20T21:40:24.9143281Z Node 2: 2024-08-20T21:40:24.9143532Z 2024-08-20T21:40:24.9143694Z :: 2024-08-20T21:40:24.9143905Z 2024-08-20T21:40:24.9144601Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:40:24.9145926Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-08-20T21:40:24.9147258Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-20T21:40:24.9148298Z and all other arguments of your training script) 2024-08-20T21:40:24.9148887Z 2024-08-20T21:40:24.9149208Z 3. To look up what optional arguments this module offers: 2024-08-20T21:40:24.9149863Z 2024-08-20T21:40:24.9150026Z :: 2024-08-20T21:40:24.9150275Z 2024-08-20T21:40:24.9150706Z python -m torch.distributed.launch --help 2024-08-20T21:40:24.9151264Z 2024-08-20T21:40:24.9151272Z 2024-08-20T21:40:24.9151496Z **Important Notices:** 2024-08-20T21:40:24.9151844Z 2024-08-20T21:40:24.9152406Z 1. This utility and multi-process distributed (single-node or 2024-08-20T21:40:24.9153856Z multi-node) GPU training currently only achieves the best performance using 2024-08-20T21:40:24.9155413Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-08-20T21:40:24.9156543Z use for GPU training. 2024-08-20T21:40:24.9156913Z 2024-08-20T21:40:24.9157580Z 2. In your training program, you must parse the command-line argument: 2024-08-20T21:40:24.9159083Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-08-20T21:40:24.9176242Z If your training program uses GPUs, you should ensure that your code only 2024-08-20T21:40:24.9177604Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-08-20T21:40:24.9178357Z 2024-08-20T21:40:24.9178573Z Parsing the local_rank argument 2024-08-20T21:40:24.9179036Z 2024-08-20T21:40:24.9179206Z :: 2024-08-20T21:40:24.9179419Z 2024-08-20T21:40:24.9179655Z >>> # xdoctest: +SKIP 2024-08-20T21:40:24.9180186Z >>> import argparse 2024-08-20T21:40:24.9180794Z >>> parser = argparse.ArgumentParser() 2024-08-20T21:40:24.9181993Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-08-20T21:40:24.9182862Z >>> args = parser.parse_args() 2024-08-20T21:40:24.9183325Z 2024-08-20T21:40:24.9183574Z Set your device to local rank using either 2024-08-20T21:40:24.9184128Z 2024-08-20T21:40:24.9184277Z :: 2024-08-20T21:40:24.9184504Z 2024-08-20T21:40:24.9184995Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-08-20T21:40:24.9185747Z 2024-08-20T21:40:24.9185891Z or 2024-08-20T21:40:24.9186122Z 2024-08-20T21:40:24.9186269Z :: 2024-08-20T21:40:24.9186478Z 2024-08-20T21:40:24.9186767Z >>> with torch.cuda.device(args.local_rank): 2024-08-20T21:40:24.9187492Z >>> # your code to run 2024-08-20T21:40:24.9188079Z >>> ... 2024-08-20T21:40:24.9188349Z 2024-08-20T21:40:24.9188753Z .. versionchanged:: 2.0.0 2024-08-20T21:40:24.9189164Z 2024-08-20T21:40:24.9189937Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-08-20T21:40:24.9191717Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-08-20T21:40:24.9193017Z previously used underscored ``--local_rank``. 2024-08-20T21:40:24.9193634Z 2024-08-20T21:40:24.9194225Z For backward compatibility, it may be necessary for users to handle both 2024-08-20T21:40:24.9195789Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-08-20T21:40:24.9197333Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-08-20T21:40:24.9198654Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-08-20T21:40:24.9200217Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-08-20T21:40:24.9201434Z including ``"--local-rank"`` should be sufficient. 2024-08-20T21:40:24.9201962Z 2024-08-20T21:40:24.9202405Z 3. In your training program, you are supposed to call the following function 2024-08-20T21:40:24.9203788Z at the beginning to start the distributed backend. It is strongly recommended 2024-08-20T21:40:24.9205057Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-08-20T21:40:24.9206284Z but ``env://`` is the one that is officially supported by this module. 2024-08-20T21:40:24.9207286Z 2024-08-20T21:40:24.9207458Z :: 2024-08-20T21:40:24.9207706Z 2024-08-20T21:40:24.9208348Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-08-20T21:40:24.9209587Z >>> init_method='env://') 2024-08-20T21:40:24.9210266Z 2024-08-20T21:40:24.9210843Z 4. In your training program, you can either use regular distributed functions 2024-08-20T21:40:24.9212341Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-08-20T21:40:24.9213748Z training program uses GPUs for training and you would like to use 2024-08-20T21:40:24.9214983Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-08-20T21:40:24.9215965Z here is how to configure it. 2024-08-20T21:40:24.9216402Z 2024-08-20T21:40:24.9216600Z :: 2024-08-20T21:40:24.9216840Z 2024-08-20T21:40:24.9217259Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-08-20T21:40:24.9218382Z >>> device_ids=[args.local_rank], 2024-08-20T21:40:24.9219409Z >>> output_device=args.local_rank) 2024-08-20T21:40:24.9220052Z 2024-08-20T21:40:24.9220635Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-08-20T21:40:24.9222040Z that your code will be operating on. This is generally the local rank of the 2024-08-20T21:40:24.9223547Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-08-20T21:40:24.9224924Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-08-20T21:40:24.9225905Z utility 2024-08-20T21:40:24.9226178Z 2024-08-20T21:40:24.9226819Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-08-20T21:40:24.9228203Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-08-20T21:40:24.9229629Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-08-20T21:40:24.9230987Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-08-20T21:40:24.9232169Z will not pass ``--local-rank`` when you specify this flag. 2024-08-20T21:40:24.9232853Z 2024-08-20T21:40:24.9233063Z .. warning:: 2024-08-20T21:40:24.9233339Z 2024-08-20T21:40:24.9233815Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-08-20T21:40:24.9235242Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-08-20T21:40:24.9236265Z write to a networked filesystem. See 2024-08-20T21:40:24.9237531Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-08-20T21:40:24.9238972Z how things can go wrong if you don't do this correctly. 2024-08-20T21:40:24.9239712Z 2024-08-20T21:40:24.9239721Z 2024-08-20T21:40:24.9239729Z 2024-08-20T21:40:24.9239735Z 2024-08-20T21:40:24.9240510Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:24.9241427Z 2024-08-20T21:40:24.9485535Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=130. 2024-08-20T21:40:24.9488337Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:24.9489346Z 2024-08-20T21:40:24.9489913Z DistributedOptimizer takes remote references to parameters scattered 2024-08-20T21:40:24.9491726Z across workers and applies the given optimizer locally for each parameter. 2024-08-20T21:40:24.9492696Z 2024-08-20T21:40:24.9493324Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-08-20T21:40:24.9494634Z to retrieve the gradients for specific parameters. 2024-08-20T21:40:24.9495312Z 2024-08-20T21:40:24.9495492Z Concurrent calls to 2024-08-20T21:40:24.9496356Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-08-20T21:40:24.9497531Z either from the same or different clients, will 2024-08-20T21:40:24.9498918Z be serialized on each worker -- as each worker's optimizer can only work 2024-08-20T21:40:24.9500730Z on one set of gradients at a time. However, there is no guarantee that 2024-08-20T21:40:24.9502370Z the full forward-backward-optimizer sequence will execute for one client 2024-08-20T21:40:24.9503797Z at a time. This means that the gradients being applied may not correspond 2024-08-20T21:40:24.9505202Z to the latest forward pass executed on a given worker. Also, there is no 2024-08-20T21:40:24.9506346Z guaranteed ordering across workers. 2024-08-20T21:40:24.9506858Z 2024-08-20T21:40:24.9507483Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-08-20T21:40:24.9508986Z by default, so that optimizer updates are not blocked by the Python Global 2024-08-20T21:40:24.9510554Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-08-20T21:40:24.9512119Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-08-20T21:40:24.9513667Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-08-20T21:40:24.9514903Z for your own custom optimizers. 2024-08-20T21:40:24.9515386Z 2024-08-20T21:40:24.9515568Z Args: 2024-08-20T21:40:24.9516284Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-08-20T21:40:24.9517319Z instantiate on each worker. 2024-08-20T21:40:24.9518363Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-08-20T21:40:24.9519397Z to optimize. 2024-08-20T21:40:24.9520291Z args: arguments to pass to the optimizer constructor on each worker. 2024-08-20T21:40:24.9521736Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-08-20T21:40:24.9522631Z 2024-08-20T21:40:24.9522839Z Example:: 2024-08-20T21:40:24.9523379Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:24.9524330Z >>> import torch.distributed.autograd as dist_autograd 2024-08-20T21:40:24.9525324Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:24.9526152Z >>> from torch import optim 2024-08-20T21:40:24.9527098Z >>> from torch.distributed.optim import DistributedOptimizer 2024-08-20T21:40:24.9528050Z >>> 2024-08-20T21:40:24.9528628Z >>> with dist_autograd.context() as context_id: 2024-08-20T21:40:24.9529468Z >>> # Forward pass. 2024-08-20T21:40:24.9530446Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-08-20T21:40:24.9531791Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-08-20T21:40:24.9532915Z >>> loss = rref1.to_here() + rref2.to_here() 2024-08-20T21:40:24.9533940Z >>> 2024-08-20T21:40:24.9534412Z >>> # Backward pass. 2024-08-20T21:40:24.9535188Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-08-20T21:40:24.9536044Z >>> 2024-08-20T21:40:24.9536484Z >>> # Optimizer. 2024-08-20T21:40:24.9537142Z >>> dist_optim = DistributedOptimizer( 2024-08-20T21:40:24.9537937Z >>> optim.SGD, 2024-08-20T21:40:24.9538499Z >>> [rref1, rref2], 2024-08-20T21:40:24.9539116Z >>> lr=0.05, 2024-08-20T21:40:24.9539642Z >>> ) 2024-08-20T21:40:24.9540147Z >>> dist_optim.step(context_id) 2024-08-20T21:40:24.9540656Z 2024-08-20T21:40:24.9541011Z __ https://github.com/pytorch/tutorials/pull/1465 2024-08-20T21:40:24.9541653Z 2024-08-20T21:40:24.9542457Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:24.9543355Z 2024-08-20T21:40:24.9545521Z msg = Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2024-08-20T21:40:24.9548386Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:24.9549370Z 2024-08-20T21:40:24.9550572Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-08-20T21:40:24.9552274Z This optimizer runs local optimizer at every step. 2024-08-20T21:40:24.9554154Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-08-20T21:40:24.9555125Z 2024-08-20T21:40:24.9555254Z Args: 2024-08-20T21:40:24.9555649Z optim: The local optimizer. 2024-08-20T21:40:24.9556679Z averager: A model averager instance to run post-localSGD algorithm. 2024-08-20T21:40:24.9557439Z 2024-08-20T21:40:24.9557607Z Example:: 2024-08-20T21:40:24.9557853Z 2024-08-20T21:40:24.9558109Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:24.9558792Z >>> import torch 2024-08-20T21:40:24.9559325Z >>> import torch.distributed as dist 2024-08-20T21:40:24.9560457Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-20T21:40:24.9561602Z >>> import torch.nn as nn 2024-08-20T21:40:24.9562457Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-08-20T21:40:24.9563846Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-20T21:40:24.9564918Z >>> PostLocalSGDState, 2024-08-20T21:40:24.9565516Z >>> post_localSGD_hook, 2024-08-20T21:40:24.9566084Z >>> ) 2024-08-20T21:40:24.9566528Z >>> 2024-08-20T21:40:24.9567073Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:40:24.9567848Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:40:24.9568543Z >>> ) 2024-08-20T21:40:24.9568887Z >>> 2024-08-20T21:40:24.9569602Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:40:24.9570895Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-20T21:40:24.9572111Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:40:24.9572866Z >>> 2024-08-20T21:40:24.9573711Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-08-20T21:40:24.9574992Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-08-20T21:40:24.9576211Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:40:24.9577243Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-08-20T21:40:24.9578194Z >>> opt = PostLocalSGDOptimizer( 2024-08-20T21:40:24.9578867Z >>> optim=local_optim, 2024-08-20T21:40:24.9579807Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-20T21:40:24.9580888Z >>> ) 2024-08-20T21:40:24.9581229Z >>> 2024-08-20T21:40:24.9581976Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-08-20T21:40:24.9583931Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-08-20T21:40:24.9586056Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-08-20T21:40:24.9587467Z >>> for step in range(0, 200): 2024-08-20T21:40:24.9588045Z >>> opt.zero_grad() 2024-08-20T21:40:24.9588618Z >>> loss = loss_fn(output, labels) 2024-08-20T21:40:24.9589233Z >>> loss.backward() 2024-08-20T21:40:24.9589752Z >>> opt.step() 2024-08-20T21:40:24.9590055Z 2024-08-20T21:40:24.9590984Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:24.9591801Z 2024-08-20T21:40:24.9615601Z msg = Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282. 2024-08-20T21:40:24.9618480Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:24.9619507Z 2024-08-20T21:40:24.9620505Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-08-20T21:40:24.9621739Z 2024-08-20T21:40:24.9622016Z The sharing is done as described by ZeRO_. 2024-08-20T21:40:24.9622531Z 2024-08-20T21:40:24.9622838Z The local optimizer instance in each rank is only 2024-08-20T21:40:24.9624323Z responsible for updating approximately ``1 / world_size`` parameters and 2024-08-20T21:40:24.9625664Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-08-20T21:40:24.9627038Z parameters are updated locally, each rank will broadcast its parameters to 2024-08-20T21:40:24.9628403Z all other peers to keep all model replicas in the same state. 2024-08-20T21:40:24.9629545Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-08-20T21:40:24.9631090Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-08-20T21:40:24.9632227Z memory consumption. 2024-08-20T21:40:24.9632589Z 2024-08-20T21:40:24.9633317Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-08-20T21:40:24.9634823Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-08-20T21:40:24.9636265Z not divided among ranks. The partition is arbitrary and might not match the 2024-08-20T21:40:24.9637384Z the parameter registration or usage order. 2024-08-20T21:40:24.9637881Z 2024-08-20T21:40:24.9638055Z Arguments: 2024-08-20T21:40:24.9638730Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-08-20T21:40:24.9639931Z or :class:`dict` s giving all parameters, which will be sharded 2024-08-20T21:40:24.9640877Z across ranks. 2024-08-20T21:40:24.9641223Z 2024-08-20T21:40:24.9641403Z Keyword Args: 2024-08-20T21:40:24.9642228Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-08-20T21:40:24.9643240Z optimizer. 2024-08-20T21:40:24.9644050Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-08-20T21:40:24.9645322Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-08-20T21:40:24.9646427Z :meth:`torch.distributed.init_process_group`). 2024-08-20T21:40:24.9647557Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-08-20T21:40:24.9648860Z packed into buckets to speed up communication, and ``param.data`` 2024-08-20T21:40:24.9650272Z fields point to bucket views at different offsets; if ``False``, 2024-08-20T21:40:24.9651562Z each individual parameter is communicated separately, and each 2024-08-20T21:40:24.9652703Z ``params.data`` stays intact (default: ``False``). 2024-08-20T21:40:24.9653765Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-08-20T21:40:24.9655147Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-08-20T21:40:24.9656607Z synchronization; this requires (1) either a functional optimizer 2024-08-20T21:40:24.9657720Z for the ``optimizer_class`` argument or one with a functional 2024-08-20T21:40:24.9658818Z equivalent and (2) registering a DDP communication hook 2024-08-20T21:40:24.9659983Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-08-20T21:40:24.9661140Z parameters are packed into buckets matching those in 2024-08-20T21:40:24.9662203Z :class:`DistributedDataParallel`, meaning that the 2024-08-20T21:40:24.9663257Z ``parameters_as_bucket_view`` argument is ignored. 2024-08-20T21:40:24.9664347Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-08-20T21:40:24.9665291Z (per normal). 2024-08-20T21:40:24.9665821Z (default: ``False``) 2024-08-20T21:40:24.9666702Z **defaults: any trailing arguments, which are forwarded to the local 2024-08-20T21:40:24.9667700Z optimizer. 2024-08-20T21:40:24.9668015Z 2024-08-20T21:40:24.9668226Z Example:: 2024-08-20T21:40:24.9668505Z 2024-08-20T21:40:24.9668707Z >>> # xdoctest: +SKIP 2024-08-20T21:40:24.9669308Z >>> import torch.nn as nn 2024-08-20T21:40:24.9670221Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-08-20T21:40:24.9671439Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-08-20T21:40:24.9672788Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-08-20T21:40:24.9674553Z >>> ddp = DDP(model, device_ids=[rank]) 2024-08-20T21:40:24.9675366Z >>> opt = ZeroRedundancyOptimizer( 2024-08-20T21:40:24.9676124Z >>> ddp.parameters(), 2024-08-20T21:40:24.9676814Z >>> optimizer_class=torch.optim.Adam, 2024-08-20T21:40:24.9677511Z >>> lr=0.01 2024-08-20T21:40:24.9677982Z >>> ) 2024-08-20T21:40:24.9678469Z >>> ddp(inputs).sum().backward() 2024-08-20T21:40:24.9679115Z >>> opt.step() 2024-08-20T21:40:24.9679440Z 2024-08-20T21:40:24.9679629Z .. warning:: 2024-08-20T21:40:24.9680417Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-08-20T21:40:24.9681693Z passed-in parameters are the same dense type. 2024-08-20T21:40:24.9682324Z 2024-08-20T21:40:24.9682515Z .. warning:: 2024-08-20T21:40:24.9683316Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-08-20T21:40:24.9684581Z the way that overlapping :class:`DistributedDataParallel` with 2024-08-20T21:40:24.9685868Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-08-20T21:40:24.9687281Z two or three training iterations do not perform parameter updates in 2024-08-20T21:40:24.9688585Z the optimizer step, depending on if ``static_graph=False`` or 2024-08-20T21:40:24.9689775Z ``static_graph=True``, respectively. This is because it needs 2024-08-20T21:40:24.9691286Z information about the gradient bucketing strategy used by 2024-08-20T21:40:24.9692484Z :class:`DistributedDataParallel`, which is not finalized until the 2024-08-20T21:40:24.9693774Z second forward pass if ``static_graph=False`` or until the third 2024-08-20T21:40:24.9695006Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-08-20T21:40:24.9695917Z is to prepend dummy inputs. 2024-08-20T21:40:24.9696322Z 2024-08-20T21:40:24.9696881Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-08-20T21:40:24.9697664Z 2024-08-20T21:40:24.9697915Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-08-20T21:40:24.9698458Z 2024-08-20T21:40:24.9698467Z 2024-08-20T21:40:24.9699187Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:24.9700027Z 2024-08-20T21:40:25.0047400Z msg = Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2024-08-20T21:40:25.0050199Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.0051095Z 2024-08-20T21:40:25.0052111Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-08-20T21:40:25.0053355Z Needs to be called on all ranks in an SPMD fashion. 2024-08-20T21:40:25.0053989Z 2024-08-20T21:40:25.0054159Z Args: 2024-08-20T21:40:25.0055023Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-08-20T21:40:25.0056380Z of shards that represent the local shards on this rank. 2024-08-20T21:40:25.0057636Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-08-20T21:40:25.0058796Z shape of the overall sharded tensor. 2024-08-20T21:40:25.0059363Z 2024-08-20T21:40:25.0059550Z Keyword args: 2024-08-20T21:40:25.0060417Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-08-20T21:40:25.0061616Z the default process group will be used. 2024-08-20T21:40:25.0062527Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-20T21:40:25.0063688Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-20T21:40:25.0064935Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-20T21:40:25.0065889Z Default: ``False``. 2024-08-20T21:40:25.0066305Z 2024-08-20T21:40:25.0066473Z Returns: 2024-08-20T21:40:25.0067070Z A :class:`ShardedTensor` object handle on this rank 2024-08-20T21:40:25.0067713Z 2024-08-20T21:40:25.0067721Z 2024-08-20T21:40:25.0068183Z Examples: 2024-08-20T21:40:25.0069103Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-08-20T21:40:25.0070500Z each shard have a (5, 5) local tensor, we can do it like below: 2024-08-20T21:40:25.0071260Z 2024-08-20T21:40:25.0071459Z on rank 0: 2024-08-20T21:40:25.0072000Z >>> # xdoctest: +SKIP("not distributed") 2024-08-20T21:40:25.0072835Z >>> local_shard_metadata = ShardMetadata( 2024-08-20T21:40:25.0073595Z >>> shard_offsets=[0, 0], 2024-08-20T21:40:25.0074249Z >>> shard_lengths=[5, 5], 2024-08-20T21:40:25.0074950Z >>> placement="rank:0/cuda:0" 2024-08-20T21:40:25.0075594Z >>> ) 2024-08-20T21:40:25.0076292Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-20T21:40:25.0077481Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-20T21:40:25.0078241Z 2024-08-20T21:40:25.0078441Z on rank 1: 2024-08-20T21:40:25.0078990Z >>> # xdoctest: +SKIP("not distributed") 2024-08-20T21:40:25.0079840Z >>> local_shard_metadata = ShardMetadata( 2024-08-20T21:40:25.0080611Z >>> shard_offsets=[5, 0], 2024-08-20T21:40:25.0081264Z >>> shard_lengths=[5, 5], 2024-08-20T21:40:25.0081933Z >>> placement="rank:1/cuda:1" 2024-08-20T21:40:25.0082600Z >>> ) 2024-08-20T21:40:25.0083281Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-20T21:40:25.0084468Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-20T21:40:25.0085226Z 2024-08-20T21:40:25.0086127Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.0087024Z 2024-08-20T21:40:25.0161559Z msg = Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784. 2024-08-20T21:40:25.0164479Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.0165450Z 2024-08-20T21:40:25.0166101Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-08-20T21:40:25.0167272Z size and sharding spec on each rank. 2024-08-20T21:40:25.0167779Z 2024-08-20T21:40:25.0167958Z Args: 2024-08-20T21:40:25.0168688Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-08-20T21:40:25.0170023Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-08-20T21:40:25.0171309Z The specification describing how to shard the Tensor. 2024-08-20T21:40:25.0172592Z global_size (Sequence[int]): Size of the sharded tensor. 2024-08-20T21:40:25.0173858Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-08-20T21:40:25.0174932Z Default: None 2024-08-20T21:40:25.0175649Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-20T21:40:25.0176823Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-20T21:40:25.0178092Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-20T21:40:25.0179062Z Default: ``False``. 2024-08-20T21:40:25.0179459Z 2024-08-20T21:40:25.0179622Z Returns: 2024-08-20T21:40:25.0180482Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-08-20T21:40:25.0181661Z tensor stored in the current rank. 2024-08-20T21:40:25.0182201Z 2024-08-20T21:40:25.0182374Z Examples: 2024-08-20T21:40:25.0182826Z >>> # xdoctest: +SKIP 2024-08-20T21:40:25.0183550Z >>> # All tensors below are of torch.int64 type. 2024-08-20T21:40:25.0184395Z >>> # We have 2 process groups, 2 ranks. 2024-08-20T21:40:25.0185405Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-08-20T21:40:25.0186589Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-08-20T21:40:25.0187535Z >>> local_tensor 2024-08-20T21:40:25.0188052Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-08-20T21:40:25.0188682Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-08-20T21:40:25.0189571Z >>> sharding_dim = 0 2024-08-20T21:40:25.0190150Z >>> sharding_spec = ChunkShardingSpec( 2024-08-20T21:40:25.0191172Z dim=sharding_dim, 2024-08-20T21:40:25.0191755Z placements=[ 2024-08-20T21:40:25.0192328Z "rank:0/cuda:0", 2024-08-20T21:40:25.0192936Z "rank:1/cuda:1", 2024-08-20T21:40:25.0193508Z ], 2024-08-20T21:40:25.0193954Z ) 2024-08-20T21:40:25.0194777Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-08-20T21:40:25.0195815Z >>> st 2024-08-20T21:40:25.0196249Z ShardedTensor( 2024-08-20T21:40:25.0196765Z ShardedTensorMetadata( 2024-08-20T21:40:25.0197371Z shards_metadata=[ 2024-08-20T21:40:25.0198397Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-08-20T21:40:25.0199735Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-08-20T21:40:25.0200720Z ], 2024-08-20T21:40:25.0201191Z size=torch.Size([2, 4]) 2024-08-20T21:40:25.0201776Z ) 2024-08-20T21:40:25.0202194Z >>> st.local_tensor() 2024-08-20T21:40:25.0202755Z tensor([1, 2, 3, 4]) # Rank 0 2024-08-20T21:40:25.0203395Z tensor([3, 4, 5, 6]) # Rank 1 2024-08-20T21:40:25.0203810Z 2024-08-20T21:40:25.0204406Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-08-20T21:40:25.0205865Z rank validations, and we only validate the local shard on the current rank. 2024-08-20T21:40:25.0207240Z We fully rely on the user to ensure local tensor is sharded based on the 2024-08-20T21:40:25.0208258Z sharding spec. 2024-08-20T21:40:25.0208647Z 2024-08-20T21:40:25.0209476Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.0210455Z 2024-08-20T21:40:25.0212436Z msg = Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023. 2024-08-20T21:40:25.0215111Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.0216032Z 2024-08-20T21:40:25.0216681Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-08-20T21:40:25.0217801Z single local shard. 2024-08-20T21:40:25.0218160Z 2024-08-20T21:40:25.0218857Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-08-20T21:40:25.0220509Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-08-20T21:40:25.0221660Z we swap local shards directly. 2024-08-20T21:40:25.0222710Z For more generic cases, we merge different shards across different ranks and split 2024-08-20T21:40:25.0224328Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-08-20T21:40:25.0225274Z 2024-08-20T21:40:25.0225455Z Args: 2024-08-20T21:40:25.0226372Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-08-20T21:40:25.0227742Z specification describing how the tensor is sharded. 2024-08-20T21:40:25.0228411Z 2024-08-20T21:40:25.0228591Z Returns: 2024-08-20T21:40:25.0229289Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-08-20T21:40:25.0230113Z 2024-08-20T21:40:25.0230289Z Examples: 2024-08-20T21:40:25.0230775Z >>> # xdoctest: +SKIP 2024-08-20T21:40:25.0231417Z >>> # We have 2 process groups, 2 ranks. 2024-08-20T21:40:25.0232402Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-08-20T21:40:25.0233415Z >>> tensor = torch.stack([tensor, tensor]) 2024-08-20T21:40:25.0234153Z >>> tensor 2024-08-20T21:40:25.0234712Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-08-20T21:40:25.0235528Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-08-20T21:40:25.0236346Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-08-20T21:40:25.0237414Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-08-20T21:40:25.0238213Z >>> sharding_dim = 0 2024-08-20T21:40:25.0238811Z >>> spec = ChunkShardingSpec( 2024-08-20T21:40:25.0239476Z dim=sharding_dim, 2024-08-20T21:40:25.0240074Z placements=[ 2024-08-20T21:40:25.0240588Z "rank:0/cuda:0", 2024-08-20T21:40:25.0241191Z "rank:1/cuda:1", 2024-08-20T21:40:25.0241797Z "rank:2/cuda:2", 2024-08-20T21:40:25.0242336Z "rank:3/cuda:3", 2024-08-20T21:40:25.0242887Z ], 2024-08-20T21:40:25.0243323Z ) 2024-08-20T21:40:25.0243774Z >>> current_offsets = [0] * 2 2024-08-20T21:40:25.0244441Z >>> current_offsets[0] = rank * 2 2024-08-20T21:40:25.0245148Z >>> shard_metadata = ShardMetadata( 2024-08-20T21:40:25.0245978Z shard_offsets=copy.deepcopy(current_offsets), 2024-08-20T21:40:25.0246844Z shard_sizes=tensor.size(), 2024-08-20T21:40:25.0247598Z placement=spec.placements[rank], 2024-08-20T21:40:25.0248336Z ) 2024-08-20T21:40:25.0248778Z >>> local_shards = [ 2024-08-20T21:40:25.0249280Z Shard( 2024-08-20T21:40:25.0249716Z tensor=tensor, 2024-08-20T21:40:25.0250393Z metadata=shard_metadata, 2024-08-20T21:40:25.0251036Z ) 2024-08-20T21:40:25.0251446Z ] 2024-08-20T21:40:25.0252206Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-08-20T21:40:25.0253155Z >>> sharding_dim = 1 2024-08-20T21:40:25.0253839Z >>> resharding_spec = ChunkShardingSpec( 2024-08-20T21:40:25.0254686Z dim=sharding_dim, 2024-08-20T21:40:25.0255266Z placements=[ 2024-08-20T21:40:25.0255823Z "rank:0/cuda:0", 2024-08-20T21:40:25.0256411Z "rank:1/cuda:1", 2024-08-20T21:40:25.0257014Z "rank:2/cuda:2", 2024-08-20T21:40:25.0257583Z "rank:3/cuda:3", 2024-08-20T21:40:25.0258150Z ], 2024-08-20T21:40:25.0258591Z ) 2024-08-20T21:40:25.0259048Z >>> st.reshard(resharding_spec) 2024-08-20T21:40:25.0259836Z >>> tensor = st.local_shards()[0].tensor 2024-08-20T21:40:25.0260552Z >>> tensor 2024-08-20T21:40:25.0261124Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-08-20T21:40:25.0262070Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-08-20T21:40:25.0262976Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-08-20T21:40:25.0263878Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-08-20T21:40:25.0264675Z 2024-08-20T21:40:25.0265499Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.0266348Z 2024-08-20T21:40:25.0267457Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:40:25.0305746Z msg = Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2024-08-20T21:40:25.0308391Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.0309275Z 2024-08-20T21:40:25.0309771Z Representation of a sharding plan, describes how to shard a module 2024-08-20T21:40:25.0311201Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-08-20T21:40:25.0312823Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-08-20T21:40:25.0314416Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-08-20T21:40:25.0315377Z 2024-08-20T21:40:25.0315532Z Args: 2024-08-20T21:40:25.0316453Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-08-20T21:40:25.0317821Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-08-20T21:40:25.0319437Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-08-20T21:40:25.0321289Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-08-20T21:40:25.0322491Z a parameter to a `ShardingSpec`. 2024-08-20T21:40:25.0323681Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-08-20T21:40:25.0324781Z to a `Sharder` object. 2024-08-20T21:40:25.0325986Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-08-20T21:40:25.0327839Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-08-20T21:40:25.0329395Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-08-20T21:40:25.0330633Z Default: `None` 2024-08-20T21:40:25.0331609Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-08-20T21:40:25.0333352Z a module's sharded output to be returned as a Tensor from its local shards to 2024-08-20T21:40:25.0334813Z ensure further processing in a data parallel fashion. ("" in list means the 2024-08-20T21:40:25.0335874Z root module). 2024-08-20T21:40:25.0336383Z Default: None 2024-08-20T21:40:25.0336883Z Example: 2024-08-20T21:40:25.0337876Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-08-20T21:40:25.0339619Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-08-20T21:40:25.0340708Z 2024-08-20T21:40:25.0341117Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-08-20T21:40:25.0342020Z >>> class MyModule(nn.Module): 2024-08-20T21:40:25.0342858Z >>> def __init__(self) -> None: 2024-08-20T21:40:25.0343570Z >>> super().__init__() 2024-08-20T21:40:25.0344236Z >>> self.fc1 = nn.Linear() 2024-08-20T21:40:25.0344895Z >>> self.gelu = nn.GELU() 2024-08-20T21:40:25.0345552Z >>> self.fc2 = nn.Linear() 2024-08-20T21:40:25.0346256Z >>> self.relu = nn.Linear() 2024-08-20T21:40:25.0346902Z >>> 2024-08-20T21:40:25.0347374Z >>> def forward(self, input): 2024-08-20T21:40:25.0348233Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-08-20T21:40:25.0348937Z 2024-08-20T21:40:25.0348947Z 2024-08-20T21:40:25.0349250Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-08-20T21:40:25.0350025Z >>> sharding_plan = ShardingPlan( 2024-08-20T21:40:25.0350648Z >>> plan={ 2024-08-20T21:40:25.0351379Z >>> "fc1.weight": spec1, 2024-08-20T21:40:25.0352016Z >>> "fc2.weight": spec2 2024-08-20T21:40:25.0352564Z >>> }, 2024-08-20T21:40:25.0352975Z >>> output_plan={ 2024-08-20T21:40:25.0353509Z >>> "fc2": output_spec 2024-08-20T21:40:25.0354091Z >>> }, 2024-08-20T21:40:25.0354562Z >>> return_local_tensor=["fc2"] 2024-08-20T21:40:25.0355227Z >>> ) 2024-08-20T21:40:25.0355498Z 2024-08-20T21:40:25.0356334Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.0357274Z 2024-08-20T21:40:25.0642955Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:40:25.0965646Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:40:25.0986519Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/experimental/func_map.py line=33. 2024-08-20T21:40:25.0989267Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.0991168Z 2024-08-20T21:40:25.0991865Z ``local_map`` is an experimental API that allows users to apply on :class:`DTensors` 2024-08-20T21:40:25.0993411Z a function that is written to be applied on :class:`~torch.Tensors`. 2024-08-20T21:40:25.0994264Z 2024-08-20T21:40:25.0994434Z Args: 2024-08-20T21:40:25.0995549Z func (Callable): the function to be applied on each local shard of 2024-08-20T21:40:25.0996599Z :class:`DTensor`s. 2024-08-20T21:40:25.0997544Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-08-20T21:40:25.0999272Z the desired placements of the :class:`DTensor`s in ``func``'s flattened output. 2024-08-20T21:40:25.1000893Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2024-08-20T21:40:25.1002468Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2024-08-20T21:40:25.1004026Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2024-08-20T21:40:25.1005268Z mapping to the flattened ``output``. 2024-08-20T21:40:25.1006372Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-08-20T21:40:25.1007926Z placements (a `Tuple[Placement]` value). For non-:class:`Tensor` output, 2024-08-20T21:40:25.1009130Z the `PlacementType` should be `None`. 2024-08-20T21:40:25.1010417Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-08-20T21:40:25.1011975Z in. In this case, even if `out_placements` is not `None`, the result function 2024-08-20T21:40:25.1013502Z should ignore the desired placements because the application is not on 2024-08-20T21:40:25.1014618Z :class:`DTensors`. 2024-08-20T21:40:25.1015407Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-08-20T21:40:25.1016955Z the required placements of the :class:`DTensor`s in ``func``'s flattened input. 2024-08-20T21:40:25.1018515Z If ``in_placements`` is specified, ``local_map`` would examine whether the 2024-08-20T21:40:25.1019994Z placements of each :class:`DTensor` argument is the same as the required 2024-08-20T21:40:25.1021343Z placements or not. If the placements are not the same and 2024-08-20T21:40:25.1022724Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2024-08-20T21:40:25.1024300Z ``redistribute_inputs`` is `True`, the argument will be first redistributed to 2024-08-20T21:40:25.1025902Z the required sharding placements before passing its local tensor to ``func``. 2024-08-20T21:40:25.1027464Z The only exception is when required placements are not ``None`` and the 2024-08-20T21:40:25.1029009Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-08-20T21:40:25.1030781Z will be skipped and the argument will be directly passed to ``func``. 2024-08-20T21:40:25.1032283Z If ``in_placements`` is ``None``, no placements examination will be performed. 2024-08-20T21:40:25.1033408Z Default: None 2024-08-20T21:40:25.1034082Z device_mesh (:class:`DeviceMesh`, optional): 2024-08-20T21:40:25.1035231Z the device mesh that all the :class:`DTensor`s are placed on. If not 2024-08-20T21:40:25.1036870Z specified, this will be inferred from the input :class:`DTensor`s' device 2024-08-20T21:40:25.1038400Z mesh. `local_map` requires every :class:`DTensor`s to be placed on the same 2024-08-20T21:40:25.1039550Z device mesh. Default: None. 2024-08-20T21:40:25.1040329Z redistribute_inputs (bool, optional): 2024-08-20T21:40:25.1041537Z the bool value indicating whether to reshard the input :class:`DTensor`s when 2024-08-20T21:40:25.1043131Z their placements are different from the required input placements. If this 2024-08-20T21:40:25.1044690Z value is ``False`` and some :class:`DTensor` input has a different placement, 2024-08-20T21:40:25.1045952Z an exception will be raised. Default: False. 2024-08-20T21:40:25.1046588Z 2024-08-20T21:40:25.1046785Z Returns: 2024-08-20T21:40:25.1047715Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2024-08-20T21:40:25.1049335Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2024-08-20T21:40:25.1050491Z 2024-08-20T21:40:25.1050683Z Raises: 2024-08-20T21:40:25.1051597Z AssertionError: If the input :class:`DTensor`s are not placed on the same device 2024-08-20T21:40:25.1053230Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2024-08-20T21:40:25.1054421Z argument passed in. 2024-08-20T21:40:25.1054796Z 2024-08-20T21:40:25.1055661Z AssertionError: For any non-:class:`DTensor` output, we require its corresponding 2024-08-20T21:40:25.1057324Z output placement in ``out_placements`` be None. An AssertionError will be raised 2024-08-20T21:40:25.1058535Z if this is not the case. 2024-08-20T21:40:25.1058973Z 2024-08-20T21:40:25.1059616Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2024-08-20T21:40:25.1060961Z a redistribution according to ``in_placements``. 2024-08-20T21:40:25.1061638Z 2024-08-20T21:40:25.1061810Z Example: 2024-08-20T21:40:25.1062344Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:25.1063211Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-08-20T21:40:25.1064141Z >>> partial_sum_tensor = torch.mm(W, X) 2024-08-20T21:40:25.1065329Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-08-20T21:40:25.1066514Z >>> return reduced_tensor 2024-08-20T21:40:25.1067165Z >>> 2024-08-20T21:40:25.1067729Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-08-20T21:40:25.1068630Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-08-20T21:40:25.1069472Z >>> Y = torch.mm(W, X) 2024-08-20T21:40:25.1070536Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-08-20T21:40:25.1071956Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-08-20T21:40:25.1072930Z >>> 2024-08-20T21:40:25.1073878Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-08-20T21:40:25.1075173Z >>> local_mm_allreduce_forward = local_map( 2024-08-20T21:40:25.1076006Z >>> mm_allreduce_forward, 2024-08-20T21:40:25.1076733Z >>> out_placements=[Replicate()], 2024-08-20T21:40:25.1077552Z >>> in_placements=[col_wise, row_wise], 2024-08-20T21:40:25.1078362Z >>> device_mesh=device_mesh, 2024-08-20T21:40:25.1079048Z >>> ) 2024-08-20T21:40:25.1079477Z >>> 2024-08-20T21:40:25.1080577Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-08-20T21:40:25.1082531Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-08-20T21:40:25.1084392Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-08-20T21:40:25.1085614Z 2024-08-20T21:40:25.1086094Z NOTE: This API is currently experimental and subject to change 2024-08-20T21:40:25.1086899Z 2024-08-20T21:40:25.1087672Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.1088624Z 2024-08-20T21:40:25.1091032Z msg = Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/experimental/register_sharding.py line=25. 2024-08-20T21:40:25.1093943Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.1094894Z 2024-08-20T21:40:25.1095558Z ``register_sharding`` is an experimental API that allows users to register sharding 2024-08-20T21:40:25.1097260Z strategies for an operator when the tensor inputs and outputs are :class:`DTensor`s. 2024-08-20T21:40:25.1099145Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-08-20T21:40:25.1100804Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-08-20T21:40:25.1102500Z when users would like to overwrite default sharding strategies of existing operators. 2024-08-20T21:40:25.1103542Z 2024-08-20T21:40:25.1103713Z Args: 2024-08-20T21:40:25.1104480Z op (Union[OpOverload, List[OpOverload]]): 2024-08-20T21:40:25.1105611Z An op or a list of ops to register the customized sharding function. 2024-08-20T21:40:25.1106495Z 2024-08-20T21:40:25.1106674Z Returns: 2024-08-20T21:40:25.1107642Z A function decorator which can be used to wrap a function that defines the sharding 2024-08-20T21:40:25.1109367Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-08-20T21:40:25.1111111Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-08-20T21:40:25.1112923Z already implemented the operator. The customized sharding function takes the same inputs 2024-08-20T21:40:25.1114659Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-08-20T21:40:25.1116533Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-08-20T21:40:25.1118452Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-08-20T21:40:25.1119769Z corresponding intput placements. 2024-08-20T21:40:25.1120306Z 2024-08-20T21:40:25.1120481Z Example: 2024-08-20T21:40:25.1121013Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:25.1121865Z >>> @register_sharding(aten._softmax.default) 2024-08-20T21:40:25.1122840Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-08-20T21:40:25.1123883Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-08-20T21:40:25.1124805Z >>> acceptable_shardings = [] 2024-08-20T21:40:25.1125499Z >>> 2024-08-20T21:40:25.1126197Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-08-20T21:40:25.1127223Z >>> acceptable_shardings.append(all_replicate) 2024-08-20T21:40:25.1128045Z >>> 2024-08-20T21:40:25.1128585Z >>> for sharding_dim in range(x.ndim): 2024-08-20T21:40:25.1129445Z >>> if sharding_dim != softmax_dim: 2024-08-20T21:40:25.1130312Z >>> all_sharded = ( 2024-08-20T21:40:25.1131080Z >>> [Shard(sharding_dim)], 2024-08-20T21:40:25.1131951Z >>> [Shard(sharding_dim), None, None], 2024-08-20T21:40:25.1132728Z >>> ) 2024-08-20T21:40:25.1133422Z >>> acceptable_shardings.append(all_sharded) 2024-08-20T21:40:25.1134264Z >>> 2024-08-20T21:40:25.1134755Z >>> return acceptable_shardings 2024-08-20T21:40:25.1135315Z 2024-08-20T21:40:25.1136122Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.1137058Z 2024-08-20T21:40:25.1762897Z msg = Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2024-08-20T21:40:25.1764519Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.1765027Z 2024-08-20T21:40:25.1765236Z Run post-localSGD algorithm. 2024-08-20T21:40:25.1765496Z 2024-08-20T21:40:25.1765877Z This DDP communication hook is used for running post-localSGD algorithm, 2024-08-20T21:40:25.1766561Z by combining with a model averaging component (e.g., 2024-08-20T21:40:25.1767367Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-08-20T21:40:25.1768101Z that runs after the optimizer step. 2024-08-20T21:40:25.1768393Z 2024-08-20T21:40:25.1768482Z Args: 2024-08-20T21:40:25.1768990Z state (PostLocalSGDState): State information to run post-localSGD. 2024-08-20T21:40:25.1769813Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-08-20T21:40:25.1771123Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-20T21:40:25.1772212Z Note that since DDP comm hook only supports single process single device mode, 2024-08-20T21:40:25.1772938Z only exactly one tensor is stored in this bucket. 2024-08-20T21:40:25.1773429Z 2024-08-20T21:40:25.1773524Z Returns: 2024-08-20T21:40:25.1774007Z Future handler of the communication, which updates the gradients in place. 2024-08-20T21:40:25.1774496Z 2024-08-20T21:40:25.1774629Z Example:: 2024-08-20T21:40:25.1774886Z >>> # xdoctest: +SKIP 2024-08-20T21:40:25.1775436Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-08-20T21:40:25.1776085Z start_localSGD_iter=10) 2024-08-20T21:40:25.1776617Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:40:25.1777485Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-08-20T21:40:25.1778610Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-08-20T21:40:25.1779250Z 2024-08-20T21:40:25.1779651Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.1780193Z 2024-08-20T21:40:25.1811170Z msg = Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2024-08-20T21:40:25.1812739Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.1813258Z 2024-08-20T21:40:25.1813390Z Implement PowerSGD algorithm. 2024-08-20T21:40:25.1813635Z 2024-08-20T21:40:25.1813943Z This DDP communication hook implements PowerSGD gradient compression 2024-08-20T21:40:25.1814714Z algorithm described in the `paper `_. 2024-08-20T21:40:25.1815509Z Once gradient tensors are aggregated across all workers, this hook applies 2024-08-20T21:40:25.1816114Z compression as follows: 2024-08-20T21:40:25.1816325Z 2024-08-20T21:40:25.1817058Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-08-20T21:40:25.1817841Z 2024-08-20T21:40:25.1818434Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-08-20T21:40:25.1819187Z 2024-08-20T21:40:25.1819757Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-08-20T21:40:25.1820483Z 2024-08-20T21:40:25.1820627Z 2. Handles uncompressed tensors: 2024-08-20T21:40:25.1820888Z 2024-08-20T21:40:25.1821742Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-08-20T21:40:25.1822589Z 2024-08-20T21:40:25.1823054Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-08-20T21:40:25.1823691Z 2024-08-20T21:40:25.1824003Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-08-20T21:40:25.1824478Z 2024-08-20T21:40:25.1824931Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-08-20T21:40:25.1825894Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-08-20T21:40:25.1826513Z 2024-08-20T21:40:25.1826705Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-08-20T21:40:25.1827074Z 2024-08-20T21:40:25.1827203Z 3.3. Allreduces Ps as a batch; 2024-08-20T21:40:25.1827464Z 2024-08-20T21:40:25.1827616Z 3.4. Orthogonalizes each P in Ps; 2024-08-20T21:40:25.1827893Z 2024-08-20T21:40:25.1828171Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-08-20T21:40:25.1828617Z 2024-08-20T21:40:25.1828744Z 3.6. Allreduces Qs as a batch; 2024-08-20T21:40:25.1829004Z 2024-08-20T21:40:25.1829437Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-08-20T21:40:25.1830018Z 2024-08-20T21:40:25.1830583Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-08-20T21:40:25.1831808Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-08-20T21:40:25.1832969Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-08-20T21:40:25.1833714Z 2024-08-20T21:40:25.1833821Z Args: 2024-08-20T21:40:25.1834522Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-08-20T21:40:25.1835760Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-08-20T21:40:25.1836559Z and ``min_compression_rate``. 2024-08-20T21:40:25.1837613Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-20T21:40:25.1838691Z Note that since DDP comm hook only supports single process single device mode, 2024-08-20T21:40:25.1839413Z only exactly one tensor is stored in this bucket. 2024-08-20T21:40:25.1839775Z 2024-08-20T21:40:25.1839886Z Returns: 2024-08-20T21:40:25.1840352Z Future handler of the communication, which updates the gradients in place. 2024-08-20T21:40:25.1840853Z 2024-08-20T21:40:25.1840966Z Example:: 2024-08-20T21:40:25.1841237Z >>> # xdoctest: +SKIP 2024-08-20T21:40:25.1841795Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-08-20T21:40:25.1842539Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-08-20T21:40:25.1843143Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-08-20T21:40:25.1843507Z 2024-08-20T21:40:25.1843925Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.1844414Z 2024-08-20T21:40:25.1849521Z msg = Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=36. 2024-08-20T21:40:25.1851160Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.1851704Z 2024-08-20T21:40:25.1851997Z Averages parameters periodically after the warm-up stage. 2024-08-20T21:40:25.1852394Z 2024-08-20T21:40:25.1852845Z This can be used for running `post-local SGD `_, 2024-08-20T21:40:25.1853608Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-08-20T21:40:25.1854337Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-08-20T21:40:25.1854821Z 2024-08-20T21:40:25.1855013Z Args: 2024-08-20T21:40:25.1855382Z period (int): The number of steps per model averaging. 2024-08-20T21:40:25.1856124Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-08-20T21:40:25.1856839Z Otherwise, only DDP needs to be used. 2024-08-20T21:40:25.1857531Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-08-20T21:40:25.1858127Z model averaging is skipped. 2024-08-20T21:40:25.1858761Z process_group: The process group to be used for all-reduce. 2024-08-20T21:40:25.1859368Z If ``None``, the default process group, which 2024-08-20T21:40:25.1859975Z is created by :func:`torch.distributed.init_process_group`, 2024-08-20T21:40:25.1860567Z will be used. (default: ``None``) 2024-08-20T21:40:25.1860886Z 2024-08-20T21:40:25.1861010Z Example:: 2024-08-20T21:40:25.1861159Z 2024-08-20T21:40:25.1861325Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:25.1861764Z >>> import torch 2024-08-20T21:40:25.1862103Z >>> import torch.distributed as dist 2024-08-20T21:40:25.1862803Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-08-20T21:40:25.1863941Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-20T21:40:25.1865042Z >>> import torch.nn as nn 2024-08-20T21:40:25.1865621Z >>> 2024-08-20T21:40:25.1866311Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-20T21:40:25.1866846Z >>> torch.cuda.set_device(rank) 2024-08-20T21:40:25.1867289Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-08-20T21:40:25.1867912Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:40:25.1868468Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:40:25.1868913Z >>> ) 2024-08-20T21:40:25.1869357Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:40:25.1870101Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-20T21:40:25.1870867Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:40:25.1871325Z >>> 2024-08-20T21:40:25.1871855Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-20T21:40:25.1872620Z >>> # After 100 steps, run model averaging every 4 steps. 2024-08-20T21:40:25.1873435Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:40:25.1874373Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-20T21:40:25.1874995Z >>> for step in range(0, 200): 2024-08-20T21:40:25.1875379Z >>> optimizer.zero_grad() 2024-08-20T21:40:25.1875781Z >>> loss = loss_fn(output, labels) 2024-08-20T21:40:25.1876193Z >>> loss.backward() 2024-08-20T21:40:25.1876530Z >>> optimizer.step() 2024-08-20T21:40:25.1877022Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-08-20T21:40:25.1877805Z >>> # inter-node communication only occurs every 4 iterations after 2024-08-20T21:40:25.1878535Z >>> # the initial ``warmup_steps`` period. 2024-08-20T21:40:25.1879110Z >>> averager.average_parameters(model.parameters()) 2024-08-20T21:40:25.1879492Z 2024-08-20T21:40:25.1879903Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.1880388Z 2024-08-20T21:40:25.1881725Z msg = Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18. 2024-08-20T21:40:25.1883409Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.1883910Z 2024-08-20T21:40:25.1884490Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-08-20T21:40:25.1885090Z 2024-08-20T21:40:25.1885507Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-08-20T21:40:25.1886431Z by using different periods concurrently after the warm-up stage. 2024-08-20T21:40:25.1887430Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-08-20T21:40:25.1888697Z that supports `post-local SGD `_, which essentially only supports 2024-08-20T21:40:25.1889828Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-08-20T21:40:25.1891188Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-08-20T21:40:25.1892327Z Similarly, the process groups within this class do not have such an intra-machine process 2024-08-20T21:40:25.1893336Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-08-20T21:40:25.1893895Z 2024-08-20T21:40:25.1893987Z Args: 2024-08-20T21:40:25.1894487Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-08-20T21:40:25.1895269Z process group size, used for initializing process groups of 2024-08-20T21:40:25.1896026Z different sizes in a hierarchy to average parameters concurrently. 2024-08-20T21:40:25.1896953Z Particularly, at each iteration, there will be at most a single 2024-08-20T21:40:25.1897807Z process group that runs averaging -- the period of such group should 2024-08-20T21:40:25.1898598Z have the largest period which the current step can be divided by. 2024-08-20T21:40:25.1899298Z For example, if the dict has three keys: 2, 4, and 8, 2024-08-20T21:40:25.1899994Z then this means totally three process groups will be created to 2024-08-20T21:40:25.1900761Z average parameters every 2, 4, and 8 iterations, respectively. 2024-08-20T21:40:25.1901488Z At the 4th iteration, only the second process group will run 2024-08-20T21:40:25.1902177Z averaging, because the first process group should be a 2024-08-20T21:40:25.1902894Z subset of the second process group, and no need to execute the first 2024-08-20T21:40:25.1903546Z process group redundantly. 2024-08-20T21:40:25.1904164Z On the other hand, the third process group can only be triggered 2024-08-20T21:40:25.1904947Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-08-20T21:40:25.1905939Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-08-20T21:40:25.1907118Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-08-20T21:40:25.1908121Z If ``None``, the default process group, which is created 2024-08-20T21:40:25.1908828Z by :func:`torch.distributed.init_process_group`, will be used. 2024-08-20T21:40:25.1909457Z (default: ``None``) 2024-08-20T21:40:25.1909807Z 2024-08-20T21:40:25.1909914Z Example:: 2024-08-20T21:40:25.1910259Z >>> # xdoctest: +SKIP('undefined rank') 2024-08-20T21:40:25.1910727Z >>> from collections import OrderedDict 2024-08-20T21:40:25.1911144Z >>> import torch 2024-08-20T21:40:25.1911474Z >>> import torch.distributed as dist 2024-08-20T21:40:25.1912137Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-20T21:40:25.1912797Z >>> PostLocalSGDState, 2024-08-20T21:40:25.1913167Z >>> post_localSGD_hook, 2024-08-20T21:40:25.1913586Z >>> ) 2024-08-20T21:40:25.1914234Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-08-20T21:40:25.1915023Z >>> import torch.nn as nn 2024-08-20T21:40:25.1915347Z >>> 2024-08-20T21:40:25.1915722Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-20T21:40:25.1916249Z >>> torch.cuda.set_device(rank) 2024-08-20T21:40:25.1916687Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-08-20T21:40:25.1917239Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:40:25.1917799Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:40:25.1918231Z >>> ) 2024-08-20T21:40:25.1918634Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:40:25.1919477Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-08-20T21:40:25.1920173Z >>> subgroup, _ = dist.new_subgroups() 2024-08-20T21:40:25.1920896Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-08-20T21:40:25.1921683Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:40:25.1922136Z >>> 2024-08-20T21:40:25.1922672Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-08-20T21:40:25.1923389Z >>> # the 16 processes every 16 iterations. 2024-08-20T21:40:25.1923951Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-08-20T21:40:25.1924752Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-08-20T21:40:25.1925681Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:40:25.1926677Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-20T21:40:25.1927416Z >>> # After 100 steps, run model averaging at two levels. 2024-08-20T21:40:25.1927916Z >>> for step in range(0, 200): 2024-08-20T21:40:25.1928313Z >>> optimizer.zero_grad() 2024-08-20T21:40:25.1928696Z >>> loss = loss_fn(output, labels) 2024-08-20T21:40:25.1929109Z >>> loss.backward() 2024-08-20T21:40:25.1929451Z >>> optimizer.step() 2024-08-20T21:40:25.1929867Z >>> # Average parameters after ``optimizer.step()``. 2024-08-20T21:40:25.1930838Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-08-20T21:40:25.1931606Z >>> averager.average_parameters(model.parameters()) 2024-08-20T21:40:25.1931972Z 2024-08-20T21:40:25.1932079Z .. warning :: 2024-08-20T21:40:25.1932620Z The last group size in the dict must be the size of the provided ``process_group``, 2024-08-20T21:40:25.1933471Z which indicates model averaging at the highest level of the hierarchy. 2024-08-20T21:40:25.1934363Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-08-20T21:40:25.1934966Z 2024-08-20T21:40:25.1935073Z .. warning :: 2024-08-20T21:40:25.1935541Z `HierarchicalModelAverager` is experimental and subject to change. 2024-08-20T21:40:25.1935996Z 2024-08-20T21:40:25.1936413Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.1936900Z 2024-08-20T21:40:25.2059570Z msg = Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2024-08-20T21:40:25.2061106Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.2061652Z 2024-08-20T21:40:25.2062100Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-08-20T21:40:25.2063484Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-08-20T21:40:25.2064145Z 2024-08-20T21:40:25.2064361Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-08-20T21:40:25.2064751Z 2024-08-20T21:40:25.2065037Z .. warning:: 2024-08-20T21:40:25.2065430Z Current implementation only supports loading Tensors. 2024-08-20T21:40:25.2065804Z 2024-08-20T21:40:25.2065958Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:25.2066361Z >>> sd = {"mode": model} 2024-08-20T21:40:25.2066681Z >>> dcp.load( 2024-08-20T21:40:25.2066949Z >>> sd, 2024-08-20T21:40:25.2067286Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-20T21:40:25.2067807Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-20T21:40:25.2068250Z >>> checkpoint_id="path_to_model.pt" 2024-08-20T21:40:25.2068618Z >>> ) 2024-08-20T21:40:25.2068773Z 2024-08-20T21:40:25.2069197Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.2069680Z 2024-08-20T21:40:25.2070757Z msg = Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2024-08-20T21:40:25.2072237Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.2072740Z 2024-08-20T21:40:25.2073225Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-08-20T21:40:25.2074429Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-08-20T21:40:25.2075204Z metadata file, like Torch Save files. 2024-08-20T21:40:25.2075613Z 2024-08-20T21:40:25.2075857Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-08-20T21:40:25.2076276Z 2024-08-20T21:40:25.2076379Z .. warning:: 2024-08-20T21:40:25.2076764Z Current implementation only supports loading Tensors. 2024-08-20T21:40:25.2077139Z 2024-08-20T21:40:25.2077279Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:25.2077688Z >>> sd = {"mode": model} 2024-08-20T21:40:25.2078009Z >>> dcp.load( 2024-08-20T21:40:25.2078260Z >>> sd, 2024-08-20T21:40:25.2078610Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-20T21:40:25.2079131Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-20T21:40:25.2079566Z >>> checkpoint_id="path_to_model.pt" 2024-08-20T21:40:25.2079951Z >>> ) 2024-08-20T21:40:25.2080088Z 2024-08-20T21:40:25.2080505Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.2080992Z 2024-08-20T21:40:25.2115384Z msg = Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=220. 2024-08-20T21:40:25.2116885Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.2117423Z 2024-08-20T21:40:25.2117704Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-08-20T21:40:25.2118165Z 2024-08-20T21:40:25.2118380Z This is the current recommended way to checkpoint FSDP. 2024-08-20T21:40:25.2118872Z >>> # xdoctest: +SKIP 2024-08-20T21:40:25.2119255Z >>> import torch.distributed.checkpoint as dist_cp 2024-08-20T21:40:25.2119721Z >>> # Save 2024-08-20T21:40:25.2119993Z >>> model: torch.nn.Model 2024-08-20T21:40:25.2120347Z >>> optim_params = model.parameters() 2024-08-20T21:40:25.2120819Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-08-20T21:40:25.2121260Z >>> # Save 2024-08-20T21:40:25.2121760Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-08-20T21:40:25.2122332Z >>> state_dict = { 2024-08-20T21:40:25.2122742Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-08-20T21:40:25.2123231Z >>> "model": model.state_dict() 2024-08-20T21:40:25.2123613Z >>> } 2024-08-20T21:40:25.2123886Z >>> dist_cp.save_state_dict( 2024-08-20T21:40:25.2124248Z >>> state_dict=optim_state, 2024-08-20T21:40:25.2124748Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-08-20T21:40:25.2125312Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-08-20T21:40:25.2125728Z >>> ) 2024-08-20T21:40:25.2125968Z >>> 2024-08-20T21:40:25.2126344Z >>> # Load 2024-08-20T21:40:25.2126794Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-08-20T21:40:25.2127430Z >>> model_state_dict = model_tp.state_dict() 2024-08-20T21:40:25.2127870Z >>> checkpoint = { 2024-08-20T21:40:25.2128187Z >>> "model": model_state_dict 2024-08-20T21:40:25.2128557Z >>> } 2024-08-20T21:40:25.2128828Z >>> dist_cp.load_state_dict( 2024-08-20T21:40:25.2129197Z >>> state_dict=checkpoint, 2024-08-20T21:40:25.2129699Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-08-20T21:40:25.2130388Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-08-20T21:40:25.2130805Z >>> ) 2024-08-20T21:40:25.2131152Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-08-20T21:40:25.2131603Z >>> 2024-08-20T21:40:25.2131957Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-08-20T21:40:25.2132465Z >>> model_state_dict, 2024-08-20T21:40:25.2132837Z >>> optimizer_key="optimizer", 2024-08-20T21:40:25.2133336Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-08-20T21:40:25.2133836Z >>> ) 2024-08-20T21:40:25.2134077Z >>> 2024-08-20T21:40:25.2134387Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-08-20T21:40:25.2134902Z >>> model, optim, optim_state["optimizer"] 2024-08-20T21:40:25.2135323Z >>> ) 2024-08-20T21:40:25.2135551Z >>> 2024-08-20T21:40:25.2135936Z >>> optim.load_state_dict(flattened_osd) 2024-08-20T21:40:25.2136245Z 2024-08-20T21:40:25.2136689Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.2137175Z 2024-08-20T21:40:25.2140120Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=110. 2024-08-20T21:40:25.2141557Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.2142061Z 2024-08-20T21:40:25.2142447Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-08-20T21:40:25.2143003Z 2024-08-20T21:40:25.2163292Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-08-20T21:40:25.2164394Z 2024-08-20T21:40:25.2165149Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-20T21:40:25.2166282Z will be visible to the whole process. 2024-08-20T21:40:25.2166581Z 2024-08-20T21:40:25.2166977Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-08-20T21:40:25.2167524Z 2024-08-20T21:40:25.2167792Z 1) set_up_planner - called on all ranks. 2024-08-20T21:40:25.2168241Z Signals the start of a checkpoint save. 2024-08-20T21:40:25.2168544Z 2024-08-20T21:40:25.2168749Z 2) create_local_plan - called on all ranks. 2024-08-20T21:40:25.2169439Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-08-20T21:40:25.2169994Z 2024-08-20T21:40:25.2170374Z 3) create_global_plan - called on the coordinator rank only. 2024-08-20T21:40:25.2171032Z Takes the SavePlan from all ranks and make any global decision. 2024-08-20T21:40:25.2171457Z 2024-08-20T21:40:25.2171658Z 4) finish_plan - called on all ranks. 2024-08-20T21:40:25.2172213Z This gives each rank a chance to adjust to global planning decisions. 2024-08-20T21:40:25.2172696Z 2024-08-20T21:40:25.2172949Z 5) resolve_data - called multiple times on each rank 2024-08-20T21:40:25.2173580Z Lookups a value on the `state_dict` for the storage layer to write. 2024-08-20T21:40:25.2174015Z 2024-08-20T21:40:25.2174415Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-08-20T21:40:25.2175208Z most changes can be expressed by changes in a single method. 2024-08-20T21:40:25.2175614Z 2024-08-20T21:40:25.2175755Z There are 3 usual patterns of extension: 2024-08-20T21:40:25.2176047Z 2024-08-20T21:40:25.2176504Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-08-20T21:40:25.2177364Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-08-20T21:40:25.2177826Z 2024-08-20T21:40:25.2177961Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:25.2178423Z >>> class RenamePlanner(DefaultSavePlanner): 2024-08-20T21:40:25.2178856Z >>> def set_up_planner( 2024-08-20T21:40:25.2179161Z >>> self, 2024-08-20T21:40:25.2179457Z >>> state_dict: STATE_DICT_TYPE, 2024-08-20T21:40:25.2179960Z >>> storage_meta: Optional[StorageMeta], 2024-08-20T21:40:25.2180410Z >>> is_coordinator: bool, 2024-08-20T21:40:25.2180808Z >>> ) -> None: 2024-08-20T21:40:25.2181119Z >>> # prefix all keys with `foo_`` 2024-08-20T21:40:25.2181847Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-08-20T21:40:25.2182444Z 2024-08-20T21:40:25.2182910Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-08-20T21:40:25.2183537Z 2024-08-20T21:40:25.2183689Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:25.2184129Z >>> class FP16Planner(DefaultSavePlanner): 2024-08-20T21:40:25.2184583Z >>> def create_local_plan(self): 2024-08-20T21:40:25.2185015Z >>> plan = super().create_local_plan() 2024-08-20T21:40:25.2185514Z >>> for p in plan: 2024-08-20T21:40:25.2185883Z >>> if p.tensor_data is not None: 2024-08-20T21:40:25.2186481Z >>> p.tensor_data.properties.dtype = torch.float16 2024-08-20T21:40:25.2186958Z >>> return plan 2024-08-20T21:40:25.2187262Z >>> 2024-08-20T21:40:25.2187547Z >>> def resolve_data(self, write_item): 2024-08-20T21:40:25.2188002Z >>> item = super().resolve_data(write_item) 2024-08-20T21:40:25.2188713Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-08-20T21:40:25.2189251Z 2024-08-20T21:40:25.2189835Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-08-20T21:40:25.2190673Z 2024-08-20T21:40:25.2190813Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:25.2191239Z >>> from itertools import islice 2024-08-20T21:40:25.2191632Z >>> from dataclasses import replace 2024-08-20T21:40:25.2192116Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-08-20T21:40:25.2192988Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-08-20T21:40:25.2193781Z >>> # This sample doesn't handle ShardedTensors 2024-08-20T21:40:25.2194270Z >>> def create_global_plan(self, all_plans): 2024-08-20T21:40:25.2194715Z >>> def chunk(it, size): 2024-08-20T21:40:25.2195078Z >>> it = iter(it) 2024-08-20T21:40:25.2195514Z >>> return list(iter(lambda: tuple(islice(it, size)), ())) 2024-08-20T21:40:25.2196010Z >>> all_plans = [ 2024-08-20T21:40:25.2196416Z >>> replace(plan, items=items) for plan, items in 2024-08-20T21:40:25.2197003Z >>> zip(all_plans, chunk(all_plans[0].items, len(all_plans))) 2024-08-20T21:40:25.2197511Z >>> ] 2024-08-20T21:40:25.2197854Z >>> return super().create_global_plan(all_plans) 2024-08-20T21:40:25.2198193Z 2024-08-20T21:40:25.2198566Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-08-20T21:40:25.2199450Z accomplished by having each rank contribute their data items in the local plan and 2024-08-20T21:40:25.2200127Z the global planner aggregate them: 2024-08-20T21:40:25.2200395Z 2024-08-20T21:40:25.2200545Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:25.2201022Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-08-20T21:40:25.2201647Z >>> def create_local_plan(self) -> SavePlan: 2024-08-20T21:40:25.2202127Z >>> plan = super().create_local_plan() 2024-08-20T21:40:25.2202702Z >>> return replace(plan, planner_data="per-rank-data") 2024-08-20T21:40:25.2203172Z >>> 2024-08-20T21:40:25.2203930Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-08-20T21:40:25.2204748Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-08-20T21:40:25.2205382Z >>> merged_data = [p.planner_data for p in global_plan] 2024-08-20T21:40:25.2205982Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-08-20T21:40:25.2206494Z >>> return global_plan, metadata 2024-08-20T21:40:25.2206797Z 2024-08-20T21:40:25.2207197Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.2207696Z 2024-08-20T21:40:25.2208669Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=270. 2024-08-20T21:40:25.2210037Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.2210604Z 2024-08-20T21:40:25.2211069Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-08-20T21:40:25.2212030Z 2024-08-20T21:40:25.2212413Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-08-20T21:40:25.2212976Z 2024-08-20T21:40:25.2213363Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-20T21:40:25.2214071Z will be visible to the whole process. 2024-08-20T21:40:25.2214471Z 2024-08-20T21:40:25.2214862Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-08-20T21:40:25.2215404Z 2024-08-20T21:40:25.2215616Z 1) set_up_planner - called on all ranks. 2024-08-20T21:40:25.2216091Z Signals the start of loading a checkpoint. 2024-08-20T21:40:25.2216412Z 2024-08-20T21:40:25.2216625Z 2) create_local_plan - called on all ranks. 2024-08-20T21:40:25.2217016Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-08-20T21:40:25.2217022Z 2024-08-20T21:40:25.2217325Z 3) create_global_plan - called on the coordinator rank only. 2024-08-20T21:40:25.2217582Z Takes the LoadPlan from all ranks and make any global decision. 2024-08-20T21:40:25.2217587Z 2024-08-20T21:40:25.2217823Z 4) load_bytes - called multiple times on each rank 2024-08-20T21:40:25.2218113Z This is called once per non-tensor value in state_dict. 2024-08-20T21:40:25.2218119Z 2024-08-20T21:40:25.2218479Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-08-20T21:40:25.2218727Z They are called in pair for each Tensor value in state_dict. 2024-08-20T21:40:25.2218732Z 2024-08-20T21:40:25.2219150Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-08-20T21:40:25.2219392Z most changes can be expressed by changes in a single method. 2024-08-20T21:40:25.2219397Z 2024-08-20T21:40:25.2219566Z There are two usual patterns of extension: 2024-08-20T21:40:25.2219572Z 2024-08-20T21:40:25.2219916Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-08-20T21:40:25.2220327Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-08-20T21:40:25.2220651Z to keep a reference to the original state_dict as load happens in place so 2024-08-20T21:40:25.2220802Z we need to be able to perform it in place 2024-08-20T21:40:25.2220807Z 2024-08-20T21:40:25.2220947Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:25.2221136Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-08-20T21:40:25.2221249Z >>> def set_up_planner( 2024-08-20T21:40:25.2221360Z >>> self, 2024-08-20T21:40:25.2221492Z >>> state_dict: STATE_DICT_TYPE, 2024-08-20T21:40:25.2221606Z >>> metadata: Metadata, 2024-08-20T21:40:25.2221743Z >>> is_coordinator: bool, 2024-08-20T21:40:25.2221873Z >>> ) -> None: 2024-08-20T21:40:25.2222021Z >>> self.original_state_dict = state_dict 2024-08-20T21:40:25.2222265Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-08-20T21:40:25.2222441Z >>> 2024-08-20T21:40:25.2222591Z >>> if self.flatten_sharded_tensors: 2024-08-20T21:40:25.2222806Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-08-20T21:40:25.2222896Z >>> 2024-08-20T21:40:25.2223026Z >>> if self.flatten_state_dict: 2024-08-20T21:40:25.2223283Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-08-20T21:40:25.2223371Z >>> 2024-08-20T21:40:25.2223507Z >>> self.state_dict = state_dict 2024-08-20T21:40:25.2223644Z >>> self.metadata = metadata 2024-08-20T21:40:25.2223836Z >>> self.is_coordinator = is_coordinator 2024-08-20T21:40:25.2223924Z >>> 2024-08-20T21:40:25.2224100Z >>> def load_bytes(self, read_item, value): 2024-08-20T21:40:25.2224228Z >>> # Remove the "foo_" prefix 2024-08-20T21:40:25.2224673Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-08-20T21:40:25.2224679Z 2024-08-20T21:40:25.2224684Z 2024-08-20T21:40:25.2225023Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-08-20T21:40:25.2225028Z 2024-08-20T21:40:25.2225162Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:25.2225370Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-08-20T21:40:25.2225518Z >>> def resolve_tensor(self, read_item): 2024-08-20T21:40:25.2225686Z >>> tensor = super().resolve_tensor(read_item) 2024-08-20T21:40:25.2225946Z >>> return torch.empty_like(tensor, device="cpu") 2024-08-20T21:40:25.2226038Z >>> 2024-08-20T21:40:25.2226215Z >>> def commit_tensor(self, read_item, tensor): 2024-08-20T21:40:25.2226416Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-08-20T21:40:25.2226421Z 2024-08-20T21:40:25.2226822Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.2226828Z 2024-08-20T21:40:25.2328392Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=61. 2024-08-20T21:40:25.2328819Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.2328826Z 2024-08-20T21:40:25.2329064Z Load a distributed ``state_dict`` in SPMD style. 2024-08-20T21:40:25.2329069Z 2024-08-20T21:40:25.2329315Z Each rank will try to read the least amount of data necessary 2024-08-20T21:40:25.2329647Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-08-20T21:40:25.2329987Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-08-20T21:40:25.2329992Z 2024-08-20T21:40:25.2330399Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-20T21:40:25.2330765Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-08-20T21:40:25.2330978Z ``load_state_dict`` once the deserialization is complete. 2024-08-20T21:40:25.2330983Z 2024-08-20T21:40:25.2331116Z .. warning:: 2024-08-20T21:40:25.2331340Z All tensors in ``state_dict`` must be allocated on their 2024-08-20T21:40:25.2331548Z destination device *prior to* calling this function. 2024-08-20T21:40:25.2331553Z 2024-08-20T21:40:25.2331956Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-08-20T21:40:25.2332058Z on state_dict. 2024-08-20T21:40:25.2332064Z 2024-08-20T21:40:25.2332164Z .. warning:: 2024-08-20T21:40:25.2332460Z Users must call `load_state_dict` on the root module to ensure load 2024-08-20T21:40:25.2332752Z pos-processing and non-tensor data properly propagates. 2024-08-20T21:40:25.2332757Z 2024-08-20T21:40:25.2332851Z .. note: 2024-08-20T21:40:25.2333175Z If no process group is initialized, this function will assume the intent 2024-08-20T21:40:25.2333491Z is to load a checkpoint into the local process. This can be useful in the 2024-08-20T21:40:25.2333841Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-08-20T21:40:25.2334100Z or ShardedTensor) 2024-08-20T21:40:25.2334108Z 2024-08-20T21:40:25.2334203Z .. note: 2024-08-20T21:40:25.2334396Z Rank 0 is assumed to be the coordinator rank. 2024-08-20T21:40:25.2334401Z 2024-08-20T21:40:25.2334491Z Args: 2024-08-20T21:40:25.2334687Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:40:25.2334880Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:40:25.2335178Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:40:25.2335462Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:40:25.2335778Z It can also be a key if the storage is a key-value store. 2024-08-20T21:40:25.2335890Z (Default: ``None``) 2024-08-20T21:40:25.2336066Z storage_reader (Optional[StorageReader]): 2024-08-20T21:40:25.2336339Z Instance of StorageWriter used to perform reads. If this is not 2024-08-20T21:40:25.2336610Z specified, DCP will automatically infer the reader based on the 2024-08-20T21:40:25.2336886Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:40:25.2337019Z be raised. (Default: ``None``) 2024-08-20T21:40:25.2337153Z planner (Optional[LoadPlanner]): 2024-08-20T21:40:25.2337438Z Instance of LoadPlanner. If this is not specificed, the default 2024-08-20T21:40:25.2337597Z planner will be used. (Default: ``None``) 2024-08-20T21:40:25.2337845Z process_group (Optional[ProcessGroup]): 2024-08-20T21:40:25.2338159Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:40:25.2338271Z (Default: ``None``) 2024-08-20T21:40:25.2338277Z 2024-08-20T21:40:25.2338388Z Returns: 2024-08-20T21:40:25.2338484Z None. 2024-08-20T21:40:25.2338489Z 2024-08-20T21:40:25.2338581Z Examples 2024-08-20T21:40:25.2338708Z >>> # xdoctest: +SKIP 2024-08-20T21:40:25.2338825Z >>> my_model = MyModule() 2024-08-20T21:40:25.2339002Z >>> optimizer = Adagrad(my_model.parameters()) 2024-08-20T21:40:25.2339177Z >>> model_state_dict = my_model.state_dict() 2024-08-20T21:40:25.2339578Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-08-20T21:40:25.2339584Z 2024-08-20T21:40:25.2339783Z >>> torch.distributed.checkpoint.load_state_dict( 2024-08-20T21:40:25.2339928Z >>> state_dict=model_state_dict, 2024-08-20T21:40:25.2340075Z >>> storage_reader=fs_storage_reader, 2024-08-20T21:40:25.2340171Z >>> ) 2024-08-20T21:40:25.2340175Z 2024-08-20T21:40:25.2340447Z >>> # module.load_state_dict() function might have customized steps 2024-08-20T21:40:25.2340607Z >>> # to flush the state_dict, must call it to 2024-08-20T21:40:25.2340743Z >>> # ensure correct behavior. 2024-08-20T21:40:25.2340902Z >>> my_model.load_state_dict(model_state_dict) 2024-08-20T21:40:25.2340907Z 2024-08-20T21:40:25.2341002Z .. note:: 2024-08-20T21:40:25.2341296Z load_state_dict uses collectives to coordinate reads across ranks. 2024-08-20T21:40:25.2341648Z For NCCL-based process groups, internal tensor representations of 2024-08-20T21:40:25.2341966Z objects must be moved to the GPU device before communication takes place. 2024-08-20T21:40:25.2342287Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-20T21:40:25.2342687Z and it is the user's responsibility to ensure that this is set so that each 2024-08-20T21:40:25.2342938Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-20T21:40:25.2342957Z 2024-08-20T21:40:25.2343353Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.2343358Z 2024-08-20T21:40:25.2354576Z msg = Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=67. 2024-08-20T21:40:25.2355016Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.2355053Z 2024-08-20T21:40:25.2355357Z Save a distributed model in SPMD style. 2024-08-20T21:40:25.2355381Z 2024-08-20T21:40:25.2355632Z This function is different from ``torch.save()`` as it handles 2024-08-20T21:40:25.2356041Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-08-20T21:40:25.2356048Z 2024-08-20T21:40:25.2356414Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-20T21:40:25.2356610Z save will call ``state_dict`` before serialization. 2024-08-20T21:40:25.2356618Z 2024-08-20T21:40:25.2356776Z .. warning:: 2024-08-20T21:40:25.2357117Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-08-20T21:40:25.2357232Z for saved state_dicts. 2024-08-20T21:40:25.2357237Z 2024-08-20T21:40:25.2357335Z .. warning:: 2024-08-20T21:40:25.2357694Z If using the `process_group` argument, make sure that only its ranks 2024-08-20T21:40:25.2357977Z call `save_state_dict` and that all data in state_dict belong to it. 2024-08-20T21:40:25.2357982Z 2024-08-20T21:40:25.2358091Z .. note:: 2024-08-20T21:40:25.2358698Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-08-20T21:40:25.2359213Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-08-20T21:40:25.2359414Z group needs to be passed in. 2024-08-20T21:40:25.2359421Z 2024-08-20T21:40:25.2359667Z .. note:: 2024-08-20T21:40:25.2360248Z If no process group is available, this function assumes the intention is to save the 2024-08-20T21:40:25.2360528Z state_dict in the local process. 2024-08-20T21:40:25.2360538Z 2024-08-20T21:40:25.2360722Z .. note: 2024-08-20T21:40:25.2361002Z Rank 0 is assumed to be the coordinator rank. 2024-08-20T21:40:25.2361008Z 2024-08-20T21:40:25.2361012Z 2024-08-20T21:40:25.2361103Z Args: 2024-08-20T21:40:25.2361301Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:40:25.2361497Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:40:25.2361794Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:40:25.2362081Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:40:25.2362417Z It can also be a key if the storage is a key-value store. 2024-08-20T21:40:25.2362528Z (Default: ``None``) 2024-08-20T21:40:25.2362689Z storage_writer (Optional[StorageWriter]): 2024-08-20T21:40:25.2362986Z Instance of StorageWriter used to perform writes. If this is not 2024-08-20T21:40:25.2363257Z specified, DCP will automatically infer the writer based on the 2024-08-20T21:40:25.2363533Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:40:25.2363664Z be raised. (Default: ``None``) 2024-08-20T21:40:25.2363797Z planner (Optional[SavePlanner]): 2024-08-20T21:40:25.2364084Z Instance of SavePlanner. If this is not specificed, the default 2024-08-20T21:40:25.2364250Z planner will be used. (Default: ``None``) 2024-08-20T21:40:25.2364407Z process_group (Optional[ProcessGroup]): 2024-08-20T21:40:25.2364719Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:40:25.2364829Z (Default: ``None``) 2024-08-20T21:40:25.2364835Z 2024-08-20T21:40:25.2364972Z Returns: 2024-08-20T21:40:25.2365184Z Metadata: Metadata object for the saved checkpoint. 2024-08-20T21:40:25.2365193Z 2024-08-20T21:40:25.2365289Z Example: 2024-08-20T21:40:25.2365402Z >>> # xdoctest: +SKIP 2024-08-20T21:40:25.2365532Z >>> my_model = MyModule() 2024-08-20T21:40:25.2365537Z 2024-08-20T21:40:25.2365669Z >>> state_dict = {"model": my_model} 2024-08-20T21:40:25.2365674Z 2024-08-20T21:40:25.2366087Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-20T21:40:25.2366250Z >>> torch.distributed.checkpoint.save( 2024-08-20T21:40:25.2366370Z >>> state_dict=state_dict, 2024-08-20T21:40:25.2366615Z >>> storage_writer=fs_storage_writer, 2024-08-20T21:40:25.2366711Z >>> ) 2024-08-20T21:40:25.2366717Z 2024-08-20T21:40:25.2366812Z .. note:: 2024-08-20T21:40:25.2367108Z save_state_dict uses collectives to coordinate writes across ranks. 2024-08-20T21:40:25.2367461Z For NCCL-based process groups, internal tensor representations of 2024-08-20T21:40:25.2367795Z objects must be moved to the GPU device before communication takes place. 2024-08-20T21:40:25.2368110Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-20T21:40:25.2368479Z and it is the user's responsibility to ensure that this is set so that 2024-08-20T21:40:25.2368767Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-20T21:40:25.2368772Z 2024-08-20T21:40:25.2369171Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.2369176Z 2024-08-20T21:40:25.2370267Z msg = Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=170. 2024-08-20T21:40:25.2370691Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.2371123Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-08-20T21:40:25.2371530Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-08-20T21:40:25.2371596Z 2024-08-20T21:40:25.2371701Z .. warning:: 2024-08-20T21:40:25.2371925Z This feature is experimental and subject to change. 2024-08-20T21:40:25.2371931Z 2024-08-20T21:40:25.2372027Z Args: 2024-08-20T21:40:25.2372231Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:40:25.2372431Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:40:25.2372726Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:40:25.2373019Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:40:25.2373340Z It can also be a key if the storage is a key-value store. 2024-08-20T21:40:25.2373455Z (Default: ``None``) 2024-08-20T21:40:25.2373622Z storage_writer (Optional[StorageWriter]): 2024-08-20T21:40:25.2373995Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-08-20T21:40:25.2374330Z this is not specified, DCP will automatically infer the writer based on the 2024-08-20T21:40:25.2374618Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:40:25.2374753Z be raised. (Default: ``None``) 2024-08-20T21:40:25.2374896Z planner (Optional[SavePlanner]): 2024-08-20T21:40:25.2375180Z Instance of SavePlanner. If this is not specificed, the default 2024-08-20T21:40:25.2375348Z planner will be used. (Default: ``None``) 2024-08-20T21:40:25.2375510Z process_group (Optional[ProcessGroup]): 2024-08-20T21:40:25.2375830Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:40:25.2375941Z (Default: ``None``) 2024-08-20T21:40:25.2375946Z 2024-08-20T21:40:25.2376043Z Returns: 2024-08-20T21:40:25.2376336Z Future: A future holding the resultant Metadata object from `save`. 2024-08-20T21:40:25.2376342Z 2024-08-20T21:40:25.2376438Z Example: 2024-08-20T21:40:25.2376574Z >>> # xdoctest: +SKIP 2024-08-20T21:40:25.2376697Z >>> my_model = MyModule() 2024-08-20T21:40:25.2376702Z 2024-08-20T21:40:25.2376843Z >>> state_dict = {"model": my_model} 2024-08-20T21:40:25.2376849Z 2024-08-20T21:40:25.2377268Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-20T21:40:25.2377539Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-08-20T21:40:25.2377662Z >>> state_dict=state_dict, 2024-08-20T21:40:25.2377902Z >>> storage_writer=fs_storage_writer, 2024-08-20T21:40:25.2377999Z >>> ) 2024-08-20T21:40:25.2378091Z >>> 2024-08-20T21:40:25.2378227Z >>> # ... do some work ... 2024-08-20T21:40:25.2378317Z >>> 2024-08-20T21:40:25.2378449Z >>> checkpoint_future.result() 2024-08-20T21:40:25.2378467Z 2024-08-20T21:40:25.2378556Z 2024-08-20T21:40:25.2378952Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.2378961Z 2024-08-20T21:40:25.2451191Z msg = Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2024-08-20T21:40:25.2451621Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.2451649Z 2024-08-20T21:40:25.2451913Z Initialize rendezvous event object and record its operations. 2024-08-20T21:40:25.2451918Z 2024-08-20T21:40:25.2452009Z Args: 2024-08-20T21:40:25.2452183Z run_id (str): The run id of the rendezvous. 2024-08-20T21:40:25.2452384Z message (str): The message describing the event. 2024-08-20T21:40:25.2452729Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-08-20T21:40:25.2452978Z name (str): Event name. (E.g. Current action being performed). 2024-08-20T21:40:25.2453133Z hostname (str): Hostname of the node. 2024-08-20T21:40:25.2453319Z pid (Optional[int]): The process id of the node. 2024-08-20T21:40:25.2453806Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-08-20T21:40:25.2454180Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-08-20T21:40:25.2454384Z rank (Optional[int]): The rank of the node, if known. 2024-08-20T21:40:25.2454496Z Returns: 2024-08-20T21:40:25.2454590Z None 2024-08-20T21:40:25.2454684Z Example: 2024-08-20T21:40:25.2454861Z >>> # See DynamicRendezvousHandler class 2024-08-20T21:40:25.2454964Z >>> def _record( 2024-08-20T21:40:25.2455068Z ... self, 2024-08-20T21:40:25.2455191Z ... message: str, 2024-08-20T21:40:25.2455374Z ... node_state: NodeState = NodeState.RUNNING, 2024-08-20T21:40:25.2455502Z ... rank: Optional[int] = None, 2024-08-20T21:40:25.2455651Z ... ) -> None: 2024-08-20T21:40:25.2455798Z ... construct_and_record_rdzv_event( 2024-08-20T21:40:25.2456017Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-08-20T21:40:25.2456182Z ... run_id=self._settings.run_id, 2024-08-20T21:40:25.2456297Z ... message=message, 2024-08-20T21:40:25.2456419Z ... node_state=node_state, 2024-08-20T21:40:25.2456579Z ... hostname=self._this_node.addr, 2024-08-20T21:40:25.2456708Z ... pid=self._this_node.pid, 2024-08-20T21:40:25.2456876Z ... local_id=self._this_node.local_id, 2024-08-20T21:40:25.2456981Z ... rank=rank, 2024-08-20T21:40:25.2457075Z ... ) 2024-08-20T21:40:25.2457085Z 2024-08-20T21:40:25.2457497Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.2457503Z 2024-08-20T21:40:25.4024526Z msg = Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=113. 2024-08-20T21:40:25.4025930Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.4026469Z 2024-08-20T21:40:25.4026735Z This configures FSDP-native mixed precision training. 2024-08-20T21:40:25.4027117Z 2024-08-20T21:40:25.4027219Z Attributes: 2024-08-20T21:40:25.4027697Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-08-20T21:40:25.4028427Z parameters during forward and backward and thus the dtype for 2024-08-20T21:40:25.4029157Z forward and backward computation. Outside forward and backward, the 2024-08-20T21:40:25.4029874Z *sharded* parameters are kept in full precision (e.g. for the 2024-08-20T21:40:25.4030774Z optimizer step), and for model checkpointing, the parameters are 2024-08-20T21:40:25.4031434Z always saved in full precision. (Default: ``None``) 2024-08-20T21:40:25.4032086Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-20T21:40:25.4032885Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-08-20T21:40:25.4033593Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-08-20T21:40:25.4034289Z the ``param_dtype`` value, still running gradient reduction in low 2024-08-20T21:40:25.4034993Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-08-20T21:40:25.4035712Z to force gradient reduction to run in full precision. (Default: 2024-08-20T21:40:25.4036250Z ``None``) 2024-08-20T21:40:25.4036707Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-20T21:40:25.4037415Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-08-20T21:40:25.4038122Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-08-20T21:40:25.4038835Z dtype thereafter. For model checkpointing, the buffers are saved 2024-08-20T21:40:25.4039517Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-08-20T21:40:25.4040042Z ``None``) 2024-08-20T21:40:25.4040477Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-08-20T21:40:25.4041288Z gradients to full precision after the backward pass in preparation 2024-08-20T21:40:25.4042034Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-08-20T21:40:25.4042768Z in the dtype used for gradient reduction, which can save memory if 2024-08-20T21:40:25.4043481Z using a custom optimizer that supports running in low precision. 2024-08-20T21:40:25.4044041Z (Default: ``False``) 2024-08-20T21:40:25.4044557Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-08-20T21:40:25.4045266Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-08-20T21:40:25.4045992Z that parameter and input dtypes match for forward computation, as 2024-08-20T21:40:25.4046732Z required by many ops. This may need to be set to ``True`` when only 2024-08-20T21:40:25.4047485Z applying mixed precision to some but not all FSDP modules, in which 2024-08-20T21:40:25.4048301Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-08-20T21:40:25.4048869Z (Default: ``False``) 2024-08-20T21:40:25.4049394Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-08-20T21:40:25.4050190Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-08-20T21:40:25.4050976Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-08-20T21:40:25.4051591Z this does not do anything. (Default: ``True``) 2024-08-20T21:40:25.4052223Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-08-20T21:40:25.4052916Z module classes to ignore for mixed precision when using an 2024-08-20T21:40:25.4053576Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-08-20T21:40:25.4054249Z applied to them separately with mixed precision disabled (meaning 2024-08-20T21:40:25.4054977Z that the final FSDP construction would deviate from the specified 2024-08-20T21:40:25.4055696Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-08-20T21:40:25.4056395Z not do anything. This API is experimental and subject to change. 2024-08-20T21:40:25.4056941Z (Default: ``(_BatchNorm,)``) 2024-08-20T21:40:25.4057228Z 2024-08-20T21:40:25.4057465Z .. note:: This API is experimental and subject to change. 2024-08-20T21:40:25.4057833Z 2024-08-20T21:40:25.4058140Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-08-20T21:40:25.4058681Z 2024-08-20T21:40:25.4058931Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-08-20T21:40:25.4059504Z precision, but buffers are not. 2024-08-20T21:40:25.4059777Z 2024-08-20T21:40:25.4060079Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-08-20T21:40:25.4060828Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-08-20T21:40:25.4061661Z Disabling FSDP's mixed precision for those norm modules only means that 2024-08-20T21:40:25.4062433Z the affine parameters are kept in ``float32``. However, this incurs 2024-08-20T21:40:25.4063267Z separate all-gathers and reduce-scatters for those norm modules, which 2024-08-20T21:40:25.4064036Z may be inefficient, so if the workload permits, the user should prefer 2024-08-20T21:40:25.4064697Z to still apply mixed precision to those modules. 2024-08-20T21:40:25.4065046Z 2024-08-20T21:40:25.4065353Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-08-20T21:40:25.4066075Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-08-20T21:40:25.4066859Z modules will have FSDP applied to them separately with mixed precision 2024-08-20T21:40:25.4067551Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-08-20T21:40:25.4067936Z 2024-08-20T21:40:25.4068221Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-08-20T21:40:25.4069007Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-08-20T21:40:25.4069685Z its ``cast_root_forward_inputs`` takes precedence over its 2024-08-20T21:40:25.4070375Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-08-20T21:40:25.4071047Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-08-20T21:40:25.4071843Z sufficient for the typical case where each FSDP instance has the same 2024-08-20T21:40:25.4072609Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-08-20T21:40:25.4073363Z ``param_dtype`` at the beginning of the model's forward pass. 2024-08-20T21:40:25.4073777Z 2024-08-20T21:40:25.4074054Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-08-20T21:40:25.4074809Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-08-20T21:40:25.4075593Z values to configure casting inputs or not before each instance's 2024-08-20T21:40:25.4076293Z forward. In such a case, since the casts happen before each FSDP 2024-08-20T21:40:25.4077086Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-08-20T21:40:25.4077840Z submodules run before its FSDP submodules to avoid the activation dtype 2024-08-20T21:40:25.4078589Z being changed due to a different ``MixedPrecision`` configuration. 2024-08-20T21:40:25.4079046Z 2024-08-20T21:40:25.4079149Z Example:: 2024-08-20T21:40:25.4079310Z 2024-08-20T21:40:25.4079494Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:25.4080048Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-08-20T21:40:25.4080555Z >>> model[1] = FSDP( 2024-08-20T21:40:25.4080891Z >>> model[1], 2024-08-20T21:40:25.4081480Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-08-20T21:40:25.4082135Z >>> ) 2024-08-20T21:40:25.4082409Z >>> model = FSDP( 2024-08-20T21:40:25.4082715Z >>> model, 2024-08-20T21:40:25.4083309Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-08-20T21:40:25.4083967Z >>> ) 2024-08-20T21:40:25.4084123Z 2024-08-20T21:40:25.4084431Z The above shows a working example. On the other hand, if ``model[1]`` 2024-08-20T21:40:25.4085157Z were replaced with ``model[0]``, meaning that the submodule using 2024-08-20T21:40:25.4085881Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-08-20T21:40:25.4086701Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-08-20T21:40:25.4087244Z ones. 2024-08-20T21:40:25.4087405Z 2024-08-20T21:40:25.4087410Z 2024-08-20T21:40:25.4087818Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.4088302Z 2024-08-20T21:40:25.4166400Z msg = Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=648. 2024-08-20T21:40:25.4168265Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.4169492Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-20T21:40:25.4170512Z 2024-08-20T21:40:25.4171065Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-08-20T21:40:25.4171884Z The target module does not have to be a FSDP module. If the target 2024-08-20T21:40:25.4172653Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-08-20T21:40:25.4173115Z 2024-08-20T21:40:25.4173478Z .. note:: This API should be called for only the top-level (root) 2024-08-20T21:40:25.4174015Z module. 2024-08-20T21:40:25.4174193Z 2024-08-20T21:40:25.4174500Z .. note:: This API enables users to transparently use the conventional 2024-08-20T21:40:25.4175467Z ``state_dict`` API to take model checkpoints in cases where the 2024-08-20T21:40:25.4176169Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-08-20T21:40:25.4176988Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-08-20T21:40:25.4177736Z instances, while dispatching into `sharded_state_dict` implementation 2024-08-20T21:40:25.4178301Z for FSDP: 2024-08-20T21:40:25.4178504Z 2024-08-20T21:40:25.4178612Z Example:: 2024-08-20T21:40:25.4178784Z 2024-08-20T21:40:25.4178983Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:25.4179446Z >>> model = DDP(FSDP(...)) 2024-08-20T21:40:25.4179862Z >>> FSDP.set_state_dict_type( 2024-08-20T21:40:25.4180260Z >>> model, 2024-08-20T21:40:25.4180635Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-20T21:40:25.4181260Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-08-20T21:40:25.4182030Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-08-20T21:40:25.4182588Z >>> ) 2024-08-20T21:40:25.4182930Z >>> param_state_dict = model.state_dict() 2024-08-20T21:40:25.4183491Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-20T21:40:25.4183876Z 2024-08-20T21:40:25.4183987Z Args: 2024-08-20T21:40:25.4184300Z module (torch.nn.Module): Root module. 2024-08-20T21:40:25.4184943Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-20T21:40:25.4185731Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-08-20T21:40:25.4186341Z target ``state_dict_type``. 2024-08-20T21:40:25.4186984Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-08-20T21:40:25.4187632Z for the optimizer state dict. 2024-08-20T21:40:25.4187938Z 2024-08-20T21:40:25.4188039Z Returns: 2024-08-20T21:40:25.4188505Z A StateDictSettings that include the previous state_dict type and 2024-08-20T21:40:25.4189102Z configuration for the module. 2024-08-20T21:40:25.4189482Z 2024-08-20T21:40:25.4190036Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.4190743Z 2024-08-20T21:40:25.4192152Z msg = Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=804. 2024-08-20T21:40:25.4193784Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.4194627Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-20T21:40:25.4195156Z 2024-08-20T21:40:25.4195591Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-08-20T21:40:25.4196369Z :meth:`set_state_dict_type` for the detail. 2024-08-20T21:40:25.4196694Z 2024-08-20T21:40:25.4196818Z Example:: 2024-08-20T21:40:25.4196992Z 2024-08-20T21:40:25.4197171Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:25.4197649Z >>> model = DDP(FSDP(...)) 2024-08-20T21:40:25.4198072Z >>> with FSDP.state_dict_type( 2024-08-20T21:40:25.4198463Z >>> model, 2024-08-20T21:40:25.4198856Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-20T21:40:25.4199295Z >>> ): 2024-08-20T21:40:25.4199617Z >>> checkpoint = model.state_dict() 2024-08-20T21:40:25.4199954Z 2024-08-20T21:40:25.4200050Z Args: 2024-08-20T21:40:25.4200374Z module (torch.nn.Module): Root module. 2024-08-20T21:40:25.4200992Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-20T21:40:25.4201775Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-08-20T21:40:25.4202562Z configuration for the target ``state_dict_type``. 2024-08-20T21:40:25.4203231Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-08-20T21:40:25.4203982Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-08-20T21:40:25.4204519Z 2024-08-20T21:40:25.4205076Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.4205561Z 2024-08-20T21:40:25.4226061Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1801. 2024-08-20T21:40:25.4227717Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.4228458Z 2024-08-20T21:40:25.4229029Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-08-20T21:40:25.4229859Z 2024-08-20T21:40:25.4230411Z The given state-dict can be transformed to one of three types: 2024-08-20T21:40:25.4231236Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-08-20T21:40:25.4231803Z 2024-08-20T21:40:25.4232120Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-08-20T21:40:25.4232904Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-08-20T21:40:25.4233471Z avoid OOM. 2024-08-20T21:40:25.4233627Z 2024-08-20T21:40:25.4233939Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-08-20T21:40:25.4234707Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-08-20T21:40:25.4235257Z memory. 2024-08-20T21:40:25.4235395Z 2024-08-20T21:40:25.4235688Z For local state_dict, no transformation will be performed. But a state 2024-08-20T21:40:25.4236478Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-08-20T21:40:25.4237114Z nature (this is not supported yet). 2024-08-20T21:40:25.4237386Z 2024-08-20T21:40:25.4237507Z Example:: 2024-08-20T21:40:25.4237655Z 2024-08-20T21:40:25.4237819Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:25.4238468Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-20T21:40:25.4239162Z >>> from torch.distributed.fsdp import StateDictType 2024-08-20T21:40:25.4239756Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-20T21:40:25.4240601Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-20T21:40:25.4241161Z >>> # Save a checkpoint 2024-08-20T21:40:25.4241494Z >>> model, optim = ... 2024-08-20T21:40:25.4241843Z >>> FSDP.set_state_dict_type( 2024-08-20T21:40:25.4242204Z >>> model, 2024-08-20T21:40:25.4242516Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:40:25.4242994Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:40:25.4243523Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:40:25.4243971Z >>> ) 2024-08-20T21:40:25.4244256Z >>> state_dict = model.state_dict() 2024-08-20T21:40:25.4244764Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-20T21:40:25.4245315Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-20T21:40:25.4245781Z >>> # Load a checkpoint 2024-08-20T21:40:25.4246125Z >>> model, optim = ... 2024-08-20T21:40:25.4246534Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-20T21:40:25.4247026Z >>> FSDP.set_state_dict_type( 2024-08-20T21:40:25.4247389Z >>> model, 2024-08-20T21:40:25.4247703Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:40:25.4248175Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:40:25.4248699Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:40:25.4249135Z >>> ) 2024-08-20T21:40:25.4249423Z >>> model.load_state_dict(state_dict) 2024-08-20T21:40:25.4249999Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-20T21:40:25.4250572Z >>> model, optim, optim_state_dict 2024-08-20T21:40:25.4250972Z >>> ) 2024-08-20T21:40:25.4251304Z >>> optim.load_state_dict(optim_state_dict) 2024-08-20T21:40:25.4251619Z 2024-08-20T21:40:25.4251708Z Args: 2024-08-20T21:40:25.4252113Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-20T21:40:25.4252814Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-20T21:40:25.4253416Z were passed into the optimizer ``optim``. 2024-08-20T21:40:25.4254053Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-20T21:40:25.4254566Z parameters. 2024-08-20T21:40:25.4255044Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-08-20T21:40:25.4255771Z transform. If the value is None, optim.state_dict() will be used. ( 2024-08-20T21:40:25.4256341Z Default: ``None``) 2024-08-20T21:40:25.4256962Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-20T21:40:25.4257698Z are sharded or ``None`` if using the default process group. ( 2024-08-20T21:40:25.4258228Z Default: ``None``) 2024-08-20T21:40:25.4258441Z 2024-08-20T21:40:25.4258550Z Returns: 2024-08-20T21:40:25.4258954Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-08-20T21:40:25.4259612Z ``model``. The sharding of the optimizer state is based on 2024-08-20T21:40:25.4260113Z ``state_dict_type``. 2024-08-20T21:40:25.4260319Z 2024-08-20T21:40:25.4260725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.4261229Z 2024-08-20T21:40:25.4262551Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1899. 2024-08-20T21:40:25.4264212Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.4264730Z 2024-08-20T21:40:25.4265335Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-08-20T21:40:25.4266005Z 2024-08-20T21:40:25.4266234Z Given a ``optim_state_dict`` that is transformed through 2024-08-20T21:40:25.4266880Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-08-20T21:40:25.4267617Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-08-20T21:40:25.4268456Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-08-20T21:40:25.4268872Z 2024-08-20T21:40:25.4269050Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:25.4269678Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-20T21:40:25.4270373Z >>> from torch.distributed.fsdp import StateDictType 2024-08-20T21:40:25.4270982Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-20T21:40:25.4271639Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-20T21:40:25.4272191Z >>> # Save a checkpoint 2024-08-20T21:40:25.4272536Z >>> model, optim = ... 2024-08-20T21:40:25.4272871Z >>> FSDP.set_state_dict_type( 2024-08-20T21:40:25.4273231Z >>> model, 2024-08-20T21:40:25.4273552Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:40:25.4274007Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:40:25.4274535Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:40:25.4274987Z >>> ) 2024-08-20T21:40:25.4275262Z >>> state_dict = model.state_dict() 2024-08-20T21:40:25.4275694Z >>> original_osd = optim.state_dict() 2024-08-20T21:40:25.4276156Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-08-20T21:40:25.4276573Z >>> model, 2024-08-20T21:40:25.4276850Z >>> optim, 2024-08-20T21:40:25.4277163Z >>> optim_state_dict=original_osd 2024-08-20T21:40:25.4277536Z >>> ) 2024-08-20T21:40:25.4277945Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-20T21:40:25.4278408Z >>> # Load a checkpoint 2024-08-20T21:40:25.4278736Z >>> model, optim = ... 2024-08-20T21:40:25.4279158Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-20T21:40:25.4279645Z >>> FSDP.set_state_dict_type( 2024-08-20T21:40:25.4279987Z >>> model, 2024-08-20T21:40:25.4280316Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:40:25.4280788Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:40:25.4281308Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:40:25.4281765Z >>> ) 2024-08-20T21:40:25.4282050Z >>> model.load_state_dict(state_dict) 2024-08-20T21:40:25.4282530Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-20T21:40:25.4283029Z >>> model, optim, optim_state_dict 2024-08-20T21:40:25.4283421Z >>> ) 2024-08-20T21:40:25.4283712Z >>> optim.load_state_dict(optim_state_dict) 2024-08-20T21:40:25.4284040Z 2024-08-20T21:40:25.4284135Z Args: 2024-08-20T21:40:25.4284539Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-20T21:40:25.4285221Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-20T21:40:25.4285824Z were passed into the optimizer ``optim``. 2024-08-20T21:40:25.4286454Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-20T21:40:25.4286948Z parameters. 2024-08-20T21:40:25.4287419Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-08-20T21:40:25.4288145Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-08-20T21:40:25.4288861Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-08-20T21:40:25.4289560Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-08-20T21:40:25.4290523Z load_directly (bool): If this is set to True, this API will also 2024-08-20T21:40:25.4291233Z call optim.load_state_dict(result) before returning the result. 2024-08-20T21:40:25.4291956Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-08-20T21:40:25.4292532Z (Default: ``False``) 2024-08-20T21:40:25.4293166Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-20T21:40:25.4293888Z are sharded or ``None`` if using the default process group. ( 2024-08-20T21:40:25.4294416Z Default: ``None``) 2024-08-20T21:40:25.4294636Z 2024-08-20T21:40:25.4295045Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.4295682Z 2024-08-20T21:40:25.4422608Z msg = Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=137. 2024-08-20T21:40:25.4424034Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.4424553Z 2024-08-20T21:40:25.4424884Z RemoteModule instance can only be created after RPC initialization. 2024-08-20T21:40:25.4425360Z 2024-08-20T21:40:25.4425684Z It creates a user-specified module on a specified remote node. 2024-08-20T21:40:25.4426463Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-20T21:40:25.4427065Z executed on the remote node. 2024-08-20T21:40:25.4427626Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-20T21:40:25.4428306Z gradients back to the corresponding remote module. 2024-08-20T21:40:25.4429159Z It can be shared across processors using `RPC framework `__, 2024-08-20T21:40:25.4430201Z without incurring any overheads of copying the actual module, 2024-08-20T21:40:25.4431014Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-08-20T21:40:25.4431797Z pointing to the remote module. 2024-08-20T21:40:25.4432273Z 2024-08-20T21:40:25.4432764Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-08-20T21:40:25.4433616Z the ``forward`` method of the module returned by the ``module_cls``. 2024-08-20T21:40:25.4434245Z 2024-08-20T21:40:25.4434686Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-08-20T21:40:25.4435272Z 2024-08-20T21:40:25.4435611Z Particularly, to create a hybrid model, typically the local modules should be 2024-08-20T21:40:25.4436627Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-08-20T21:40:25.4437412Z Hybrid Example: 2024-08-20T21:40:25.4437780Z >>> class HybridModel(nn.Module): 2024-08-20T21:40:25.4438291Z >>> def __init__(self) -> None: 2024-08-20T21:40:25.4438736Z >>> nn.Module.__init__(self) 2024-08-20T21:40:25.4439227Z >>> self.remote_embedding = RemoteModule(...) 2024-08-20T21:40:25.4439833Z >>> self.local_linear = nn.Linear(...) 2024-08-20T21:40:25.4440173Z 2024-08-20T21:40:25.4440565Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-20T21:40:25.4441974Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-08-20T21:40:25.4442742Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-08-20T21:40:25.4443407Z ``def forward(input: Tensor) -> Tensor:`` and 2024-08-20T21:40:25.4443998Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-08-20T21:40:25.4444361Z 2024-08-20T21:40:25.4444474Z .. note:: 2024-08-20T21:40:25.4444820Z If the remote module is placed on a cuda device, 2024-08-20T21:40:25.4445502Z any input CPU tensors will be automatically moved to the same cuda device, 2024-08-20T21:40:25.4446570Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-08-20T21:40:25.4447300Z 2024-08-20T21:40:25.4447392Z Args: 2024-08-20T21:40:25.4448034Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:40:25.4449031Z The device can be a local device or a remote device specified by one of the following remote 2024-08-20T21:40:25.4449705Z formats: 2024-08-20T21:40:25.4449891Z 2024-08-20T21:40:25.4450147Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-20T21:40:25.4450700Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-20T21:40:25.4451055Z 2024-08-20T21:40:25.4451393Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:40:25.4452049Z module_cls (nn.Module): For example, 2024-08-20T21:40:25.4452616Z >>> class MyModule(nn.Module): 2024-08-20T21:40:25.4453027Z >>> def forward(input): 2024-08-20T21:40:25.4453401Z >>> return input + 1 2024-08-20T21:40:25.4453770Z >>> 2024-08-20T21:40:25.4454045Z >>> module_cls = MyModule 2024-08-20T21:40:25.4454542Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-20T21:40:25.4455223Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-20T21:40:25.4456016Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-20T21:40:25.4456874Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-20T21:40:25.4457769Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-20T21:40:25.4458563Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-20T21:40:25.4459049Z 2024-08-20T21:40:25.4459145Z Returns: 2024-08-20T21:40:25.4459623Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:40:25.4460500Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-20T21:40:25.4461320Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:40:25.4462084Z on the user-provided module on the remote side. 2024-08-20T21:40:25.4462444Z 2024-08-20T21:40:25.4462622Z Example:: 2024-08-20T21:40:25.4462977Z Run the following code in two different processes: 2024-08-20T21:40:25.4463333Z 2024-08-20T21:40:25.4463473Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:25.4463877Z >>> # On worker 0: 2024-08-20T21:40:25.4464190Z >>> import torch 2024-08-20T21:40:25.4464526Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:25.4464977Z >>> from torch import nn, Tensor 2024-08-20T21:40:25.4465548Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:40:25.4466091Z >>> 2024-08-20T21:40:25.4466418Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:40:25.4466909Z >>> remote_linear_module = RemoteModule( 2024-08-20T21:40:25.4467375Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:40:25.4467802Z >>> ) 2024-08-20T21:40:25.4468084Z >>> input = torch.randn(128, 20) 2024-08-20T21:40:25.4468553Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-20T21:40:25.4469078Z >>> ret = ret_fut.wait() 2024-08-20T21:40:25.4469419Z >>> rpc.shutdown() 2024-08-20T21:40:25.4469617Z 2024-08-20T21:40:25.4469724Z >>> # On worker 1: 2024-08-20T21:40:25.4470027Z >>> import torch 2024-08-20T21:40:25.4470374Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:25.4470769Z >>> 2024-08-20T21:40:25.4471086Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:40:25.4471536Z >>> rpc.shutdown() 2024-08-20T21:40:25.4471734Z 2024-08-20T21:40:25.4472140Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.4472640Z 2024-08-20T21:40:25.4473797Z msg = Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=514. 2024-08-20T21:40:25.4475280Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.4475793Z 2024-08-20T21:40:25.4476216Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-08-20T21:40:25.4476812Z 2024-08-20T21:40:25.4477235Z This alternate initialization method can be particularly useful if we want to create multiple 2024-08-20T21:40:25.4478242Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-08-20T21:40:25.4478822Z 2024-08-20T21:40:25.4479189Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-08-20T21:40:25.4480069Z which is not supported. The recommended way is as follows: 2024-08-20T21:40:25.4480464Z 2024-08-20T21:40:25.4480626Z 1. the sender creates a RemoteModule; 2024-08-20T21:40:25.4481105Z 2. the sender sends its ``module_rref`` over RPC; 2024-08-20T21:40:25.4481924Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-08-20T21:40:25.4482559Z 2024-08-20T21:40:25.4482659Z Example:: 2024-08-20T21:40:25.4483020Z Run the following code in two different processes: 2024-08-20T21:40:25.4483374Z 2024-08-20T21:40:25.4483516Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:25.4483918Z >>> # On worker 0: 2024-08-20T21:40:25.4484228Z >>> import torch 2024-08-20T21:40:25.4484563Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:25.4485008Z >>> from torch import nn, Tensor 2024-08-20T21:40:25.4485582Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:40:25.4486120Z >>> 2024-08-20T21:40:25.4486447Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:40:25.4486922Z >>> remote_module = RemoteModule( 2024-08-20T21:40:25.4487355Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:40:25.4487779Z >>> ) 2024-08-20T21:40:25.4488021Z >>> 2024-08-20T21:40:25.4488283Z >>> remote_module1 = rpc.rpc_sync( 2024-08-20T21:40:25.4488684Z >>> "worker1/cpu", 2024-08-20T21:40:25.4489063Z >>> RemoteModule.init_from_module_rref, 2024-08-20T21:40:25.4489641Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-08-20T21:40:25.4490169Z >>> ) 2024-08-20T21:40:25.4490652Z >>> rpc.shutdown() 2024-08-20T21:40:25.4490849Z 2024-08-20T21:40:25.4490955Z >>> # On worker 1: 2024-08-20T21:40:25.4491262Z >>> import torch 2024-08-20T21:40:25.4491615Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:25.4492013Z >>> 2024-08-20T21:40:25.4492333Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:40:25.4492791Z >>> rpc.shutdown() 2024-08-20T21:40:25.4492991Z 2024-08-20T21:40:25.4493082Z Args: 2024-08-20T21:40:25.4493750Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:40:25.4494756Z The device can be a local device or a remote device specified by one of the following remote 2024-08-20T21:40:25.4495438Z formats: 2024-08-20T21:40:25.4495624Z 2024-08-20T21:40:25.4495810Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-20T21:40:25.4496365Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-20T21:40:25.4496722Z 2024-08-20T21:40:25.4497076Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:40:25.4497934Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-08-20T21:40:25.4498575Z the created remote module. 2024-08-20T21:40:25.4499203Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-20T21:40:25.4500061Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-20T21:40:25.4500946Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-20T21:40:25.4501738Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-20T21:40:25.4502225Z 2024-08-20T21:40:25.4502320Z Returns: 2024-08-20T21:40:25.4502796Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:40:25.4503686Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-08-20T21:40:25.4504522Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:40:25.4505274Z on the user-provided module on the remote side. 2024-08-20T21:40:25.4505632Z 2024-08-20T21:40:25.4506025Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.4506504Z 2024-08-20T21:40:25.4507592Z msg = Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=606. 2024-08-20T21:40:25.4508960Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.4509476Z 2024-08-20T21:40:25.4509775Z A RemoteModule instance can only be created after RPC initialization. 2024-08-20T21:40:25.4510253Z 2024-08-20T21:40:25.4510581Z It creates a user-specified module on a specified remote node. 2024-08-20T21:40:25.4511348Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-20T21:40:25.4511958Z executed on the remote node. 2024-08-20T21:40:25.4512608Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-20T21:40:25.4513302Z gradients back to the corresponding remote module. 2024-08-20T21:40:25.4513657Z 2024-08-20T21:40:25.4513952Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-08-20T21:40:25.4514715Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-08-20T21:40:25.4515512Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-08-20T21:40:25.4516283Z and ``forward`` are the same as the ``forward`` method of the module 2024-08-20T21:40:25.4516842Z returned by the ``module_cls``. 2024-08-20T21:40:25.4517126Z 2024-08-20T21:40:25.4517400Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-20T21:40:25.4518360Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-08-20T21:40:25.4519157Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-08-20T21:40:25.4519636Z 2024-08-20T21:40:25.4519847Z | ``def forward(input: Tensor) -> Tensor:`` 2024-08-20T21:40:25.4520444Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-08-20T21:40:25.4520809Z 2024-08-20T21:40:25.4520899Z Args: 2024-08-20T21:40:25.4521541Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:40:25.4522585Z The format should be "/", where the device field can be parsed as torch.device type. 2024-08-20T21:40:25.4523405Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-08-20T21:40:25.4524092Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:40:25.4524956Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-08-20T21:40:25.4525469Z 2024-08-20T21:40:25.4525617Z >>> class MyModule(nn.Module): 2024-08-20T21:40:25.4526013Z >>> def forward(input): 2024-08-20T21:40:25.4526405Z >>> return input + 1 2024-08-20T21:40:25.4526774Z >>> 2024-08-20T21:40:25.4527035Z >>> module_cls = MyModule 2024-08-20T21:40:25.4527297Z 2024-08-20T21:40:25.4527548Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-20T21:40:25.4528232Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-20T21:40:25.4528647Z 2024-08-20T21:40:25.4528742Z Returns: 2024-08-20T21:40:25.4529215Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:40:25.4530165Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-20T21:40:25.4531009Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:40:25.4531767Z on the user-provided module on the remote side. 2024-08-20T21:40:25.4532127Z 2024-08-20T21:40:25.4532234Z Example:: 2024-08-20T21:40:25.4532589Z Run the following code in two different processes: 2024-08-20T21:40:25.4532947Z 2024-08-20T21:40:25.4533089Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:25.4533491Z >>> # On worker 0: 2024-08-20T21:40:25.4533798Z >>> import torch 2024-08-20T21:40:25.4534136Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:25.4534682Z >>> from torch import nn, Tensor 2024-08-20T21:40:25.4535262Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:40:25.4535804Z >>> 2024-08-20T21:40:25.4536128Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:40:25.4536631Z >>> remote_linear_module = RemoteModule( 2024-08-20T21:40:25.4537096Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:40:25.4537524Z >>> ) 2024-08-20T21:40:25.4537810Z >>> input = torch.randn(128, 20) 2024-08-20T21:40:25.4538277Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-20T21:40:25.4538759Z >>> ret = ret_fut.wait() 2024-08-20T21:40:25.4539104Z >>> rpc.shutdown() 2024-08-20T21:40:25.4539301Z 2024-08-20T21:40:25.4539408Z >>> # On worker 1: 2024-08-20T21:40:25.4539712Z >>> import torch 2024-08-20T21:40:25.4540060Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:25.4540456Z >>> 2024-08-20T21:40:25.4540775Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:40:25.4541232Z >>> rpc.shutdown() 2024-08-20T21:40:25.4541427Z 2024-08-20T21:40:25.4541671Z Furthermore, a more practical example that is combined with 2024-08-20T21:40:25.4542715Z `DistributedDataParallel `__ (DDP) 2024-08-20T21:40:25.4543953Z can be found in this `tutorial `__. 2024-08-20T21:40:25.4544628Z 2024-08-20T21:40:25.4545050Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.4545535Z 2024-08-20T21:40:25.4588417Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2024-08-20T21:40:25.4589832Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.4590531Z 2024-08-20T21:40:25.4590854Z Custom reducer class that can be used to specify a custom operation that 2024-08-20T21:40:25.4591549Z reduces losses of multiple microbatches into one value. 2024-08-20T21:40:25.4591924Z 2024-08-20T21:40:25.4592019Z Example: 2024-08-20T21:40:25.4592282Z >>> # xdoctest: +SKIP 2024-08-20T21:40:25.4592614Z >>> sum_reducer = _CustomReducer( 2024-08-20T21:40:25.4592982Z >>> torch.tensor(0.0), 2024-08-20T21:40:25.4593316Z >>> lambda a, b: a + b 2024-08-20T21:40:25.4593631Z >>> ) 2024-08-20T21:40:25.4593777Z 2024-08-20T21:40:25.4594180Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.4594683Z 2024-08-20T21:40:25.4999966Z msg = Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6. 2024-08-20T21:40:25.5001600Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.5002127Z 2024-08-20T21:40:25.5002448Z A decorator for a function indicating that the return value of the function 2024-08-20T21:40:25.5003244Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-08-20T21:40:25.5004020Z function can run asynchronously on the RPC callee. More specifically, the 2024-08-20T21:40:25.5004813Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-08-20T21:40:25.5005604Z function and installs subsequent processing steps as a callback to that 2024-08-20T21:40:25.5006402Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-08-20T21:40:25.5007150Z from the :class:`~torch.futures.Future` when completed and send the 2024-08-20T21:40:25.5007840Z value back as the RPC response. That also means the returned 2024-08-20T21:40:25.5008561Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-08-20T21:40:25.5009356Z sent through RPC. This decorator is useful when the wrapped function's 2024-08-20T21:40:25.5010146Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-08-20T21:40:25.5011151Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-08-20T21:40:25.5011619Z 2024-08-20T21:40:25.5011943Z .. note:: To enable asynchronous execution, applications must pass the 2024-08-20T21:40:25.5012689Z function object returned by this decorator to RPC APIs. If RPC detected 2024-08-20T21:40:25.5013465Z attributes installed by this decorator, it knows that this function 2024-08-20T21:40:25.5013716Z returns a ``Future`` object and will handle that accordingly. 2024-08-20T21:40:25.5014025Z However, this does not mean this decorator has to be outmost one when 2024-08-20T21:40:25.5014325Z defining a function. For example, when combined with ``@staticmethod`` 2024-08-20T21:40:25.5014609Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-08-20T21:40:25.5014923Z inner decorator to allow the target function be recognized as a static 2024-08-20T21:40:25.5015236Z or class function. This target function can still execute asynchronously 2024-08-20T21:40:25.5015539Z because, when accessed, the static or class method preserves attributes 2024-08-20T21:40:25.5015742Z installed by ``@rpc.functions.async_execution``. 2024-08-20T21:40:25.5015749Z 2024-08-20T21:40:25.5015753Z 2024-08-20T21:40:25.5015854Z Example:: 2024-08-20T21:40:25.5016144Z The returned :class:`~torch.futures.Future` object can come from 2024-08-20T21:40:25.5016309Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-08-20T21:40:25.5016715Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-08-20T21:40:25.5016956Z constructor. The example below shows directly using the 2024-08-20T21:40:25.5017123Z :class:`~torch.futures.Future` returned by 2024-08-20T21:40:25.5017270Z :meth:`~torch.futures.Future.then`. 2024-08-20T21:40:25.5017275Z 2024-08-20T21:40:25.5017437Z >>> from torch.distributed import rpc 2024-08-20T21:40:25.5017531Z >>> 2024-08-20T21:40:25.5017688Z >>> # omitting setup and shutdown RPC 2024-08-20T21:40:25.5017783Z >>> 2024-08-20T21:40:25.5017890Z >>> # On all workers 2024-08-20T21:40:25.5018044Z >>> @rpc.functions.async_execution 2024-08-20T21:40:25.5018186Z >>> def async_add_chained(to, x, y, z): 2024-08-20T21:40:25.5018443Z >>> # This function runs on "worker1" and returns immediately when 2024-08-20T21:40:25.5018711Z >>> # the callback is installed through the `then(cb)` API. In the 2024-08-20T21:40:25.5018972Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-08-20T21:40:25.5019190Z >>> # When the return value of that `rpc_async` arrives at 2024-08-20T21:40:25.5019451Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-08-20T21:40:25.5019710Z >>> # and set the value for the previously returned `Future`, which 2024-08-20T21:40:25.5019963Z >>> # will then trigger RPC to send the result back to "worker0". 2024-08-20T21:40:25.5020199Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:40:25.5020336Z >>> lambda fut: fut.wait() + z 2024-08-20T21:40:25.5020446Z >>> ) 2024-08-20T21:40:25.5020537Z >>> 2024-08-20T21:40:25.5020640Z >>> # On worker0 2024-08-20T21:40:25.5020813Z >>> # xdoctest: +SKIP 2024-08-20T21:40:25.5020925Z >>> ret = rpc.rpc_sync( 2024-08-20T21:40:25.5021026Z >>> "worker1", 2024-08-20T21:40:25.5021155Z >>> async_add_chained, 2024-08-20T21:40:25.5021312Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-08-20T21:40:25.5021406Z >>> ) 2024-08-20T21:40:25.5021569Z >>> print(ret) # prints tensor([3., 3.]) 2024-08-20T21:40:25.5021575Z 2024-08-20T21:40:25.5021872Z When combined with TorchScript decorators, this decorator must be the 2024-08-20T21:40:25.5021974Z outmost one. 2024-08-20T21:40:25.5021979Z 2024-08-20T21:40:25.5022115Z >>> from torch import Tensor 2024-08-20T21:40:25.5022258Z >>> from torch.futures import Future 2024-08-20T21:40:25.5022462Z >>> from torch.distributed import rpc 2024-08-20T21:40:25.5022570Z >>> 2024-08-20T21:40:25.5022709Z >>> # omitting setup and shutdown RPC 2024-08-20T21:40:25.5022814Z >>> 2024-08-20T21:40:25.5022921Z >>> # On all workers 2024-08-20T21:40:25.5023031Z >>> @torch.jit.script 2024-08-20T21:40:25.5023323Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-08-20T21:40:25.5023430Z >>> return x + y 2024-08-20T21:40:25.5023526Z >>> 2024-08-20T21:40:25.5023678Z >>> @rpc.functions.async_execution 2024-08-20T21:40:25.5023789Z >>> @torch.jit.script 2024-08-20T21:40:25.5024117Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-08-20T21:40:25.5024316Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-08-20T21:40:25.5024410Z >>> 2024-08-20T21:40:25.5024512Z >>> # On worker0 2024-08-20T21:40:25.5024638Z >>> ret = rpc.rpc_sync( 2024-08-20T21:40:25.5024739Z >>> "worker1", 2024-08-20T21:40:25.5024840Z >>> async_add, 2024-08-20T21:40:25.5025008Z >>> args=("worker2", torch.ones(2), 1) 2024-08-20T21:40:25.5025102Z >>> ) 2024-08-20T21:40:25.5025251Z >>> print(ret) # prints tensor([2., 2.]) 2024-08-20T21:40:25.5025256Z 2024-08-20T21:40:25.5025566Z When combined with static or class method, this decorator must be the 2024-08-20T21:40:25.5025665Z inner one. 2024-08-20T21:40:25.5025670Z 2024-08-20T21:40:25.5025830Z >>> from torch.distributed import rpc 2024-08-20T21:40:25.5025984Z >>> 2024-08-20T21:40:25.5026123Z >>> # omitting setup and shutdown RPC 2024-08-20T21:40:25.5026228Z >>> 2024-08-20T21:40:25.5026334Z >>> # On all workers 2024-08-20T21:40:25.5026466Z >>> class AsyncExecutionClass: 2024-08-20T21:40:25.5026570Z >>> 2024-08-20T21:40:25.5026679Z >>> @staticmethod 2024-08-20T21:40:25.5026820Z >>> @rpc.functions.async_execution 2024-08-20T21:40:25.5026982Z >>> def static_async_add(to, x, y, z): 2024-08-20T21:40:25.5027213Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:40:25.5027358Z >>> lambda fut: fut.wait() + z 2024-08-20T21:40:25.5027468Z >>> ) 2024-08-20T21:40:25.5027560Z >>> 2024-08-20T21:40:25.5027664Z >>> @classmethod 2024-08-20T21:40:25.5027818Z >>> @rpc.functions.async_execution 2024-08-20T21:40:25.5027975Z >>> def class_async_add(cls, to, x, y, z): 2024-08-20T21:40:25.5028132Z >>> ret_fut = torch.futures.Future() 2024-08-20T21:40:25.5028345Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:40:25.5028540Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-08-20T21:40:25.5028654Z >>> ) 2024-08-20T21:40:25.5028764Z >>> return ret_fut 2024-08-20T21:40:25.5028856Z >>> 2024-08-20T21:40:25.5029012Z >>> @rpc.functions.async_execution 2024-08-20T21:40:25.5029171Z >>> def bound_async_add(self, to, x, y, z): 2024-08-20T21:40:25.5029403Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:40:25.5029560Z >>> lambda fut: fut.wait() + z 2024-08-20T21:40:25.5029655Z >>> ) 2024-08-20T21:40:25.5029751Z >>> 2024-08-20T21:40:25.5029866Z >>> # On worker0 2024-08-20T21:40:25.5029978Z >>> ret = rpc.rpc_sync( 2024-08-20T21:40:25.5030079Z >>> "worker1", 2024-08-20T21:40:25.5030266Z >>> AsyncExecutionClass.static_async_add, 2024-08-20T21:40:25.5030421Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-20T21:40:25.5030514Z >>> ) 2024-08-20T21:40:25.5030679Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:40:25.5030769Z >>> 2024-08-20T21:40:25.5030881Z >>> ret = rpc.rpc_sync( 2024-08-20T21:40:25.5030997Z >>> "worker1", 2024-08-20T21:40:25.5031163Z >>> AsyncExecutionClass.class_async_add, 2024-08-20T21:40:25.5031311Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-20T21:40:25.5031416Z >>> ) 2024-08-20T21:40:25.5031624Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:40:25.5031630Z 2024-08-20T21:40:25.5031852Z This decorator also works with RRef helpers, i.e., . 2024-08-20T21:40:25.5032033Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-08-20T21:40:25.5032238Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-08-20T21:40:25.5032429Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-08-20T21:40:25.5032434Z 2024-08-20T21:40:25.5032585Z >>> from torch.distributed import rpc 2024-08-20T21:40:25.5032676Z >>> 2024-08-20T21:40:25.5032862Z >>> # reuse the AsyncExecutionClass class above 2024-08-20T21:40:25.5033065Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:40:25.5033346Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-08-20T21:40:25.5033508Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:40:25.5033600Z >>> 2024-08-20T21:40:25.5033800Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:40:25.5034134Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-08-20T21:40:25.5034284Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:40:25.5034390Z >>> 2024-08-20T21:40:25.5034587Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:40:25.5034901Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-08-20T21:40:25.5035061Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:40:25.5035137Z 2024-08-20T21:40:25.5035551Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.5035557Z 2024-08-20T21:40:25.5039884Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=108. 2024-08-20T21:40:25.5040306Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.5040353Z 2024-08-20T21:40:25.5040625Z Set device mapping between each RPC caller and callee pair. This 2024-08-20T21:40:25.5040874Z function can be called multiple times to incrementally add 2024-08-20T21:40:25.5041009Z device placement configurations. 2024-08-20T21:40:25.5041015Z 2024-08-20T21:40:25.5041118Z Args: 2024-08-20T21:40:25.5041229Z to (str): Callee name. 2024-08-20T21:40:25.5041493Z device_map (Dict of int, str, or torch.device): Device placement 2024-08-20T21:40:25.5041751Z mappings from this worker to the callee. This map must be 2024-08-20T21:40:25.5041853Z invertible. 2024-08-20T21:40:25.5041859Z 2024-08-20T21:40:25.5041951Z Example: 2024-08-20T21:40:25.5042104Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:25.5042209Z >>> # both workers 2024-08-20T21:40:25.5042314Z >>> def add(x, y): 2024-08-20T21:40:25.5042572Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-08-20T21:40:25.5042702Z >>> return x + y, (x + y).to(2) 2024-08-20T21:40:25.5042794Z >>> 2024-08-20T21:40:25.5042917Z >>> # on worker 0 2024-08-20T21:40:25.5043096Z >>> options = TensorPipeRpcBackendOptions( 2024-08-20T21:40:25.5043213Z >>> num_worker_threads=8, 2024-08-20T21:40:25.5043362Z >>> device_maps={"worker1": {0: 1}} 2024-08-20T21:40:25.5043596Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-08-20T21:40:25.5043705Z >>> ) 2024-08-20T21:40:25.5043866Z >>> options.set_device_map("worker1", {1: 2}) 2024-08-20T21:40:25.5044090Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-08-20T21:40:25.5044196Z >>> 2024-08-20T21:40:25.5044302Z >>> rpc.init_rpc( 2024-08-20T21:40:25.5044402Z >>> "worker0", 2024-08-20T21:40:25.5044512Z >>> rank=0, 2024-08-20T21:40:25.5044618Z >>> world_size=2, 2024-08-20T21:40:25.5044787Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-08-20T21:40:25.5044929Z >>> rpc_backend_options=options 2024-08-20T21:40:25.5045021Z >>> ) 2024-08-20T21:40:25.5045111Z >>> 2024-08-20T21:40:25.5045372Z >>> x = torch.ones(2) 2024-08-20T21:40:25.5045586Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-08-20T21:40:25.5045837Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-08-20T21:40:25.5046102Z >>> # sending the return value back, it will follow the invert of 2024-08-20T21:40:25.5046345Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-08-20T21:40:25.5046461Z >>> # cuda:1 on worker0 2024-08-20T21:40:25.5046734Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-08-20T21:40:25.5046984Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-08-20T21:40:25.5046990Z 2024-08-20T21:40:25.5047402Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.5047408Z 2024-08-20T21:40:25.5247980Z msg = Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=378. 2024-08-20T21:40:25.5248417Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.5248470Z 2024-08-20T21:40:25.5249109Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-08-20T21:40:25.5249527Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-08-20T21:40:25.5249550Z 2024-08-20T21:40:25.5249881Z Keyword Args: 2024-08-20T21:40:25.5250193Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-20T21:40:25.5250672Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-08-20T21:40:25.5251188Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-08-20T21:40:25.5251334Z as a placeholder. default: None. 2024-08-20T21:40:25.5251754Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-20T21:40:25.5252406Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-20T21:40:25.5253138Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-08-20T21:40:25.5253428Z input_kwarg_layouts (Dict[str, Placement]): 2024-08-20T21:40:25.5254306Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-08-20T21:40:25.5254432Z default: None 2024-08-20T21:40:25.5254633Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-08-20T21:40:25.5255161Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-20T21:40:25.5255366Z have the desired DTensor layouts. default: None. 2024-08-20T21:40:25.5255501Z use_local_output (bool, optional): 2024-08-20T21:40:25.5256006Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-08-20T21:40:25.5256115Z Returns: 2024-08-20T21:40:25.5256650Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-08-20T21:40:25.5256656Z 2024-08-20T21:40:25.5256788Z Example:: 2024-08-20T21:40:25.5256912Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:40:25.5257327Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-08-20T21:40:25.5257597Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-20T21:40:25.5257690Z >>> ... 2024-08-20T21:40:25.5258114Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-20T21:40:25.5258284Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-20T21:40:25.5258376Z >>> 2024-08-20T21:40:25.5258976Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-08-20T21:40:25.5259187Z >>> # and then redistributed to Replicated DTensor. 2024-08-20T21:40:25.5259303Z >>> parallelize_module( 2024-08-20T21:40:25.5259479Z >>> block, # this can be a submodule or module 2024-08-20T21:40:25.5259593Z >>> tp_mesh, 2024-08-20T21:40:25.5259710Z >>> parallelize_plan={ 2024-08-20T21:40:25.5259873Z >>> "attn": PrepareModuleInput( 2024-08-20T21:40:25.5260057Z >>> input_layouts=(Shard(0), None, None, ...), 2024-08-20T21:40:25.5260277Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-08-20T21:40:25.5260394Z >>> ), 2024-08-20T21:40:25.5260489Z >>> } 2024-08-20T21:40:25.5260582Z >>> ) 2024-08-20T21:40:25.5260587Z 2024-08-20T21:40:25.5261005Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.5261011Z 2024-08-20T21:40:25.5262048Z msg = Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=533. 2024-08-20T21:40:25.5262471Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.5262476Z 2024-08-20T21:40:25.5263123Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-08-20T21:40:25.5263550Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-08-20T21:40:25.5263642Z 2024-08-20T21:40:25.5263746Z Keyword Args: 2024-08-20T21:40:25.5263953Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-20T21:40:25.5264451Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-08-20T21:40:25.5264992Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-08-20T21:40:25.5265179Z ``None`` need to be specified as a placeholder. 2024-08-20T21:40:25.5265438Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-20T21:40:25.5265988Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-08-20T21:40:25.5266128Z have the desired DTensor layouts. 2024-08-20T21:40:25.5266280Z use_local_output (bool, optional): 2024-08-20T21:40:25.5278087Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-08-20T21:40:25.5278283Z Returns: 2024-08-20T21:40:25.5278863Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-08-20T21:40:25.5278888Z 2024-08-20T21:40:25.5278996Z Example:: 2024-08-20T21:40:25.5279126Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:40:25.5279570Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-08-20T21:40:25.5279886Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-20T21:40:25.5279982Z >>> ... 2024-08-20T21:40:25.5280427Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-20T21:40:25.5280585Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-20T21:40:25.5280679Z >>> 2024-08-20T21:40:25.5281254Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-08-20T21:40:25.5281437Z >>> # and then redistributed to Sharded DTensor. 2024-08-20T21:40:25.5281555Z >>> parallelize_module( 2024-08-20T21:40:25.5281747Z >>> block, # this can be a submodule or module 2024-08-20T21:40:25.5281849Z >>> tp_mesh, 2024-08-20T21:40:25.5282042Z >>> parallelize_plan = PrepareModuleOutput( 2024-08-20T21:40:25.5282181Z >>> output_layouts=Replicate(), 2024-08-20T21:40:25.5282333Z >>> desired_output_layouts=Shard(0) 2024-08-20T21:40:25.5282442Z >>> ) 2024-08-20T21:40:25.5282664Z >>> ) 2024-08-20T21:40:25.5282670Z 2024-08-20T21:40:25.5283085Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.5283091Z 2024-08-20T21:40:25.7410932Z msg = Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=230. 2024-08-20T21:40:25.7411573Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.7411975Z Return a new dict with new, potentially nested, key value pair 2024-08-20T21:40:25.7412042Z 2024-08-20T21:40:25.7412450Z >>> purchase = {'name': 'Alice', 2024-08-20T21:40:25.7412933Z ... 'order': {'items': ['Apple', 'Orange'], 2024-08-20T21:40:25.7413343Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:40:25.7413770Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:40:25.7414452Z >>> assoc_in(purchase, ['order', 'costs'], [0.25, 1.00]) # doctest: +SKIP 2024-08-20T21:40:25.7414704Z {'credit card': '5555-1234-1234-1234', 2024-08-20T21:40:25.7414903Z 'name': 'Alice', 2024-08-20T21:40:25.7415440Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-08-20T21:40:25.7415597Z 2024-08-20T21:40:25.7416369Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.7416381Z 2024-08-20T21:40:25.7417755Z msg = Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2024-08-20T21:40:25.7418359Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.7418587Z Update value in a (potentially) nested dictionary 2024-08-20T21:40:25.7418595Z 2024-08-20T21:40:25.7418707Z inputs: 2024-08-20T21:40:25.7419039Z d - dictionary on which to operate 2024-08-20T21:40:25.7419721Z keys - list or tuple giving the location of the value to be changed in d 2024-08-20T21:40:25.7419935Z func - function to operate on that value 2024-08-20T21:40:25.7419942Z 2024-08-20T21:40:25.7420227Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-08-20T21:40:25.7420558Z original dictionary with v replaced by func(v), but does not mutate the 2024-08-20T21:40:25.7420674Z original dictionary. 2024-08-20T21:40:25.7420680Z 2024-08-20T21:40:25.7421018Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-08-20T21:40:25.7421315Z specified by the keys, with the innermost value set to func(default). 2024-08-20T21:40:25.7421320Z 2024-08-20T21:40:25.7421439Z >>> inc = lambda x: x + 1 2024-08-20T21:40:25.7421637Z >>> update_in({'a': 0}, ['a'], inc) 2024-08-20T21:40:25.7421769Z {'a': 1} 2024-08-20T21:40:25.7421775Z 2024-08-20T21:40:25.7421958Z >>> transaction = {'name': 'Alice', 2024-08-20T21:40:25.7422231Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-20T21:40:25.7422465Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:40:25.7422718Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:40:25.7423081Z >>> update_in(transaction, ['purchase', 'costs'], sum) # doctest: +SKIP 2024-08-20T21:40:25.7423269Z {'credit card': '5555-1234-1234-1234', 2024-08-20T21:40:25.7423428Z 'name': 'Alice', 2024-08-20T21:40:25.7423713Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-08-20T21:40:25.7423728Z 2024-08-20T21:40:25.7423886Z >>> # updating a value when k0 is not in d 2024-08-20T21:40:25.7424065Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-08-20T21:40:25.7424214Z {1: {2: {3: 'bar'}}} 2024-08-20T21:40:25.7424423Z >>> update_in({1: 'foo'}, [2, 3, 4], inc, 0) 2024-08-20T21:40:25.7424601Z {1: 'foo', 2: {3: {4: 1}}} 2024-08-20T21:40:25.7424695Z 2024-08-20T21:40:25.7425095Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.7425292Z 2024-08-20T21:40:25.7426365Z msg = Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=303. 2024-08-20T21:40:25.7426786Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.7427029Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-08-20T21:40:25.7427035Z 2024-08-20T21:40:25.7427307Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-08-20T21:40:25.7427573Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-08-20T21:40:25.7427579Z 2024-08-20T21:40:25.7427877Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-08-20T21:40:25.7428042Z structures such as dictionaries and lists. 2024-08-20T21:40:25.7428047Z 2024-08-20T21:40:25.7428249Z >>> transaction = {'name': 'Alice', 2024-08-20T21:40:25.7428508Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-20T21:40:25.7428740Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:40:25.7428993Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:40:25.7429230Z >>> get_in(['purchase', 'items', 0], transaction) 2024-08-20T21:40:25.7429357Z 'Apple' 2024-08-20T21:40:25.7429548Z >>> get_in(['name'], transaction) 2024-08-20T21:40:25.7429672Z 'Alice' 2024-08-20T21:40:25.7429970Z >>> get_in(['purchase', 'total'], transaction) 2024-08-20T21:40:25.7430248Z >>> get_in(['purchase', 'items', 'apple'], transaction) 2024-08-20T21:40:25.7430485Z >>> get_in(['purchase', 'items', 10], transaction) 2024-08-20T21:40:25.7430718Z >>> get_in(['purchase', 'total'], transaction, 0) 2024-08-20T21:40:25.7430827Z 0 2024-08-20T21:40:25.7431014Z >>> get_in(['y'], {}, no_default=True) 2024-08-20T21:40:25.7431154Z Traceback (most recent call last): 2024-08-20T21:40:25.7431267Z ... 2024-08-20T21:40:25.7431406Z KeyError: 'y' 2024-08-20T21:40:25.7431413Z 2024-08-20T21:40:25.7431531Z See Also: 2024-08-20T21:40:25.7431638Z itertoolz.get 2024-08-20T21:40:25.7431750Z operator.getitem 2024-08-20T21:40:25.7431856Z 2024-08-20T21:40:25.7432259Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.7432266Z 2024-08-20T21:40:25.7433287Z msg = Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=355. 2024-08-20T21:40:25.7433725Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:25.7433863Z Group a collection by a key function 2024-08-20T21:40:25.7433868Z 2024-08-20T21:40:25.7434173Z >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank'] 2024-08-20T21:40:25.7434342Z >>> groupby(len, names) # doctest: +SKIP 2024-08-20T21:40:25.7434650Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-08-20T21:40:25.7434656Z 2024-08-20T21:40:25.7434809Z >>> iseven = lambda x: x % 2 == 0 2024-08-20T21:40:25.7435045Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-08-20T21:40:25.7435189Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-08-20T21:40:25.7435194Z 2024-08-20T21:40:25.7435443Z Non-callable keys imply grouping on a member. 2024-08-20T21:40:25.7435449Z 2024-08-20T21:40:25.7435717Z >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'}, 2024-08-20T21:40:25.7435951Z ... {'name': 'Bob', 'gender': 'M'}, 2024-08-20T21:40:25.7436265Z ... {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP 2024-08-20T21:40:25.7436469Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-08-20T21:40:25.7436673Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-08-20T21:40:25.7436880Z {'gender': 'M', 'name': 'Charlie'}]} 2024-08-20T21:40:25.7436887Z 2024-08-20T21:40:25.7437065Z Not to be confused with ``itertools.groupby`` 2024-08-20T21:40:25.7437070Z 2024-08-20T21:40:25.7437245Z See Also: 2024-08-20T21:40:25.7437350Z countby 2024-08-20T21:40:25.7437440Z 2024-08-20T21:40:25.7437863Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:25.7437871Z 2024-08-20T21:40:26.0910771Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601. 2024-08-20T21:40:26.0913239Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.0915168Z Applies Batch Normalization over a N-Dimensional input. 2024-08-20T21:40:26.0915883Z 2024-08-20T21:40:26.0916961Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-08-20T21:40:26.0918024Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-08-20T21:40:26.0918755Z Internal Covariate Shift `__ . 2024-08-20T21:40:26.0919210Z 2024-08-20T21:40:26.0919351Z .. math:: 2024-08-20T21:40:26.0919513Z 2024-08-20T21:40:26.0919952Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-08-20T21:40:26.0920450Z 2024-08-20T21:40:26.0920849Z The mean and standard-deviation are calculated per-dimension over all 2024-08-20T21:40:26.0921705Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-08-20T21:40:26.0922777Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-08-20T21:40:26.0923542Z By default, the elements of :math:`\gamma` are sampled from 2024-08-20T21:40:26.0924232Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-08-20T21:40:26.0925119Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-08-20T21:40:26.0925781Z `torch.var(input, unbiased=False)`. 2024-08-20T21:40:26.0926071Z 2024-08-20T21:40:26.0926398Z Also by default, during training this layer keeps running estimates of its 2024-08-20T21:40:26.0927199Z computed mean and variance, which are then used for normalization during 2024-08-20T21:40:26.0928007Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-08-20T21:40:26.0928589Z of 0.1. 2024-08-20T21:40:26.0928752Z 2024-08-20T21:40:26.0929061Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-08-20T21:40:26.0929840Z keep running estimates, and batch statistics are instead used during 2024-08-20T21:40:26.0930533Z evaluation time as well. 2024-08-20T21:40:26.0930770Z 2024-08-20T21:40:26.0930875Z .. note:: 2024-08-20T21:40:26.0931344Z This :attr:`momentum` argument is different from one used in optimizer 2024-08-20T21:40:26.0932120Z classes and the conventional notion of momentum. Mathematically, the 2024-08-20T21:40:26.0932747Z update rule for running statistics here is 2024-08-20T21:40:26.0933604Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-08-20T21:40:26.0934474Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-08-20T21:40:26.0935039Z new observed value. 2024-08-20T21:40:26.0935279Z 2024-08-20T21:40:26.0935690Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-08-20T21:40:26.0936706Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-08-20T21:40:26.0937512Z Normalization or Spatio-temporal Batch Normalization. 2024-08-20T21:40:26.0937890Z 2024-08-20T21:40:26.0938069Z Currently :class:`SyncBatchNorm` only supports 2024-08-20T21:40:26.0938792Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-08-20T21:40:26.0939622Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-08-20T21:40:26.0940335Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-08-20T21:40:26.0941014Z Network with DDP. 2024-08-20T21:40:26.0941218Z 2024-08-20T21:40:26.0941333Z Args: 2024-08-20T21:40:26.0941690Z num_features: :math:`C` from an expected input of size 2024-08-20T21:40:26.0942194Z :math:`(N, C, +)` 2024-08-20T21:40:26.0942703Z eps: a value added to the denominator for numerical stability. 2024-08-20T21:40:26.0943285Z Default: ``1e-5`` 2024-08-20T21:40:26.0943789Z momentum: the value used for the running_mean and running_var 2024-08-20T21:40:26.0944494Z computation. Can be set to ``None`` for cumulative moving average 2024-08-20T21:40:26.0945103Z (i.e. simple average). Default: 0.1 2024-08-20T21:40:26.0945685Z affine: a boolean value that when set to ``True``, this module has 2024-08-20T21:40:26.0946327Z learnable affine parameters. Default: ``True`` 2024-08-20T21:40:26.0946977Z track_running_stats: a boolean value that when set to ``True``, this 2024-08-20T21:40:26.0947733Z module tracks the running mean and variance, and when set to ``False``, 2024-08-20T21:40:26.0948529Z this module does not track such statistics, and initializes statistics 2024-08-20T21:40:26.0949281Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-08-20T21:40:26.0950032Z When these buffers are ``None``, this module always uses batch statistics. 2024-08-20T21:40:26.0950809Z in both training and eval modes. Default: ``True`` 2024-08-20T21:40:26.0951504Z process_group: synchronization of stats happen within each process group 2024-08-20T21:40:26.0952285Z individually. Default behavior is synchronization across the whole 2024-08-20T21:40:26.0952856Z world 2024-08-20T21:40:26.0953046Z 2024-08-20T21:40:26.0953147Z Shape: 2024-08-20T21:40:26.0953485Z - Input: :math:`(N, C, +)` 2024-08-20T21:40:26.0953986Z - Output: :math:`(N, C, +)` (same shape as input) 2024-08-20T21:40:26.0954345Z 2024-08-20T21:40:26.0954458Z .. note:: 2024-08-20T21:40:26.0954962Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-08-20T21:40:26.0955709Z synchronization is disabled when ``model.eval()`` is set or if 2024-08-20T21:40:26.0956304Z ``self.training`` is otherwise ``False``. 2024-08-20T21:40:26.0956625Z 2024-08-20T21:40:26.0956746Z Examples:: 2024-08-20T21:40:26.0956921Z 2024-08-20T21:40:26.0957041Z >>> # xdoctest: +SKIP 2024-08-20T21:40:26.0957419Z >>> # With Learnable Parameters 2024-08-20T21:40:26.0957845Z >>> m = nn.SyncBatchNorm(100) 2024-08-20T21:40:26.0958269Z >>> # creating process group (optional) 2024-08-20T21:40:26.0958778Z >>> # ranks is a list of int identifying rank ids. 2024-08-20T21:40:26.0959253Z >>> ranks = list(range(8)) 2024-08-20T21:40:26.0959634Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-20T21:40:26.0960123Z >>> # Note: every rank calls into new_group for every 2024-08-20T21:40:26.0960694Z >>> # process group created, even if that rank is not 2024-08-20T21:40:26.0961172Z >>> # part of the group. 2024-08-20T21:40:26.0961754Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-20T21:40:26.0962522Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-20T21:40:26.0963105Z >>> # Without Learnable Parameters 2024-08-20T21:40:26.0963683Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-08-20T21:40:26.0964299Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-08-20T21:40:26.0964746Z >>> output = m(input) 2024-08-20T21:40:26.0964976Z 2024-08-20T21:40:26.0965120Z >>> # network is nn.BatchNorm layer 2024-08-20T21:40:26.0965796Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-08-20T21:40:26.0966557Z >>> # only single gpu per process is currently supported 2024-08-20T21:40:26.0967306Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-08-20T21:40:26.0967943Z >>> sync_bn_network, 2024-08-20T21:40:26.0968440Z >>> device_ids=[args.local_rank], 2024-08-20T21:40:26.0968953Z >>> output_device=args.local_rank) 2024-08-20T21:40:26.0969398Z 2024-08-20T21:40:26.0969956Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.0970523Z 2024-08-20T21:40:26.0971553Z msg = Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=824. 2024-08-20T21:40:26.0972976Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.0973925Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-08-20T21:40:26.0974512Z 2024-08-20T21:40:26.0974632Z Args: 2024-08-20T21:40:26.0975115Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-08-20T21:40:26.0975937Z process_group (optional): process group to scope synchronization, 2024-08-20T21:40:26.0976539Z default is the whole world 2024-08-20T21:40:26.0976837Z 2024-08-20T21:40:26.0976953Z Returns: 2024-08-20T21:40:26.0977469Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-08-20T21:40:26.0978396Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-08-20T21:40:26.0979168Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-08-20T21:40:26.0979725Z instead. 2024-08-20T21:40:26.0979929Z 2024-08-20T21:40:26.0980038Z Example:: 2024-08-20T21:40:26.0980215Z 2024-08-20T21:40:26.0980390Z >>> # Network with nn.BatchNorm layer 2024-08-20T21:40:26.0980898Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:40:26.0981410Z >>> module = torch.nn.Sequential( 2024-08-20T21:40:26.0981880Z >>> torch.nn.Linear(20, 100), 2024-08-20T21:40:26.0982372Z >>> torch.nn.BatchNorm1d(100), 2024-08-20T21:40:26.0982800Z >>> ).cuda() 2024-08-20T21:40:26.0983213Z >>> # creating process group (optional) 2024-08-20T21:40:26.0983783Z >>> # ranks is a list of int identifying rank ids. 2024-08-20T21:40:26.0984260Z >>> ranks = list(range(8)) 2024-08-20T21:40:26.0984682Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-20T21:40:26.0985185Z >>> # Note: every rank calls into new_group for every 2024-08-20T21:40:26.0985751Z >>> # process group created, even if that rank is not 2024-08-20T21:40:26.0986251Z >>> # part of the group. 2024-08-20T21:40:26.0986679Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:26.0987322Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-20T21:40:26.0988096Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-20T21:40:26.0988933Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-08-20T21:40:26.0989493Z 2024-08-20T21:40:26.0989588Z 2024-08-20T21:40:26.0990143Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.0990892Z 2024-08-20T21:40:26.1162691Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60. 2024-08-20T21:40:26.1164808Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.1165351Z 2024-08-20T21:40:26.1165902Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-08-20T21:40:26.1166558Z 2024-08-20T21:40:26.1167242Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-08-20T21:40:26.1168187Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-08-20T21:40:26.1168733Z 2024-08-20T21:40:26.1169190Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-08-20T21:40:26.1170332Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-08-20T21:40:26.1171179Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-08-20T21:40:26.1171579Z 2024-08-20T21:40:26.1171694Z Shape: 2024-08-20T21:40:26.1172306Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-08-20T21:40:26.1173201Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-08-20T21:40:26.1174231Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-08-20T21:40:26.1174939Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-08-20T21:40:26.1175264Z 2024-08-20T21:40:26.1175356Z Args: 2024-08-20T21:40:26.1175745Z dim (Union[int, str]): Dimension to be unflattened 2024-08-20T21:40:26.1176619Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-08-20T21:40:26.1177299Z 2024-08-20T21:40:26.1177398Z Examples: 2024-08-20T21:40:26.1177685Z >>> input = torch.randn(2, 50) 2024-08-20T21:40:26.1178260Z >>> # With tuple of ints 2024-08-20T21:40:26.1178639Z >>> m = nn.Sequential( 2024-08-20T21:40:26.1179002Z >>> nn.Linear(50, 50), 2024-08-20T21:40:26.1179426Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-08-20T21:40:26.1179802Z >>> ) 2024-08-20T21:40:26.1180066Z >>> output = m(input) 2024-08-20T21:40:26.1180456Z >>> output.size() 2024-08-20T21:40:26.1180758Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:40:26.1181164Z >>> # With torch.Size 2024-08-20T21:40:26.1181500Z >>> m = nn.Sequential( 2024-08-20T21:40:26.1181887Z >>> nn.Linear(50, 50), 2024-08-20T21:40:26.1182283Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-08-20T21:40:26.1182766Z >>> ) 2024-08-20T21:40:26.1183017Z >>> output = m(input) 2024-08-20T21:40:26.1183404Z >>> output.size() 2024-08-20T21:40:26.1183722Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:40:26.1184140Z >>> # With namedshape (tuple of tuples) 2024-08-20T21:40:26.1184787Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-08-20T21:40:26.1185578Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-08-20T21:40:26.1186152Z >>> output = unflatten(input) 2024-08-20T21:40:26.1186593Z >>> output.size() 2024-08-20T21:40:26.1186915Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:40:26.1187201Z 2024-08-20T21:40:26.1187603Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.1188164Z 2024-08-20T21:40:26.1507655Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1696. 2024-08-20T21:40:26.1510226Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.1511033Z Creates a criterion that measures the triplet loss given input 2024-08-20T21:40:26.1511704Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-08-20T21:40:26.1512465Z positive, and negative examples, respectively), and a nonnegative, 2024-08-20T21:40:26.1513331Z real-valued function ("distance function") used to compute the relationship 2024-08-20T21:40:26.1514127Z between the anchor and positive example ("positive distance") and the 2024-08-20T21:40:26.1514778Z anchor and negative example ("negative distance"). 2024-08-20T21:40:26.1515158Z 2024-08-20T21:40:26.1515511Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-08-20T21:40:26.1516080Z can be described as: 2024-08-20T21:40:26.1516295Z 2024-08-20T21:40:26.1516704Z .. math:: 2024-08-20T21:40:26.1517061Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-08-20T21:40:26.1517723Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-08-20T21:40:26.1518113Z 2024-08-20T21:40:26.1518569Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-08-20T21:40:26.1519471Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-08-20T21:40:26.1520384Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-08-20T21:40:26.1521230Z between the positive and negative distances that is required for the loss to 2024-08-20T21:40:26.1522062Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-08-20T21:40:26.1522689Z that the distance function can handle. 2024-08-20T21:40:26.1523009Z 2024-08-20T21:40:26.1523214Z If :attr:`reduction` is not ``'none'`` 2024-08-20T21:40:26.1523687Z (default ``'mean'``), then: 2024-08-20T21:40:26.1523935Z 2024-08-20T21:40:26.1524039Z .. math:: 2024-08-20T21:40:26.1524311Z \ell(x, y) = 2024-08-20T21:40:26.1524611Z \begin{cases} 2024-08-20T21:40:26.1525153Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-08-20T21:40:26.1525936Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-08-20T21:40:26.1526580Z \end{cases} 2024-08-20T21:40:26.1526766Z 2024-08-20T21:40:26.1527089Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-08-20T21:40:26.1527929Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-08-20T21:40:26.1528440Z 2024-08-20T21:40:26.1528554Z Args: 2024-08-20T21:40:26.1529138Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-08-20T21:40:26.1529923Z quantifies the closeness of two tensors. If not specified, 2024-08-20T21:40:26.1530686Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-08-20T21:40:26.1531448Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-08-20T21:40:26.1532344Z between the positive and negative distances required for the loss to be 0. Larger 2024-08-20T21:40:26.1533278Z margins penalize cases where the negative examples are not distant enough from the 2024-08-20T21:40:26.1534058Z anchors, relative to the positives. Default: :math:`1`. 2024-08-20T21:40:26.1534779Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-08-20T21:40:26.1535633Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-08-20T21:40:26.1536495Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-08-20T21:40:26.1537392Z negative example than the anchor is, swaps the positive example and the anchor in 2024-08-20T21:40:26.1538102Z the loss computation. Default: ``False``. 2024-08-20T21:40:26.1538817Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-08-20T21:40:26.1539718Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-08-20T21:40:26.1540479Z ``'mean'``: the sum of the output will be divided by the number of 2024-08-20T21:40:26.1541340Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-08-20T21:40:26.1541842Z 2024-08-20T21:40:26.1541847Z 2024-08-20T21:40:26.1541959Z Shape: 2024-08-20T21:40:26.1542530Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-08-20T21:40:26.1543203Z as supported by the distance function. 2024-08-20T21:40:26.1543977Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-08-20T21:40:26.1544645Z otherwise. 2024-08-20T21:40:26.1544908Z 2024-08-20T21:40:26.1545019Z Examples:: 2024-08-20T21:40:26.1545201Z 2024-08-20T21:40:26.1545325Z >>> # Initialize embeddings 2024-08-20T21:40:26.1545733Z >>> embedding = nn.Embedding(1000, 128) 2024-08-20T21:40:26.1546193Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:40:26.1546694Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:40:26.1547195Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:40:26.1547658Z >>> anchor = embedding(anchor_ids) 2024-08-20T21:40:26.1548094Z >>> positive = embedding(positive_ids) 2024-08-20T21:40:26.1548548Z >>> negative = embedding(negative_ids) 2024-08-20T21:40:26.1548935Z >>> 2024-08-20T21:40:26.1549256Z >>> # Built-in Distance Function 2024-08-20T21:40:26.1549654Z >>> triplet_loss = \ 2024-08-20T21:40:26.1550213Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-08-20T21:40:26.1550941Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:40:26.1551426Z >>> output.backward() 2024-08-20T21:40:26.1551730Z >>> 2024-08-20T21:40:26.1552002Z >>> # Custom Distance Function 2024-08-20T21:40:26.1552389Z >>> def l_infinity(x1, x2): 2024-08-20T21:40:26.1552900Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-08-20T21:40:26.1553370Z >>> 2024-08-20T21:40:26.1553773Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-08-20T21:40:26.1554373Z >>> triplet_loss = ( 2024-08-20T21:40:26.1554951Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-08-20T21:40:26.1555693Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:40:26.1556162Z >>> output.backward() 2024-08-20T21:40:26.1556484Z >>> 2024-08-20T21:40:26.1556774Z >>> # Custom Distance Function (Lambda) 2024-08-20T21:40:26.1557183Z >>> triplet_loss = ( 2024-08-20T21:40:26.1557570Z >>> nn.TripletMarginWithDistanceLoss( 2024-08-20T21:40:26.1558266Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-08-20T21:40:26.1558896Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:40:26.1559375Z >>> output.backward() 2024-08-20T21:40:26.1559603Z 2024-08-20T21:40:26.1559708Z Reference: 2024-08-20T21:40:26.1560295Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-08-20T21:40:26.1561117Z http://www.bmva.org/bmvc/2016/papers/paper119/index.html 2024-08-20T21:40:26.1561616Z 2024-08-20T21:40:26.1562160Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-08-20T21:40:26.1562650Z 2024-08-20T21:40:26.2086074Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=395. 2024-08-20T21:40:26.2088542Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.2089344Z Computes a partial inverse of :class:`MaxPool2d`. 2024-08-20T21:40:26.2089694Z 2024-08-20T21:40:26.2090212Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-08-20T21:40:26.2090936Z 2024-08-20T21:40:26.2091254Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-08-20T21:40:26.2092050Z including the indices of the maximal values and computes a partial inverse 2024-08-20T21:40:26.2092824Z in which all non-maximal values are set to zero. 2024-08-20T21:40:26.2093172Z 2024-08-20T21:40:26.2093285Z Note: 2024-08-20T21:40:26.2093837Z This operation may behave nondeterministically when the input indices has repeat values. 2024-08-20T21:40:26.2094938Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-08-20T21:40:26.2095597Z 2024-08-20T21:40:26.2095940Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-08-20T21:40:26.2096891Z sizes. Hence, the inversion process can get ambiguous. 2024-08-20T21:40:26.2097549Z To accommodate this, you can provide the needed output size 2024-08-20T21:40:26.2098269Z as an additional argument :attr:`output_size` in the forward call. 2024-08-20T21:40:26.2098891Z See the Inputs and Example below. 2024-08-20T21:40:26.2099205Z 2024-08-20T21:40:26.2099307Z Args: 2024-08-20T21:40:26.2099712Z kernel_size (int or tuple): Size of the max pooling window. 2024-08-20T21:40:26.2100354Z stride (int or tuple): Stride of the max pooling window. 2024-08-20T21:40:26.2100918Z It is set to :attr:`kernel_size` by default. 2024-08-20T21:40:26.2101518Z padding (int or tuple): Padding that was added to the input 2024-08-20T21:40:26.2101930Z 2024-08-20T21:40:26.2102045Z Inputs: 2024-08-20T21:40:26.2102406Z - `input`: the input Tensor to invert 2024-08-20T21:40:26.2103075Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-08-20T21:40:26.2103789Z - `output_size` (optional): the targeted output size 2024-08-20T21:40:26.2104156Z 2024-08-20T21:40:26.2104268Z Shape: 2024-08-20T21:40:26.2104751Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-08-20T21:40:26.2105577Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-08-20T21:40:26.2106598Z 2024-08-20T21:40:26.2106720Z .. math:: 2024-08-20T21:40:26.2107421Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-08-20T21:40:26.2108010Z 2024-08-20T21:40:26.2108114Z .. math:: 2024-08-20T21:40:26.2108812Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-08-20T21:40:26.2109387Z 2024-08-20T21:40:26.2109627Z or as given by :attr:`output_size` in the call operator 2024-08-20T21:40:26.2110013Z 2024-08-20T21:40:26.2110122Z Example:: 2024-08-20T21:40:26.2110300Z 2024-08-20T21:40:26.2110518Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-08-20T21:40:26.2111067Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-08-20T21:40:26.2111553Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-08-20T21:40:26.2112049Z [ 5., 6., 7., 8.], 2024-08-20T21:40:26.2112523Z [ 9., 10., 11., 12.], 2024-08-20T21:40:26.2112978Z [13., 14., 15., 16.]]]]) 2024-08-20T21:40:26.2113439Z >>> output, indices = pool(input) 2024-08-20T21:40:26.2113863Z >>> unpool(output, indices) 2024-08-20T21:40:26.2114248Z tensor([[[[ 0., 0., 0., 0.], 2024-08-20T21:40:26.2114666Z [ 0., 6., 0., 8.], 2024-08-20T21:40:26.2115074Z [ 0., 0., 0., 0.], 2024-08-20T21:40:26.2115478Z [ 0., 14., 0., 16.]]]]) 2024-08-20T21:40:26.2116068Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-08-20T21:40:26.2116724Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-08-20T21:40:26.2117223Z [ 6., 7., 8., 9., 10.], 2024-08-20T21:40:26.2117706Z [11., 12., 13., 14., 15.], 2024-08-20T21:40:26.2118193Z [16., 17., 18., 19., 20.]]]]) 2024-08-20T21:40:26.2118667Z >>> output, indices = pool(input) 2024-08-20T21:40:26.2119188Z >>> # This call will not work without specifying output_size 2024-08-20T21:40:26.2119793Z >>> unpool(output, indices, output_size=input.size()) 2024-08-20T21:40:26.2120307Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-08-20T21:40:26.2120725Z [ 0., 7., 0., 9., 0.], 2024-08-20T21:40:26.2121149Z [ 0., 0., 0., 0., 0.], 2024-08-20T21:40:26.2121663Z [ 0., 17., 0., 19., 0.]]]]) 2024-08-20T21:40:26.2121959Z 2024-08-20T21:40:26.2121964Z 2024-08-20T21:40:26.2122058Z 2024-08-20T21:40:26.2122625Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.2123114Z 2024-08-20T21:40:26.2371637Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=270. 2024-08-20T21:40:26.2373900Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.2375699Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-08-20T21:40:26.2376395Z 2024-08-20T21:40:26.2376893Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-08-20T21:40:26.2377706Z and with 2D inputs, this class 2024-08-20T21:40:26.2378023Z 2024-08-20T21:40:26.2378496Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-08-20T21:40:26.2379650Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-08-20T21:40:26.2380749Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-08-20T21:40:26.2381411Z 2024-08-20T21:40:26.2381960Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-08-20T21:40:26.2383036Z operations. 2024-08-20T21:40:26.2383209Z 2024-08-20T21:40:26.2383696Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-08-20T21:40:26.2384597Z pass. This scales the output of the Embedding before performing a weighted 2024-08-20T21:40:26.2385502Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-08-20T21:40:26.2386448Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-08-20T21:40:26.2387088Z :attr:`per_sample_weights`. 2024-08-20T21:40:26.2387407Z 2024-08-20T21:40:26.2387503Z Args: 2024-08-20T21:40:26.2387927Z num_embeddings (int): size of the dictionary of embeddings 2024-08-20T21:40:26.2388564Z embedding_dim (int): the size of each embedding vector 2024-08-20T21:40:26.2389483Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-08-20T21:40:26.2390531Z is renormalized to have norm :attr:`max_norm`. 2024-08-20T21:40:26.2391581Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-08-20T21:40:26.2392822Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-08-20T21:40:26.2393839Z the words in the mini-batch. Default ``False``. 2024-08-20T21:40:26.2394558Z Note: this option is not supported when ``mode="max"``. 2024-08-20T21:40:26.2395409Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-08-20T21:40:26.2396305Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-08-20T21:40:26.2397142Z into consideration. ``"mean"`` computes the average of the values 2024-08-20T21:40:26.2397923Z in the bag, ``"max"`` computes the max value over each bag. 2024-08-20T21:40:26.2398554Z Default: ``"mean"`` 2024-08-20T21:40:26.2399391Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-08-20T21:40:26.2400474Z Notes for more details regarding sparse gradients. Note: this option is not 2024-08-20T21:40:26.2401222Z supported when ``mode="max"``. 2024-08-20T21:40:26.2402272Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-08-20T21:40:26.2403386Z is equivalent to the size of `indices`. This matches the CSR format. 2024-08-20T21:40:26.2404389Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-08-20T21:40:26.2405460Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-08-20T21:40:26.2406471Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-08-20T21:40:26.2407398Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-08-20T21:40:26.2408281Z zeros, but can be updated to another value to be used as the padding vector. 2024-08-20T21:40:26.2409216Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-08-20T21:40:26.2409866Z reduction. 2024-08-20T21:40:26.2410238Z 2024-08-20T21:40:26.2410345Z Attributes: 2024-08-20T21:40:26.2411019Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-08-20T21:40:26.2411933Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-08-20T21:40:26.2412298Z 2024-08-20T21:40:26.2412429Z Examples:: 2024-08-20T21:40:26.2412614Z 2024-08-20T21:40:26.2412847Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-08-20T21:40:26.2413558Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-08-20T21:40:26.2414110Z >>> # a batch of 2 samples of 4 indices each 2024-08-20T21:40:26.2414750Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-08-20T21:40:26.2415388Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-20T21:40:26.2416014Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:40:26.2416531Z >>> embedding_sum(input, offsets) 2024-08-20T21:40:26.2417023Z tensor([[-0.8861, -5.4350, -0.0523], 2024-08-20T21:40:26.2417511Z [ 1.1306, -2.5798, -1.0044]]) 2024-08-20T21:40:26.2417801Z 2024-08-20T21:40:26.2417934Z >>> # Example with padding_idx 2024-08-20T21:40:26.2418587Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-08-20T21:40:26.2419302Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-08-20T21:40:26.2419917Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-20T21:40:26.2420421Z >>> embedding_sum(input, offsets) 2024-08-20T21:40:26.2420922Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-08-20T21:40:26.2421390Z [-0.7082, 3.2145, -2.6251]]) 2024-08-20T21:40:26.2421758Z 2024-08-20T21:40:26.2421999Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-08-20T21:40:26.2422605Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-08-20T21:40:26.2423200Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-08-20T21:40:26.2423679Z embedding.weight, 2024-08-20T21:40:26.2424104Z padding_idx=embedding.padding_idx, 2024-08-20T21:40:26.2424599Z mode='sum') 2024-08-20T21:40:26.2424907Z 2024-08-20T21:40:26.2425450Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.2425936Z 2024-08-20T21:40:26.2729190Z msg = Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1748. 2024-08-20T21:40:26.2731510Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.2732120Z 2024-08-20T21:40:26.2732818Z Context manager for training with uneven inputs across processes in DDP. 2024-08-20T21:40:26.2733423Z 2024-08-20T21:40:26.2734139Z This context manager will keep track of already-joined DDP processes, 2024-08-20T21:40:26.2735277Z and "shadow" the forward and backward passes by inserting collective 2024-08-20T21:40:26.2736102Z communication operations to match with the ones created by non-joined 2024-08-20T21:40:26.2736857Z DDP processes. This will ensure each collective call has a corresponding 2024-08-20T21:40:26.2737701Z call by already-joined DDP processes, preventing hangs or errors that 2024-08-20T21:40:26.2738416Z would otherwise happen when training with uneven inputs across 2024-08-20T21:40:26.2739124Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-08-20T21:40:26.2739894Z specified to be ``True``, all trainers will throw an error once one rank 2024-08-20T21:40:26.2740627Z runs out of inputs, allowing these errors to be caught and handled 2024-08-20T21:40:26.2741181Z according to application logic. 2024-08-20T21:40:26.2741459Z 2024-08-20T21:40:26.2741754Z Once all DDP processes have joined, the context manager will broadcast 2024-08-20T21:40:26.2742526Z the model corresponding to the last joined process to all processes to 2024-08-20T21:40:26.2743177Z ensure the model is the same across all processes 2024-08-20T21:40:26.2743637Z (which is guaranteed by DDP). 2024-08-20T21:40:26.2743893Z 2024-08-20T21:40:26.2744170Z To use this to enable training with uneven inputs across processes, 2024-08-20T21:40:26.2745056Z simply wrap this context manager around your training loop. No further 2024-08-20T21:40:26.2745725Z modifications to the model or data loading is required. 2024-08-20T21:40:26.2746118Z 2024-08-20T21:40:26.2746241Z .. warning:: 2024-08-20T21:40:26.2746705Z If the model or training loop this context manager is wrapped around 2024-08-20T21:40:26.2747395Z has additional distributed collective operations, such as 2024-08-20T21:40:26.2748109Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-08-20T21:40:26.2748787Z ``throw_on_early_termination`` must be enabled. This is because this 2024-08-20T21:40:26.2749552Z context manager is not aware of non-DDP collective communication. 2024-08-20T21:40:26.2750220Z This flag will cause all ranks to throw when any one rank 2024-08-20T21:40:26.2750887Z exhausts inputs, allowing these errors to be caught and recovered 2024-08-20T21:40:26.2751453Z from across all ranks. 2024-08-20T21:40:26.2751672Z 2024-08-20T21:40:26.2751763Z Args: 2024-08-20T21:40:26.2752145Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-08-20T21:40:26.2752825Z gradients by the initial ``world_size`` DDP training was launched 2024-08-20T21:40:26.2753479Z with. If ``False``, will compute the effective world size 2024-08-20T21:40:26.2754120Z (number of ranks that have not depleted their inputs yet) and 2024-08-20T21:40:26.2754729Z divide gradients by that during allreduce. Set 2024-08-20T21:40:26.2755308Z ``divide_by_initial_world_size=True`` to ensure every input 2024-08-20T21:40:26.2755989Z sample including the uneven inputs have equal weight in terms of 2024-08-20T21:40:26.2756662Z how much they contribute to the global gradient. This is 2024-08-20T21:40:26.2757269Z achieved by always dividing the gradient by the initial 2024-08-20T21:40:26.2757945Z ``world_size`` even when we encounter uneven inputs. If you set 2024-08-20T21:40:26.2758933Z this to ``False``, we divide the gradient by the remaining 2024-08-20T21:40:26.2759984Z number of nodes. This ensures parity with training on a smaller 2024-08-20T21:40:26.2761086Z ``world_size`` although it also means the uneven inputs would 2024-08-20T21:40:26.2762054Z contribute more towards the global gradient. Typically, you 2024-08-20T21:40:26.2762806Z would want to set this to ``True`` for cases where the last few 2024-08-20T21:40:26.2763710Z inputs of your training job are uneven. In extreme cases, where 2024-08-20T21:40:26.2764544Z there is a large discrepancy in the number of inputs, setting 2024-08-20T21:40:26.2765165Z this to ``False`` might provide better results. 2024-08-20T21:40:26.2765843Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-08-20T21:40:26.2766548Z in ``enable=False`` to disable in cases where you know that 2024-08-20T21:40:26.2767212Z inputs are even across participating processes. Default is 2024-08-20T21:40:26.2767713Z ``True``. 2024-08-20T21:40:26.2768188Z throw_on_early_termination (bool): Whether to throw an error 2024-08-20T21:40:26.2768832Z or continue training when at least one rank has exhausted 2024-08-20T21:40:26.2769482Z inputs. If ``True``, will throw upon the first rank reaching end 2024-08-20T21:40:26.2770239Z of data. If ``False``, will continue training with a smaller 2024-08-20T21:40:26.2770917Z effective world size until all ranks are joined. Note that if 2024-08-20T21:40:26.2771502Z this flag is specified, then the flag 2024-08-20T21:40:26.2772094Z ``divide_by_initial_world_size`` would be ignored. Default 2024-08-20T21:40:26.2772597Z is ``False``. 2024-08-20T21:40:26.2772791Z 2024-08-20T21:40:26.2772796Z 2024-08-20T21:40:26.2772916Z Example:: 2024-08-20T21:40:26.2773063Z 2024-08-20T21:40:26.2773203Z >>> # xdoctest: +SKIP("Distributed") 2024-08-20T21:40:26.2773692Z >>> import torch 2024-08-20T21:40:26.2774036Z >>> import torch.distributed as dist 2024-08-20T21:40:26.2774429Z >>> import os 2024-08-20T21:40:26.2774767Z >>> import torch.multiprocessing as mp 2024-08-20T21:40:26.2775209Z >>> import torch.nn as nn 2024-08-20T21:40:26.2775562Z >>> # On each spawned worker 2024-08-20T21:40:26.2775934Z >>> def worker(rank): 2024-08-20T21:40:26.2776387Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-08-20T21:40:26.2776910Z >>> torch.cuda.set_device(rank) 2024-08-20T21:40:26.2777378Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-08-20T21:40:26.2777959Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-08-20T21:40:26.2778539Z >>> model, device_ids=[rank], output_device=rank 2024-08-20T21:40:26.2779048Z >>> ) 2024-08-20T21:40:26.2779387Z >>> # Rank 1 gets one more input than rank 0. 2024-08-20T21:40:26.2779962Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-08-20T21:40:26.2780511Z >>> with model.join(): 2024-08-20T21:40:26.2780873Z >>> for _ in range(5): 2024-08-20T21:40:26.2781244Z >>> for inp in inputs: 2024-08-20T21:40:26.2781673Z >>> loss = model(inp).sum() 2024-08-20T21:40:26.2782111Z >>> loss.backward() 2024-08-20T21:40:26.2782665Z >>> # Without the join() API, the below synchronization will hang 2024-08-20T21:40:26.2783419Z >>> # blocking for rank 1's allreduce to complete. 2024-08-20T21:40:26.2783939Z >>> torch.cuda.synchronize(device=rank) 2024-08-20T21:40:26.2784257Z 2024-08-20T21:40:26.2784674Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.2785161Z 2024-08-20T21:40:26.2786316Z msg = Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2039. 2024-08-20T21:40:26.2787818Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.2788339Z 2024-08-20T21:40:26.2788757Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-08-20T21:40:26.2789338Z 2024-08-20T21:40:26.2789626Z Registers an optimizer with DDP such that the optimization for a 2024-08-20T21:40:26.2790577Z parameter will run immediately when that parameter's gradient is 2024-08-20T21:40:26.2791427Z finished with reduction, instead of waiting for all parameters' 2024-08-20T21:40:26.2792225Z gradients to finish reduction. This can result in a training speedup 2024-08-20T21:40:26.2792983Z depending on your workload since the optimizer can run while gradient 2024-08-20T21:40:26.2793741Z reduction for other parameters are still ongoing. In addition, this has 2024-08-20T21:40:26.2794521Z the potential to reduce peak memory consumption during training, as it 2024-08-20T21:40:26.2795349Z only needs to load the per-parameter optimizer states of a single 2024-08-20T21:40:26.2796121Z parameter at a time, instead of loading all per-parameter optimizer 2024-08-20T21:40:26.2796678Z states at once. 2024-08-20T21:40:26.2796853Z 2024-08-20T21:40:26.2796957Z Args: 2024-08-20T21:40:26.2797353Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-08-20T21:40:26.2797909Z as a fused optimizer. 2024-08-20T21:40:26.2798344Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-08-20T21:40:26.2798988Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-08-20T21:40:26.2799731Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-08-20T21:40:26.2800464Z Optimizers. If this is omitted, all DDP model parameters will be 2024-08-20T21:40:26.2800990Z optimized. 2024-08-20T21:40:26.2801431Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-08-20T21:40:26.2801864Z 2024-08-20T21:40:26.2802088Z .. warning :: 2024-08-20T21:40:26.2802525Z _register_fused_optim should only be called once on a DDP instance, 2024-08-20T21:40:26.2803251Z and registering multiple fused optimizers for the same DDP model 2024-08-20T21:40:26.2803853Z is not currently supported. Please ping 2024-08-20T21:40:26.2804486Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:40:26.2805050Z for your use case. 2024-08-20T21:40:26.2805264Z 2024-08-20T21:40:26.2805364Z .. warning :: 2024-08-20T21:40:26.2805786Z _register_fused_optim and register_comm_hook currently do not 2024-08-20T21:40:26.2806463Z compose together, meaning that custom DDP communication hooks are 2024-08-20T21:40:26.2807124Z not supported with overlapped optimizers. Please ping 2024-08-20T21:40:26.2807801Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:40:26.2808362Z for your use case. 2024-08-20T21:40:26.2808574Z 2024-08-20T21:40:26.2808676Z .. warning :: 2024-08-20T21:40:26.2809145Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-08-20T21:40:26.2809738Z with overlapped optimizer. Please ping 2024-08-20T21:40:26.2810423Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:40:26.2810534Z for your use case. 2024-08-20T21:40:26.2810545Z 2024-08-20T21:40:26.2810664Z Example:: 2024-08-20T21:40:26.2810669Z 2024-08-20T21:40:26.2810840Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-08-20T21:40:26.2811348Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-08-20T21:40:26.2811619Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-08-20T21:40:26.2811738Z >>> lr = 1e-2 2024-08-20T21:40:26.2811863Z >>> betas = (0.9, 0.99) 2024-08-20T21:40:26.2811982Z >>> eps = 1e-6 2024-08-20T21:40:26.2812279Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-08-20T21:40:26.2812446Z >>> # Example with subset of parameters 2024-08-20T21:40:26.2812620Z >>> params_to_opt = [list(net.parameters())[0]] 2024-08-20T21:40:26.2812742Z >>> net._register_fused_optim( 2024-08-20T21:40:26.2813068Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-08-20T21:40:26.2813161Z ... ) 2024-08-20T21:40:26.2813167Z 2024-08-20T21:40:26.2813582Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.2813588Z 2024-08-20T21:40:26.3009089Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=6. 2024-08-20T21:40:26.3009526Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.3009825Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-08-20T21:40:26.3009869Z 2024-08-20T21:40:26.3010283Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-20T21:40:26.3010695Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-20T21:40:26.3011050Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-20T21:40:26.3011472Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-20T21:40:26.3011494Z 2024-08-20T21:40:26.3011606Z .. note:: 2024-08-20T21:40:26.3011917Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-08-20T21:40:26.3012237Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-08-20T21:40:26.3012533Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-20T21:40:26.3012823Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-20T21:40:26.3013151Z One place we are confident in is that NHWC(channels_last) conversion for 2024-08-20T21:40:26.3013595Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2024-08-20T21:40:26.3013912Z even in cases where we have to apply permutation to input tensors. 2024-08-20T21:40:26.3013934Z 2024-08-20T21:40:26.3014303Z Hence our strategy here is to convert only the weight of convolution to 2024-08-20T21:40:26.3014480Z channels_last. This ensures that; 2024-08-20T21:40:26.3014959Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-20T21:40:26.3015436Z outweigh overhead of permutation (if input is not in the same format). 2024-08-20T21:40:26.3015750Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-20T21:40:26.3015899Z from memory_format conversion. 2024-08-20T21:40:26.3015905Z 2024-08-20T21:40:26.3016215Z The optimal case is that, layers between convolution layers are channels 2024-08-20T21:40:26.3016531Z last compatible. Input tensor would be permuted to channels last when it 2024-08-20T21:40:26.3016856Z encounters the first convolution layer and stay in that memory format. 2024-08-20T21:40:26.3017168Z Hence following convolutions will not need to permute its input tensor. 2024-08-20T21:40:26.3017173Z 2024-08-20T21:40:26.3017496Z In case where a channels last incompatible layer is between convolution 2024-08-20T21:40:26.3017789Z layers, we need to permute the input tensor back to contiguous format 2024-08-20T21:40:26.3018097Z for that layer. The input tensor will go through the remaining layers in 2024-08-20T21:40:26.3018418Z contiguous format and be permuted to channels last when it encounters 2024-08-20T21:40:26.3018786Z another convolution layer. There's no point in propagating that 2024-08-20T21:40:26.3019084Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-20T21:40:26.3019211Z ``memory_format``. 2024-08-20T21:40:26.3019216Z 2024-08-20T21:40:26.3019527Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-20T21:40:26.3019851Z there might have been a better spot to fuse the permutation other than 2024-08-20T21:40:26.3019995Z immediately before a convolution. 2024-08-20T21:40:26.3020000Z 2024-08-20T21:40:26.3020096Z Args: 2024-08-20T21:40:26.3020410Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-08-20T21:40:26.3020534Z ``nn.Module`` 2024-08-20T21:40:26.3020725Z memory_format: user specified ``memory_format``, 2024-08-20T21:40:26.3021070Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-20T21:40:26.3021076Z 2024-08-20T21:40:26.3021176Z Returns: 2024-08-20T21:40:26.3021362Z The original module with updated ``nn.Conv2d`` 2024-08-20T21:40:26.3021381Z 2024-08-20T21:40:26.3021479Z Example: 2024-08-20T21:40:26.3021665Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:40:26.3021888Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-20T21:40:26.3022214Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-08-20T21:40:26.3022339Z >>> model = nn.Sequential( 2024-08-20T21:40:26.3022507Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-08-20T21:40:26.3022632Z >>> # This is identical to: 2024-08-20T21:40:26.3023006Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-20T21:40:26.3023375Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-20T21:40:26.3023491Z >>> out = model(input) 2024-08-20T21:40:26.3023580Z 2024-08-20T21:40:26.3023996Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.3024002Z 2024-08-20T21:40:26.3025010Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=81. 2024-08-20T21:40:26.3025522Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.3025796Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-08-20T21:40:26.3026173Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-20T21:40:26.3026555Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-20T21:40:26.3026914Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-20T21:40:26.3027355Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-20T21:40:26.3027361Z 2024-08-20T21:40:26.3027466Z .. note:: 2024-08-20T21:40:26.3027796Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2024-08-20T21:40:26.3028110Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-08-20T21:40:26.3028411Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-20T21:40:26.3028721Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-20T21:40:26.3029050Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2024-08-20T21:40:26.3029350Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2024-08-20T21:40:26.3029647Z even in cases where we have to apply permutation to input tensors. 2024-08-20T21:40:26.3029652Z 2024-08-20T21:40:26.3029961Z Hence our strategy here is to convert only the weight of convolution to 2024-08-20T21:40:26.3030110Z channels_last_3d. This ensures that; 2024-08-20T21:40:26.3030413Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-20T21:40:26.3030721Z outweigh overhead of permutation (if input is not in the same format). 2024-08-20T21:40:26.3031049Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-20T21:40:26.3031187Z from memory_format conversion. 2024-08-20T21:40:26.3031193Z 2024-08-20T21:40:26.3031506Z The optimal case is that, layers between convolution layers are channels 2024-08-20T21:40:26.3031834Z last compatible. Input tensor would be permuted to channels last when it 2024-08-20T21:40:26.3032137Z encounters the first convolution layer and stay in that memory format. 2024-08-20T21:40:26.3032449Z Hence following convolutions will not need to permute its input tensor. 2024-08-20T21:40:26.3032536Z 2024-08-20T21:40:26.3032862Z In case where a channels last incompatible layer is between convolution 2024-08-20T21:40:26.3033158Z layers, we need to permute the input tensor back to contiguous format 2024-08-20T21:40:26.3033481Z for that layer. The input tensor will go through the remaining layers in 2024-08-20T21:40:26.3033782Z contiguous format and be permuted to channels last when it encounters 2024-08-20T21:40:26.3034133Z another convolution layer. There's no point in propagating that 2024-08-20T21:40:26.3034446Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-20T21:40:26.3034558Z ``memory_format``. 2024-08-20T21:40:26.3034564Z 2024-08-20T21:40:26.3034875Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-20T21:40:26.3035200Z there might have been a better spot to fuse the permutation other than 2024-08-20T21:40:26.3035346Z immediately before a convolution. 2024-08-20T21:40:26.3035351Z 2024-08-20T21:40:26.3035463Z Args: 2024-08-20T21:40:26.3035756Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-08-20T21:40:26.3035879Z ``nn.Module`` 2024-08-20T21:40:26.3036087Z memory_format: user specified ``memory_format``, 2024-08-20T21:40:26.3036329Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-20T21:40:26.3036422Z 2024-08-20T21:40:26.3036521Z Returns: 2024-08-20T21:40:26.3036720Z The original module with updated ``nn.Conv3d`` 2024-08-20T21:40:26.3036726Z 2024-08-20T21:40:26.3036824Z Example: 2024-08-20T21:40:26.3037006Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:40:26.3037221Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-20T21:40:26.3037555Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-08-20T21:40:26.3037700Z >>> model = nn.Sequential( 2024-08-20T21:40:26.3037847Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-08-20T21:40:26.3037972Z >>> # This is identical to: 2024-08-20T21:40:26.3038319Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-20T21:40:26.3038683Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-20T21:40:26.3038804Z >>> out = model(input) 2024-08-20T21:40:26.3038910Z 2024-08-20T21:40:26.3039309Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.3039315Z 2024-08-20T21:40:26.3234892Z msg = Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=936. 2024-08-20T21:40:26.3235313Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.3235640Z Prune tensor by removing random channels along the specified dimension. 2024-08-20T21:40:26.3235654Z 2024-08-20T21:40:26.3235982Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-20T21:40:26.3236280Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-20T21:40:26.3236477Z along the specified ``dim`` selected at random. 2024-08-20T21:40:26.3236735Z Modifies module in place (and also return the modified module) 2024-08-20T21:40:26.3236836Z by: 2024-08-20T21:40:26.3236842Z 2024-08-20T21:40:26.3237220Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:40:26.3237513Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:40:26.3237850Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:40:26.3238299Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:40:26.3238455Z ``name+'_orig'``. 2024-08-20T21:40:26.3238461Z 2024-08-20T21:40:26.3238572Z Args: 2024-08-20T21:40:26.3239077Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:40:26.3239496Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:40:26.3239706Z will act. 2024-08-20T21:40:26.3240142Z amount (int or float): quantity of parameters to prune. 2024-08-20T21:40:26.3240455Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:40:26.3240743Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:40:26.3240911Z absolute number of parameters to prune. 2024-08-20T21:40:26.3241194Z dim (int): index of the dim along which we define channels to prune. 2024-08-20T21:40:26.3241199Z 2024-08-20T21:40:26.3241312Z Returns: 2024-08-20T21:40:26.3241613Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:40:26.3241617Z 2024-08-20T21:40:26.3241731Z Examples: 2024-08-20T21:40:26.3241850Z >>> # xdoctest: +SKIP 2024-08-20T21:40:26.3241989Z >>> m = prune.random_structured( 2024-08-20T21:40:26.3242265Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-08-20T21:40:26.3242360Z ... ) 2024-08-20T21:40:26.3242598Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-08-20T21:40:26.3242733Z >>> print(columns_pruned) 2024-08-20T21:40:26.3242826Z 3 2024-08-20T21:40:26.3243013Z 2024-08-20T21:40:26.3243435Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.3243440Z 2024-08-20T21:40:26.3244277Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=977. 2024-08-20T21:40:26.3244881Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.3245493Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-08-20T21:40:26.3245500Z 2024-08-20T21:40:26.3246017Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-20T21:40:26.3246431Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-20T21:40:26.3246740Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-08-20T21:40:26.3247026Z Modifies module in place (and also return the modified module) 2024-08-20T21:40:26.3247160Z by: 2024-08-20T21:40:26.3247172Z 2024-08-20T21:40:26.3247536Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:40:26.3247909Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:40:26.3248197Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:40:26.3248467Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:40:26.3248685Z ``name+'_orig'``. 2024-08-20T21:40:26.3248691Z 2024-08-20T21:40:26.3248786Z Args: 2024-08-20T21:40:26.3249028Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:40:26.3249290Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:40:26.3249450Z will act. 2024-08-20T21:40:26.3249692Z amount (int or float): quantity of parameters to prune. 2024-08-20T21:40:26.3249931Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:40:26.3250336Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:40:26.3250525Z absolute number of parameters to prune. 2024-08-20T21:40:26.3251017Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-08-20T21:40:26.3251292Z entries for argument ``p`` in :func:`torch.norm`. 2024-08-20T21:40:26.3251597Z dim (int): index of the dim along which we define channels to prune. 2024-08-20T21:40:26.3251903Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-08-20T21:40:26.3252260Z shape as module parameter) used to compute mask for pruning. 2024-08-20T21:40:26.3252595Z The values in this tensor indicate the importance of the corresponding 2024-08-20T21:40:26.3252762Z elements in the parameter being pruned. 2024-08-20T21:40:26.3253091Z If unspecified or None, the module parameter will be used in its place. 2024-08-20T21:40:26.3253096Z 2024-08-20T21:40:26.3253200Z Returns: 2024-08-20T21:40:26.3253502Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:40:26.3253507Z 2024-08-20T21:40:26.3253627Z Examples: 2024-08-20T21:40:26.3253781Z >>> from torch.nn.utils import prune 2024-08-20T21:40:26.3253908Z >>> m = prune.ln_structured( 2024-08-20T21:40:26.3254274Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-08-20T21:40:26.3254369Z ... ) 2024-08-20T21:40:26.3254458Z 2024-08-20T21:40:26.3254873Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.3254878Z 2024-08-20T21:40:26.3255802Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1024. 2024-08-20T21:40:26.3256212Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.3256217Z 2024-08-20T21:40:26.3256779Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-08-20T21:40:26.3256853Z 2024-08-20T21:40:26.3256993Z Modifies modules in place by: 2024-08-20T21:40:26.3256998Z 2024-08-20T21:40:26.3257354Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:40:26.3257658Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:40:26.3257941Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:40:26.3258214Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:40:26.3258369Z ``name+'_orig'``. 2024-08-20T21:40:26.3258374Z 2024-08-20T21:40:26.3258465Z Args: 2024-08-20T21:40:26.3258723Z parameters (Iterable of (module, name) tuples): parameters of 2024-08-20T21:40:26.3259009Z the model to prune in a global fashion, i.e. by aggregating all 2024-08-20T21:40:26.3259284Z weights prior to deciding which ones to prune. module must be of 2024-08-20T21:40:26.3259486Z type :class:`nn.Module`, and name must be a string. 2024-08-20T21:40:26.3259791Z pruning_method (function): a valid pruning function from this module, 2024-08-20T21:40:26.3260035Z or a custom one implemented by the user that satisfies the 2024-08-20T21:40:26.3260414Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-08-20T21:40:26.3260714Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-08-20T21:40:26.3261078Z the corresponding parameter's importance scores tensor. The tensor 2024-08-20T21:40:26.3261388Z should be the same shape as the parameter, and is used for computing 2024-08-20T21:40:26.3261500Z mask for pruning. 2024-08-20T21:40:26.3261783Z If unspecified or None, the parameter will be used in place of its 2024-08-20T21:40:26.3261911Z importance scores. 2024-08-20T21:40:26.3262069Z kwargs: other keyword arguments such as: 2024-08-20T21:40:26.3262348Z amount (int or float): quantity of parameters to prune across the 2024-08-20T21:40:26.3262482Z specified parameters. 2024-08-20T21:40:26.3262720Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:40:26.3262998Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:40:26.3263162Z absolute number of parameters to prune. 2024-08-20T21:40:26.3263168Z 2024-08-20T21:40:26.3263262Z Raises: 2024-08-20T21:40:26.3263526Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-08-20T21:40:26.3263590Z 2024-08-20T21:40:26.3263683Z Note: 2024-08-20T21:40:26.3264033Z Since global structured pruning doesn't make much sense unless the 2024-08-20T21:40:26.3264322Z norm is normalized by the size of the parameter, we now limit the 2024-08-20T21:40:26.3264515Z scope of global pruning to unstructured methods. 2024-08-20T21:40:26.3264520Z 2024-08-20T21:40:26.3264617Z Examples: 2024-08-20T21:40:26.3264776Z >>> from torch.nn.utils import prune 2024-08-20T21:40:26.3264930Z >>> from collections import OrderedDict 2024-08-20T21:40:26.3265093Z >>> net = nn.Sequential(OrderedDict([ 2024-08-20T21:40:26.3265271Z ... ('first', nn.Linear(10, 4)), 2024-08-20T21:40:26.3265444Z ... ('second', nn.Linear(4, 1)), 2024-08-20T21:40:26.3265553Z ... ])) 2024-08-20T21:40:26.3265675Z >>> parameters_to_prune = ( 2024-08-20T21:40:26.3265836Z ... (net.first, 'weight'), 2024-08-20T21:40:26.3266013Z ... (net.second, 'weight'), 2024-08-20T21:40:26.3266105Z ... ) 2024-08-20T21:40:26.3266236Z >>> prune.global_unstructured( 2024-08-20T21:40:26.3266370Z ... parameters_to_prune, 2024-08-20T21:40:26.3266536Z ... pruning_method=prune.L1Unstructured, 2024-08-20T21:40:26.3266638Z ... amount=10, 2024-08-20T21:40:26.3266744Z ... ) 2024-08-20T21:40:26.3267040Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-08-20T21:40:26.3267139Z tensor(10) 2024-08-20T21:40:26.3267223Z 2024-08-20T21:40:26.3267228Z 2024-08-20T21:40:26.3267644Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.3267650Z 2024-08-20T21:40:26.3268482Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1143. 2024-08-20T21:40:26.3268911Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.3269550Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-08-20T21:40:26.3269555Z 2024-08-20T21:40:26.3269839Z Modifies module in place (and also return the modified module) by: 2024-08-20T21:40:26.3269844Z 2024-08-20T21:40:26.3270224Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:40:26.3270518Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:40:26.3270825Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:40:26.3271098Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:40:26.3271242Z ``name+'_orig'``. 2024-08-20T21:40:26.3271247Z 2024-08-20T21:40:26.3271356Z Args: 2024-08-20T21:40:26.3271590Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:40:26.3271835Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:40:26.3271950Z will act. 2024-08-20T21:40:26.3272188Z mask (Tensor): binary mask to be applied to the parameter. 2024-08-20T21:40:26.3272193Z 2024-08-20T21:40:26.3272290Z Returns: 2024-08-20T21:40:26.3272605Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:40:26.3272610Z 2024-08-20T21:40:26.3272710Z Examples: 2024-08-20T21:40:26.3272877Z >>> from torch.nn.utils import prune 2024-08-20T21:40:26.3273009Z >>> m = prune.custom_from_mask( 2024-08-20T21:40:26.3273328Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-08-20T21:40:26.3273437Z ... ) 2024-08-20T21:40:26.3273552Z >>> print(m.bias_mask) 2024-08-20T21:40:26.3273660Z tensor([0., 1., 0.]) 2024-08-20T21:40:26.3273666Z 2024-08-20T21:40:26.3273770Z 2024-08-20T21:40:26.3274168Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.3274173Z 2024-08-20T21:40:26.4289240Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=106. 2024-08-20T21:40:26.4290806Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.4291851Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-08-20T21:40:26.4292506Z 2024-08-20T21:40:26.4292821Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-08-20T21:40:26.4293631Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-08-20T21:40:26.4294363Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-08-20T21:40:26.4294918Z (UAI 2018). 2024-08-20T21:40:26.4295087Z 2024-08-20T21:40:26.4295382Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-08-20T21:40:26.4296135Z but using exponential weights instead of equal weights across iterations. 2024-08-20T21:40:26.4296633Z 2024-08-20T21:40:26.4296953Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-08-20T21:40:26.4297923Z on the device :attr:`device` and allows to compute running averages of the 2024-08-20T21:40:26.4298651Z parameters of the :attr:`model`. 2024-08-20T21:40:26.4299083Z 2024-08-20T21:40:26.4299191Z Args: 2024-08-20T21:40:26.4299607Z model (torch.nn.Module): model to use with SWA/EMA 2024-08-20T21:40:26.4300356Z device (torch.device, optional): if provided, the averaged model will be 2024-08-20T21:40:26.4301213Z stored on the :attr:`device` 2024-08-20T21:40:26.4301836Z avg_fn (function, optional): the averaging function used to update 2024-08-20T21:40:26.4302608Z parameters; the function must take in the current value of the 2024-08-20T21:40:26.4303380Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-08-20T21:40:26.4304169Z parameter, and the number of models already averaged; if None, 2024-08-20T21:40:26.4304886Z an equally weighted average is used (default: None) 2024-08-20T21:40:26.4305615Z multi_avg_fn (function, optional): the averaging function used to update 2024-08-20T21:40:26.4306465Z parameters inplace; the function must take in the current values of the 2024-08-20T21:40:26.4307364Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-08-20T21:40:26.4308271Z parameters as a list, and the number of models already averaged; if None, 2024-08-20T21:40:26.4309027Z an equally weighted average is used (default: None) 2024-08-20T21:40:26.4309744Z use_buffers (bool): if ``True``, it will compute running averages for 2024-08-20T21:40:26.4310563Z both the parameters and the buffers of the model. (default: ``False``) 2024-08-20T21:40:26.4311093Z 2024-08-20T21:40:26.4311190Z Example: 2024-08-20T21:40:26.4311532Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:26.4312101Z >>> loader, optimizer, model, loss_fn = ... 2024-08-20T21:40:26.4312709Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-08-20T21:40:26.4313454Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-08-20T21:40:26.4314138Z >>> T_max=300) 2024-08-20T21:40:26.4314569Z >>> swa_start = 160 2024-08-20T21:40:26.4315022Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-08-20T21:40:26.4315556Z >>> for i in range(300): 2024-08-20T21:40:26.4315957Z >>> for input, target in loader: 2024-08-20T21:40:26.4316445Z >>> optimizer.zero_grad() 2024-08-20T21:40:26.4316940Z >>> loss_fn(model(input), target).backward() 2024-08-20T21:40:26.4317450Z >>> optimizer.step() 2024-08-20T21:40:26.4317884Z >>> if i > swa_start: 2024-08-20T21:40:26.4318309Z >>> swa_model.update_parameters(model) 2024-08-20T21:40:26.4318969Z >>> swa_scheduler.step() 2024-08-20T21:40:26.4319408Z >>> else: 2024-08-20T21:40:26.4319732Z >>> scheduler.step() 2024-08-20T21:40:26.4320155Z >>> 2024-08-20T21:40:26.4320507Z >>> # Update bn statistics for the swa_model at the end 2024-08-20T21:40:26.4321150Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-08-20T21:40:26.4321537Z 2024-08-20T21:40:26.4322007Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-08-20T21:40:26.4322909Z If no averaging function is provided, the default is to compute 2024-08-20T21:40:26.4323712Z equally-weighted average of the weights (SWA). 2024-08-20T21:40:26.4324117Z 2024-08-20T21:40:26.4324235Z Example: 2024-08-20T21:40:26.4324567Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:26.4325220Z >>> # Compute exponential moving averages of the weights and buffers 2024-08-20T21:40:26.4325953Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-08-20T21:40:26.4326647Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-08-20T21:40:26.4327167Z 2024-08-20T21:40:26.4327294Z .. note:: 2024-08-20T21:40:26.4327763Z When using SWA/EMA with models containing Batch Normalization you may 2024-08-20T21:40:26.4328515Z need to update the activation statistics for Batch Normalization. 2024-08-20T21:40:26.4329424Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-08-20T21:40:26.4330321Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-08-20T21:40:26.4331238Z statistics in a post-training step by passing data through the model. The 2024-08-20T21:40:26.4332049Z second does it during the parameter update phase by averaging all buffers. 2024-08-20T21:40:26.4332923Z Empirical evidence has shown that updating the statistics in normalization 2024-08-20T21:40:26.4333749Z layers increases accuracy, but you may wish to empirically test which 2024-08-20T21:40:26.4334423Z approach yields the best results in your problem. 2024-08-20T21:40:26.4334785Z 2024-08-20T21:40:26.4334886Z .. note:: 2024-08-20T21:40:26.4335413Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-08-20T21:40:26.4335942Z 2024-08-20T21:40:26.4336053Z .. note:: 2024-08-20T21:40:26.4336478Z When :meth:`update_parameters` is called for the first time (i.e. 2024-08-20T21:40:26.4337171Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-08-20T21:40:26.4337865Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-08-20T21:40:26.4338559Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-08-20T21:40:26.4339107Z to update the parameters. 2024-08-20T21:40:26.4339363Z 2024-08-20T21:40:26.4339669Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-20T21:40:26.4340346Z https://arxiv.org/abs/1803.05407 2024-08-20T21:40:26.4340953Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-08-20T21:40:26.4341543Z Average: 2024-08-20T21:40:26.4341858Z https://arxiv.org/abs/1806.05594 2024-08-20T21:40:26.4342485Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-08-20T21:40:26.4343060Z https://arxiv.org/abs/1904.11943 2024-08-20T21:40:26.4343726Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-08-20T21:40:26.4344287Z Generalizes Well: 2024-08-20T21:40:26.4344646Z https://arxiv.org/abs/2001.02312 2024-08-20T21:40:26.4345056Z .. _Polyak averaging: 2024-08-20T21:40:26.4345545Z https://paperswithcode.com/method/polyak-averaging 2024-08-20T21:40:26.4346021Z 2024-08-20T21:40:26.4346565Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.4347053Z 2024-08-20T21:40:26.4347989Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=357. 2024-08-20T21:40:26.4349213Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.4350013Z Anneals the learning rate in each parameter group to a fixed value. 2024-08-20T21:40:26.4350458Z 2024-08-20T21:40:26.4350783Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-08-20T21:40:26.4351537Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-08-20T21:40:26.4352005Z 2024-08-20T21:40:26.4352100Z Args: 2024-08-20T21:40:26.4352490Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-08-20T21:40:26.4353147Z swa_lrs (float or list): the learning rate value for all param groups 2024-08-20T21:40:26.4353842Z together or separately for each group. 2024-08-20T21:40:26.4354447Z annealing_epochs (int): number of epochs in the annealing phase 2024-08-20T21:40:26.4354992Z (default: 10) 2024-08-20T21:40:26.4355479Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-08-20T21:40:26.4356218Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-08-20T21:40:26.4356785Z (default: "cos") 2024-08-20T21:40:26.4357317Z last_epoch (int): the index of the last epoch (default: -1) 2024-08-20T21:40:26.4357810Z 2024-08-20T21:40:26.4358051Z The :class:`SWALR` scheduler can be used together with other 2024-08-20T21:40:26.4358760Z schedulers to switch to a constant learning rate late in the training 2024-08-20T21:40:26.4359330Z as in the example below. 2024-08-20T21:40:26.4359580Z 2024-08-20T21:40:26.4359678Z Example: 2024-08-20T21:40:26.4360015Z >>> # xdoctest: +SKIP("Undefined variables") 2024-08-20T21:40:26.4360484Z >>> loader, optimizer, model = ... 2024-08-20T21:40:26.4360924Z >>> lr_lambda = lambda epoch: 0.9 2024-08-20T21:40:26.4361520Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-08-20T21:40:26.4362092Z >>> lr_lambda=lr_lambda) 2024-08-20T21:40:26.4362600Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-08-20T21:40:26.4363215Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-08-20T21:40:26.4375413Z >>> swa_start = 160 2024-08-20T21:40:26.4375788Z >>> for i in range(300): 2024-08-20T21:40:26.4376199Z >>> for input, target in loader: 2024-08-20T21:40:26.4376642Z >>> optimizer.zero_grad() 2024-08-20T21:40:26.4377133Z >>> loss_fn(model(input), target).backward() 2024-08-20T21:40:26.4377610Z >>> optimizer.step() 2024-08-20T21:40:26.4377997Z >>> if i > swa_start: 2024-08-20T21:40:26.4378396Z >>> swa_scheduler.step() 2024-08-20T21:40:26.4378795Z >>> else: 2024-08-20T21:40:26.4379116Z >>> scheduler.step() 2024-08-20T21:40:26.4379415Z 2024-08-20T21:40:26.4379782Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-20T21:40:26.4380399Z https://arxiv.org/abs/1803.05407 2024-08-20T21:40:26.4380779Z 2024-08-20T21:40:26.4381393Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.4381884Z 2024-08-20T21:40:26.4426982Z msg = Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=111. 2024-08-20T21:40:26.4428317Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.4429059Z Register a container-like type as pytree node. 2024-08-20T21:40:26.4429425Z 2024-08-20T21:40:26.4429523Z Args: 2024-08-20T21:40:26.4429943Z cls (type): A Python type to treat as an internal pytree node. 2024-08-20T21:40:26.4430960Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-08-20T21:40:26.4431961Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-08-20T21:40:26.4432951Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-08-20T21:40:26.4433652Z passed to the ``unflatten_fn``. 2024-08-20T21:40:26.4434341Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-08-20T21:40:26.4435265Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-08-20T21:40:26.4436015Z The function should return an instance of ``cls``. 2024-08-20T21:40:26.4436737Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-08-20T21:40:26.4437481Z qualified name used when serializing the tree spec. 2024-08-20T21:40:26.4438287Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-08-20T21:40:26.4439269Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-08-20T21:40:26.4440192Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-08-20T21:40:26.4441132Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-08-20T21:40:26.4442070Z how to convert the custom json dumpable representation of the context back to the 2024-08-20T21:40:26.4443063Z original context. This is used for json deserialization, which is being used in 2024-08-20T21:40:26.4443735Z :mod:`torch.export` right now. 2024-08-20T21:40:26.4444033Z 2024-08-20T21:40:26.4444172Z Example:: 2024-08-20T21:40:26.4444335Z 2024-08-20T21:40:26.4444471Z >>> # xdoctest: +SKIP 2024-08-20T21:40:26.4444892Z >>> # Registry a Python type with lambda functions 2024-08-20T21:40:26.4445375Z >>> register_pytree_node( 2024-08-20T21:40:26.4445740Z ... set, 2024-08-20T21:40:26.4446074Z ... lambda s: (sorted(s), None, None), 2024-08-20T21:40:26.4446564Z ... lambda children, _: set(children), 2024-08-20T21:40:26.4446992Z ... ) 2024-08-20T21:40:26.4447233Z 2024-08-20T21:40:26.4447802Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.4448287Z 2024-08-20T21:40:26.4930219Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1201. 2024-08-20T21:40:26.4931602Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.4932126Z 2024-08-20T21:40:26.4932420Z Context passed to policy function during selective checkpointing. 2024-08-20T21:40:26.4932870Z 2024-08-20T21:40:26.4933185Z This class is used to pass relevant metadata to the policy function during 2024-08-20T21:40:26.4934017Z selective checkpointing. The metadata includes whether the current invocation 2024-08-20T21:40:26.4934725Z of the policy function is during recomputation or not. 2024-08-20T21:40:26.4935179Z 2024-08-20T21:40:26.4935278Z Example: 2024-08-20T21:40:26.4935556Z >>> # xdoctest: +SKIP(stub) 2024-08-20T21:40:26.4935977Z >>> 2024-08-20T21:40:26.4936464Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-20T21:40:26.4936989Z >>> print(ctx.is_recompute) 2024-08-20T21:40:26.4937388Z >>> 2024-08-20T21:40:26.4937902Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-20T21:40:26.4938586Z >>> 2024-08-20T21:40:26.4938957Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-20T21:40:26.4939414Z >>> fn, x, y, 2024-08-20T21:40:26.4939787Z >>> use_reentrant=False, 2024-08-20T21:40:26.4940153Z >>> context_fn=context_fn, 2024-08-20T21:40:26.4940565Z >>> ) 2024-08-20T21:40:26.4940712Z 2024-08-20T21:40:26.4941386Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.4941892Z 2024-08-20T21:40:26.4943031Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1335. 2024-08-20T21:40:26.4944540Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.4945119Z 2024-08-20T21:40:26.4945443Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-08-20T21:40:26.4945992Z 2024-08-20T21:40:26.4946285Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-08-20T21:40:26.4947003Z operations are recomputed during the backward pass. 2024-08-20T21:40:26.4947419Z 2024-08-20T21:40:26.4947513Z Args: 2024-08-20T21:40:26.4947805Z policy_fn_or_list (Callable or List): 2024-08-20T21:40:26.4948467Z - If a policy function is provided, it should accept a 2024-08-20T21:40:26.4949204Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-08-20T21:40:26.4950031Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-08-20T21:40:26.4950861Z indicating whether the execution of the op should be recomputed or not. 2024-08-20T21:40:26.4951754Z - If a list of operations is provided, it is equivalent to a policy 2024-08-20T21:40:26.4952505Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-08-20T21:40:26.4953367Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-08-20T21:40:26.4953969Z operations. 2024-08-20T21:40:26.4954492Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-08-20T21:40:26.4955286Z raised if any tensors cached by selective activation checkpoint are 2024-08-20T21:40:26.4956105Z mutated in order to ensure correctness. If set to `True`, this check 2024-08-20T21:40:26.4956717Z is disabled. 2024-08-20T21:40:26.4957011Z Returns: 2024-08-20T21:40:26.4957360Z A tuple of two context managers. 2024-08-20T21:40:26.4957637Z 2024-08-20T21:40:26.4957733Z Example: 2024-08-20T21:40:26.4958050Z >>> # xdoctest: +REQUIRES(LINUX) 2024-08-20T21:40:26.4958462Z >>> import functools 2024-08-20T21:40:26.4958780Z >>> 2024-08-20T21:40:26.4959121Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-08-20T21:40:26.4959665Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-08-20T21:40:26.4960079Z >>> 2024-08-20T21:40:26.4960386Z >>> ops_to_save = [ 2024-08-20T21:40:26.4960742Z >>> torch.ops.aten.mm.default, 2024-08-20T21:40:26.4961166Z >>> ] 2024-08-20T21:40:26.4961459Z >>> 2024-08-20T21:40:26.4961770Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-20T21:40:26.4962264Z >>> if op in ops_to_save: 2024-08-20T21:40:26.4962741Z >>> return CheckpointPolicy.MUST_SAVE 2024-08-20T21:40:26.4963176Z >>> else: 2024-08-20T21:40:26.4963579Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-08-20T21:40:26.4964036Z >>> 2024-08-20T21:40:26.4964589Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-20T21:40:26.4965250Z >>> 2024-08-20T21:40:26.4965511Z >>> # or equivalently 2024-08-20T21:40:26.4966146Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-08-20T21:40:26.4966819Z >>> 2024-08-20T21:40:26.4967072Z >>> def fn(x, y): 2024-08-20T21:40:26.4967594Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-08-20T21:40:26.4968163Z >>> 2024-08-20T21:40:26.4968502Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-20T21:40:26.4969011Z >>> fn, x, y, 2024-08-20T21:40:26.4969312Z >>> use_reentrant=False, 2024-08-20T21:40:26.4969745Z >>> context_fn=context_fn, 2024-08-20T21:40:26.4970169Z >>> ) 2024-08-20T21:40:26.4970375Z 2024-08-20T21:40:26.4970810Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.4971474Z 2024-08-20T21:40:26.5140439Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=925. 2024-08-20T21:40:26.5141748Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.5142257Z 2024-08-20T21:40:26.5142474Z Create a :class:`setuptools.Extension` for C++. 2024-08-20T21:40:26.5142812Z 2024-08-20T21:40:26.5143139Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-20T21:40:26.5143919Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-08-20T21:40:26.5144377Z 2024-08-20T21:40:26.5144658Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-20T21:40:26.5145265Z constructor. Full list arguments can be found at 2024-08-20T21:40:26.5146370Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-20T21:40:26.5147123Z 2024-08-20T21:40:26.5147280Z Example: 2024-08-20T21:40:26.5147740Z >>> # xdoctest: +SKIP 2024-08-20T21:40:26.5148536Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:40:26.5149065Z >>> from setuptools import setup 2024-08-20T21:40:26.5149645Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-08-20T21:40:26.5150195Z >>> setup( 2024-08-20T21:40:26.5150537Z ... name='extension', 2024-08-20T21:40:26.5151114Z ... ext_modules=[ 2024-08-20T21:40:26.5151433Z ... CppExtension( 2024-08-20T21:40:26.5151843Z ... name='extension', 2024-08-20T21:40:26.5152325Z ... sources=['extension.cpp'], 2024-08-20T21:40:26.5152821Z ... extra_compile_args=['-g'], 2024-08-20T21:40:26.5153400Z ... extra_link_flags=['-Wl,--no-as-needed', '-lm']) 2024-08-20T21:40:26.5153876Z ... ], 2024-08-20T21:40:26.5154135Z ... cmdclass={ 2024-08-20T21:40:26.5154529Z ... 'build_ext': BuildExtension 2024-08-20T21:40:26.5154938Z ... }) 2024-08-20T21:40:26.5155100Z 2024-08-20T21:40:26.5155499Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.5156098Z 2024-08-20T21:40:26.5157495Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=976. 2024-08-20T21:40:26.5159317Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.5159831Z 2024-08-20T21:40:26.5160051Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-08-20T21:40:26.5160417Z 2024-08-20T21:40:26.5160744Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-20T21:40:26.5161474Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-08-20T21:40:26.5162218Z extension. This includes the CUDA include path, library path and runtime 2024-08-20T21:40:26.5162798Z library. 2024-08-20T21:40:26.5162942Z 2024-08-20T21:40:26.5163218Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-20T21:40:26.5163843Z constructor. Full list arguments can be found at 2024-08-20T21:40:26.5164704Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-20T21:40:26.5165294Z 2024-08-20T21:40:26.5165389Z Example: 2024-08-20T21:40:26.5165657Z >>> # xdoctest: +SKIP 2024-08-20T21:40:26.5166157Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:40:26.5166758Z >>> from setuptools import setup 2024-08-20T21:40:26.5167824Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-08-20T21:40:26.5168433Z >>> setup( 2024-08-20T21:40:26.5168771Z ... name='cuda_extension', 2024-08-20T21:40:26.5169148Z ... ext_modules=[ 2024-08-20T21:40:26.5169485Z ... CUDAExtension( 2024-08-20T21:40:26.5169913Z ... name='cuda_extension', 2024-08-20T21:40:26.5170748Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:40:26.5171378Z ... extra_compile_args={'cxx': ['-g'], 2024-08-20T21:40:26.5171936Z ... 'nvcc': ['-O2']}, 2024-08-20T21:40:26.5172566Z ... extra_link_flags=['-Wl,--no-as-needed', '-lcuda']) 2024-08-20T21:40:26.5173054Z ... ], 2024-08-20T21:40:26.5173356Z ... cmdclass={ 2024-08-20T21:40:26.5173733Z ... 'build_ext': BuildExtension 2024-08-20T21:40:26.5174147Z ... }) 2024-08-20T21:40:26.5174310Z 2024-08-20T21:40:26.5174441Z Compute capabilities: 2024-08-20T21:40:26.5174644Z 2024-08-20T21:40:26.5175067Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-08-20T21:40:26.5176065Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-08-20T21:40:26.5177133Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-08-20T21:40:26.5178212Z newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch 2024-08-20T21:40:26.5179206Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-08-20T21:40:26.5179923Z support (see below for details on PTX). 2024-08-20T21:40:26.5180219Z 2024-08-20T21:40:26.5180662Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-08-20T21:40:26.5181461Z CCs you want the extension to support: 2024-08-20T21:40:26.5181768Z 2024-08-20T21:40:26.5182011Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-08-20T21:40:26.5182770Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-08-20T21:40:26.5183283Z 2024-08-20T21:40:26.5183714Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-08-20T21:40:26.5184841Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-08-20T21:40:26.5185965Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-08-20T21:40:26.5187063Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-08-20T21:40:26.5188146Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-08-20T21:40:26.5189244Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-08-20T21:40:26.5190474Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-08-20T21:40:26.5191610Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-08-20T21:40:26.5192333Z "8.0 8.6" would be better. 2024-08-20T21:40:26.5192550Z 2024-08-20T21:40:26.5193065Z Note that while it's possible to include all supported archs, the more archs get included the 2024-08-20T21:40:26.5194054Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-08-20T21:40:26.5194652Z 2024-08-20T21:40:26.5195209Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-08-20T21:40:26.5196137Z To workaround the issue, move python binding logic to pure C++ file. 2024-08-20T21:40:26.5196586Z 2024-08-20T21:40:26.5196702Z Example use: 2024-08-20T21:40:26.5196979Z #include 2024-08-20T21:40:26.5197430Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-08-20T21:40:26.5197778Z 2024-08-20T21:40:26.5197892Z Instead of: 2024-08-20T21:40:26.5198163Z #include 2024-08-20T21:40:26.5198621Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-08-20T21:40:26.5198976Z 2024-08-20T21:40:26.5199356Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-08-20T21:40:26.5200555Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-08-20T21:40:26.5201505Z 2024-08-20T21:40:26.5201643Z Relocatable device code linking: 2024-08-20T21:40:26.5201919Z 2024-08-20T21:40:26.5202306Z If you want to reference device symbols across compilation units (across object files), 2024-08-20T21:40:26.5203319Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-08-20T21:40:26.5204340Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-08-20T21:40:26.5205480Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-08-20T21:40:26.5206638Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-08-20T21:40:26.5207528Z help reduce the protentional perf degradation of `-rdc`. 2024-08-20T21:40:26.5208134Z Note that it needs to be used at both steps to be useful. 2024-08-20T21:40:26.5208536Z 2024-08-20T21:40:26.5209179Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-08-20T21:40:26.5210238Z There is also a case where `-dlink` is used without `-rdc`: 2024-08-20T21:40:26.5211059Z when an extension is linked against a static lib containing rdc-compiled objects 2024-08-20T21:40:26.5211859Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-08-20T21:40:26.5212300Z 2024-08-20T21:40:26.5212594Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-08-20T21:40:26.5213131Z 2024-08-20T21:40:26.5213245Z Example: 2024-08-20T21:40:26.5213500Z >>> # xdoctest: +SKIP 2024-08-20T21:40:26.5213915Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:40:26.5214386Z >>> CUDAExtension( 2024-08-20T21:40:26.5214754Z ... name='cuda_extension', 2024-08-20T21:40:26.5215304Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:40:26.5215793Z ... dlink=True, 2024-08-20T21:40:26.5216154Z ... dlink_libraries=["dlink_lib"], 2024-08-20T21:40:26.5216673Z ... extra_compile_args={'cxx': ['-g'], 2024-08-20T21:40:26.5217239Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-08-20T21:40:26.5217575Z 2024-08-20T21:40:26.5217973Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.5218477Z 2024-08-20T21:40:26.5219383Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1234. 2024-08-20T21:40:26.5220629Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.5221148Z 2024-08-20T21:40:26.5221385Z Load a PyTorch C++ extension just-in-time (JIT). 2024-08-20T21:40:26.5221727Z 2024-08-20T21:40:26.5222026Z To load an extension, a Ninja build file is emitted, which is used to 2024-08-20T21:40:26.5222738Z compile the given sources into a dynamic library. This library is 2024-08-20T21:40:26.5223463Z subsequently loaded into the current Python process as a module and 2024-08-20T21:40:26.5224077Z returned from this function, ready for use. 2024-08-20T21:40:26.5224395Z 2024-08-20T21:40:26.5224694Z By default, the directory to which the build file is emitted and the 2024-08-20T21:40:26.5225441Z resulting library compiled to is ``/torch_extensions/``, where 2024-08-20T21:40:26.5226207Z ```` is the temporary folder on the current platform and ```` 2024-08-20T21:40:26.5226972Z the name of the extension. This location can be overridden in two ways. 2024-08-20T21:40:26.5227717Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-08-20T21:40:26.5228461Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-08-20T21:40:26.5229219Z into subfolders of this directory. Second, if the ``build_directory`` 2024-08-20T21:40:26.5229972Z argument to this function is supplied, it overrides the entire path, i.e. 2024-08-20T21:40:26.5230758Z the library will be compiled into that folder directly. 2024-08-20T21:40:26.5231154Z 2024-08-20T21:40:26.5231444Z To compile the sources, the default system compiler (``c++``) is used, 2024-08-20T21:40:26.5232233Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-08-20T21:40:26.5233013Z additional arguments to the compilation process, ``extra_cflags`` or 2024-08-20T21:40:26.5233776Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-08-20T21:40:26.5234593Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-08-20T21:40:26.5235217Z ``extra_cflags`` to pass further include directories. 2024-08-20T21:40:26.5235583Z 2024-08-20T21:40:26.5235897Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-08-20T21:40:26.5236643Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-08-20T21:40:26.5237387Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-08-20T21:40:26.5238174Z passing the CUDA lib64 directory as a library directory, and linking 2024-08-20T21:40:26.5238826Z ``cudart``. You can pass additional flags to nvcc via 2024-08-20T21:40:26.5239467Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-08-20T21:40:26.5240209Z heuristics for finding the CUDA install directory are used, which usually 2024-08-20T21:40:26.5240988Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-08-20T21:40:26.5241637Z safest option. 2024-08-20T21:40:26.5241806Z 2024-08-20T21:40:26.5241897Z Args: 2024-08-20T21:40:26.5242336Z name: The name of the extension to build. This MUST be the same as the 2024-08-20T21:40:26.5242936Z name of the pybind11 module! 2024-08-20T21:40:26.5243481Z sources: A list of relative or absolute paths to C++ source files. 2024-08-20T21:40:26.5244222Z extra_cflags: optional list of compiler flags to forward to the build. 2024-08-20T21:40:26.5244980Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-08-20T21:40:26.5245554Z when building CUDA sources. 2024-08-20T21:40:26.5246129Z extra_ldflags: optional list of linker flags to forward to the build. 2024-08-20T21:40:26.5246875Z extra_include_paths: optional list of include directories to forward 2024-08-20T21:40:26.5247421Z to the build. 2024-08-20T21:40:26.5247853Z build_directory: optional path to use as build workspace. 2024-08-20T21:40:26.5248492Z verbose: If ``True``, turns on verbose logging of load steps. 2024-08-20T21:40:26.5249177Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-20T21:40:26.5249854Z the build. If set to ``None`` (default), this value is 2024-08-20T21:40:26.5250576Z automatically determined based on the existence of ``.cu`` or 2024-08-20T21:40:26.5251254Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-08-20T21:40:26.5251789Z and libraries to be included. 2024-08-20T21:40:26.5252356Z is_python_module: If ``True`` (default), imports the produced shared 2024-08-20T21:40:26.5253060Z library as a Python module. If ``False``, behavior depends on 2024-08-20T21:40:26.5253582Z ``is_standalone``. 2024-08-20T21:40:26.5254094Z is_standalone: If ``False`` (default) loads the constructed extension 2024-08-20T21:40:26.5254818Z into the process as a plain dynamic library. If ``True``, build a 2024-08-20T21:40:26.5255366Z standalone executable. 2024-08-20T21:40:26.5255627Z 2024-08-20T21:40:26.5255726Z Returns: 2024-08-20T21:40:26.5256016Z If ``is_python_module`` is ``True``: 2024-08-20T21:40:26.5256527Z Returns the loaded PyTorch extension as a Python module. 2024-08-20T21:40:26.5256934Z 2024-08-20T21:40:26.5257215Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-08-20T21:40:26.5257951Z Returns nothing. (The shared library is loaded into the process as 2024-08-20T21:40:26.5258515Z a side effect.) 2024-08-20T21:40:26.5258717Z 2024-08-20T21:40:26.5258939Z If ``is_standalone`` is ``True``. 2024-08-20T21:40:26.5259501Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-08-20T21:40:26.5260188Z added to the PATH environment variable as a side effect.) 2024-08-20T21:40:26.5260587Z 2024-08-20T21:40:26.5260686Z Example: 2024-08-20T21:40:26.5260956Z >>> # xdoctest: +SKIP 2024-08-20T21:40:26.5261354Z >>> from torch.utils.cpp_extension import load 2024-08-20T21:40:26.5261799Z >>> module = load( 2024-08-20T21:40:26.5262188Z ... name='extension', 2024-08-20T21:40:26.5262692Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:40:26.5263216Z ... extra_cflags=['-O2'], 2024-08-20T21:40:26.5263580Z ... verbose=True) 2024-08-20T21:40:26.5263787Z 2024-08-20T21:40:26.5264199Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.5264687Z 2024-08-20T21:40:26.5265528Z msg = Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1523. 2024-08-20T21:40:26.5266828Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.5267344Z 2024-08-20T21:40:26.5267693Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-08-20T21:40:26.5268138Z 2024-08-20T21:40:26.5268459Z This function behaves exactly like :func:`load`, but takes its sources as 2024-08-20T21:40:26.5269294Z strings rather than filenames. These strings are stored to files in the 2024-08-20T21:40:26.5270046Z build directory, after which the behavior of :func:`load_inline` is 2024-08-20T21:40:26.5270610Z identical to :func:`load`. 2024-08-20T21:40:26.5270831Z 2024-08-20T21:40:26.5270926Z See `the 2024-08-20T21:40:26.5271497Z tests `_ 2024-08-20T21:40:26.5272239Z for good examples of using this function. 2024-08-20T21:40:26.5272539Z 2024-08-20T21:40:26.5272949Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-08-20T21:40:26.5273744Z the necessary header includes, as well as the (pybind11) binding code. More 2024-08-20T21:40:26.5274551Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-08-20T21:40:26.5275291Z single ``.cpp`` file. This file is then prepended with ``#include 2024-08-20T21:40:26.5275817Z ``. 2024-08-20T21:40:26.5276040Z 2024-08-20T21:40:26.5276338Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-08-20T21:40:26.5277110Z automatically generated for each function specified. ``functions`` can 2024-08-20T21:40:26.5277872Z either be a list of function names, or a dictionary mapping from function 2024-08-20T21:40:26.5278661Z names to docstrings. If a list is given, the name of each function is used 2024-08-20T21:40:26.5279250Z as its docstring. 2024-08-20T21:40:26.5279429Z 2024-08-20T21:40:26.5279730Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-08-20T21:40:26.5280417Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-08-20T21:40:26.5281092Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-08-20T21:40:26.5281830Z separately, but ultimately linked into a single library. Note that no 2024-08-20T21:40:26.5282584Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-08-20T21:40:26.5283373Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-08-20T21:40:26.5284141Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-08-20T21:40:26.5284727Z include its name in ``functions``). 2024-08-20T21:40:26.5285017Z 2024-08-20T21:40:26.5285257Z See :func:`load` for a description of arguments omitted below. 2024-08-20T21:40:26.5285664Z 2024-08-20T21:40:26.5285768Z Args: 2024-08-20T21:40:26.5286197Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-08-20T21:40:26.5287035Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-08-20T21:40:26.5287785Z functions: A list of function names for which to generate function 2024-08-20T21:40:26.5288518Z bindings. If a dictionary is given, it should map function names to 2024-08-20T21:40:26.5289201Z docstrings (which are otherwise just the function names). 2024-08-20T21:40:26.5289899Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-20T21:40:26.5290829Z the build. If set to ``None`` (default), this value is 2024-08-20T21:40:26.5291463Z automatically determined based on whether ``cuda_sources`` is 2024-08-20T21:40:26.5292101Z provided. Set it to ``True`` to force CUDA headers 2024-08-20T21:40:26.5292610Z and libraries to be included. 2024-08-20T21:40:26.5293146Z with_pytorch_error_handling: Determines whether pytorch error and 2024-08-20T21:40:26.5293855Z warning macros are handled by pytorch instead of pybind. To do 2024-08-20T21:40:26.5294599Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-08-20T21:40:26.5295322Z function. This redirection might cause issues in obscure cases 2024-08-20T21:40:26.5296022Z of cpp. This flag should be set to ``False`` when this redirect 2024-08-20T21:40:26.5296562Z causes issues. 2024-08-20T21:40:26.5296757Z 2024-08-20T21:40:26.5296858Z Example: 2024-08-20T21:40:26.5297347Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:40:26.5297919Z >>> from torch.utils.cpp_extension import load_inline 2024-08-20T21:40:26.5298401Z >>> source = """ 2024-08-20T21:40:26.5298784Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-08-20T21:40:26.5299262Z return x.sin() + y.sin(); 2024-08-20T21:40:26.5299612Z } 2024-08-20T21:40:26.5299836Z """ 2024-08-20T21:40:26.5300250Z >>> module = load_inline(name='inline_extension', 2024-08-20T21:40:26.5300754Z ... cpp_sources=[source], 2024-08-20T21:40:26.5301274Z ... functions=['sin_add']) 2024-08-20T21:40:26.5301606Z 2024-08-20T21:40:26.5301710Z .. note:: 2024-08-20T21:40:26.5302138Z By default, the Ninja backend uses #CPUS + 2 workers to build the 2024-08-20T21:40:26.5302846Z extension. This may use up too many resources on some systems. One 2024-08-20T21:40:26.5303602Z can control the number of workers by setting the `MAX_JOBS` environment 2024-08-20T21:40:26.5304272Z variable to a non-negative number. 2024-08-20T21:40:26.5304554Z 2024-08-20T21:40:26.5304953Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.5305454Z 2024-08-20T21:40:26.5351313Z msg = Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61. 2024-08-20T21:40:26.5352706Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.5353213Z 2024-08-20T21:40:26.5353644Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-08-20T21:40:26.5354232Z 2024-08-20T21:40:26.5354631Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-08-20T21:40:26.5355512Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-08-20T21:40:26.5356351Z server like load. It can emulate multiple calling threads to a single module 2024-08-20T21:40:26.5357190Z provided. In the future we plan to enhance this component to support inter and 2024-08-20T21:40:26.5358117Z intra-op parallelism as well as multiple models running in a single process. 2024-08-20T21:40:26.5358629Z 2024-08-20T21:40:26.5358972Z Please note that even though nn.Module is supported, it might incur an overhead 2024-08-20T21:40:26.5359802Z from the need to hold GIL every time we execute Python code or pass around 2024-08-20T21:40:26.5360781Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-08-20T21:40:26.5361604Z model for inference deployment it is better to switch to using it in this 2024-08-20T21:40:26.5362190Z benchmark. 2024-08-20T21:40:26.5362338Z 2024-08-20T21:40:26.5362453Z Example:: 2024-08-20T21:40:26.5362602Z 2024-08-20T21:40:26.5362750Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:26.5363245Z >>> from torch.utils import ThroughputBenchmark 2024-08-20T21:40:26.5363759Z >>> bench = ThroughputBenchmark(my_module) 2024-08-20T21:40:26.5364351Z >>> # Pre-populate benchmark's data set with the inputs 2024-08-20T21:40:26.5364848Z >>> for input in inputs: 2024-08-20T21:40:26.5365394Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-08-20T21:40:26.5366024Z ... bench.add_input(input[0], x2=input[1]) 2024-08-20T21:40:26.5366616Z >>> # Inputs supplied above are randomly used during the execution 2024-08-20T21:40:26.5367171Z >>> stats = bench.benchmark( 2024-08-20T21:40:26.5367549Z ... num_calling_threads=4, 2024-08-20T21:40:26.5367934Z ... num_warmup_iters = 100, 2024-08-20T21:40:26.5368046Z ... num_iters = 1000, 2024-08-20T21:40:26.5368152Z ... ) 2024-08-20T21:40:26.5368382Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-08-20T21:40:26.5368614Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-08-20T21:40:26.5368635Z 2024-08-20T21:40:26.5369136Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.5369142Z 2024-08-20T21:40:26.6196380Z msg = Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=17. 2024-08-20T21:40:26.6196821Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:26.6197102Z Sampler that restricts data loading to a subset of the dataset. 2024-08-20T21:40:26.6197141Z 2024-08-20T21:40:26.6197336Z It is especially useful in conjunction with 2024-08-20T21:40:26.6197699Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-08-20T21:40:26.6198062Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-08-20T21:40:26.6198374Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-08-20T21:40:26.6198555Z original dataset that is exclusive to it. 2024-08-20T21:40:26.6198573Z 2024-08-20T21:40:26.6198695Z .. note:: 2024-08-20T21:40:26.6199031Z Dataset is assumed to be of constant size and that any instance of it always 2024-08-20T21:40:26.6199222Z returns the same elements in the same order. 2024-08-20T21:40:26.6199229Z 2024-08-20T21:40:26.6199323Z Args: 2024-08-20T21:40:26.6199484Z dataset: Dataset used for sampling. 2024-08-20T21:40:26.6199764Z num_replicas (int, optional): Number of processes participating in 2024-08-20T21:40:26.6200094Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-08-20T21:40:26.6200240Z current distributed group. 2024-08-20T21:40:26.6200573Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-08-20T21:40:26.6200847Z By default, :attr:`rank` is retrieved from the current distributed 2024-08-20T21:40:26.6200958Z group. 2024-08-20T21:40:26.6201261Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-08-20T21:40:26.6201365Z indices. 2024-08-20T21:40:26.6201641Z seed (int, optional): random seed used to shuffle the sampler if 2024-08-20T21:40:26.6201904Z :attr:`shuffle=True`. This number should be identical across all 2024-08-20T21:40:26.6202115Z processes in the distributed group. Default: ``0``. 2024-08-20T21:40:26.6202418Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-08-20T21:40:26.6202693Z tail of the data to make it evenly divisible across the number of 2024-08-20T21:40:26.6203273Z replicas. If ``False``, the sampler will add extra indices to make 2024-08-20T21:40:26.6203559Z the data evenly divisible across the replicas. Default: ``False``. 2024-08-20T21:40:26.6203564Z 2024-08-20T21:40:26.6203667Z .. warning:: 2024-08-20T21:40:26.6203932Z In distributed mode, calling the :meth:`set_epoch` method at 2024-08-20T21:40:26.6204288Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-08-20T21:40:26.6204646Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-08-20T21:40:26.6204817Z the same ordering will be always used. 2024-08-20T21:40:26.6204822Z 2024-08-20T21:40:26.6204922Z Example:: 2024-08-20T21:40:26.6204927Z 2024-08-20T21:40:26.6205054Z >>> # xdoctest: +SKIP 2024-08-20T21:40:26.6205343Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-08-20T21:40:26.6205572Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-08-20T21:40:26.6205736Z ... sampler=sampler) 2024-08-20T21:40:26.6205911Z >>> for epoch in range(start_epoch, n_epochs): 2024-08-20T21:40:26.6206031Z ... if is_distributed: 2024-08-20T21:40:26.6206187Z ... sampler.set_epoch(epoch) 2024-08-20T21:40:26.6206319Z ... train(loader) 2024-08-20T21:40:26.6206515Z 2024-08-20T21:40:26.6206953Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:26.6206959Z 2024-08-20T21:40:27.3172257Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=40. 2024-08-20T21:40:27.3173533Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:27.3174043Z 2024-08-20T21:40:27.3174419Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-08-20T21:40:27.3175182Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-08-20T21:40:27.3175918Z pushes the map into PyTorch operations called by ``func``, effectively 2024-08-20T21:40:27.3176499Z vectorizing those operations. 2024-08-20T21:40:27.3176757Z 2024-08-20T21:40:27.3177055Z vmap is useful for handling batch dimensions: one can write a function 2024-08-20T21:40:27.3177798Z ``func`` that runs on examples and then lift it to a function that can 2024-08-20T21:40:27.3178549Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-08-20T21:40:27.3179196Z compute batched gradients when composed with autograd. 2024-08-20T21:40:27.3179582Z 2024-08-20T21:40:27.3179696Z .. note:: 2024-08-20T21:40:27.3180094Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-08-20T21:40:27.3180716Z convenience. Use whichever one you'd like. 2024-08-20T21:40:27.3181054Z 2024-08-20T21:40:27.3181148Z Args: 2024-08-20T21:40:27.3181576Z func (function): A Python function that takes one or more arguments. 2024-08-20T21:40:27.3182164Z Must return one or more Tensors. 2024-08-20T21:40:27.3182745Z in_dims (int or nested structure): Specifies which dimension of the 2024-08-20T21:40:27.3183407Z inputs should be mapped over. ``in_dims`` should have a 2024-08-20T21:40:27.3184034Z structure like the inputs. If the ``in_dim`` for a particular 2024-08-20T21:40:27.3184711Z input is None, then that indicates there is no map dimension. 2024-08-20T21:40:27.3185241Z Default: 0. 2024-08-20T21:40:27.3185686Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-08-20T21:40:27.3186384Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-08-20T21:40:27.3187012Z it should have one element per output. Default: 0. 2024-08-20T21:40:27.3187613Z randomness (str): Specifies whether the randomness in this 2024-08-20T21:40:27.3188372Z vmap should be the same or different across batches. If 'different', 2024-08-20T21:40:27.3189440Z the randomness for each batch will be different. If 'same', the 2024-08-20T21:40:27.3190462Z randomness will be the same across batches. If 'error', any calls to 2024-08-20T21:40:27.3191276Z random functions will error. Default: 'error'. WARNING: this flag 2024-08-20T21:40:27.3192005Z only applies to random PyTorch operations and does not apply to 2024-08-20T21:40:27.3192674Z Python's random module or numpy randomness. 2024-08-20T21:40:27.3193337Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-08-20T21:40:27.3194137Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-08-20T21:40:27.3195060Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-08-20T21:40:27.3196040Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-08-20T21:40:27.3196580Z 2024-08-20T21:40:27.3196674Z Returns: 2024-08-20T21:40:27.3197081Z Returns a new "batched" function. It takes the same inputs as 2024-08-20T21:40:27.3197739Z ``func``, except each input has an extra dimension at the index 2024-08-20T21:40:27.3198386Z specified by ``in_dims``. It takes returns the same outputs as 2024-08-20T21:40:27.3199052Z ``func``, except each output has an extra dimension at the index 2024-08-20T21:40:27.3199589Z specified by ``out_dims``. 2024-08-20T21:40:27.3199962Z 2024-08-20T21:40:27.3200061Z .. warning: 2024-08-20T21:40:27.3200563Z :func:`vmap` works best with functional-style code. Please do not 2024-08-20T21:40:27.3201312Z perform any side-effects in ``func``, with the exception of 2024-08-20T21:40:27.3202088Z in-place PyTorch operations. Examples of side-effects include mutating 2024-08-20T21:40:27.3202865Z Python data structures and assigning values to variables not captured 2024-08-20T21:40:27.3203438Z in ``func``. 2024-08-20T21:40:27.3203609Z 2024-08-20T21:40:27.3203951Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-08-20T21:40:27.3204795Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-08-20T21:40:27.3205545Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-08-20T21:40:27.3205993Z 2024-08-20T21:40:27.3206265Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:40:27.3206949Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-08-20T21:40:27.3207560Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-20T21:40:27.3208004Z >>> batched_dot(x, y) 2024-08-20T21:40:27.3208215Z 2024-08-20T21:40:27.3208532Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-08-20T21:40:27.3209136Z model authoring experience. 2024-08-20T21:40:27.3209365Z 2024-08-20T21:40:27.3209508Z >>> batch_size, feature_size = 3, 5 2024-08-20T21:40:27.3210004Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-08-20T21:40:27.3210591Z >>> 2024-08-20T21:40:27.3210857Z >>> def model(feature_vec): 2024-08-20T21:40:27.3211271Z >>> # Very simple linear model with activation 2024-08-20T21:40:27.3211782Z >>> return feature_vec.dot(weights).relu() 2024-08-20T21:40:27.3212207Z >>> 2024-08-20T21:40:27.3212528Z >>> examples = torch.randn(batch_size, feature_size) 2024-08-20T21:40:27.3213033Z >>> result = torch.vmap(model)(examples) 2024-08-20T21:40:27.3213357Z 2024-08-20T21:40:27.3213701Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-08-20T21:40:27.3214613Z or impossible to batch. One example is higher-order gradient computation. 2024-08-20T21:40:27.3215446Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-08-20T21:40:27.3216281Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-08-20T21:40:27.3217093Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-08-20T21:40:27.3218015Z we can vectorize the whole computation, computing the Jacobian in a single 2024-08-20T21:40:27.3218626Z call to ``autograd.grad``. 2024-08-20T21:40:27.3218847Z 2024-08-20T21:40:27.3218963Z >>> # Setup 2024-08-20T21:40:27.3219218Z >>> N = 5 2024-08-20T21:40:27.3219501Z >>> f = lambda x: x ** 2 2024-08-20T21:40:27.3219894Z >>> x = torch.randn(N, requires_grad=True) 2024-08-20T21:40:27.3220296Z >>> y = f(x) 2024-08-20T21:40:27.3220586Z >>> I_N = torch.eye(N) 2024-08-20T21:40:27.3220901Z >>> 2024-08-20T21:40:27.3221150Z >>> # Sequential approach 2024-08-20T21:40:27.3221673Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-08-20T21:40:27.3222270Z >>> for v in I_N.unbind()] 2024-08-20T21:40:27.3222716Z >>> jacobian = torch.stack(jacobian_rows) 2024-08-20T21:40:27.3223122Z >>> 2024-08-20T21:40:27.3223403Z >>> # vectorized gradient computation 2024-08-20T21:40:27.3223796Z >>> def get_vjp(v): 2024-08-20T21:40:27.3224167Z >>> return torch.autograd.grad(y, x, v) 2024-08-20T21:40:27.3224634Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-08-20T21:40:27.3224935Z 2024-08-20T21:40:27.3225299Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-08-20T21:40:27.3225843Z 2024-08-20T21:40:27.3226109Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:40:27.3226932Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-08-20T21:40:27.3227743Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-08-20T21:40:27.3228244Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-08-20T21:40:27.3228584Z 2024-08-20T21:40:27.3228916Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-08-20T21:40:27.3229620Z the dimension that each inputs are batched along as 2024-08-20T21:40:27.3229974Z 2024-08-20T21:40:27.3230228Z >>> torch.dot # [N], [N] -> [] 2024-08-20T21:40:27.3230969Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-08-20T21:40:27.3231606Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-20T21:40:27.3232285Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-08-20T21:40:27.3232794Z 2024-08-20T21:40:27.3233144Z If there are multiple inputs each of which is batched along different dimensions, 2024-08-20T21:40:27.3233954Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-08-20T21:40:27.3234390Z 2024-08-20T21:40:27.3234660Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:40:27.3235402Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-08-20T21:40:27.3236059Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-20T21:40:27.3236818Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-08-20T21:40:27.3237331Z 2024-08-20T21:40:27.3237688Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-08-20T21:40:27.3238320Z matching the shape of the input: 2024-08-20T21:40:27.3238595Z 2024-08-20T21:40:27.3238838Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-08-20T21:40:27.3239343Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-20T21:40:27.3239809Z >>> input = {'x': x, 'y': y} 2024-08-20T21:40:27.3240370Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-08-20T21:40:27.3240888Z >>> batched_dot(input) 2024-08-20T21:40:27.3241103Z 2024-08-20T21:40:27.3241482Z By default, the output is batched along the first dimension. However, it can be batched 2024-08-20T21:40:27.3242196Z along any dimension by using ``out_dims`` 2024-08-20T21:40:27.3242491Z 2024-08-20T21:40:27.3242618Z >>> f = lambda x: x ** 2 2024-08-20T21:40:27.3242955Z >>> x = torch.randn(2, 5) 2024-08-20T21:40:27.3243350Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-08-20T21:40:27.3243997Z >>> batched_pow(x) # [5, 2] 2024-08-20T21:40:27.3244244Z 2024-08-20T21:40:27.3244648Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-08-20T21:40:27.3245325Z accept kwargs 2024-08-20T21:40:27.3245506Z 2024-08-20T21:40:27.3245628Z >>> x = torch.randn([2, 5]) 2024-08-20T21:40:27.3245996Z >>> def fn(x, scale=4.): 2024-08-20T21:40:27.3246322Z >>> return x * scale 2024-08-20T21:40:27.3246637Z >>> 2024-08-20T21:40:27.3246906Z >>> batched_pow = torch.vmap(fn) 2024-08-20T21:40:27.3247349Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-08-20T21:40:27.3247997Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-08-20T21:40:27.3248470Z 2024-08-20T21:40:27.3248591Z .. note:: 2024-08-20T21:40:27.3249102Z vmap does not provide general autobatching or handle variable-length 2024-08-20T21:40:27.3249685Z sequences out of the box. 2024-08-20T21:40:27.3249920Z 2024-08-20T21:40:27.3250398Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:27.3250885Z 2024-08-20T21:40:27.4933579Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=17. 2024-08-20T21:40:27.4935961Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:27.4937424Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-08-20T21:40:27.4938755Z 2024-08-20T21:40:27.4939371Z Use this instead of :func:`torch.library.custom_op` when the implementation 2024-08-20T21:40:27.4940593Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-08-20T21:40:27.4941429Z custom operators as opaque (:func:`torch.compile` and 2024-08-20T21:40:27.4942319Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-08-20T21:40:27.4943584Z makes the implementation visible to these subsystems, allowing them 2024-08-20T21:40:27.4944704Z to optimize the triton kernel(s). 2024-08-20T21:40:27.4945247Z 2024-08-20T21:40:27.4945915Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-08-20T21:40:27.4947245Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-08-20T21:40:27.4948331Z must be wrapped in a call to :func:`torch._library.capture_triton``. 2024-08-20T21:40:27.4948790Z 2024-08-20T21:40:27.4948885Z Args: 2024-08-20T21:40:27.4949369Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-08-20T21:40:27.4950241Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-08-20T21:40:27.4950917Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-08-20T21:40:27.4951629Z To avoid name collisions, please use your project name as the namespace; 2024-08-20T21:40:27.4952412Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-08-20T21:40:27.4953235Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-08-20T21:40:27.4954106Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-08-20T21:40:27.4954956Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-08-20T21:40:27.4955737Z schema (None | str): A schema string for the operator. If None 2024-08-20T21:40:27.4956506Z (recommended) we'll infer a schema for the operator from its type 2024-08-20T21:40:27.4957220Z annotations. We recommend letting us infer a schema unless you 2024-08-20T21:40:27.4957799Z have a specific reason not to. 2024-08-20T21:40:27.4958345Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-08-20T21:40:27.4958714Z 2024-08-20T21:40:27.4958816Z Example:: 2024-08-20T21:40:27.4958976Z 2024-08-20T21:40:27.4959168Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:40:27.4959779Z >>> import torch 2024-08-20T21:40:27.4960210Z >>> from torch._library import triton_op, capture_triton 2024-08-20T21:40:27.4960687Z >>> 2024-08-20T21:40:27.4960934Z >>> import triton 2024-08-20T21:40:27.4961303Z >>> from triton import language as tl 2024-08-20T21:40:27.4961709Z >>> 2024-08-20T21:40:27.4961954Z >>> @triton.jit 2024-08-20T21:40:27.4962276Z >>> def add_kernel( 2024-08-20T21:40:27.4962607Z >>> in_ptr0, 2024-08-20T21:40:27.4962901Z >>> in_ptr1, 2024-08-20T21:40:27.4963206Z >>> out_ptr, 2024-08-20T21:40:27.4963513Z >>> n_elements, 2024-08-20T21:40:27.4963857Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-20T21:40:27.4964256Z >>> ): 2024-08-20T21:40:27.4964602Z >>> pid = tl.program_id(axis=0) 2024-08-20T21:40:27.4965037Z >>> block_start = pid * BLOCK_SIZE 2024-08-20T21:40:27.4965551Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-20T21:40:27.4966053Z >>> mask = offsets < n_elements 2024-08-20T21:40:27.4966508Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-20T21:40:27.4967015Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-20T21:40:27.4967469Z >>> output = x + y 2024-08-20T21:40:27.4967885Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-20T21:40:27.4968454Z >>> 2024-08-20T21:40:27.4968777Z >>> @triton_op("mylib::add", mutates_args={}) 2024-08-20T21:40:27.4969442Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-08-20T21:40:27.4969991Z >>> output = torch.empty_like(x) 2024-08-20T21:40:27.4970534Z >>> n_elements = output.numel() 2024-08-20T21:40:27.4970938Z >>> 2024-08-20T21:40:27.4971190Z >>> def grid(meta): 2024-08-20T21:40:27.4971665Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-20T21:40:27.4972168Z >>> 2024-08-20T21:40:27.4972588Z >>> # NB: we need to wrap the triton kernel in a call to capture_triton 2024-08-20T21:40:27.4973308Z >>> capture_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-08-20T21:40:27.4973861Z >>> return output 2024-08-20T21:40:27.4974176Z >>> 2024-08-20T21:40:27.4974436Z >>> @torch.compile 2024-08-20T21:40:27.4974766Z >>> def f(x, y): 2024-08-20T21:40:27.4975081Z >>> return add(x, y) 2024-08-20T21:40:27.4975425Z >>> 2024-08-20T21:40:27.4975723Z >>> x = torch.randn(3, device="cuda") 2024-08-20T21:40:27.4976163Z >>> y = torch.randn(3, device="cuda") 2024-08-20T21:40:27.4976561Z >>> 2024-08-20T21:40:27.4976814Z >>> z = f(x, y) 2024-08-20T21:40:27.4977144Z >>> assert torch.allclose(z, x + y) 2024-08-20T21:40:27.4977456Z 2024-08-20T21:40:27.4977545Z 2024-08-20T21:40:27.4978113Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:27.4978603Z 2024-08-20T21:40:27.4979481Z msg = Cannot scrape callname=capture_triton in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=163. 2024-08-20T21:40:27.4980742Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:27.4981495Z Allows capture of a triton kernel into a graph via make_fx or 2024-08-20T21:40:27.4982094Z non-strict export (coming soon). 2024-08-20T21:40:27.4982368Z 2024-08-20T21:40:27.4982664Z These technologies perform Dispatcher-based tracing (via 2024-08-20T21:40:27.4983313Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-08-20T21:40:27.4984005Z The ``capture_triton`` API returns a new callable that can actually 2024-08-20T21:40:27.4984549Z be traced into a graph. 2024-08-20T21:40:27.4984789Z 2024-08-20T21:40:27.4984890Z Examples: 2024-08-20T21:40:27.4985050Z 2024-08-20T21:40:27.4985178Z >>> # xdoctest: +SKIP 2024-08-20T21:40:27.4985623Z >>> import torch 2024-08-20T21:40:27.4985951Z >>> import triton 2024-08-20T21:40:27.4986316Z >>> from triton import language as tl 2024-08-20T21:40:27.4986855Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-20T21:40:27.4987564Z >>> from torch._higher_order_ops.triton_kernel_wrap import capture_triton 2024-08-20T21:40:27.4988132Z >>> 2024-08-20T21:40:27.4988397Z >>> @triton.jit 2024-08-20T21:40:27.4988696Z >>> def add_kernel( 2024-08-20T21:40:27.4989024Z >>> in_ptr0, 2024-08-20T21:40:27.4989321Z >>> in_ptr1, 2024-08-20T21:40:27.4989625Z >>> out_ptr, 2024-08-20T21:40:27.4989934Z >>> n_elements, 2024-08-20T21:40:27.4990501Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-20T21:40:27.4990908Z >>> ): 2024-08-20T21:40:27.4991214Z >>> pid = tl.program_id(axis=0) 2024-08-20T21:40:27.4991647Z >>> block_start = pid * BLOCK_SIZE 2024-08-20T21:40:27.4992166Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-20T21:40:27.4992671Z >>> mask = offsets < n_elements 2024-08-20T21:40:27.4993139Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-20T21:40:27.4993635Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-20T21:40:27.4994108Z >>> output = x + y 2024-08-20T21:40:27.4994537Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-20T21:40:27.4995133Z >>> 2024-08-20T21:40:27.4995400Z >>> def add(x, y): 2024-08-20T21:40:27.4995769Z >>> output = torch.empty_like(x) 2024-08-20T21:40:27.4996205Z >>> n_elements = output.numel() 2024-08-20T21:40:27.4996610Z >>> 2024-08-20T21:40:27.4996881Z >>> def grid_fn(meta): 2024-08-20T21:40:27.4997346Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-20T21:40:27.4997836Z >>> 2024-08-20T21:40:27.4998266Z >>> capture_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-08-20T21:40:27.4998813Z >>> return output 2024-08-20T21:40:27.4999140Z >>> 2024-08-20T21:40:27.4999434Z >>> x = torch.randn(3, device="cuda") 2024-08-20T21:40:27.4999873Z >>> y = torch.randn(3, device="cuda") 2024-08-20T21:40:27.5000300Z >>> gm = make_fx(add)(x, y) 2024-08-20T21:40:27.5000677Z >>> print(gm.code) 2024-08-20T21:40:27.5001028Z >>> # def forward(self, x_1, y_1): 2024-08-20T21:40:27.5001648Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-08-20T21:40:27.5002451Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-08-20T21:40:27.5003093Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-08-20T21:40:27.5003589Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-08-20T21:40:27.5004262Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-08-20T21:40:27.5004872Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-08-20T21:40:27.5005313Z >>> # }) 2024-08-20T21:40:27.5005641Z >>> # return empty_like 2024-08-20T21:40:27.5005902Z 2024-08-20T21:40:27.5005989Z 2024-08-20T21:40:27.5006528Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:27.5007027Z 2024-08-20T21:40:27.5667692Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=330. 2024-08-20T21:40:27.5670002Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:27.5671084Z 2024-08-20T21:40:27.5671533Z Raises an AssertionError if two items are not equal up to desired 2024-08-20T21:40:27.5672153Z precision. 2024-08-20T21:40:27.5672330Z 2024-08-20T21:40:27.5672577Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:40:27.5673979Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:40:27.5674653Z instead of this function for more consistent floating point 2024-08-20T21:40:27.5675261Z comparisons. 2024-08-20T21:40:27.5675555Z 2024-08-20T21:40:27.5675866Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-08-20T21:40:27.5676606Z 2024-08-20T21:40:27.5677141Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-08-20T21:40:27.5677761Z 2024-08-20T21:40:27.5678300Z That is a looser test than originally documented, but agrees with what the 2024-08-20T21:40:27.5679576Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-08-20T21:40:27.5680998Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-08-20T21:40:27.5682068Z delegates to assert_array_almost_equal 2024-08-20T21:40:27.5682569Z 2024-08-20T21:40:27.5682747Z Parameters 2024-08-20T21:40:27.5683246Z ---------- 2024-08-20T21:40:27.5683651Z actual : array_like 2024-08-20T21:40:27.5684112Z The object to check. 2024-08-20T21:40:27.5684610Z desired : array_like 2024-08-20T21:40:27.5685098Z The expected object. 2024-08-20T21:40:27.5685653Z decimal : int, optional 2024-08-20T21:40:27.5686289Z Desired precision, default is 7. 2024-08-20T21:40:27.5687013Z err_msg : str, optional 2024-08-20T21:40:27.5687714Z The error message to be printed in case of failure. 2024-08-20T21:40:27.5688821Z verbose : bool, optional 2024-08-20T21:40:27.5689586Z If True, the conflicting values are appended to the error message. 2024-08-20T21:40:27.5690027Z 2024-08-20T21:40:27.5690190Z Raises 2024-08-20T21:40:27.5690670Z ------ 2024-08-20T21:40:27.5690918Z AssertionError 2024-08-20T21:40:27.5691346Z If actual and desired are not equal up to specified precision. 2024-08-20T21:40:27.5691786Z 2024-08-20T21:40:27.5691881Z See Also 2024-08-20T21:40:27.5692143Z -------- 2024-08-20T21:40:27.5692580Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:40:27.5693229Z relative and/or absolute precision. 2024-08-20T21:40:27.5693821Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:40:27.5694247Z 2024-08-20T21:40:27.5694339Z Examples 2024-08-20T21:40:27.5694604Z -------- 2024-08-20T21:40:27.5694957Z >>> from torch._numpy.testing import assert_almost_equal 2024-08-20T21:40:27.5695478Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-08-20T21:40:27.5696032Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-08-20T21:40:27.5696548Z Traceback (most recent call last): 2024-08-20T21:40:27.5696899Z ... 2024-08-20T21:40:27.5697148Z AssertionError: 2024-08-20T21:40:27.5697478Z Arrays are not almost equal to 10 decimals 2024-08-20T21:40:27.5697882Z ACTUAL: 2.3333333333333 2024-08-20T21:40:27.5698198Z DESIRED: 2.33333334 2024-08-20T21:40:27.5698385Z 2024-08-20T21:40:27.5698582Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-08-20T21:40:27.5699105Z ... np.array([1.0,2.33333334]), decimal=9) 2024-08-20T21:40:27.5699559Z Traceback (most recent call last): 2024-08-20T21:40:27.5699920Z ... 2024-08-20T21:40:27.5700169Z AssertionError: 2024-08-20T21:40:27.5700480Z Arrays are not almost equal to 9 decimals 2024-08-20T21:40:27.5700883Z 2024-08-20T21:40:27.5701153Z Mismatched elements: 1 / 2 (50%) 2024-08-20T21:40:27.5701635Z Max absolute difference: 6.666699636781459e-09 2024-08-20T21:40:27.5702182Z Max relative difference: 2.8571569790287484e-09 2024-08-20T21:40:27.5702677Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-20T21:40:27.5703168Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-20T21:40:27.5703510Z 2024-08-20T21:40:27.5703515Z 2024-08-20T21:40:27.5703918Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:27.5704403Z 2024-08-20T21:40:27.5705449Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=455. 2024-08-20T21:40:27.5706771Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:27.5707286Z 2024-08-20T21:40:27.5707575Z Raises an AssertionError if two items are not equal up to significant 2024-08-20T21:40:27.5708132Z digits. 2024-08-20T21:40:27.5708267Z 2024-08-20T21:40:27.5708516Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:40:27.5709130Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:40:27.5709782Z instead of this function for more consistent floating point 2024-08-20T21:40:27.5710311Z comparisons. 2024-08-20T21:40:27.5710507Z 2024-08-20T21:40:27.5710741Z Given two numbers, check that they are approximately equal. 2024-08-20T21:40:27.5711422Z Approximately equal is defined as the number of significant digits 2024-08-20T21:40:27.5711970Z that agree. 2024-08-20T21:40:27.5712121Z 2024-08-20T21:40:27.5712220Z Parameters 2024-08-20T21:40:27.5712497Z ---------- 2024-08-20T21:40:27.5712747Z actual : scalar 2024-08-20T21:40:27.5713021Z The object to check. 2024-08-20T21:40:27.5713342Z desired : scalar 2024-08-20T21:40:27.5713634Z The expected object. 2024-08-20T21:40:27.5713956Z significant : int, optional 2024-08-20T21:40:27.5714330Z Desired precision, default is 7. 2024-08-20T21:40:27.5714721Z err_msg : str, optional 2024-08-20T21:40:27.5715118Z The error message to be printed in case of failure. 2024-08-20T21:40:27.5715711Z verbose : bool, optional 2024-08-20T21:40:27.5716208Z If True, the conflicting values are appended to the error message. 2024-08-20T21:40:27.5716641Z 2024-08-20T21:40:27.5716731Z Raises 2024-08-20T21:40:27.5716984Z ------ 2024-08-20T21:40:27.5717234Z AssertionError 2024-08-20T21:40:27.5717654Z If actual and desired are not equal up to specified precision. 2024-08-20T21:40:27.5718086Z 2024-08-20T21:40:27.5718179Z See Also 2024-08-20T21:40:27.5718442Z -------- 2024-08-20T21:40:27.5718885Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:40:27.5719530Z relative and/or absolute precision. 2024-08-20T21:40:27.5720124Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:40:27.5720545Z 2024-08-20T21:40:27.5720640Z Examples 2024-08-20T21:40:27.5720903Z -------- 2024-08-20T21:40:27.5721496Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-08-20T21:40:27.5722446Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-08-20T21:40:27.5723084Z ... significant=8) 2024-08-20T21:40:27.5723805Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-08-20T21:40:27.5724454Z ... significant=8) 2024-08-20T21:40:27.5724888Z Traceback (most recent call last): 2024-08-20T21:40:27.5725253Z ... 2024-08-20T21:40:27.5725501Z AssertionError: 2024-08-20T21:40:27.5725833Z Items are not equal to 8 significant digits: 2024-08-20T21:40:27.5726301Z ACTUAL: 1.234567e-21 2024-08-20T21:40:27.5726642Z DESIRED: 1.2345672e-21 2024-08-20T21:40:27.5726846Z 2024-08-20T21:40:27.5727043Z the evaluated condition that raises the exception is 2024-08-20T21:40:27.5727415Z 2024-08-20T21:40:27.5727717Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-08-20T21:40:27.5728198Z True 2024-08-20T21:40:27.5728333Z 2024-08-20T21:40:27.5728337Z 2024-08-20T21:40:27.5728732Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:27.5729236Z 2024-08-20T21:40:27.5730288Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=734. 2024-08-20T21:40:27.5731597Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:27.5732112Z 2024-08-20T21:40:27.5732497Z Raises an AssertionError if two array_like objects are not equal. 2024-08-20T21:40:27.5732943Z 2024-08-20T21:40:27.5733216Z Given two array_like objects, check that the shape is equal and all 2024-08-20T21:40:27.5733957Z elements of these objects are equal (but see the Notes for the special 2024-08-20T21:40:27.5734851Z handling of a scalar). An exception is raised at shape mismatch or 2024-08-20T21:40:27.5735622Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-08-20T21:40:27.5736838Z are compared like numbers, no assertion is raised if both objects have 2024-08-20T21:40:27.5737821Z NaNs in the same positions. 2024-08-20T21:40:27.5738275Z 2024-08-20T21:40:27.5738835Z The usual caution for verifying equality with floating point numbers is 2024-08-20T21:40:27.5739897Z advised. 2024-08-20T21:40:27.5740162Z 2024-08-20T21:40:27.5740265Z Parameters 2024-08-20T21:40:27.5740566Z ---------- 2024-08-20T21:40:27.5740818Z x : array_like 2024-08-20T21:40:27.5741096Z The actual object to check. 2024-08-20T21:40:27.5741453Z y : array_like 2024-08-20T21:40:27.5741760Z The desired, expected object. 2024-08-20T21:40:27.5742121Z err_msg : str, optional 2024-08-20T21:40:27.5742531Z The error message to be printed in case of failure. 2024-08-20T21:40:27.5743006Z verbose : bool, optional 2024-08-20T21:40:27.5743492Z If True, the conflicting values are appended to the error message. 2024-08-20T21:40:27.5744050Z strict : bool, optional 2024-08-20T21:40:27.5744536Z If True, raise an AssertionError when either the shape or the data 2024-08-20T21:40:27.5745307Z type of the array_like objects does not match. The special 2024-08-20T21:40:27.5745983Z handling for scalars mentioned in the Notes section is disabled. 2024-08-20T21:40:27.5746431Z 2024-08-20T21:40:27.5746519Z Raises 2024-08-20T21:40:27.5746777Z ------ 2024-08-20T21:40:27.5747007Z AssertionError 2024-08-20T21:40:27.5747349Z If actual and desired objects are not equal. 2024-08-20T21:40:27.5747676Z 2024-08-20T21:40:27.5747781Z See Also 2024-08-20T21:40:27.5748033Z -------- 2024-08-20T21:40:27.5748488Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:40:27.5749127Z relative and/or absolute precision. 2024-08-20T21:40:27.5761377Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:40:27.5761851Z 2024-08-20T21:40:27.5761945Z Notes 2024-08-20T21:40:27.5762276Z ----- 2024-08-20T21:40:27.5762682Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-08-20T21:40:27.5763461Z function checks that each element of the array_like object is equal to 2024-08-20T21:40:27.5764239Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-08-20T21:40:27.5764708Z 2024-08-20T21:40:27.5764809Z Examples 2024-08-20T21:40:27.5765066Z -------- 2024-08-20T21:40:27.5765383Z The first assert does not raise an exception: 2024-08-20T21:40:27.5765709Z 2024-08-20T21:40:27.5765918Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-08-20T21:40:27.5766445Z ... [np.exp(0),2.33333, np.nan]) 2024-08-20T21:40:27.5766789Z 2024-08-20T21:40:27.5767097Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-08-20T21:40:27.5767702Z functions for these cases instead: 2024-08-20T21:40:27.5767967Z 2024-08-20T21:40:27.5768147Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-08-20T21:40:27.5768665Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-08-20T21:40:27.5769220Z ... rtol=1e-10, atol=0) 2024-08-20T21:40:27.5769527Z 2024-08-20T21:40:27.5769802Z As mentioned in the Notes section, `assert_array_equal` has special 2024-08-20T21:40:27.5770636Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-08-20T21:40:27.5771116Z 2024-08-20T21:40:27.5771248Z >>> x = np.full((2, 5), fill_value=3) 2024-08-20T21:40:27.5771670Z >>> np.testing.assert_array_equal(x, 3) 2024-08-20T21:40:27.5771959Z 2024-08-20T21:40:27.5772384Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-08-20T21:40:27.5772951Z array: 2024-08-20T21:40:27.5773119Z 2024-08-20T21:40:27.5773312Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-08-20T21:40:27.5773802Z Traceback (most recent call last): 2024-08-20T21:40:27.5774173Z ... 2024-08-20T21:40:27.5774509Z AssertionError: 2024-08-20T21:40:27.5774804Z Arrays are not equal 2024-08-20T21:40:27.5775103Z 2024-08-20T21:40:27.5775370Z (shapes (2, 5), () mismatch) 2024-08-20T21:40:27.5775733Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-08-20T21:40:27.5776128Z [3, 3, 3, 3, 3]]) 2024-08-20T21:40:27.5776435Z y: torch.ndarray(3) 2024-08-20T21:40:27.5776644Z 2024-08-20T21:40:27.5776923Z The `strict` parameter also ensures that the array data types match: 2024-08-20T21:40:27.5777369Z 2024-08-20T21:40:27.5777495Z >>> x = np.array([2, 2, 2]) 2024-08-20T21:40:27.5777878Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-08-20T21:40:27.5778397Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-08-20T21:40:27.5778889Z Traceback (most recent call last): 2024-08-20T21:40:27.5779247Z ... 2024-08-20T21:40:27.5779493Z AssertionError: 2024-08-20T21:40:27.5779785Z Arrays are not equal 2024-08-20T21:40:27.5780073Z 2024-08-20T21:40:27.5780416Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-08-20T21:40:27.5780883Z x: torch.ndarray([2, 2, 2]) 2024-08-20T21:40:27.5781234Z y: torch.ndarray([2., 2., 2.]) 2024-08-20T21:40:27.5781593Z 2024-08-20T21:40:27.5782031Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:27.5782520Z 2024-08-20T21:40:27.5783457Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=840. 2024-08-20T21:40:27.5784790Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:27.5785307Z 2024-08-20T21:40:27.5785587Z Raises an AssertionError if two objects are not equal up to desired 2024-08-20T21:40:27.5786141Z precision. 2024-08-20T21:40:27.5786295Z 2024-08-20T21:40:27.5786554Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:40:27.5787169Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:40:27.5787822Z instead of this function for more consistent floating point 2024-08-20T21:40:27.5788352Z comparisons. 2024-08-20T21:40:27.5788549Z 2024-08-20T21:40:27.5788869Z The test verifies identical shapes and that the elements of ``actual`` and 2024-08-20T21:40:27.5789470Z ``desired`` satisfy. 2024-08-20T21:40:27.5789661Z 2024-08-20T21:40:27.5789897Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-08-20T21:40:27.5790224Z 2024-08-20T21:40:27.5790742Z That is a looser test than originally documented, but agrees with what the 2024-08-20T21:40:27.5791550Z actual implementation did up to rounding vagaries. An exception is raised 2024-08-20T21:40:27.5792358Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-08-20T21:40:27.5793158Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-08-20T21:40:27.5793772Z objects have NaNs in the same positions. 2024-08-20T21:40:27.5794088Z 2024-08-20T21:40:27.5794187Z Parameters 2024-08-20T21:40:27.5794475Z ---------- 2024-08-20T21:40:27.5794718Z x : array_like 2024-08-20T21:40:27.5795015Z The actual object to check. 2024-08-20T21:40:27.5795373Z y : array_like 2024-08-20T21:40:27.5795663Z The desired, expected object. 2024-08-20T21:40:27.5796044Z decimal : int, optional 2024-08-20T21:40:27.5796393Z Desired precision, default is 6. 2024-08-20T21:40:27.5796776Z err_msg : str, optional 2024-08-20T21:40:27.5797191Z The error message to be printed in case of failure. 2024-08-20T21:40:27.5797670Z verbose : bool, optional 2024-08-20T21:40:27.5798154Z If True, the conflicting values are appended to the error message. 2024-08-20T21:40:27.5798608Z 2024-08-20T21:40:27.5798700Z Raises 2024-08-20T21:40:27.5798953Z ------ 2024-08-20T21:40:27.5799326Z AssertionError 2024-08-20T21:40:27.5799772Z If actual and desired are not equal up to specified precision. 2024-08-20T21:40:27.5800193Z 2024-08-20T21:40:27.5800302Z See Also 2024-08-20T21:40:27.5800555Z -------- 2024-08-20T21:40:27.5801010Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:40:27.5801661Z relative and/or absolute precision. 2024-08-20T21:40:27.5802246Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:40:27.5802682Z 2024-08-20T21:40:27.5802778Z Examples 2024-08-20T21:40:27.5803043Z -------- 2024-08-20T21:40:27.5803338Z the first assert does not raise an exception 2024-08-20T21:40:27.5803674Z 2024-08-20T21:40:27.5803895Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-08-20T21:40:27.5804441Z ... [1.0,2.333,np.nan]) 2024-08-20T21:40:27.5804759Z 2024-08-20T21:40:27.5805002Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-20T21:40:27.5805560Z ... [1.0,2.33339,np.nan], decimal=5) 2024-08-20T21:40:27.5806043Z Traceback (most recent call last): 2024-08-20T21:40:27.5806410Z ... 2024-08-20T21:40:27.5806648Z AssertionError: 2024-08-20T21:40:27.5806982Z Arrays are not almost equal to 5 decimals 2024-08-20T21:40:27.5807391Z 2024-08-20T21:40:27.5807663Z Mismatched elements: 1 / 3 (33.3%) 2024-08-20T21:40:27.5808263Z Max absolute difference: 5.999999999994898e-05 2024-08-20T21:40:27.5808811Z Max relative difference: 2.5713661239633743e-05 2024-08-20T21:40:27.5809333Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-20T21:40:27.5809919Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-08-20T21:40:27.5810356Z 2024-08-20T21:40:27.5810600Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-20T21:40:27.5811155Z ... [1.0,2.33333, 5], decimal=5) 2024-08-20T21:40:27.5811640Z Traceback (most recent call last): 2024-08-20T21:40:27.5812011Z ... 2024-08-20T21:40:27.5812251Z AssertionError: 2024-08-20T21:40:27.5812581Z Arrays are not almost equal to 5 decimals 2024-08-20T21:40:27.5812991Z 2024-08-20T21:40:27.5813264Z x and y nan location mismatch: 2024-08-20T21:40:27.5813722Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-20T21:40:27.5814297Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-08-20T21:40:27.5814667Z 2024-08-20T21:40:27.5814672Z 2024-08-20T21:40:27.5815073Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:27.5815578Z 2024-08-20T21:40:27.5816532Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1790. 2024-08-20T21:40:27.5817889Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:27.5818678Z Context manager that resets warning registry for catching warnings 2024-08-20T21:40:27.5819109Z 2024-08-20T21:40:27.5819452Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-08-20T21:40:27.5820236Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-08-20T21:40:27.5821026Z it impossible to retrigger the warning in this module, whatever you put in 2024-08-20T21:40:27.5821841Z the warnings filters. This context manager accepts a sequence of `modules` 2024-08-20T21:40:27.5822499Z as a keyword argument to its constructor and: 2024-08-20T21:40:27.5822850Z 2024-08-20T21:40:27.5823154Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-08-20T21:40:27.5823731Z on entry; 2024-08-20T21:40:27.5824135Z * resets ``__warningregistry__`` to its previous state on exit. 2024-08-20T21:40:27.5824549Z 2024-08-20T21:40:27.5824855Z This makes it possible to trigger any warning afresh inside the context 2024-08-20T21:40:27.5825638Z manager without disturbing the state of warnings outside. 2024-08-20T21:40:27.5826032Z 2024-08-20T21:40:27.5826358Z For compatibility with Python 3.0, please consider all arguments to be 2024-08-20T21:40:27.5826962Z keyword-only. 2024-08-20T21:40:27.5827157Z 2024-08-20T21:40:27.5827258Z Parameters 2024-08-20T21:40:27.5827558Z ---------- 2024-08-20T21:40:27.5827831Z record : bool, optional 2024-08-20T21:40:27.5828307Z Specifies whether warnings should be captured by a custom 2024-08-20T21:40:27.5829031Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-08-20T21:40:27.5829782Z returned by the context manager. Otherwise None is returned by the 2024-08-20T21:40:27.5830535Z context manager. The objects appended to the list are arguments whose 2024-08-20T21:40:27.5831221Z attributes mirror the arguments to ``showwarning()``. 2024-08-20T21:40:27.5831716Z modules : sequence, optional 2024-08-20T21:40:27.5832284Z Sequence of modules for which to reset warnings registry on entry and 2024-08-20T21:40:27.5833093Z restore on exit. To work correctly, all 'ignore' filters should 2024-08-20T21:40:27.5833649Z filter by one of these modules. 2024-08-20T21:40:27.5833948Z 2024-08-20T21:40:27.5834046Z Examples 2024-08-20T21:40:27.5834334Z -------- 2024-08-20T21:40:27.5834586Z >>> import warnings 2024-08-20T21:40:27.5835047Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-08-20T21:40:27.5835705Z ... modules=[np.core.fromnumeric]): 2024-08-20T21:40:27.5836214Z ... warnings.simplefilter('always') 2024-08-20T21:40:27.5836887Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-08-20T21:40:27.5837589Z ... # do something that raises a warning but ignore those in 2024-08-20T21:40:27.5838120Z ... # np.core.fromnumeric 2024-08-20T21:40:27.5838459Z 2024-08-20T21:40:27.5839005Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:27.5839495Z 2024-08-20T21:40:27.8270789Z msg = Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=355. 2024-08-20T21:40:27.8273225Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:27.8274188Z Applies a 1D convolution over a quantized input signal composed of 2024-08-20T21:40:27.8274822Z several quantized input planes. 2024-08-20T21:40:27.8275100Z 2024-08-20T21:40:27.8275404Z For details on input arguments, parameters, and implementation see 2024-08-20T21:40:27.8275966Z :class:`~torch.nn.Conv1d`. 2024-08-20T21:40:27.8276222Z 2024-08-20T21:40:27.8276341Z .. note:: 2024-08-20T21:40:27.8276777Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-08-20T21:40:27.8277207Z 2024-08-20T21:40:27.8277308Z .. note:: 2024-08-20T21:40:27.8277708Z Only `torch.quint8` is supported for the input data type. 2024-08-20T21:40:27.8278128Z 2024-08-20T21:40:27.8278133Z 2024-08-20T21:40:27.8278239Z Attributes: 2024-08-20T21:40:27.8278699Z weight (Tensor): packed tensor derived from the learnable weight 2024-08-20T21:40:27.8279258Z parameter. 2024-08-20T21:40:27.8279732Z scale (Tensor): scalar for the output scale 2024-08-20T21:40:27.8280298Z zero_point (Tensor): scalar for the output zero point 2024-08-20T21:40:27.8280677Z 2024-08-20T21:40:27.8280874Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-08-20T21:40:27.8281242Z 2024-08-20T21:40:27.8281349Z Examples:: 2024-08-20T21:40:27.8281516Z 2024-08-20T21:40:27.8281723Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-08-20T21:40:27.8282250Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-08-20T21:40:27.8282746Z >>> input = torch.randn(20, 16, 100) 2024-08-20T21:40:27.8283188Z >>> # quantize input to quint8 2024-08-20T21:40:27.8283888Z >>> # xdoctest: +SKIP 2024-08-20T21:40:27.8284418Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-08-20T21:40:27.8285049Z ... dtype=torch.quint8) 2024-08-20T21:40:27.8285521Z >>> output = m(q_input) 2024-08-20T21:40:27.8285767Z 2024-08-20T21:40:27.8285859Z 2024-08-20T21:40:27.8286427Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:27.8286919Z 2024-08-20T21:40:27.8465074Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11. 2024-08-20T21:40:27.8467037Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:27.8467780Z A quantized long short-term memory (LSTM). 2024-08-20T21:40:27.8468111Z 2024-08-20T21:40:27.8468490Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-08-20T21:40:27.8469036Z 2024-08-20T21:40:27.8469164Z Attributes: 2024-08-20T21:40:27.8469489Z layers : instances of the `_LSTMLayer` 2024-08-20T21:40:27.8469816Z 2024-08-20T21:40:27.8469932Z .. note:: 2024-08-20T21:40:27.8470397Z To access the weights and biases, you need to access them per layer. 2024-08-20T21:40:27.8471077Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-08-20T21:40:27.8471481Z 2024-08-20T21:40:27.8471776Z Examples:: 2024-08-20T21:40:27.8472068Z >>> # xdoctest: +SKIP 2024-08-20T21:40:27.8472419Z >>> custom_module_config = { 2024-08-20T21:40:27.8472947Z ... 'float_to_observed_custom_module_class': { 2024-08-20T21:40:27.8473458Z ... nn.LSTM: nn.quantizable.LSTM, 2024-08-20T21:40:27.8473884Z ... }, 2024-08-20T21:40:27.8474316Z ... 'observed_to_quantized_custom_module_class': { 2024-08-20T21:40:27.8474874Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-08-20T21:40:27.8475330Z ... } 2024-08-20T21:40:27.8475599Z ... } 2024-08-20T21:40:27.8476053Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-08-20T21:40:27.8476799Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-08-20T21:40:27.8477332Z 2024-08-20T21:40:27.8477877Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:27.8478368Z 2024-08-20T21:40:27.9321907Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227. 2024-08-20T21:40:27.9324241Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:27.9324984Z Squashes the sparse masks into the appropriate tensors. 2024-08-20T21:40:27.9325396Z 2024-08-20T21:40:27.9325700Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-08-20T21:40:27.9326408Z the module will have a `sparse_params` dict attached to it. 2024-08-20T21:40:27.9326834Z 2024-08-20T21:40:27.9326933Z Args: 2024-08-20T21:40:27.9327351Z params_to_keep: List of keys to save in the module or a dict 2024-08-20T21:40:27.9327979Z representing the modules and keys that will have 2024-08-20T21:40:27.9328567Z sparsity parameters saved 2024-08-20T21:40:27.9329313Z params_to_keep_per_layer: Dict to specify the params that should be 2024-08-20T21:40:27.9329973Z saved for specific layers. The keys in the dict 2024-08-20T21:40:27.9330633Z should be the module fqn, while the values should 2024-08-20T21:40:27.9331254Z be a list of strings with the names of the variables 2024-08-20T21:40:27.9331825Z to save in the `sparse_params` 2024-08-20T21:40:27.9332155Z 2024-08-20T21:40:27.9332258Z Examples: 2024-08-20T21:40:27.9332847Z >>> # xdoctest: +SKIP("locals are undefined") 2024-08-20T21:40:27.9333429Z >>> # Don't save any sparse params 2024-08-20T21:40:27.9333874Z >>> sparsifier.squash_mask() 2024-08-20T21:40:27.9334419Z >>> hasattr(model.submodule1, 'sparse_params') 2024-08-20T21:40:27.9334869Z False 2024-08-20T21:40:27.9335044Z 2024-08-20T21:40:27.9335199Z >>> # Keep sparse params per layer 2024-08-20T21:40:27.9335656Z >>> sparsifier.squash_mask( 2024-08-20T21:40:27.9336090Z ... params_to_keep_per_layer={ 2024-08-20T21:40:27.9336632Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-08-20T21:40:27.9337210Z ... 'submodule2.linear42': ('baz',) 2024-08-20T21:40:27.9337644Z ... }) 2024-08-20T21:40:27.9338067Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:40:27.9338610Z {'foo': 42, 'bar': 24} 2024-08-20T21:40:27.9339085Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:40:27.9339596Z {'baz': 0.1} 2024-08-20T21:40:27.9339812Z 2024-08-20T21:40:27.9339975Z >>> # Keep sparse params for all layers 2024-08-20T21:40:27.9340603Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-08-20T21:40:27.9341208Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:40:27.9341848Z {'foo': 42, 'bar': 24} 2024-08-20T21:40:27.9342317Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:40:27.9342856Z {'foo': 42, 'bar': 24} 2024-08-20T21:40:27.9343102Z 2024-08-20T21:40:27.9343369Z >>> # Keep some sparse params for all layers, and specific ones for 2024-08-20T21:40:27.9343938Z >>> # some other layers 2024-08-20T21:40:27.9344345Z >>> sparsifier.squash_mask( 2024-08-20T21:40:27.9344825Z ... params_to_keep=('foo', 'bar'), 2024-08-20T21:40:27.9345297Z ... params_to_keep_per_layer={ 2024-08-20T21:40:27.9345835Z ... 'submodule2.linear42': ('baz',) 2024-08-20T21:40:27.9346255Z ... }) 2024-08-20T21:40:27.9346661Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:40:27.9347198Z {'foo': 42, 'bar': 24} 2024-08-20T21:40:27.9347650Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:40:27.9348225Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-08-20T21:40:27.9348625Z 2024-08-20T21:40:27.9349162Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:27.9349658Z 2024-08-20T21:40:28.0096172Z msg = Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2024-08-20T21:40:28.0098908Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:28.0099822Z 2024-08-20T21:40:28.0100595Z Config object that specifies the supported data types passed as arguments to 2024-08-20T21:40:28.0101579Z quantize ops in the reference model spec, for input and output activations, 2024-08-20T21:40:28.0102176Z weights, and biases. 2024-08-20T21:40:28.0102389Z 2024-08-20T21:40:28.0102586Z For example, consider the following reference model: 2024-08-20T21:40:28.0102948Z 2024-08-20T21:40:28.0103233Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-08-20T21:40:28.0103597Z 2024-08-20T21:40:28.0103890Z The pattern in the square brackets refers to the reference pattern of 2024-08-20T21:40:28.0104653Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-08-20T21:40:28.0105435Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-08-20T21:40:28.0106203Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-08-20T21:40:28.0106977Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-08-20T21:40:28.0107831Z the second quantize op (quant2). 2024-08-20T21:40:28.0108089Z 2024-08-20T21:40:28.0108405Z Note that the dtype here does not refer to the interface dtypes of the 2024-08-20T21:40:28.0109140Z op. For example, the "input dtype" here is not the dtype of the input 2024-08-20T21:40:28.0109880Z tensor passed to the quantized linear op. Though it can still be the 2024-08-20T21:40:28.0110611Z same as the interface dtype, this is not always the case, e.g. the 2024-08-20T21:40:28.0111341Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-08-20T21:40:28.0112091Z specified in the DTypeConfig would still be quint8. The semantics of 2024-08-20T21:40:28.0112836Z dtypes here are the same as the semantics of the dtypes specified in 2024-08-20T21:40:28.0113387Z the observers. 2024-08-20T21:40:28.0113570Z 2024-08-20T21:40:28.0113883Z These dtypes are matched against the ones specified in the user's 2024-08-20T21:40:28.0114619Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-08-20T21:40:28.0115378Z specified in the DTypeConfig (if any), then we will quantize the given 2024-08-20T21:40:28.0116140Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-08-20T21:40:28.0116742Z the pattern will not be quantized. 2024-08-20T21:40:28.0117008Z 2024-08-20T21:40:28.0117157Z Example usage:: 2024-08-20T21:40:28.0117328Z 2024-08-20T21:40:28.0117460Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:40:28.0117987Z >>> dtype_config1 = DTypeConfig( 2024-08-20T21:40:28.0118406Z ... input_dtype=torch.quint8, 2024-08-20T21:40:28.0118804Z ... output_dtype=torch.quint8, 2024-08-20T21:40:28.0119219Z ... weight_dtype=torch.qint8, 2024-08-20T21:40:28.0119624Z ... bias_dtype=torch.float) 2024-08-20T21:40:28.0119887Z 2024-08-20T21:40:28.0120016Z >>> dtype_config2 = DTypeConfig( 2024-08-20T21:40:28.0120460Z ... input_dtype=DTypeWithConstraints( 2024-08-20T21:40:28.0120912Z ... dtype=torch.quint8, 2024-08-20T21:40:28.0121298Z ... quant_min_lower_bound=0, 2024-08-20T21:40:28.0121720Z ... quant_max_upper_bound=255, 2024-08-20T21:40:28.0122117Z ... ), 2024-08-20T21:40:28.0122433Z ... output_dtype=DTypeWithConstraints( 2024-08-20T21:40:28.0122882Z ... dtype=torch.quint8, 2024-08-20T21:40:28.0123280Z ... quant_min_lower_bound=0, 2024-08-20T21:40:28.0123688Z ... quant_max_upper_bound=255, 2024-08-20T21:40:28.0124091Z ... ), 2024-08-20T21:40:28.0124421Z ... weight_dtype=DTypeWithConstraints( 2024-08-20T21:40:28.0124851Z ... dtype=torch.qint8, 2024-08-20T21:40:28.0125311Z ... quant_min_lower_bound=-128, 2024-08-20T21:40:28.0125746Z ... quant_max_upper_bound=127, 2024-08-20T21:40:28.0126120Z ... ), 2024-08-20T21:40:28.0126411Z ... bias_dtype=torch.float) 2024-08-20T21:40:28.0126671Z 2024-08-20T21:40:28.0126810Z >>> dtype_config1.input_dtype 2024-08-20T21:40:28.0127162Z torch.quint8 2024-08-20T21:40:28.0127355Z 2024-08-20T21:40:28.0127478Z >>> dtype_config2.input_dtype 2024-08-20T21:40:28.0127841Z torch.quint8 2024-08-20T21:40:28.0128014Z 2024-08-20T21:40:28.0128212Z >>> dtype_config2.input_dtype_with_constraints 2024-08-20T21:40:28.0129270Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-08-20T21:40:28.0130253Z 2024-08-20T21:40:28.0130671Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:28.0131158Z 2024-08-20T21:40:28.1140603Z msg = Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2024-08-20T21:40:28.1144052Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:28.1145035Z 2024-08-20T21:40:28.1146163Z Takes in optional filter values and generates two tables with desired information. 2024-08-20T21:40:28.1147097Z 2024-08-20T21:40:28.1147791Z The generated tables are presented in both a list-of-lists format 2024-08-20T21:40:28.1148636Z 2024-08-20T21:40:28.1149167Z The reason for the two tables are that they handle different things: 2024-08-20T21:40:28.1150430Z 1.) the first table handles all tensor level information 2024-08-20T21:40:28.1151628Z 2.) the second table handles and displays all channel based information 2024-08-20T21:40:28.1152424Z 2024-08-20T21:40:28.1153274Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-20T21:40:28.1155485Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-20T21:40:28.1157557Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-20T21:40:28.1158774Z 2024-08-20T21:40:28.1158977Z Tensor table columns: 2024-08-20T21:40:28.1159840Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:40:28.1161127Z ---- --------- --------- --------- --------- --------- 2024-08-20T21:40:28.1161782Z 2024-08-20T21:40:28.1162094Z Per-Channel table columns: 2024-08-20T21:40:28.1163026Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:40:28.1164413Z ---- --------- ------- --------- --------- --------- --------- 2024-08-20T21:40:28.1165385Z 2024-08-20T21:40:28.1165565Z Args: 2024-08-20T21:40:28.1166455Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:40:28.1167700Z contain this filter substring 2024-08-20T21:40:28.1168616Z Default = "", results in all the features being printed 2024-08-20T21:40:28.1169935Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:40:28.1171589Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:40:28.1172562Z 2024-08-20T21:40:28.1172830Z Returns a dictionary with two keys: 2024-08-20T21:40:28.1173654Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-08-20T21:40:28.1174619Z "tensor_level_info", "channel_level_info" 2024-08-20T21:40:28.1175448Z Each key maps to a tuple with: 2024-08-20T21:40:28.1176255Z A list of the headers of each table 2024-08-20T21:40:28.1177260Z A list of lists containing the table information row by row 2024-08-20T21:40:28.1178440Z The 0th index row will contain the headers of the columns 2024-08-20T21:40:28.1179482Z The rest of the rows will contain data 2024-08-20T21:40:28.1180090Z 2024-08-20T21:40:28.1180273Z Example Use: 2024-08-20T21:40:28.1180873Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:28.1181828Z >>> mod_report_visualizer.generate_filtered_tables( 2024-08-20T21:40:28.1182783Z ... feature_filter = "per_channel_min", 2024-08-20T21:40:28.1183636Z ... module_fqn_filter = "block1" 2024-08-20T21:40:28.1184866Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-08-20T21:40:28.1185883Z 2024-08-20T21:40:28.1186697Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:28.1187971Z 2024-08-20T21:40:28.1190824Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400. 2024-08-20T21:40:28.1194204Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:28.1195153Z 2024-08-20T21:40:28.1195865Z Takes in optional filter values and prints out formatted tables of the information. 2024-08-20T21:40:28.1196870Z 2024-08-20T21:40:28.1197953Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-08-20T21:40:28.1199506Z 1.) the first table handles all tensor level information 2024-08-20T21:40:28.1200743Z 2.) the second table handles and displays all channel based information 2024-08-20T21:40:28.1201580Z 2024-08-20T21:40:28.1202459Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-20T21:40:28.1204701Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-20T21:40:28.1206821Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-20T21:40:28.1208067Z 2024-08-20T21:40:28.1208290Z Tensor table columns: 2024-08-20T21:40:28.1209132Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:40:28.1210486Z ---- --------- --------- --------- --------- --------- 2024-08-20T21:40:28.1211147Z 2024-08-20T21:40:28.1211432Z Per-Channel table columns: 2024-08-20T21:40:28.1211834Z 2024-08-20T21:40:28.1212393Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:40:28.1213773Z ---- --------- ------- --------- --------- --------- --------- 2024-08-20T21:40:28.1214455Z 2024-08-20T21:40:28.1214613Z Args: 2024-08-20T21:40:28.1215493Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:40:28.1216948Z contain this filter substring 2024-08-20T21:40:28.1217868Z Default = "", results in all the features being printed 2024-08-20T21:40:28.1219229Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:40:28.1220837Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:40:28.1221840Z 2024-08-20T21:40:28.1222022Z Example Use: 2024-08-20T21:40:28.1222618Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:28.1223571Z >>> mod_report_visualizer.generate_table_visualization( 2024-08-20T21:40:28.1224486Z ... feature_filter = "per_channel_min", 2024-08-20T21:40:28.1225267Z ... module_fqn_filter = "block1" 2024-08-20T21:40:28.1225942Z ... ) 2024-08-20T21:40:28.1226633Z >>> # prints out neatly formatted table with per_channel_min info 2024-08-20T21:40:28.1227699Z >>> # for all modules in block 1 of the model 2024-08-20T21:40:28.1228301Z 2024-08-20T21:40:28.1229093Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:28.1230009Z 2024-08-20T21:40:28.1232704Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=565. 2024-08-20T21:40:28.1236035Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:28.1236971Z 2024-08-20T21:40:28.1237602Z Takes in a feature and optional module_filter and plots of the desired data. 2024-08-20T21:40:28.1238515Z 2024-08-20T21:40:28.1239179Z For per channel features, it averages the value across the channels and plots a point 2024-08-20T21:40:28.1240811Z per module. The reason for this is that for models with hundreds of channels, it can 2024-08-20T21:40:28.1242426Z be hard to differentiate one channel line from another, and so the point of generating 2024-08-20T21:40:28.1244130Z a single average point per module is to give a sense of general trends that encourage 2024-08-20T21:40:28.1245348Z further deep dives. 2024-08-20T21:40:28.1245699Z 2024-08-20T21:40:28.1245865Z Note: 2024-08-20T21:40:28.1246799Z Only features in the report that have tensor value data are plottable by this class 2024-08-20T21:40:28.1248202Z When the tensor information is plotted, it will plot: 2024-08-20T21:40:28.1249213Z idx as the x val, feature value as the y_val 2024-08-20T21:40:28.1250296Z When the channel information is plotted, it will plot: 2024-08-20T21:40:28.1251889Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-08-20T21:40:28.1253583Z The reason for this is that we want to be able to compare values across the 2024-08-20T21:40:28.1255130Z channels for same layer, and it will be hard if values are staggered by idx 2024-08-20T21:40:28.1256478Z This means each module is represented by only 1 x value 2024-08-20T21:40:28.1257379Z Args: 2024-08-20T21:40:28.1258176Z feature_filter (str): Filters the features presented to only those that 2024-08-20T21:40:28.1259258Z contain this filter substring 2024-08-20T21:40:28.1260422Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:40:28.1262046Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:40:28.1263042Z 2024-08-20T21:40:28.1263221Z Example Use: 2024-08-20T21:40:28.1263822Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:28.1264804Z >>> mod_report_visualizer.generate_plot_visualization( 2024-08-20T21:40:28.1265793Z ... feature_filter = "per_channel_min", 2024-08-20T21:40:28.1266589Z ... module_fqn_filter = "block1" 2024-08-20T21:40:28.1267255Z ... ) 2024-08-20T21:40:28.1267916Z >>> # outputs line plot of per_channel_min information for all 2024-08-20T21:40:28.1269261Z >>> # modules in block1 of model each channel gets it's own line, 2024-08-20T21:40:28.1270746Z >>> # and it's plotted across the in-order modules on the x-axis 2024-08-20T21:40:28.1271506Z 2024-08-20T21:40:28.1272268Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:28.1273201Z 2024-08-20T21:40:28.1275832Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=645. 2024-08-20T21:40:28.1279177Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:28.1280103Z 2024-08-20T21:40:28.1280826Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-08-20T21:40:28.1281849Z 2024-08-20T21:40:28.1282009Z Note: 2024-08-20T21:40:28.1282954Z Only features in the report that have tensor value data can be viewed as a histogram 2024-08-20T21:40:28.1284669Z If you want to plot a histogram from all the channel values of a specific feature for 2024-08-20T21:40:28.1286329Z a specific model, make sure to specify both the model and the feature properly 2024-08-20T21:40:28.1288146Z in the filters and you should be able to see a distribution of the channel data 2024-08-20T21:40:28.1288877Z 2024-08-20T21:40:28.1289067Z Args: 2024-08-20T21:40:28.1289941Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:40:28.1291446Z contain this filter substring 2024-08-20T21:40:28.1292369Z Default = "", results in all the features being printed 2024-08-20T21:40:28.1293696Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:40:28.1295214Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:40:28.1296710Z num_bins (int, optional): The number of bins to create the histogram with 2024-08-20T21:40:28.1298014Z Default = 10, the values will be split into 10 equal sized bins 2024-08-20T21:40:28.1298834Z 2024-08-20T21:40:28.1299016Z Example Use: 2024-08-20T21:40:28.1299503Z >>> # xdoctest: +SKIP 2024-08-20T21:40:28.1300601Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-08-20T21:40:28.1301911Z ... feature_filter = "per_channel_min", 2024-08-20T21:40:28.1302674Z ... module_fqn_filter = "block1" 2024-08-20T21:40:28.1303381Z ... ) 2024-08-20T21:40:28.1304539Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-08-20T21:40:28.1306175Z information is gathered across all channels for all modules in block 1 for the 2024-08-20T21:40:28.1307643Z per_channel_min and is displayed in a histogram of equally sized bins 2024-08-20T21:40:28.1308501Z 2024-08-20T21:40:28.1309318Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:28.1310243Z 2024-08-20T21:40:28.3854714Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=13. 2024-08-20T21:40:28.3857463Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:28.3858426Z 2024-08-20T21:40:28.3858967Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-08-20T21:40:28.3860445Z distribution where all component are from different parameterizations of 2024-08-20T21:40:28.3861865Z the same distribution type. It is parameterized by a `Categorical` 2024-08-20T21:40:28.3863088Z "selecting distribution" (over `k` component) and a component 2024-08-20T21:40:28.3864338Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-08-20T21:40:28.3865551Z (equal to `[k]`) which indexes each (batch of) component. 2024-08-20T21:40:28.3866243Z 2024-08-20T21:40:28.3866469Z Examples:: 2024-08-20T21:40:28.3866738Z 2024-08-20T21:40:28.3867007Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:28.3868420Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-08-20T21:40:28.3869500Z >>> # weighted normal distributions 2024-08-20T21:40:28.3870293Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-20T21:40:28.3871169Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-08-20T21:40:28.3872078Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:40:28.3872645Z 2024-08-20T21:40:28.3873133Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-08-20T21:40:28.3874258Z >>> # weighted bivariate normal distributions 2024-08-20T21:40:28.3875101Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-20T21:40:28.3875885Z >>> comp = D.Independent(D.Normal( 2024-08-20T21:40:28.3876686Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-08-20T21:40:28.3877555Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:40:28.3878116Z 2024-08-20T21:40:28.3878572Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-08-20T21:40:28.3879820Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-08-20T21:40:28.3880897Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-08-20T21:40:28.3881758Z >>> comp = D.Independent(D.Normal( 2024-08-20T21:40:28.3882601Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-08-20T21:40:28.3883516Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:40:28.3884070Z 2024-08-20T21:40:28.3884233Z Args: 2024-08-20T21:40:28.3885182Z mixture_distribution: `torch.distributions.Categorical`-like 2024-08-20T21:40:28.3886404Z instance. Manages the probability of selecting component. 2024-08-20T21:40:28.3887973Z The number of categories must match the rightmost batch 2024-08-20T21:40:28.3889140Z dimension of the `component_distribution`. Must have either 2024-08-20T21:40:28.3890596Z scalar `batch_shape` or `batch_shape` matching 2024-08-20T21:40:28.3891728Z `component_distribution.batch_shape[:-1]` 2024-08-20T21:40:28.3892996Z component_distribution: `torch.distributions.Distribution`-like 2024-08-20T21:40:28.3894362Z instance. Right-most batch dimension indexes component. 2024-08-20T21:40:28.3895081Z 2024-08-20T21:40:28.3895808Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:28.3896740Z 2024-08-20T21:40:28.3984343Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=111. 2024-08-20T21:40:28.3987395Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:28.3988859Z 2024-08-20T21:40:28.3989331Z Creates a RelaxedBernoulli distribution, parametrized by 2024-08-20T21:40:28.3990728Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-08-20T21:40:28.3991997Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-08-20T21:40:28.3993321Z so the values are in (0, 1), and has reparametrizable samples. 2024-08-20T21:40:28.3994139Z 2024-08-20T21:40:28.3994334Z Example:: 2024-08-20T21:40:28.3994611Z 2024-08-20T21:40:28.3995113Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:40:28.3996035Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-08-20T21:40:28.3996946Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-08-20T21:40:28.3997754Z >>> m.sample() 2024-08-20T21:40:28.3998341Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-08-20T21:40:28.3998919Z 2024-08-20T21:40:28.3999095Z Args: 2024-08-20T21:40:28.3999630Z temperature (Tensor): relaxation temperature 2024-08-20T21:40:28.4000601Z probs (Number, Tensor): the probability of sampling `1` 2024-08-20T21:40:28.4001804Z logits (Number, Tensor): the log-odds of sampling `1` 2024-08-20T21:40:28.4002519Z 2024-08-20T21:40:28.4003186Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:28.4004343Z 2024-08-20T21:40:28.4006468Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=99. 2024-08-20T21:40:28.4009261Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:28.4010311Z 2024-08-20T21:40:28.4010818Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-08-20T21:40:28.4012125Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-08-20T21:40:28.4013558Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-08-20T21:40:28.4014848Z its samples are on simplex, and are reparametrizable. 2024-08-20T21:40:28.4015525Z 2024-08-20T21:40:28.4015702Z Example:: 2024-08-20T21:40:28.4015972Z 2024-08-20T21:40:28.4016474Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:40:28.4017487Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-08-20T21:40:28.4018483Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-08-20T21:40:28.4019324Z >>> m.sample() 2024-08-20T21:40:28.4019860Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-08-20T21:40:28.4020411Z 2024-08-20T21:40:28.4020567Z Args: 2024-08-20T21:40:28.4021141Z temperature (Tensor): relaxation temperature 2024-08-20T21:40:28.4021992Z probs (Tensor): event probabilities 2024-08-20T21:40:28.4022970Z logits (Tensor): unnormalized log probability for each event 2024-08-20T21:40:28.4023745Z 2024-08-20T21:40:28.4024546Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:28.4025408Z 2024-08-20T21:40:28.9310027Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1274. 2024-08-20T21:40:28.9312528Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:28.9313982Z Asserts that ``actual`` and ``expected`` are close. 2024-08-20T21:40:28.9314655Z 2024-08-20T21:40:28.9315795Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-08-20T21:40:28.9317050Z 2024-08-20T21:40:28.9317260Z .. math:: 2024-08-20T21:40:28.9317565Z 2024-08-20T21:40:28.9318814Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-08-20T21:40:28.9320061Z 2024-08-20T21:40:28.9321550Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-08-20T21:40:28.9323249Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-08-20T21:40:28.9324067Z 2024-08-20T21:40:28.9324572Z In addition, they are only considered close if they have the same 2024-08-20T21:40:28.9325398Z 2024-08-20T21:40:28.9326041Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-08-20T21:40:28.9327215Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-08-20T21:40:28.9328192Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-08-20T21:40:28.9329194Z - stride (if ``check_stride`` is ``True``). 2024-08-20T21:40:28.9329797Z 2024-08-20T21:40:28.9330712Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-08-20T21:40:28.9331791Z 2024-08-20T21:40:28.9332758Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-08-20T21:40:28.9334848Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-08-20T21:40:28.9336756Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-08-20T21:40:28.9338725Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-08-20T21:40:28.9340080Z 2024-08-20T21:40:28.9340841Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-08-20T21:40:28.9343128Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-08-20T21:40:28.9344479Z definition above. 2024-08-20T21:40:28.9344813Z 2024-08-20T21:40:28.9345915Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-08-20T21:40:28.9348157Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-08-20T21:40:28.9350421Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-08-20T21:40:28.9352871Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-08-20T21:40:28.9354637Z their elements are considered close according to the above definition. 2024-08-20T21:40:28.9355505Z 2024-08-20T21:40:28.9355710Z .. note:: 2024-08-20T21:40:28.9356013Z 2024-08-20T21:40:28.9356814Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-08-20T21:40:28.9358928Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-08-20T21:40:28.9360701Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-08-20T21:40:28.9361672Z 2024-08-20T21:40:28.9361853Z Args: 2024-08-20T21:40:28.9362351Z actual (Any): Actual input. 2024-08-20T21:40:28.9363081Z expected (Any): Expected input. 2024-08-20T21:40:28.9364470Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-08-20T21:40:28.9366072Z are allowed. Otherwise type equality is required. 2024-08-20T21:40:28.9367689Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-08-20T21:40:28.9369608Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-20T21:40:28.9371655Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-08-20T21:40:28.9373553Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-20T21:40:28.9375197Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-08-20T21:40:28.9377136Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-08-20T21:40:28.9378815Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-08-20T21:40:28.9380572Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-08-20T21:40:28.9382200Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-08-20T21:40:28.9384487Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-08-20T21:40:28.9386069Z :func:`torch.promote_types`) before being compared. 2024-08-20T21:40:28.9388047Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-08-20T21:40:28.9390582Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-08-20T21:40:28.9391960Z compared. 2024-08-20T21:40:28.9393238Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-08-20T21:40:28.9395370Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-08-20T21:40:28.9397528Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-08-20T21:40:28.9399067Z should return the new message. 2024-08-20T21:40:28.9399562Z 2024-08-20T21:40:28.9399737Z Raises: 2024-08-20T21:40:28.9400587Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-08-20T21:40:28.9401875Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-08-20T21:40:28.9403393Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-08-20T21:40:28.9405383Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-08-20T21:40:28.9406778Z different types. 2024-08-20T21:40:28.9408336Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-08-20T21:40:28.9410794Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-08-20T21:40:28.9412857Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-08-20T21:40:28.9414688Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-08-20T21:40:28.9416011Z :attr:`~torch.Tensor.layout`. 2024-08-20T21:40:28.9417060Z AssertionError: If only one of corresponding tensors is quantized. 2024-08-20T21:40:28.9419064Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-08-20T21:40:28.9421051Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-08-20T21:40:28.9422305Z :attr:`~torch.Tensor.device`. 2024-08-20T21:40:28.9423673Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-08-20T21:40:28.9425678Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-08-20T21:40:28.9427801Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-08-20T21:40:28.9429002Z 2024-08-20T21:40:28.9430088Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-08-20T21:40:28.9432001Z ``dtype``'s, the maximum of both tolerances is used. 2024-08-20T21:40:28.9432600Z 2024-08-20T21:40:28.9432981Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9434015Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-08-20T21:40:28.9434880Z +===========================+============+==========+ 2024-08-20T21:40:28.9435892Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-08-20T21:40:28.9436914Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9437943Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-08-20T21:40:28.9438978Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9440016Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:28.9441035Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9442069Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-08-20T21:40:28.9443014Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9443964Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-08-20T21:40:28.9444931Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9445936Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:28.9446926Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9447957Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-08-20T21:40:28.9448997Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9450005Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:28.9451314Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9452360Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:28.9453373Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9454385Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:28.9455426Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9456455Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:28.9457478Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9458536Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:28.9459602Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9460511Z | other | ``0.0`` | ``0.0`` | 2024-08-20T21:40:28.9461527Z +---------------------------+------------+----------+ 2024-08-20T21:40:28.9462064Z 2024-08-20T21:40:28.9462278Z .. note:: 2024-08-20T21:40:28.9462583Z 2024-08-20T21:40:28.9463556Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-08-20T21:40:28.9465791Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-08-20T21:40:28.9467733Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-08-20T21:40:28.9468722Z 2024-08-20T21:40:28.9468935Z >>> import functools 2024-08-20T21:40:28.9470012Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-08-20T21:40:28.9471334Z >>> assert_equal(1e-9, 1e-10) 2024-08-20T21:40:28.9472106Z Traceback (most recent call last): 2024-08-20T21:40:28.9472843Z ... 2024-08-20T21:40:28.9473405Z AssertionError: Scalars are not equal! 2024-08-20T21:40:28.9474176Z 2024-08-20T21:40:28.9474839Z Expected 1e-10 but got 1e-09. 2024-08-20T21:40:28.9475755Z Absolute difference: 9.000000000000001e-10 2024-08-20T21:40:28.9476590Z Relative difference: 9.0 2024-08-20T21:40:28.9476970Z 2024-08-20T21:40:28.9477114Z Examples: 2024-08-20T21:40:28.9477622Z >>> # tensor to tensor comparison 2024-08-20T21:40:28.9478613Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-08-20T21:40:28.9479544Z >>> actual = torch.acos(torch.cos(expected)) 2024-08-20T21:40:28.9480487Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:40:28.9481132Z 2024-08-20T21:40:28.9481558Z >>> # scalar to scalar comparison 2024-08-20T21:40:28.9482294Z >>> import math 2024-08-20T21:40:28.9482888Z >>> expected = math.sqrt(2.0) 2024-08-20T21:40:28.9483644Z >>> actual = 2.0 / math.sqrt(2.0) 2024-08-20T21:40:28.9484504Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:40:28.9485155Z 2024-08-20T21:40:28.9485448Z >>> # numpy array to numpy array comparison 2024-08-20T21:40:28.9486290Z >>> import numpy as np 2024-08-20T21:40:28.9487137Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-08-20T21:40:28.9488430Z >>> actual = np.arccos(np.cos(expected)) 2024-08-20T21:40:28.9489355Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:40:28.9489997Z 2024-08-20T21:40:28.9490480Z >>> # sequence to sequence comparison 2024-08-20T21:40:28.9491276Z >>> import numpy as np 2024-08-20T21:40:28.9492378Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-08-20T21:40:28.9493705Z >>> # length and their elements have to match. 2024-08-20T21:40:28.9494684Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-08-20T21:40:28.9495628Z >>> actual = tuple(expected) 2024-08-20T21:40:28.9496449Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:40:28.9497158Z 2024-08-20T21:40:28.9497416Z >>> # mapping to mapping comparison 2024-08-20T21:40:28.9498435Z >>> from collections import OrderedDict 2024-08-20T21:40:28.9499158Z >>> import numpy as np 2024-08-20T21:40:28.9499751Z >>> foo = torch.tensor(1.0) 2024-08-20T21:40:28.9500428Z >>> bar = 2.0 2024-08-20T21:40:28.9500982Z >>> baz = np.array(3.0) 2024-08-20T21:40:28.9502069Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-08-20T21:40:28.9503487Z >>> # have to have the same set of keys and their elements have to match. 2024-08-20T21:40:28.9504835Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-08-20T21:40:28.9505971Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-08-20T21:40:28.9506950Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:40:28.9507599Z 2024-08-20T21:40:28.9507916Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-20T21:40:28.9508776Z >>> actual = expected.clone() 2024-08-20T21:40:28.9509724Z >>> # By default, directly related instances can be compared 2024-08-20T21:40:28.9511006Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-08-20T21:40:28.9512361Z >>> # This check can be made more strict with allow_subclasses=False 2024-08-20T21:40:28.9513435Z >>> torch.testing.assert_close( 2024-08-20T21:40:28.9514491Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-08-20T21:40:28.9515495Z ... ) 2024-08-20T21:40:28.9516042Z Traceback (most recent call last): 2024-08-20T21:40:28.9516796Z ... 2024-08-20T21:40:28.9517580Z TypeError: No comparison pair was able to handle inputs of type 2024-08-20T21:40:28.9519135Z and . 2024-08-20T21:40:28.9520574Z >>> # If the inputs are not directly related, they are never considered close 2024-08-20T21:40:28.9521878Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-08-20T21:40:28.9522835Z Traceback (most recent call last): 2024-08-20T21:40:28.9523478Z ... 2024-08-20T21:40:28.9524598Z TypeError: No comparison pair was able to handle inputs of type 2024-08-20T21:40:28.9525874Z and . 2024-08-20T21:40:28.9527015Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-08-20T21:40:28.9528268Z >>> # their type if check_dtype=False. 2024-08-20T21:40:28.9529217Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-08-20T21:40:28.9530240Z 2024-08-20T21:40:28.9530483Z >>> # NaN != NaN by default. 2024-08-20T21:40:28.9531285Z >>> expected = torch.tensor(float("Nan")) 2024-08-20T21:40:28.9532108Z >>> actual = expected.clone() 2024-08-20T21:40:28.9532933Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:40:28.9533847Z Traceback (most recent call last): 2024-08-20T21:40:28.9534592Z ... 2024-08-20T21:40:28.9535117Z AssertionError: Scalars are not close! 2024-08-20T21:40:28.9535893Z 2024-08-20T21:40:28.9536413Z Expected nan but got nan. 2024-08-20T21:40:28.9537350Z Absolute difference: nan (up to 1e-05 allowed) 2024-08-20T21:40:28.9538355Z Relative difference: nan (up to 1.3e-06 allowed) 2024-08-20T21:40:28.9539483Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-08-20T21:40:28.9540263Z 2024-08-20T21:40:28.9540550Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-20T21:40:28.9541358Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-08-20T21:40:28.9542272Z >>> # The default error message can be overwritten. 2024-08-20T21:40:28.9543642Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-08-20T21:40:28.9544964Z Traceback (most recent call last): 2024-08-20T21:40:28.9545709Z ... 2024-08-20T21:40:28.9546316Z AssertionError: Argh, the tensors are not close! 2024-08-20T21:40:28.9547680Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-08-20T21:40:28.9548780Z >>> # extra information 2024-08-20T21:40:28.9549440Z >>> torch.testing.assert_close( 2024-08-20T21:40:28.9550454Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-08-20T21:40:28.9551425Z ... ) 2024-08-20T21:40:28.9551965Z Traceback (most recent call last): 2024-08-20T21:40:28.9552699Z ... 2024-08-20T21:40:28.9553203Z AssertionError: Header 2024-08-20T21:40:28.9553818Z 2024-08-20T21:40:28.9554516Z Tensor-likes are not close! 2024-08-20T21:40:28.9555204Z 2024-08-20T21:40:28.9555773Z Mismatched elements: 2 / 3 (66.7%) 2024-08-20T21:40:28.9557001Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-08-20T21:40:28.9558560Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-08-20T21:40:28.9559649Z 2024-08-20T21:40:28.9560135Z Footer 2024-08-20T21:40:28.9560591Z 2024-08-20T21:40:28.9561572Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:28.9562499Z 2024-08-20T21:40:30.1830046Z gathering tests 2024-08-20T21:40:30.1843602Z running 695 test(s) 2024-08-20T21:40:30.1851105Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0, line 980 <- wrt source file 2024-08-20T21:40:30.1860155Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0 2024-08-20T21:40:30.1862772Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0, line 1016 <- wrt source file 2024-08-20T21:40:30.1866609Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0 2024-08-20T21:40:30.1869498Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0, line 1085 <- wrt source file 2024-08-20T21:40:30.1872489Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0 2024-08-20T21:40:30.1875499Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1134 <- wrt source file 2024-08-20T21:40:30.1878870Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0 2024-08-20T21:40:30.1881818Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0, line 1171 <- wrt source file 2024-08-20T21:40:30.1884684Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0 2024-08-20T21:40:30.1888390Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1326 <- wrt source file 2024-08-20T21:40:30.1891961Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2024-08-20T21:40:30.1894923Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0, line 2412 <- wrt source file 2024-08-20T21:40:30.1897359Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0 2024-08-20T21:40:30.1900351Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2661 <- wrt source file 2024-08-20T21:40:30.1903474Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2024-08-20T21:40:30.1906889Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file 2024-08-20T21:40:30.1909663Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0 2024-08-20T21:40:30.1912629Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file 2024-08-20T21:40:30.1915975Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0 2024-08-20T21:40:30.1918435Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2024-08-20T21:40:30.1920575Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0 2024-08-20T21:40:30.1922774Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0, line 137 <- wrt source file 2024-08-20T21:40:30.1924825Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0 2024-08-20T21:40:30.1927007Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0, line 206 <- wrt source file 2024-08-20T21:40:30.2158205Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0 2024-08-20T21:40:30.2160145Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2024-08-20T21:40:30.2162011Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0 2024-08-20T21:40:30.2163883Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 546 <- wrt source file 2024-08-20T21:40:30.2178377Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0 2024-08-20T21:40:30.2180313Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 603 <- wrt source file 2024-08-20T21:40:30.2292899Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2024-08-20T21:40:30.2294679Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1232 <- wrt source file 2024-08-20T21:40:30.2413982Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0 2024-08-20T21:40:30.2418145Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1277 <- wrt source file 2024-08-20T21:40:30.2424220Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0 2024-08-20T21:40:30.2427338Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0, line 1350 <- wrt source file 2024-08-20T21:40:30.2447610Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0 2024-08-20T21:40:30.2449766Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1380 <- wrt source file 2024-08-20T21:40:30.2520766Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2024-08-20T21:40:30.2523519Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0, line 1403 <- wrt source file 2024-08-20T21:40:30.2526733Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0 2024-08-20T21:40:30.2529181Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2024-08-20T21:40:30.2555247Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0 2024-08-20T21:40:30.2572698Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0, line 63 <- wrt source file 2024-08-20T21:40:30.2575663Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0 2024-08-20T21:40:30.2578502Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0, line 91 <- wrt source file 2024-08-20T21:40:30.2581282Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0 2024-08-20T21:40:30.2584128Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0, line 178 <- wrt source file 2024-08-20T21:40:30.2591574Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0 2024-08-20T21:40:30.2594070Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0, line 287 <- wrt source file 2024-08-20T21:40:30.2706481Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0 2024-08-20T21:40:30.2709186Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1014 <- wrt source file 2024-08-20T21:40:30.2719746Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0 2024-08-20T21:40:30.2722586Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0, line 1289 <- wrt source file 2024-08-20T21:40:30.2760696Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0 2024-08-20T21:40:30.2763543Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0, line 1373 <- wrt source file 2024-08-20T21:40:30.2769946Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0 2024-08-20T21:40:30.2772880Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0, line 1407 <- wrt source file 2024-08-20T21:40:30.2780486Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0 2024-08-20T21:40:30.2783153Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0, line 1458 <- wrt source file 2024-08-20T21:40:30.2813508Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0 2024-08-20T21:40:30.2815070Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0, line 1499 <- wrt source file 2024-08-20T21:40:30.2828555Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0 2024-08-20T21:40:30.2830132Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0, line 1535 <- wrt source file 2024-08-20T21:40:30.2844278Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0 2024-08-20T21:40:30.2845833Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0, line 1573 <- wrt source file 2024-08-20T21:40:30.2864157Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0 2024-08-20T21:40:30.2865692Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0, line 1746 <- wrt source file 2024-08-20T21:40:30.2920407Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0 2024-08-20T21:40:30.2921969Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0, line 1913 <- wrt source file 2024-08-20T21:40:30.2955188Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0 2024-08-20T21:40:30.2957252Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0, line 2013 <- wrt source file 2024-08-20T21:40:30.2959413Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0 2024-08-20T21:40:30.2960972Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0, line 2113 <- wrt source file 2024-08-20T21:40:30.2962486Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0 2024-08-20T21:40:30.2964278Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0, line 468 <- wrt source file 2024-08-20T21:40:30.2965652Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0 2024-08-20T21:40:30.2967031Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0, line 528 <- wrt source file 2024-08-20T21:40:30.2968403Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0 2024-08-20T21:40:30.2969836Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0, line 667 <- wrt source file 2024-08-20T21:40:30.2971375Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0 2024-08-20T21:40:30.2972896Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0, line 129 <- wrt source file 2024-08-20T21:40:30.2974628Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0 2024-08-20T21:40:30.2976307Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 217 <- wrt source file 2024-08-20T21:40:30.3020627Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2024-08-20T21:40:30.3022308Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0, line 268 <- wrt source file 2024-08-20T21:40:30.3024681Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0 2024-08-20T21:40:30.3026201Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0, line 452 <- wrt source file 2024-08-20T21:40:30.3134298Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0 2024-08-20T21:40:30.3135770Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0, line 519 <- wrt source file 2024-08-20T21:40:30.3143746Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0 2024-08-20T21:40:30.3145699Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0, line 641 <- wrt source file 2024-08-20T21:40:30.3147507Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0 2024-08-20T21:40:30.3149118Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0, line 958 <- wrt source file 2024-08-20T21:40:30.3237180Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0 2024-08-20T21:40:30.3238820Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0, line 1047 <- wrt source file 2024-08-20T21:40:30.3357872Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0 2024-08-20T21:40:30.3359481Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0, line 111 <- wrt source file 2024-08-20T21:40:30.3363954Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0 2024-08-20T21:40:30.3373840Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0, line 418 <- wrt source file 2024-08-20T21:40:30.3399152Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0 2024-08-20T21:40:30.3400936Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0, line 1564 <- wrt source file 2024-08-20T21:40:30.3403054Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0 2024-08-20T21:40:30.3404986Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0, line 1699 <- wrt source file 2024-08-20T21:40:30.3406689Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0 2024-08-20T21:40:30.3408410Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1947 <- wrt source file 2024-08-20T21:40:30.3432940Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2024-08-20T21:40:30.3434872Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0, line 1966 <- wrt source file 2024-08-20T21:40:30.3440038Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0 2024-08-20T21:40:30.3442374Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2024-08-20T21:40:30.3443987Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0 2024-08-20T21:40:30.3445616Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0, line 216 <- wrt source file 2024-08-20T21:40:30.3447670Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0 2024-08-20T21:40:30.3449960Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0, line 241 <- wrt source file 2024-08-20T21:40:30.3451826Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0 2024-08-20T21:40:30.3453481Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0, line 304 <- wrt source file 2024-08-20T21:40:30.3455572Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0 2024-08-20T21:40:30.3457391Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0, line 765 <- wrt source file 2024-08-20T21:40:30.3458950Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0 2024-08-20T21:40:30.3460974Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0, line 18 <- wrt source file 2024-08-20T21:40:30.3462639Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0 2024-08-20T21:40:30.3464297Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 134 <- wrt source file 2024-08-20T21:40:30.3466076Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2024-08-20T21:40:30.3467750Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0, line 164 <- wrt source file 2024-08-20T21:40:30.3469376Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0 2024-08-20T21:40:30.3470981Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0, line 146 <- wrt source file 2024-08-20T21:40:30.3472495Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0 2024-08-20T21:40:30.3474201Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1747 <- wrt source file 2024-08-20T21:40:30.3476086Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0 2024-08-20T21:40:30.3477886Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 103 <- wrt source file 2024-08-20T21:40:30.3479524Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2024-08-20T21:40:30.3481335Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 146 <- wrt source file 2024-08-20T21:40:31.1557781Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::substitute_in_graph:0 2024-08-20T21:40:31.1559873Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 255 <- wrt source file 2024-08-20T21:40:31.1561534Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2024-08-20T21:40:31.1563801Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0, line 286 <- wrt source file 2024-08-20T21:40:31.1566279Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0 2024-08-20T21:40:31.1568429Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 307 <- wrt source file 2024-08-20T21:40:31.1570831Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2024-08-20T21:40:31.1572545Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0, line 214 <- wrt source file 2024-08-20T21:40:31.1574069Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0 2024-08-20T21:40:31.1575899Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0, line 298 <- wrt source file 2024-08-20T21:40:31.1577418Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0 2024-08-20T21:40:31.1579030Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0, line 396 <- wrt source file 2024-08-20T21:40:31.1580737Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0 2024-08-20T21:40:31.1582496Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 196 <- wrt source file 2024-08-20T21:40:31.1584275Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2024-08-20T21:40:31.1586046Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 258 <- wrt source file 2024-08-20T21:40:31.1588127Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0 2024-08-20T21:40:31.1589831Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0, line 289 <- wrt source file 2024-08-20T21:40:31.1591674Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0 2024-08-20T21:40:31.1593381Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 22 <- wrt source file 2024-08-20T21:40:31.1595161Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0 2024-08-20T21:40:31.1596907Z * 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 2024-08-20T21:40:31.1679464Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::as_nested_tensor:0 2024-08-20T21:40:31.1682457Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0, line 215 <- wrt source file 2024-08-20T21:40:31.1685661Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0 2024-08-20T21:40:31.1688561Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0, line 277 <- wrt source file 2024-08-20T21:40:31.1749584Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0 2024-08-20T21:40:31.1752674Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0, line 361 <- wrt source file 2024-08-20T21:40:31.1769912Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2024-08-20T21:40:31.1773258Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0, line 454 <- wrt source file 2024-08-20T21:40:31.1789521Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2024-08-20T21:40:31.1793063Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0, line 540 <- wrt source file 2024-08-20T21:40:31.1915161Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0 2024-08-20T21:40:31.1919331Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0, line 1166 <- wrt source file 2024-08-20T21:40:31.1922926Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0 2024-08-20T21:40:31.1926356Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 265 <- wrt source file 2024-08-20T21:40:31.1929448Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0 2024-08-20T21:40:31.1932818Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0, line 682 <- wrt source file 2024-08-20T21:40:31.1936292Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0 2024-08-20T21:40:31.1939686Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0, line 755 <- wrt source file 2024-08-20T21:40:31.1942915Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0 2024-08-20T21:40:31.1946311Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0, line 2251 <- wrt source file 2024-08-20T21:40:31.1949806Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0 2024-08-20T21:40:31.1953284Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::batch_isend_irecv:0, line 2343 <- wrt source file 2024-08-20T21:40:31.1956684Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::batch_isend_irecv:0 2024-08-20T21:40:31.1960086Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_reduce:0, line 2452 <- wrt source file 2024-08-20T21:40:31.1963368Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_reduce:0 2024-08-20T21:40:31.1966933Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 2696 <- wrt source file 2024-08-20T21:40:31.1970412Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0 2024-08-20T21:40:31.1973851Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0, line 2896 <- wrt source file 2024-08-20T21:40:31.1977251Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0 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SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0 2024-08-20T21:40:31.2022476Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter:0, line 3655 <- wrt source file 2024-08-20T21:40:31.2025654Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter:0 2024-08-20T21:40:31.2029026Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0, line 3790 <- wrt source file 2024-08-20T21:40:31.2032794Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0 2024-08-20T21:40:31.2036299Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_to_all_single:0, line 3917 <- wrt source file 2024-08-20T21:40:31.2039689Z * 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0, line 284 <- wrt source file 2024-08-20T21:40:31.2727938Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0 2024-08-20T21:40:31.2732279Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0, line 411 <- wrt source file 2024-08-20T21:40:31.2736411Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0 2024-08-20T21:40:31.2740517Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0, line 16 <- wrt source file 2024-08-20T21:40:31.2744505Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0 2024-08-20T21:40:31.2748481Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0, line 39 <- wrt source file 2024-08-20T21:40:31.2752470Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0 2024-08-20T21:40:31.2756437Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0, line 67 <- wrt source file 2024-08-20T21:40:31.2760388Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0 2024-08-20T21:40:31.2764333Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0, line 86 <- wrt source file 2024-08-20T21:40:31.2768173Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0 2024-08-20T21:40:31.2772125Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0, line 116 <- wrt source file 2024-08-20T21:40:31.2776008Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0 2024-08-20T21:40:31.2780003Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 46 <- wrt source file 2024-08-20T21:40:31.2784081Z * SKIPPED: 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file 2024-08-20T21:40:31.2805651Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0 2024-08-20T21:40:31.2808735Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/split_module.py::split_module:0, line 76 <- wrt source file 2024-08-20T21:40:31.2811869Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/split_module.py::split_module:0 2024-08-20T21:40:31.2815578Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0, line 53 <- wrt source file 2024-08-20T21:40:31.2819772Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0 2024-08-20T21:40:31.2823552Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file 2024-08-20T21:40:31.2826854Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0 2024-08-20T21:40:31.2830295Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0, line 22 <- wrt source file 2024-08-20T21:40:31.2833591Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0 2024-08-20T21:40:31.2837019Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0, line 122 <- wrt source file 2024-08-20T21:40:31.2840465Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0 2024-08-20T21:40:31.2843824Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0, line 214 <- wrt source file 2024-08-20T21:40:31.2847046Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0 2024-08-20T21:40:31.2850434Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 467 <- wrt source file 2024-08-20T21:40:31.2853839Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0 2024-08-20T21:40:31.2857260Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 586 <- wrt source file 2024-08-20T21:40:31.3332609Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0 2024-08-20T21:40:31.3343736Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2181 <- wrt source file 2024-08-20T21:40:31.3461111Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0 2024-08-20T21:40:31.3464121Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0, line 2487 <- wrt source file 2024-08-20T21:40:31.3477726Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0 2024-08-20T21:40:31.3480679Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0, line 2627 <- wrt source file 2024-08-20T21:40:31.3495883Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0 2024-08-20T21:40:31.3498801Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0, line 3049 <- wrt source file 2024-08-20T21:40:31.3556750Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0 2024-08-20T21:40:31.3559872Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0, line 3126 <- wrt source file 2024-08-20T21:40:31.3606289Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0 2024-08-20T21:40:31.3609775Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0, line 3451 <- wrt source file 2024-08-20T21:40:31.3628826Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0 2024-08-20T21:40:31.3632564Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3523 <- wrt source file 2024-08-20T21:40:31.3660343Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2024-08-20T21:40:31.3664167Z * 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 2024-08-20T21:40:31.3677753Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2024-08-20T21:40:31.3680889Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0, line 5065 <- wrt source file 2024-08-20T21:40:31.3694954Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0 2024-08-20T21:40:31.3697798Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2024-08-20T21:40:31.3756745Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0 2024-08-20T21:40:31.3759575Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2024-08-20T21:40:31.3762325Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0 2024-08-20T21:40:31.3765106Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2024-08-20T21:40:31.3769569Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0 2024-08-20T21:40:31.3772752Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2024-08-20T21:40:31.3775964Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0 2024-08-20T21:40:31.3778765Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2024-08-20T21:40:31.4172321Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0 2024-08-20T21:40:31.4175164Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2024-08-20T21:40:31.4259142Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0 2024-08-20T21:40:31.4262267Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0, line 102 <- wrt source file 2024-08-20T21:40:31.4265093Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0 2024-08-20T21:40:31.4267821Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0, line 159 <- wrt source file 2024-08-20T21:40:31.4270470Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0 2024-08-20T21:40:31.4273164Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0, line 186 <- wrt source file 2024-08-20T21:40:31.4275754Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0 2024-08-20T21:40:31.4278468Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0, line 221 <- wrt source file 2024-08-20T21:40:31.4295633Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0 2024-08-20T21:40:31.4298391Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0, line 235 <- wrt source file 2024-08-20T21:40:31.4301056Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0 2024-08-20T21:40:31.4304013Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0, line 252 <- wrt source file 2024-08-20T21:40:31.4306575Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0 2024-08-20T21:40:31.4309200Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0, line 265 <- wrt source file 2024-08-20T21:40:31.4311795Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0 2024-08-20T21:40:31.4314401Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0, line 281 <- wrt source file 2024-08-20T21:40:31.4316941Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0 2024-08-20T21:40:31.4319546Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0, line 303 <- wrt source file 2024-08-20T21:40:31.4322275Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0 2024-08-20T21:40:31.4324999Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0, line 389 <- wrt source file 2024-08-20T21:40:31.4327787Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0 2024-08-20T21:40:31.4330665Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0, line 429 <- wrt source file 2024-08-20T21:40:31.4333613Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0 2024-08-20T21:40:31.4336436Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 488 <- wrt source file 2024-08-20T21:40:31.4339257Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0 2024-08-20T21:40:31.4342104Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0, line 553 <- wrt source file 2024-08-20T21:40:31.4344876Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0 2024-08-20T21:40:31.4347651Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0, line 592 <- wrt source file 2024-08-20T21:40:31.4350402Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0 2024-08-20T21:40:31.4353100Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0, line 645 <- wrt source file 2024-08-20T21:40:31.4355721Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0 2024-08-20T21:40:31.4358603Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 81 <- wrt source file 2024-08-20T21:40:31.4361625Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2024-08-20T21:40:31.4364673Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 94 <- wrt source file 2024-08-20T21:40:31.4367623Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0 2024-08-20T21:40:31.4370691Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0, line 70 <- wrt source file 2024-08-20T21:40:31.4374471Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0 2024-08-20T21:40:31.4377774Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0, line 112 <- wrt source file 2024-08-20T21:40:31.4380865Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0 2024-08-20T21:40:31.4383853Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0, line 171 <- wrt source file 2024-08-20T21:40:31.4393707Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0 2024-08-20T21:40:31.4396752Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 227 <- wrt source file 2024-08-20T21:40:31.4402665Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2024-08-20T21:40:31.4405746Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 292 <- wrt source file 2024-08-20T21:40:31.4408711Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0 2024-08-20T21:40:31.4411832Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 320 <- wrt source file 2024-08-20T21:40:31.4414869Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2024-08-20T21:40:31.4418223Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 352 <- wrt source file 2024-08-20T21:40:31.4421348Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2024-08-20T21:40:31.4424430Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0, line 385 <- wrt source file 2024-08-20T21:40:31.4434674Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0 2024-08-20T21:40:31.4437804Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0, line 418 <- wrt source file 2024-08-20T21:40:31.4440757Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0 2024-08-20T21:40:31.4443773Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0, line 457 <- wrt source file 2024-08-20T21:40:31.4446723Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0 2024-08-20T21:40:31.4449822Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 502 <- wrt source file 2024-08-20T21:40:31.4452967Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0 2024-08-20T21:40:31.4455999Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0, line 545 <- wrt source file 2024-08-20T21:40:31.4458939Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0 2024-08-20T21:40:31.4461907Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0, line 587 <- wrt source file 2024-08-20T21:40:31.4464853Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0 2024-08-20T21:40:31.4467856Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0, line 640 <- wrt source file 2024-08-20T21:40:31.4470976Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0 2024-08-20T21:40:31.4473944Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0, line 678 <- wrt source file 2024-08-20T21:40:31.4476858Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0 2024-08-20T21:40:31.4479844Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0, line 720 <- wrt source file 2024-08-20T21:40:31.4549598Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0 2024-08-20T21:40:31.4552666Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 763 <- wrt source file 2024-08-20T21:40:31.4563512Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2024-08-20T21:40:31.4566651Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 812 <- wrt source file 2024-08-20T21:40:31.4569717Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2024-08-20T21:40:31.4572887Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 848 <- wrt source file 2024-08-20T21:40:31.4576180Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2024-08-20T21:40:31.4579272Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0, line 881 <- wrt source file 2024-08-20T21:40:31.4582311Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0 2024-08-20T21:40:31.4585415Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 924 <- wrt source file 2024-08-20T21:40:31.4588457Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0 2024-08-20T21:40:31.4591881Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1026 <- wrt source file 2024-08-20T21:40:31.4595228Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2024-08-20T21:40:31.4598408Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1489 <- wrt source file 2024-08-20T21:40:31.4601346Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0 2024-08-20T21:40:31.4604381Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1531 <- wrt source file 2024-08-20T21:40:31.4607433Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0 2024-08-20T21:40:31.4610597Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1554 <- wrt source file 2024-08-20T21:40:31.4613688Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2024-08-20T21:40:31.4616761Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1589 <- wrt source file 2024-08-20T21:40:31.4620082Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0 2024-08-20T21:40:31.4623627Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1647 <- wrt source file 2024-08-20T21:40:31.4627063Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0 2024-08-20T21:40:31.4630131Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1688 <- wrt source file 2024-08-20T21:40:31.4633211Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2024-08-20T21:40:31.4636324Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1724 <- wrt source file 2024-08-20T21:40:31.4639370Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2024-08-20T21:40:31.4642494Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file 2024-08-20T21:40:31.4663484Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2024-08-20T21:40:31.4666592Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file 2024-08-20T21:40:31.4904481Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2024-08-20T21:40:31.7526422Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file 2024-08-20T21:40:31.7529545Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2024-08-20T21:40:31.7609819Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2024-08-20T21:40:31.7639542Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2024-08-20T21:40:31.7642813Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0, line 86 <- wrt source file 2024-08-20T21:40:31.7645881Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0 2024-08-20T21:40:31.7648965Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0, line 292 <- wrt source file 2024-08-20T21:40:31.7652139Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0 2024-08-20T21:40:31.7655299Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 474 <- wrt source file 2024-08-20T21:40:31.7658363Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0 2024-08-20T21:40:31.7661535Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0, line 606 <- wrt source file 2024-08-20T21:40:31.7664748Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0 2024-08-20T21:40:31.7667936Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 758 <- wrt source file 2024-08-20T21:40:31.7671068Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0 2024-08-20T21:40:31.7674768Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2024-08-20T21:40:31.7677949Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2024-08-20T21:40:31.7681155Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 77 <- wrt source file 2024-08-20T21:40:31.7684338Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2024-08-20T21:40:31.7687457Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2024-08-20T21:40:31.7690563Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0 2024-08-20T21:40:31.7693574Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file 2024-08-20T21:40:31.7696548Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2024-08-20T21:40:31.7699540Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file 2024-08-20T21:40:31.7710522Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2024-08-20T21:40:31.7714110Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file 2024-08-20T21:40:31.7787764Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2024-08-20T21:40:31.7791627Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file 2024-08-20T21:40:31.7794678Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2024-08-20T21:40:31.7797867Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file 2024-08-20T21:40:31.7872898Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2024-08-20T21:40:31.7876418Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2024-08-20T21:40:31.7879288Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0 2024-08-20T21:40:31.7882161Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file 2024-08-20T21:40:31.7885453Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0 2024-08-20T21:40:31.7888279Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0, line 261 <- wrt source file 2024-08-20T21:40:31.7902262Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0 2024-08-20T21:40:31.7905356Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2024-08-20T21:40:31.7917565Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2024-08-20T21:40:31.7921165Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2024-08-20T21:40:31.8119066Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2024-08-20T21:40:31.8122450Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2024-08-20T21:40:32.0767742Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2024-08-20T21:40:32.0851378Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 87 <- wrt source file 2024-08-20T21:40:32.0854420Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2024-08-20T21:40:32.0857436Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2024-08-20T21:40:32.0860403Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0 2024-08-20T21:40:32.0863309Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file 2024-08-20T21:40:32.0878137Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0 2024-08-20T21:40:32.0881052Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file 2024-08-20T21:40:32.0903558Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0 2024-08-20T21:40:32.0906480Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2024-08-20T21:40:32.0911990Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0 2024-08-20T21:40:32.0914911Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 211 <- wrt source file 2024-08-20T21:40:32.0977307Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2024-08-20T21:40:32.0980403Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 321 <- wrt source file 2024-08-20T21:40:32.0985277Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2024-08-20T21:40:32.0988388Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 406 <- wrt source file 2024-08-20T21:40:32.1014090Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2024-08-20T21:40:32.1017126Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 517 <- wrt source file 2024-08-20T21:40:32.1031310Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2024-08-20T21:40:32.1034235Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 595 <- wrt source file 2024-08-20T21:40:32.1052308Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0 2024-08-20T21:40:32.1055228Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 677 <- wrt source file 2024-08-20T21:40:32.1059057Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0 2024-08-20T21:40:32.1062098Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 748 <- wrt source file 2024-08-20T21:40:32.1071713Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2024-08-20T21:40:32.1074954Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 941 <- wrt source file 2024-08-20T21:40:32.1080081Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2024-08-20T21:40:32.1083250Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1261 <- wrt source file 2024-08-20T21:40:32.1089556Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2024-08-20T21:40:32.1093559Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1401 <- wrt source file 2024-08-20T21:40:32.1116492Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2024-08-20T21:40:32.1119945Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1466 <- wrt source file 2024-08-20T21:40:32.1123414Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2024-08-20T21:40:32.1126543Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1545 <- wrt source file 2024-08-20T21:40:32.1132189Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2024-08-20T21:40:32.1135290Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1645 <- wrt source file 2024-08-20T21:40:32.1144149Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2024-08-20T21:40:32.1147196Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1886 <- wrt source file 2024-08-20T21:40:32.1176904Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0 2024-08-20T21:40:32.1180038Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 545 <- wrt source file 2024-08-20T21:40:32.1183104Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2024-08-20T21:40:32.1186044Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1005 <- wrt source file 2024-08-20T21:40:32.1195525Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0 2024-08-20T21:40:32.1203623Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0, line 1259 <- wrt source file 2024-08-20T21:40:32.1206636Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0 2024-08-20T21:40:32.1209799Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2167 <- wrt source file 2024-08-20T21:40:32.1213174Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2024-08-20T21:40:32.1216502Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2609 <- wrt source file 2024-08-20T21:40:32.1219518Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0 2024-08-20T21:40:32.1222888Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2637 <- wrt source file 2024-08-20T21:40:32.1226273Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2024-08-20T21:40:32.1229753Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2664 <- wrt source file 2024-08-20T21:40:32.1232732Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0 2024-08-20T21:40:32.1236555Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2691 <- wrt source file 2024-08-20T21:40:32.1240762Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2024-08-20T21:40:32.1244525Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2722 <- wrt source file 2024-08-20T21:40:32.1247924Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0 2024-08-20T21:40:32.1250848Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2746 <- wrt source file 2024-08-20T21:40:32.1253815Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0 2024-08-20T21:40:32.1256081Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2784 <- wrt source file 2024-08-20T21:40:32.1258129Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2024-08-20T21:40:32.1260209Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2024-08-20T21:40:32.1281948Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2024-08-20T21:40:32.1285918Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file 2024-08-20T21:40:32.1317987Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2024-08-20T21:40:32.1321614Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file 2024-08-20T21:40:32.1344689Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2024-08-20T21:40:32.1347591Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file 2024-08-20T21:40:32.1351200Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2024-08-20T21:40:32.1354095Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 69 <- wrt source file 2024-08-20T21:40:32.1360452Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2024-08-20T21:40:32.1363624Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 120 <- wrt source file 2024-08-20T21:40:32.1381510Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2024-08-20T21:40:32.1384626Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 184 <- wrt source file 2024-08-20T21:40:32.8042390Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2024-08-20T21:40:32.8234856Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 238 <- wrt source file 2024-08-20T21:40:32.8245225Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2024-08-20T21:40:32.8248360Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 291 <- wrt source file 2024-08-20T21:40:32.8251488Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2024-08-20T21:40:32.8254920Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 347 <- wrt source file 2024-08-20T21:40:32.8277244Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2024-08-20T21:40:32.8280482Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 391 <- wrt source file 2024-08-20T21:40:32.8295000Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2024-08-20T21:40:32.8298644Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 435 <- wrt source file 2024-08-20T21:40:32.8301790Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2024-08-20T21:40:32.8304966Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 492 <- wrt source file 2024-08-20T21:40:32.8315383Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2024-08-20T21:40:32.8318581Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 550 <- wrt source file 2024-08-20T21:40:32.8331251Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2024-08-20T21:40:32.8334452Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 593 <- wrt source file 2024-08-20T21:40:32.8337762Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2024-08-20T21:40:32.8340974Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 650 <- wrt source file 2024-08-20T21:40:33.3786869Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2024-08-20T21:40:33.3984389Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 684 <- wrt source file 2024-08-20T21:40:33.3995207Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2024-08-20T21:40:33.3998284Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 739 <- wrt source file 2024-08-20T21:40:33.4001257Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2024-08-20T21:40:33.4004262Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 798 <- wrt source file 2024-08-20T21:40:33.4026566Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2024-08-20T21:40:33.4029700Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2024-08-20T21:40:33.4032901Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2024-08-20T21:40:33.4036146Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file 2024-08-20T21:40:33.4039374Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2024-08-20T21:40:33.4042724Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 118 <- wrt source file 2024-08-20T21:40:33.4045690Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2024-08-20T21:40:33.4048685Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 195 <- wrt source file 2024-08-20T21:40:33.4110600Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2024-08-20T21:40:33.4113645Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 278 <- wrt source file 2024-08-20T21:40:33.6473886Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2024-08-20T21:40:33.6504826Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 352 <- wrt source file 2024-08-20T21:40:33.6516873Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2024-08-20T21:40:33.6519958Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 534 <- wrt source file 2024-08-20T21:40:33.7306466Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2024-08-20T21:40:33.7330566Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 622 <- wrt source file 2024-08-20T21:40:33.7352655Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2024-08-20T21:40:33.7355729Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 714 <- wrt source file 2024-08-20T21:40:33.7397075Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2024-08-20T21:40:33.7400104Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 827 <- wrt source file 2024-08-20T21:40:33.9152848Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2024-08-20T21:40:33.9182234Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 917 <- wrt source file 2024-08-20T21:40:33.9233299Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2024-08-20T21:40:33.9236410Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1003 <- wrt source file 2024-08-20T21:40:34.0009791Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2024-08-20T21:40:34.0012860Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1117 <- wrt source file 2024-08-20T21:40:34.0037213Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2024-08-20T21:40:34.0040627Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1168 <- wrt source file 2024-08-20T21:40:34.0092599Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2024-08-20T21:40:34.0096113Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1227 <- wrt source file 2024-08-20T21:40:34.2357323Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2024-08-20T21:40:34.2418761Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1282 <- wrt source file 2024-08-20T21:40:34.2433905Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2024-08-20T21:40:34.2437153Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1316 <- wrt source file 2024-08-20T21:40:34.2445528Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2024-08-20T21:40:34.2448812Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1359 <- wrt source file 2024-08-20T21:40:34.2477375Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2024-08-20T21:40:34.2480664Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1406 <- wrt source file 2024-08-20T21:40:34.2494990Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2024-08-20T21:40:34.2498305Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1437 <- wrt source file 2024-08-20T21:40:34.2503497Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2024-08-20T21:40:34.2506759Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1476 <- wrt source file 2024-08-20T21:40:34.2527995Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2024-08-20T21:40:34.2531071Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0, line 589 <- wrt source file 2024-08-20T21:40:34.2542279Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0 2024-08-20T21:40:34.2546042Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 946 <- wrt source file 2024-08-20T21:40:34.3002442Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0 2024-08-20T21:40:34.3005002Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1284 <- wrt source file 2024-08-20T21:40:34.3023359Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0 2024-08-20T21:40:34.3026224Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1535 <- wrt source file 2024-08-20T21:40:34.3034845Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2024-08-20T21:40:34.3037963Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1657 <- wrt source file 2024-08-20T21:40:34.3046937Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2024-08-20T21:40:34.3050069Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1771 <- wrt source file 2024-08-20T21:40:34.3061154Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2024-08-20T21:40:34.3064457Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 69 <- wrt source file 2024-08-20T21:40:34.3075077Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0 2024-08-20T21:40:34.3078263Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 241 <- wrt source file 2024-08-20T21:40:34.3081587Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2024-08-20T21:40:34.3084969Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 519 <- wrt source file 2024-08-20T21:40:34.3088360Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2024-08-20T21:40:34.3091895Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file 2024-08-20T21:40:34.9301322Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0 2024-08-20T21:40:34.9317590Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 258 <- wrt source file 2024-08-20T21:40:34.9320728Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0 2024-08-20T21:40:34.9323719Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 323 <- wrt source file 2024-08-20T21:40:34.9957856Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2024-08-20T21:40:35.0008147Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 536 <- wrt source file 2024-08-20T21:40:35.1287571Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2024-08-20T21:40:35.1296972Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 657 <- wrt source file 2024-08-20T21:40:35.1517997Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2024-08-20T21:40:35.1521559Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 961 <- wrt source file 2024-08-20T21:40:35.1892992Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2024-08-20T21:40:35.1896317Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2024-08-20T21:40:35.1944582Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2024-08-20T21:40:35.1947845Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file 2024-08-20T21:40:35.1957249Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2024-08-20T21:40:35.1960675Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file 2024-08-20T21:40:35.1967168Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2024-08-20T21:40:35.1970545Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 126 <- wrt source file 2024-08-20T21:40:35.1973753Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2024-08-20T21:40:35.1977154Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 619 <- wrt source file 2024-08-20T21:40:35.1980685Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2024-08-20T21:40:35.1984350Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1418 <- wrt source file 2024-08-20T21:40:35.1988044Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2024-08-20T21:40:35.1992086Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1981 <- wrt source file 2024-08-20T21:40:35.1996070Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2024-08-20T21:40:35.2000078Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 1991 <- wrt source file 2024-08-20T21:40:35.2004040Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2024-08-20T21:40:35.2008138Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2026 <- wrt source file 2024-08-20T21:40:35.2012280Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2024-08-20T21:40:35.2016267Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 35 <- wrt source file 2024-08-20T21:40:35.2019687Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0 2024-08-20T21:40:35.2022828Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0, line 33 <- wrt source file 2024-08-20T21:40:35.2025667Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0 2024-08-20T21:40:35.2028718Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2024-08-20T21:40:35.2031911Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2024-08-20T21:40:35.2035139Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0, line 360 <- wrt source file 2024-08-20T21:40:35.2038356Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2024-08-20T21:40:35.2041624Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 591 <- wrt source file 2024-08-20T21:40:35.2045504Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2024-08-20T21:40:35.2048856Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0, line 506 <- wrt source file 2024-08-20T21:40:35.2052308Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0 2024-08-20T21:40:35.2055445Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0, line 845 <- wrt source file 2024-08-20T21:40:35.2058260Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0 2024-08-20T21:40:35.2061236Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 881 <- wrt source file 2024-08-20T21:40:35.2064336Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2024-08-20T21:40:35.2067403Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 924 <- wrt source file 2024-08-20T21:40:35.2070374Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2024-08-20T21:40:35.2073289Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0, line 1191 <- wrt source file 2024-08-20T21:40:35.2076076Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0 2024-08-20T21:40:35.2078918Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1219 <- wrt source file 2024-08-20T21:40:35.2081751Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0 2024-08-20T21:40:35.2084711Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 357 <- wrt source file 2024-08-20T21:40:35.2094543Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0 2024-08-20T21:40:35.2097758Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 435 <- wrt source file 2024-08-20T21:40:35.2100618Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2024-08-20T21:40:35.2103521Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 493 <- wrt source file 2024-08-20T21:40:35.2113143Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2024-08-20T21:40:35.2116089Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 549 <- wrt source file 2024-08-20T21:40:35.2120184Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2024-08-20T21:40:35.2123101Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 577 <- wrt source file 2024-08-20T21:40:35.2136011Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2024-08-20T21:40:35.2139122Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 313 <- wrt source file 2024-08-20T21:40:35.2145187Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2024-08-20T21:40:35.2148559Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 345 <- wrt source file 2024-08-20T21:40:35.2152166Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2024-08-20T21:40:35.2155432Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 214 <- wrt source file 2024-08-20T21:40:35.2158538Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0 2024-08-20T21:40:35.2160835Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 133 <- wrt source file 2024-08-20T21:40:35.2172004Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2024-08-20T21:40:35.2175159Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 155 <- wrt source file 2024-08-20T21:40:35.2178506Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2024-08-20T21:40:35.2181946Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 317 <- wrt source file 2024-08-20T21:40:35.2185279Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2024-08-20T21:40:35.2189063Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0, line 178 <- wrt source file 2024-08-20T21:40:35.2208614Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0 2024-08-20T21:40:35.2212190Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 308 <- wrt source file 2024-08-20T21:40:35.2215314Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2024-08-20T21:40:35.2221012Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 410 <- wrt source file 2024-08-20T21:40:35.2224088Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2024-08-20T21:40:35.2227064Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 510 <- wrt source file 2024-08-20T21:40:35.2229881Z * SKIPPED: 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2024-08-20T21:40:35.2250612Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 842 <- wrt source file 2024-08-20T21:40:35.2253570Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0 2024-08-20T21:40:35.2256568Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 979 <- wrt source file 2024-08-20T21:40:35.2259531Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2024-08-20T21:40:35.2262600Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1135 <- wrt source file 2024-08-20T21:40:35.2265679Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2024-08-20T21:40:35.2268818Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 1278 <- wrt source file 2024-08-20T21:40:35.2271928Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0 2024-08-20T21:40:35.2274958Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1510 <- wrt source file 2024-08-20T21:40:35.2277814Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0 2024-08-20T21:40:35.2281028Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1780 <- wrt source file 2024-08-20T21:40:35.2284540Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2024-08-20T21:40:35.2288090Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1796 <- wrt source file 2024-08-20T21:40:35.2292048Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2024-08-20T21:40:35.2295285Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1941 <- wrt source file 2024-08-20T21:40:35.2298180Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0 2024-08-20T21:40:35.2301066Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0, line 319 <- wrt source file 2024-08-20T21:40:35.2303879Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0 2024-08-20T21:40:35.2306749Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 257 <- wrt source file 2024-08-20T21:40:35.2309639Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2024-08-20T21:40:35.2312619Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 299 <- wrt source file 2024-08-20T21:40:35.2315561Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2024-08-20T21:40:35.2318476Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 329 <- wrt source file 2024-08-20T21:40:35.2321424Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2024-08-20T21:40:35.2324305Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 364 <- wrt source file 2024-08-20T21:40:35.2327160Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2024-08-20T21:40:35.2330144Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 399 <- wrt source file 2024-08-20T21:40:35.2344080Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2024-08-20T21:40:35.2347280Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 436 <- wrt source file 2024-08-20T21:40:35.2350119Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2024-08-20T21:40:35.2353062Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 812 <- wrt source file 2024-08-20T21:40:35.2356044Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2024-08-20T21:40:35.2358964Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0, line 933 <- wrt source file 2024-08-20T21:40:35.2361692Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0 2024-08-20T21:40:35.2364819Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 69 <- wrt source file 2024-08-20T21:40:35.2368328Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2024-08-20T21:40:35.2372079Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 322 <- wrt source file 2024-08-20T21:40:35.2376034Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0 2024-08-20T21:40:35.2379614Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0, line 354 <- wrt source file 2024-08-20T21:40:35.2382959Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0 2024-08-20T21:40:35.2386323Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 548 <- wrt source file 2024-08-20T21:40:35.2389459Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0 2024-08-20T21:40:35.2392830Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 750 <- wrt source file 2024-08-20T21:40:35.2396049Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0 2024-08-20T21:40:35.2399084Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 72 <- wrt source file 2024-08-20T21:40:35.2401867Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0 2024-08-20T21:40:35.2405098Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 98 <- wrt source file 2024-08-20T21:40:35.2408149Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0 2024-08-20T21:40:35.2411272Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 223 <- wrt source file 2024-08-20T21:40:35.2414284Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0 2024-08-20T21:40:35.2417325Z * 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 2024-08-20T21:40:35.2420287Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::random_split:0 2024-08-20T21:40:35.2423245Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0, line 42 <- wrt source file 2024-08-20T21:40:35.2426108Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0 2024-08-20T21:40:35.2429206Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0, line 240 <- wrt source file 2024-08-20T21:40:35.2432439Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0 2024-08-20T21:40:35.2435596Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 303 <- wrt source file 2024-08-20T21:40:35.2438575Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0 2024-08-20T21:40:35.2441692Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 39 <- wrt source file 2024-08-20T21:40:35.2444858Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2024-08-20T21:40:35.2448000Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 137 <- wrt source file 2024-08-20T21:40:35.2451171Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0 2024-08-20T21:40:35.2454304Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 364 <- wrt source file 2024-08-20T21:40:35.2457478Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0 2024-08-20T21:40:35.2460800Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0, line 96 <- wrt source file 2024-08-20T21:40:35.2464122Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0 2024-08-20T21:40:35.2467487Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 263 <- wrt source file 2024-08-20T21:40:35.2470780Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0 2024-08-20T21:40:35.2474307Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 51 <- wrt source file 2024-08-20T21:40:35.2477950Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0 2024-08-20T21:40:35.2481763Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 197 <- wrt source file 2024-08-20T21:40:35.2485450Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0 2024-08-20T21:40:35.2489276Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0, line 87 <- wrt source file 2024-08-20T21:40:35.2493330Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0 2024-08-20T21:40:35.2497161Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 48 <- wrt source file 2024-08-20T21:40:35.2500978Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2024-08-20T21:40:35.2504720Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 98 <- wrt source file 2024-08-20T21:40:35.2508378Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0 2024-08-20T21:40:35.2512047Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 317 <- wrt source file 2024-08-20T21:40:35.2515604Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0 2024-08-20T21:40:35.2519379Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0, line 403 <- wrt source file 2024-08-20T21:40:35.2523266Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0 2024-08-20T21:40:35.2527156Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0, line 613 <- wrt source file 2024-08-20T21:40:35.2531160Z * SKIPPED: 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line 34 <- wrt source file 2024-08-20T21:40:35.2553949Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0 2024-08-20T21:40:35.2557733Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 62 <- wrt source file 2024-08-20T21:40:35.2561532Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0 2024-08-20T21:40:35.2565278Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 122 <- wrt source file 2024-08-20T21:40:35.2568998Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0 2024-08-20T21:40:35.2572808Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0, line 189 <- wrt source file 2024-08-20T21:40:35.2576493Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0 2024-08-20T21:40:35.2580214Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0, line 36 <- wrt source file 2024-08-20T21:40:35.2583892Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0 2024-08-20T21:40:35.2587742Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 24 <- wrt source file 2024-08-20T21:40:35.2591840Z * SKIPPED: 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line 33 <- wrt source file 2024-08-20T21:40:35.2614753Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2024-08-20T21:40:35.2618523Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 28 <- wrt source file 2024-08-20T21:40:35.2622207Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2024-08-20T21:40:35.2625885Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 72 <- wrt source file 2024-08-20T21:40:35.2629541Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2024-08-20T21:40:35.2633203Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 28 <- wrt source file 2024-08-20T21:40:35.2636813Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2024-08-20T21:40:35.2640536Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 26 <- wrt source file 2024-08-20T21:40:35.2644395Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2024-08-20T21:40:35.2648089Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 36 <- wrt source file 2024-08-20T21:40:35.2651731Z * SKIPPED: 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* SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2024-08-20T21:40:35.2675788Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 216 <- wrt source file 2024-08-20T21:40:35.2679151Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2024-08-20T21:40:35.2682649Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 314 <- wrt source file 2024-08-20T21:40:35.2686130Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2024-08-20T21:40:35.2689787Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 362 <- wrt source file 2024-08-20T21:40:35.2693451Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2024-08-20T21:40:35.2696975Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 394 <- wrt source file 2024-08-20T21:40:35.2700472Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2024-08-20T21:40:35.2703983Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0, line 441 <- wrt source file 2024-08-20T21:40:35.2707438Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2024-08-20T21:40:35.2710961Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 480 <- wrt source file 2024-08-20T21:40:35.2714489Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2024-08-20T21:40:35.2718092Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 533 <- wrt source file 2024-08-20T21:40:35.2721831Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2024-08-20T21:40:35.2725423Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 599 <- wrt source file 2024-08-20T21:40:35.2728858Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2024-08-20T21:40:35.2732400Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 648 <- wrt source file 2024-08-20T21:40:35.2735846Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2024-08-20T21:40:35.2739342Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 811 <- wrt source file 2024-08-20T21:40:35.2742750Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2024-08-20T21:40:35.2746264Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 878 <- wrt source file 2024-08-20T21:40:35.2749841Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2024-08-20T21:40:35.2753378Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 989 <- wrt source file 2024-08-20T21:40:35.2756886Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2024-08-20T21:40:35.2760696Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1063 <- wrt source file 2024-08-20T21:40:35.2764647Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2024-08-20T21:40:35.2768762Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1084 <- wrt source file 2024-08-20T21:40:35.2772725Z * SKIPPED: 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file 2024-08-20T21:40:35.2793736Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0 2024-08-20T21:40:35.2797138Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 203 <- wrt source file 2024-08-20T21:40:35.2800541Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2024-08-20T21:40:35.2803801Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 825 <- wrt source file 2024-08-20T21:40:35.2806838Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2024-08-20T21:40:35.2809724Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0, line 324 <- wrt source file 2024-08-20T21:40:35.2812448Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0 2024-08-20T21:40:35.2815517Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0, line 184 <- wrt source file 2024-08-20T21:40:35.2818840Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2024-08-20T21:40:35.2822000Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 271 <- wrt source file 2024-08-20T21:40:35.2824945Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2024-08-20T21:40:35.2827965Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 510 <- wrt source file 2024-08-20T21:40:35.2830993Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2024-08-20T21:40:35.2834020Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1064 <- wrt source file 2024-08-20T21:40:35.3920122Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2024-08-20T21:40:35.3923559Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1219 <- wrt source file 2024-08-20T21:40:35.3978308Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2024-08-20T21:40:35.3981464Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1384 <- wrt source file 2024-08-20T21:40:35.3996655Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2024-08-20T21:40:35.3999936Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1548 <- wrt source file 2024-08-20T21:40:35.4003159Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2024-08-20T21:40:35.4006367Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1748 <- wrt source file 2024-08-20T21:40:35.4177021Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2024-08-20T21:40:35.4180254Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0, line 36 <- wrt source file 2024-08-20T21:40:35.4183427Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0 2024-08-20T21:40:35.4186950Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2024-08-20T21:40:35.4189894Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2024-08-20T21:40:35.4193517Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0, line 110 <- wrt source file 2024-08-20T21:40:35.4196366Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0 2024-08-20T21:40:35.4198409Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 69 <- wrt source file 2024-08-20T21:40:35.4200316Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2024-08-20T21:40:35.4202075Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 81 <- wrt source file 2024-08-20T21:40:35.4203681Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0 2024-08-20T21:40:35.4205383Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0, line 90 <- wrt source file 2024-08-20T21:40:35.4207148Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0 2024-08-20T21:40:35.4208856Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0, line 83 <- wrt source file 2024-08-20T21:40:35.4576062Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0 2024-08-20T21:40:35.4577928Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 212 <- wrt source file 2024-08-20T21:40:35.4645366Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2024-08-20T21:40:35.4647601Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 281 <- wrt source file 2024-08-20T21:40:35.4649451Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2024-08-20T21:40:35.4651392Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0, line 401 <- wrt source file 2024-08-20T21:40:35.4709441Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0 2024-08-20T21:40:35.4711347Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 524 <- wrt source file 2024-08-20T21:40:35.4840554Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2024-08-20T21:40:35.4842467Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 698 <- wrt source file 2024-08-20T21:40:35.4968167Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2024-08-20T21:40:35.4970639Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 184 <- wrt source file 2024-08-20T21:40:35.4972485Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2024-08-20T21:40:35.4974352Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 159 <- wrt source file 2024-08-20T21:40:35.5023489Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2024-08-20T21:40:35.5025272Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0, line 45 <- wrt source file 2024-08-20T21:40:35.5029418Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0 2024-08-20T21:40:35.5031228Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0, line 417 <- wrt source file 2024-08-20T21:40:35.5032828Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0 2024-08-20T21:40:35.5034494Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 170 <- wrt source file 2024-08-20T21:40:35.5082468Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2024-08-20T21:40:35.5084203Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0, line 305 <- wrt source file 2024-08-20T21:40:35.5085954Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0 2024-08-20T21:40:35.5087722Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 996 <- wrt source file 2024-08-20T21:40:35.5132639Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2024-08-20T21:40:35.5134688Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1061 <- wrt source file 2024-08-20T21:40:35.5136502Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2024-08-20T21:40:35.5138280Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1282 <- wrt source file 2024-08-20T21:40:35.5184996Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2024-08-20T21:40:35.5186924Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1348 <- wrt source file 2024-08-20T21:40:35.5189043Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2024-08-20T21:40:35.5191122Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1411 <- wrt source file 2024-08-20T21:40:35.5193809Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2024-08-20T21:40:35.5195739Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1456 <- wrt source file 2024-08-20T21:40:35.5197593Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2024-08-20T21:40:35.5199731Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1566 <- wrt source file 2024-08-20T21:40:35.5201816Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2024-08-20T21:40:35.5204266Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 85 <- wrt source file 2024-08-20T21:40:35.5206538Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0 2024-08-20T21:40:35.5208214Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 60 <- wrt source file 2024-08-20T21:40:35.5209953Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2024-08-20T21:40:35.5211906Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 23 <- wrt source file 2024-08-20T21:40:35.5213900Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2024-08-20T21:40:35.5216075Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file 2024-08-20T21:40:35.5218333Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2024-08-20T21:40:35.5220525Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 25 <- wrt source file 2024-08-20T21:40:35.5222621Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2024-08-20T21:40:35.5224793Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 66 <- wrt source file 2024-08-20T21:40:35.5227388Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2024-08-20T21:40:35.5229583Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 140 <- wrt source file 2024-08-20T21:40:35.5231695Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2024-08-20T21:40:35.5233663Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 25 <- wrt source file 2024-08-20T21:40:35.5235491Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2024-08-20T21:40:35.5237303Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 318 <- wrt source file 2024-08-20T21:40:35.5257764Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2024-08-20T21:40:35.5259992Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 210 <- wrt source file 2024-08-20T21:40:35.5262369Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2024-08-20T21:40:35.5264292Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 282 <- wrt source file 2024-08-20T21:40:35.5266035Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2024-08-20T21:40:35.5268063Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 358 <- wrt source file 2024-08-20T21:40:35.5269806Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2024-08-20T21:40:35.5271936Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 95 <- wrt source file 2024-08-20T21:40:35.5274550Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2024-08-20T21:40:35.5277562Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 145 <- wrt source file 2024-08-20T21:40:35.5280910Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2024-08-20T21:40:35.5283143Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 42 <- wrt source file 2024-08-20T21:40:35.5285096Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2024-08-20T21:40:35.5287563Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 123 <- wrt source file 2024-08-20T21:40:35.5290744Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2024-08-20T21:40:35.5293578Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 207 <- wrt source file 2024-08-20T21:40:35.5295505Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2024-08-20T21:40:35.5297696Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 293 <- wrt source file 2024-08-20T21:40:35.5299752Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2024-08-20T21:40:35.5301890Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 375 <- wrt source file 2024-08-20T21:40:35.5304933Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2024-08-20T21:40:35.5309220Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 457 <- wrt source file 2024-08-20T21:40:35.5313537Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2024-08-20T21:40:35.5317701Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 30 <- wrt source file 2024-08-20T21:40:35.5321796Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2024-08-20T21:40:35.5325630Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 516 <- wrt source file 2024-08-20T21:40:35.5329123Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2024-08-20T21:40:35.5332623Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 801 <- wrt source file 2024-08-20T21:40:35.5335970Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2024-08-20T21:40:35.5339431Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 1203 <- wrt source file 2024-08-20T21:40:35.5342913Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2024-08-20T21:40:35.5346417Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1269 <- wrt source file 2024-08-20T21:40:35.5349902Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2024-08-20T21:40:35.5353419Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1322 <- wrt source file 2024-08-20T21:40:35.5356886Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2024-08-20T21:40:35.5360291Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 36 <- wrt source file 2024-08-20T21:40:35.5363648Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2024-08-20T21:40:35.5366965Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 506 <- wrt source file 2024-08-20T21:40:35.5370180Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2024-08-20T21:40:35.5373388Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 635 <- wrt source file 2024-08-20T21:40:35.5376653Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2024-08-20T21:40:35.5380021Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 892 <- wrt source file 2024-08-20T21:40:35.5383471Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2024-08-20T21:40:35.5386970Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1014 <- wrt source file 2024-08-20T21:40:35.5390550Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2024-08-20T21:40:35.5394061Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1140 <- wrt source file 2024-08-20T21:40:35.5397517Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2024-08-20T21:40:35.5401030Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 112 <- wrt source file 2024-08-20T21:40:35.5404578Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2024-08-20T21:40:35.5408342Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 276 <- wrt source file 2024-08-20T21:40:35.5411981Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2024-08-20T21:40:35.5415734Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 24 <- wrt source file 2024-08-20T21:40:35.5419545Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2024-08-20T21:40:35.5423344Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 177 <- wrt source file 2024-08-20T21:40:35.5427070Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2024-08-20T21:40:35.5430574Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 138 <- wrt source file 2024-08-20T21:40:35.5433797Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2024-08-20T21:40:35.5437689Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0, line 62 <- wrt source file 2024-08-20T21:40:35.5447958Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0 2024-08-20T21:40:35.5452542Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0, line 98 <- wrt source file 2024-08-20T21:40:35.5457075Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0 2024-08-20T21:40:35.5461619Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 55 <- wrt source file 2024-08-20T21:40:35.5465784Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2024-08-20T21:40:35.5469679Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 22 <- wrt source file 2024-08-20T21:40:35.5473124Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2024-08-20T21:40:35.5476716Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 47 <- wrt source file 2024-08-20T21:40:35.5480339Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2024-08-20T21:40:35.5483835Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 178 <- wrt source file 2024-08-20T21:40:35.5487118Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2024-08-20T21:40:35.5490825Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 31 <- wrt source file 2024-08-20T21:40:35.5494442Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2024-08-20T21:40:35.5498041Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 76 <- wrt source file 2024-08-20T21:40:35.5501627Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2024-08-20T21:40:35.5505245Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 130 <- wrt source file 2024-08-20T21:40:35.5508782Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2024-08-20T21:40:35.5512454Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 163 <- wrt source file 2024-08-20T21:40:35.5516171Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2024-08-20T21:40:35.5519639Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 93 <- wrt source file 2024-08-20T21:40:35.5522766Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0 2024-08-20T21:40:35.5526036Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 115 <- wrt source file 2024-08-20T21:40:35.5529526Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2024-08-20T21:40:35.5532874Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 218 <- wrt source file 2024-08-20T21:40:35.5536017Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2024-08-20T21:40:35.5539366Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 286 <- wrt source file 2024-08-20T21:40:35.5542593Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2024-08-20T21:40:35.5545913Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 424 <- wrt source file 2024-08-20T21:40:35.5549227Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2024-08-20T21:40:35.5552539Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 595 <- wrt source file 2024-08-20T21:40:35.5555727Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2024-08-20T21:40:35.5559153Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 654 <- wrt source file 2024-08-20T21:40:35.5562672Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2024-08-20T21:40:35.5566367Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 706 <- wrt source file 2024-08-20T21:40:35.5570342Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2024-08-20T21:40:35.5573984Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 49 <- wrt source file 2024-08-20T21:40:35.5577329Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2024-08-20T21:40:35.5580763Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 123 <- wrt source file 2024-08-20T21:40:35.5584168Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2024-08-20T21:40:35.5587604Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 215 <- wrt source file 2024-08-20T21:40:35.5591139Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2024-08-20T21:40:35.5594475Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 145 <- wrt source file 2024-08-20T21:40:35.5597712Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2024-08-20T21:40:35.5601013Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 512 <- wrt source file 2024-08-20T21:40:35.5604448Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2024-08-20T21:40:35.5607774Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 534 <- wrt source file 2024-08-20T21:40:35.5611130Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2024-08-20T21:40:35.5614479Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 548 <- wrt source file 2024-08-20T21:40:35.5617872Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2024-08-20T21:40:35.5621169Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 570 <- wrt source file 2024-08-20T21:40:35.5624398Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2024-08-20T21:40:35.5627672Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 696 <- wrt source file 2024-08-20T21:40:35.5630892Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2024-08-20T21:40:35.5634442Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0, line 85 <- wrt source file 2024-08-20T21:40:35.5638252Z * 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 2024-08-20T21:40:35.5642012Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0, line 84 <- wrt source file 2024-08-20T21:40:35.5645671Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0 2024-08-20T21:40:35.5649458Z * 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 185 <- wrt source file 2024-08-20T21:40:35.5653196Z * 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 2024-08-20T21:40:35.5657022Z * 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 439 <- wrt source file 2024-08-20T21:40:35.5660913Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2024-08-20T21:40:35.5664472Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 27 <- wrt source file 2024-08-20T21:40:35.5667601Z * SKIPPED: 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DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0, line 885 <- wrt source file 2024-08-20T21:40:35.5751043Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0 2024-08-20T21:40:35.5753975Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0, line 1001 <- wrt source file 2024-08-20T21:40:35.5756909Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0 2024-08-20T21:40:35.5759817Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0, line 1100 <- wrt source file 2024-08-20T21:40:35.5762647Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0 2024-08-20T21:40:35.5765525Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0, line 50 <- wrt source file 2024-08-20T21:40:35.5768398Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0 2024-08-20T21:40:35.5771486Z * 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 2024-08-20T21:40:35.5774446Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2024-08-20T21:40:35.5777506Z * 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 2024-08-20T21:40:35.5780574Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0 2024-08-20T21:40:35.5783683Z * 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 2024-08-20T21:40:35.5786730Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2024-08-20T21:40:35.5808189Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 272 <- wrt source file 2024-08-20T21:40:35.5811297Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0 2024-08-20T21:40:35.5814316Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::save_on_cpu:0, line 337 <- wrt source file 2024-08-20T21:40:35.5817197Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::save_on_cpu:0 2024-08-20T21:40:35.5820280Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0, line 394 <- wrt source file 2024-08-20T21:40:35.5823530Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0 2024-08-20T21:40:35.5826795Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0, line 471 <- wrt source file 2024-08-20T21:40:35.5830058Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0 2024-08-20T21:40:35.5833445Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 727 <- wrt source file 2024-08-20T21:40:35.5836805Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2024-08-20T21:40:35.5839982Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0, line 176 <- wrt source file 2024-08-20T21:40:35.5842864Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0 2024-08-20T21:40:35.5845993Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::record_function:0, line 695 <- wrt source file 2024-08-20T21:40:35.5849075Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::record_function:0 2024-08-20T21:40:35.5852147Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_itt:0, line 829 <- wrt source file 2024-08-20T21:40:35.5855050Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_itt:0 2024-08-20T21:40:35.5857984Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_nvtx:0, line 902 <- wrt source file 2024-08-20T21:40:35.5860889Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_nvtx:0 2024-08-20T21:40:35.5863860Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0, line 114 <- wrt source file 2024-08-20T21:40:35.5866827Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0 2024-08-20T21:40:35.5871039Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1, line 125 <- wrt source file 2024-08-20T21:40:35.5874059Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1 2024-08-20T21:40:35.5877038Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2, line 138 <- wrt source file 2024-08-20T21:40:35.5879953Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2 2024-08-20T21:40:35.5883128Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0, line 171 <- wrt source file 2024-08-20T21:40:35.5886380Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0 2024-08-20T21:40:35.5889418Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file 2024-08-20T21:40:35.5892392Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/profiler.py::profile:0 2024-08-20T21:40:35.5895348Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 29 <- wrt source file 2024-08-20T21:40:35.5898445Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2024-08-20T21:40:35.5901449Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0, line 20 <- wrt source file 2024-08-20T21:40:35.5904265Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0 2024-08-20T21:40:35.5907347Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0, line 28 <- wrt source file 2024-08-20T21:40:35.5910381Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0 2024-08-20T21:40:35.5913549Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0, line 40 <- wrt source file 2024-08-20T21:40:35.5916752Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0 2024-08-20T21:40:35.5919941Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 23 <- wrt source file 2024-08-20T21:40:35.5922867Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0 2024-08-20T21:40:35.5925785Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0, line 15 <- wrt source file 2024-08-20T21:40:35.5928608Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0 2024-08-20T21:40:35.5931759Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0, line 160 <- wrt source file 2024-08-20T21:40:35.5934994Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0 2024-08-20T21:40:35.5938355Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 181 <- wrt source file 2024-08-20T21:40:35.5941740Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/geometric.py::Geometric:0 2024-08-20T21:40:35.5983941Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 21 <- wrt source file 2024-08-20T21:40:35.5986859Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0 2024-08-20T21:40:35.5989932Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 23 <- wrt source file 2024-08-20T21:40:35.5993199Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0 2024-08-20T21:40:35.5996512Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 23 <- wrt source file 2024-08-20T21:40:35.5999650Z * SUCCESS: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0 2024-08-20T21:40:35.6022212Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0, line 19 <- wrt source file 2024-08-20T21:40:35.6025259Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0 2024-08-20T21:40:35.6028382Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 41 <- wrt source file 2024-08-20T21:40:35.6031577Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0 2024-08-20T21:40:35.6034753Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 20 <- wrt source file 2024-08-20T21:40:35.6037845Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0 2024-08-20T21:40:35.6041121Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 25 <- wrt source file 2024-08-20T21:40:35.6044477Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0 2024-08-20T21:40:35.6048147Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0, line 61 <- wrt source file 2024-08-20T21:40:35.6052103Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0 2024-08-20T21:40:35.6055704Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 36 <- wrt source file 2024-08-20T21:40:35.6058989Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0 2024-08-20T21:40:35.6062432Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 101 <- wrt source file 2024-08-20T21:40:35.6066036Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0 2024-08-20T21:40:35.6069221Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0, line 21 <- wrt source file 2024-08-20T21:40:35.6072161Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0 2024-08-20T21:40:35.6075373Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 31 <- wrt source file 2024-08-20T21:40:35.6082099Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0 2024-08-20T21:40:35.6085349Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0, line 17 <- wrt source file 2024-08-20T21:40:35.6088260Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0 2024-08-20T21:40:35.6105302Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0, line 23 <- wrt source file 2024-08-20T21:40:35.6108348Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0 2024-08-20T21:40:35.6111394Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/studentT.py::StudentT:0, line 21 <- wrt source file 2024-08-20T21:40:35.6114422Z * SUCCESS: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/von_mises.py::VonMises:0 2024-08-20T21:40:35.6156673Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/weibull.py::Weibull:0, line 19 <- wrt source file 2024-08-20T21:40:35.6159650Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/weibull.py::Weibull:0 2024-08-20T21:40:35.6162662Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/wishart.py::Wishart:0, line 40 <- wrt source file 2024-08-20T21:40:35.6165766Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/wishart.py::Wishart:0 2024-08-20T21:40:35.6168915Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0, line 720 <- wrt source file 2024-08-20T21:40:35.6172169Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0 2024-08-20T21:40:35.6212288Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 591 <- wrt source file 2024-08-20T21:40:35.6215539Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0 2024-08-20T21:40:35.6218818Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0, line 746 <- wrt source file 2024-08-20T21:40:35.6222063Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0 2024-08-20T21:40:35.6225507Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 4346 <- wrt source file 2024-08-20T21:40:35.6229191Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0 2024-08-20T21:40:35.6232838Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4360 <- wrt source file 2024-08-20T21:40:35.6236429Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2024-08-20T21:40:35.6240157Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4390 <- wrt source file 2024-08-20T21:40:35.6243713Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0 2024-08-20T21:40:35.6247198Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0, line 192 <- wrt source file 2024-08-20T21:40:35.6250555Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0 2024-08-20T21:40:35.6254263Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0, line 288 <- wrt source file 2024-08-20T21:40:35.6258207Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0 2024-08-20T21:40:35.6262237Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0, line 29 <- wrt source file 2024-08-20T21:40:35.6266255Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0 2024-08-20T21:40:35.6268379Z ============ 2024-08-20T21:40:35.6268941Z Finished doctests 2024-08-20T21:40:35.6269439Z 335 / 695 passed 2024-08-20T21:40:35.6269945Z  2024-08-20T21:40:35.6270557Z === Found 101 parse-time warnings === 2024-08-20T21:40:35.6271505Z --- Parse Warning: 1 / 101 --- 2024-08-20T21:40:35.6274456Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=426. 2024-08-20T21:40:35.6277558Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.6279041Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-08-20T21:40:35.6280095Z 2024-08-20T21:40:35.6280745Z This is helpful when you want to visualize data over some 2024-08-20T21:40:35.6281833Z range of inputs. See below for a plotting example. 2024-08-20T21:40:35.6282652Z 2024-08-20T21:40:35.6283391Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-08-20T21:40:35.6284656Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-08-20T21:40:35.6285980Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-08-20T21:40:35.6287270Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-08-20T21:40:35.6288407Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-08-20T21:40:35.6289391Z to the result shape. 2024-08-20T21:40:35.6289998Z 2024-08-20T21:40:35.6290617Z .. note:: 2024-08-20T21:40:35.6291340Z 0D inputs are treated equivalently to 1D inputs of a 2024-08-20T21:40:35.6292242Z single element. 2024-08-20T21:40:35.6292946Z 2024-08-20T21:40:35.6293381Z .. warning:: 2024-08-20T21:40:35.6294183Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-08-20T21:40:35.6295432Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-08-20T21:40:35.6296269Z 2024-08-20T21:40:35.6296870Z In the future `torch.meshgrid` will transition to 2024-08-20T21:40:35.6297873Z `indexing='xy'` as the default. 2024-08-20T21:40:35.6298580Z 2024-08-20T21:40:35.6299235Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-08-20T21:40:35.6300492Z this issue with the goal of migrating to NumPy's behavior. 2024-08-20T21:40:35.6301391Z 2024-08-20T21:40:35.6301947Z .. seealso:: 2024-08-20T21:40:35.6302458Z 2024-08-20T21:40:35.6303098Z :func:`torch.cartesian_prod` has the same effect but it 2024-08-20T21:40:35.6304115Z collects the data in a tensor of vectors. 2024-08-20T21:40:35.6304874Z 2024-08-20T21:40:35.6305292Z Args: 2024-08-20T21:40:35.6306251Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-08-20T21:40:35.6307591Z treated as tensors of size :math:`(1,)` automatically 2024-08-20T21:40:35.6308440Z 2024-08-20T21:40:35.6309093Z indexing: (str, optional): the indexing mode, either "xy" 2024-08-20T21:40:35.6310228Z or "ij", defaults to "ij". See warning for future changes. 2024-08-20T21:40:35.6311121Z 2024-08-20T21:40:35.6311739Z If "xy" is selected, the first dimension corresponds 2024-08-20T21:40:35.6312815Z to the cardinality of the second input and the second 2024-08-20T21:40:35.6313937Z dimension corresponds to the cardinality of the first 2024-08-20T21:40:35.6314834Z input. 2024-08-20T21:40:35.6315353Z 2024-08-20T21:40:35.6315958Z If "ij" is selected, the dimensions are in the same 2024-08-20T21:40:35.6316942Z order as the cardinality of the inputs. 2024-08-20T21:40:35.6317770Z 2024-08-20T21:40:35.6318169Z Returns: 2024-08-20T21:40:35.6318885Z seq (sequence of Tensors): If the input has :math:`N` 2024-08-20T21:40:35.6320096Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-08-20T21:40:35.6321194Z output will also have :math:`N` tensors, where each tensor 2024-08-20T21:40:35.6322338Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-08-20T21:40:35.6323084Z 2024-08-20T21:40:35.6323518Z Example:: 2024-08-20T21:40:35.6324008Z 2024-08-20T21:40:35.6324512Z >>> x = torch.tensor([1, 2, 3]) 2024-08-20T21:40:35.6325292Z >>> y = torch.tensor([4, 5, 6]) 2024-08-20T21:40:35.6325991Z 2024-08-20T21:40:35.6326799Z Observe the element-wise pairings across the grid, (1, 4), 2024-08-20T21:40:35.6327852Z (1, 5), ..., (3, 6). This is the same thing as the 2024-08-20T21:40:35.6328699Z cartesian product. 2024-08-20T21:40:35.6329665Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-08-20T21:40:35.6330592Z >>> grid_x 2024-08-20T21:40:35.6331168Z tensor([[1, 1, 1], 2024-08-20T21:40:35.6331803Z [2, 2, 2], 2024-08-20T21:40:35.6332411Z [3, 3, 3]]) 2024-08-20T21:40:35.6333042Z >>> grid_y 2024-08-20T21:40:35.6333627Z tensor([[4, 5, 6], 2024-08-20T21:40:35.6334241Z [4, 5, 6], 2024-08-20T21:40:35.6334870Z [4, 5, 6]]) 2024-08-20T21:40:35.6335473Z 2024-08-20T21:40:35.6336084Z This correspondence can be seen when these grids are 2024-08-20T21:40:35.6336988Z stacked properly. 2024-08-20T21:40:35.6337984Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-08-20T21:40:35.6339050Z ... torch.cartesian_prod(x, y)) 2024-08-20T21:40:35.6339836Z True 2024-08-20T21:40:35.6340320Z 2024-08-20T21:40:35.6340953Z `torch.meshgrid` is commonly used to produce a grid for 2024-08-20T21:40:35.6341853Z plotting. 2024-08-20T21:40:35.6342532Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-08-20T21:40:35.6343428Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-08-20T21:40:35.6344316Z >>> import matplotlib.pyplot as plt 2024-08-20T21:40:35.6345316Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-08-20T21:40:35.6346381Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-08-20T21:40:35.6347406Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-08-20T21:40:35.6348335Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-08-20T21:40:35.6349294Z >>> ax = plt.axes(projection='3d') 2024-08-20T21:40:35.6350224Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-08-20T21:40:35.6351081Z >>> plt.show() 2024-08-20T21:40:35.6351651Z 2024-08-20T21:40:35.6352145Z .. image:: ../_static/img/meshgrid.png 2024-08-20T21:40:35.6352905Z :width: 512 2024-08-20T21:40:35.6353454Z 2024-08-20T21:40:35.6353844Z 2024-08-20T21:40:35.6354847Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.6355888Z 2024-08-20T21:40:35.6356326Z warnings.warn(msg) 2024-08-20T21:40:35.6356859Z 2024-08-20T21:40:35.6357458Z --- Parse Warning: 2 / 101 --- 2024-08-20T21:40:35.6360446Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=815. 2024-08-20T21:40:35.6363566Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.6365599Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-08-20T21:40:35.6367006Z 2024-08-20T21:40:35.6367586Z Returns the unique elements of the input tensor. 2024-08-20T21:40:35.6368390Z 2024-08-20T21:40:35.6369337Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-08-20T21:40:35.6371062Z this function also eliminates non-consecutive duplicate values. 2024-08-20T21:40:35.6372025Z 2024-08-20T21:40:35.6372824Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-08-20T21:40:35.6374428Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-08-20T21:40:35.6376181Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-08-20T21:40:35.6377647Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-08-20T21:40:35.6378520Z 2024-08-20T21:40:35.6378930Z Args: 2024-08-20T21:40:35.6379465Z input (Tensor): the input tensor 2024-08-20T21:40:35.6380516Z sorted (bool): Whether to sort the unique elements in ascending order 2024-08-20T21:40:35.6381588Z before returning as output. 2024-08-20T21:40:35.6382608Z return_inverse (bool): Whether to also return the indices for where 2024-08-20T21:40:35.6383946Z elements in the original input ended up in the returned unique list. 2024-08-20T21:40:35.6385337Z return_counts (bool): Whether to also return the counts for each unique 2024-08-20T21:40:35.6386371Z element. 2024-08-20T21:40:35.6387219Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-08-20T21:40:35.6388631Z unique of the flattened input is returned. Otherwise, each of the 2024-08-20T21:40:35.6389935Z tensors indexed by the given dimension is treated as one of the 2024-08-20T21:40:35.6391433Z elements to apply the unique operation upon. See examples for more 2024-08-20T21:40:35.6392503Z details. Default: ``None`` 2024-08-20T21:40:35.6393192Z 2024-08-20T21:40:35.6393607Z Returns: 2024-08-20T21:40:35.6394581Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-08-20T21:40:35.6395735Z 2024-08-20T21:40:35.6396594Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-08-20T21:40:35.6397937Z - **inverse_indices** (*Tensor*): (optional) if 2024-08-20T21:40:35.6399003Z :attr:`return_inverse` is True, there will be an additional 2024-08-20T21:40:35.6400220Z returned tensor (same shape as input) representing the indices 2024-08-20T21:40:35.6401491Z for where elements in the original input map to in the output; 2024-08-20T21:40:35.6402722Z otherwise, this function will only return a single tensor. 2024-08-20T21:40:35.6403858Z - **counts** (*Tensor*): (optional) if 2024-08-20T21:40:35.6404841Z :attr:`return_counts` is True, there will be an additional 2024-08-20T21:40:35.6406006Z returned tensor (same shape as output or output.size(dim), 2024-08-20T21:40:35.6407228Z if dim was specified) representing the number of occurrences 2024-08-20T21:40:35.6408244Z for each unique value or tensor. 2024-08-20T21:40:35.6408971Z 2024-08-20T21:40:35.6409401Z Example:: 2024-08-20T21:40:35.6409853Z 2024-08-20T21:40:35.6410650Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-08-20T21:40:35.6411653Z >>> output 2024-08-20T21:40:35.6412181Z tensor([1, 2, 3]) 2024-08-20T21:40:35.6412752Z 2024-08-20T21:40:35.6413289Z >>> output, inverse_indices = torch.unique( 2024-08-20T21:40:35.6414480Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-20T21:40:35.6415637Z >>> output 2024-08-20T21:40:35.6416177Z tensor([1, 2, 3]) 2024-08-20T21:40:35.6416780Z >>> inverse_indices 2024-08-20T21:40:35.6417390Z tensor([0, 2, 1, 2]) 2024-08-20T21:40:35.6417985Z 2024-08-20T21:40:35.6418504Z >>> output, inverse_indices = torch.unique( 2024-08-20T21:40:35.6419699Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-08-20T21:40:35.6420784Z >>> output 2024-08-20T21:40:35.6421318Z tensor([1, 2, 3]) 2024-08-20T21:40:35.6421900Z >>> inverse_indices 2024-08-20T21:40:35.6422517Z tensor([[0, 2], 2024-08-20T21:40:35.6423093Z [1, 2]]) 2024-08-20T21:40:35.6423619Z 2024-08-20T21:40:35.6424067Z >>> a = torch.tensor([ 2024-08-20T21:40:35.6424688Z ... [ 2024-08-20T21:40:35.6425206Z ... [1, 1, 0, 0], 2024-08-20T21:40:35.6425862Z ... [1, 1, 0, 0], 2024-08-20T21:40:35.6426520Z ... [0, 0, 1, 1], 2024-08-20T21:40:35.6427137Z ... ], 2024-08-20T21:40:35.6427641Z ... [ 2024-08-20T21:40:35.6428145Z ... [0, 0, 1, 1], 2024-08-20T21:40:35.6428793Z ... [0, 0, 1, 1], 2024-08-20T21:40:35.6429439Z ... [1, 1, 1, 1], 2024-08-20T21:40:35.6430074Z ... ], 2024-08-20T21:40:35.6430570Z ... [ 2024-08-20T21:40:35.6431096Z ... [1, 1, 0, 0], 2024-08-20T21:40:35.6431730Z ... [1, 1, 0, 0], 2024-08-20T21:40:35.6432385Z ... [0, 0, 1, 1], 2024-08-20T21:40:35.6433101Z ... ], 2024-08-20T21:40:35.6433590Z ... ]) 2024-08-20T21:40:35.6434100Z 2024-08-20T21:40:35.6434879Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-08-20T21:40:35.6436277Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-08-20T21:40:35.6437425Z >>> # each other, so one of them will be removed. 2024-08-20T21:40:35.6438291Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-08-20T21:40:35.6439015Z tensor(True) 2024-08-20T21:40:35.6439659Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-08-20T21:40:35.6440442Z >>> a_unique_dim0 2024-08-20T21:40:35.6441050Z tensor([[[0, 0, 1, 1], 2024-08-20T21:40:35.6441758Z [0, 0, 1, 1], 2024-08-20T21:40:35.6442390Z [1, 1, 1, 1]], 2024-08-20T21:40:35.6443031Z [[1, 1, 0, 0], 2024-08-20T21:40:35.6443645Z [1, 1, 0, 0], 2024-08-20T21:40:35.6444282Z [0, 0, 1, 1]]]) 2024-08-20T21:40:35.6444910Z 2024-08-20T21:40:35.6445837Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-08-20T21:40:35.6446886Z >>> # `a_unique_dim0`: 2024-08-20T21:40:35.6447634Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-08-20T21:40:35.6448403Z tensor(True) 2024-08-20T21:40:35.6449055Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-08-20T21:40:35.6449829Z tensor(True) 2024-08-20T21:40:35.6450415Z 2024-08-20T21:40:35.6451164Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-08-20T21:40:35.6452460Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-08-20T21:40:35.6453435Z >>> # them will be removed. 2024-08-20T21:40:35.6454176Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-08-20T21:40:35.6454896Z tensor(True) 2024-08-20T21:40:35.6455470Z >>> torch.unique(a, dim=1) 2024-08-20T21:40:35.6456160Z tensor([[[0, 0, 1, 1], 2024-08-20T21:40:35.6456791Z [1, 1, 0, 0]], 2024-08-20T21:40:35.6490718Z [[1, 1, 1, 1], 2024-08-20T21:40:35.6491396Z [0, 0, 1, 1]], 2024-08-20T21:40:35.6492023Z [[0, 0, 1, 1], 2024-08-20T21:40:35.6492655Z [1, 1, 0, 0]]]) 2024-08-20T21:40:35.6493274Z 2024-08-20T21:40:35.6494037Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-08-20T21:40:35.6495298Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-08-20T21:40:35.6496499Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-08-20T21:40:35.6497677Z >>> # sub-tensors will be removed. 2024-08-20T21:40:35.6498465Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-08-20T21:40:35.6499204Z tensor(True) 2024-08-20T21:40:35.6499790Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-08-20T21:40:35.6500514Z tensor(True) 2024-08-20T21:40:35.6501106Z >>> torch.unique(a, dim=2) 2024-08-20T21:40:35.6501782Z tensor([[[0, 1], 2024-08-20T21:40:35.6502356Z [0, 1], 2024-08-20T21:40:35.6502920Z [1, 0]], 2024-08-20T21:40:35.6503480Z [[1, 0], 2024-08-20T21:40:35.6504043Z [1, 0], 2024-08-20T21:40:35.6504608Z [1, 1]], 2024-08-20T21:40:35.6505160Z [[0, 1], 2024-08-20T21:40:35.6505718Z [0, 1], 2024-08-20T21:40:35.6506282Z [1, 0]]]) 2024-08-20T21:40:35.6506834Z 2024-08-20T21:40:35.6507834Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.6508891Z 2024-08-20T21:40:35.6509305Z warnings.warn(msg) 2024-08-20T21:40:35.6510056Z 2024-08-20T21:40:35.6510668Z --- Parse Warning: 3 / 101 --- 2024-08-20T21:40:35.6513475Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=560. 2024-08-20T21:40:35.6516485Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.6517568Z 2024-08-20T21:40:35.6518161Z Load a model from a github repo or a local directory. 2024-08-20T21:40:35.6518987Z 2024-08-20T21:40:35.6519793Z Note: Loading a model is the typical use case, but this can also be used to 2024-08-20T21:40:35.6521169Z for loading other objects such as tokenizers, loss functions, etc. 2024-08-20T21:40:35.6522235Z 2024-08-20T21:40:35.6522997Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-08-20T21:40:35.6524100Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-08-20T21:40:35.6525004Z ref (a tag or a branch). 2024-08-20T21:40:35.6525581Z 2024-08-20T21:40:35.6526348Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-08-20T21:40:35.6527269Z path to a local directory. 2024-08-20T21:40:35.6527859Z 2024-08-20T21:40:35.6528265Z Args: 2024-08-20T21:40:35.6528928Z repo_or_dir (str): If ``source`` is 'github', 2024-08-20T21:40:35.6530346Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-08-20T21:40:35.6532329Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-08-20T21:40:35.6534058Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-08-20T21:40:35.6535699Z If ``source`` is 'local' then it should be a path to a local directory. 2024-08-20T21:40:35.6536983Z model (str): the name of a callable (entrypoint) defined in the 2024-08-20T21:40:35.6538052Z repo/dir's ``hubconf.py``. 2024-08-20T21:40:35.6539013Z *args (optional): the corresponding args for callable ``model``. 2024-08-20T21:40:35.6540317Z source (str, optional): 'github' or 'local'. Specifies how 2024-08-20T21:40:35.6541626Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-08-20T21:40:35.6542882Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-08-20T21:40:35.6544271Z This parameter was introduced in v1.12 and helps ensuring that users 2024-08-20T21:40:35.6545419Z only run code from repos that they trust. 2024-08-20T21:40:35.6546170Z 2024-08-20T21:40:35.6547002Z - If ``False``, a prompt will ask the user whether the repo should 2024-08-20T21:40:35.6547964Z be trusted. 2024-08-20T21:40:35.6548924Z - If ``True``, the repo will be added to the trusted list and loaded 2024-08-20T21:40:35.6550012Z without requiring explicit confirmation. 2024-08-20T21:40:35.6551160Z - If ``"check"``, the repo will be checked against the list of 2024-08-20T21:40:35.6552371Z trusted repos in the cache. If it is not present in that list, the 2024-08-20T21:40:35.6553666Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-08-20T21:40:35.6555034Z - If ``None``: this will raise a warning, inviting the user to set 2024-08-20T21:40:35.6556226Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-08-20T21:40:35.6557489Z is only present for backward compatibility and will be removed in 2024-08-20T21:40:35.6558481Z v2.0. 2024-08-20T21:40:35.6558933Z 2024-08-20T21:40:35.6559689Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-08-20T21:40:35.6561003Z force_reload (bool, optional): whether to force a fresh download of 2024-08-20T21:40:35.6562318Z the github repo unconditionally. Does not have any effect if 2024-08-20T21:40:35.6563525Z ``source = 'local'``. Default is ``False``. 2024-08-20T21:40:35.6564605Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-08-20T21:40:35.6565914Z local caches. Note that the message about first download cannot be 2024-08-20T21:40:35.6567229Z muted. Does not have any effect if ``source = 'local'``. 2024-08-20T21:40:35.6568145Z Default is ``True``. 2024-08-20T21:40:35.6569292Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-08-20T21:40:35.6571039Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-08-20T21:40:35.6572974Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-08-20T21:40:35.6574399Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-08-20T21:40:35.6575636Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-08-20T21:40:35.6576593Z 2024-08-20T21:40:35.6577007Z Returns: 2024-08-20T21:40:35.6577747Z The output of the ``model`` callable when called with the given 2024-08-20T21:40:35.6578714Z ``*args`` and ``**kwargs``. 2024-08-20T21:40:35.6579341Z 2024-08-20T21:40:35.6579744Z Example: 2024-08-20T21:40:35.6580312Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:40:35.6581133Z >>> # from a github repo 2024-08-20T21:40:35.6581787Z >>> repo = "pytorch/vision" 2024-08-20T21:40:35.6582460Z >>> model = torch.hub.load( 2024-08-20T21:40:35.6583369Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-08-20T21:40:35.6584279Z ... ) 2024-08-20T21:40:35.6584757Z >>> # from a local directory 2024-08-20T21:40:35.6585526Z >>> path = "/some/local/path/pytorch/vision" 2024-08-20T21:40:35.6586319Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.6587307Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-08-20T21:40:35.6588366Z 2024-08-20T21:40:35.6589337Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.6590599Z 2024-08-20T21:40:35.6591044Z warnings.warn(msg) 2024-08-20T21:40:35.6591578Z 2024-08-20T21:40:35.6592181Z --- Parse Warning: 4 / 101 --- 2024-08-20T21:40:35.6595179Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=687. 2024-08-20T21:40:35.6598308Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.6599549Z Download object at the given URL to a local path. 2024-08-20T21:40:35.6600348Z 2024-08-20T21:40:35.6600756Z Args: 2024-08-20T21:40:35.6601314Z url (str): URL of the object to download 2024-08-20T21:40:35.6602495Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-08-20T21:40:35.6604217Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-08-20T21:40:35.6605526Z Default: None 2024-08-20T21:40:35.6606540Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-08-20T21:40:35.6607651Z Default: True 2024-08-20T21:40:35.6608214Z 2024-08-20T21:40:35.6608613Z Example: 2024-08-20T21:40:35.6609234Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:40:35.6610151Z >>> # xdoctest: +REQUIRES(POSIX) 2024-08-20T21:40:35.6610954Z >>> torch.hub.download_url_to_file( 2024-08-20T21:40:35.6612197Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-08-20T21:40:35.6613266Z ... "/tmp/temporary_file", 2024-08-20T21:40:35.6614073Z ... ) 2024-08-20T21:40:35.6614532Z 2024-08-20T21:40:35.6614939Z 2024-08-20T21:40:35.6615905Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.6616966Z 2024-08-20T21:40:35.6617393Z warnings.warn(msg) 2024-08-20T21:40:35.6617912Z 2024-08-20T21:40:35.6618508Z --- Parse Warning: 5 / 101 --- 2024-08-20T21:40:35.6621524Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=812. 2024-08-20T21:40:35.6624787Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.6626065Z Loads the Torch serialized object at the given URL. 2024-08-20T21:40:35.6626873Z 2024-08-20T21:40:35.6627527Z If downloaded file is a zip file, it will be automatically 2024-08-20T21:40:35.6628460Z decompressed. 2024-08-20T21:40:35.6628963Z 2024-08-20T21:40:35.6629866Z If the object is already present in `model_dir`, it's deserialized and 2024-08-20T21:40:35.6630888Z returned. 2024-08-20T21:40:35.6631706Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-08-20T21:40:35.6633003Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-08-20T21:40:35.6633927Z 2024-08-20T21:40:35.6634331Z Args: 2024-08-20T21:40:35.6634904Z url (str): URL of the object to download 2024-08-20T21:40:35.6635940Z model_dir (str, optional): directory in which to save the object 2024-08-20T21:40:35.6637580Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-08-20T21:40:35.6639358Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-08-20T21:40:35.6640462Z Default: True 2024-08-20T21:40:35.6641687Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-08-20T21:40:35.6643557Z ``filename-.ext`` where ```` is the first eight or more 2024-08-20T21:40:35.6644941Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-08-20T21:40:35.6646283Z ensure unique names and to verify the contents of the file. 2024-08-20T21:40:35.6647237Z Default: False 2024-08-20T21:40:35.6648491Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-08-20T21:40:35.6650473Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-08-20T21:40:35.6652201Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-08-20T21:40:35.6653294Z 2024-08-20T21:40:35.6653693Z Example: 2024-08-20T21:40:35.6654324Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-08-20T21:40:35.6655293Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-08-20T21:40:35.6656595Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-08-20T21:40:35.6657581Z ... ) 2024-08-20T21:40:35.6658043Z 2024-08-20T21:40:35.6658426Z 2024-08-20T21:40:35.6659403Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.6660444Z 2024-08-20T21:40:35.6660861Z warnings.warn(msg) 2024-08-20T21:40:35.6661393Z 2024-08-20T21:40:35.6661994Z --- Parse Warning: 6 / 101 --- 2024-08-20T21:40:35.6665007Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=334. 2024-08-20T21:40:35.6668189Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:40:35.6669640Z Registers the function implementation as the fallback for the given key. 2024-08-20T21:40:35.6670676Z 2024-08-20T21:40:35.6671423Z This function only works for a library with global namespace ("_"). 2024-08-20T21:40:35.6672422Z 2024-08-20T21:40:35.6672812Z Args: 2024-08-20T21:40:35.6673817Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-08-20T21:40:35.6675096Z to register a fallthrough. 2024-08-20T21:40:35.6676445Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-08-20T21:40:35.6678019Z the dispatch key that the library was created with. 2024-08-20T21:40:35.6679642Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-08-20T21:40:35.6681708Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-08-20T21:40:35.6683014Z 2024-08-20T21:40:35.6683473Z Example:: 2024-08-20T21:40:35.6684061Z >>> my_lib = Library("_", "IMPL") 2024-08-20T21:40:35.6684897Z >>> def fallback_kernel(op, *args, **kwargs): 2024-08-20T21:40:35.6685814Z >>> # Handle all autocast ops generically 2024-08-20T21:40:35.6686610Z >>> # ... 2024-08-20T21:40:35.6687322Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-08-20T21:40:35.6688123Z 2024-08-20T21:40:35.6690435Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-08-20T21:40:35.6692430Z 2024-08-20T21:40:35.6692949Z my_lib.fallback(fallback_kernel, "Autocast") 2024-08-20T21:40:35.6693706Z ^ 2024-08-20T21:40:35.6694144Z warnings.warn(msg) 2024-08-20T21:40:35.6694661Z 2024-08-20T21:40:35.6695266Z --- Parse Warning: 7 / 101 --- 2024-08-20T21:40:35.6698327Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=692. 2024-08-20T21:40:35.6701387Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:40:35.6702814Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-08-20T21:40:35.6703811Z 2024-08-20T21:40:35.6704455Z Also sometimes known as a "meta kernel", "abstract impl". 2024-08-20T21:40:35.6705330Z 2024-08-20T21:40:35.6706140Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-08-20T21:40:35.6707564Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-08-20T21:40:35.6708980Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-08-20T21:40:35.6710173Z what the properties of the output Tensors are. 2024-08-20T21:40:35.6710951Z 2024-08-20T21:40:35.6711727Z The FakeTensor implementation has the same signature as the operator. 2024-08-20T21:40:35.6713115Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-08-20T21:40:35.6714463Z implementation, assume that all Tensor inputs to the operator are 2024-08-20T21:40:35.6715740Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-08-20T21:40:35.6717033Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-08-20T21:40:35.6718390Z The FakeTensor implementation must consist of only PyTorch operations 2024-08-20T21:40:35.6719703Z (and may not directly access the storage or data of any input or 2024-08-20T21:40:35.6720769Z intermediate Tensors). 2024-08-20T21:40:35.6721359Z 2024-08-20T21:40:35.6721951Z This API may be used as a decorator (see examples). 2024-08-20T21:40:35.6722774Z 2024-08-20T21:40:35.6723340Z For a detailed guide on custom ops, please see 2024-08-20T21:40:35.6724488Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-08-20T21:40:35.6725491Z 2024-08-20T21:40:35.6725901Z Examples: 2024-08-20T21:40:35.6726385Z >>> import torch 2024-08-20T21:40:35.6726998Z >>> import numpy as np 2024-08-20T21:40:35.6727690Z >>> from torch import Tensor 2024-08-20T21:40:35.6728356Z >>> 2024-08-20T21:40:35.6729364Z >>> # Example 1: an operator without data-dependent output shape 2024-08-20T21:40:35.6730704Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-08-20T21:40:35.6732177Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-08-20T21:40:35.6733443Z >>> raise NotImplementedError("Implementation goes here") 2024-08-20T21:40:35.6734338Z >>> 2024-08-20T21:40:35.6735022Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-08-20T21:40:35.6735927Z >>> def _(x, weight, bias): 2024-08-20T21:40:35.6736648Z >>> assert x.dim() == 2 2024-08-20T21:40:35.6737386Z >>> assert weight.dim() == 2 2024-08-20T21:40:35.6738135Z >>> assert bias.dim() == 1 2024-08-20T21:40:35.6738943Z >>> assert x.shape[1] == weight.shape[1] 2024-08-20T21:40:35.6739833Z >>> assert weight.shape[0] == bias.shape[0] 2024-08-20T21:40:35.6740701Z >>> assert x.device == weight.device 2024-08-20T21:40:35.6741443Z >>> 2024-08-20T21:40:35.6741996Z >>> return (x @ weight.t()) + bias 2024-08-20T21:40:35.6742708Z >>> 2024-08-20T21:40:35.6743391Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-08-20T21:40:35.6744320Z >>> x = torch.randn(2, 3) 2024-08-20T21:40:35.6745044Z >>> w = torch.randn(3, 3) 2024-08-20T21:40:35.6745826Z >>> b = torch.randn(3) 2024-08-20T21:40:35.6746647Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-08-20T21:40:35.6747435Z >>> 2024-08-20T21:40:35.6747954Z >>> assert y.shape == (2, 3) 2024-08-20T21:40:35.6748632Z >>> 2024-08-20T21:40:35.6749465Z >>> # Example 2: an operator with data-dependent output shape 2024-08-20T21:40:35.6750709Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-08-20T21:40:35.6751944Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-08-20T21:40:35.6752775Z >>> x_np = x.numpy(force=True) 2024-08-20T21:40:35.6753617Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-08-20T21:40:35.6754544Z >>> return torch.tensor(res, device=x.device) 2024-08-20T21:40:35.6755314Z >>> 2024-08-20T21:40:35.6756007Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-08-20T21:40:35.6756897Z >>> def _(x): 2024-08-20T21:40:35.6757718Z >>> # Number of nonzero-elements is data-dependent. 2024-08-20T21:40:35.6758743Z >>> # Since we cannot peek at the data in an fake impl, 2024-08-20T21:40:35.6759803Z >>> # we use the ctx object to construct a new symint that 2024-08-20T21:40:35.6760897Z >>> # represents the data-dependent size. 2024-08-20T21:40:35.6761740Z >>> ctx = torch.library.get_ctx() 2024-08-20T21:40:35.6762553Z >>> nnz = ctx.new_dynamic_size() 2024-08-20T21:40:35.6763339Z >>> shape = [nnz, x.dim()] 2024-08-20T21:40:35.6764185Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-08-20T21:40:35.6765035Z >>> return result 2024-08-20T21:40:35.6765691Z >>> 2024-08-20T21:40:35.6766392Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-20T21:40:35.6767299Z >>> 2024-08-20T21:40:35.6767857Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-08-20T21:40:35.6769009Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-08-20T21:40:35.6770219Z >>> trace.print_readable() 2024-08-20T21:40:35.6770889Z >>> 2024-08-20T21:40:35.6771687Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-08-20T21:40:35.6772690Z 2024-08-20T21:40:35.6773099Z 2024-08-20T21:40:35.6774997Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-08-20T21:40:35.6776751Z 2024-08-20T21:40:35.6777160Z _._ = None 2024-08-20T21:40:35.6777599Z ^ 2024-08-20T21:40:35.6778042Z warnings.warn(msg) 2024-08-20T21:40:35.6778581Z 2024-08-20T21:40:35.6779166Z --- Parse Warning: 8 / 101 --- 2024-08-20T21:40:35.6782161Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=813. 2024-08-20T21:40:35.6785318Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.6786565Z Register a backward formula for this custom op. 2024-08-20T21:40:35.6787339Z 2024-08-20T21:40:35.6788099Z In order for an operator to work with autograd, you need to register 2024-08-20T21:40:35.6789133Z a backward formula: 2024-08-20T21:40:35.6790048Z 1. You must tell us how to compute gradients during the backward pass 2024-08-20T21:40:35.6791296Z by providing us a "backward" function. 2024-08-20T21:40:35.6792408Z 2. If you need any values from the forward to compute gradients, you can 2024-08-20T21:40:35.6793582Z use `setup_context` to save values for backward. 2024-08-20T21:40:35.6794374Z 2024-08-20T21:40:35.6795153Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-08-20T21:40:35.6796695Z - ``grads`` is one or more gradients. The number of gradients matches 2024-08-20T21:40:35.6797762Z the number of outputs of the operator. 2024-08-20T21:40:35.6798879Z The ``ctx`` object is `the same ctx object `_ used by 2024-08-20T21:40:35.6800305Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-08-20T21:40:35.6801567Z same as :meth:`torch.autograd.Function.backward`. 2024-08-20T21:40:35.6802392Z 2024-08-20T21:40:35.6803119Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-08-20T21:40:35.6804464Z Please save quantities needed for backward onto the ``ctx`` object via 2024-08-20T21:40:35.6805882Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-08-20T21:40:35.6807233Z or assigning them as attributes of ``ctx``. If your custom op has 2024-08-20T21:40:35.6808653Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-08-20T21:40:35.6809932Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-08-20T21:40:35.6810920Z 2024-08-20T21:40:35.6811670Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-08-20T21:40:35.6813067Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-08-20T21:40:35.6814659Z not depend on or mutate global state. If you need a non-traceable backward, 2024-08-20T21:40:35.6816120Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-08-20T21:40:35.6817162Z 2024-08-20T21:40:35.6817568Z Examples: 2024-08-20T21:40:35.6818053Z >>> import torch 2024-08-20T21:40:35.6818750Z >>> import numpy as np 2024-08-20T21:40:35.6819449Z >>> from torch import Tensor 2024-08-20T21:40:35.6820124Z >>> 2024-08-20T21:40:35.6820894Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-08-20T21:40:35.6822046Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-08-20T21:40:35.6822828Z >>> x_np = x.cpu().numpy() 2024-08-20T21:40:35.6823560Z >>> y_np = np.sin(x_np) 2024-08-20T21:40:35.6824412Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-20T21:40:35.6825233Z >>> 2024-08-20T21:40:35.6825998Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-08-20T21:40:35.6826848Z >>> x, = inputs 2024-08-20T21:40:35.6827593Z >>> ctx.save_for_backward(x) 2024-08-20T21:40:35.6828291Z >>> 2024-08-20T21:40:35.6828827Z >>> def backward(ctx, grad): 2024-08-20T21:40:35.6829542Z >>> x, = ctx.saved_tensors 2024-08-20T21:40:35.6830286Z >>> return grad * x.cos() 2024-08-20T21:40:35.6830966Z >>> 2024-08-20T21:40:35.6831524Z >>> torch.library.register_autograd( 2024-08-20T21:40:35.6832523Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-08-20T21:40:35.6833410Z ... ) 2024-08-20T21:40:35.6833865Z >>> 2024-08-20T21:40:35.6834447Z >>> x = torch.randn(3, requires_grad=True) 2024-08-20T21:40:35.6835237Z >>> y = numpy_sin(x) 2024-08-20T21:40:35.6836061Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-20T21:40:35.6837057Z >>> assert torch.allclose(grad_x, x.cos()) 2024-08-20T21:40:35.6837817Z >>> 2024-08-20T21:40:35.6838486Z >>> # Example with a keyword-only arg 2024-08-20T21:40:35.6839507Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-20T21:40:35.6840753Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-08-20T21:40:35.6841641Z >>> x_np = x.cpu().numpy() 2024-08-20T21:40:35.6842353Z >>> y_np = x_np * val 2024-08-20T21:40:35.6843237Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-08-20T21:40:35.6844074Z >>> 2024-08-20T21:40:35.6845040Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-08-20T21:40:35.6846176Z >>> ctx.val = keyword_only_inputs["val"] 2024-08-20T21:40:35.6846923Z >>> 2024-08-20T21:40:35.6847429Z >>> def backward(ctx, grad): 2024-08-20T21:40:35.6848161Z >>> return grad * ctx.val 2024-08-20T21:40:35.6848840Z >>> 2024-08-20T21:40:35.6849396Z >>> torch.library.register_autograd( 2024-08-20T21:40:35.6850454Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-08-20T21:40:35.6851358Z ... ) 2024-08-20T21:40:35.6851819Z >>> 2024-08-20T21:40:35.6852404Z >>> x = torch.randn(3, requires_grad=True) 2024-08-20T21:40:35.6853231Z >>> y = numpy_mul(x, val=3.14) 2024-08-20T21:40:35.6854141Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-08-20T21:40:35.6855261Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-08-20T21:40:35.6856136Z 2024-08-20T21:40:35.6856526Z 2024-08-20T21:40:35.6857512Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.6858565Z 2024-08-20T21:40:35.6858992Z warnings.warn(msg) 2024-08-20T21:40:35.6859528Z 2024-08-20T21:40:35.6860126Z --- Parse Warning: 9 / 101 --- 2024-08-20T21:40:35.6863024Z /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=1221. 2024-08-20T21:40:35.6866139Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.6867596Z Given an operator and some sample arguments, tests if the operator is 2024-08-20T21:40:35.6868641Z registered correctly. 2024-08-20T21:40:35.6869210Z 2024-08-20T21:40:35.6869978Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-08-20T21:40:35.6871420Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-08-20T21:40:35.6872862Z and these APIs require that the functions you pass them satisfy certain 2024-08-20T21:40:35.6874277Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-08-20T21:40:35.6875553Z ``opcheck`` tests these metadata and properties. 2024-08-20T21:40:35.6876334Z 2024-08-20T21:40:35.6876821Z Concretely, we test the following: 2024-08-20T21:40:35.6877823Z - test_schema: if the operator's schema is correct. 2024-08-20T21:40:35.6879118Z - test_autograd_registration: if autograd was registered correctly. 2024-08-20T21:40:35.6880447Z - test_faketensor: If the operator has a FakeTensor kernel 2024-08-20T21:40:35.6881599Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-08-20T21:40:35.6882849Z but not sufficient) for the operator to work with PyTorch compilation 2024-08-20T21:40:35.6883926Z APIs (torch.compile/export/FX). 2024-08-20T21:40:35.6885039Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-08-20T21:40:35.6886227Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-08-20T21:40:35.6887428Z This checks that the outputs (and gradients, if applicable) are the 2024-08-20T21:40:35.6888707Z same under eager-mode PyTorch and torch.compile. 2024-08-20T21:40:35.6889673Z This test is a superset of ``test_faketensor``. 2024-08-20T21:40:35.6890641Z 2024-08-20T21:40:35.6891351Z For best results, please call ``opcheck`` multiple times with a 2024-08-20T21:40:35.6892537Z representative set of inputs. If your operator supports 2024-08-20T21:40:35.6893788Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-08-20T21:40:35.6895294Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-08-20T21:40:35.6896510Z use ``opcheck`` with inputs on all supported devices. 2024-08-20T21:40:35.6897319Z 2024-08-20T21:40:35.6897727Z Args: 2024-08-20T21:40:35.6898435Z op: The operator. Must either be a function decorated with 2024-08-20T21:40:35.6899666Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-08-20T21:40:35.6901026Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-08-20T21:40:35.6902101Z args: The args to the operator 2024-08-20T21:40:35.6902878Z kwargs: The kwargs to the operator 2024-08-20T21:40:35.6903868Z test_utils: Tests that we should run. Default: all of them. 2024-08-20T21:40:35.6904911Z Example: ("test_schema", "test_faketensor") 2024-08-20T21:40:35.6905974Z raise_exception: If we should raise an exception on the first 2024-08-20T21:40:35.6907121Z error. If False, we will return a dict with information 2024-08-20T21:40:35.6908080Z on if each test passed or not. 2024-08-20T21:40:35.6908779Z 2024-08-20T21:40:35.6909186Z .. warning:: 2024-08-20T21:40:35.6909671Z 2024-08-20T21:40:35.6910429Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-08-20T21:40:35.6911756Z opcheck tests if your usage of torch.library APIs is correct while 2024-08-20T21:40:35.6913094Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-08-20T21:40:35.6914412Z mathematically correct. Use both to test custom ops that support 2024-08-20T21:40:35.6915483Z gradient computation. 2024-08-20T21:40:35.6916097Z 2024-08-20T21:40:35.6916506Z Example: 2024-08-20T21:40:35.6916953Z 2024-08-20T21:40:35.6917514Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:40:35.6918621Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-08-20T21:40:35.6919855Z >>> def numpy_add(x: Tensor, y: float) -> Tensor: 2024-08-20T21:40:35.6920116Z >>> x_np = x.numpy(force=True) 2024-08-20T21:40:35.6920324Z >>> z_np = x_np + y 2024-08-20T21:40:35.6920644Z >>> return torch.from_numpy(z_np).to(x.device) 2024-08-20T21:40:35.6920830Z >>> 2024-08-20T21:40:35.6921059Z >>> @numpy_sin.register_fake 2024-08-20T21:40:35.6921349Z >>> def _(x, y): 2024-08-20T21:40:35.6921620Z >>> return torch.empty_like(x) 2024-08-20T21:40:35.6921788Z >>> 2024-08-20T21:40:35.6922096Z >>> def setup_context(ctx, inputs, output): 2024-08-20T21:40:35.6922290Z >>> y, = inputs 2024-08-20T21:40:35.6922476Z >>> ctx.y = y 2024-08-20T21:40:35.6922663Z >>> 2024-08-20T21:40:35.6922895Z >>> def backward(ctx, grad): 2024-08-20T21:40:35.6923140Z >>> return grad * ctx.y, None 2024-08-20T21:40:35.6923322Z >>> 2024-08-20T21:40:35.6923873Z >>> numpy_sin.register_autograd(backward, setup_context=setup_context) 2024-08-20T21:40:35.6924039Z >>> 2024-08-20T21:40:35.6924265Z >>> sample_inputs = [ 2024-08-20T21:40:35.6924491Z >>> (torch.randn(3), 3.14), 2024-08-20T21:40:35.6924914Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-08-20T21:40:35.6925278Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-08-20T21:40:35.6925898Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-08-20T21:40:35.6926071Z >>> ] 2024-08-20T21:40:35.6926256Z >>> 2024-08-20T21:40:35.6926492Z >>> for args in sample_inputs: 2024-08-20T21:40:35.6926797Z >>> torch.library.opcheck(foo, args) 2024-08-20T21:40:35.6926993Z 2024-08-20T21:40:35.6927158Z 2024-08-20T21:40:35.6927893Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.6928049Z 2024-08-20T21:40:35.6928246Z warnings.warn(msg) 2024-08-20T21:40:35.6928416Z 2024-08-20T21:40:35.6928784Z --- Parse Warning: 10 / 101 --- 2024-08-20T21:40:35.6931255Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1042. 2024-08-20T21:40:35.6932023Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.6932828Z load(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args) 2024-08-20T21:40:35.6932991Z 2024-08-20T21:40:35.6933426Z Loads an object saved with :func:`torch.save` from a file. 2024-08-20T21:40:35.6933584Z 2024-08-20T21:40:35.6934318Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-08-20T21:40:35.6934849Z which underlie tensors, specially. They are first deserialized on the 2024-08-20T21:40:35.6935398Z CPU and are then moved to the device they were saved from. If this fails 2024-08-20T21:40:35.6936145Z (e.g. because the run time system doesn't have certain devices), an exception 2024-08-20T21:40:35.6936717Z is raised. However, storages can be dynamically remapped to an alternative 2024-08-20T21:40:35.6937107Z set of devices using the :attr:`map_location` argument. 2024-08-20T21:40:35.6937275Z 2024-08-20T21:40:35.6937884Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-08-20T21:40:35.6938470Z storage with two arguments: storage and location. The storage argument 2024-08-20T21:40:35.6939054Z will be the initial deserialization of the storage, residing on the CPU. 2024-08-20T21:40:35.6939573Z Each serialized storage has a location tag associated with it which 2024-08-20T21:40:35.6940096Z identifies the device it was saved from, and this tag is the second 2024-08-20T21:40:35.6940860Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-08-20T21:40:35.6941568Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-08-20T21:40:35.6942059Z :attr:`map_location` should return either ``None`` or a storage. If 2024-08-20T21:40:35.6942798Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-08-20T21:40:35.6943394Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-08-20T21:40:35.6944169Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-08-20T21:40:35.6944327Z 2024-08-20T21:40:35.6944923Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-08-20T21:40:35.6945518Z a device tag, it indicates the location where all tensors should be loaded. 2024-08-20T21:40:35.6945675Z 2024-08-20T21:40:35.6946337Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-08-20T21:40:35.6946837Z appearing in the file (keys), to ones that specify where to put the 2024-08-20T21:40:35.6947037Z storages (values). 2024-08-20T21:40:35.6947206Z 2024-08-20T21:40:35.6947735Z User extensions can register their own location tags and tagging and 2024-08-20T21:40:35.6948351Z deserialization methods using :func:`torch.serialization.register_package`. 2024-08-20T21:40:35.6948521Z 2024-08-20T21:40:35.6948690Z Args: 2024-08-20T21:40:35.6949706Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-08-20T21:40:35.6950144Z or a string or os.PathLike object containing a file name 2024-08-20T21:40:35.6950956Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-08-20T21:40:35.6951156Z locations 2024-08-20T21:40:35.6951690Z pickle_module: module used for unpickling metadata and objects (has to 2024-08-20T21:40:35.6952091Z match the :attr:`pickle_module` used to serialize file) 2024-08-20T21:40:35.6952600Z weights_only: Indicates whether unpickler should be restricted to 2024-08-20T21:40:35.6952988Z loading only tensors, primitive types, dictionaries 2024-08-20T21:40:35.6953529Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-08-20T21:40:35.6954379Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-08-20T21:40:35.6955220Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-08-20T21:40:35.6956124Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-08-20T21:40:35.6957213Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-08-20T21:40:35.6957800Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-08-20T21:40:35.6958380Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-08-20T21:40:35.6958912Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-08-20T21:40:35.6959114Z :attr:`errors=...`. 2024-08-20T21:40:35.6959285Z 2024-08-20T21:40:35.6959475Z .. warning:: 2024-08-20T21:40:35.6960025Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-08-20T21:40:35.6960510Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-08-20T21:40:35.6961187Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-08-20T21:40:35.6961785Z during unpickling. Never load data that could have come from an untrusted 2024-08-20T21:40:35.6962554Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-08-20T21:40:35.6962718Z 2024-08-20T21:40:35.6962910Z .. note:: 2024-08-20T21:40:35.6963581Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-08-20T21:40:35.6964492Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-08-20T21:40:35.6965188Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-08-20T21:40:35.6965348Z 2024-08-20T21:40:35.6965514Z .. note:: 2024-08-20T21:40:35.6966324Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-08-20T21:40:35.6966952Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-08-20T21:40:35.6967473Z when loading files saved by Python 2 in Python 3. If this default 2024-08-20T21:40:35.6968124Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-08-20T21:40:35.6968868Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-08-20T21:40:35.6969620Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-08-20T21:40:35.6970250Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-08-20T21:40:35.6970413Z 2024-08-20T21:40:35.6970602Z Example: 2024-08-20T21:40:35.6970914Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-08-20T21:40:35.6971234Z >>> torch.load("tensors.pt", weights_only=True) 2024-08-20T21:40:35.6971497Z # Load all tensors onto the CPU 2024-08-20T21:40:35.6972134Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-08-20T21:40:35.6972489Z # Load all tensors onto the CPU, using a function 2024-08-20T21:40:35.6972681Z >>> torch.load( 2024-08-20T21:40:35.6973250Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-08-20T21:40:35.6973433Z ... ) 2024-08-20T21:40:35.6973668Z # Load all tensors onto GPU 1 2024-08-20T21:40:35.6973858Z >>> torch.load( 2024-08-20T21:40:35.6974078Z ... "tensors.pt", 2024-08-20T21:40:35.6974455Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-08-20T21:40:35.6974667Z ... weights_only=True, 2024-08-20T21:40:35.6975042Z ... ) # type: ignore[attr-defined] 2024-08-20T21:40:35.6975297Z # Map tensors from GPU 1 to GPU 0 2024-08-20T21:40:35.6975886Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-08-20T21:40:35.6976175Z # Load tensor from io.BytesIO object 2024-08-20T21:40:35.6976778Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-08-20T21:40:35.6977056Z >>> with open("tensor.pt", "rb") as f: 2024-08-20T21:40:35.6977307Z ... buffer = io.BytesIO(f.read()) 2024-08-20T21:40:35.6977594Z >>> torch.load(buffer, weights_only=False) 2024-08-20T21:40:35.6978100Z # Load a module with 'ascii' encoding for unpickling 2024-08-20T21:40:35.6978704Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-08-20T21:40:35.6979157Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-08-20T21:40:35.6979382Z 2024-08-20T21:40:35.6980103Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.6980259Z 2024-08-20T21:40:35.6980476Z warnings.warn(msg) 2024-08-20T21:40:35.6980631Z 2024-08-20T21:40:35.6980994Z --- Parse Warning: 11 / 101 --- 2024-08-20T21:40:35.6983397Z /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=341. 2024-08-20T21:40:35.6984109Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:40:35.6984384Z Retrieves the CUDA runtime API module. 2024-08-20T21:40:35.6984613Z 2024-08-20T21:40:35.6984771Z 2024-08-20T21:40:35.6985383Z This function initializes the CUDA runtime environment if it is not already 2024-08-20T21:40:35.6985934Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-08-20T21:40:35.6986467Z runtime API module provides access to various CUDA runtime functions. 2024-08-20T21:40:35.6986642Z 2024-08-20T21:40:35.6986812Z Args: 2024-08-20T21:40:35.6986981Z ``None`` 2024-08-20T21:40:35.6987150Z 2024-08-20T21:40:35.6987322Z Returns: 2024-08-20T21:40:35.6987650Z module: The CUDA runtime API module (_cudart). 2024-08-20T21:40:35.6987821Z 2024-08-20T21:40:35.6987984Z Raises: 2024-08-20T21:40:35.6988670Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-08-20T21:40:35.6989571Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-08-20T21:40:35.6989732Z 2024-08-20T21:40:35.6990047Z Example of CUDA operations with profiling: 2024-08-20T21:40:35.6990447Z >>> import torch 2024-08-20T21:40:35.6990772Z >>> from torch.cuda import cudart, check_error 2024-08-20T21:40:35.6990981Z >>> import os 2024-08-20T21:40:35.6991144Z >>> 2024-08-20T21:40:35.6991507Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-08-20T21:40:35.6991778Z >>> 2024-08-20T21:40:35.6992096Z >>> def perform_cuda_operations_with_streams(): 2024-08-20T21:40:35.6992360Z >>> stream = torch.cuda.Stream() 2024-08-20T21:40:35.6992650Z >>> with torch.cuda.stream(stream): 2024-08-20T21:40:35.6993076Z >>> x = torch.randn(100, 100, device='cuda') 2024-08-20T21:40:35.6993495Z >>> y = torch.randn(100, 100, device='cuda') 2024-08-20T21:40:35.6993741Z >>> z = torch.mul(x, y) 2024-08-20T21:40:35.6993932Z >>> return z 2024-08-20T21:40:35.6994095Z >>> 2024-08-20T21:40:35.6994357Z >>> torch.cuda.synchronize() 2024-08-20T21:40:35.6994669Z >>> print("====== Start nsys profiling ======") 2024-08-20T21:40:35.6994996Z >>> check_error(cudart().cudaProfilerStart()) 2024-08-20T21:40:35.6995322Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-08-20T21:40:35.6995670Z >>> result = perform_cuda_operations_with_streams() 2024-08-20T21:40:35.6995971Z >>> print("CUDA operations completed.") 2024-08-20T21:40:35.6996345Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-08-20T21:40:35.6996643Z >>> print("====== End nsys profiling ======") 2024-08-20T21:40:35.6996818Z 2024-08-20T21:40:35.6997303Z To run this example and save the profiling information, execute: 2024-08-20T21:40:35.6998347Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-20T21:40:35.6998519Z 2024-08-20T21:40:35.6999109Z This command profiles the CUDA operations in the provided script and saves 2024-08-20T21:40:35.6999573Z the profiling information to a file named `trace_name.prof`. 2024-08-20T21:40:35.7000323Z The `--profile-from-start off` option ensures that profiling starts only 2024-08-20T21:40:35.7000677Z after the `cudaProfilerStart` call in the script. 2024-08-20T21:40:35.7001368Z The `--csv` and `--print-summary` options format the profiling output as a 2024-08-20T21:40:35.7001668Z CSV file and print a summary, respectively. 2024-08-20T21:40:35.7002402Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-08-20T21:40:35.7002765Z overwrite of the output file if it already exists. 2024-08-20T21:40:35.7002931Z 2024-08-20T21:40:35.7004867Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2024-08-20T21:40:35.7005043Z 2024-08-20T21:40:35.7006044Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-08-20T21:40:35.7006224Z ^ 2024-08-20T21:40:35.7006422Z warnings.warn(msg) 2024-08-20T21:40:35.7006584Z 2024-08-20T21:40:35.7006970Z --- Parse Warning: 12 / 101 --- 2024-08-20T21:40:35.7009454Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101. 2024-08-20T21:40:35.7010252Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7010453Z 2024-08-20T21:40:35.7011007Z Append the given callback function to this ``Future``, which will be run 2024-08-20T21:40:35.7011519Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-08-20T21:40:35.7012056Z the same ``Future``, but the order in which they will be executed cannot 2024-08-20T21:40:35.7012489Z be guaranteed (to enforce a certain order consider chaining: 2024-08-20T21:40:35.7013019Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-08-20T21:40:35.7013533Z is the reference to this ``Future``. The callback function can use the 2024-08-20T21:40:35.7014084Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-08-20T21:40:35.7014624Z already completed, the given callback will be run immediately inline. 2024-08-20T21:40:35.7014781Z 2024-08-20T21:40:35.7015389Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-08-20T21:40:35.7015940Z callback might be invoked while the async kernels that are populating 2024-08-20T21:40:35.7016621Z those tensors haven't yet finished executing on the device. However, the 2024-08-20T21:40:35.7017134Z callback will be invoked with some dedicated streams set as current 2024-08-20T21:40:35.7017642Z (fetched from a global pool) which will be synchronized with those 2024-08-20T21:40:35.7018191Z kernels. Hence any operation performed by the callback on these tensors 2024-08-20T21:40:35.7018730Z will be scheduled on the device after the kernels complete. In other 2024-08-20T21:40:35.7019363Z words, as long as the callback doesn't switch streams, it can safely 2024-08-20T21:40:35.7019894Z manipulate the result without any additional synchronization. This is 2024-08-20T21:40:35.7020381Z similar to the non-blocking behavior of :meth:`wait`. 2024-08-20T21:40:35.7020538Z 2024-08-20T21:40:35.7021051Z Similarly, if the callback returns a value that contains tensors that 2024-08-20T21:40:35.7021566Z reside on a GPU, it can do so even if the kernels that are producing 2024-08-20T21:40:35.7022100Z these tensors are still running on the device, as long as the callback 2024-08-20T21:40:35.7022717Z didn't change streams during its execution. If one wants to change 2024-08-20T21:40:35.7023379Z streams, one must be careful to re-synchronize them with the original 2024-08-20T21:40:35.7023982Z streams, that is, those that were current when the callback was invoked. 2024-08-20T21:40:35.7024158Z 2024-08-20T21:40:35.7024322Z Args: 2024-08-20T21:40:35.7024799Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-08-20T21:40:35.7025065Z the only argument. 2024-08-20T21:40:35.7025223Z 2024-08-20T21:40:35.7025392Z Returns: 2024-08-20T21:40:35.7025809Z A new ``Future`` object that holds the return value of the 2024-08-20T21:40:35.7026218Z ``callback`` and will be marked as completed when the given 2024-08-20T21:40:35.7026421Z ``callback`` finishes. 2024-08-20T21:40:35.7026598Z 2024-08-20T21:40:35.7027080Z .. note:: Note that if the callback function throws, either 2024-08-20T21:40:35.7027577Z through the original future being completed with an exception and 2024-08-20T21:40:35.7028060Z calling ``fut.wait()``, or through other code in the callback, the 2024-08-20T21:40:35.7028541Z future returned by ``then`` will be marked appropriately with the 2024-08-20T21:40:35.7028989Z encountered error. However, if this callback later completes 2024-08-20T21:40:35.7029515Z additional futures, those futures are not marked as completed with 2024-08-20T21:40:35.7030041Z an error and the user is responsible for handling completion/waiting 2024-08-20T21:40:35.7030298Z on those futures independently. 2024-08-20T21:40:35.7030455Z 2024-08-20T21:40:35.7030633Z Example:: 2024-08-20T21:40:35.7030987Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-20T21:40:35.7031192Z >>> def callback(fut): 2024-08-20T21:40:35.7031518Z ... print(f"RPC return value is {fut.wait()}.") 2024-08-20T21:40:35.7031787Z >>> fut = torch.futures.Future() 2024-08-20T21:40:35.7032184Z >>> # The inserted callback will print the return value when 2024-08-20T21:40:35.7032465Z >>> # receiving the response from "worker1" 2024-08-20T21:40:35.7032709Z >>> cb_fut = fut.then(callback) 2024-08-20T21:40:35.7032927Z >>> chain_cb_fut = cb_fut.then( 2024-08-20T21:40:35.7033277Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-08-20T21:40:35.7033504Z ... ) 2024-08-20T21:40:35.7033703Z >>> fut.set_result(5) 2024-08-20T21:40:35.7033930Z RPC return value is 5. 2024-08-20T21:40:35.7034135Z Chained cb done. None 2024-08-20T21:40:35.7034298Z 2024-08-20T21:40:35.7035043Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7035204Z 2024-08-20T21:40:35.7035395Z warnings.warn(msg) 2024-08-20T21:40:35.7035571Z 2024-08-20T21:40:35.7035945Z --- Parse Warning: 13 / 101 --- 2024-08-20T21:40:35.7038502Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=209. 2024-08-20T21:40:35.7039274Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7039433Z 2024-08-20T21:40:35.7039949Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-08-20T21:40:35.7040485Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-08-20T21:40:35.7040726Z cannot be marked completed twice. 2024-08-20T21:40:35.7040883Z 2024-08-20T21:40:35.7041431Z If the result contains tensors that reside on GPUs, this method can be 2024-08-20T21:40:35.7041917Z called even if the asynchronous kernels that are populating those 2024-08-20T21:40:35.7042597Z tensors haven't yet completed running on the device, provided that the 2024-08-20T21:40:35.7043153Z streams on which those kernels were enqueued are set as the current ones 2024-08-20T21:40:35.7043798Z when this method is called. Put simply, it's safe to call this method 2024-08-20T21:40:35.7044343Z immediately after launching those kernels, without any additional 2024-08-20T21:40:35.7045023Z synchronization, as long as one doesn't change streams in between. This 2024-08-20T21:40:35.7045560Z method will record events on all the relevant current streams and will 2024-08-20T21:40:35.7046064Z use them to ensure proper scheduling for all the consumers of this 2024-08-20T21:40:35.7046235Z ``Future``. 2024-08-20T21:40:35.7046391Z 2024-08-20T21:40:35.7046573Z Args: 2024-08-20T21:40:35.7046938Z result (object): the result object of this ``Future``. 2024-08-20T21:40:35.7047113Z 2024-08-20T21:40:35.7047294Z Example:: 2024-08-20T21:40:35.7047626Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-08-20T21:40:35.7047915Z >>> import threading 2024-08-20T21:40:35.7048103Z >>> import time 2024-08-20T21:40:35.7048350Z >>> def slow_set_future(fut, value): 2024-08-20T21:40:35.7048566Z ... time.sleep(0.5) 2024-08-20T21:40:35.7048783Z ... fut.set_result(value) 2024-08-20T21:40:35.7049026Z >>> fut = torch.futures.Future() 2024-08-20T21:40:35.7049257Z >>> t = threading.Thread( 2024-08-20T21:40:35.7049478Z ... target=slow_set_future, 2024-08-20T21:40:35.7049750Z ... args=(fut, torch.ones(2) * 3) 2024-08-20T21:40:35.7049933Z ... ) 2024-08-20T21:40:35.7050171Z >>> t.start() 2024-08-20T21:40:35.7050373Z >>> print(fut.wait()) 2024-08-20T21:40:35.7050578Z tensor([3., 3.]) 2024-08-20T21:40:35.7050755Z >>> t.join() 2024-08-20T21:40:35.7050916Z 2024-08-20T21:40:35.7051662Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7051819Z 2024-08-20T21:40:35.7052026Z warnings.warn(msg) 2024-08-20T21:40:35.7052198Z 2024-08-20T21:40:35.7052564Z --- Parse Warning: 14 / 101 --- 2024-08-20T21:40:35.7054968Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=201. 2024-08-20T21:40:35.7055718Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7056146Z Return the sum of each row of the given sparse tensor. 2024-08-20T21:40:35.7056320Z 2024-08-20T21:40:35.7056897Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-08-20T21:40:35.7057349Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-08-20T21:40:35.7057893Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-08-20T21:40:35.7058250Z returns a dense tensor instead of a sparse tensor. 2024-08-20T21:40:35.7058411Z 2024-08-20T21:40:35.7059071Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-08-20T21:40:35.7059521Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-08-20T21:40:35.7059703Z 2024-08-20T21:40:35.7060225Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-08-20T21:40:35.7060805Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-08-20T21:40:35.7060979Z 2024-08-20T21:40:35.7061147Z Args: 2024-08-20T21:40:35.7061426Z input (Tensor): the input sparse tensor 2024-08-20T21:40:35.7062155Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-08-20T21:40:35.7062353Z over all dims. 2024-08-20T21:40:35.7062974Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-08-20T21:40:35.7063256Z Default: dtype of :attr:`input`. 2024-08-20T21:40:35.7063412Z 2024-08-20T21:40:35.7063592Z Example:: 2024-08-20T21:40:35.7063767Z 2024-08-20T21:40:35.7063947Z >>> nnz = 3 2024-08-20T21:40:35.7064192Z >>> dims = [5, 5, 2, 3] 2024-08-20T21:40:35.7064583Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-08-20T21:40:35.7065031Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-08-20T21:40:35.7065336Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-08-20T21:40:35.7065564Z >>> size = torch.Size(dims) 2024-08-20T21:40:35.7066010Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:40:35.7066314Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-08-20T21:40:35.7066483Z >>> S 2024-08-20T21:40:35.7066731Z tensor(indices=tensor([[2, 0, 3], 2024-08-20T21:40:35.7067049Z [2, 4, 1]]), 2024-08-20T21:40:35.7067464Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-08-20T21:40:35.7067856Z [ 0.3411, 0.0918, -0.2312]], 2024-08-20T21:40:35.7068031Z 2024-08-20T21:40:35.7068414Z [[ 0.5348, 0.0634, -2.0494], 2024-08-20T21:40:35.7068799Z [-0.7125, -1.0646, 2.1844]], 2024-08-20T21:40:35.7068977Z 2024-08-20T21:40:35.7069362Z [[ 0.1276, 0.1874, -0.6334], 2024-08-20T21:40:35.7069755Z [-1.9682, -0.5340, 0.7483]]]), 2024-08-20T21:40:35.7070116Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-08-20T21:40:35.7070276Z 2024-08-20T21:40:35.7070768Z # when sum over only part of sparse_dims, return a sparse tensor 2024-08-20T21:40:35.7071014Z >>> torch.sparse.sum(S, [1, 3]) 2024-08-20T21:40:35.7071274Z tensor(indices=tensor([[0, 2, 3]]), 2024-08-20T21:40:35.7071656Z values=tensor([[-1.4512, 0.4073], 2024-08-20T21:40:35.7072000Z [-0.8901, 0.2017], 2024-08-20T21:40:35.7072347Z [-0.3183, -1.7539]]), 2024-08-20T21:40:35.7072681Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-08-20T21:40:35.7072834Z 2024-08-20T21:40:35.7073202Z # when sum over all sparse dim, return a dense tensor 2024-08-20T21:40:35.7073489Z # with summed dims squeezed 2024-08-20T21:40:35.7073742Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-08-20T21:40:35.7074043Z tensor([-2.6596, -1.1450]) 2024-08-20T21:40:35.7074218Z 2024-08-20T21:40:35.7074941Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7075117Z 2024-08-20T21:40:35.7075314Z warnings.warn(msg) 2024-08-20T21:40:35.7075470Z 2024-08-20T21:40:35.7075857Z --- Parse Warning: 15 / 101 --- 2024-08-20T21:40:35.7078550Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py line=473. 2024-08-20T21:40:35.7079262Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-08-20T21:40:35.7079433Z 2024-08-20T21:40:35.7080095Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2024-08-20T21:40:35.7080749Z The submesh created consists of the dimensions and the communicators indicated by 2024-08-20T21:40:35.7080948Z ``mesh_dim_names`` 2024-08-20T21:40:35.7081107Z 2024-08-20T21:40:35.7081273Z Args: 2024-08-20T21:40:35.7081863Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2024-08-20T21:40:35.7082300Z mesh dimension of the DeviceMesh to create the submesh for. 2024-08-20T21:40:35.7082496Z Returns: 2024-08-20T21:40:35.7082717Z A :class:`DeviceMesh` object 2024-08-20T21:40:35.7082868Z 2024-08-20T21:40:35.7083587Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2024-08-20T21:40:35.7083833Z In the first example: 2024-08-20T21:40:35.7084489Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2024-08-20T21:40:35.7085174Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2024-08-20T21:40:35.7085754Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2024-08-20T21:40:35.7086335Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2024-08-20T21:40:35.7086934Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2024-08-20T21:40:35.7087582Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2024-08-20T21:40:35.7087754Z 2024-08-20T21:40:35.7087960Z In the second example: 2024-08-20T21:40:35.7088695Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2024-08-20T21:40:35.7089437Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2024-08-20T21:40:35.7090386Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2024-08-20T21:40:35.7091105Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2024-08-20T21:40:35.7091284Z 2024-08-20T21:40:35.7091465Z Example:: 2024-08-20T21:40:35.7091698Z >>> # xdoctest: +SKIP("no rank") 2024-08-20T21:40:35.7092111Z >>> from torch.distributed.device_mesh import DeviceMesh 2024-08-20T21:40:35.7092278Z >>> 2024-08-20T21:40:35.7092766Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2024-08-20T21:40:35.7093250Z >>> # of cross-host(dim 0), and within-host (dim 1). 2024-08-20T21:40:35.7093856Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-08-20T21:40:35.7094083Z >>> tp_mesh = mesh_2d["tp"] 2024-08-20T21:40:35.7094289Z >>> dp_mesh = mesh_2d["dp"] 2024-08-20T21:40:35.7094457Z >>> 2024-08-20T21:40:35.7094687Z >>> # Initialize a 3D mesh. 2024-08-20T21:40:35.7095458Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2024-08-20T21:40:35.7096221Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2024-08-20T21:40:35.7096488Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2024-08-20T21:40:35.7096736Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2024-08-20T21:40:35.7096892Z 2024-08-20T21:40:35.7098847Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2024-08-20T21:40:35.7099007Z 2024-08-20T21:40:35.7099614Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-08-20T21:40:35.7099909Z ^ 2024-08-20T21:40:35.7100107Z warnings.warn(msg) 2024-08-20T21:40:35.7100281Z 2024-08-20T21:40:35.7100651Z --- Parse Warning: 16 / 101 --- 2024-08-20T21:40:35.7103329Z /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=2745. 2024-08-20T21:40:35.7104099Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7104261Z 2024-08-20T21:40:35.7104782Z Gathers picklable objects from the whole group in a single process. 2024-08-20T21:40:35.7104956Z 2024-08-20T21:40:35.7105530Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-08-20T21:40:35.7105944Z object must be picklable in order to be gathered. 2024-08-20T21:40:35.7106103Z 2024-08-20T21:40:35.7106268Z Args: 2024-08-20T21:40:35.7106579Z obj (Any): Input object. Must be picklable. 2024-08-20T21:40:35.7107078Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-08-20T21:40:35.7107517Z should be correctly sized as the size of the group for this 2024-08-20T21:40:35.7108180Z collective and will contain the output. Must be ``None`` on non-dst 2024-08-20T21:40:35.7108408Z ranks. (default is ``None``) 2024-08-20T21:40:35.7109344Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0) 2024-08-20T21:40:35.7109991Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-08-20T21:40:35.7110442Z the default process group will be used. Default is ``None``. 2024-08-20T21:40:35.7110614Z 2024-08-20T21:40:35.7110786Z Returns: 2024-08-20T21:40:35.7111239Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-08-20T21:40:35.7111476Z output of the collective. 2024-08-20T21:40:35.7111632Z 2024-08-20T21:40:35.7112163Z .. note:: Note that this API differs slightly from the gather collective 2024-08-20T21:40:35.7112735Z since it does not provide an async_op handle and thus will be a blocking 2024-08-20T21:40:35.7112905Z call. 2024-08-20T21:40:35.7113065Z 2024-08-20T21:40:35.7113773Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-08-20T21:40:35.7114300Z of objects must be moved to the GPU device before communication takes 2024-08-20T21:40:35.7114639Z place. In this case, the device used is given by 2024-08-20T21:40:35.7115317Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-08-20T21:40:35.7115833Z ensure that this is set so that each rank has an individual GPU, via 2024-08-20T21:40:35.7116072Z ``torch.cuda.set_device()``. 2024-08-20T21:40:35.7116231Z 2024-08-20T21:40:35.7116409Z .. warning:: 2024-08-20T21:40:35.7116893Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-08-20T21:40:35.7117486Z known to be insecure. It is possible to construct malicious pickle data 2024-08-20T21:40:35.7118003Z which will execute arbitrary code during unpickling. Only call this 2024-08-20T21:40:35.7118254Z function with data you trust. 2024-08-20T21:40:35.7118407Z 2024-08-20T21:40:35.7118589Z .. warning:: 2024-08-20T21:40:35.7119103Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-08-20T21:40:35.7119798Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-08-20T21:40:35.7120175Z pickled. Please consider using :func:`gather` instead. 2024-08-20T21:40:35.7120349Z 2024-08-20T21:40:35.7120529Z Example:: 2024-08-20T21:40:35.7120847Z >>> # xdoctest: +SKIP("need process group init") 2024-08-20T21:40:35.7121288Z >>> # Note: Process group initialization omitted on each rank. 2024-08-20T21:40:35.7121550Z >>> import torch.distributed as dist 2024-08-20T21:40:35.7121795Z >>> # Assumes world_size of 3. 2024-08-20T21:40:35.7122212Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-08-20T21:40:35.7122488Z >>> output = [None for _ in gather_objects] 2024-08-20T21:40:35.7122706Z >>> dist.gather_object( 2024-08-20T21:40:35.7122973Z ... gather_objects[dist.get_rank()], 2024-08-20T21:40:35.7123274Z ... output if dist.get_rank() == 0 else None, 2024-08-20T21:40:35.7123466Z ... dst=0 2024-08-20T21:40:35.7123629Z ... ) 2024-08-20T21:40:35.7123818Z >>> # On rank 0 2024-08-20T21:40:35.7124005Z >>> output 2024-08-20T21:40:35.7124258Z ['foo', 12, {1: 2}] 2024-08-20T21:40:35.7124412Z 2024-08-20T21:40:35.7125157Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7125390Z 2024-08-20T21:40:35.7125585Z warnings.warn(msg) 2024-08-20T21:40:35.7125760Z 2024-08-20T21:40:35.7126130Z --- Parse Warning: 17 / 101 --- 2024-08-20T21:40:35.7128566Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2024-08-20T21:40:35.7129311Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7129471Z 2024-08-20T21:40:35.7129749Z Module ``torch.distributed.launch``. 2024-08-20T21:40:35.7129911Z 2024-08-20T21:40:35.7130681Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-08-20T21:40:35.7131044Z training processes on each of the training nodes. 2024-08-20T21:40:35.7131204Z 2024-08-20T21:40:35.7131387Z .. warning:: 2024-08-20T21:40:35.7131562Z 2024-08-20T21:40:35.7132362Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-08-20T21:40:35.7132520Z 2024-08-20T21:40:35.7133272Z The utility can be used for single-node distributed training, in which one or 2024-08-20T21:40:35.7133863Z more processes per node will be spawned. The utility can be used for either 2024-08-20T21:40:35.7134396Z CPU training or GPU training. If the utility is used for GPU training, 2024-08-20T21:40:35.7135013Z each distributed process will be operating on a single GPU. This can achieve 2024-08-20T21:40:35.7135691Z well-improved single-node training performance. It can also be used in 2024-08-20T21:40:35.7136494Z multi-node distributed training, by spawning up multiple processes on each node 2024-08-20T21:40:35.7137169Z for well-improved multi-node distributed training performance as well. 2024-08-20T21:40:35.7137720Z This will especially be beneficial for systems with multiple Infiniband 2024-08-20T21:40:35.7138488Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-08-20T21:40:35.7138736Z aggregated communication bandwidth. 2024-08-20T21:40:35.7138938Z 2024-08-20T21:40:35.7139681Z In both cases of single-node distributed training or multi-node distributed 2024-08-20T21:40:35.7140237Z training, this utility will launch the given number of processes per node 2024-08-20T21:40:35.7140949Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-08-20T21:40:35.7141508Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-08-20T21:40:35.7141996Z and each process will be operating on a single GPU from *GPU 0 to 2024-08-20T21:40:35.7142310Z GPU (nproc_per_node - 1)*. 2024-08-20T21:40:35.7142462Z 2024-08-20T21:40:35.7142671Z **How to use this module:** 2024-08-20T21:40:35.7142842Z 2024-08-20T21:40:35.7143282Z 1. Single-Node multi-process distributed training 2024-08-20T21:40:35.7143442Z 2024-08-20T21:40:35.7143621Z :: 2024-08-20T21:40:35.7143775Z 2024-08-20T21:40:35.7144448Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:40:35.7145036Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-08-20T21:40:35.7145325Z arguments of your training script) 2024-08-20T21:40:35.7145479Z 2024-08-20T21:40:35.7146117Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-08-20T21:40:35.7146287Z 2024-08-20T21:40:35.7146440Z 2024-08-20T21:40:35.7146789Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-08-20T21:40:35.7146948Z 2024-08-20T21:40:35.7147104Z :: 2024-08-20T21:40:35.7147280Z 2024-08-20T21:40:35.7147948Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:40:35.7148454Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-08-20T21:40:35.7149132Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-20T21:40:35.7149492Z and all other arguments of your training script) 2024-08-20T21:40:35.7149670Z 2024-08-20T21:40:35.7149836Z Node 2: 2024-08-20T21:40:35.7149989Z 2024-08-20T21:40:35.7150168Z :: 2024-08-20T21:40:35.7150325Z 2024-08-20T21:40:35.7150998Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-08-20T21:40:35.7151494Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-08-20T21:40:35.7152109Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-08-20T21:40:35.7152540Z and all other arguments of your training script) 2024-08-20T21:40:35.7152714Z 2024-08-20T21:40:35.7153116Z 3. To look up what optional arguments this module offers: 2024-08-20T21:40:35.7153269Z 2024-08-20T21:40:35.7153445Z :: 2024-08-20T21:40:35.7153606Z 2024-08-20T21:40:35.7154007Z python -m torch.distributed.launch --help 2024-08-20T21:40:35.7154178Z 2024-08-20T21:40:35.7154332Z 2024-08-20T21:40:35.7154535Z **Important Notices:** 2024-08-20T21:40:35.7154712Z 2024-08-20T21:40:35.7155272Z 1. This utility and multi-process distributed (single-node or 2024-08-20T21:40:35.7156006Z multi-node) GPU training currently only achieves the best performance using 2024-08-20T21:40:35.7156610Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-08-20T21:40:35.7156812Z use for GPU training. 2024-08-20T21:40:35.7156980Z 2024-08-20T21:40:35.7157633Z 2. In your training program, you must parse the command-line argument: 2024-08-20T21:40:35.7158314Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-08-20T21:40:35.7158889Z If your training program uses GPUs, you should ensure that your code only 2024-08-20T21:40:35.7159383Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-08-20T21:40:35.7159539Z 2024-08-20T21:40:35.7159784Z Parsing the local_rank argument 2024-08-20T21:40:35.7159939Z 2024-08-20T21:40:35.7160147Z :: 2024-08-20T21:40:35.7160318Z 2024-08-20T21:40:35.7160525Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.7160738Z >>> import argparse 2024-08-20T21:40:35.7161040Z >>> parser = argparse.ArgumentParser() 2024-08-20T21:40:35.7161605Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-08-20T21:40:35.7161855Z >>> args = parser.parse_args() 2024-08-20T21:40:35.7162012Z 2024-08-20T21:40:35.7162298Z Set your device to local rank using either 2024-08-20T21:40:35.7162468Z 2024-08-20T21:40:35.7162633Z :: 2024-08-20T21:40:35.7162788Z 2024-08-20T21:40:35.7163281Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-08-20T21:40:35.7163436Z 2024-08-20T21:40:35.7163589Z or 2024-08-20T21:40:35.7163758Z 2024-08-20T21:40:35.7163920Z :: 2024-08-20T21:40:35.7164074Z 2024-08-20T21:40:35.7164387Z >>> with torch.cuda.device(args.local_rank): 2024-08-20T21:40:35.7164597Z >>> # your code to run 2024-08-20T21:40:35.7164769Z >>> ... 2024-08-20T21:40:35.7164939Z 2024-08-20T21:40:35.7165156Z .. versionchanged:: 2.0.0 2024-08-20T21:40:35.7165314Z 2024-08-20T21:40:35.7166058Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-08-20T21:40:35.7166793Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-08-20T21:40:35.7167215Z previously used underscored ``--local_rank``. 2024-08-20T21:40:35.7167386Z 2024-08-20T21:40:35.7167961Z For backward compatibility, it may be necessary for users to handle both 2024-08-20T21:40:35.7168779Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-08-20T21:40:35.7169495Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-08-20T21:40:35.7170161Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-08-20T21:40:35.7170873Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-08-20T21:40:35.7171319Z including ``"--local-rank"`` should be sufficient. 2024-08-20T21:40:35.7171479Z 2024-08-20T21:40:35.7172077Z 3. In your training program, you are supposed to call the following function 2024-08-20T21:40:35.7172683Z at the beginning to start the distributed backend. It is strongly recommended 2024-08-20T21:40:35.7173208Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-08-20T21:40:35.7173798Z but ``env://`` is the one that is officially supported by this module. 2024-08-20T21:40:35.7173958Z 2024-08-20T21:40:35.7174124Z :: 2024-08-20T21:40:35.7174296Z 2024-08-20T21:40:35.7174901Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-08-20T21:40:35.7175355Z >>> init_method='env://') 2024-08-20T21:40:35.7175509Z 2024-08-20T21:40:35.7176104Z 4. In your training program, you can either use regular distributed functions 2024-08-20T21:40:35.7176715Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-08-20T21:40:35.7177208Z training program uses GPUs for training and you would like to use 2024-08-20T21:40:35.7177655Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-08-20T21:40:35.7177892Z here is how to configure it. 2024-08-20T21:40:35.7178052Z 2024-08-20T21:40:35.7178212Z :: 2024-08-20T21:40:35.7178384Z 2024-08-20T21:40:35.7178854Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-08-20T21:40:35.7179195Z >>> device_ids=[args.local_rank], 2024-08-20T21:40:35.7179552Z >>> output_device=args.local_rank) 2024-08-20T21:40:35.7179733Z 2024-08-20T21:40:35.7180332Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-08-20T21:40:35.7180976Z that your code will be operating on. This is generally the local rank of the 2024-08-20T21:40:35.7181554Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-08-20T21:40:35.7182094Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-08-20T21:40:35.7182258Z utility 2024-08-20T21:40:35.7182416Z 2024-08-20T21:40:35.7183044Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-08-20T21:40:35.7183562Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-08-20T21:40:35.7184229Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-08-20T21:40:35.7184838Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-08-20T21:40:35.7185358Z will not pass ``--local-rank`` when you specify this flag. 2024-08-20T21:40:35.7185514Z 2024-08-20T21:40:35.7185710Z .. warning:: 2024-08-20T21:40:35.7185864Z 2024-08-20T21:40:35.7186365Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-08-20T21:40:35.7186963Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-08-20T21:40:35.7187230Z write to a networked filesystem. See 2024-08-20T21:40:35.7187747Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-08-20T21:40:35.7188251Z how things can go wrong if you don't do this correctly. 2024-08-20T21:40:35.7188407Z 2024-08-20T21:40:35.7188578Z 2024-08-20T21:40:35.7188742Z 2024-08-20T21:40:35.7188895Z 2024-08-20T21:40:35.7189639Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7189790Z 2024-08-20T21:40:35.7190034Z warnings.warn(msg) 2024-08-20T21:40:35.7190204Z 2024-08-20T21:40:35.7190745Z --- Parse Warning: 18 / 101 --- 2024-08-20T21:40:35.7193548Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=130. 2024-08-20T21:40:35.7194323Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7194482Z 2024-08-20T21:40:35.7195034Z DistributedOptimizer takes remote references to parameters scattered 2024-08-20T21:40:35.7195619Z across workers and applies the given optimizer locally for each parameter. 2024-08-20T21:40:35.7195910Z 2024-08-20T21:40:35.7196509Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-08-20T21:40:35.7196856Z to retrieve the gradients for specific parameters. 2024-08-20T21:40:35.7197015Z 2024-08-20T21:40:35.7197229Z Concurrent calls to 2024-08-20T21:40:35.7197723Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-08-20T21:40:35.7198037Z either from the same or different clients, will 2024-08-20T21:40:35.7198747Z be serialized on each worker -- as each worker's optimizer can only work 2024-08-20T21:40:35.7199269Z on one set of gradients at a time. However, there is no guarantee that 2024-08-20T21:40:35.7199987Z the full forward-backward-optimizer sequence will execute for one client 2024-08-20T21:40:35.7200552Z at a time. This means that the gradients being applied may not correspond 2024-08-20T21:40:35.7201094Z to the latest forward pass executed on a given worker. Also, there is no 2024-08-20T21:40:35.7201361Z guaranteed ordering across workers. 2024-08-20T21:40:35.7201520Z 2024-08-20T21:40:35.7202103Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-08-20T21:40:35.7202689Z by default, so that optimizer updates are not blocked by the Python Global 2024-08-20T21:40:35.7203290Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-08-20T21:40:35.7203861Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-08-20T21:40:35.7204556Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-08-20T21:40:35.7204788Z for your own custom optimizers. 2024-08-20T21:40:35.7204940Z 2024-08-20T21:40:35.7205120Z Args: 2024-08-20T21:40:35.7205572Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-08-20T21:40:35.7205822Z instantiate on each worker. 2024-08-20T21:40:35.7206333Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-08-20T21:40:35.7206519Z to optimize. 2024-08-20T21:40:35.7207057Z args: arguments to pass to the optimizer constructor on each worker. 2024-08-20T21:40:35.7207591Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-08-20T21:40:35.7207750Z 2024-08-20T21:40:35.7207942Z Example:: 2024-08-20T21:40:35.7208198Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:35.7208584Z >>> import torch.distributed.autograd as dist_autograd 2024-08-20T21:40:35.7208884Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:35.7209106Z >>> from torch import optim 2024-08-20T21:40:35.7209550Z >>> from torch.distributed.optim import DistributedOptimizer 2024-08-20T21:40:35.7209734Z >>> 2024-08-20T21:40:35.7210043Z >>> with dist_autograd.context() as context_id: 2024-08-20T21:40:35.7210294Z >>> # Forward pass. 2024-08-20T21:40:35.7210798Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-08-20T21:40:35.7211267Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-08-20T21:40:35.7211570Z >>> loss = rref1.to_here() + rref2.to_here() 2024-08-20T21:40:35.7211796Z >>> 2024-08-20T21:40:35.7211996Z >>> # Backward pass. 2024-08-20T21:40:35.7212362Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-08-20T21:40:35.7212537Z >>> 2024-08-20T21:40:35.7212732Z >>> # Optimizer. 2024-08-20T21:40:35.7213029Z >>> dist_optim = DistributedOptimizer( 2024-08-20T21:40:35.7213217Z >>> optim.SGD, 2024-08-20T21:40:35.7213418Z >>> [rref1, rref2], 2024-08-20T21:40:35.7213617Z >>> lr=0.05, 2024-08-20T21:40:35.7213786Z >>> ) 2024-08-20T21:40:35.7214024Z >>> dist_optim.step(context_id) 2024-08-20T21:40:35.7214199Z 2024-08-20T21:40:35.7214536Z __ https://github.com/pytorch/tutorials/pull/1465 2024-08-20T21:40:35.7214763Z 2024-08-20T21:40:35.7215527Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7215683Z 2024-08-20T21:40:35.7215877Z warnings.warn(msg) 2024-08-20T21:40:35.7216053Z 2024-08-20T21:40:35.7216420Z --- Parse Warning: 19 / 101 --- 2024-08-20T21:40:35.7219385Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2024-08-20T21:40:35.7220136Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7220295Z 2024-08-20T21:40:35.7221444Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-08-20T21:40:35.7221790Z This optimizer runs local optimizer at every step. 2024-08-20T21:40:35.7222772Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-08-20T21:40:35.7222949Z 2024-08-20T21:40:35.7223114Z Args: 2024-08-20T21:40:35.7223340Z optim: The local optimizer. 2024-08-20T21:40:35.7224005Z averager: A model averager instance to run post-localSGD algorithm. 2024-08-20T21:40:35.7224163Z 2024-08-20T21:40:35.7224343Z Example:: 2024-08-20T21:40:35.7224565Z 2024-08-20T21:40:35.7224857Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:35.7225061Z >>> import torch 2024-08-20T21:40:35.7225323Z >>> import torch.distributed as dist 2024-08-20T21:40:35.7225963Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-20T21:40:35.7226190Z >>> import torch.nn as nn 2024-08-20T21:40:35.7226646Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-08-20T21:40:35.7227299Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-20T21:40:35.7227532Z >>> PostLocalSGDState, 2024-08-20T21:40:35.7227741Z >>> post_localSGD_hook, 2024-08-20T21:40:35.7227907Z >>> ) 2024-08-20T21:40:35.7228115Z >>> 2024-08-20T21:40:35.7228473Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:40:35.7228796Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:40:35.7228980Z >>> ) 2024-08-20T21:40:35.7229144Z >>> 2024-08-20T21:40:35.7229590Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:40:35.7230291Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-20T21:40:35.7230660Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:40:35.7230845Z >>> 2024-08-20T21:40:35.7231465Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-08-20T21:40:35.7232091Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-08-20T21:40:35.7232490Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:40:35.7232979Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-08-20T21:40:35.7233277Z >>> opt = PostLocalSGDOptimizer( 2024-08-20T21:40:35.7233503Z >>> optim=local_optim, 2024-08-20T21:40:35.7234086Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-20T21:40:35.7234259Z >>> ) 2024-08-20T21:40:35.7234441Z >>> 2024-08-20T21:40:35.7235008Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-08-20T21:40:35.7236045Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-08-20T21:40:35.7237221Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-08-20T21:40:35.7237609Z >>> for step in range(0, 200): 2024-08-20T21:40:35.7237902Z >>> opt.zero_grad() 2024-08-20T21:40:35.7238189Z >>> loss = loss_fn(output, labels) 2024-08-20T21:40:35.7238659Z >>> loss.backward() 2024-08-20T21:40:35.7238909Z >>> opt.step() 2024-08-20T21:40:35.7239156Z 2024-08-20T21:40:35.7239923Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7240128Z 2024-08-20T21:40:35.7240415Z warnings.warn(msg) 2024-08-20T21:40:35.7240660Z 2024-08-20T21:40:35.7241088Z --- Parse Warning: 20 / 101 --- 2024-08-20T21:40:35.7244209Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282. 2024-08-20T21:40:35.7245002Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7245192Z 2024-08-20T21:40:35.7246267Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-08-20T21:40:35.7246533Z 2024-08-20T21:40:35.7246859Z The sharing is done as described by ZeRO_. 2024-08-20T21:40:35.7247101Z 2024-08-20T21:40:35.7247470Z The local optimizer instance in each rank is only 2024-08-20T21:40:35.7248167Z responsible for updating approximately ``1 / world_size`` parameters and 2024-08-20T21:40:35.7248705Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-08-20T21:40:35.7249377Z parameters are updated locally, each rank will broadcast its parameters to 2024-08-20T21:40:35.7249910Z all other peers to keep all model replicas in the same state. 2024-08-20T21:40:35.7250500Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-08-20T21:40:35.7251311Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-08-20T21:40:35.7251599Z memory consumption. 2024-08-20T21:40:35.7251811Z 2024-08-20T21:40:35.7252622Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-08-20T21:40:35.7253284Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-08-20T21:40:35.7253907Z not divided among ranks. The partition is arbitrary and might not match the 2024-08-20T21:40:35.7254294Z the parameter registration or usage order. 2024-08-20T21:40:35.7254501Z 2024-08-20T21:40:35.7254715Z Arguments: 2024-08-20T21:40:35.7255305Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-08-20T21:40:35.7255796Z or :class:`dict` s giving all parameters, which will be sharded 2024-08-20T21:40:35.7256026Z across ranks. 2024-08-20T21:40:35.7256274Z 2024-08-20T21:40:35.7256502Z Keyword Args: 2024-08-20T21:40:35.7257081Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-08-20T21:40:35.7257403Z optimizer. 2024-08-20T21:40:35.7257927Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-08-20T21:40:35.7258410Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-08-20T21:40:35.7258889Z :meth:`torch.distributed.init_process_group`). 2024-08-20T21:40:35.7259453Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-08-20T21:40:35.7259996Z packed into buckets to speed up communication, and ``param.data`` 2024-08-20T21:40:35.7260616Z fields point to bucket views at different offsets; if ``False``, 2024-08-20T21:40:35.7261168Z each individual parameter is communicated separately, and each 2024-08-20T21:40:35.7261616Z ``params.data`` stays intact (default: ``False``). 2024-08-20T21:40:35.7262104Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-08-20T21:40:35.7262816Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-08-20T21:40:35.7263444Z synchronization; this requires (1) either a functional optimizer 2024-08-20T21:40:35.7263919Z for the ``optimizer_class`` argument or one with a functional 2024-08-20T21:40:35.7264368Z equivalent and (2) registering a DDP communication hook 2024-08-20T21:40:35.7264941Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-08-20T21:40:35.7265365Z parameters are packed into buckets matching those in 2024-08-20T21:40:35.7265827Z :class:`DistributedDataParallel`, meaning that the 2024-08-20T21:40:35.7266264Z ``parameters_as_bucket_view`` argument is ignored. 2024-08-20T21:40:35.7266754Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-08-20T21:40:35.7267047Z (per normal). 2024-08-20T21:40:35.7267282Z (default: ``False``) 2024-08-20T21:40:35.7267830Z **defaults: any trailing arguments, which are forwarded to the local 2024-08-20T21:40:35.7268095Z optimizer. 2024-08-20T21:40:35.7268329Z 2024-08-20T21:40:35.7268544Z Example:: 2024-08-20T21:40:35.7268802Z 2024-08-20T21:40:35.7269044Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.7269290Z >>> import torch.nn as nn 2024-08-20T21:40:35.7269851Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-08-20T21:40:35.7270491Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-08-20T21:40:35.7271086Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-08-20T21:40:35.7271441Z >>> ddp = DDP(model, device_ids=[rank]) 2024-08-20T21:40:35.7282175Z >>> opt = ZeroRedundancyOptimizer( 2024-08-20T21:40:35.7282469Z >>> ddp.parameters(), 2024-08-20T21:40:35.7282758Z >>> optimizer_class=torch.optim.Adam, 2024-08-20T21:40:35.7282952Z >>> lr=0.01 2024-08-20T21:40:35.7283130Z >>> ) 2024-08-20T21:40:35.7283367Z >>> ddp(inputs).sum().backward() 2024-08-20T21:40:35.7283571Z >>> opt.step() 2024-08-20T21:40:35.7283732Z 2024-08-20T21:40:35.7283958Z .. warning:: 2024-08-20T21:40:35.7284474Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-08-20T21:40:35.7284983Z passed-in parameters are the same dense type. 2024-08-20T21:40:35.7285188Z 2024-08-20T21:40:35.7285388Z .. warning:: 2024-08-20T21:40:35.7285906Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-08-20T21:40:35.7286404Z the way that overlapping :class:`DistributedDataParallel` with 2024-08-20T21:40:35.7286943Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-08-20T21:40:35.7287470Z two or three training iterations do not perform parameter updates in 2024-08-20T21:40:35.7287942Z the optimizer step, depending on if ``static_graph=False`` or 2024-08-20T21:40:35.7288371Z ``static_graph=True``, respectively. This is because it needs 2024-08-20T21:40:35.7288790Z information about the gradient bucketing strategy used by 2024-08-20T21:40:35.7289317Z :class:`DistributedDataParallel`, which is not finalized until the 2024-08-20T21:40:35.7289898Z second forward pass if ``static_graph=False`` or until the third 2024-08-20T21:40:35.7290660Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-08-20T21:40:35.7290913Z is to prepend dummy inputs. 2024-08-20T21:40:35.7291078Z 2024-08-20T21:40:35.7291683Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-08-20T21:40:35.7291846Z 2024-08-20T21:40:35.7292129Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-08-20T21:40:35.7292307Z 2024-08-20T21:40:35.7292464Z 2024-08-20T21:40:35.7293212Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7293385Z 2024-08-20T21:40:35.7293737Z warnings.warn(msg) 2024-08-20T21:40:35.7293893Z 2024-08-20T21:40:35.7294290Z --- Parse Warning: 21 / 101 --- 2024-08-20T21:40:35.7297232Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2024-08-20T21:40:35.7297986Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7298159Z 2024-08-20T21:40:35.7298740Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-08-20T21:40:35.7299103Z Needs to be called on all ranks in an SPMD fashion. 2024-08-20T21:40:35.7299260Z 2024-08-20T21:40:35.7299491Z Args: 2024-08-20T21:40:35.7300150Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-08-20T21:40:35.7300558Z of shards that represent the local shards on this rank. 2024-08-20T21:40:35.7301114Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-08-20T21:40:35.7301406Z shape of the overall sharded tensor. 2024-08-20T21:40:35.7301570Z 2024-08-20T21:40:35.7301753Z Keyword args: 2024-08-20T21:40:35.7302382Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-08-20T21:40:35.7302727Z the default process group will be used. 2024-08-20T21:40:35.7303123Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-20T21:40:35.7303629Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-20T21:40:35.7304100Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-20T21:40:35.7304321Z Default: ``False``. 2024-08-20T21:40:35.7304476Z 2024-08-20T21:40:35.7304646Z Returns: 2024-08-20T21:40:35.7305025Z A :class:`ShardedTensor` object handle on this rank 2024-08-20T21:40:35.7305183Z 2024-08-20T21:40:35.7305343Z 2024-08-20T21:40:35.7305535Z Examples: 2024-08-20T21:40:35.7306156Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-08-20T21:40:35.7306610Z each shard have a (5, 5) local tensor, we can do it like below: 2024-08-20T21:40:35.7306785Z 2024-08-20T21:40:35.7306964Z on rank 0: 2024-08-20T21:40:35.7307238Z >>> # xdoctest: +SKIP("not distributed") 2024-08-20T21:40:35.7307531Z >>> local_shard_metadata = ShardMetadata( 2024-08-20T21:40:35.7307754Z >>> shard_offsets=[0, 0], 2024-08-20T21:40:35.7307971Z >>> shard_lengths=[5, 5], 2024-08-20T21:40:35.7308211Z >>> placement="rank:0/cuda:0" 2024-08-20T21:40:35.7308376Z >>> ) 2024-08-20T21:40:35.7308821Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-20T21:40:35.7309288Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-20T21:40:35.7309446Z 2024-08-20T21:40:35.7309631Z on rank 1: 2024-08-20T21:40:35.7309901Z >>> # xdoctest: +SKIP("not distributed") 2024-08-20T21:40:35.7310172Z >>> local_shard_metadata = ShardMetadata( 2024-08-20T21:40:35.7310467Z >>> shard_offsets=[5, 0], 2024-08-20T21:40:35.7310680Z >>> shard_lengths=[5, 5], 2024-08-20T21:40:35.7310907Z >>> placement="rank:1/cuda:1" 2024-08-20T21:40:35.7311081Z >>> ) 2024-08-20T21:40:35.7311529Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-08-20T21:40:35.7311972Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-08-20T21:40:35.7312142Z 2024-08-20T21:40:35.7312876Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7313081Z 2024-08-20T21:40:35.7313289Z warnings.warn(msg) 2024-08-20T21:40:35.7313442Z 2024-08-20T21:40:35.7313885Z --- Parse Warning: 22 / 101 --- 2024-08-20T21:40:35.7316956Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784. 2024-08-20T21:40:35.7317713Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7317885Z 2024-08-20T21:40:35.7318491Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-08-20T21:40:35.7318737Z size and sharding spec on each rank. 2024-08-20T21:40:35.7318916Z 2024-08-20T21:40:35.7319084Z Args: 2024-08-20T21:40:35.7319620Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-08-20T21:40:35.7320237Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-08-20T21:40:35.7320624Z The specification describing how to shard the Tensor. 2024-08-20T21:40:35.7321016Z global_size (Sequence[int]): Size of the sharded tensor. 2024-08-20T21:40:35.7321610Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-08-20T21:40:35.7321798Z Default: None 2024-08-20T21:40:35.7322205Z init_rrefs (bool, optional): Whether or not to initialize 2024-08-20T21:40:35.7322691Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-08-20T21:40:35.7323225Z Need to initialize the RPC Framework if specified as ``True``. 2024-08-20T21:40:35.7323441Z Default: ``False``. 2024-08-20T21:40:35.7323628Z 2024-08-20T21:40:35.7323790Z Returns: 2024-08-20T21:40:35.7324389Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-08-20T21:40:35.7324655Z tensor stored in the current rank. 2024-08-20T21:40:35.7324812Z 2024-08-20T21:40:35.7324992Z Examples: 2024-08-20T21:40:35.7325194Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.7325508Z >>> # All tensors below are of torch.int64 type. 2024-08-20T21:40:35.7325796Z >>> # We have 2 process groups, 2 ranks. 2024-08-20T21:40:35.7326218Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-08-20T21:40:35.7326681Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-08-20T21:40:35.7326883Z >>> local_tensor 2024-08-20T21:40:35.7327092Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-08-20T21:40:35.7327319Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-08-20T21:40:35.7327515Z >>> sharding_dim = 0 2024-08-20T21:40:35.7327790Z >>> sharding_spec = ChunkShardingSpec( 2024-08-20T21:40:35.7328008Z dim=sharding_dim, 2024-08-20T21:40:35.7328195Z placements=[ 2024-08-20T21:40:35.7328392Z "rank:0/cuda:0", 2024-08-20T21:40:35.7328598Z "rank:1/cuda:1", 2024-08-20T21:40:35.7328770Z ], 2024-08-20T21:40:35.7328936Z ) 2024-08-20T21:40:35.7329557Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-08-20T21:40:35.7329723Z >>> st 2024-08-20T21:40:35.7329956Z ShardedTensor( 2024-08-20T21:40:35.7330271Z ShardedTensorMetadata( 2024-08-20T21:40:35.7330471Z shards_metadata=[ 2024-08-20T21:40:35.7331103Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-08-20T21:40:35.7331737Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-08-20T21:40:35.7331911Z ], 2024-08-20T21:40:35.7332145Z size=torch.Size([2, 4]) 2024-08-20T21:40:35.7332308Z ) 2024-08-20T21:40:35.7332501Z >>> st.local_tensor() 2024-08-20T21:40:35.7332732Z tensor([1, 2, 3, 4]) # Rank 0 2024-08-20T21:40:35.7332938Z tensor([3, 4, 5, 6]) # Rank 1 2024-08-20T21:40:35.7333169Z 2024-08-20T21:40:35.7333850Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-08-20T21:40:35.7334444Z rank validations, and we only validate the local shard on the current rank. 2024-08-20T21:40:35.7335008Z We fully rely on the user to ensure local tensor is sharded based on the 2024-08-20T21:40:35.7335218Z sharding spec. 2024-08-20T21:40:35.7335384Z 2024-08-20T21:40:35.7336119Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7336297Z 2024-08-20T21:40:35.7336490Z warnings.warn(msg) 2024-08-20T21:40:35.7336648Z 2024-08-20T21:40:35.7337040Z --- Parse Warning: 23 / 101 --- 2024-08-20T21:40:35.7339953Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023. 2024-08-20T21:40:35.7340715Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7340869Z 2024-08-20T21:40:35.7341493Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-08-20T21:40:35.7341717Z single local shard. 2024-08-20T21:40:35.7341872Z 2024-08-20T21:40:35.7342538Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-08-20T21:40:35.7343186Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-08-20T21:40:35.7343413Z we swap local shards directly. 2024-08-20T21:40:35.7344058Z For more generic cases, we merge different shards across different ranks and split 2024-08-20T21:40:35.7344700Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-08-20T21:40:35.7344858Z 2024-08-20T21:40:35.7345025Z Args: 2024-08-20T21:40:35.7345722Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-08-20T21:40:35.7346101Z specification describing how the tensor is sharded. 2024-08-20T21:40:35.7346283Z 2024-08-20T21:40:35.7346456Z Returns: 2024-08-20T21:40:35.7346942Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-08-20T21:40:35.7347113Z 2024-08-20T21:40:35.7347284Z Examples: 2024-08-20T21:40:35.7347492Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.7347785Z >>> # We have 2 process groups, 2 ranks. 2024-08-20T21:40:35.7348206Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-08-20T21:40:35.7348481Z >>> tensor = torch.stack([tensor, tensor]) 2024-08-20T21:40:35.7348670Z >>> tensor 2024-08-20T21:40:35.7348948Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-08-20T21:40:35.7349222Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-08-20T21:40:35.7349516Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-08-20T21:40:35.7349798Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-08-20T21:40:35.7349999Z >>> sharding_dim = 0 2024-08-20T21:40:35.7350253Z >>> spec = ChunkShardingSpec( 2024-08-20T21:40:35.7350497Z dim=sharding_dim, 2024-08-20T21:40:35.7350703Z placements=[ 2024-08-20T21:40:35.7350903Z "rank:0/cuda:0", 2024-08-20T21:40:35.7351110Z "rank:1/cuda:1", 2024-08-20T21:40:35.7351316Z "rank:2/cuda:2", 2024-08-20T21:40:35.7351511Z "rank:3/cuda:3", 2024-08-20T21:40:35.7351683Z ], 2024-08-20T21:40:35.7351866Z ) 2024-08-20T21:40:35.7352085Z >>> current_offsets = [0] * 2 2024-08-20T21:40:35.7352319Z >>> current_offsets[0] = rank * 2 2024-08-20T21:40:35.7352585Z >>> shard_metadata = ShardMetadata( 2024-08-20T21:40:35.7352919Z shard_offsets=copy.deepcopy(current_offsets), 2024-08-20T21:40:35.7353216Z shard_sizes=tensor.size(), 2024-08-20T21:40:35.7353512Z placement=spec.placements[rank], 2024-08-20T21:40:35.7353677Z ) 2024-08-20T21:40:35.7353876Z >>> local_shards = [ 2024-08-20T21:40:35.7354064Z Shard( 2024-08-20T21:40:35.7354259Z tensor=tensor, 2024-08-20T21:40:35.7354498Z metadata=shard_metadata, 2024-08-20T21:40:35.7354689Z ) 2024-08-20T21:40:35.7354856Z ] 2024-08-20T21:40:35.7355385Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-08-20T21:40:35.7355594Z >>> sharding_dim = 1 2024-08-20T21:40:35.7355871Z >>> resharding_spec = ChunkShardingSpec( 2024-08-20T21:40:35.7356089Z dim=sharding_dim, 2024-08-20T21:40:35.7356281Z placements=[ 2024-08-20T21:40:35.7356483Z "rank:0/cuda:0", 2024-08-20T21:40:35.7356694Z "rank:1/cuda:1", 2024-08-20T21:40:35.7356895Z "rank:2/cuda:2", 2024-08-20T21:40:35.7357088Z "rank:3/cuda:3", 2024-08-20T21:40:35.7357272Z ], 2024-08-20T21:40:35.7357435Z ) 2024-08-20T21:40:35.7357674Z >>> st.reshard(resharding_spec) 2024-08-20T21:40:35.7357948Z >>> tensor = st.local_shards()[0].tensor 2024-08-20T21:40:35.7358115Z >>> tensor 2024-08-20T21:40:35.7358448Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-08-20T21:40:35.7358834Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-08-20T21:40:35.7359159Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-08-20T21:40:35.7359503Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-08-20T21:40:35.7359670Z 2024-08-20T21:40:35.7360410Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7360586Z 2024-08-20T21:40:35.7360786Z warnings.warn(msg) 2024-08-20T21:40:35.7360950Z 2024-08-20T21:40:35.7361334Z --- Parse Warning: 24 / 101 --- 2024-08-20T21:40:35.7364105Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2024-08-20T21:40:35.7364859Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7365035Z 2024-08-20T21:40:35.7365540Z Representation of a sharding plan, describes how to shard a module 2024-08-20T21:40:35.7366222Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-08-20T21:40:35.7366923Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-08-20T21:40:35.7367560Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-08-20T21:40:35.7367730Z 2024-08-20T21:40:35.7367903Z Args: 2024-08-20T21:40:35.7368558Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-08-20T21:40:35.7368963Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-08-20T21:40:35.7369869Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-08-20T21:40:35.7370560Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-08-20T21:40:35.7370855Z a parameter to a `ShardingSpec`. 2024-08-20T21:40:35.7371501Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-08-20T21:40:35.7371729Z to a `Sharder` object. 2024-08-20T21:40:35.7372536Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-08-20T21:40:35.7373411Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-08-20T21:40:35.7374034Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-08-20T21:40:35.7374234Z Default: `None` 2024-08-20T21:40:35.7374850Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-08-20T21:40:35.7375628Z a module's sharded output to be returned as a Tensor from its local shards to 2024-08-20T21:40:35.7376224Z ensure further processing in a data parallel fashion. ("" in list means the 2024-08-20T21:40:35.7376411Z root module). 2024-08-20T21:40:35.7376620Z Default: None 2024-08-20T21:40:35.7376791Z Example: 2024-08-20T21:40:35.7377512Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-08-20T21:40:35.7378276Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-08-20T21:40:35.7378439Z 2024-08-20T21:40:35.7378857Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-08-20T21:40:35.7379087Z >>> class MyModule(nn.Module): 2024-08-20T21:40:35.7379416Z >>> def __init__(self) -> None: 2024-08-20T21:40:35.7379645Z >>> super().__init__() 2024-08-20T21:40:35.7379870Z >>> self.fc1 = nn.Linear() 2024-08-20T21:40:35.7380096Z >>> self.gelu = nn.GELU() 2024-08-20T21:40:35.7380365Z >>> self.fc2 = nn.Linear() 2024-08-20T21:40:35.7380595Z >>> self.relu = nn.Linear() 2024-08-20T21:40:35.7380762Z >>> 2024-08-20T21:40:35.7381007Z >>> def forward(self, input): 2024-08-20T21:40:35.7381400Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-08-20T21:40:35.7381556Z 2024-08-20T21:40:35.7381723Z 2024-08-20T21:40:35.7382022Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-08-20T21:40:35.7382258Z >>> sharding_plan = ShardingPlan( 2024-08-20T21:40:35.7382456Z >>> plan={ 2024-08-20T21:40:35.7382672Z >>> "fc1.weight": spec1, 2024-08-20T21:40:35.7382879Z >>> "fc2.weight": spec2 2024-08-20T21:40:35.7383055Z >>> }, 2024-08-20T21:40:35.7383255Z >>> output_plan={ 2024-08-20T21:40:35.7383473Z >>> "fc2": output_spec 2024-08-20T21:40:35.7383638Z >>> }, 2024-08-20T21:40:35.7383868Z >>> return_local_tensor=["fc2"] 2024-08-20T21:40:35.7384054Z >>> ) 2024-08-20T21:40:35.7384209Z 2024-08-20T21:40:35.7384931Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7385098Z 2024-08-20T21:40:35.7385298Z warnings.warn(msg) 2024-08-20T21:40:35.7385449Z 2024-08-20T21:40:35.7385824Z --- Parse Warning: 25 / 101 --- 2024-08-20T21:40:35.7388606Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/experimental/func_map.py line=33. 2024-08-20T21:40:35.7389351Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7389562Z 2024-08-20T21:40:35.7390193Z ``local_map`` is an experimental API that allows users to apply on :class:`DTensors` 2024-08-20T21:40:35.7390847Z a function that is written to be applied on :class:`~torch.Tensors`. 2024-08-20T21:40:35.7391030Z 2024-08-20T21:40:35.7391198Z Args: 2024-08-20T21:40:35.7391709Z func (Callable): the function to be applied on each local shard of 2024-08-20T21:40:35.7391908Z :class:`DTensor`s. 2024-08-20T21:40:35.7392427Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-08-20T21:40:35.7393217Z the desired placements of the :class:`DTensor`s in ``func``'s flattened output. 2024-08-20T21:40:35.7393966Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2024-08-20T21:40:35.7394551Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2024-08-20T21:40:35.7395158Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2024-08-20T21:40:35.7395432Z mapping to the flattened ``output``. 2024-08-20T21:40:35.7395922Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-08-20T21:40:35.7396631Z placements (a `Tuple[Placement]` value). For non-:class:`Tensor` output, 2024-08-20T21:40:35.7396909Z the `PlacementType` should be `None`. 2024-08-20T21:40:35.7397514Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-08-20T21:40:35.7398089Z in. In this case, even if `out_placements` is not `None`, the result function 2024-08-20T21:40:35.7398646Z should ignore the desired placements because the application is not on 2024-08-20T21:40:35.7398870Z :class:`DTensors`. 2024-08-20T21:40:35.7399240Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-08-20T21:40:35.7400014Z the required placements of the :class:`DTensor`s in ``func``'s flattened input. 2024-08-20T21:40:35.7400564Z If ``in_placements`` is specified, ``local_map`` would examine whether the 2024-08-20T21:40:35.7401115Z placements of each :class:`DTensor` argument is the same as the required 2024-08-20T21:40:35.7401612Z placements or not. If the placements are not the same and 2024-08-20T21:40:35.7402205Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2024-08-20T21:40:35.7402794Z ``redistribute_inputs`` is `True`, the argument will be first redistributed to 2024-08-20T21:40:35.7403432Z the required sharding placements before passing its local tensor to ``func``. 2024-08-20T21:40:35.7403976Z The only exception is when required placements are not ``None`` and the 2024-08-20T21:40:35.7404579Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-08-20T21:40:35.7405123Z will be skipped and the argument will be directly passed to ``func``. 2024-08-20T21:40:35.7405676Z If ``in_placements`` is ``None``, no placements examination will be performed. 2024-08-20T21:40:35.7405882Z Default: None 2024-08-20T21:40:35.7406185Z device_mesh (:class:`DeviceMesh`, optional): 2024-08-20T21:40:35.7406696Z the device mesh that all the :class:`DTensor`s are placed on. If not 2024-08-20T21:40:35.7407417Z specified, this will be inferred from the input :class:`DTensor`s' device 2024-08-20T21:40:35.7407976Z mesh. `local_map` requires every :class:`DTensor`s to be placed on the same 2024-08-20T21:40:35.7408201Z device mesh. Default: None. 2024-08-20T21:40:35.7408484Z redistribute_inputs (bool, optional): 2024-08-20T21:40:35.7409101Z the bool value indicating whether to reshard the input :class:`DTensor`s when 2024-08-20T21:40:35.7409690Z their placements are different from the required input placements. If this 2024-08-20T21:40:35.7410325Z value is ``False`` and some :class:`DTensor` input has a different placement, 2024-08-20T21:40:35.7410700Z an exception will be raised. Default: False. 2024-08-20T21:40:35.7410879Z 2024-08-20T21:40:35.7411051Z Returns: 2024-08-20T21:40:35.7411686Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2024-08-20T21:40:35.7412288Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2024-08-20T21:40:35.7412444Z 2024-08-20T21:40:35.7412610Z Raises: 2024-08-20T21:40:35.7413258Z AssertionError: If the input :class:`DTensor`s are not placed on the same device 2024-08-20T21:40:35.7413867Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2024-08-20T21:40:35.7414144Z argument passed in. 2024-08-20T21:40:35.7414326Z 2024-08-20T21:40:35.7415123Z AssertionError: For any non-:class:`DTensor` output, we require its corresponding 2024-08-20T21:40:35.7415742Z output placement in ``out_placements`` be None. An AssertionError will be raised 2024-08-20T21:40:35.7415974Z if this is not the case. 2024-08-20T21:40:35.7416136Z 2024-08-20T21:40:35.7416755Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2024-08-20T21:40:35.7417112Z a redistribution according to ``in_placements``. 2024-08-20T21:40:35.7417269Z 2024-08-20T21:40:35.7417461Z Example: 2024-08-20T21:40:35.7417720Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:35.7418029Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-08-20T21:40:35.7418316Z >>> partial_sum_tensor = torch.mm(W, X) 2024-08-20T21:40:35.7418888Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-08-20T21:40:35.7419113Z >>> return reduced_tensor 2024-08-20T21:40:35.7419289Z >>> 2024-08-20T21:40:35.7419586Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-08-20T21:40:35.7419882Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-08-20T21:40:35.7420099Z >>> Y = torch.mm(W, X) 2024-08-20T21:40:35.7420698Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-08-20T21:40:35.7421331Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-08-20T21:40:35.7421515Z >>> 2024-08-20T21:40:35.7422162Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-08-20T21:40:35.7422458Z >>> local_mm_allreduce_forward = local_map( 2024-08-20T21:40:35.7422680Z >>> mm_allreduce_forward, 2024-08-20T21:40:35.7422932Z >>> out_placements=[Replicate()], 2024-08-20T21:40:35.7423221Z >>> in_placements=[col_wise, row_wise], 2024-08-20T21:40:35.7423450Z >>> device_mesh=device_mesh, 2024-08-20T21:40:35.7423622Z >>> ) 2024-08-20T21:40:35.7423808Z >>> 2024-08-20T21:40:35.7424595Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-08-20T21:40:35.7425387Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-08-20T21:40:35.7426237Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-08-20T21:40:35.7426398Z 2024-08-20T21:40:35.7426855Z NOTE: This API is currently experimental and subject to change 2024-08-20T21:40:35.7427036Z 2024-08-20T21:40:35.7427758Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7427932Z 2024-08-20T21:40:35.7428130Z warnings.warn(msg) 2024-08-20T21:40:35.7428289Z 2024-08-20T21:40:35.7428684Z --- Parse Warning: 26 / 101 --- 2024-08-20T21:40:35.7431665Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tensor/experimental/register_sharding.py line=25. 2024-08-20T21:40:35.7432469Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7432649Z 2024-08-20T21:40:35.7433280Z ``register_sharding`` is an experimental API that allows users to register sharding 2024-08-20T21:40:35.7433947Z strategies for an operator when the tensor inputs and outputs are :class:`DTensor`s. 2024-08-20T21:40:35.7434771Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-08-20T21:40:35.7435399Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-08-20T21:40:35.7436173Z when users would like to overwrite default sharding strategies of existing operators. 2024-08-20T21:40:35.7436336Z 2024-08-20T21:40:35.7436505Z Args: 2024-08-20T21:40:35.7436809Z op (Union[OpOverload, List[OpOverload]]): 2024-08-20T21:40:35.7437326Z An op or a list of ops to register the customized sharding function. 2024-08-20T21:40:35.7437531Z 2024-08-20T21:40:35.7437720Z Returns: 2024-08-20T21:40:35.7438387Z A function decorator which can be used to wrap a function that defines the sharding 2024-08-20T21:40:35.7439061Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-08-20T21:40:35.7439758Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-08-20T21:40:35.7440483Z already implemented the operator. The customized sharding function takes the same inputs 2024-08-20T21:40:35.7441113Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-08-20T21:40:35.7441945Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-08-20T21:40:35.7442775Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-08-20T21:40:35.7443048Z corresponding intput placements. 2024-08-20T21:40:35.7443210Z 2024-08-20T21:40:35.7443385Z Example: 2024-08-20T21:40:35.7443661Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:35.7443999Z >>> @register_sharding(aten._softmax.default) 2024-08-20T21:40:35.7444357Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-08-20T21:40:35.7444718Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-08-20T21:40:35.7444961Z >>> acceptable_shardings = [] 2024-08-20T21:40:35.7445128Z >>> 2024-08-20T21:40:35.7445562Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-08-20T21:40:35.7445894Z >>> acceptable_shardings.append(all_replicate) 2024-08-20T21:40:35.7446068Z >>> 2024-08-20T21:40:35.7446354Z >>> for sharding_dim in range(x.ndim): 2024-08-20T21:40:35.7446621Z >>> if sharding_dim != softmax_dim: 2024-08-20T21:40:35.7446856Z >>> all_sharded = ( 2024-08-20T21:40:35.7447111Z >>> [Shard(sharding_dim)], 2024-08-20T21:40:35.7447404Z >>> [Shard(sharding_dim), None, None], 2024-08-20T21:40:35.7447604Z >>> ) 2024-08-20T21:40:35.7447931Z >>> acceptable_shardings.append(all_sharded) 2024-08-20T21:40:35.7448096Z >>> 2024-08-20T21:40:35.7448360Z >>> return acceptable_shardings 2024-08-20T21:40:35.7448516Z 2024-08-20T21:40:35.7449235Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7449412Z 2024-08-20T21:40:35.7449612Z warnings.warn(msg) 2024-08-20T21:40:35.7449772Z 2024-08-20T21:40:35.7450215Z --- Parse Warning: 27 / 101 --- 2024-08-20T21:40:35.7453329Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2024-08-20T21:40:35.7454153Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7454312Z 2024-08-20T21:40:35.7454609Z Run post-localSGD algorithm. 2024-08-20T21:40:35.7454781Z 2024-08-20T21:40:35.7455473Z This DDP communication hook is used for running post-localSGD algorithm, 2024-08-20T21:40:35.7455835Z by combining with a model averaging component (e.g., 2024-08-20T21:40:35.7456630Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-08-20T21:40:35.7456881Z that runs after the optimizer step. 2024-08-20T21:40:35.7457036Z 2024-08-20T21:40:35.7457223Z Args: 2024-08-20T21:40:35.7457934Z state (PostLocalSGDState): State information to run post-localSGD. 2024-08-20T21:40:35.7458615Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-08-20T21:40:35.7459876Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-20T21:40:35.7460504Z Note that since DDP comm hook only supports single process single device mode, 2024-08-20T21:40:35.7460877Z only exactly one tensor is stored in this bucket. 2024-08-20T21:40:35.7461039Z 2024-08-20T21:40:35.7461207Z Returns: 2024-08-20T21:40:35.7461811Z Future handler of the communication, which updates the gradients in place. 2024-08-20T21:40:35.7461966Z 2024-08-20T21:40:35.7462154Z Example:: 2024-08-20T21:40:35.7462375Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.7462944Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-08-20T21:40:35.7463223Z start_localSGD_iter=10) 2024-08-20T21:40:35.7463644Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:40:35.7464495Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-08-20T21:40:35.7465360Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-08-20T21:40:35.7465576Z 2024-08-20T21:40:35.7466298Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7466465Z 2024-08-20T21:40:35.7466658Z warnings.warn(msg) 2024-08-20T21:40:35.7466816Z 2024-08-20T21:40:35.7467199Z --- Parse Warning: 28 / 101 --- 2024-08-20T21:40:35.7470181Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2024-08-20T21:40:35.7470926Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7471107Z 2024-08-20T21:40:35.7471341Z Implement PowerSGD algorithm. 2024-08-20T21:40:35.7471496Z 2024-08-20T21:40:35.7472038Z This DDP communication hook implements PowerSGD gradient compression 2024-08-20T21:40:35.7472596Z algorithm described in the `paper `_. 2024-08-20T21:40:35.7473197Z Once gradient tensors are aggregated across all workers, this hook applies 2024-08-20T21:40:35.7473408Z compression as follows: 2024-08-20T21:40:35.7473561Z 2024-08-20T21:40:35.7474913Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-08-20T21:40:35.7475069Z 2024-08-20T21:40:35.7476125Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-08-20T21:40:35.7476295Z 2024-08-20T21:40:35.7477322Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-08-20T21:40:35.7477546Z 2024-08-20T21:40:35.7477792Z 2. Handles uncompressed tensors: 2024-08-20T21:40:35.7477944Z 2024-08-20T21:40:35.7479196Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-08-20T21:40:35.7479364Z 2024-08-20T21:40:35.7480191Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-08-20T21:40:35.7480357Z 2024-08-20T21:40:35.7480916Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-08-20T21:40:35.7481075Z 2024-08-20T21:40:35.7481852Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-08-20T21:40:35.7482736Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-08-20T21:40:35.7482894Z 2024-08-20T21:40:35.7483252Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-08-20T21:40:35.7483407Z 2024-08-20T21:40:35.7483638Z 3.3. Allreduces Ps as a batch; 2024-08-20T21:40:35.7483809Z 2024-08-20T21:40:35.7484056Z 3.4. Orthogonalizes each P in Ps; 2024-08-20T21:40:35.7484243Z 2024-08-20T21:40:35.7484748Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-08-20T21:40:35.7484904Z 2024-08-20T21:40:35.7485126Z 3.6. Allreduces Qs as a batch; 2024-08-20T21:40:35.7485292Z 2024-08-20T21:40:35.7486043Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-08-20T21:40:35.7486200Z 2024-08-20T21:40:35.7487230Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-08-20T21:40:35.7487946Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-08-20T21:40:35.7489041Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-08-20T21:40:35.7489202Z 2024-08-20T21:40:35.7489361Z Args: 2024-08-20T21:40:35.7490965Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-08-20T21:40:35.7491869Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-08-20T21:40:35.7492015Z and ``min_compression_rate``. 2024-08-20T21:40:35.7492853Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-08-20T21:40:35.7493209Z Note that since DDP comm hook only supports single process single device mode, 2024-08-20T21:40:35.7493445Z only exactly one tensor is stored in this bucket. 2024-08-20T21:40:35.7493540Z 2024-08-20T21:40:35.7493642Z Returns: 2024-08-20T21:40:35.7493995Z Future handler of the communication, which updates the gradients in place. 2024-08-20T21:40:35.7494091Z 2024-08-20T21:40:35.7494205Z Example:: 2024-08-20T21:40:35.7494337Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.7494846Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-08-20T21:40:35.7495073Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-08-20T21:40:35.7495306Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-08-20T21:40:35.7495404Z 2024-08-20T21:40:35.7495828Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7495939Z 2024-08-20T21:40:35.7496056Z warnings.warn(msg) 2024-08-20T21:40:35.7496150Z 2024-08-20T21:40:35.7496399Z --- Parse Warning: 29 / 101 --- 2024-08-20T21:40:35.7498066Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=36. 2024-08-20T21:40:35.7498610Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7498709Z 2024-08-20T21:40:35.7499020Z Averages parameters periodically after the warm-up stage. 2024-08-20T21:40:35.7499132Z 2024-08-20T21:40:35.7499580Z This can be used for running `post-local SGD `_, 2024-08-20T21:40:35.7499838Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-08-20T21:40:35.7500176Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-08-20T21:40:35.7500270Z 2024-08-20T21:40:35.7500370Z Args: 2024-08-20T21:40:35.7500706Z period (int): The number of steps per model averaging. 2024-08-20T21:40:35.7501085Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-08-20T21:40:35.7501285Z Otherwise, only DDP needs to be used. 2024-08-20T21:40:35.7501649Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-08-20T21:40:35.7501818Z model averaging is skipped. 2024-08-20T21:40:35.7502152Z process_group: The process group to be used for all-reduce. 2024-08-20T21:40:35.7502354Z If ``None``, the default process group, which 2024-08-20T21:40:35.7502623Z is created by :func:`torch.distributed.init_process_group`, 2024-08-20T21:40:35.7502807Z will be used. (default: ``None``) 2024-08-20T21:40:35.7502901Z 2024-08-20T21:40:35.7503008Z Example:: 2024-08-20T21:40:35.7503117Z 2024-08-20T21:40:35.7503292Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:35.7503406Z >>> import torch 2024-08-20T21:40:35.7503575Z >>> import torch.distributed as dist 2024-08-20T21:40:35.7503994Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-08-20T21:40:35.7504376Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-08-20T21:40:35.7504542Z >>> import torch.nn as nn 2024-08-20T21:40:35.7504644Z >>> 2024-08-20T21:40:35.7504898Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-20T21:40:35.7505030Z >>> torch.cuda.set_device(rank) 2024-08-20T21:40:35.7505206Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-08-20T21:40:35.7505429Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:40:35.7505617Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:40:35.7505714Z >>> ) 2024-08-20T21:40:35.7505999Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:40:35.7506392Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-08-20T21:40:35.7506607Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:40:35.7506722Z >>> 2024-08-20T21:40:35.7507100Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-20T21:40:35.7507329Z >>> # After 100 steps, run model averaging every 4 steps. 2024-08-20T21:40:35.7507776Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:40:35.7508103Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-08-20T21:40:35.7508250Z >>> for step in range(0, 200): 2024-08-20T21:40:35.7508380Z >>> optimizer.zero_grad() 2024-08-20T21:40:35.7508528Z >>> loss = loss_fn(output, labels) 2024-08-20T21:40:35.7508665Z >>> loss.backward() 2024-08-20T21:40:35.7508787Z >>> optimizer.step() 2024-08-20T21:40:35.7509052Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-08-20T21:40:35.7509483Z >>> # inter-node communication only occurs every 4 iterations after 2024-08-20T21:40:35.7509648Z >>> # the initial ``warmup_steps`` period. 2024-08-20T21:40:35.7509875Z >>> averager.average_parameters(model.parameters()) 2024-08-20T21:40:35.7509966Z 2024-08-20T21:40:35.7510373Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7510480Z 2024-08-20T21:40:35.7510593Z warnings.warn(msg) 2024-08-20T21:40:35.7510684Z 2024-08-20T21:40:35.7510970Z --- Parse Warning: 30 / 101 --- 2024-08-20T21:40:35.7512880Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18. 2024-08-20T21:40:35.7513322Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7513433Z 2024-08-20T21:40:35.7513871Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-08-20T21:40:35.7513982Z 2024-08-20T21:40:35.7514412Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-08-20T21:40:35.7514767Z by using different periods concurrently after the warm-up stage. 2024-08-20T21:40:35.7515363Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-08-20T21:40:35.7515917Z that supports `post-local SGD `_, which essentially only supports 2024-08-20T21:40:35.7516445Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-08-20T21:40:35.7516952Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-08-20T21:40:35.7517451Z Similarly, the process groups within this class do not have such an intra-machine process 2024-08-20T21:40:35.7517935Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-08-20T21:40:35.7518071Z 2024-08-20T21:40:35.7518171Z Args: 2024-08-20T21:40:35.7518551Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-08-20T21:40:35.7518835Z process group size, used for initializing process groups of 2024-08-20T21:40:35.7519149Z different sizes in a hierarchy to average parameters concurrently. 2024-08-20T21:40:35.7519464Z Particularly, at each iteration, there will be at most a single 2024-08-20T21:40:35.7519901Z process group that runs averaging -- the period of such group should 2024-08-20T21:40:35.7520203Z have the largest period which the current step can be divided by. 2024-08-20T21:40:35.7520454Z For example, if the dict has three keys: 2, 4, and 8, 2024-08-20T21:40:35.7520743Z then this means totally three process groups will be created to 2024-08-20T21:40:35.7521051Z average parameters every 2, 4, and 8 iterations, respectively. 2024-08-20T21:40:35.7521324Z At the 4th iteration, only the second process group will run 2024-08-20T21:40:35.7521574Z averaging, because the first process group should be a 2024-08-20T21:40:35.7521901Z subset of the second process group, and no need to execute the first 2024-08-20T21:40:35.7522078Z process group redundantly. 2024-08-20T21:40:35.7522373Z On the other hand, the third process group can only be triggered 2024-08-20T21:40:35.7522714Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-08-20T21:40:35.7523306Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-08-20T21:40:35.7523908Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-08-20T21:40:35.7524170Z If ``None``, the default process group, which is created 2024-08-20T21:40:35.7524465Z by :func:`torch.distributed.init_process_group`, will be used. 2024-08-20T21:40:35.7524650Z (default: ``None``) 2024-08-20T21:40:35.7524740Z 2024-08-20T21:40:35.7524849Z Example:: 2024-08-20T21:40:35.7525169Z >>> # xdoctest: +SKIP('undefined rank') 2024-08-20T21:40:35.7525335Z >>> from collections import OrderedDict 2024-08-20T21:40:35.7525444Z >>> import torch 2024-08-20T21:40:35.7525619Z >>> import torch.distributed as dist 2024-08-20T21:40:35.7525995Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-08-20T21:40:35.7526141Z >>> PostLocalSGDState, 2024-08-20T21:40:35.7526268Z >>> post_localSGD_hook, 2024-08-20T21:40:35.7526367Z >>> ) 2024-08-20T21:40:35.7526879Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-08-20T21:40:35.7527003Z >>> import torch.nn as nn 2024-08-20T21:40:35.7527098Z >>> 2024-08-20T21:40:35.7527349Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-08-20T21:40:35.7527478Z >>> torch.cuda.set_device(rank) 2024-08-20T21:40:35.7527667Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-08-20T21:40:35.7527896Z >>> model = nn.parallel.DistributedDataParallel( 2024-08-20T21:40:35.7528088Z >>> module, device_ids=[rank], output_device=rank 2024-08-20T21:40:35.7528184Z >>> ) 2024-08-20T21:40:35.7528464Z >>> # Register a post-localSGD communication hook. 2024-08-20T21:40:35.7528957Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-08-20T21:40:35.7529162Z >>> subgroup, _ = dist.new_subgroups() 2024-08-20T21:40:35.7529570Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-08-20T21:40:35.7529782Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-08-20T21:40:35.7529891Z >>> 2024-08-20T21:40:35.7530383Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-08-20T21:40:35.7530555Z >>> # the 16 processes every 16 iterations. 2024-08-20T21:40:35.7530828Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-08-20T21:40:35.7531140Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-08-20T21:40:35.7531601Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-08-20T21:40:35.7532000Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-08-20T21:40:35.7532212Z >>> # After 100 steps, run model averaging at two levels. 2024-08-20T21:40:35.7532357Z >>> for step in range(0, 200): 2024-08-20T21:40:35.7532486Z >>> optimizer.zero_grad() 2024-08-20T21:40:35.7532633Z >>> loss = loss_fn(output, labels) 2024-08-20T21:40:35.7532762Z >>> loss.backward() 2024-08-20T21:40:35.7532883Z >>> optimizer.step() 2024-08-20T21:40:35.7533089Z >>> # Average parameters after ``optimizer.step()``. 2024-08-20T21:40:35.7533604Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-08-20T21:40:35.7533817Z >>> averager.average_parameters(model.parameters()) 2024-08-20T21:40:35.7533954Z 2024-08-20T21:40:35.7534081Z .. warning :: 2024-08-20T21:40:35.7534453Z The last group size in the dict must be the size of the provided ``process_group``, 2024-08-20T21:40:35.7534783Z which indicates model averaging at the highest level of the hierarchy. 2024-08-20T21:40:35.7535215Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-08-20T21:40:35.7535311Z 2024-08-20T21:40:35.7535434Z .. warning :: 2024-08-20T21:40:35.7535733Z `HierarchicalModelAverager` is experimental and subject to change. 2024-08-20T21:40:35.7535830Z 2024-08-20T21:40:35.7536264Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7536353Z 2024-08-20T21:40:35.7536529Z warnings.warn(msg) 2024-08-20T21:40:35.7536637Z 2024-08-20T21:40:35.7536860Z --- Parse Warning: 31 / 101 --- 2024-08-20T21:40:35.7538506Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2024-08-20T21:40:35.7538964Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7539057Z 2024-08-20T21:40:35.7539472Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-08-20T21:40:35.7539820Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-08-20T21:40:35.7539917Z 2024-08-20T21:40:35.7540151Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-08-20T21:40:35.7540243Z 2024-08-20T21:40:35.7540352Z .. warning:: 2024-08-20T21:40:35.7540591Z Current implementation only supports loading Tensors. 2024-08-20T21:40:35.7540682Z 2024-08-20T21:40:35.7540828Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:35.7540959Z >>> sd = {"mode": model} 2024-08-20T21:40:35.7541061Z >>> dcp.load( 2024-08-20T21:40:35.7541161Z >>> sd, 2024-08-20T21:40:35.7541376Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-20T21:40:35.7541574Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-20T21:40:35.7541722Z >>> checkpoint_id="path_to_model.pt" 2024-08-20T21:40:35.7541842Z >>> ) 2024-08-20T21:40:35.7541971Z 2024-08-20T21:40:35.7542565Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7542735Z 2024-08-20T21:40:35.7542896Z warnings.warn(msg) 2024-08-20T21:40:35.7542984Z 2024-08-20T21:40:35.7543235Z --- Parse Warning: 32 / 101 --- 2024-08-20T21:40:35.7544844Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2024-08-20T21:40:35.7545288Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7545381Z 2024-08-20T21:40:35.7545872Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-08-20T21:40:35.7546450Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-08-20T21:40:35.7546598Z metadata file, like Torch Save files. 2024-08-20T21:40:35.7546729Z 2024-08-20T21:40:35.7546992Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-08-20T21:40:35.7547084Z 2024-08-20T21:40:35.7547191Z .. warning:: 2024-08-20T21:40:35.7547427Z Current implementation only supports loading Tensors. 2024-08-20T21:40:35.7547524Z 2024-08-20T21:40:35.7547688Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:35.7547804Z >>> sd = {"mode": model} 2024-08-20T21:40:35.7547909Z >>> dcp.load( 2024-08-20T21:40:35.7548089Z >>> sd, 2024-08-20T21:40:35.7548292Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-08-20T21:40:35.7548454Z >>> planner=DynamicMetaLoadPlanner(), 2024-08-20T21:40:35.7548620Z >>> checkpoint_id="path_to_model.pt" 2024-08-20T21:40:35.7548715Z >>> ) 2024-08-20T21:40:35.7548808Z 2024-08-20T21:40:35.7549245Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7549335Z 2024-08-20T21:40:35.7549449Z warnings.warn(msg) 2024-08-20T21:40:35.7549566Z 2024-08-20T21:40:35.7549785Z --- Parse Warning: 33 / 101 --- 2024-08-20T21:40:35.7551479Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=220. 2024-08-20T21:40:35.7551930Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7552028Z 2024-08-20T21:40:35.7552330Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-08-20T21:40:35.7552422Z 2024-08-20T21:40:35.7552648Z This is the current recommended way to checkpoint FSDP. 2024-08-20T21:40:35.7552782Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.7552987Z >>> import torch.distributed.checkpoint as dist_cp 2024-08-20T21:40:35.7553087Z >>> # Save 2024-08-20T21:40:35.7553220Z >>> model: torch.nn.Model 2024-08-20T21:40:35.7553369Z >>> optim_params = model.parameters() 2024-08-20T21:40:35.7553555Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-08-20T21:40:35.7553677Z >>> # Save 2024-08-20T21:40:35.7553979Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-08-20T21:40:35.7554091Z >>> state_dict = { 2024-08-20T21:40:35.7554311Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-08-20T21:40:35.7554451Z >>> "model": model.state_dict() 2024-08-20T21:40:35.7554554Z >>> } 2024-08-20T21:40:35.7554693Z >>> dist_cp.save_state_dict( 2024-08-20T21:40:35.7554819Z >>> state_dict=optim_state, 2024-08-20T21:40:35.7555099Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-08-20T21:40:35.7555270Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-08-20T21:40:35.7555365Z >>> ) 2024-08-20T21:40:35.7555470Z >>> 2024-08-20T21:40:35.7555568Z >>> # Load 2024-08-20T21:40:35.7555875Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-08-20T21:40:35.7556060Z >>> model_state_dict = model_tp.state_dict() 2024-08-20T21:40:35.7556171Z >>> checkpoint = { 2024-08-20T21:40:35.7556303Z >>> "model": model_state_dict 2024-08-20T21:40:35.7556413Z >>> } 2024-08-20T21:40:35.7556539Z >>> dist_cp.load_state_dict( 2024-08-20T21:40:35.7556666Z >>> state_dict=checkpoint, 2024-08-20T21:40:35.7556925Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-08-20T21:40:35.7557098Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-08-20T21:40:35.7557191Z >>> ) 2024-08-20T21:40:35.7557404Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-08-20T21:40:35.7557499Z >>> 2024-08-20T21:40:35.7557726Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-08-20T21:40:35.7557856Z >>> model_state_dict, 2024-08-20T21:40:35.7557992Z >>> optimizer_key="optimizer", 2024-08-20T21:40:35.7558236Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-08-20T21:40:35.7558335Z >>> ) 2024-08-20T21:40:35.7558426Z >>> 2024-08-20T21:40:35.7558634Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-08-20T21:40:35.7558805Z >>> model, optim, optim_state["optimizer"] 2024-08-20T21:40:35.7558898Z >>> ) 2024-08-20T21:40:35.7559008Z >>> 2024-08-20T21:40:35.7559200Z >>> optim.load_state_dict(flattened_osd) 2024-08-20T21:40:35.7559286Z 2024-08-20T21:40:35.7559724Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7559819Z 2024-08-20T21:40:35.7559928Z warnings.warn(msg) 2024-08-20T21:40:35.7560029Z 2024-08-20T21:40:35.7560247Z --- Parse Warning: 34 / 101 --- 2024-08-20T21:40:35.7561733Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=110. 2024-08-20T21:40:35.7562176Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7562322Z 2024-08-20T21:40:35.7562726Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-08-20T21:40:35.7562814Z 2024-08-20T21:40:35.7563210Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-08-20T21:40:35.7563314Z 2024-08-20T21:40:35.7563709Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-20T21:40:35.7563854Z will be visible to the whole process. 2024-08-20T21:40:35.7563956Z 2024-08-20T21:40:35.7564337Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-08-20T21:40:35.7564425Z 2024-08-20T21:40:35.7564647Z 1) set_up_planner - called on all ranks. 2024-08-20T21:40:35.7564813Z Signals the start of a checkpoint save. 2024-08-20T21:40:35.7564900Z 2024-08-20T21:40:35.7565125Z 2) create_local_plan - called on all ranks. 2024-08-20T21:40:35.7565524Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-08-20T21:40:35.7565628Z 2024-08-20T21:40:35.7565931Z 3) create_global_plan - called on the coordinator rank only. 2024-08-20T21:40:35.7566201Z Takes the SavePlan from all ranks and make any global decision. 2024-08-20T21:40:35.7566304Z 2024-08-20T21:40:35.7566495Z 4) finish_plan - called on all ranks. 2024-08-20T21:40:35.7566829Z This gives each rank a chance to adjust to global planning decisions. 2024-08-20T21:40:35.7566933Z 2024-08-20T21:40:35.7567192Z 5) resolve_data - called multiple times on each rank 2024-08-20T21:40:35.7567475Z Lookups a value on the `state_dict` for the storage layer to write. 2024-08-20T21:40:35.7567578Z 2024-08-20T21:40:35.7567985Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-08-20T21:40:35.7568229Z most changes can be expressed by changes in a single method. 2024-08-20T21:40:35.7568334Z 2024-08-20T21:40:35.7568496Z There are 3 usual patterns of extension: 2024-08-20T21:40:35.7568584Z 2024-08-20T21:40:35.7568945Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-08-20T21:40:35.7569317Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-08-20T21:40:35.7569420Z 2024-08-20T21:40:35.7569566Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:35.7569742Z >>> class RenamePlanner(DefaultSavePlanner): 2024-08-20T21:40:35.7569872Z >>> def set_up_planner( 2024-08-20T21:40:35.7569970Z >>> self, 2024-08-20T21:40:35.7570203Z >>> state_dict: STATE_DICT_TYPE, 2024-08-20T21:40:35.7570391Z >>> storage_meta: Optional[StorageMeta], 2024-08-20T21:40:35.7570521Z >>> is_coordinator: bool, 2024-08-20T21:40:35.7570664Z >>> ) -> None: 2024-08-20T21:40:35.7570832Z >>> # prefix all keys with `foo_`` 2024-08-20T21:40:35.7571261Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-08-20T21:40:35.7571353Z 2024-08-20T21:40:35.7571836Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-08-20T21:40:35.7571986Z 2024-08-20T21:40:35.7572131Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:35.7572312Z >>> class FP16Planner(DefaultSavePlanner): 2024-08-20T21:40:35.7572455Z >>> def create_local_plan(self): 2024-08-20T21:40:35.7572635Z >>> plan = super().create_local_plan() 2024-08-20T21:40:35.7572753Z >>> for p in plan: 2024-08-20T21:40:35.7572905Z >>> if p.tensor_data is not None: 2024-08-20T21:40:35.7573138Z >>> p.tensor_data.properties.dtype = torch.float16 2024-08-20T21:40:35.7573246Z >>> return plan 2024-08-20T21:40:35.7573336Z >>> 2024-08-20T21:40:35.7573501Z >>> def resolve_data(self, write_item): 2024-08-20T21:40:35.7573721Z >>> item = super().resolve_data(write_item) 2024-08-20T21:40:35.7574103Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-08-20T21:40:35.7574211Z 2024-08-20T21:40:35.7574790Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-08-20T21:40:35.7574881Z 2024-08-20T21:40:35.7575037Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:35.7575170Z >>> from itertools import islice 2024-08-20T21:40:35.7575305Z >>> from dataclasses import replace 2024-08-20T21:40:35.7575549Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-08-20T21:40:35.7576032Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-08-20T21:40:35.7576284Z >>> # This sample doesn't handle ShardedTensors 2024-08-20T21:40:35.7576450Z >>> def create_global_plan(self, all_plans): 2024-08-20T21:40:35.7576574Z >>> def chunk(it, size): 2024-08-20T21:40:35.7576700Z >>> it = iter(it) 2024-08-20T21:40:35.7576922Z >>> return list(iter(lambda: tuple(islice(it, size)), ())) 2024-08-20T21:40:35.7577034Z >>> all_plans = [ 2024-08-20T21:40:35.7577246Z >>> replace(plan, items=items) for plan, items in 2024-08-20T21:40:35.7577488Z >>> zip(all_plans, chunk(all_plans[0].items, len(all_plans))) 2024-08-20T21:40:35.7577621Z >>> ] 2024-08-20T21:40:35.7577819Z >>> return super().create_global_plan(all_plans) 2024-08-20T21:40:35.7577910Z 2024-08-20T21:40:35.7578274Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-08-20T21:40:35.7578654Z accomplished by having each rank contribute their data items in the local plan and 2024-08-20T21:40:35.7578795Z the global planner aggregate them: 2024-08-20T21:40:35.7578902Z 2024-08-20T21:40:35.7579052Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:35.7579259Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-08-20T21:40:35.7579509Z >>> def create_local_plan(self) -> SavePlan: 2024-08-20T21:40:35.7579663Z >>> plan = super().create_local_plan() 2024-08-20T21:40:35.7579952Z >>> return replace(plan, planner_data="per-rank-data") 2024-08-20T21:40:35.7580063Z >>> 2024-08-20T21:40:35.7580562Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-08-20T21:40:35.7580818Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-08-20T21:40:35.7581045Z >>> merged_data = [p.planner_data for p in global_plan] 2024-08-20T21:40:35.7581268Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-08-20T21:40:35.7581412Z >>> return global_plan, metadata 2024-08-20T21:40:35.7581520Z 2024-08-20T21:40:35.7581943Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7582051Z 2024-08-20T21:40:35.7582163Z warnings.warn(msg) 2024-08-20T21:40:35.7582250Z 2024-08-20T21:40:35.7582482Z --- Parse Warning: 35 / 101 --- 2024-08-20T21:40:35.7584019Z /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=270. 2024-08-20T21:40:35.7584446Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7584551Z 2024-08-20T21:40:35.7584937Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-08-20T21:40:35.7585025Z 2024-08-20T21:40:35.7585435Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-08-20T21:40:35.7585528Z 2024-08-20T21:40:35.7585980Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-08-20T21:40:35.7586139Z will be visible to the whole process. 2024-08-20T21:40:35.7586230Z 2024-08-20T21:40:35.7586631Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-08-20T21:40:35.7586727Z 2024-08-20T21:40:35.7586937Z 1) set_up_planner - called on all ranks. 2024-08-20T21:40:35.7587120Z Signals the start of loading a checkpoint. 2024-08-20T21:40:35.7587210Z 2024-08-20T21:40:35.7587419Z 2) create_local_plan - called on all ranks. 2024-08-20T21:40:35.7587830Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-08-20T21:40:35.7587920Z 2024-08-20T21:40:35.7588223Z 3) create_global_plan - called on the coordinator rank only. 2024-08-20T21:40:35.7588509Z Takes the LoadPlan from all ranks and make any global decision. 2024-08-20T21:40:35.7589023Z 2024-08-20T21:40:35.7589290Z 4) load_bytes - called multiple times on each rank 2024-08-20T21:40:35.7589596Z This is called once per non-tensor value in state_dict. 2024-08-20T21:40:35.7589689Z 2024-08-20T21:40:35.7590063Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-08-20T21:40:35.7590659Z They are called in pair for each Tensor value in state_dict. 2024-08-20T21:40:35.7590798Z 2024-08-20T21:40:35.7591448Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-08-20T21:40:35.7591837Z most changes can be expressed by changes in a single method. 2024-08-20T21:40:35.7591930Z 2024-08-20T21:40:35.7592114Z There are two usual patterns of extension: 2024-08-20T21:40:35.7592204Z 2024-08-20T21:40:35.7592550Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-08-20T21:40:35.7593004Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-08-20T21:40:35.7593335Z to keep a reference to the original state_dict as load happens in place so 2024-08-20T21:40:35.7593499Z we need to be able to perform it in place 2024-08-20T21:40:35.7593610Z 2024-08-20T21:40:35.7593754Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:35.7593931Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-08-20T21:40:35.7594066Z >>> def set_up_planner( 2024-08-20T21:40:35.7594168Z >>> self, 2024-08-20T21:40:35.7594319Z >>> state_dict: STATE_DICT_TYPE, 2024-08-20T21:40:35.7594461Z >>> metadata: Metadata, 2024-08-20T21:40:35.7594592Z >>> is_coordinator: bool, 2024-08-20T21:40:35.7594747Z >>> ) -> None: 2024-08-20T21:40:35.7594914Z >>> self.original_state_dict = state_dict 2024-08-20T21:40:35.7595153Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-08-20T21:40:35.7595266Z >>> 2024-08-20T21:40:35.7595420Z >>> if self.flatten_sharded_tensors: 2024-08-20T21:40:35.7595634Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-08-20T21:40:35.7595743Z >>> 2024-08-20T21:40:35.7595878Z >>> if self.flatten_state_dict: 2024-08-20T21:40:35.7596124Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-08-20T21:40:35.7596298Z >>> 2024-08-20T21:40:35.7596442Z >>> self.state_dict = state_dict 2024-08-20T21:40:35.7596579Z >>> self.metadata = metadata 2024-08-20T21:40:35.7596767Z >>> self.is_coordinator = is_coordinator 2024-08-20T21:40:35.7596857Z >>> 2024-08-20T21:40:35.7597019Z >>> def load_bytes(self, read_item, value): 2024-08-20T21:40:35.7597178Z >>> # Remove the "foo_" prefix 2024-08-20T21:40:35.7597608Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-08-20T21:40:35.7597716Z 2024-08-20T21:40:35.7597838Z 2024-08-20T21:40:35.7598263Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-08-20T21:40:35.7598369Z 2024-08-20T21:40:35.7598515Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:35.7598721Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-08-20T21:40:35.7598887Z >>> def resolve_tensor(self, read_item): 2024-08-20T21:40:35.7599064Z >>> tensor = super().resolve_tensor(read_item) 2024-08-20T21:40:35.7599254Z >>> return torch.empty_like(tensor, device="cpu") 2024-08-20T21:40:35.7599369Z >>> 2024-08-20T21:40:35.7599545Z >>> def commit_tensor(self, read_item, tensor): 2024-08-20T21:40:35.7599754Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-08-20T21:40:35.7599860Z 2024-08-20T21:40:35.7600269Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7600361Z 2024-08-20T21:40:35.7600486Z warnings.warn(msg) 2024-08-20T21:40:35.7600576Z 2024-08-20T21:40:35.7600795Z --- Parse Warning: 36 / 101 --- 2024-08-20T21:40:35.7602304Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=61. 2024-08-20T21:40:35.7602724Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7602828Z 2024-08-20T21:40:35.7603055Z Load a distributed ``state_dict`` in SPMD style. 2024-08-20T21:40:35.7603144Z 2024-08-20T21:40:35.7603406Z Each rank will try to read the least amount of data necessary 2024-08-20T21:40:35.7603725Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-08-20T21:40:35.7604065Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-08-20T21:40:35.7604166Z 2024-08-20T21:40:35.7604523Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-20T21:40:35.7604893Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-08-20T21:40:35.7605114Z ``load_state_dict`` once the deserialization is complete. 2024-08-20T21:40:35.7605205Z 2024-08-20T21:40:35.7605336Z .. warning:: 2024-08-20T21:40:35.7605560Z All tensors in ``state_dict`` must be allocated on their 2024-08-20T21:40:35.7605773Z destination device *prior to* calling this function. 2024-08-20T21:40:35.7605877Z 2024-08-20T21:40:35.7606269Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-08-20T21:40:35.7606372Z on state_dict. 2024-08-20T21:40:35.7606474Z 2024-08-20T21:40:35.7606573Z .. warning:: 2024-08-20T21:40:35.7606852Z Users must call `load_state_dict` on the root module to ensure load 2024-08-20T21:40:35.7607170Z pos-processing and non-tensor data properly propagates. 2024-08-20T21:40:35.7607258Z 2024-08-20T21:40:35.7607353Z .. note: 2024-08-20T21:40:35.7607689Z If no process group is initialized, this function will assume the intent 2024-08-20T21:40:35.7608006Z is to load a checkpoint into the local process. This can be useful in the 2024-08-20T21:40:35.7608403Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-08-20T21:40:35.7608523Z or ShardedTensor) 2024-08-20T21:40:35.7608611Z 2024-08-20T21:40:35.7608722Z .. note: 2024-08-20T21:40:35.7608901Z Rank 0 is assumed to be the coordinator rank. 2024-08-20T21:40:35.7608989Z 2024-08-20T21:40:35.7609098Z Args: 2024-08-20T21:40:35.7609297Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:40:35.7609480Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:40:35.7609791Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:40:35.7610076Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:40:35.7610538Z It can also be a key if the storage is a key-value store. 2024-08-20T21:40:35.7610676Z (Default: ``None``) 2024-08-20T21:40:35.7610845Z storage_reader (Optional[StorageReader]): 2024-08-20T21:40:35.7611126Z Instance of StorageWriter used to perform reads. If this is not 2024-08-20T21:40:35.7611413Z specified, DCP will automatically infer the reader based on the 2024-08-20T21:40:35.7611682Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:40:35.7611836Z be raised. (Default: ``None``) 2024-08-20T21:40:35.7611981Z planner (Optional[LoadPlanner]): 2024-08-20T21:40:35.7612254Z Instance of LoadPlanner. If this is not specificed, the default 2024-08-20T21:40:35.7612436Z planner will be used. (Default: ``None``) 2024-08-20T21:40:35.7612598Z process_group (Optional[ProcessGroup]): 2024-08-20T21:40:35.7612908Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:40:35.7613039Z (Default: ``None``) 2024-08-20T21:40:35.7613132Z 2024-08-20T21:40:35.7613230Z Returns: 2024-08-20T21:40:35.7613339Z None. 2024-08-20T21:40:35.7613430Z 2024-08-20T21:40:35.7613527Z Examples 2024-08-20T21:40:35.7613659Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.7613783Z >>> my_model = MyModule() 2024-08-20T21:40:35.7613967Z >>> optimizer = Adagrad(my_model.parameters()) 2024-08-20T21:40:35.7614250Z >>> model_state_dict = my_model.state_dict() 2024-08-20T21:40:35.7614660Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-08-20T21:40:35.7614750Z 2024-08-20T21:40:35.7614975Z >>> torch.distributed.checkpoint.load_state_dict( 2024-08-20T21:40:35.7615118Z >>> state_dict=model_state_dict, 2024-08-20T21:40:35.7615289Z >>> storage_reader=fs_storage_reader, 2024-08-20T21:40:35.7615384Z >>> ) 2024-08-20T21:40:35.7615478Z 2024-08-20T21:40:35.7615759Z >>> # module.load_state_dict() function might have customized steps 2024-08-20T21:40:35.7615931Z >>> # to flush the state_dict, must call it to 2024-08-20T21:40:35.7616065Z >>> # ensure correct behavior. 2024-08-20T21:40:35.7616249Z >>> my_model.load_state_dict(model_state_dict) 2024-08-20T21:40:35.7616339Z 2024-08-20T21:40:35.7616443Z .. note:: 2024-08-20T21:40:35.7616738Z load_state_dict uses collectives to coordinate reads across ranks. 2024-08-20T21:40:35.7617097Z For NCCL-based process groups, internal tensor representations of 2024-08-20T21:40:35.7617422Z objects must be moved to the GPU device before communication takes place. 2024-08-20T21:40:35.7617751Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-20T21:40:35.7618155Z and it is the user's responsibility to ensure that this is set so that each 2024-08-20T21:40:35.7618428Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-20T21:40:35.7618520Z 2024-08-20T21:40:35.7618925Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7619032Z 2024-08-20T21:40:35.7619182Z warnings.warn(msg) 2024-08-20T21:40:35.7619273Z 2024-08-20T21:40:35.7619503Z --- Parse Warning: 37 / 101 --- 2024-08-20T21:40:35.7620986Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=67. 2024-08-20T21:40:35.7621405Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7621508Z 2024-08-20T21:40:35.7621660Z Save a distributed model in SPMD style. 2024-08-20T21:40:35.7621747Z 2024-08-20T21:40:35.7622011Z This function is different from ``torch.save()`` as it handles 2024-08-20T21:40:35.7622411Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-08-20T21:40:35.7622521Z 2024-08-20T21:40:35.7622875Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-08-20T21:40:35.7623076Z save will call ``state_dict`` before serialization. 2024-08-20T21:40:35.7623177Z 2024-08-20T21:40:35.7623280Z .. warning:: 2024-08-20T21:40:35.7623602Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-08-20T21:40:35.7623731Z for saved state_dicts. 2024-08-20T21:40:35.7623817Z 2024-08-20T21:40:35.7623923Z .. warning:: 2024-08-20T21:40:35.7624221Z If using the `process_group` argument, make sure that only its ranks 2024-08-20T21:40:35.7624502Z call `save_state_dict` and that all data in state_dict belong to it. 2024-08-20T21:40:35.7624589Z 2024-08-20T21:40:35.7624706Z .. note:: 2024-08-20T21:40:35.7625153Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-08-20T21:40:35.7625523Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-08-20T21:40:35.7625651Z group needs to be passed in. 2024-08-20T21:40:35.7625741Z 2024-08-20T21:40:35.7625854Z .. note:: 2024-08-20T21:40:35.7626232Z If no process group is available, this function assumes the intention is to save the 2024-08-20T21:40:35.7626404Z state_dict in the local process. 2024-08-20T21:40:35.7626507Z 2024-08-20T21:40:35.7626600Z .. note: 2024-08-20T21:40:35.7626782Z Rank 0 is assumed to be the coordinator rank. 2024-08-20T21:40:35.7626882Z 2024-08-20T21:40:35.7626972Z 2024-08-20T21:40:35.7627065Z Args: 2024-08-20T21:40:35.7627281Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:40:35.7627465Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:40:35.7627763Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:40:35.7628063Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:40:35.7628372Z It can also be a key if the storage is a key-value store. 2024-08-20T21:40:35.7628487Z (Default: ``None``) 2024-08-20T21:40:35.7628665Z storage_writer (Optional[StorageWriter]): 2024-08-20T21:40:35.7628945Z Instance of StorageWriter used to perform writes. If this is not 2024-08-20T21:40:35.7629235Z specified, DCP will automatically infer the writer based on the 2024-08-20T21:40:35.7629507Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:40:35.7629643Z be raised. (Default: ``None``) 2024-08-20T21:40:35.7629798Z planner (Optional[SavePlanner]): 2024-08-20T21:40:35.7630073Z Instance of SavePlanner. If this is not specificed, the default 2024-08-20T21:40:35.7630242Z planner will be used. (Default: ``None``) 2024-08-20T21:40:35.7630430Z process_group (Optional[ProcessGroup]): 2024-08-20T21:40:35.7630737Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:40:35.7630851Z (Default: ``None``) 2024-08-20T21:40:35.7630990Z 2024-08-20T21:40:35.7631089Z Returns: 2024-08-20T21:40:35.7631301Z Metadata: Metadata object for the saved checkpoint. 2024-08-20T21:40:35.7631411Z 2024-08-20T21:40:35.7631513Z Example: 2024-08-20T21:40:35.7631630Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.7631768Z >>> my_model = MyModule() 2024-08-20T21:40:35.7631857Z 2024-08-20T21:40:35.7632017Z >>> state_dict = {"model": my_model} 2024-08-20T21:40:35.7632107Z 2024-08-20T21:40:35.7632515Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-20T21:40:35.7632709Z >>> torch.distributed.checkpoint.save( 2024-08-20T21:40:35.7632837Z >>> state_dict=state_dict, 2024-08-20T21:40:35.7633043Z >>> storage_writer=fs_storage_writer, 2024-08-20T21:40:35.7633157Z >>> ) 2024-08-20T21:40:35.7633247Z 2024-08-20T21:40:35.7633347Z .. note:: 2024-08-20T21:40:35.7633648Z save_state_dict uses collectives to coordinate writes across ranks. 2024-08-20T21:40:35.7634014Z For NCCL-based process groups, internal tensor representations of 2024-08-20T21:40:35.7634340Z objects must be moved to the GPU device before communication takes place. 2024-08-20T21:40:35.7634674Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-08-20T21:40:35.7635048Z and it is the user's responsibility to ensure that this is set so that 2024-08-20T21:40:35.7635343Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-08-20T21:40:35.7635435Z 2024-08-20T21:40:35.7635839Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7635944Z 2024-08-20T21:40:35.7636058Z warnings.warn(msg) 2024-08-20T21:40:35.7636145Z 2024-08-20T21:40:35.7636376Z --- Parse Warning: 38 / 101 --- 2024-08-20T21:40:35.7637914Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=170. 2024-08-20T21:40:35.7638377Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7638830Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-08-20T21:40:35.7639230Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-08-20T21:40:35.7639333Z 2024-08-20T21:40:35.7639440Z .. warning:: 2024-08-20T21:40:35.7639651Z This feature is experimental and subject to change. 2024-08-20T21:40:35.7639750Z 2024-08-20T21:40:35.7639846Z Args: 2024-08-20T21:40:35.7640050Z state_dict (Dict[str, Any]): The state_dict to save. 2024-08-20T21:40:35.7640259Z checkpoint_id (Union[str, os.PathLike, None]): 2024-08-20T21:40:35.7640560Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-08-20T21:40:35.7640848Z depends on the storage. It can be a path to a folder or to a file. 2024-08-20T21:40:35.7641173Z It can also be a key if the storage is a key-value store. 2024-08-20T21:40:35.7641289Z (Default: ``None``) 2024-08-20T21:40:35.7641463Z storage_writer (Optional[StorageWriter]): 2024-08-20T21:40:35.7641840Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-08-20T21:40:35.7642181Z this is not specified, DCP will automatically infer the writer based on the 2024-08-20T21:40:35.7642469Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-08-20T21:40:35.7642617Z be raised. (Default: ``None``) 2024-08-20T21:40:35.7642768Z planner (Optional[SavePlanner]): 2024-08-20T21:40:35.7643055Z Instance of SavePlanner. If this is not specificed, the default 2024-08-20T21:40:35.7643278Z planner will be used. (Default: ``None``) 2024-08-20T21:40:35.7643511Z process_group (Optional[ProcessGroup]): 2024-08-20T21:40:35.7643881Z ProcessGroup to be used for cross-rank synchronization. 2024-08-20T21:40:35.7643998Z (Default: ``None``) 2024-08-20T21:40:35.7644089Z 2024-08-20T21:40:35.7644206Z Returns: 2024-08-20T21:40:35.7644491Z Future: A future holding the resultant Metadata object from `save`. 2024-08-20T21:40:35.7644580Z 2024-08-20T21:40:35.7644692Z Example: 2024-08-20T21:40:35.7644810Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.7644934Z >>> my_model = MyModule() 2024-08-20T21:40:35.7645042Z 2024-08-20T21:40:35.7645255Z >>> state_dict = {"model": my_model} 2024-08-20T21:40:35.7645346Z 2024-08-20T21:40:35.7645772Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-08-20T21:40:35.7646053Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-08-20T21:40:35.7646198Z >>> state_dict=state_dict, 2024-08-20T21:40:35.7646360Z >>> storage_writer=fs_storage_writer, 2024-08-20T21:40:35.7646455Z >>> ) 2024-08-20T21:40:35.7646563Z >>> 2024-08-20T21:40:35.7646684Z >>> # ... do some work ... 2024-08-20T21:40:35.7646777Z >>> 2024-08-20T21:40:35.7646931Z >>> checkpoint_future.result() 2024-08-20T21:40:35.7647019Z 2024-08-20T21:40:35.7647113Z 2024-08-20T21:40:35.7647537Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7647624Z 2024-08-20T21:40:35.7647738Z warnings.warn(msg) 2024-08-20T21:40:35.7647843Z 2024-08-20T21:40:35.7648056Z --- Parse Warning: 39 / 101 --- 2024-08-20T21:40:35.7649660Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2024-08-20T21:40:35.7650209Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7650325Z 2024-08-20T21:40:35.7650598Z Initialize rendezvous event object and record its operations. 2024-08-20T21:40:35.7650686Z 2024-08-20T21:40:35.7650816Z Args: 2024-08-20T21:40:35.7651002Z run_id (str): The run id of the rendezvous. 2024-08-20T21:40:35.7651220Z message (str): The message describing the event. 2024-08-20T21:40:35.7651569Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-08-20T21:40:35.7651844Z name (str): Event name. (E.g. Current action being performed). 2024-08-20T21:40:35.7651996Z hostname (str): Hostname of the node. 2024-08-20T21:40:35.7652187Z pid (Optional[int]): The process id of the node. 2024-08-20T21:40:35.7652544Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-08-20T21:40:35.7652921Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-08-20T21:40:35.7653144Z rank (Optional[int]): The rank of the node, if known. 2024-08-20T21:40:35.7653241Z Returns: 2024-08-20T21:40:35.7653334Z None 2024-08-20T21:40:35.7653441Z Example: 2024-08-20T21:40:35.7653613Z >>> # See DynamicRendezvousHandler class 2024-08-20T21:40:35.7653718Z >>> def _record( 2024-08-20T21:40:35.7653829Z ... self, 2024-08-20T21:40:35.7653937Z ... message: str, 2024-08-20T21:40:35.7654129Z ... node_state: NodeState = NodeState.RUNNING, 2024-08-20T21:40:35.7654277Z ... rank: Optional[int] = None, 2024-08-20T21:40:35.7654420Z ... ) -> None: 2024-08-20T21:40:35.7654571Z ... construct_and_record_rdzv_event( 2024-08-20T21:40:35.7654848Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-08-20T21:40:35.7655000Z ... run_id=self._settings.run_id, 2024-08-20T21:40:35.7655119Z ... message=message, 2024-08-20T21:40:35.7655260Z ... node_state=node_state, 2024-08-20T21:40:35.7655413Z ... hostname=self._this_node.addr, 2024-08-20T21:40:35.7655547Z ... pid=self._this_node.pid, 2024-08-20T21:40:35.7655723Z ... local_id=self._this_node.local_id, 2024-08-20T21:40:35.7655830Z ... rank=rank, 2024-08-20T21:40:35.7655940Z ... ) 2024-08-20T21:40:35.7656030Z 2024-08-20T21:40:35.7656506Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7656611Z 2024-08-20T21:40:35.7656720Z warnings.warn(msg) 2024-08-20T21:40:35.7656808Z 2024-08-20T21:40:35.7657036Z --- Parse Warning: 40 / 101 --- 2024-08-20T21:40:35.7658457Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=113. 2024-08-20T21:40:35.7658877Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7658981Z 2024-08-20T21:40:35.7659252Z This configures FSDP-native mixed precision training. 2024-08-20T21:40:35.7659338Z 2024-08-20T21:40:35.7659455Z Attributes: 2024-08-20T21:40:35.7659766Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-08-20T21:40:35.7660046Z parameters during forward and backward and thus the dtype for 2024-08-20T21:40:35.7660345Z forward and backward computation. Outside forward and backward, the 2024-08-20T21:40:35.7660601Z *sharded* parameters are kept in full precision (e.g. for the 2024-08-20T21:40:35.7660897Z optimizer step), and for model checkpointing, the parameters are 2024-08-20T21:40:35.7661106Z always saved in full precision. (Default: ``None``) 2024-08-20T21:40:35.7661386Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-20T21:40:35.7661802Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-08-20T21:40:35.7662051Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-08-20T21:40:35.7662319Z the ``param_dtype`` value, still running gradient reduction in low 2024-08-20T21:40:35.7662611Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-08-20T21:40:35.7662877Z to force gradient reduction to run in full precision. (Default: 2024-08-20T21:40:35.7662994Z ``None``) 2024-08-20T21:40:35.7663497Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-08-20T21:40:35.7663803Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-08-20T21:40:35.7664088Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-08-20T21:40:35.7664367Z dtype thereafter. For model checkpointing, the buffers are saved 2024-08-20T21:40:35.7664617Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-08-20T21:40:35.7664737Z ``None``) 2024-08-20T21:40:35.7664991Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-08-20T21:40:35.7665281Z gradients to full precision after the backward pass in preparation 2024-08-20T21:40:35.7665582Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-08-20T21:40:35.7665870Z in the dtype used for gradient reduction, which can save memory if 2024-08-20T21:40:35.7666161Z using a custom optimizer that supports running in low precision. 2024-08-20T21:40:35.7666277Z (Default: ``False``) 2024-08-20T21:40:35.7666594Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-08-20T21:40:35.7666884Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-08-20T21:40:35.7667169Z that parameter and input dtypes match for forward computation, as 2024-08-20T21:40:35.7667453Z required by many ops. This may need to be set to ``True`` when only 2024-08-20T21:40:35.7667759Z applying mixed precision to some but not all FSDP modules, in which 2024-08-20T21:40:35.7668140Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-08-20T21:40:35.7668254Z (Default: ``False``) 2024-08-20T21:40:35.7668560Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-08-20T21:40:35.7668889Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-08-20T21:40:35.7669244Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-08-20T21:40:35.7669434Z this does not do anything. (Default: ``True``) 2024-08-20T21:40:35.7669719Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-08-20T21:40:35.7669985Z module classes to ignore for mixed precision when using an 2024-08-20T21:40:35.7670230Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-08-20T21:40:35.7670513Z applied to them separately with mixed precision disabled (meaning 2024-08-20T21:40:35.7670807Z that the final FSDP construction would deviate from the specified 2024-08-20T21:40:35.7671065Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-08-20T21:40:35.7671353Z not do anything. This API is experimental and subject to change. 2024-08-20T21:40:35.7671488Z (Default: ``(_BatchNorm,)``) 2024-08-20T21:40:35.7671581Z 2024-08-20T21:40:35.7671821Z .. note:: This API is experimental and subject to change. 2024-08-20T21:40:35.7671911Z 2024-08-20T21:40:35.7672205Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-08-20T21:40:35.7672307Z 2024-08-20T21:40:35.7672555Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-08-20T21:40:35.7672724Z precision, but buffers are not. 2024-08-20T21:40:35.7672825Z 2024-08-20T21:40:35.7673107Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-08-20T21:40:35.7673398Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-08-20T21:40:35.7673797Z Disabling FSDP's mixed precision for those norm modules only means that 2024-08-20T21:40:35.7674151Z the affine parameters are kept in ``float32``. However, this incurs 2024-08-20T21:40:35.7674736Z separate all-gathers and reduce-scatters for those norm modules, which 2024-08-20T21:40:35.7675045Z may be inefficient, so if the workload permits, the user should prefer 2024-08-20T21:40:35.7675243Z to still apply mixed precision to those modules. 2024-08-20T21:40:35.7675356Z 2024-08-20T21:40:35.7675653Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-08-20T21:40:35.7675936Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-08-20T21:40:35.7676259Z modules will have FSDP applied to them separately with mixed precision 2024-08-20T21:40:35.7676489Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-08-20T21:40:35.7676597Z 2024-08-20T21:40:35.7676875Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-08-20T21:40:35.7677157Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-08-20T21:40:35.7677407Z its ``cast_root_forward_inputs`` takes precedence over its 2024-08-20T21:40:35.7677714Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-08-20T21:40:35.7678003Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-08-20T21:40:35.7678411Z sufficient for the typical case where each FSDP instance has the same 2024-08-20T21:40:35.7678706Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-08-20T21:40:35.7679021Z ``param_dtype`` at the beginning of the model's forward pass. 2024-08-20T21:40:35.7679129Z 2024-08-20T21:40:35.7679413Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-08-20T21:40:35.7679738Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-08-20T21:40:35.7680051Z values to configure casting inputs or not before each instance's 2024-08-20T21:40:35.7680322Z forward. In such a case, since the casts happen before each FSDP 2024-08-20T21:40:35.7680761Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-08-20T21:40:35.7681080Z submodules run before its FSDP submodules to avoid the activation dtype 2024-08-20T21:40:35.7681371Z being changed due to a different ``MixedPrecision`` configuration. 2024-08-20T21:40:35.7681479Z 2024-08-20T21:40:35.7681585Z Example:: 2024-08-20T21:40:35.7681676Z 2024-08-20T21:40:35.7681871Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:35.7682101Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-08-20T21:40:35.7682217Z >>> model[1] = FSDP( 2024-08-20T21:40:35.7682342Z >>> model[1], 2024-08-20T21:40:35.7682735Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-08-20T21:40:35.7682848Z >>> ) 2024-08-20T21:40:35.7682956Z >>> model = FSDP( 2024-08-20T21:40:35.7683055Z >>> model, 2024-08-20T21:40:35.7683469Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-08-20T21:40:35.7683566Z >>> ) 2024-08-20T21:40:35.7683654Z 2024-08-20T21:40:35.7683967Z The above shows a working example. On the other hand, if ``model[1]`` 2024-08-20T21:40:35.7684236Z were replaced with ``model[0]``, meaning that the submodule using 2024-08-20T21:40:35.7684564Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-08-20T21:40:35.7684864Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-08-20T21:40:35.7684960Z ones. 2024-08-20T21:40:35.7685049Z 2024-08-20T21:40:35.7685152Z 2024-08-20T21:40:35.7685561Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7685649Z 2024-08-20T21:40:35.7685775Z warnings.warn(msg) 2024-08-20T21:40:35.7685862Z 2024-08-20T21:40:35.7686100Z --- Parse Warning: 41 / 101 --- 2024-08-20T21:40:35.7687886Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=648. 2024-08-20T21:40:35.7688305Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7689021Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-20T21:40:35.7689112Z 2024-08-20T21:40:35.7689556Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-08-20T21:40:35.7689863Z The target module does not have to be a FSDP module. If the target 2024-08-20T21:40:35.7690209Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-08-20T21:40:35.7690513Z 2024-08-20T21:40:35.7690890Z .. note:: This API should be called for only the top-level (root) 2024-08-20T21:40:35.7690991Z module. 2024-08-20T21:40:35.7691096Z 2024-08-20T21:40:35.7691395Z .. note:: This API enables users to transparently use the conventional 2024-08-20T21:40:35.7691780Z ``state_dict`` API to take model checkpoints in cases where the 2024-08-20T21:40:35.7692088Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-08-20T21:40:35.7692449Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-08-20T21:40:35.7692756Z instances, while dispatching into `sharded_state_dict` implementation 2024-08-20T21:40:35.7692878Z for FSDP: 2024-08-20T21:40:35.7692971Z 2024-08-20T21:40:35.7693075Z Example:: 2024-08-20T21:40:35.7693181Z 2024-08-20T21:40:35.7693361Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:35.7693585Z >>> model = DDP(FSDP(...)) 2024-08-20T21:40:35.7693732Z >>> FSDP.set_state_dict_type( 2024-08-20T21:40:35.7693837Z >>> model, 2024-08-20T21:40:35.7694029Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-20T21:40:35.7694319Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-08-20T21:40:35.7694627Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-08-20T21:40:35.7694746Z >>> ) 2024-08-20T21:40:35.7694918Z >>> param_state_dict = model.state_dict() 2024-08-20T21:40:35.7695147Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-20T21:40:35.7695250Z 2024-08-20T21:40:35.7695346Z Args: 2024-08-20T21:40:35.7695510Z module (torch.nn.Module): Root module. 2024-08-20T21:40:35.7695834Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-20T21:40:35.7696153Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-08-20T21:40:35.7696313Z target ``state_dict_type``. 2024-08-20T21:40:35.7696644Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-08-20T21:40:35.7696830Z for the optimizer state dict. 2024-08-20T21:40:35.7696932Z 2024-08-20T21:40:35.7697071Z Returns: 2024-08-20T21:40:35.7697362Z A StateDictSettings that include the previous state_dict type and 2024-08-20T21:40:35.7697523Z configuration for the module. 2024-08-20T21:40:35.7697617Z 2024-08-20T21:40:35.7698025Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7698127Z 2024-08-20T21:40:35.7698238Z warnings.warn(msg) 2024-08-20T21:40:35.7698325Z 2024-08-20T21:40:35.7698553Z --- Parse Warning: 42 / 101 --- 2024-08-20T21:40:35.7700327Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=804. 2024-08-20T21:40:35.7700761Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7701112Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-08-20T21:40:35.7701200Z 2024-08-20T21:40:35.7701656Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-08-20T21:40:35.7701824Z :meth:`set_state_dict_type` for the detail. 2024-08-20T21:40:35.7701912Z 2024-08-20T21:40:35.7702031Z Example:: 2024-08-20T21:40:35.7702121Z 2024-08-20T21:40:35.7702302Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:35.7702456Z >>> model = DDP(FSDP(...)) 2024-08-20T21:40:35.7702602Z >>> with FSDP.state_dict_type( 2024-08-20T21:40:35.7702706Z >>> model, 2024-08-20T21:40:35.7702899Z >>> StateDictType.SHARDED_STATE_DICT, 2024-08-20T21:40:35.7703031Z >>> ): 2024-08-20T21:40:35.7703205Z >>> checkpoint = model.state_dict() 2024-08-20T21:40:35.7703295Z 2024-08-20T21:40:35.7703393Z Args: 2024-08-20T21:40:35.7703570Z module (torch.nn.Module): Root module. 2024-08-20T21:40:35.7703877Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-08-20T21:40:35.7704186Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-08-20T21:40:35.7704408Z configuration for the target ``state_dict_type``. 2024-08-20T21:40:35.7704717Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-08-20T21:40:35.7705050Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-08-20T21:40:35.7705161Z 2024-08-20T21:40:35.7705564Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7705654Z 2024-08-20T21:40:35.7705779Z warnings.warn(msg) 2024-08-20T21:40:35.7705871Z 2024-08-20T21:40:35.7706092Z --- Parse Warning: 43 / 101 --- 2024-08-20T21:40:35.7707883Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1801. 2024-08-20T21:40:35.7708301Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7708403Z 2024-08-20T21:40:35.7708800Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-08-20T21:40:35.7708894Z 2024-08-20T21:40:35.7709232Z The given state-dict can be transformed to one of three types: 2024-08-20T21:40:35.7709636Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-08-20T21:40:35.7709727Z 2024-08-20T21:40:35.7710052Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-08-20T21:40:35.7710353Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-08-20T21:40:35.7710499Z avoid OOM. 2024-08-20T21:40:35.7710586Z 2024-08-20T21:40:35.7710901Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-08-20T21:40:35.7711202Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-08-20T21:40:35.7711296Z memory. 2024-08-20T21:40:35.7711385Z 2024-08-20T21:40:35.7711694Z For local state_dict, no transformation will be performed. But a state 2024-08-20T21:40:35.7712026Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-08-20T21:40:35.7712162Z nature (this is not supported yet). 2024-08-20T21:40:35.7712263Z 2024-08-20T21:40:35.7712364Z Example:: 2024-08-20T21:40:35.7712454Z 2024-08-20T21:40:35.7712642Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:35.7712964Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-20T21:40:35.7713175Z >>> from torch.distributed.fsdp import StateDictType 2024-08-20T21:40:35.7713424Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-20T21:40:35.7713691Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-20T21:40:35.7713826Z >>> # Save a checkpoint 2024-08-20T21:40:35.7713939Z >>> model, optim = ... 2024-08-20T21:40:35.7714066Z >>> FSDP.set_state_dict_type( 2024-08-20T21:40:35.7714178Z >>> model, 2024-08-20T21:40:35.7714335Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:40:35.7714510Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:40:35.7714721Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:40:35.7714815Z >>> ) 2024-08-20T21:40:35.7714990Z >>> state_dict = model.state_dict() 2024-08-20T21:40:35.7715225Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-08-20T21:40:35.7715415Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-20T21:40:35.7715531Z >>> # Load a checkpoint 2024-08-20T21:40:35.7715662Z >>> model, optim = ... 2024-08-20T21:40:35.7715859Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-20T21:40:35.7715985Z >>> FSDP.set_state_dict_type( 2024-08-20T21:40:35.7716097Z >>> model, 2024-08-20T21:40:35.7716250Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:40:35.7716437Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:40:35.7716692Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:40:35.7716787Z >>> ) 2024-08-20T21:40:35.7716947Z >>> model.load_state_dict(state_dict) 2024-08-20T21:40:35.7717145Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-20T21:40:35.7717290Z >>> model, optim, optim_state_dict 2024-08-20T21:40:35.7717397Z >>> ) 2024-08-20T21:40:35.7717558Z >>> optim.load_state_dict(optim_state_dict) 2024-08-20T21:40:35.7717646Z 2024-08-20T21:40:35.7717752Z Args: 2024-08-20T21:40:35.7718016Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-20T21:40:35.7718374Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-20T21:40:35.7718617Z were passed into the optimizer ``optim``. 2024-08-20T21:40:35.7718945Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-20T21:40:35.7719054Z parameters. 2024-08-20T21:40:35.7719365Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-08-20T21:40:35.7719656Z transform. If the value is None, optim.state_dict() will be used. ( 2024-08-20T21:40:35.7719786Z Default: ``None``) 2024-08-20T21:40:35.7720191Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-20T21:40:35.7720446Z are sharded or ``None`` if using the default process group. ( 2024-08-20T21:40:35.7720616Z Default: ``None``) 2024-08-20T21:40:35.7720708Z 2024-08-20T21:40:35.7720810Z Returns: 2024-08-20T21:40:35.7721099Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-08-20T21:40:35.7721328Z ``model``. The sharding of the optimizer state is based on 2024-08-20T21:40:35.7721444Z ``state_dict_type``. 2024-08-20T21:40:35.7721552Z 2024-08-20T21:40:35.7721958Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7722075Z 2024-08-20T21:40:35.7722215Z warnings.warn(msg) 2024-08-20T21:40:35.7722309Z 2024-08-20T21:40:35.7722527Z --- Parse Warning: 44 / 101 --- 2024-08-20T21:40:35.7724357Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1899. 2024-08-20T21:40:35.7724783Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7724888Z 2024-08-20T21:40:35.7725497Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-08-20T21:40:35.7725590Z 2024-08-20T21:40:35.7725832Z Given a ``optim_state_dict`` that is transformed through 2024-08-20T21:40:35.7726119Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-08-20T21:40:35.7726415Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-08-20T21:40:35.7726688Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-08-20T21:40:35.7726780Z 2024-08-20T21:40:35.7726953Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:35.7727323Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-08-20T21:40:35.7727536Z >>> from torch.distributed.fsdp import StateDictType 2024-08-20T21:40:35.7727795Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-08-20T21:40:35.7728064Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-08-20T21:40:35.7728183Z >>> # Save a checkpoint 2024-08-20T21:40:35.7728317Z >>> model, optim = ... 2024-08-20T21:40:35.7728448Z >>> FSDP.set_state_dict_type( 2024-08-20T21:40:35.7728546Z >>> model, 2024-08-20T21:40:35.7728712Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:40:35.7728941Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:40:35.7729143Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:40:35.7729257Z >>> ) 2024-08-20T21:40:35.7729398Z >>> state_dict = model.state_dict() 2024-08-20T21:40:35.7729550Z >>> original_osd = optim.state_dict() 2024-08-20T21:40:35.7729732Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-08-20T21:40:35.7729829Z >>> model, 2024-08-20T21:40:35.7729943Z >>> optim, 2024-08-20T21:40:35.7730090Z >>> optim_state_dict=original_osd 2024-08-20T21:40:35.7730265Z >>> ) 2024-08-20T21:40:35.7730472Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-08-20T21:40:35.7730591Z >>> # Load a checkpoint 2024-08-20T21:40:35.7730706Z >>> model, optim = ... 2024-08-20T21:40:35.7730925Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-08-20T21:40:35.7731056Z >>> FSDP.set_state_dict_type( 2024-08-20T21:40:35.7731159Z >>> model, 2024-08-20T21:40:35.7731330Z >>> StateDictType.FULL_STATE_DICT, 2024-08-20T21:40:35.7731503Z >>> FullStateDictConfig(rank0_only=False), 2024-08-20T21:40:35.7731703Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-08-20T21:40:35.7731816Z >>> ) 2024-08-20T21:40:35.7731964Z >>> model.load_state_dict(state_dict) 2024-08-20T21:40:35.7732166Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-08-20T21:40:35.7732363Z >>> model, optim, optim_state_dict 2024-08-20T21:40:35.7732459Z >>> ) 2024-08-20T21:40:35.7732632Z >>> optim.load_state_dict(optim_state_dict) 2024-08-20T21:40:35.7732739Z 2024-08-20T21:40:35.7732834Z Args: 2024-08-20T21:40:35.7733114Z model (torch.nn.Module): Root module (which may or may not be a 2024-08-20T21:40:35.7733382Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-08-20T21:40:35.7733560Z were passed into the optimizer ``optim``. 2024-08-20T21:40:35.7733858Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-08-20T21:40:35.7733967Z parameters. 2024-08-20T21:40:35.7734252Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-08-20T21:40:35.7734533Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-08-20T21:40:35.7734822Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-08-20T21:40:35.7735120Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-08-20T21:40:35.7735394Z load_directly (bool): If this is set to True, this API will also 2024-08-20T21:40:35.7735661Z call optim.load_state_dict(result) before returning the result. 2024-08-20T21:40:35.7735965Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-08-20T21:40:35.7736086Z (Default: ``False``) 2024-08-20T21:40:35.7736482Z group (dist.ProcessGroup): Model's process group across which parameters 2024-08-20T21:40:35.7736752Z are sharded or ``None`` if using the default process group. ( 2024-08-20T21:40:35.7736864Z Default: ``None``) 2024-08-20T21:40:35.7736992Z 2024-08-20T21:40:35.7737417Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7737508Z 2024-08-20T21:40:35.7737621Z warnings.warn(msg) 2024-08-20T21:40:35.7737728Z 2024-08-20T21:40:35.7737943Z --- Parse Warning: 45 / 101 --- 2024-08-20T21:40:35.7739479Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=137. 2024-08-20T21:40:35.7739910Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7740000Z 2024-08-20T21:40:35.7740358Z RemoteModule instance can only be created after RPC initialization. 2024-08-20T21:40:35.7740448Z 2024-08-20T21:40:35.7740778Z It creates a user-specified module on a specified remote node. 2024-08-20T21:40:35.7741117Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-20T21:40:35.7741243Z executed on the remote node. 2024-08-20T21:40:35.7741563Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-20T21:40:35.7741772Z gradients back to the corresponding remote module. 2024-08-20T21:40:35.7742262Z It can be shared across processors using `RPC framework `__, 2024-08-20T21:40:35.7742519Z without incurring any overheads of copying the actual module, 2024-08-20T21:40:35.7742800Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-08-20T21:40:35.7742928Z pointing to the remote module. 2024-08-20T21:40:35.7743029Z 2024-08-20T21:40:35.7743297Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-08-20T21:40:35.7743569Z the ``forward`` method of the module returned by the ``module_cls``. 2024-08-20T21:40:35.7743670Z 2024-08-20T21:40:35.7744100Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-08-20T21:40:35.7744191Z 2024-08-20T21:40:35.7744539Z Particularly, to create a hybrid model, typically the local modules should be 2024-08-20T21:40:35.7745089Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-08-20T21:40:35.7745195Z Hybrid Example: 2024-08-20T21:40:35.7745356Z >>> class HybridModel(nn.Module): 2024-08-20T21:40:35.7745558Z >>> def __init__(self) -> None: 2024-08-20T21:40:35.7745700Z >>> nn.Module.__init__(self) 2024-08-20T21:40:35.7745904Z >>> self.remote_embedding = RemoteModule(...) 2024-08-20T21:40:35.7746076Z >>> self.local_linear = nn.Linear(...) 2024-08-20T21:40:35.7746176Z 2024-08-20T21:40:35.7746452Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-20T21:40:35.7746869Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-08-20T21:40:35.7747160Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-08-20T21:40:35.7747378Z ``def forward(input: Tensor) -> Tensor:`` and 2024-08-20T21:40:35.7747652Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-08-20T21:40:35.7747758Z 2024-08-20T21:40:35.7747864Z .. note:: 2024-08-20T21:40:35.7748054Z If the remote module is placed on a cuda device, 2024-08-20T21:40:35.7748392Z any input CPU tensors will be automatically moved to the same cuda device, 2024-08-20T21:40:35.7748961Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-08-20T21:40:35.7749048Z 2024-08-20T21:40:35.7749163Z Args: 2024-08-20T21:40:35.7749658Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:40:35.7750096Z The device can be a local device or a remote device specified by one of the following remote 2024-08-20T21:40:35.7750241Z formats: 2024-08-20T21:40:35.7750328Z 2024-08-20T21:40:35.7750529Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-20T21:40:35.7750737Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-20T21:40:35.7750826Z 2024-08-20T21:40:35.7751181Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:40:35.7751328Z module_cls (nn.Module): For example, 2024-08-20T21:40:35.7751463Z >>> class MyModule(nn.Module): 2024-08-20T21:40:35.7751601Z >>> def forward(input): 2024-08-20T21:40:35.7751727Z >>> return input + 1 2024-08-20T21:40:35.7751872Z >>> 2024-08-20T21:40:35.7752008Z >>> module_cls = MyModule 2024-08-20T21:40:35.7752266Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-20T21:40:35.7752525Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-20T21:40:35.7752907Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-20T21:40:35.7753247Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-20T21:40:35.7753642Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-20T21:40:35.7753966Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-20T21:40:35.7754054Z 2024-08-20T21:40:35.7754164Z Returns: 2024-08-20T21:40:35.7754487Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:40:35.7754871Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-20T21:40:35.7755240Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:40:35.7755486Z on the user-provided module on the remote side. 2024-08-20T21:40:35.7755579Z 2024-08-20T21:40:35.7755693Z Example:: 2024-08-20T21:40:35.7755895Z Run the following code in two different processes: 2024-08-20T21:40:35.7756026Z 2024-08-20T21:40:35.7756176Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:35.7756286Z >>> # On worker 0: 2024-08-20T21:40:35.7756410Z >>> import torch 2024-08-20T21:40:35.7756576Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:35.7756711Z >>> from torch import nn, Tensor 2024-08-20T21:40:35.7757018Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:40:35.7757114Z >>> 2024-08-20T21:40:35.7757291Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:40:35.7757464Z >>> remote_linear_module = RemoteModule( 2024-08-20T21:40:35.7757632Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:40:35.7757725Z >>> ) 2024-08-20T21:40:35.7757874Z >>> input = torch.randn(128, 20) 2024-08-20T21:40:35.7758083Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-20T21:40:35.7758200Z >>> ret = ret_fut.wait() 2024-08-20T21:40:35.7758326Z >>> rpc.shutdown() 2024-08-20T21:40:35.7758442Z 2024-08-20T21:40:35.7758548Z >>> # On worker 1: 2024-08-20T21:40:35.7758667Z >>> import torch 2024-08-20T21:40:35.7758833Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:35.7758940Z >>> 2024-08-20T21:40:35.7759118Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:40:35.7759228Z >>> rpc.shutdown() 2024-08-20T21:40:35.7759333Z 2024-08-20T21:40:35.7759743Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7759836Z 2024-08-20T21:40:35.7759962Z warnings.warn(msg) 2024-08-20T21:40:35.7760049Z 2024-08-20T21:40:35.7760260Z --- Parse Warning: 46 / 101 --- 2024-08-20T21:40:35.7761939Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=514. 2024-08-20T21:40:35.7762359Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7762447Z 2024-08-20T21:40:35.7762888Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-08-20T21:40:35.7762979Z 2024-08-20T21:40:35.7763426Z This alternate initialization method can be particularly useful if we want to create multiple 2024-08-20T21:40:35.7763900Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-08-20T21:40:35.7763995Z 2024-08-20T21:40:35.7764383Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-08-20T21:40:35.7764621Z which is not supported. The recommended way is as follows: 2024-08-20T21:40:35.7764715Z 2024-08-20T21:40:35.7764920Z 1. the sender creates a RemoteModule; 2024-08-20T21:40:35.7765114Z 2. the sender sends its ``module_rref`` over RPC; 2024-08-20T21:40:35.7765580Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-08-20T21:40:35.7765689Z 2024-08-20T21:40:35.7765795Z Example:: 2024-08-20T21:40:35.7765998Z Run the following code in two different processes: 2024-08-20T21:40:35.7766104Z 2024-08-20T21:40:35.7766254Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:35.7766368Z >>> # On worker 0: 2024-08-20T21:40:35.7766499Z >>> import torch 2024-08-20T21:40:35.7766669Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:35.7766821Z >>> from torch import nn, Tensor 2024-08-20T21:40:35.7767119Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:40:35.7767217Z >>> 2024-08-20T21:40:35.7767419Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:40:35.7767560Z >>> remote_module = RemoteModule( 2024-08-20T21:40:35.7767761Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:40:35.7767878Z >>> ) 2024-08-20T21:40:35.7767974Z >>> 2024-08-20T21:40:35.7768114Z >>> remote_module1 = rpc.rpc_sync( 2024-08-20T21:40:35.7768242Z >>> "worker1/cpu", 2024-08-20T21:40:35.7768403Z >>> RemoteModule.init_from_module_rref, 2024-08-20T21:40:35.7768603Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-08-20T21:40:35.7768712Z >>> ) 2024-08-20T21:40:35.7768826Z >>> rpc.shutdown() 2024-08-20T21:40:35.7768996Z 2024-08-20T21:40:35.7769189Z >>> # On worker 1: 2024-08-20T21:40:35.7776337Z >>> import torch 2024-08-20T21:40:35.7776598Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:35.7776724Z >>> 2024-08-20T21:40:35.7776920Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:40:35.7777034Z >>> rpc.shutdown() 2024-08-20T21:40:35.7777141Z 2024-08-20T21:40:35.7777243Z Args: 2024-08-20T21:40:35.7777836Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:40:35.7778274Z The device can be a local device or a remote device specified by one of the following remote 2024-08-20T21:40:35.7778377Z formats: 2024-08-20T21:40:35.7778488Z 2024-08-20T21:40:35.7778680Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-08-20T21:40:35.7778881Z 2. "/" (ex: "trainer0/cuda:0"). 2024-08-20T21:40:35.7778988Z 2024-08-20T21:40:35.7779337Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:40:35.7779687Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-08-20T21:40:35.7779934Z the created remote module. 2024-08-20T21:40:35.7780302Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-08-20T21:40:35.7780644Z to be created. The type object should be decorated by @torch.jit.interface. 2024-08-20T21:40:35.7781046Z If not provided, the generated RemoteModule is not torchscript-able. 2024-08-20T21:40:35.7781373Z Warning, this is an experimental API and susceptible to frequent changes. 2024-08-20T21:40:35.7781484Z 2024-08-20T21:40:35.7781584Z Returns: 2024-08-20T21:40:35.7781916Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:40:35.7782440Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-08-20T21:40:35.7782800Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:40:35.7783050Z on the user-provided module on the remote side. 2024-08-20T21:40:35.7783163Z 2024-08-20T21:40:35.7783574Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7783667Z 2024-08-20T21:40:35.7783800Z warnings.warn(msg) 2024-08-20T21:40:35.7783892Z 2024-08-20T21:40:35.7784109Z --- Parse Warning: 47 / 101 --- 2024-08-20T21:40:35.7785622Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=606. 2024-08-20T21:40:35.7786040Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7786143Z 2024-08-20T21:40:35.7786449Z A RemoteModule instance can only be created after RPC initialization. 2024-08-20T21:40:35.7786540Z 2024-08-20T21:40:35.7786887Z It creates a user-specified module on a specified remote node. 2024-08-20T21:40:35.7787222Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-08-20T21:40:35.7787351Z executed on the remote node. 2024-08-20T21:40:35.7787729Z It takes care of autograd recording to ensure the backward pass propagates 2024-08-20T21:40:35.7787933Z gradients back to the corresponding remote module. 2024-08-20T21:40:35.7788023Z 2024-08-20T21:40:35.7788336Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-08-20T21:40:35.7789044Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-08-20T21:40:35.7789399Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-08-20T21:40:35.7789680Z and ``forward`` are the same as the ``forward`` method of the module 2024-08-20T21:40:35.7789814Z returned by the ``module_cls``. 2024-08-20T21:40:35.7789922Z 2024-08-20T21:40:35.7790201Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-08-20T21:40:35.7790867Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-08-20T21:40:35.7791189Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-08-20T21:40:35.7791284Z 2024-08-20T21:40:35.7791506Z | ``def forward(input: Tensor) -> Tensor:`` 2024-08-20T21:40:35.7791802Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-08-20T21:40:35.7791893Z 2024-08-20T21:40:35.7791988Z Args: 2024-08-20T21:40:35.7792503Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-08-20T21:40:35.7792977Z The format should be "/", where the device field can be parsed as torch.device type. 2024-08-20T21:40:35.7793184Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-08-20T21:40:35.7793527Z In addition, the device field can be optional and the default value is "cpu". 2024-08-20T21:40:35.7794006Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-08-20T21:40:35.7794116Z 2024-08-20T21:40:35.7794258Z >>> class MyModule(nn.Module): 2024-08-20T21:40:35.7794388Z >>> def forward(input): 2024-08-20T21:40:35.7794536Z >>> return input + 1 2024-08-20T21:40:35.7794633Z >>> 2024-08-20T21:40:35.7794758Z >>> module_cls = MyModule 2024-08-20T21:40:35.7794872Z 2024-08-20T21:40:35.7795137Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-08-20T21:40:35.7795411Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-08-20T21:40:35.7795502Z 2024-08-20T21:40:35.7795679Z Returns: 2024-08-20T21:40:35.7796026Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-08-20T21:40:35.7796411Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-08-20T21:40:35.7796771Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-08-20T21:40:35.7797033Z on the user-provided module on the remote side. 2024-08-20T21:40:35.7797129Z 2024-08-20T21:40:35.7797243Z Example:: 2024-08-20T21:40:35.7797464Z Run the following code in two different processes: 2024-08-20T21:40:35.7797559Z 2024-08-20T21:40:35.7797711Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:35.7797841Z >>> # On worker 0: 2024-08-20T21:40:35.7797950Z >>> import torch 2024-08-20T21:40:35.7798116Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:35.7798267Z >>> from torch import nn, Tensor 2024-08-20T21:40:35.7798566Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-08-20T21:40:35.7798663Z >>> 2024-08-20T21:40:35.7798858Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-08-20T21:40:35.7799017Z >>> remote_linear_module = RemoteModule( 2024-08-20T21:40:35.7799203Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-08-20T21:40:35.7799298Z >>> ) 2024-08-20T21:40:35.7799433Z >>> input = torch.randn(128, 20) 2024-08-20T21:40:35.7799698Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-08-20T21:40:35.7799818Z >>> ret = ret_fut.wait() 2024-08-20T21:40:35.7799930Z >>> rpc.shutdown() 2024-08-20T21:40:35.7800033Z 2024-08-20T21:40:35.7800142Z >>> # On worker 1: 2024-08-20T21:40:35.7800249Z >>> import torch 2024-08-20T21:40:35.7800428Z >>> import torch.distributed.rpc as rpc 2024-08-20T21:40:35.7800521Z >>> 2024-08-20T21:40:35.7800702Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-08-20T21:40:35.7800831Z >>> rpc.shutdown() 2024-08-20T21:40:35.7800922Z 2024-08-20T21:40:35.7801173Z Furthermore, a more practical example that is combined with 2024-08-20T21:40:35.7801822Z `DistributedDataParallel `__ (DDP) 2024-08-20T21:40:35.7802279Z can be found in this `tutorial `__. 2024-08-20T21:40:35.7802383Z 2024-08-20T21:40:35.7802797Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7802886Z 2024-08-20T21:40:35.7803014Z warnings.warn(msg) 2024-08-20T21:40:35.7803105Z 2024-08-20T21:40:35.7803321Z --- Parse Warning: 48 / 101 --- 2024-08-20T21:40:35.7804839Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2024-08-20T21:40:35.7805256Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7805345Z 2024-08-20T21:40:35.7805705Z Custom reducer class that can be used to specify a custom operation that 2024-08-20T21:40:35.7805927Z reduces losses of multiple microbatches into one value. 2024-08-20T21:40:35.7806019Z 2024-08-20T21:40:35.7806134Z Example: 2024-08-20T21:40:35.7806249Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.7806401Z >>> sum_reducer = _CustomReducer( 2024-08-20T21:40:35.7806519Z >>> torch.tensor(0.0), 2024-08-20T21:40:35.7806633Z >>> lambda a, b: a + b 2024-08-20T21:40:35.7806738Z >>> ) 2024-08-20T21:40:35.7806828Z 2024-08-20T21:40:35.7807230Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7807335Z 2024-08-20T21:40:35.7807447Z warnings.warn(msg) 2024-08-20T21:40:35.7807536Z 2024-08-20T21:40:35.7807870Z --- Parse Warning: 49 / 101 --- 2024-08-20T21:40:35.7809318Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6. 2024-08-20T21:40:35.7809740Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7809849Z 2024-08-20T21:40:35.7810251Z A decorator for a function indicating that the return value of the function 2024-08-20T21:40:35.7810548Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-08-20T21:40:35.7810890Z function can run asynchronously on the RPC callee. More specifically, the 2024-08-20T21:40:35.7811209Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-08-20T21:40:35.7811534Z function and installs subsequent processing steps as a callback to that 2024-08-20T21:40:35.7811856Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-08-20T21:40:35.7812144Z from the :class:`~torch.futures.Future` when completed and send the 2024-08-20T21:40:35.7812410Z value back as the RPC response. That also means the returned 2024-08-20T21:40:35.7812728Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-08-20T21:40:35.7813116Z sent through RPC. This decorator is useful when the wrapped function's 2024-08-20T21:40:35.7813405Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-08-20T21:40:35.7813709Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-08-20T21:40:35.7813820Z 2024-08-20T21:40:35.7814112Z .. note:: To enable asynchronous execution, applications must pass the 2024-08-20T21:40:35.7814431Z function object returned by this decorator to RPC APIs. If RPC detected 2024-08-20T21:40:35.7814745Z attributes installed by this decorator, it knows that this function 2024-08-20T21:40:35.7814996Z returns a ``Future`` object and will handle that accordingly. 2024-08-20T21:40:35.7815292Z However, this does not mean this decorator has to be outmost one when 2024-08-20T21:40:35.7815619Z defining a function. For example, when combined with ``@staticmethod`` 2024-08-20T21:40:35.7815912Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-08-20T21:40:35.7816221Z inner decorator to allow the target function be recognized as a static 2024-08-20T21:40:35.7816554Z or class function. This target function can still execute asynchronously 2024-08-20T21:40:35.7816863Z because, when accessed, the static or class method preserves attributes 2024-08-20T21:40:35.7817077Z installed by ``@rpc.functions.async_execution``. 2024-08-20T21:40:35.7817172Z 2024-08-20T21:40:35.7817318Z 2024-08-20T21:40:35.7817441Z Example:: 2024-08-20T21:40:35.7817725Z The returned :class:`~torch.futures.Future` object can come from 2024-08-20T21:40:35.7817896Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-08-20T21:40:35.7818217Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-08-20T21:40:35.7818482Z constructor. The example below shows directly using the 2024-08-20T21:40:35.7818655Z :class:`~torch.futures.Future` returned by 2024-08-20T21:40:35.7818830Z :meth:`~torch.futures.Future.then`. 2024-08-20T21:40:35.7818929Z 2024-08-20T21:40:35.7819088Z >>> from torch.distributed import rpc 2024-08-20T21:40:35.7819204Z >>> 2024-08-20T21:40:35.7819358Z >>> # omitting setup and shutdown RPC 2024-08-20T21:40:35.7819502Z >>> 2024-08-20T21:40:35.7819636Z >>> # On all workers 2024-08-20T21:40:35.7819782Z >>> @rpc.functions.async_execution 2024-08-20T21:40:35.7819956Z >>> def async_add_chained(to, x, y, z): 2024-08-20T21:40:35.7820278Z >>> # This function runs on "worker1" and returns immediately when 2024-08-20T21:40:35.7820542Z >>> # the callback is installed through the `then(cb)` API. In the 2024-08-20T21:40:35.7820822Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-08-20T21:40:35.7821044Z >>> # When the return value of that `rpc_async` arrives at 2024-08-20T21:40:35.7821294Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-08-20T21:40:35.7821576Z >>> # and set the value for the previously returned `Future`, which 2024-08-20T21:40:35.7821833Z >>> # will then trigger RPC to send the result back to "worker0". 2024-08-20T21:40:35.7822056Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:40:35.7822217Z >>> lambda fut: fut.wait() + z 2024-08-20T21:40:35.7822316Z >>> ) 2024-08-20T21:40:35.7822422Z >>> 2024-08-20T21:40:35.7822530Z >>> # On worker0 2024-08-20T21:40:35.7822649Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.7822780Z >>> ret = rpc.rpc_sync( 2024-08-20T21:40:35.7822885Z >>> "worker1", 2024-08-20T21:40:35.7823001Z >>> async_add_chained, 2024-08-20T21:40:35.7823178Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-08-20T21:40:35.7823275Z >>> ) 2024-08-20T21:40:35.7823430Z >>> print(ret) # prints tensor([3., 3.]) 2024-08-20T21:40:35.7823562Z 2024-08-20T21:40:35.7823870Z When combined with TorchScript decorators, this decorator must be the 2024-08-20T21:40:35.7823972Z outmost one. 2024-08-20T21:40:35.7824079Z 2024-08-20T21:40:35.7824205Z >>> from torch import Tensor 2024-08-20T21:40:35.7824358Z >>> from torch.futures import Future 2024-08-20T21:40:35.7824528Z >>> from torch.distributed import rpc 2024-08-20T21:40:35.7824622Z >>> 2024-08-20T21:40:35.7824773Z >>> # omitting setup and shutdown RPC 2024-08-20T21:40:35.7824886Z >>> 2024-08-20T21:40:35.7824998Z >>> # On all workers 2024-08-20T21:40:35.7825113Z >>> @torch.jit.script 2024-08-20T21:40:35.7825400Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-08-20T21:40:35.7825514Z >>> return x + y 2024-08-20T21:40:35.7825606Z >>> 2024-08-20T21:40:35.7825764Z >>> @rpc.functions.async_execution 2024-08-20T21:40:35.7825881Z >>> @torch.jit.script 2024-08-20T21:40:35.7826235Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-08-20T21:40:35.7826425Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-08-20T21:40:35.7826520Z >>> 2024-08-20T21:40:35.7826645Z >>> # On worker0 2024-08-20T21:40:35.7826761Z >>> ret = rpc.rpc_sync( 2024-08-20T21:40:35.7826868Z >>> "worker1", 2024-08-20T21:40:35.7826984Z >>> async_add, 2024-08-20T21:40:35.7827139Z >>> args=("worker2", torch.ones(2), 1) 2024-08-20T21:40:35.7827239Z >>> ) 2024-08-20T21:40:35.7827412Z >>> print(ret) # prints tensor([2., 2.]) 2024-08-20T21:40:35.7827503Z 2024-08-20T21:40:35.7827808Z When combined with static or class method, this decorator must be the 2024-08-20T21:40:35.7827965Z inner one. 2024-08-20T21:40:35.7828057Z 2024-08-20T21:40:35.7828214Z >>> from torch.distributed import rpc 2024-08-20T21:40:35.7828321Z >>> 2024-08-20T21:40:35.7828478Z >>> # omitting setup and shutdown RPC 2024-08-20T21:40:35.7828571Z >>> 2024-08-20T21:40:35.7828696Z >>> # On all workers 2024-08-20T21:40:35.7828835Z >>> class AsyncExecutionClass: 2024-08-20T21:40:35.7828930Z >>> 2024-08-20T21:40:35.7829055Z >>> @staticmethod 2024-08-20T21:40:35.7829207Z >>> @rpc.functions.async_execution 2024-08-20T21:40:35.7829372Z >>> def static_async_add(to, x, y, z): 2024-08-20T21:40:35.7829656Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:40:35.7829809Z >>> lambda fut: fut.wait() + z 2024-08-20T21:40:35.7829925Z >>> ) 2024-08-20T21:40:35.7830019Z >>> 2024-08-20T21:40:35.7830126Z >>> @classmethod 2024-08-20T21:40:35.7830295Z >>> @rpc.functions.async_execution 2024-08-20T21:40:35.7830459Z >>> def class_async_add(cls, to, x, y, z): 2024-08-20T21:40:35.7830625Z >>> ret_fut = torch.futures.Future() 2024-08-20T21:40:35.7830839Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:40:35.7831040Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-08-20T21:40:35.7831140Z >>> ) 2024-08-20T21:40:35.7831271Z >>> return ret_fut 2024-08-20T21:40:35.7831365Z >>> 2024-08-20T21:40:35.7831517Z >>> @rpc.functions.async_execution 2024-08-20T21:40:35.7831695Z >>> def bound_async_add(self, to, x, y, z): 2024-08-20T21:40:35.7831928Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-08-20T21:40:35.7832093Z >>> lambda fut: fut.wait() + z 2024-08-20T21:40:35.7832197Z >>> ) 2024-08-20T21:40:35.7832294Z >>> 2024-08-20T21:40:35.7832412Z >>> # On worker0 2024-08-20T21:40:35.7832528Z >>> ret = rpc.rpc_sync( 2024-08-20T21:40:35.7832637Z >>> "worker1", 2024-08-20T21:40:35.7832866Z >>> AsyncExecutionClass.static_async_add, 2024-08-20T21:40:35.7833026Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-20T21:40:35.7833124Z >>> ) 2024-08-20T21:40:35.7833293Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:40:35.7833387Z >>> 2024-08-20T21:40:35.7833504Z >>> ret = rpc.rpc_sync( 2024-08-20T21:40:35.7833623Z >>> "worker1", 2024-08-20T21:40:35.7833802Z >>> AsyncExecutionClass.class_async_add, 2024-08-20T21:40:35.7833959Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-08-20T21:40:35.7834073Z >>> ) 2024-08-20T21:40:35.7834228Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:40:35.7834320Z 2024-08-20T21:40:35.7834551Z This decorator also works with RRef helpers, i.e., . 2024-08-20T21:40:35.7834742Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-08-20T21:40:35.7834964Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-08-20T21:40:35.7835150Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-08-20T21:40:35.7835244Z 2024-08-20T21:40:35.7835419Z >>> from torch.distributed import rpc 2024-08-20T21:40:35.7835514Z >>> 2024-08-20T21:40:35.7835695Z >>> # reuse the AsyncExecutionClass class above 2024-08-20T21:40:35.7835916Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:40:35.7836201Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-08-20T21:40:35.7836357Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:40:35.7836470Z >>> 2024-08-20T21:40:35.7836676Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:40:35.7836996Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-08-20T21:40:35.7837195Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:40:35.7837289Z >>> 2024-08-20T21:40:35.7837492Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-08-20T21:40:35.7837825Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-08-20T21:40:35.7837977Z >>> print(ret) # prints tensor([4., 4.]) 2024-08-20T21:40:35.7838081Z 2024-08-20T21:40:35.7838506Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7838604Z 2024-08-20T21:40:35.7838730Z warnings.warn(msg) 2024-08-20T21:40:35.7838849Z 2024-08-20T21:40:35.7839068Z --- Parse Warning: 50 / 101 --- 2024-08-20T21:40:35.7840770Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=108. 2024-08-20T21:40:35.7841195Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7841288Z 2024-08-20T21:40:35.7841577Z Set device mapping between each RPC caller and callee pair. This 2024-08-20T21:40:35.7841817Z function can be called multiple times to incrementally add 2024-08-20T21:40:35.7841970Z device placement configurations. 2024-08-20T21:40:35.7842061Z 2024-08-20T21:40:35.7842157Z Args: 2024-08-20T21:40:35.7842293Z to (str): Callee name. 2024-08-20T21:40:35.7842559Z device_map (Dict of int, str, or torch.device): Device placement 2024-08-20T21:40:35.7842802Z mappings from this worker to the callee. This map must be 2024-08-20T21:40:35.7842932Z invertible. 2024-08-20T21:40:35.7843022Z 2024-08-20T21:40:35.7843120Z Example: 2024-08-20T21:40:35.7843284Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:35.7843393Z >>> # both workers 2024-08-20T21:40:35.7843506Z >>> def add(x, y): 2024-08-20T21:40:35.7843768Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-08-20T21:40:35.7843905Z >>> return x + y, (x + y).to(2) 2024-08-20T21:40:35.7844031Z >>> 2024-08-20T21:40:35.7844153Z >>> # on worker 0 2024-08-20T21:40:35.7844335Z >>> options = TensorPipeRpcBackendOptions( 2024-08-20T21:40:35.7844461Z >>> num_worker_threads=8, 2024-08-20T21:40:35.7844622Z >>> device_maps={"worker1": {0: 1}} 2024-08-20T21:40:35.7844868Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-08-20T21:40:35.7844963Z >>> ) 2024-08-20T21:40:35.7845145Z >>> options.set_device_map("worker1", {1: 2}) 2024-08-20T21:40:35.7845382Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-08-20T21:40:35.7845490Z >>> 2024-08-20T21:40:35.7845601Z >>> rpc.init_rpc( 2024-08-20T21:40:35.7845706Z >>> "worker0", 2024-08-20T21:40:35.7845823Z >>> rank=0, 2024-08-20T21:40:35.7845931Z >>> world_size=2, 2024-08-20T21:40:35.7846101Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-08-20T21:40:35.7846252Z >>> rpc_backend_options=options 2024-08-20T21:40:35.7846351Z >>> ) 2024-08-20T21:40:35.7846445Z >>> 2024-08-20T21:40:35.7846577Z >>> x = torch.ones(2) 2024-08-20T21:40:35.7846799Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-08-20T21:40:35.7847054Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-08-20T21:40:35.7847322Z >>> # sending the return value back, it will follow the invert of 2024-08-20T21:40:35.7847572Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-08-20T21:40:35.7847695Z >>> # cuda:1 on worker0 2024-08-20T21:40:35.7847984Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-08-20T21:40:35.7848247Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-08-20T21:40:35.7848395Z 2024-08-20T21:40:35.7848809Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7848903Z 2024-08-20T21:40:35.7849038Z warnings.warn(msg) 2024-08-20T21:40:35.7849131Z 2024-08-20T21:40:35.7849349Z --- Parse Warning: 51 / 101 --- 2024-08-20T21:40:35.7851005Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=378. 2024-08-20T21:40:35.7851435Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7851529Z 2024-08-20T21:40:35.7852265Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-08-20T21:40:35.7852693Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-08-20T21:40:35.7852812Z 2024-08-20T21:40:35.7852919Z Keyword Args: 2024-08-20T21:40:35.7853177Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-20T21:40:35.7853668Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-08-20T21:40:35.7854187Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-08-20T21:40:35.7854341Z as a placeholder. default: None. 2024-08-20T21:40:35.7854650Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-08-20T21:40:35.7855196Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-20T21:40:35.7855752Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-08-20T21:40:35.7855944Z input_kwarg_layouts (Dict[str, Placement]): 2024-08-20T21:40:35.7856493Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-08-20T21:40:35.7856652Z default: None 2024-08-20T21:40:35.7856866Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-08-20T21:40:35.7857403Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-08-20T21:40:35.7857624Z have the desired DTensor layouts. default: None. 2024-08-20T21:40:35.7857772Z use_local_output (bool, optional): 2024-08-20T21:40:35.7858280Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-08-20T21:40:35.7858392Z Returns: 2024-08-20T21:40:35.7858918Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-08-20T21:40:35.7859027Z 2024-08-20T21:40:35.7859135Z Example:: 2024-08-20T21:40:35.7859264Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:40:35.7859694Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-08-20T21:40:35.7859952Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-20T21:40:35.7860049Z >>> ... 2024-08-20T21:40:35.7860488Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-20T21:40:35.7860655Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-20T21:40:35.7860750Z >>> 2024-08-20T21:40:35.7861240Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-08-20T21:40:35.7861438Z >>> # and then redistributed to Replicated DTensor. 2024-08-20T21:40:35.7861557Z >>> parallelize_module( 2024-08-20T21:40:35.7861754Z >>> block, # this can be a submodule or module 2024-08-20T21:40:35.7861899Z >>> tp_mesh, 2024-08-20T21:40:35.7862037Z >>> parallelize_plan={ 2024-08-20T21:40:35.7862205Z >>> "attn": PrepareModuleInput( 2024-08-20T21:40:35.7862397Z >>> input_layouts=(Shard(0), None, None, ...), 2024-08-20T21:40:35.7862636Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-08-20T21:40:35.7862738Z >>> ), 2024-08-20T21:40:35.7862836Z >>> } 2024-08-20T21:40:35.7862945Z >>> ) 2024-08-20T21:40:35.7863035Z 2024-08-20T21:40:35.7863441Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7863546Z 2024-08-20T21:40:35.7863658Z warnings.warn(msg) 2024-08-20T21:40:35.7863840Z 2024-08-20T21:40:35.7864071Z --- Parse Warning: 52 / 101 --- 2024-08-20T21:40:35.7865637Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=533. 2024-08-20T21:40:35.7866071Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7866166Z 2024-08-20T21:40:35.7866817Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-08-20T21:40:35.7867268Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-08-20T21:40:35.7867361Z 2024-08-20T21:40:35.7867465Z Keyword Args: 2024-08-20T21:40:35.7867696Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-20T21:40:35.7868180Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-08-20T21:40:35.7868723Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-08-20T21:40:35.7868930Z ``None`` need to be specified as a placeholder. 2024-08-20T21:40:35.7869180Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-08-20T21:40:35.7869788Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-08-20T21:40:35.7869942Z have the desired DTensor layouts. 2024-08-20T21:40:35.7870085Z use_local_output (bool, optional): 2024-08-20T21:40:35.7870605Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-08-20T21:40:35.7870705Z Returns: 2024-08-20T21:40:35.7871185Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-08-20T21:40:35.7871290Z 2024-08-20T21:40:35.7871394Z Example:: 2024-08-20T21:40:35.7871525Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:40:35.7871967Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-08-20T21:40:35.7872222Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-08-20T21:40:35.7872335Z >>> ... 2024-08-20T21:40:35.7872762Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-08-20T21:40:35.7872927Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-08-20T21:40:35.7873035Z >>> 2024-08-20T21:40:35.7873592Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-08-20T21:40:35.7873773Z >>> # and then redistributed to Sharded DTensor. 2024-08-20T21:40:35.7873939Z >>> parallelize_module( 2024-08-20T21:40:35.7874127Z >>> block, # this can be a submodule or module 2024-08-20T21:40:35.7874234Z >>> tp_mesh, 2024-08-20T21:40:35.7874436Z >>> parallelize_plan = PrepareModuleOutput( 2024-08-20T21:40:35.7874633Z >>> output_layouts=Replicate(), 2024-08-20T21:40:35.7874788Z >>> desired_output_layouts=Shard(0) 2024-08-20T21:40:35.7874899Z >>> ) 2024-08-20T21:40:35.7874997Z >>> ) 2024-08-20T21:40:35.7875085Z 2024-08-20T21:40:35.7875509Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7875600Z 2024-08-20T21:40:35.7875728Z warnings.warn(msg) 2024-08-20T21:40:35.7875819Z 2024-08-20T21:40:35.7876032Z --- Parse Warning: 53 / 101 --- 2024-08-20T21:40:35.7877684Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=230. 2024-08-20T21:40:35.7878110Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7878371Z Return a new dict with new, potentially nested, key value pair 2024-08-20T21:40:35.7878476Z 2024-08-20T21:40:35.7878652Z >>> purchase = {'name': 'Alice', 2024-08-20T21:40:35.7878894Z ... 'order': {'items': ['Apple', 'Orange'], 2024-08-20T21:40:35.7879130Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:40:35.7879361Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:40:35.7879731Z >>> assoc_in(purchase, ['order', 'costs'], [0.25, 1.00]) # doctest: +SKIP 2024-08-20T21:40:35.7879922Z {'credit card': '5555-1234-1234-1234', 2024-08-20T21:40:35.7880062Z 'name': 'Alice', 2024-08-20T21:40:35.7880376Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-08-20T21:40:35.7880474Z 2024-08-20T21:40:35.7880877Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7880981Z 2024-08-20T21:40:35.7881091Z warnings.warn(msg) 2024-08-20T21:40:35.7881181Z 2024-08-20T21:40:35.7881405Z --- Parse Warning: 54 / 101 --- 2024-08-20T21:40:35.7882978Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2024-08-20T21:40:35.7883435Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7883639Z Update value in a (potentially) nested dictionary 2024-08-20T21:40:35.7883729Z 2024-08-20T21:40:35.7883841Z inputs: 2024-08-20T21:40:35.7884032Z d - dictionary on which to operate 2024-08-20T21:40:35.7884413Z keys - list or tuple giving the location of the value to be changed in d 2024-08-20T21:40:35.7884633Z func - function to operate on that value 2024-08-20T21:40:35.7884723Z 2024-08-20T21:40:35.7885014Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-08-20T21:40:35.7885337Z original dictionary with v replaced by func(v), but does not mutate the 2024-08-20T21:40:35.7885453Z original dictionary. 2024-08-20T21:40:35.7885542Z 2024-08-20T21:40:35.7885869Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-08-20T21:40:35.7886164Z specified by the keys, with the innermost value set to func(default). 2024-08-20T21:40:35.7886251Z 2024-08-20T21:40:35.7886380Z >>> inc = lambda x: x + 1 2024-08-20T21:40:35.7886560Z >>> update_in({'a': 0}, ['a'], inc) 2024-08-20T21:40:35.7886685Z {'a': 1} 2024-08-20T21:40:35.7886846Z 2024-08-20T21:40:35.7887026Z >>> transaction = {'name': 'Alice', 2024-08-20T21:40:35.7887285Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-20T21:40:35.7887522Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:40:35.7887755Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:40:35.7888157Z >>> update_in(transaction, ['purchase', 'costs'], sum) # doctest: +SKIP 2024-08-20T21:40:35.7888350Z {'credit card': '5555-1234-1234-1234', 2024-08-20T21:40:35.7888491Z 'name': 'Alice', 2024-08-20T21:40:35.7889189Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-08-20T21:40:35.7889278Z 2024-08-20T21:40:35.7889438Z >>> # updating a value when k0 is not in d 2024-08-20T21:40:35.7889619Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-08-20T21:40:35.7889767Z {1: {2: {3: 'bar'}}} 2024-08-20T21:40:35.7889973Z >>> update_in({1: 'foo'}, [2, 3, 4], inc, 0) 2024-08-20T21:40:35.7890191Z {1: 'foo', 2: {3: {4: 1}}} 2024-08-20T21:40:35.7890500Z 2024-08-20T21:40:35.7891117Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7891221Z 2024-08-20T21:40:35.7891334Z warnings.warn(msg) 2024-08-20T21:40:35.7891423Z 2024-08-20T21:40:35.7891651Z --- Parse Warning: 55 / 101 --- 2024-08-20T21:40:35.7893265Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=303. 2024-08-20T21:40:35.7893695Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7893909Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-08-20T21:40:35.7893996Z 2024-08-20T21:40:35.7894268Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-08-20T21:40:35.7894536Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-08-20T21:40:35.7894623Z 2024-08-20T21:40:35.7894923Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-08-20T21:40:35.7895093Z structures such as dictionaries and lists. 2024-08-20T21:40:35.7895190Z 2024-08-20T21:40:35.7895393Z >>> transaction = {'name': 'Alice', 2024-08-20T21:40:35.7895654Z ... 'purchase': {'items': ['Apple', 'Orange'], 2024-08-20T21:40:35.7895942Z ... 'costs': [0.50, 1.25]}, 2024-08-20T21:40:35.7896177Z ... 'credit card': '5555-1234-1234-1234'} 2024-08-20T21:40:35.7896416Z >>> get_in(['purchase', 'items', 0], transaction) 2024-08-20T21:40:35.7896557Z 'Apple' 2024-08-20T21:40:35.7896735Z >>> get_in(['name'], transaction) 2024-08-20T21:40:35.7896859Z 'Alice' 2024-08-20T21:40:35.7897100Z >>> get_in(['purchase', 'total'], transaction) 2024-08-20T21:40:35.7897364Z >>> get_in(['purchase', 'items', 'apple'], transaction) 2024-08-20T21:40:35.7897601Z >>> get_in(['purchase', 'items', 10], transaction) 2024-08-20T21:40:35.7897903Z >>> get_in(['purchase', 'total'], transaction, 0) 2024-08-20T21:40:35.7898001Z 0 2024-08-20T21:40:35.7898196Z >>> get_in(['y'], {}, no_default=True) 2024-08-20T21:40:35.7898358Z Traceback (most recent call last): 2024-08-20T21:40:35.7898458Z ... 2024-08-20T21:40:35.7898602Z KeyError: 'y' 2024-08-20T21:40:35.7898711Z 2024-08-20T21:40:35.7898810Z See Also: 2024-08-20T21:40:35.7898919Z itertoolz.get 2024-08-20T21:40:35.7899053Z operator.getitem 2024-08-20T21:40:35.7899152Z 2024-08-20T21:40:35.7899578Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7899673Z 2024-08-20T21:40:35.7899790Z warnings.warn(msg) 2024-08-20T21:40:35.7899898Z 2024-08-20T21:40:35.7900114Z --- Parse Warning: 56 / 101 --- 2024-08-20T21:40:35.7901692Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=355. 2024-08-20T21:40:35.7902281Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7902430Z Group a collection by a key function 2024-08-20T21:40:35.7902526Z 2024-08-20T21:40:35.7902852Z >>> names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank'] 2024-08-20T21:40:35.7903014Z >>> groupby(len, names) # doctest: +SKIP 2024-08-20T21:40:35.7903322Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-08-20T21:40:35.7903434Z 2024-08-20T21:40:35.7903570Z >>> iseven = lambda x: x % 2 == 0 2024-08-20T21:40:35.7903825Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-08-20T21:40:35.7904042Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-08-20T21:40:35.7904137Z 2024-08-20T21:40:35.7904392Z Non-callable keys imply grouping on a member. 2024-08-20T21:40:35.7904487Z 2024-08-20T21:40:35.7904759Z >>> groupby('gender', [{'name': 'Alice', 'gender': 'F'}, 2024-08-20T21:40:35.7905011Z ... {'name': 'Bob', 'gender': 'M'}, 2024-08-20T21:40:35.7905309Z ... {'name': 'Charlie', 'gender': 'M'}]) # doctest:+SKIP 2024-08-20T21:40:35.7905519Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-08-20T21:40:35.7905734Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-08-20T21:40:35.7905944Z {'gender': 'M', 'name': 'Charlie'}]} 2024-08-20T21:40:35.7906033Z 2024-08-20T21:40:35.7906236Z Not to be confused with ``itertools.groupby`` 2024-08-20T21:40:35.7906326Z 2024-08-20T21:40:35.7906425Z See Also: 2024-08-20T21:40:35.7906539Z countby 2024-08-20T21:40:35.7906633Z 2024-08-20T21:40:35.7907041Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7907148Z 2024-08-20T21:40:35.7907259Z warnings.warn(msg) 2024-08-20T21:40:35.7907362Z 2024-08-20T21:40:35.7907574Z --- Parse Warning: 57 / 101 --- 2024-08-20T21:40:35.7908979Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601. 2024-08-20T21:40:35.7909448Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7909731Z Applies Batch Normalization over a N-Dimensional input. 2024-08-20T21:40:35.7909822Z 2024-08-20T21:40:35.7910443Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-08-20T21:40:35.7910739Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-08-20T21:40:35.7911025Z Internal Covariate Shift `__ . 2024-08-20T21:40:35.7911133Z 2024-08-20T21:40:35.7911240Z .. math:: 2024-08-20T21:40:35.7911343Z 2024-08-20T21:40:35.7911764Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-08-20T21:40:35.7911855Z 2024-08-20T21:40:35.7912255Z The mean and standard-deviation are calculated per-dimension over all 2024-08-20T21:40:35.7912656Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-08-20T21:40:35.7912977Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-08-20T21:40:35.7913232Z By default, the elements of :math:`\gamma` are sampled from 2024-08-20T21:40:35.7913528Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-08-20T21:40:35.7913952Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-08-20T21:40:35.7914113Z `torch.var(input, unbiased=False)`. 2024-08-20T21:40:35.7914204Z 2024-08-20T21:40:35.7914524Z Also by default, during training this layer keeps running estimates of its 2024-08-20T21:40:35.7914887Z computed mean and variance, which are then used for normalization during 2024-08-20T21:40:35.7915208Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-08-20T21:40:35.7915323Z of 0.1. 2024-08-20T21:40:35.7915412Z 2024-08-20T21:40:35.7915724Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-08-20T21:40:35.7916037Z keep running estimates, and batch statistics are instead used during 2024-08-20T21:40:35.7916161Z evaluation time as well. 2024-08-20T21:40:35.7916252Z 2024-08-20T21:40:35.7916372Z .. note:: 2024-08-20T21:40:35.7916676Z This :attr:`momentum` argument is different from one used in optimizer 2024-08-20T21:40:35.7917047Z classes and the conventional notion of momentum. Mathematically, the 2024-08-20T21:40:35.7917241Z update rule for running statistics here is 2024-08-20T21:40:35.7917748Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-08-20T21:40:35.7918061Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-08-20T21:40:35.7918179Z new observed value. 2024-08-20T21:40:35.7918272Z 2024-08-20T21:40:35.7918702Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-08-20T21:40:35.7919139Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-08-20T21:40:35.7919424Z Normalization or Spatio-temporal Batch Normalization. 2024-08-20T21:40:35.7919528Z 2024-08-20T21:40:35.7919716Z Currently :class:`SyncBatchNorm` only supports 2024-08-20T21:40:35.7920099Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-08-20T21:40:35.7920407Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-08-20T21:40:35.7920682Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-08-20T21:40:35.7920798Z Network with DDP. 2024-08-20T21:40:35.7920902Z 2024-08-20T21:40:35.7920999Z Args: 2024-08-20T21:40:35.7921232Z num_features: :math:`C` from an expected input of size 2024-08-20T21:40:35.7921378Z :math:`(N, C, +)` 2024-08-20T21:40:35.7921642Z eps: a value added to the denominator for numerical stability. 2024-08-20T21:40:35.7921817Z Default: ``1e-5`` 2024-08-20T21:40:35.7922071Z momentum: the value used for the running_mean and running_var 2024-08-20T21:40:35.7922353Z computation. Can be set to ``None`` for cumulative moving average 2024-08-20T21:40:35.7922523Z (i.e. simple average). Default: 0.1 2024-08-20T21:40:35.7922804Z affine: a boolean value that when set to ``True``, this module has 2024-08-20T21:40:35.7923002Z learnable affine parameters. Default: ``True`` 2024-08-20T21:40:35.7923302Z track_running_stats: a boolean value that when set to ``True``, this 2024-08-20T21:40:35.7923623Z module tracks the running mean and variance, and when set to ``False``, 2024-08-20T21:40:35.7923951Z this module does not track such statistics, and initializes statistics 2024-08-20T21:40:35.7924220Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-08-20T21:40:35.7924569Z When these buffers are ``None``, this module always uses batch statistics. 2024-08-20T21:40:35.7924787Z in both training and eval modes. Default: ``True`` 2024-08-20T21:40:35.7925110Z process_group: synchronization of stats happen within each process group 2024-08-20T21:40:35.7925410Z individually. Default behavior is synchronization across the whole 2024-08-20T21:40:35.7925526Z world 2024-08-20T21:40:35.7925616Z 2024-08-20T21:40:35.7925717Z Shape: 2024-08-20T21:40:35.7925941Z - Input: :math:`(N, C, +)` 2024-08-20T21:40:35.7926193Z - Output: :math:`(N, C, +)` (same shape as input) 2024-08-20T21:40:35.7926284Z 2024-08-20T21:40:35.7926400Z .. note:: 2024-08-20T21:40:35.7926740Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-08-20T21:40:35.7927018Z synchronization is disabled when ``model.eval()`` is set or if 2024-08-20T21:40:35.7927185Z ``self.training`` is otherwise ``False``. 2024-08-20T21:40:35.7927276Z 2024-08-20T21:40:35.7927395Z Examples:: 2024-08-20T21:40:35.7927484Z 2024-08-20T21:40:35.7927604Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.7927762Z >>> # With Learnable Parameters 2024-08-20T21:40:35.7927952Z >>> m = nn.SyncBatchNorm(100) 2024-08-20T21:40:35.7928117Z >>> # creating process group (optional) 2024-08-20T21:40:35.7928322Z >>> # ranks is a list of int identifying rank ids. 2024-08-20T21:40:35.7928448Z >>> ranks = list(range(8)) 2024-08-20T21:40:35.7928587Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-20T21:40:35.7928800Z >>> # Note: every rank calls into new_group for every 2024-08-20T21:40:35.7929006Z >>> # process group created, even if that rank is not 2024-08-20T21:40:35.7929129Z >>> # part of the group. 2024-08-20T21:40:35.7929472Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-20T21:40:35.7929740Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-20T21:40:35.7929907Z >>> # Without Learnable Parameters 2024-08-20T21:40:35.7930269Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-08-20T21:40:35.7930444Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-08-20T21:40:35.7930583Z >>> output = m(input) 2024-08-20T21:40:35.7930676Z 2024-08-20T21:40:35.7930829Z >>> # network is nn.BatchNorm layer 2024-08-20T21:40:35.7931218Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-08-20T21:40:35.7931442Z >>> # only single gpu per process is currently supported 2024-08-20T21:40:35.7931787Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-08-20T21:40:35.7931956Z >>> sync_bn_network, 2024-08-20T21:40:35.7932133Z >>> device_ids=[args.local_rank], 2024-08-20T21:40:35.7932329Z >>> output_device=args.local_rank) 2024-08-20T21:40:35.7932424Z 2024-08-20T21:40:35.7932836Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7932947Z 2024-08-20T21:40:35.7933061Z warnings.warn(msg) 2024-08-20T21:40:35.7933151Z 2024-08-20T21:40:35.7933380Z --- Parse Warning: 58 / 101 --- 2024-08-20T21:40:35.7934935Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=824. 2024-08-20T21:40:35.7935357Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7935797Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-08-20T21:40:35.7935889Z 2024-08-20T21:40:35.7935988Z Args: 2024-08-20T21:40:35.7936332Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-08-20T21:40:35.7936622Z process_group (optional): process group to scope synchronization, 2024-08-20T21:40:35.7936782Z default is the whole world 2024-08-20T21:40:35.7936872Z 2024-08-20T21:40:35.7936973Z Returns: 2024-08-20T21:40:35.7937338Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-08-20T21:40:35.7937667Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-08-20T21:40:35.7937963Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-08-20T21:40:35.7938082Z instead. 2024-08-20T21:40:35.7938173Z 2024-08-20T21:40:35.7938281Z Example:: 2024-08-20T21:40:35.7938386Z 2024-08-20T21:40:35.7938552Z >>> # Network with nn.BatchNorm layer 2024-08-20T21:40:35.7938743Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:40:35.7938912Z >>> module = torch.nn.Sequential( 2024-08-20T21:40:35.7939130Z >>> torch.nn.Linear(20, 100), 2024-08-20T21:40:35.7939314Z >>> torch.nn.BatchNorm1d(100), 2024-08-20T21:40:35.7939426Z >>> ).cuda() 2024-08-20T21:40:35.7939593Z >>> # creating process group (optional) 2024-08-20T21:40:35.7939804Z >>> # ranks is a list of int identifying rank ids. 2024-08-20T21:40:35.7939931Z >>> ranks = list(range(8)) 2024-08-20T21:40:35.7940077Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-08-20T21:40:35.7940295Z >>> # Note: every rank calls into new_group for every 2024-08-20T21:40:35.7940498Z >>> # process group created, even if that rank is not 2024-08-20T21:40:35.7940626Z >>> # part of the group. 2024-08-20T21:40:35.7940805Z >>> # xdoctest: +SKIP("distributed") 2024-08-20T21:40:35.7941142Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-08-20T21:40:35.7941421Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-08-20T21:40:35.7941839Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-08-20T21:40:35.7941930Z 2024-08-20T21:40:35.7942044Z 2024-08-20T21:40:35.7942454Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7942545Z 2024-08-20T21:40:35.7942705Z warnings.warn(msg) 2024-08-20T21:40:35.7942800Z 2024-08-20T21:40:35.7943016Z --- Parse Warning: 59 / 101 --- 2024-08-20T21:40:35.7944396Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60. 2024-08-20T21:40:35.7944815Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7944910Z 2024-08-20T21:40:35.7945369Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-08-20T21:40:35.7945464Z 2024-08-20T21:40:35.7945853Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-08-20T21:40:35.7946182Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-08-20T21:40:35.7946271Z 2024-08-20T21:40:35.7946726Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-08-20T21:40:35.7947107Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-08-20T21:40:35.7947329Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-08-20T21:40:35.7947435Z 2024-08-20T21:40:35.7947532Z Shape: 2024-08-20T21:40:35.7947937Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-08-20T21:40:35.7948303Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-08-20T21:40:35.7948701Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-08-20T21:40:35.7948885Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-08-20T21:40:35.7949007Z 2024-08-20T21:40:35.7949103Z Args: 2024-08-20T21:40:35.7949318Z dim (Union[int, str]): Dimension to be unflattened 2024-08-20T21:40:35.7949780Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-08-20T21:40:35.7949872Z 2024-08-20T21:40:35.7949990Z Examples: 2024-08-20T21:40:35.7950123Z >>> input = torch.randn(2, 50) 2024-08-20T21:40:35.7950239Z >>> # With tuple of ints 2024-08-20T21:40:35.7950375Z >>> m = nn.Sequential( 2024-08-20T21:40:35.7950492Z >>> nn.Linear(50, 50), 2024-08-20T21:40:35.7950625Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-08-20T21:40:35.7950749Z >>> ) 2024-08-20T21:40:35.7950916Z >>> output = m(input) 2024-08-20T21:40:35.7951031Z >>> output.size() 2024-08-20T21:40:35.7951162Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:40:35.7951273Z >>> # With torch.Size 2024-08-20T21:40:35.7951389Z >>> m = nn.Sequential( 2024-08-20T21:40:35.7951517Z >>> nn.Linear(50, 50), 2024-08-20T21:40:35.7951678Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-08-20T21:40:35.7951773Z >>> ) 2024-08-20T21:40:35.7951899Z >>> output = m(input) 2024-08-20T21:40:35.7952009Z >>> output.size() 2024-08-20T21:40:35.7952139Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:40:35.7952297Z >>> # With namedshape (tuple of tuples) 2024-08-20T21:40:35.7952568Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-08-20T21:40:35.7952934Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-08-20T21:40:35.7953061Z >>> output = unflatten(input) 2024-08-20T21:40:35.7953172Z >>> output.size() 2024-08-20T21:40:35.7953303Z torch.Size([2, 2, 5, 5]) 2024-08-20T21:40:35.7953390Z 2024-08-20T21:40:35.7953795Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7953901Z 2024-08-20T21:40:35.7954010Z warnings.warn(msg) 2024-08-20T21:40:35.7954096Z 2024-08-20T21:40:35.7954326Z --- Parse Warning: 60 / 101 --- 2024-08-20T21:40:35.7955860Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1696. 2024-08-20T21:40:35.7956293Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7956548Z Creates a criterion that measures the triplet loss given input 2024-08-20T21:40:35.7956814Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-08-20T21:40:35.7957117Z positive, and negative examples, respectively), and a nonnegative, 2024-08-20T21:40:35.7957524Z real-valued function ("distance function") used to compute the relationship 2024-08-20T21:40:35.7957824Z between the anchor and positive example ("positive distance") and the 2024-08-20T21:40:35.7958036Z anchor and negative example ("negative distance"). 2024-08-20T21:40:35.7958124Z 2024-08-20T21:40:35.7958480Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-08-20T21:40:35.7958610Z can be described as: 2024-08-20T21:40:35.7958703Z 2024-08-20T21:40:35.7958802Z .. math:: 2024-08-20T21:40:35.7959008Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-08-20T21:40:35.7959304Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-08-20T21:40:35.7959407Z 2024-08-20T21:40:35.7959841Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-08-20T21:40:35.7960240Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-08-20T21:40:35.7960587Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-08-20T21:40:35.7960952Z between the positive and negative distances that is required for the loss to 2024-08-20T21:40:35.7961273Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-08-20T21:40:35.7961444Z that the distance function can handle. 2024-08-20T21:40:35.7961533Z 2024-08-20T21:40:35.7961737Z If :attr:`reduction` is not ``'none'`` 2024-08-20T21:40:35.7961913Z (default ``'mean'``), then: 2024-08-20T21:40:35.7962002Z 2024-08-20T21:40:35.7962100Z .. math:: 2024-08-20T21:40:35.7962217Z \ell(x, y) = 2024-08-20T21:40:35.7962340Z \begin{cases} 2024-08-20T21:40:35.7962707Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-08-20T21:40:35.7963104Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-08-20T21:40:35.7963207Z \end{cases} 2024-08-20T21:40:35.7963308Z 2024-08-20T21:40:35.7963627Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-08-20T21:40:35.7963973Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-08-20T21:40:35.7964076Z 2024-08-20T21:40:35.7964170Z Args: 2024-08-20T21:40:35.7964609Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-08-20T21:40:35.7964875Z quantifies the closeness of two tensors. If not specified, 2024-08-20T21:40:35.7965102Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-08-20T21:40:35.7965460Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-08-20T21:40:35.7965852Z between the positive and negative distances required for the loss to be 0. Larger 2024-08-20T21:40:35.7966228Z margins penalize cases where the negative examples are not distant enough from the 2024-08-20T21:40:35.7966473Z anchors, relative to the positives. Default: :math:`1`. 2024-08-20T21:40:35.7966810Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-08-20T21:40:35.7967150Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-08-20T21:40:35.7967544Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-08-20T21:40:35.7967918Z negative example than the anchor is, swaps the positive example and the anchor in 2024-08-20T21:40:35.7968094Z the loss computation. Default: ``False``. 2024-08-20T21:40:35.7968481Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-08-20T21:40:35.7968841Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-08-20T21:40:35.7969200Z ``'mean'``: the sum of the output will be divided by the number of 2024-08-20T21:40:35.7969626Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-08-20T21:40:35.7969715Z 2024-08-20T21:40:35.7969817Z 2024-08-20T21:40:35.7969912Z Shape: 2024-08-20T21:40:35.7970418Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-08-20T21:40:35.7970608Z as supported by the distance function. 2024-08-20T21:40:35.7971063Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-08-20T21:40:35.7971168Z otherwise. 2024-08-20T21:40:35.7971274Z 2024-08-20T21:40:35.7971379Z Examples:: 2024-08-20T21:40:35.7971468Z 2024-08-20T21:40:35.7971608Z >>> # Initialize embeddings 2024-08-20T21:40:35.7971769Z >>> embedding = nn.Embedding(1000, 128) 2024-08-20T21:40:35.7971949Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:40:35.7972120Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:40:35.7972327Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-08-20T21:40:35.7972482Z >>> anchor = embedding(anchor_ids) 2024-08-20T21:40:35.7972634Z >>> positive = embedding(positive_ids) 2024-08-20T21:40:35.7972786Z >>> negative = embedding(negative_ids) 2024-08-20T21:40:35.7972895Z >>> 2024-08-20T21:40:35.7973078Z >>> # Built-in Distance Function 2024-08-20T21:40:35.7973187Z >>> triplet_loss = \ 2024-08-20T21:40:35.7973557Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-08-20T21:40:35.7973760Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:40:35.7973877Z >>> output.backward() 2024-08-20T21:40:35.7973987Z >>> 2024-08-20T21:40:35.7974171Z >>> # Custom Distance Function 2024-08-20T21:40:35.7974295Z >>> def l_infinity(x1, x2): 2024-08-20T21:40:35.7974586Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-08-20T21:40:35.7974683Z >>> 2024-08-20T21:40:35.7974952Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-08-20T21:40:35.7975064Z >>> triplet_loss = ( 2024-08-20T21:40:35.7975427Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-08-20T21:40:35.7975643Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:40:35.7975760Z >>> output.backward() 2024-08-20T21:40:35.7975856Z >>> 2024-08-20T21:40:35.7976024Z >>> # Custom Distance Function (Lambda) 2024-08-20T21:40:35.7976136Z >>> triplet_loss = ( 2024-08-20T21:40:35.7976310Z >>> nn.TripletMarginWithDistanceLoss( 2024-08-20T21:40:35.7976686Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-08-20T21:40:35.7976886Z >>> output = triplet_loss(anchor, positive, negative) 2024-08-20T21:40:35.7977002Z >>> output.backward() 2024-08-20T21:40:35.7977106Z 2024-08-20T21:40:35.7977210Z Reference: 2024-08-20T21:40:35.7977622Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-08-20T21:40:35.7977878Z http://www.bmva.org/bmvc/2016/papers/paper119/index.html 2024-08-20T21:40:35.7978001Z 2024-08-20T21:40:35.7978429Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-08-20T21:40:35.7978518Z 2024-08-20T21:40:35.7978632Z warnings.warn(msg) 2024-08-20T21:40:35.7978736Z 2024-08-20T21:40:35.7978950Z --- Parse Warning: 61 / 101 --- 2024-08-20T21:40:35.7980337Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=395. 2024-08-20T21:40:35.7980770Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7980970Z Computes a partial inverse of :class:`MaxPool2d`. 2024-08-20T21:40:35.7981059Z 2024-08-20T21:40:35.7981514Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-08-20T21:40:35.7981607Z 2024-08-20T21:40:35.7981907Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-08-20T21:40:35.7982248Z including the indices of the maximal values and computes a partial inverse 2024-08-20T21:40:35.7982494Z in which all non-maximal values are set to zero. 2024-08-20T21:40:35.7982595Z 2024-08-20T21:40:35.7982689Z Note: 2024-08-20T21:40:35.7983102Z This operation may behave nondeterministically when the input indices has repeat values. 2024-08-20T21:40:35.7983619Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-08-20T21:40:35.7983712Z 2024-08-20T21:40:35.7984020Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-08-20T21:40:35.7984297Z sizes. Hence, the inversion process can get ambiguous. 2024-08-20T21:40:35.7984554Z To accommodate this, you can provide the needed output size 2024-08-20T21:40:35.7984842Z as an additional argument :attr:`output_size` in the forward call. 2024-08-20T21:40:35.7985018Z See the Inputs and Example below. 2024-08-20T21:40:35.7985105Z 2024-08-20T21:40:35.7985213Z Args: 2024-08-20T21:40:35.7985452Z kernel_size (int or tuple): Size of the max pooling window. 2024-08-20T21:40:35.7985680Z stride (int or tuple): Stride of the max pooling window. 2024-08-20T21:40:35.7985873Z It is set to :attr:`kernel_size` by default. 2024-08-20T21:40:35.7986178Z padding (int or tuple): Padding that was added to the input 2024-08-20T21:40:35.7986271Z 2024-08-20T21:40:35.7986382Z Inputs: 2024-08-20T21:40:35.7986597Z - `input`: the input Tensor to invert 2024-08-20T21:40:35.7986949Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-08-20T21:40:35.7987233Z - `output_size` (optional): the targeted output size 2024-08-20T21:40:35.7987328Z 2024-08-20T21:40:35.7987429Z Shape: 2024-08-20T21:40:35.7987787Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-08-20T21:40:35.7988177Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-08-20T21:40:35.7988283Z 2024-08-20T21:40:35.7988388Z .. math:: 2024-08-20T21:40:35.7989288Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-08-20T21:40:35.7989402Z 2024-08-20T21:40:35.7989507Z .. math:: 2024-08-20T21:40:35.7990021Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-08-20T21:40:35.7990132Z 2024-08-20T21:40:35.7990560Z or as given by :attr:`output_size` in the call operator 2024-08-20T21:40:35.7990656Z 2024-08-20T21:40:35.7990780Z Example:: 2024-08-20T21:40:35.7990971Z 2024-08-20T21:40:35.7991198Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-08-20T21:40:35.7991384Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-08-20T21:40:35.7991569Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-08-20T21:40:35.7991725Z [ 5., 6., 7., 8.], 2024-08-20T21:40:35.7991898Z [ 9., 10., 11., 12.], 2024-08-20T21:40:35.7992055Z [13., 14., 15., 16.]]]]) 2024-08-20T21:40:35.7992225Z >>> output, indices = pool(input) 2024-08-20T21:40:35.7992356Z >>> unpool(output, indices) 2024-08-20T21:40:35.7992500Z tensor([[[[ 0., 0., 0., 0.], 2024-08-20T21:40:35.7992657Z [ 0., 6., 0., 8.], 2024-08-20T21:40:35.7992789Z [ 0., 0., 0., 0.], 2024-08-20T21:40:35.7992920Z [ 0., 14., 0., 16.]]]]) 2024-08-20T21:40:35.7993232Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-08-20T21:40:35.7993430Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-08-20T21:40:35.7993584Z [ 6., 7., 8., 9., 10.], 2024-08-20T21:40:35.7993757Z [11., 12., 13., 14., 15.], 2024-08-20T21:40:35.7993919Z [16., 17., 18., 19., 20.]]]]) 2024-08-20T21:40:35.7994078Z >>> output, indices = pool(input) 2024-08-20T21:40:35.7994318Z >>> # This call will not work without specifying output_size 2024-08-20T21:40:35.7994520Z >>> unpool(output, indices, output_size=input.size()) 2024-08-20T21:40:35.7994675Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-08-20T21:40:35.7994854Z [ 0., 7., 0., 9., 0.], 2024-08-20T21:40:35.7994983Z [ 0., 0., 0., 0., 0.], 2024-08-20T21:40:35.7995137Z [ 0., 17., 0., 19., 0.]]]]) 2024-08-20T21:40:35.7995229Z 2024-08-20T21:40:35.7995315Z 2024-08-20T21:40:35.7995422Z 2024-08-20T21:40:35.7995845Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.7995934Z 2024-08-20T21:40:35.7996061Z warnings.warn(msg) 2024-08-20T21:40:35.7996148Z 2024-08-20T21:40:35.7996360Z --- Parse Warning: 62 / 101 --- 2024-08-20T21:40:35.7997830Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=270. 2024-08-20T21:40:35.7998259Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.7998805Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-08-20T21:40:35.7998893Z 2024-08-20T21:40:35.7999344Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-08-20T21:40:35.7999496Z and with 2D inputs, this class 2024-08-20T21:40:35.7999587Z 2024-08-20T21:40:35.8000021Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-08-20T21:40:35.8000474Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-08-20T21:40:35.8000905Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-08-20T21:40:35.8000993Z 2024-08-20T21:40:35.8001498Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-08-20T21:40:35.8001608Z operations. 2024-08-20T21:40:35.8001707Z 2024-08-20T21:40:35.8002118Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-08-20T21:40:35.8002479Z pass. This scales the output of the Embedding before performing a weighted 2024-08-20T21:40:35.8002924Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-08-20T21:40:35.8003250Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-08-20T21:40:35.8003377Z :attr:`per_sample_weights`. 2024-08-20T21:40:35.8003479Z 2024-08-20T21:40:35.8003574Z Args: 2024-08-20T21:40:35.8003822Z num_embeddings (int): size of the dictionary of embeddings 2024-08-20T21:40:35.8004057Z embedding_dim (int): the size of each embedding vector 2024-08-20T21:40:35.8004490Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-08-20T21:40:35.8004722Z is renormalized to have norm :attr:`max_norm`. 2024-08-20T21:40:35.8005328Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-08-20T21:40:35.8005785Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-08-20T21:40:35.8006125Z the words in the mini-batch. Default ``False``. 2024-08-20T21:40:35.8006383Z Note: this option is not supported when ``mode="max"``. 2024-08-20T21:40:35.8006744Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-08-20T21:40:35.8007068Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-08-20T21:40:35.8007366Z into consideration. ``"mean"`` computes the average of the values 2024-08-20T21:40:35.8007685Z in the bag, ``"max"`` computes the max value over each bag. 2024-08-20T21:40:35.8007841Z Default: ``"mean"`` 2024-08-20T21:40:35.8008294Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-08-20T21:40:35.8008662Z Notes for more details regarding sparse gradients. Note: this option is not 2024-08-20T21:40:35.8008838Z supported when ``mode="max"``. 2024-08-20T21:40:35.8009400Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-08-20T21:40:35.8009734Z is equivalent to the size of `indices`. This matches the CSR format. 2024-08-20T21:40:35.8010254Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-08-20T21:40:35.8010645Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-08-20T21:40:35.8011025Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-08-20T21:40:35.8011390Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-08-20T21:40:35.8011763Z zeros, but can be updated to another value to be used as the padding vector. 2024-08-20T21:40:35.8012118Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-08-20T21:40:35.8012296Z reduction. 2024-08-20T21:40:35.8012387Z 2024-08-20T21:40:35.8012493Z Attributes: 2024-08-20T21:40:35.8012942Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-08-20T21:40:35.8013138Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-08-20T21:40:35.8013259Z 2024-08-20T21:40:35.8013379Z Examples:: 2024-08-20T21:40:35.8013466Z 2024-08-20T21:40:35.8013702Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-08-20T21:40:35.8014003Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-08-20T21:40:35.8014175Z >>> # a batch of 2 samples of 4 indices each 2024-08-20T21:40:35.8014434Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-08-20T21:40:35.8014648Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-20T21:40:35.8014903Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:40:35.8015062Z >>> embedding_sum(input, offsets) 2024-08-20T21:40:35.8015256Z tensor([[-0.8861, -5.4350, -0.0523], 2024-08-20T21:40:35.8015449Z [ 1.1306, -2.5798, -1.0044]]) 2024-08-20T21:40:35.8015552Z 2024-08-20T21:40:35.8015690Z >>> # Example with padding_idx 2024-08-20T21:40:35.8016047Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-08-20T21:40:35.8016322Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-08-20T21:40:35.8016515Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-08-20T21:40:35.8016659Z >>> embedding_sum(input, offsets) 2024-08-20T21:40:35.8016809Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-08-20T21:40:35.8016999Z [-0.7082, 3.2145, -2.6251]]) 2024-08-20T21:40:35.8017088Z 2024-08-20T21:40:35.8017347Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-08-20T21:40:35.8017543Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-08-20T21:40:35.8017795Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-08-20T21:40:35.8017920Z embedding.weight, 2024-08-20T21:40:35.8018085Z padding_idx=embedding.padding_idx, 2024-08-20T21:40:35.8018256Z mode='sum') 2024-08-20T21:40:35.8018353Z 2024-08-20T21:40:35.8018752Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8018855Z 2024-08-20T21:40:35.8018969Z warnings.warn(msg) 2024-08-20T21:40:35.8019056Z 2024-08-20T21:40:35.8019281Z --- Parse Warning: 63 / 101 --- 2024-08-20T21:40:35.8020886Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1748. 2024-08-20T21:40:35.8021323Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8021415Z 2024-08-20T21:40:35.8021727Z Context manager for training with uneven inputs across processes in DDP. 2024-08-20T21:40:35.8021828Z 2024-08-20T21:40:35.8022194Z This context manager will keep track of already-joined DDP processes, 2024-08-20T21:40:35.8022484Z and "shadow" the forward and backward passes by inserting collective 2024-08-20T21:40:35.8022864Z communication operations to match with the ones created by non-joined 2024-08-20T21:40:35.8023174Z DDP processes. This will ensure each collective call has a corresponding 2024-08-20T21:40:35.8023546Z call by already-joined DDP processes, preventing hangs or errors that 2024-08-20T21:40:35.8023814Z would otherwise happen when training with uneven inputs across 2024-08-20T21:40:35.8024120Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-08-20T21:40:35.8024411Z specified to be ``True``, all trainers will throw an error once one rank 2024-08-20T21:40:35.8024700Z runs out of inputs, allowing these errors to be caught and handled 2024-08-20T21:40:35.8024832Z according to application logic. 2024-08-20T21:40:35.8024932Z 2024-08-20T21:40:35.8025227Z Once all DDP processes have joined, the context manager will broadcast 2024-08-20T21:40:35.8025567Z the model corresponding to the last joined process to all processes to 2024-08-20T21:40:35.8025776Z ensure the model is the same across all processes 2024-08-20T21:40:35.8025902Z (which is guaranteed by DDP). 2024-08-20T21:40:35.8025989Z 2024-08-20T21:40:35.8026285Z To use this to enable training with uneven inputs across processes, 2024-08-20T21:40:35.8026581Z simply wrap this context manager around your training loop. No further 2024-08-20T21:40:35.8026805Z modifications to the model or data loading is required. 2024-08-20T21:40:35.8026907Z 2024-08-20T21:40:35.8027014Z .. warning:: 2024-08-20T21:40:35.8027305Z If the model or training loop this context manager is wrapped around 2024-08-20T21:40:35.8027559Z has additional distributed collective operations, such as 2024-08-20T21:40:35.8027874Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-08-20T21:40:35.8028161Z ``throw_on_early_termination`` must be enabled. This is because this 2024-08-20T21:40:35.8028509Z context manager is not aware of non-DDP collective communication. 2024-08-20T21:40:35.8028740Z This flag will cause all ranks to throw when any one rank 2024-08-20T21:40:35.8029026Z exhausts inputs, allowing these errors to be caught and recovered 2024-08-20T21:40:35.8029143Z from across all ranks. 2024-08-20T21:40:35.8029231Z 2024-08-20T21:40:35.8029342Z Args: 2024-08-20T21:40:35.8029589Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-08-20T21:40:35.8029865Z gradients by the initial ``world_size`` DDP training was launched 2024-08-20T21:40:35.8030107Z with. If ``False``, will compute the effective world size 2024-08-20T21:40:35.8030392Z (number of ranks that have not depleted their inputs yet) and 2024-08-20T21:40:35.8030581Z divide gradients by that during allreduce. Set 2024-08-20T21:40:35.8030833Z ``divide_by_initial_world_size=True`` to ensure every input 2024-08-20T21:40:35.8031110Z sample including the uneven inputs have equal weight in terms of 2024-08-20T21:40:35.8031352Z how much they contribute to the global gradient. This is 2024-08-20T21:40:35.8031581Z achieved by always dividing the gradient by the initial 2024-08-20T21:40:35.8031831Z ``world_size`` even when we encounter uneven inputs. If you set 2024-08-20T21:40:35.8032130Z this to ``False``, we divide the gradient by the remaining 2024-08-20T21:40:35.8032401Z number of nodes. This ensures parity with training on a smaller 2024-08-20T21:40:35.8032640Z ``world_size`` although it also means the uneven inputs would 2024-08-20T21:40:35.8032914Z contribute more towards the global gradient. Typically, you 2024-08-20T21:40:35.8033174Z would want to set this to ``True`` for cases where the last few 2024-08-20T21:40:35.8033454Z inputs of your training job are uneven. In extreme cases, where 2024-08-20T21:40:35.8033712Z there is a large discrepancy in the number of inputs, setting 2024-08-20T21:40:35.8033900Z this to ``False`` might provide better results. 2024-08-20T21:40:35.8034202Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-08-20T21:40:35.8034439Z in ``enable=False`` to disable in cases where you know that 2024-08-20T21:40:35.8034695Z inputs are even across participating processes. Default is 2024-08-20T21:40:35.8034813Z ``True``. 2024-08-20T21:40:35.8035059Z throw_on_early_termination (bool): Whether to throw an error 2024-08-20T21:40:35.8035296Z or continue training when at least one rank has exhausted 2024-08-20T21:40:35.8035577Z inputs. If ``True``, will throw upon the first rank reaching end 2024-08-20T21:40:35.8035820Z of data. If ``False``, will continue training with a smaller 2024-08-20T21:40:35.8036126Z effective world size until all ranks are joined. Note that if 2024-08-20T21:40:35.8036286Z this flag is specified, then the flag 2024-08-20T21:40:35.8036516Z ``divide_by_initial_world_size`` would be ignored. Default 2024-08-20T21:40:35.8036641Z is ``False``. 2024-08-20T21:40:35.8036731Z 2024-08-20T21:40:35.8036820Z 2024-08-20T21:40:35.8036939Z Example:: 2024-08-20T21:40:35.8037028Z 2024-08-20T21:40:35.8037182Z >>> # xdoctest: +SKIP("Distributed") 2024-08-20T21:40:35.8037312Z >>> import torch 2024-08-20T21:40:35.8037464Z >>> import torch.distributed as dist 2024-08-20T21:40:35.8037567Z >>> import os 2024-08-20T21:40:35.8037748Z >>> import torch.multiprocessing as mp 2024-08-20T21:40:35.8037874Z >>> import torch.nn as nn 2024-08-20T21:40:35.8038002Z >>> # On each spawned worker 2024-08-20T21:40:35.8038136Z >>> def worker(rank): 2024-08-20T21:40:35.8038374Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-08-20T21:40:35.8038513Z >>> torch.cuda.set_device(rank) 2024-08-20T21:40:35.8038708Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-08-20T21:40:35.8038953Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-08-20T21:40:35.8039152Z >>> model, device_ids=[rank], output_device=rank 2024-08-20T21:40:35.8039252Z >>> ) 2024-08-20T21:40:35.8039430Z >>> # Rank 1 gets one more input than rank 0. 2024-08-20T21:40:35.8039702Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-08-20T21:40:35.8039819Z >>> with model.join(): 2024-08-20T21:40:35.8039940Z >>> for _ in range(5): 2024-08-20T21:40:35.8040120Z >>> for inp in inputs: 2024-08-20T21:40:35.8040267Z >>> loss = model(inp).sum() 2024-08-20T21:40:35.8040400Z >>> loss.backward() 2024-08-20T21:40:35.8040698Z >>> # Without the join() API, the below synchronization will hang 2024-08-20T21:40:35.8040963Z >>> # blocking for rank 1's allreduce to complete. 2024-08-20T21:40:35.8041192Z >>> torch.cuda.synchronize(device=rank) 2024-08-20T21:40:35.8041301Z 2024-08-20T21:40:35.8041701Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8041805Z 2024-08-20T21:40:35.8041919Z warnings.warn(msg) 2024-08-20T21:40:35.8042008Z 2024-08-20T21:40:35.8042292Z --- Parse Warning: 64 / 101 --- 2024-08-20T21:40:35.8043937Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2039. 2024-08-20T21:40:35.8044356Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8044460Z 2024-08-20T21:40:35.8044879Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-08-20T21:40:35.8044970Z 2024-08-20T21:40:35.8045258Z Registers an optimizer with DDP such that the optimization for a 2024-08-20T21:40:35.8045598Z parameter will run immediately when that parameter's gradient is 2024-08-20T21:40:35.8045878Z finished with reduction, instead of waiting for all parameters' 2024-08-20T21:40:35.8046169Z gradients to finish reduction. This can result in a training speedup 2024-08-20T21:40:35.8046463Z depending on your workload since the optimizer can run while gradient 2024-08-20T21:40:35.8046780Z reduction for other parameters are still ongoing. In addition, this has 2024-08-20T21:40:35.8047081Z the potential to reduce peak memory consumption during training, as it 2024-08-20T21:40:35.8047423Z only needs to load the per-parameter optimizer states of a single 2024-08-20T21:40:35.8047830Z parameter at a time, instead of loading all per-parameter optimizer 2024-08-20T21:40:35.8047933Z states at once. 2024-08-20T21:40:35.8048021Z 2024-08-20T21:40:35.8048128Z Args: 2024-08-20T21:40:35.8048391Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-08-20T21:40:35.8048508Z as a fused optimizer. 2024-08-20T21:40:35.8048732Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-08-20T21:40:35.8049013Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-08-20T21:40:35.8049326Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-08-20T21:40:35.8049595Z Optimizers. If this is omitted, all DDP model parameters will be 2024-08-20T21:40:35.8049699Z optimized. 2024-08-20T21:40:35.8049986Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-08-20T21:40:35.8050077Z 2024-08-20T21:40:35.8050255Z .. warning :: 2024-08-20T21:40:35.8050555Z _register_fused_optim should only be called once on a DDP instance, 2024-08-20T21:40:35.8050829Z and registering multiple fused optimizers for the same DDP model 2024-08-20T21:40:35.8050996Z is not currently supported. Please ping 2024-08-20T21:40:35.8051322Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:40:35.8051433Z for your use case. 2024-08-20T21:40:35.8051522Z 2024-08-20T21:40:35.8051644Z .. warning :: 2024-08-20T21:40:35.8051897Z _register_fused_optim and register_comm_hook currently do not 2024-08-20T21:40:35.8052179Z compose together, meaning that custom DDP communication hooks are 2024-08-20T21:40:35.8052413Z not supported with overlapped optimizers. Please ping 2024-08-20T21:40:35.8052752Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:40:35.8052877Z for your use case. 2024-08-20T21:40:35.8052984Z 2024-08-20T21:40:35.8053085Z .. warning :: 2024-08-20T21:40:35.8053388Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-08-20T21:40:35.8053549Z with overlapped optimizer. Please ping 2024-08-20T21:40:35.8053841Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-08-20T21:40:35.8053962Z for your use case. 2024-08-20T21:40:35.8054049Z 2024-08-20T21:40:35.8054149Z Example:: 2024-08-20T21:40:35.8054256Z 2024-08-20T21:40:35.8054485Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-08-20T21:40:35.8054979Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-08-20T21:40:35.8055269Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-08-20T21:40:35.8055390Z >>> lr = 1e-2 2024-08-20T21:40:35.8055514Z >>> betas = (0.9, 0.99) 2024-08-20T21:40:35.8055639Z >>> eps = 1e-6 2024-08-20T21:40:35.8055937Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-08-20T21:40:35.8056110Z >>> # Example with subset of parameters 2024-08-20T21:40:35.8056289Z >>> params_to_opt = [list(net.parameters())[0]] 2024-08-20T21:40:35.8056418Z >>> net._register_fused_optim( 2024-08-20T21:40:35.8056750Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-08-20T21:40:35.8056849Z ... ) 2024-08-20T21:40:35.8056937Z 2024-08-20T21:40:35.8057362Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8057452Z 2024-08-20T21:40:35.8057561Z warnings.warn(msg) 2024-08-20T21:40:35.8057661Z 2024-08-20T21:40:35.8057875Z --- Parse Warning: 65 / 101 --- 2024-08-20T21:40:35.8059397Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=6. 2024-08-20T21:40:35.8059948Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8060233Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-08-20T21:40:35.8060337Z 2024-08-20T21:40:35.8060705Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-20T21:40:35.8061090Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-20T21:40:35.8061467Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-20T21:40:35.8061891Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-20T21:40:35.8061983Z 2024-08-20T21:40:35.8062096Z .. note:: 2024-08-20T21:40:35.8062411Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-08-20T21:40:35.8062725Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-08-20T21:40:35.8063022Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-20T21:40:35.8063313Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-20T21:40:35.8063640Z One place we are confident in is that NHWC(channels_last) conversion for 2024-08-20T21:40:35.8063936Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2024-08-20T21:40:35.8064224Z even in cases where we have to apply permutation to input tensors. 2024-08-20T21:40:35.8064327Z 2024-08-20T21:40:35.8064641Z Hence our strategy here is to convert only the weight of convolution to 2024-08-20T21:40:35.8064848Z channels_last. This ensures that; 2024-08-20T21:40:35.8065155Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-20T21:40:35.8065462Z outweigh overhead of permutation (if input is not in the same format). 2024-08-20T21:40:35.8065795Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-20T21:40:35.8065933Z from memory_format conversion. 2024-08-20T21:40:35.8066019Z 2024-08-20T21:40:35.8066345Z The optimal case is that, layers between convolution layers are channels 2024-08-20T21:40:35.8066664Z last compatible. Input tensor would be permuted to channels last when it 2024-08-20T21:40:35.8067019Z encounters the first convolution layer and stay in that memory format. 2024-08-20T21:40:35.8067351Z Hence following convolutions will not need to permute its input tensor. 2024-08-20T21:40:35.8067439Z 2024-08-20T21:40:35.8067749Z In case where a channels last incompatible layer is between convolution 2024-08-20T21:40:35.8068054Z layers, we need to permute the input tensor back to contiguous format 2024-08-20T21:40:35.8068369Z for that layer. The input tensor will go through the remaining layers in 2024-08-20T21:40:35.8068684Z contiguous format and be permuted to channels last when it encounters 2024-08-20T21:40:35.8069049Z another convolution layer. There's no point in propagating that 2024-08-20T21:40:35.8069350Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-20T21:40:35.8069476Z ``memory_format``. 2024-08-20T21:40:35.8069565Z 2024-08-20T21:40:35.8069883Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-20T21:40:35.8070238Z there might have been a better spot to fuse the permutation other than 2024-08-20T21:40:35.8070392Z immediately before a convolution. 2024-08-20T21:40:35.8070482Z 2024-08-20T21:40:35.8070588Z Args: 2024-08-20T21:40:35.8070881Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-08-20T21:40:35.8071040Z ``nn.Module`` 2024-08-20T21:40:35.8071246Z memory_format: user specified ``memory_format``, 2024-08-20T21:40:35.8071492Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-20T21:40:35.8071595Z 2024-08-20T21:40:35.8071691Z Returns: 2024-08-20T21:40:35.8071877Z The original module with updated ``nn.Conv2d`` 2024-08-20T21:40:35.8071979Z 2024-08-20T21:40:35.8072076Z Example: 2024-08-20T21:40:35.8072264Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:40:35.8072490Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-20T21:40:35.8072818Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-08-20T21:40:35.8072946Z >>> model = nn.Sequential( 2024-08-20T21:40:35.8073113Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-08-20T21:40:35.8073239Z >>> # This is identical to: 2024-08-20T21:40:35.8073568Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-20T21:40:35.8073935Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-08-20T21:40:35.8074051Z >>> out = model(input) 2024-08-20T21:40:35.8074157Z 2024-08-20T21:40:35.8074565Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8074655Z 2024-08-20T21:40:35.8074781Z warnings.warn(msg) 2024-08-20T21:40:35.8074868Z 2024-08-20T21:40:35.8075087Z --- Parse Warning: 66 / 101 --- 2024-08-20T21:40:35.8076625Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=81. 2024-08-20T21:40:35.8077080Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8077359Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-08-20T21:40:35.8077737Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-08-20T21:40:35.8078117Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-08-20T21:40:35.8078488Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-08-20T21:40:35.8078966Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-08-20T21:40:35.8079055Z 2024-08-20T21:40:35.8079173Z .. note:: 2024-08-20T21:40:35.8079504Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2024-08-20T21:40:35.8079810Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-08-20T21:40:35.8080126Z layer with 4d weight will be affected by ``model.to``, which does not 2024-08-20T21:40:35.8080421Z necessarily benefit from conversion to specified ``memory_format``. 2024-08-20T21:40:35.8080760Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2024-08-20T21:40:35.8081083Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2024-08-20T21:40:35.8081368Z even in cases where we have to apply permutation to input tensors. 2024-08-20T21:40:35.8081475Z 2024-08-20T21:40:35.8081792Z Hence our strategy here is to convert only the weight of convolution to 2024-08-20T21:40:35.8081947Z channels_last_3d. This ensures that; 2024-08-20T21:40:35.8082260Z 1. Fast convolution kernels will be used, the benefit of which could 2024-08-20T21:40:35.8082574Z outweigh overhead of permutation (if input is not in the same format). 2024-08-20T21:40:35.8082892Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-08-20T21:40:35.8083078Z from memory_format conversion. 2024-08-20T21:40:35.8083170Z 2024-08-20T21:40:35.8083490Z The optimal case is that, layers between convolution layers are channels 2024-08-20T21:40:35.8083825Z last compatible. Input tensor would be permuted to channels last when it 2024-08-20T21:40:35.8084132Z encounters the first convolution layer and stay in that memory format. 2024-08-20T21:40:35.8084466Z Hence following convolutions will not need to permute its input tensor. 2024-08-20T21:40:35.8084561Z 2024-08-20T21:40:35.8084872Z In case where a channels last incompatible layer is between convolution 2024-08-20T21:40:35.8085187Z layers, we need to permute the input tensor back to contiguous format 2024-08-20T21:40:35.8085507Z for that layer. The input tensor will go through the remaining layers in 2024-08-20T21:40:35.8085814Z contiguous format and be permuted to channels last when it encounters 2024-08-20T21:40:35.8086190Z another convolution layer. There's no point in propagating that 2024-08-20T21:40:35.8086493Z permutation to an earlier layer, as most layers are quite agnostic to 2024-08-20T21:40:35.8086609Z ``memory_format``. 2024-08-20T21:40:35.8086716Z 2024-08-20T21:40:35.8087034Z This claim might change when PyTorch supports fusion of permutation, as 2024-08-20T21:40:35.8087358Z there might have been a better spot to fuse the permutation other than 2024-08-20T21:40:35.8087517Z immediately before a convolution. 2024-08-20T21:40:35.8087609Z 2024-08-20T21:40:35.8087721Z Args: 2024-08-20T21:40:35.8088021Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-08-20T21:40:35.8088187Z ``nn.Module`` 2024-08-20T21:40:35.8088401Z memory_format: user specified ``memory_format``, 2024-08-20T21:40:35.8089003Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-08-20T21:40:35.8089094Z 2024-08-20T21:40:35.8089213Z Returns: 2024-08-20T21:40:35.8089411Z The original module with updated ``nn.Conv3d`` 2024-08-20T21:40:35.8089502Z 2024-08-20T21:40:35.8089618Z Example: 2024-08-20T21:40:35.8089804Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:40:35.8090009Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-08-20T21:40:35.8090816Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-08-20T21:40:35.8090957Z >>> model = nn.Sequential( 2024-08-20T21:40:35.8091137Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-08-20T21:40:35.8091269Z >>> # This is identical to: 2024-08-20T21:40:35.8091609Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-20T21:40:35.8091995Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-08-20T21:40:35.8092118Z >>> out = model(input) 2024-08-20T21:40:35.8092213Z 2024-08-20T21:40:35.8092664Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8092754Z 2024-08-20T21:40:35.8092869Z warnings.warn(msg) 2024-08-20T21:40:35.8092976Z 2024-08-20T21:40:35.8093193Z --- Parse Warning: 67 / 101 --- 2024-08-20T21:40:35.8094586Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=936. 2024-08-20T21:40:35.8095027Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8095335Z Prune tensor by removing random channels along the specified dimension. 2024-08-20T21:40:35.8095443Z 2024-08-20T21:40:35.8095794Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-20T21:40:35.8096090Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-20T21:40:35.8096287Z along the specified ``dim`` selected at random. 2024-08-20T21:40:35.8096553Z Modifies module in place (and also return the modified module) 2024-08-20T21:40:35.8096650Z by: 2024-08-20T21:40:35.8096752Z 2024-08-20T21:40:35.8097117Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:40:35.8097415Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:40:35.8097718Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:40:35.8097990Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:40:35.8098158Z ``name+'_orig'``. 2024-08-20T21:40:35.8098248Z 2024-08-20T21:40:35.8098347Z Args: 2024-08-20T21:40:35.8098599Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:40:35.8098847Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:40:35.8098952Z will act. 2024-08-20T21:40:35.8099194Z amount (int or float): quantity of parameters to prune. 2024-08-20T21:40:35.8099435Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:40:35.8099702Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:40:35.8099891Z absolute number of parameters to prune. 2024-08-20T21:40:35.8100177Z dim (int): index of the dim along which we define channels to prune. 2024-08-20T21:40:35.8100266Z 2024-08-20T21:40:35.8100422Z Returns: 2024-08-20T21:40:35.8100721Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:40:35.8100821Z 2024-08-20T21:40:35.8100923Z Examples: 2024-08-20T21:40:35.8101041Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.8101203Z >>> m = prune.random_structured( 2024-08-20T21:40:35.8101452Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-08-20T21:40:35.8101547Z ... ) 2024-08-20T21:40:35.8101804Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-08-20T21:40:35.8101930Z >>> print(columns_pruned) 2024-08-20T21:40:35.8102024Z 3 2024-08-20T21:40:35.8102132Z 2024-08-20T21:40:35.8102588Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8102679Z 2024-08-20T21:40:35.8102806Z warnings.warn(msg) 2024-08-20T21:40:35.8102893Z 2024-08-20T21:40:35.8103110Z --- Parse Warning: 68 / 101 --- 2024-08-20T21:40:35.8104477Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=977. 2024-08-20T21:40:35.8104892Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8105423Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-08-20T21:40:35.8105510Z 2024-08-20T21:40:35.8105816Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-08-20T21:40:35.8106126Z by removing the specified ``amount`` of (currently unpruned) channels 2024-08-20T21:40:35.8106428Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-08-20T21:40:35.8106689Z Modifies module in place (and also return the modified module) 2024-08-20T21:40:35.8106798Z by: 2024-08-20T21:40:35.8106890Z 2024-08-20T21:40:35.8107295Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:40:35.8107606Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:40:35.8107927Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:40:35.8108212Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:40:35.8108359Z ``name+'_orig'``. 2024-08-20T21:40:35.8108450Z 2024-08-20T21:40:35.8108558Z Args: 2024-08-20T21:40:35.8108796Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:40:35.8109046Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:40:35.8109169Z will act. 2024-08-20T21:40:35.8109394Z amount (int or float): quantity of parameters to prune. 2024-08-20T21:40:35.8109638Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:40:35.8109922Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:40:35.8110097Z absolute number of parameters to prune. 2024-08-20T21:40:35.8110442Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-08-20T21:40:35.8110658Z entries for argument ``p`` in :func:`torch.norm`. 2024-08-20T21:40:35.8110943Z dim (int): index of the dim along which we define channels to prune. 2024-08-20T21:40:35.8111261Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-08-20T21:40:35.8111522Z shape as module parameter) used to compute mask for pruning. 2024-08-20T21:40:35.8111837Z The values in this tensor indicate the importance of the corresponding 2024-08-20T21:40:35.8112020Z elements in the parameter being pruned. 2024-08-20T21:40:35.8112337Z If unspecified or None, the module parameter will be used in its place. 2024-08-20T21:40:35.8112456Z 2024-08-20T21:40:35.8112570Z Returns: 2024-08-20T21:40:35.8112872Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:40:35.8112960Z 2024-08-20T21:40:35.8113075Z Examples: 2024-08-20T21:40:35.8113231Z >>> from torch.nn.utils import prune 2024-08-20T21:40:35.8113361Z >>> m = prune.ln_structured( 2024-08-20T21:40:35.8113719Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-08-20T21:40:35.8113815Z ... ) 2024-08-20T21:40:35.8113922Z 2024-08-20T21:40:35.8114322Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8114462Z 2024-08-20T21:40:35.8114590Z warnings.warn(msg) 2024-08-20T21:40:35.8114678Z 2024-08-20T21:40:35.8114891Z --- Parse Warning: 69 / 101 --- 2024-08-20T21:40:35.8116317Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1024. 2024-08-20T21:40:35.8116736Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8116824Z 2024-08-20T21:40:35.8117400Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-08-20T21:40:35.8117488Z 2024-08-20T21:40:35.8117619Z Modifies modules in place by: 2024-08-20T21:40:35.8117718Z 2024-08-20T21:40:35.8118074Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:40:35.8118384Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:40:35.8118666Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:40:35.8118935Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:40:35.8119091Z ``name+'_orig'``. 2024-08-20T21:40:35.8119178Z 2024-08-20T21:40:35.8119270Z Args: 2024-08-20T21:40:35.8119539Z parameters (Iterable of (module, name) tuples): parameters of 2024-08-20T21:40:35.8119845Z the model to prune in a global fashion, i.e. by aggregating all 2024-08-20T21:40:35.8120122Z weights prior to deciding which ones to prune. module must be of 2024-08-20T21:40:35.8120336Z type :class:`nn.Module`, and name must be a string. 2024-08-20T21:40:35.8120625Z pruning_method (function): a valid pruning function from this module, 2024-08-20T21:40:35.8120881Z or a custom one implemented by the user that satisfies the 2024-08-20T21:40:35.8121256Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-08-20T21:40:35.8121557Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-08-20T21:40:35.8121936Z the corresponding parameter's importance scores tensor. The tensor 2024-08-20T21:40:35.8122236Z should be the same shape as the parameter, and is used for computing 2024-08-20T21:40:35.8122351Z mask for pruning. 2024-08-20T21:40:35.8122649Z If unspecified or None, the parameter will be used in place of its 2024-08-20T21:40:35.8122764Z importance scores. 2024-08-20T21:40:35.8122924Z kwargs: other keyword arguments such as: 2024-08-20T21:40:35.8123210Z amount (int or float): quantity of parameters to prune across the 2024-08-20T21:40:35.8123333Z specified parameters. 2024-08-20T21:40:35.8123571Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-08-20T21:40:35.8123853Z fraction of parameters to prune. If ``int``, it represents the 2024-08-20T21:40:35.8124017Z absolute number of parameters to prune. 2024-08-20T21:40:35.8124119Z 2024-08-20T21:40:35.8124216Z Raises: 2024-08-20T21:40:35.8124508Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-08-20T21:40:35.8124608Z 2024-08-20T21:40:35.8124700Z Note: 2024-08-20T21:40:35.8125057Z Since global structured pruning doesn't make much sense unless the 2024-08-20T21:40:35.8125351Z norm is normalized by the size of the parameter, we now limit the 2024-08-20T21:40:35.8125547Z scope of global pruning to unstructured methods. 2024-08-20T21:40:35.8125636Z 2024-08-20T21:40:35.8125749Z Examples: 2024-08-20T21:40:35.8125903Z >>> from torch.nn.utils import prune 2024-08-20T21:40:35.8126063Z >>> from collections import OrderedDict 2024-08-20T21:40:35.8126233Z >>> net = nn.Sequential(OrderedDict([ 2024-08-20T21:40:35.8126516Z ... ('first', nn.Linear(10, 4)), 2024-08-20T21:40:35.8126711Z ... ('second', nn.Linear(4, 1)), 2024-08-20T21:40:35.8126825Z ... ])) 2024-08-20T21:40:35.8126953Z >>> parameters_to_prune = ( 2024-08-20T21:40:35.8127125Z ... (net.first, 'weight'), 2024-08-20T21:40:35.8127312Z ... (net.second, 'weight'), 2024-08-20T21:40:35.8127409Z ... ) 2024-08-20T21:40:35.8127562Z >>> prune.global_unstructured( 2024-08-20T21:40:35.8127689Z ... parameters_to_prune, 2024-08-20T21:40:35.8127864Z ... pruning_method=prune.L1Unstructured, 2024-08-20T21:40:35.8127988Z ... amount=10, 2024-08-20T21:40:35.8128085Z ... ) 2024-08-20T21:40:35.8128388Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-08-20T21:40:35.8128506Z tensor(10) 2024-08-20T21:40:35.8128597Z 2024-08-20T21:40:35.8128687Z 2024-08-20T21:40:35.8129110Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8129199Z 2024-08-20T21:40:35.8129314Z warnings.warn(msg) 2024-08-20T21:40:35.8129421Z 2024-08-20T21:40:35.8129635Z --- Parse Warning: 70 / 101 --- 2024-08-20T21:40:35.8131098Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1143. 2024-08-20T21:40:35.8131581Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8132229Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-08-20T21:40:35.8132338Z 2024-08-20T21:40:35.8132626Z Modifies module in place (and also return the modified module) by: 2024-08-20T21:40:35.8132716Z 2024-08-20T21:40:35.8133102Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-08-20T21:40:35.8133404Z binary mask applied to the parameter ``name`` by the pruning method. 2024-08-20T21:40:35.8133696Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-08-20T21:40:35.8133992Z original (unpruned) parameter is stored in a new parameter named 2024-08-20T21:40:35.8134141Z ``name+'_orig'``. 2024-08-20T21:40:35.8134230Z 2024-08-20T21:40:35.8134346Z Args: 2024-08-20T21:40:35.8134582Z module (nn.Module): module containing the tensor to prune 2024-08-20T21:40:35.8134833Z name (str): parameter name within ``module`` on which pruning 2024-08-20T21:40:35.8134957Z will act. 2024-08-20T21:40:35.8135194Z mask (Tensor): binary mask to be applied to the parameter. 2024-08-20T21:40:35.8135297Z 2024-08-20T21:40:35.8135392Z Returns: 2024-08-20T21:40:35.8135690Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-08-20T21:40:35.8135799Z 2024-08-20T21:40:35.8135901Z Examples: 2024-08-20T21:40:35.8136057Z >>> from torch.nn.utils import prune 2024-08-20T21:40:35.8136209Z >>> m = prune.custom_from_mask( 2024-08-20T21:40:35.8136563Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-08-20T21:40:35.8136659Z ... ) 2024-08-20T21:40:35.8136790Z >>> print(m.bias_mask) 2024-08-20T21:40:35.8136909Z tensor([0., 1., 0.]) 2024-08-20T21:40:35.8136995Z 2024-08-20T21:40:35.8137099Z 2024-08-20T21:40:35.8137500Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8137588Z 2024-08-20T21:40:35.8137715Z warnings.warn(msg) 2024-08-20T21:40:35.8137804Z 2024-08-20T21:40:35.8138016Z --- Parse Warning: 71 / 101 --- 2024-08-20T21:40:35.8139473Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=106. 2024-08-20T21:40:35.8139894Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8140390Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-08-20T21:40:35.8140481Z 2024-08-20T21:40:35.8140800Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-08-20T21:40:35.8141110Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-08-20T21:40:35.8141391Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-08-20T21:40:35.8141493Z (UAI 2018). 2024-08-20T21:40:35.8141595Z 2024-08-20T21:40:35.8141871Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-08-20T21:40:35.8142195Z but using exponential weights instead of equal weights across iterations. 2024-08-20T21:40:35.8142298Z 2024-08-20T21:40:35.8142614Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-08-20T21:40:35.8142940Z on the device :attr:`device` and allows to compute running averages of the 2024-08-20T21:40:35.8143078Z parameters of the :attr:`model`. 2024-08-20T21:40:35.8143166Z 2024-08-20T21:40:35.8143275Z Args: 2024-08-20T21:40:35.8143487Z model (torch.nn.Module): model to use with SWA/EMA 2024-08-20T21:40:35.8143833Z device (torch.device, optional): if provided, the averaged model will be 2024-08-20T21:40:35.8143990Z stored on the :attr:`device` 2024-08-20T21:40:35.8144266Z avg_fn (function, optional): the averaging function used to update 2024-08-20T21:40:35.8144541Z parameters; the function must take in the current value of the 2024-08-20T21:40:35.8144844Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-08-20T21:40:35.8145179Z parameter, and the number of models already averaged; if None, 2024-08-20T21:40:35.8145479Z an equally weighted average is used (default: None) 2024-08-20T21:40:35.8145787Z multi_avg_fn (function, optional): the averaging function used to update 2024-08-20T21:40:35.8146105Z parameters inplace; the function must take in the current values of the 2024-08-20T21:40:35.8146478Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-08-20T21:40:35.8146806Z parameters as a list, and the number of models already averaged; if None, 2024-08-20T21:40:35.8147015Z an equally weighted average is used (default: None) 2024-08-20T21:40:35.8147311Z use_buffers (bool): if ``True``, it will compute running averages for 2024-08-20T21:40:35.8147618Z both the parameters and the buffers of the model. (default: ``False``) 2024-08-20T21:40:35.8147704Z 2024-08-20T21:40:35.8147824Z Example: 2024-08-20T21:40:35.8148000Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:35.8148183Z >>> loader, optimizer, model, loss_fn = ... 2024-08-20T21:40:35.8148455Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-08-20T21:40:35.8148752Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-08-20T21:40:35.8148928Z >>> T_max=300) 2024-08-20T21:40:35.8149043Z >>> swa_start = 160 2024-08-20T21:40:35.8149230Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-08-20T21:40:35.8149371Z >>> for i in range(300): 2024-08-20T21:40:35.8149520Z >>> for input, target in loader: 2024-08-20T21:40:35.8149803Z >>> optimizer.zero_grad() 2024-08-20T21:40:35.8150036Z >>> loss_fn(model(input), target).backward() 2024-08-20T21:40:35.8150232Z >>> optimizer.step() 2024-08-20T21:40:35.8150356Z >>> if i > swa_start: 2024-08-20T21:40:35.8150545Z >>> swa_model.update_parameters(model) 2024-08-20T21:40:35.8150686Z >>> swa_scheduler.step() 2024-08-20T21:40:35.8150804Z >>> else: 2024-08-20T21:40:35.8150932Z >>> scheduler.step() 2024-08-20T21:40:35.8151026Z >>> 2024-08-20T21:40:35.8151253Z >>> # Update bn statistics for the swa_model at the end 2024-08-20T21:40:35.8151465Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-08-20T21:40:35.8151553Z 2024-08-20T21:40:35.8151985Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-08-20T21:40:35.8152254Z If no averaging function is provided, the default is to compute 2024-08-20T21:40:35.8152532Z equally-weighted average of the weights (SWA). 2024-08-20T21:40:35.8152634Z 2024-08-20T21:40:35.8152738Z Example: 2024-08-20T21:40:35.8152911Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:35.8153201Z >>> # Compute exponential moving averages of the weights and buffers 2024-08-20T21:40:35.8153437Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-08-20T21:40:35.8153750Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-08-20T21:40:35.8153872Z 2024-08-20T21:40:35.8153979Z .. note:: 2024-08-20T21:40:35.8154305Z When using SWA/EMA with models containing Batch Normalization you may 2024-08-20T21:40:35.8154588Z need to update the activation statistics for Batch Normalization. 2024-08-20T21:40:35.8154926Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-08-20T21:40:35.8155259Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-08-20T21:40:35.8155672Z statistics in a post-training step by passing data through the model. The 2024-08-20T21:40:35.8156088Z second does it during the parameter update phase by averaging all buffers. 2024-08-20T21:40:35.8156503Z Empirical evidence has shown that updating the statistics in normalization 2024-08-20T21:40:35.8156815Z layers increases accuracy, but you may wish to empirically test which 2024-08-20T21:40:35.8157035Z approach yields the best results in your problem. 2024-08-20T21:40:35.8157123Z 2024-08-20T21:40:35.8157223Z .. note:: 2024-08-20T21:40:35.8157605Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-08-20T21:40:35.8157692Z 2024-08-20T21:40:35.8157790Z .. note:: 2024-08-20T21:40:35.8158074Z When :meth:`update_parameters` is called for the first time (i.e. 2024-08-20T21:40:35.8158324Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-08-20T21:40:35.8158604Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-08-20T21:40:35.8158873Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-08-20T21:40:35.8159007Z to update the parameters. 2024-08-20T21:40:35.8159141Z 2024-08-20T21:40:35.8159451Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-20T21:40:35.8159607Z https://arxiv.org/abs/1803.05407 2024-08-20T21:40:35.8159944Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-08-20T21:40:35.8160043Z Average: 2024-08-20T21:40:35.8160188Z https://arxiv.org/abs/1806.05594 2024-08-20T21:40:35.8160634Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-08-20T21:40:35.8160785Z https://arxiv.org/abs/1904.11943 2024-08-20T21:40:35.8161158Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-08-20T21:40:35.8161392Z Generalizes Well: 2024-08-20T21:40:35.8161564Z https://arxiv.org/abs/2001.02312 2024-08-20T21:40:35.8161681Z .. _Polyak averaging: 2024-08-20T21:40:35.8162046Z https://paperswithcode.com/method/polyak-averaging 2024-08-20T21:40:35.8162179Z 2024-08-20T21:40:35.8162589Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8162691Z 2024-08-20T21:40:35.8162805Z warnings.warn(msg) 2024-08-20T21:40:35.8162892Z 2024-08-20T21:40:35.8163121Z --- Parse Warning: 72 / 101 --- 2024-08-20T21:40:35.8164447Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=357. 2024-08-20T21:40:35.8164878Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8165167Z Anneals the learning rate in each parameter group to a fixed value. 2024-08-20T21:40:35.8165255Z 2024-08-20T21:40:35.8165579Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-08-20T21:40:35.8165864Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-08-20T21:40:35.8165953Z 2024-08-20T21:40:35.8166061Z Args: 2024-08-20T21:40:35.8166280Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-08-20T21:40:35.8166614Z swa_lrs (float or list): the learning rate value for all param groups 2024-08-20T21:40:35.8166800Z together or separately for each group. 2024-08-20T21:40:35.8167066Z annealing_epochs (int): number of epochs in the annealing phase 2024-08-20T21:40:35.8167188Z (default: 10) 2024-08-20T21:40:35.8167469Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-08-20T21:40:35.8167754Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-08-20T21:40:35.8167884Z (default: "cos") 2024-08-20T21:40:35.8168200Z last_epoch (int): the index of the last epoch (default: -1) 2024-08-20T21:40:35.8168291Z 2024-08-20T21:40:35.8168556Z The :class:`SWALR` scheduler can be used together with other 2024-08-20T21:40:35.8168855Z schedulers to switch to a constant learning rate late in the training 2024-08-20T21:40:35.8168977Z as in the example below. 2024-08-20T21:40:35.8169078Z 2024-08-20T21:40:35.8169178Z Example: 2024-08-20T21:40:35.8169354Z >>> # xdoctest: +SKIP("Undefined variables") 2024-08-20T21:40:35.8169519Z >>> loader, optimizer, model = ... 2024-08-20T21:40:35.8169665Z >>> lr_lambda = lambda epoch: 0.9 2024-08-20T21:40:35.8169975Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-08-20T21:40:35.8170172Z >>> lr_lambda=lr_lambda) 2024-08-20T21:40:35.8170411Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-08-20T21:40:35.8170665Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-08-20T21:40:35.8170779Z >>> swa_start = 160 2024-08-20T21:40:35.8170939Z >>> for i in range(300): 2024-08-20T21:40:35.8171109Z >>> for input, target in loader: 2024-08-20T21:40:35.8171258Z >>> optimizer.zero_grad() 2024-08-20T21:40:35.8171444Z >>> loss_fn(model(input), target).backward() 2024-08-20T21:40:35.8171597Z >>> optimizer.step() 2024-08-20T21:40:35.8171721Z >>> if i > swa_start: 2024-08-20T21:40:35.8171864Z >>> swa_scheduler.step() 2024-08-20T21:40:35.8171985Z >>> else: 2024-08-20T21:40:35.8172116Z >>> scheduler.step() 2024-08-20T21:40:35.8172208Z 2024-08-20T21:40:35.8172525Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-08-20T21:40:35.8172729Z https://arxiv.org/abs/1803.05407 2024-08-20T21:40:35.8172841Z 2024-08-20T21:40:35.8173253Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8173351Z 2024-08-20T21:40:35.8173482Z warnings.warn(msg) 2024-08-20T21:40:35.8173576Z 2024-08-20T21:40:35.8173793Z --- Parse Warning: 73 / 101 --- 2024-08-20T21:40:35.8175227Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=111. 2024-08-20T21:40:35.8175646Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8175881Z Register a container-like type as pytree node. 2024-08-20T21:40:35.8175990Z 2024-08-20T21:40:35.8176092Z Args: 2024-08-20T21:40:35.8176357Z cls (type): A Python type to treat as an internal pytree node. 2024-08-20T21:40:35.8176749Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-08-20T21:40:35.8177105Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-08-20T21:40:35.8177522Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-08-20T21:40:35.8177709Z passed to the ``unflatten_fn``. 2024-08-20T21:40:35.8178082Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-08-20T21:40:35.8185150Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-08-20T21:40:35.8185446Z The function should return an instance of ``cls``. 2024-08-20T21:40:35.8185809Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-08-20T21:40:35.8186059Z qualified name used when serializing the tree spec. 2024-08-20T21:40:35.8186472Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-08-20T21:40:35.8186884Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-08-20T21:40:35.8187258Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-08-20T21:40:35.8187659Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-08-20T21:40:35.8188046Z how to convert the custom json dumpable representation of the context back to the 2024-08-20T21:40:35.8188405Z original context. This is used for json deserialization, which is being used in 2024-08-20T21:40:35.8188852Z :mod:`torch.export` right now. 2024-08-20T21:40:35.8188965Z 2024-08-20T21:40:35.8189080Z Example:: 2024-08-20T21:40:35.8189171Z 2024-08-20T21:40:35.8189315Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.8189515Z >>> # Registry a Python type with lambda functions 2024-08-20T21:40:35.8189657Z >>> register_pytree_node( 2024-08-20T21:40:35.8189861Z ... set, 2024-08-20T21:40:35.8190029Z ... lambda s: (sorted(s), None, None), 2024-08-20T21:40:35.8190213Z ... lambda children, _: set(children), 2024-08-20T21:40:35.8190554Z ... ) 2024-08-20T21:40:35.8190655Z 2024-08-20T21:40:35.8191149Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8191240Z 2024-08-20T21:40:35.8191356Z warnings.warn(msg) 2024-08-20T21:40:35.8191461Z 2024-08-20T21:40:35.8191680Z --- Parse Warning: 74 / 101 --- 2024-08-20T21:40:35.8193308Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1201. 2024-08-20T21:40:35.8193748Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8193843Z 2024-08-20T21:40:35.8194140Z Context passed to policy function during selective checkpointing. 2024-08-20T21:40:35.8194232Z 2024-08-20T21:40:35.8194554Z This class is used to pass relevant metadata to the policy function during 2024-08-20T21:40:35.8194915Z selective checkpointing. The metadata includes whether the current invocation 2024-08-20T21:40:35.8195133Z of the policy function is during recomputation or not. 2024-08-20T21:40:35.8195227Z 2024-08-20T21:40:35.8195342Z Example: 2024-08-20T21:40:35.8195466Z >>> # xdoctest: +SKIP(stub) 2024-08-20T21:40:35.8195560Z >>> 2024-08-20T21:40:35.8195744Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-20T21:40:35.8195875Z >>> print(ctx.is_recompute) 2024-08-20T21:40:35.8195973Z >>> 2024-08-20T21:40:35.8196341Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-20T21:40:35.8196436Z >>> 2024-08-20T21:40:35.8196628Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-20T21:40:35.8196752Z >>> fn, x, y, 2024-08-20T21:40:35.8196875Z >>> use_reentrant=False, 2024-08-20T21:40:35.8196998Z >>> context_fn=context_fn, 2024-08-20T21:40:35.8197151Z >>> ) 2024-08-20T21:40:35.8197242Z 2024-08-20T21:40:35.8197663Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8197753Z 2024-08-20T21:40:35.8197867Z warnings.warn(msg) 2024-08-20T21:40:35.8197971Z 2024-08-20T21:40:35.8198187Z --- Parse Warning: 75 / 101 --- 2024-08-20T21:40:35.8199710Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1335. 2024-08-20T21:40:35.8200147Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8200242Z 2024-08-20T21:40:35.8200561Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-08-20T21:40:35.8200668Z 2024-08-20T21:40:35.8200966Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-08-20T21:40:35.8201175Z operations are recomputed during the backward pass. 2024-08-20T21:40:35.8201278Z 2024-08-20T21:40:35.8201374Z Args: 2024-08-20T21:40:35.8201526Z policy_fn_or_list (Callable or List): 2024-08-20T21:40:35.8202601Z - If a policy function is provided, it should accept a 2024-08-20T21:40:35.8202916Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-08-20T21:40:35.8203223Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-08-20T21:40:35.8203548Z indicating whether the execution of the op should be recomputed or not. 2024-08-20T21:40:35.8203906Z - If a list of operations is provided, it is equivalent to a policy 2024-08-20T21:40:35.8204226Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-08-20T21:40:35.8204520Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-08-20T21:40:35.8204631Z operations. 2024-08-20T21:40:35.8204933Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-08-20T21:40:35.8205227Z raised if any tensors cached by selective activation checkpoint are 2024-08-20T21:40:35.8205536Z mutated in order to ensure correctness. If set to `True`, this check 2024-08-20T21:40:35.8205646Z is disabled. 2024-08-20T21:40:35.8205744Z Returns: 2024-08-20T21:40:35.8205948Z A tuple of two context managers. 2024-08-20T21:40:35.8206040Z 2024-08-20T21:40:35.8206194Z Example: 2024-08-20T21:40:35.8206348Z >>> # xdoctest: +REQUIRES(LINUX) 2024-08-20T21:40:35.8206461Z >>> import functools 2024-08-20T21:40:35.8206557Z >>> 2024-08-20T21:40:35.8206741Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-08-20T21:40:35.8206905Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-08-20T21:40:35.8207001Z >>> 2024-08-20T21:40:35.8207126Z >>> ops_to_save = [ 2024-08-20T21:40:35.8207275Z >>> torch.ops.aten.mm.default, 2024-08-20T21:40:35.8207371Z >>> ] 2024-08-20T21:40:35.8207483Z >>> 2024-08-20T21:40:35.8207652Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-08-20T21:40:35.8207775Z >>> if op in ops_to_save: 2024-08-20T21:40:35.8207962Z >>> return CheckpointPolicy.MUST_SAVE 2024-08-20T21:40:35.8208061Z >>> else: 2024-08-20T21:40:35.8208268Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-08-20T21:40:35.8208364Z >>> 2024-08-20T21:40:35.8208721Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-08-20T21:40:35.8208830Z >>> 2024-08-20T21:40:35.8208946Z >>> # or equivalently 2024-08-20T21:40:35.8209313Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-08-20T21:40:35.8209421Z >>> 2024-08-20T21:40:35.8209532Z >>> def fn(x, y): 2024-08-20T21:40:35.8209830Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-08-20T21:40:35.8209937Z >>> 2024-08-20T21:40:35.8210211Z >>> out = torch.utils.checkpoint.checkpoint( 2024-08-20T21:40:35.8210322Z >>> fn, x, y, 2024-08-20T21:40:35.8210461Z >>> use_reentrant=False, 2024-08-20T21:40:35.8210587Z >>> context_fn=context_fn, 2024-08-20T21:40:35.8210683Z >>> ) 2024-08-20T21:40:35.8210788Z 2024-08-20T21:40:35.8211212Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8211303Z 2024-08-20T21:40:35.8211430Z warnings.warn(msg) 2024-08-20T21:40:35.8211522Z 2024-08-20T21:40:35.8211737Z --- Parse Warning: 76 / 101 --- 2024-08-20T21:40:35.8213151Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=925. 2024-08-20T21:40:35.8213572Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8213682Z 2024-08-20T21:40:35.8213868Z Create a :class:`setuptools.Extension` for C++. 2024-08-20T21:40:35.8213957Z 2024-08-20T21:40:35.8214287Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-20T21:40:35.8214589Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-08-20T21:40:35.8214676Z 2024-08-20T21:40:35.8214968Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-20T21:40:35.8215164Z constructor. Full list arguments can be found at 2024-08-20T21:40:35.8215689Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-20T21:40:35.8215831Z 2024-08-20T21:40:35.8215932Z Example: 2024-08-20T21:40:35.8216051Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.8216264Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:40:35.8216406Z >>> from setuptools import setup 2024-08-20T21:40:35.8216717Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-08-20T21:40:35.8216818Z >>> setup( 2024-08-20T21:40:35.8216983Z ... name='extension', 2024-08-20T21:40:35.8217109Z ... ext_modules=[ 2024-08-20T21:40:35.8217228Z ... CppExtension( 2024-08-20T21:40:35.8217404Z ... name='extension', 2024-08-20T21:40:35.8217683Z ... sources=['extension.cpp'], 2024-08-20T21:40:35.8217893Z ... extra_compile_args=['-g'], 2024-08-20T21:40:35.8218170Z ... extra_link_flags=['-Wl,--no-as-needed', '-lm']) 2024-08-20T21:40:35.8218291Z ... ], 2024-08-20T21:40:35.8218402Z ... cmdclass={ 2024-08-20T21:40:35.8218605Z ... 'build_ext': BuildExtension 2024-08-20T21:40:35.8218723Z ... }) 2024-08-20T21:40:35.8218817Z 2024-08-20T21:40:35.8219288Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8219420Z 2024-08-20T21:40:35.8219579Z warnings.warn(msg) 2024-08-20T21:40:35.8219690Z 2024-08-20T21:40:35.8219935Z --- Parse Warning: 77 / 101 --- 2024-08-20T21:40:35.8221344Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=976. 2024-08-20T21:40:35.8221787Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8221883Z 2024-08-20T21:40:35.8222097Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-08-20T21:40:35.8222211Z 2024-08-20T21:40:35.8222530Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-08-20T21:40:35.8222804Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-08-20T21:40:35.8223180Z extension. This includes the CUDA include path, library path and runtime 2024-08-20T21:40:35.8223285Z library. 2024-08-20T21:40:35.8223378Z 2024-08-20T21:40:35.8223672Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-08-20T21:40:35.8223866Z constructor. Full list arguments can be found at 2024-08-20T21:40:35.8224400Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-08-20T21:40:35.8224496Z 2024-08-20T21:40:35.8224601Z Example: 2024-08-20T21:40:35.8224737Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.8224937Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:40:35.8225079Z >>> from setuptools import setup 2024-08-20T21:40:35.8225401Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-08-20T21:40:35.8225503Z >>> setup( 2024-08-20T21:40:35.8225677Z ... name='cuda_extension', 2024-08-20T21:40:35.8225807Z ... ext_modules=[ 2024-08-20T21:40:35.8225926Z ... CUDAExtension( 2024-08-20T21:40:35.8226131Z ... name='cuda_extension', 2024-08-20T21:40:35.8226447Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:40:35.8226684Z ... extra_compile_args={'cxx': ['-g'], 2024-08-20T21:40:35.8226920Z ... 'nvcc': ['-O2']}, 2024-08-20T21:40:35.8227230Z ... extra_link_flags=['-Wl,--no-as-needed', '-lcuda']) 2024-08-20T21:40:35.8227331Z ... ], 2024-08-20T21:40:35.8227450Z ... cmdclass={ 2024-08-20T21:40:35.8227650Z ... 'build_ext': BuildExtension 2024-08-20T21:40:35.8227778Z ... }) 2024-08-20T21:40:35.8227882Z 2024-08-20T21:40:35.8228001Z Compute capabilities: 2024-08-20T21:40:35.8228091Z 2024-08-20T21:40:35.8228532Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-08-20T21:40:35.8228938Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-08-20T21:40:35.8229398Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-08-20T21:40:35.8229921Z newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch 2024-08-20T21:40:35.8230334Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-08-20T21:40:35.8230543Z support (see below for details on PTX). 2024-08-20T21:40:35.8230655Z 2024-08-20T21:40:35.8231085Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-08-20T21:40:35.8231250Z CCs you want the extension to support: 2024-08-20T21:40:35.8231341Z 2024-08-20T21:40:35.8231588Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-08-20T21:40:35.8231957Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-08-20T21:40:35.8232048Z 2024-08-20T21:40:35.8232488Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-08-20T21:40:35.8233032Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-08-20T21:40:35.8233556Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-08-20T21:40:35.8234068Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-08-20T21:40:35.8234604Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-08-20T21:40:35.8235105Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-08-20T21:40:35.8235561Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-08-20T21:40:35.8236132Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-08-20T21:40:35.8236248Z "8.0 8.6" would be better. 2024-08-20T21:40:35.8236353Z 2024-08-20T21:40:35.8236860Z Note that while it's possible to include all supported archs, the more archs get included the 2024-08-20T21:40:35.8237277Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-08-20T21:40:35.8237380Z 2024-08-20T21:40:35.8237944Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-08-20T21:40:35.8238249Z To workaround the issue, move python binding logic to pure C++ file. 2024-08-20T21:40:35.8238344Z 2024-08-20T21:40:35.8238449Z Example use: 2024-08-20T21:40:35.8238581Z #include 2024-08-20T21:40:35.8238790Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-08-20T21:40:35.8238883Z 2024-08-20T21:40:35.8239000Z Instead of: 2024-08-20T21:40:35.8239130Z #include 2024-08-20T21:40:35.8239338Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-08-20T21:40:35.8239441Z 2024-08-20T21:40:35.8239813Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-08-20T21:40:35.8240476Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-08-20T21:40:35.8240578Z 2024-08-20T21:40:35.8240717Z Relocatable device code linking: 2024-08-20T21:40:35.8240807Z 2024-08-20T21:40:35.8241206Z If you want to reference device symbols across compilation units (across object files), 2024-08-20T21:40:35.8241658Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-08-20T21:40:35.8242204Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-08-20T21:40:35.8242668Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-08-20T21:40:35.8243196Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-08-20T21:40:35.8243507Z help reduce the protentional perf degradation of `-rdc`. 2024-08-20T21:40:35.8243735Z Note that it needs to be used at both steps to be useful. 2024-08-20T21:40:35.8243824Z 2024-08-20T21:40:35.8244529Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-08-20T21:40:35.8244833Z There is also a case where `-dlink` is used without `-rdc`: 2024-08-20T21:40:35.8245284Z when an extension is linked against a static lib containing rdc-compiled objects 2024-08-20T21:40:35.8245573Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-08-20T21:40:35.8245662Z 2024-08-20T21:40:35.8245957Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-08-20T21:40:35.8246051Z 2024-08-20T21:40:35.8246152Z Example: 2024-08-20T21:40:35.8246282Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.8246478Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:40:35.8246592Z >>> CUDAExtension( 2024-08-20T21:40:35.8246784Z ... name='cuda_extension', 2024-08-20T21:40:35.8247062Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:40:35.8247173Z ... dlink=True, 2024-08-20T21:40:35.8247342Z ... dlink_libraries=["dlink_lib"], 2024-08-20T21:40:35.8247562Z ... extra_compile_args={'cxx': ['-g'], 2024-08-20T21:40:35.8247802Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-08-20T21:40:35.8247911Z 2024-08-20T21:40:35.8248316Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8248406Z 2024-08-20T21:40:35.8248562Z warnings.warn(msg) 2024-08-20T21:40:35.8248653Z 2024-08-20T21:40:35.8248882Z --- Parse Warning: 78 / 101 --- 2024-08-20T21:40:35.8250324Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1234. 2024-08-20T21:40:35.8250850Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8250959Z 2024-08-20T21:40:35.8251215Z Load a PyTorch C++ extension just-in-time (JIT). 2024-08-20T21:40:35.8251307Z 2024-08-20T21:40:35.8251615Z To load an extension, a Ninja build file is emitted, which is used to 2024-08-20T21:40:35.8251890Z compile the given sources into a dynamic library. This library is 2024-08-20T21:40:35.8252181Z subsequently loaded into the current Python process as a module and 2024-08-20T21:40:35.8252369Z returned from this function, ready for use. 2024-08-20T21:40:35.8252463Z 2024-08-20T21:40:35.8252764Z By default, the directory to which the build file is emitted and the 2024-08-20T21:40:35.8253077Z resulting library compiled to is ``/torch_extensions/``, where 2024-08-20T21:40:35.8253361Z ```` is the temporary folder on the current platform and ```` 2024-08-20T21:40:35.8253678Z the name of the extension. This location can be overridden in two ways. 2024-08-20T21:40:35.8253965Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-08-20T21:40:35.8254256Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-08-20T21:40:35.8254563Z into subfolders of this directory. Second, if the ``build_directory`` 2024-08-20T21:40:35.8254925Z argument to this function is supplied, it overrides the entire path, i.e. 2024-08-20T21:40:35.8255148Z the library will be compiled into that folder directly. 2024-08-20T21:40:35.8255252Z 2024-08-20T21:40:35.8255549Z To compile the sources, the default system compiler (``c++``) is used, 2024-08-20T21:40:35.8255893Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-08-20T21:40:35.8256184Z additional arguments to the compilation process, ``extra_cflags`` or 2024-08-20T21:40:35.8256487Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-08-20T21:40:35.8256853Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-08-20T21:40:35.8257111Z ``extra_cflags`` to pass further include directories. 2024-08-20T21:40:35.8257205Z 2024-08-20T21:40:35.8257535Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-08-20T21:40:35.8257800Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-08-20T21:40:35.8258131Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-08-20T21:40:35.8258436Z passing the CUDA lib64 directory as a library directory, and linking 2024-08-20T21:40:35.8258644Z ``cudart``. You can pass additional flags to nvcc via 2024-08-20T21:40:35.8258937Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-08-20T21:40:35.8259254Z heuristics for finding the CUDA install directory are used, which usually 2024-08-20T21:40:35.8259553Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-08-20T21:40:35.8259673Z safest option. 2024-08-20T21:40:35.8259764Z 2024-08-20T21:40:35.8259862Z Args: 2024-08-20T21:40:35.8260180Z name: The name of the extension to build. This MUST be the same as the 2024-08-20T21:40:35.8260316Z name of the pybind11 module! 2024-08-20T21:40:35.8260594Z sources: A list of relative or absolute paths to C++ source files. 2024-08-20T21:40:35.8260907Z extra_cflags: optional list of compiler flags to forward to the build. 2024-08-20T21:40:35.8261198Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-08-20T21:40:35.8261715Z when building CUDA sources. 2024-08-20T21:40:35.8262021Z extra_ldflags: optional list of linker flags to forward to the build. 2024-08-20T21:40:35.8262305Z extra_include_paths: optional list of include directories to forward 2024-08-20T21:40:35.8262429Z to the build. 2024-08-20T21:40:35.8262663Z build_directory: optional path to use as build workspace. 2024-08-20T21:40:35.8262904Z verbose: If ``True``, turns on verbose logging of load steps. 2024-08-20T21:40:35.8263218Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-20T21:40:35.8263438Z the build. If set to ``None`` (default), this value is 2024-08-20T21:40:35.8263702Z automatically determined based on the existence of ``.cu`` or 2024-08-20T21:40:35.8263960Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-08-20T21:40:35.8264098Z and libraries to be included. 2024-08-20T21:40:35.8264379Z is_python_module: If ``True`` (default), imports the produced shared 2024-08-20T21:40:35.8264652Z library as a Python module. If ``False``, behavior depends on 2024-08-20T21:40:35.8264765Z ``is_standalone``. 2024-08-20T21:40:35.8265063Z is_standalone: If ``False`` (default) loads the constructed extension 2024-08-20T21:40:35.8265339Z into the process as a plain dynamic library. If ``True``, build a 2024-08-20T21:40:35.8265460Z standalone executable. 2024-08-20T21:40:35.8265571Z 2024-08-20T21:40:35.8265671Z Returns: 2024-08-20T21:40:35.8265818Z If ``is_python_module`` is ``True``: 2024-08-20T21:40:35.8266068Z Returns the loaded PyTorch extension as a Python module. 2024-08-20T21:40:35.8266205Z 2024-08-20T21:40:35.8266486Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-08-20T21:40:35.8266784Z Returns nothing. (The shared library is loaded into the process as 2024-08-20T21:40:35.8266898Z a side effect.) 2024-08-20T21:40:35.8266988Z 2024-08-20T21:40:35.8267144Z If ``is_standalone`` is ``True``. 2024-08-20T21:40:35.8267422Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-08-20T21:40:35.8267667Z added to the PATH environment variable as a side effect.) 2024-08-20T21:40:35.8267771Z 2024-08-20T21:40:35.8267873Z Example: 2024-08-20T21:40:35.8268009Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.8268243Z >>> from torch.utils.cpp_extension import load 2024-08-20T21:40:35.8268358Z >>> module = load( 2024-08-20T21:40:35.8268546Z ... name='extension', 2024-08-20T21:40:35.8268824Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-08-20T21:40:35.8268996Z ... extra_cflags=['-O2'], 2024-08-20T21:40:35.8269128Z ... verbose=True) 2024-08-20T21:40:35.8269221Z 2024-08-20T21:40:35.8269629Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8269738Z 2024-08-20T21:40:35.8269855Z warnings.warn(msg) 2024-08-20T21:40:35.8269947Z 2024-08-20T21:40:35.8270178Z --- Parse Warning: 79 / 101 --- 2024-08-20T21:40:35.8271565Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1523. 2024-08-20T21:40:35.8271992Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8272103Z 2024-08-20T21:40:35.8272465Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-08-20T21:40:35.8272576Z 2024-08-20T21:40:35.8272888Z This function behaves exactly like :func:`load`, but takes its sources as 2024-08-20T21:40:35.8273198Z strings rather than filenames. These strings are stored to files in the 2024-08-20T21:40:35.8273527Z build directory, after which the behavior of :func:`load_inline` is 2024-08-20T21:40:35.8273654Z identical to :func:`load`. 2024-08-20T21:40:35.8273748Z 2024-08-20T21:40:35.8273868Z See `the 2024-08-20T21:40:35.8274295Z tests `_ 2024-08-20T21:40:35.8274463Z for good examples of using this function. 2024-08-20T21:40:35.8274572Z 2024-08-20T21:40:35.8274979Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-08-20T21:40:35.8275309Z the necessary header includes, as well as the (pybind11) binding code. More 2024-08-20T21:40:35.8275640Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-08-20T21:40:35.8275900Z single ``.cpp`` file. This file is then prepended with ``#include 2024-08-20T21:40:35.8276038Z ``. 2024-08-20T21:40:35.8276128Z 2024-08-20T21:40:35.8276434Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-08-20T21:40:35.8276756Z automatically generated for each function specified. ``functions`` can 2024-08-20T21:40:35.8277068Z either be a list of function names, or a dictionary mapping from function 2024-08-20T21:40:35.8277387Z names to docstrings. If a list is given, the name of each function is used 2024-08-20T21:40:35.8277509Z as its docstring. 2024-08-20T21:40:35.8277598Z 2024-08-20T21:40:35.8277896Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-08-20T21:40:35.8278142Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-08-20T21:40:35.8278430Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-08-20T21:40:35.8278754Z separately, but ultimately linked into a single library. Note that no 2024-08-20T21:40:35.8279080Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-08-20T21:40:35.8279396Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-08-20T21:40:35.8279704Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-08-20T21:40:35.8279841Z include its name in ``functions``). 2024-08-20T21:40:35.8279932Z 2024-08-20T21:40:35.8280198Z See :func:`load` for a description of arguments omitted below. 2024-08-20T21:40:35.8280289Z 2024-08-20T21:40:35.8280384Z Args: 2024-08-20T21:40:35.8280693Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-08-20T21:40:35.8281066Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-08-20T21:40:35.8281347Z functions: A list of function names for which to generate function 2024-08-20T21:40:35.8281656Z bindings. If a dictionary is given, it should map function names to 2024-08-20T21:40:35.8281893Z docstrings (which are otherwise just the function names). 2024-08-20T21:40:35.8282203Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-08-20T21:40:35.8282422Z the build. If set to ``None`` (default), this value is 2024-08-20T21:40:35.8282685Z automatically determined based on whether ``cuda_sources`` is 2024-08-20T21:40:35.8282904Z provided. Set it to ``True`` to force CUDA headers 2024-08-20T21:40:35.8283041Z and libraries to be included. 2024-08-20T21:40:35.8283314Z with_pytorch_error_handling: Determines whether pytorch error and 2024-08-20T21:40:35.8283604Z warning macros are handled by pytorch instead of pybind. To do 2024-08-20T21:40:35.8283905Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-08-20T21:40:35.8284172Z function. This redirection might cause issues in obscure cases 2024-08-20T21:40:35.8284451Z of cpp. This flag should be set to ``False`` when this redirect 2024-08-20T21:40:35.8284562Z causes issues. 2024-08-20T21:40:35.8284697Z 2024-08-20T21:40:35.8284796Z Example: 2024-08-20T21:40:35.8284993Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-08-20T21:40:35.8285214Z >>> from torch.utils.cpp_extension import load_inline 2024-08-20T21:40:35.8285321Z >>> source = """ 2024-08-20T21:40:35.8285513Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-08-20T21:40:35.8285653Z return x.sin() + y.sin(); 2024-08-20T21:40:35.8285747Z } 2024-08-20T21:40:35.8285842Z """ 2024-08-20T21:40:35.8286110Z >>> module = load_inline(name='inline_extension', 2024-08-20T21:40:35.8286272Z ... cpp_sources=[source], 2024-08-20T21:40:35.8286490Z ... functions=['sin_add']) 2024-08-20T21:40:35.8286597Z 2024-08-20T21:40:35.8286697Z .. note:: 2024-08-20T21:40:35.8286973Z By default, the Ninja backend uses #CPUS + 2 workers to build the 2024-08-20T21:40:35.8287266Z extension. This may use up too many resources on some systems. One 2024-08-20T21:40:35.8287576Z can control the number of workers by setting the `MAX_JOBS` environment 2024-08-20T21:40:35.8287784Z variable to a non-negative number. 2024-08-20T21:40:35.8287878Z 2024-08-20T21:40:35.8288283Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8288387Z 2024-08-20T21:40:35.8288501Z warnings.warn(msg) 2024-08-20T21:40:35.8288941Z 2024-08-20T21:40:35.8289187Z --- Parse Warning: 80 / 101 --- 2024-08-20T21:40:35.8290919Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61. 2024-08-20T21:40:35.8291460Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8291568Z 2024-08-20T21:40:35.8291982Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-08-20T21:40:35.8292074Z 2024-08-20T21:40:35.8292483Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-08-20T21:40:35.8292820Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-08-20T21:40:35.8293168Z server like load. It can emulate multiple calling threads to a single module 2024-08-20T21:40:35.8293513Z provided. In the future we plan to enhance this component to support inter and 2024-08-20T21:40:35.8293994Z intra-op parallelism as well as multiple models running in a single process. 2024-08-20T21:40:35.8294102Z 2024-08-20T21:40:35.8294451Z Please note that even though nn.Module is supported, it might incur an overhead 2024-08-20T21:40:35.8294773Z from the need to hold GIL every time we execute Python code or pass around 2024-08-20T21:40:35.8295117Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-08-20T21:40:35.8295439Z model for inference deployment it is better to switch to using it in this 2024-08-20T21:40:35.8295556Z benchmark. 2024-08-20T21:40:35.8295647Z 2024-08-20T21:40:35.8295748Z Example:: 2024-08-20T21:40:35.8295854Z 2024-08-20T21:40:35.8296012Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:35.8296200Z >>> from torch.utils import ThroughputBenchmark 2024-08-20T21:40:35.8296379Z >>> bench = ThroughputBenchmark(my_module) 2024-08-20T21:40:35.8296666Z >>> # Pre-populate benchmark's data set with the inputs 2024-08-20T21:40:35.8296790Z >>> for input in inputs: 2024-08-20T21:40:35.8297113Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-08-20T21:40:35.8297285Z ... bench.add_input(input[0], x2=input[1]) 2024-08-20T21:40:35.8297546Z >>> # Inputs supplied above are randomly used during the execution 2024-08-20T21:40:35.8297732Z >>> stats = bench.benchmark( 2024-08-20T21:40:35.8297861Z ... num_calling_threads=4, 2024-08-20T21:40:35.8297989Z ... num_warmup_iters = 100, 2024-08-20T21:40:35.8298119Z ... num_iters = 1000, 2024-08-20T21:40:35.8298216Z ... ) 2024-08-20T21:40:35.8298471Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-08-20T21:40:35.8298709Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-08-20T21:40:35.8298800Z 2024-08-20T21:40:35.8299231Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8299323Z 2024-08-20T21:40:35.8299436Z warnings.warn(msg) 2024-08-20T21:40:35.8299541Z 2024-08-20T21:40:35.8299756Z --- Parse Warning: 81 / 101 --- 2024-08-20T21:40:35.8301218Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=17. 2024-08-20T21:40:35.8301650Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8301912Z Sampler that restricts data loading to a subset of the dataset. 2024-08-20T21:40:35.8302004Z 2024-08-20T21:40:35.8302190Z It is especially useful in conjunction with 2024-08-20T21:40:35.8302537Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-08-20T21:40:35.8302919Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-08-20T21:40:35.8303228Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-08-20T21:40:35.8303393Z original dataset that is exclusive to it. 2024-08-20T21:40:35.8303530Z 2024-08-20T21:40:35.8303633Z .. note:: 2024-08-20T21:40:35.8303974Z Dataset is assumed to be of constant size and that any instance of it always 2024-08-20T21:40:35.8304175Z returns the same elements in the same order. 2024-08-20T21:40:35.8304265Z 2024-08-20T21:40:35.8304362Z Args: 2024-08-20T21:40:35.8304533Z dataset: Dataset used for sampling. 2024-08-20T21:40:35.8304816Z num_replicas (int, optional): Number of processes participating in 2024-08-20T21:40:35.8305146Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-08-20T21:40:35.8305299Z current distributed group. 2024-08-20T21:40:35.8305688Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-08-20T21:40:35.8305981Z By default, :attr:`rank` is retrieved from the current distributed 2024-08-20T21:40:35.8306084Z group. 2024-08-20T21:40:35.8306393Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-08-20T21:40:35.8306509Z indices. 2024-08-20T21:40:35.8306774Z seed (int, optional): random seed used to shuffle the sampler if 2024-08-20T21:40:35.8307045Z :attr:`shuffle=True`. This number should be identical across all 2024-08-20T21:40:35.8307277Z processes in the distributed group. Default: ``0``. 2024-08-20T21:40:35.8307569Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-08-20T21:40:35.8307850Z tail of the data to make it evenly divisible across the number of 2024-08-20T21:40:35.8308142Z replicas. If ``False``, the sampler will add extra indices to make 2024-08-20T21:40:35.8308427Z the data evenly divisible across the replicas. Default: ``False``. 2024-08-20T21:40:35.8308532Z 2024-08-20T21:40:35.8308637Z .. warning:: 2024-08-20T21:40:35.8308892Z In distributed mode, calling the :meth:`set_epoch` method at 2024-08-20T21:40:35.8309263Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-08-20T21:40:35.8309651Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-08-20T21:40:35.8309810Z the same ordering will be always used. 2024-08-20T21:40:35.8309916Z 2024-08-20T21:40:35.8310019Z Example:: 2024-08-20T21:40:35.8310110Z 2024-08-20T21:40:35.8310241Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.8310528Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-08-20T21:40:35.8310761Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-08-20T21:40:35.8310932Z ... sampler=sampler) 2024-08-20T21:40:35.8311111Z >>> for epoch in range(start_epoch, n_epochs): 2024-08-20T21:40:35.8311250Z ... if is_distributed: 2024-08-20T21:40:35.8311402Z ... sampler.set_epoch(epoch) 2024-08-20T21:40:35.8311514Z ... train(loader) 2024-08-20T21:40:35.8311621Z 2024-08-20T21:40:35.8312032Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8312125Z 2024-08-20T21:40:35.8312251Z warnings.warn(msg) 2024-08-20T21:40:35.8312344Z 2024-08-20T21:40:35.8312562Z --- Parse Warning: 82 / 101 --- 2024-08-20T21:40:35.8313901Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=40. 2024-08-20T21:40:35.8314331Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8314424Z 2024-08-20T21:40:35.8314747Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-08-20T21:40:35.8315052Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-08-20T21:40:35.8315365Z pushes the map into PyTorch operations called by ``func``, effectively 2024-08-20T21:40:35.8315503Z vectorizing those operations. 2024-08-20T21:40:35.8315596Z 2024-08-20T21:40:35.8315918Z vmap is useful for handling batch dimensions: one can write a function 2024-08-20T21:40:35.8316202Z ``func`` that runs on examples and then lift it to a function that can 2024-08-20T21:40:35.8316495Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-08-20T21:40:35.8316730Z compute batched gradients when composed with autograd. 2024-08-20T21:40:35.8316825Z 2024-08-20T21:40:35.8316927Z .. note:: 2024-08-20T21:40:35.8317243Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-08-20T21:40:35.8317474Z convenience. Use whichever one you'd like. 2024-08-20T21:40:35.8317568Z 2024-08-20T21:40:35.8317682Z Args: 2024-08-20T21:40:35.8317972Z func (function): A Python function that takes one or more arguments. 2024-08-20T21:40:35.8318124Z Must return one or more Tensors. 2024-08-20T21:40:35.8318424Z in_dims (int or nested structure): Specifies which dimension of the 2024-08-20T21:40:35.8318656Z inputs should be mapped over. ``in_dims`` should have a 2024-08-20T21:40:35.8318931Z structure like the inputs. If the ``in_dim`` for a particular 2024-08-20T21:40:35.8319194Z input is None, then that indicates there is no map dimension. 2024-08-20T21:40:35.8319303Z Default: 0. 2024-08-20T21:40:35.8319595Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-08-20T21:40:35.8319864Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-08-20T21:40:35.8320072Z it should have one element per output. Default: 0. 2024-08-20T21:40:35.8320327Z randomness (str): Specifies whether the randomness in this 2024-08-20T21:40:35.8320707Z vmap should be the same or different across batches. If 'different', 2024-08-20T21:40:35.8321066Z the randomness for each batch will be different. If 'same', the 2024-08-20T21:40:35.8321476Z randomness will be the same across batches. If 'error', any calls to 2024-08-20T21:40:35.8321834Z random functions will error. Default: 'error'. WARNING: this flag 2024-08-20T21:40:35.8322125Z only applies to random PyTorch operations and does not apply to 2024-08-20T21:40:35.8322363Z Python's random module or numpy randomness. 2024-08-20T21:40:35.8322691Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-08-20T21:40:35.8323019Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-08-20T21:40:35.8323466Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-08-20T21:40:35.8323929Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-08-20T21:40:35.8324040Z 2024-08-20T21:40:35.8324137Z Returns: 2024-08-20T21:40:35.8324409Z Returns a new "batched" function. It takes the same inputs as 2024-08-20T21:40:35.8324658Z ``func``, except each input has an extra dimension at the index 2024-08-20T21:40:35.8324913Z specified by ``in_dims``. It takes returns the same outputs as 2024-08-20T21:40:35.8325182Z ``func``, except each output has an extra dimension at the index 2024-08-20T21:40:35.8325307Z specified by ``out_dims``. 2024-08-20T21:40:35.8325398Z 2024-08-20T21:40:35.8325513Z .. warning: 2024-08-20T21:40:35.8325855Z :func:`vmap` works best with functional-style code. Please do not 2024-08-20T21:40:35.8326173Z perform any side-effects in ``func``, with the exception of 2024-08-20T21:40:35.8326571Z in-place PyTorch operations. Examples of side-effects include mutating 2024-08-20T21:40:35.8326903Z Python data structures and assigning values to variables not captured 2024-08-20T21:40:35.8327008Z in ``func``. 2024-08-20T21:40:35.8327112Z 2024-08-20T21:40:35.8327442Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-08-20T21:40:35.8327829Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-08-20T21:40:35.8328120Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-08-20T21:40:35.8328211Z 2024-08-20T21:40:35.8328490Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:40:35.8328838Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-08-20T21:40:35.8329078Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-20T21:40:35.8329210Z >>> batched_dot(x, y) 2024-08-20T21:40:35.8329299Z 2024-08-20T21:40:35.8329621Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-08-20T21:40:35.8329763Z model authoring experience. 2024-08-20T21:40:35.8329854Z 2024-08-20T21:40:35.8329995Z >>> batch_size, feature_size = 3, 5 2024-08-20T21:40:35.8330311Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-08-20T21:40:35.8330412Z >>> 2024-08-20T21:40:35.8330536Z >>> def model(feature_vec): 2024-08-20T21:40:35.8330736Z >>> # Very simple linear model with activation 2024-08-20T21:40:35.8330903Z >>> return feature_vec.dot(weights).relu() 2024-08-20T21:40:35.8331015Z >>> 2024-08-20T21:40:35.8331216Z >>> examples = torch.randn(batch_size, feature_size) 2024-08-20T21:40:35.8331373Z >>> result = torch.vmap(model)(examples) 2024-08-20T21:40:35.8331479Z 2024-08-20T21:40:35.8331830Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-08-20T21:40:35.8332235Z or impossible to batch. One example is higher-order gradient computation. 2024-08-20T21:40:35.8332623Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-08-20T21:40:35.8333007Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-08-20T21:40:35.8333382Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-08-20T21:40:35.8333716Z we can vectorize the whole computation, computing the Jacobian in a single 2024-08-20T21:40:35.8333839Z call to ``autograd.grad``. 2024-08-20T21:40:35.8333929Z 2024-08-20T21:40:35.8334043Z >>> # Setup 2024-08-20T21:40:35.8334142Z >>> N = 5 2024-08-20T21:40:35.8334260Z >>> f = lambda x: x ** 2 2024-08-20T21:40:35.8334434Z >>> x = torch.randn(N, requires_grad=True) 2024-08-20T21:40:35.8334536Z >>> y = f(x) 2024-08-20T21:40:35.8334670Z >>> I_N = torch.eye(N) 2024-08-20T21:40:35.8334765Z >>> 2024-08-20T21:40:35.8334888Z >>> # Sequential approach 2024-08-20T21:40:35.8335188Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-08-20T21:40:35.8335338Z >>> for v in I_N.unbind()] 2024-08-20T21:40:35.8335497Z >>> jacobian = torch.stack(jacobian_rows) 2024-08-20T21:40:35.8335609Z >>> 2024-08-20T21:40:35.8335760Z >>> # vectorized gradient computation 2024-08-20T21:40:35.8335871Z >>> def get_vjp(v): 2024-08-20T21:40:35.8336046Z >>> return torch.autograd.grad(y, x, v) 2024-08-20T21:40:35.8336199Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-08-20T21:40:35.8336288Z 2024-08-20T21:40:35.8336673Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-08-20T21:40:35.8336767Z 2024-08-20T21:40:35.8337039Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:40:35.8337534Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-08-20T21:40:35.8337727Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-08-20T21:40:35.8337939Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-08-20T21:40:35.8338031Z 2024-08-20T21:40:35.8338368Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-08-20T21:40:35.8338586Z the dimension that each inputs are batched along as 2024-08-20T21:40:35.8338678Z 2024-08-20T21:40:35.8338940Z >>> torch.dot # [N], [N] -> [] 2024-08-20T21:40:35.8339333Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-08-20T21:40:35.8339505Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-08-20T21:40:35.8339852Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-08-20T21:40:35.8340007Z 2024-08-20T21:40:35.8340370Z If there are multiple inputs each of which is batched along different dimensions, 2024-08-20T21:40:35.8340653Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-08-20T21:40:35.8340758Z 2024-08-20T21:40:35.8341020Z >>> torch.dot # [D], [D] -> [] 2024-08-20T21:40:35.8341442Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-08-20T21:40:35.8341610Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-20T21:40:35.8342046Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-08-20T21:40:35.8342152Z 2024-08-20T21:40:35.8342494Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-08-20T21:40:35.8342629Z matching the shape of the input: 2024-08-20T21:40:35.8342735Z 2024-08-20T21:40:35.8342987Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-08-20T21:40:35.8343155Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-08-20T21:40:35.8343334Z >>> input = {'x': x, 'y': y} 2024-08-20T21:40:35.8343645Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-08-20T21:40:35.8343766Z >>> batched_dot(input) 2024-08-20T21:40:35.8343870Z 2024-08-20T21:40:35.8344255Z By default, the output is batched along the first dimension. However, it can be batched 2024-08-20T21:40:35.8344458Z along any dimension by using ``out_dims`` 2024-08-20T21:40:35.8344553Z 2024-08-20T21:40:35.8344671Z >>> f = lambda x: x ** 2 2024-08-20T21:40:35.8344805Z >>> x = torch.randn(2, 5) 2024-08-20T21:40:35.8344966Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-08-20T21:40:35.8345086Z >>> batched_pow(x) # [5, 2] 2024-08-20T21:40:35.8345188Z 2024-08-20T21:40:35.8345599Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-08-20T21:40:35.8345709Z accept kwargs 2024-08-20T21:40:35.8345813Z 2024-08-20T21:40:35.8345934Z >>> x = torch.randn([2, 5]) 2024-08-20T21:40:35.8346050Z >>> def fn(x, scale=4.): 2024-08-20T21:40:35.8346175Z >>> return x * scale 2024-08-20T21:40:35.8346273Z >>> 2024-08-20T21:40:35.8346409Z >>> batched_pow = torch.vmap(fn) 2024-08-20T21:40:35.8346607Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-08-20T21:40:35.8346926Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-08-20T21:40:35.8347018Z 2024-08-20T21:40:35.8347134Z .. note:: 2024-08-20T21:40:35.8347512Z vmap does not provide general autobatching or handle variable-length 2024-08-20T21:40:35.8347651Z sequences out of the box. 2024-08-20T21:40:35.8347742Z 2024-08-20T21:40:35.8348145Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8348246Z 2024-08-20T21:40:35.8348364Z warnings.warn(msg) 2024-08-20T21:40:35.8348454Z 2024-08-20T21:40:35.8348685Z --- Parse Warning: 83 / 101 --- 2024-08-20T21:40:35.8350026Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=17. 2024-08-20T21:40:35.8350476Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8350829Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-08-20T21:40:35.8350919Z 2024-08-20T21:40:35.8351250Z Use this instead of :func:`torch.library.custom_op` when the implementation 2024-08-20T21:40:35.8351560Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-08-20T21:40:35.8351776Z custom operators as opaque (:func:`torch.compile` and 2024-08-20T21:40:35.8352156Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-08-20T21:40:35.8352450Z makes the implementation visible to these subsystems, allowing them 2024-08-20T21:40:35.8352593Z to optimize the triton kernel(s). 2024-08-20T21:40:35.8352699Z 2024-08-20T21:40:35.8353041Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-08-20T21:40:35.8353346Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-08-20T21:40:35.8353648Z must be wrapped in a call to :func:`torch._library.capture_triton``. 2024-08-20T21:40:35.8353737Z 2024-08-20T21:40:35.8353830Z Args: 2024-08-20T21:40:35.8354161Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-08-20T21:40:35.8354546Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-08-20T21:40:35.8354787Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-08-20T21:40:35.8355111Z To avoid name collisions, please use your project name as the namespace; 2024-08-20T21:40:35.8355404Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-08-20T21:40:35.8355790Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-08-20T21:40:35.8356120Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-08-20T21:40:35.8356499Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-08-20T21:40:35.8356772Z schema (None | str): A schema string for the operator. If None 2024-08-20T21:40:35.8357128Z (recommended) we'll infer a schema for the operator from its type 2024-08-20T21:40:35.8357414Z annotations. We recommend letting us infer a schema unless you 2024-08-20T21:40:35.8357564Z have a specific reason not to. 2024-08-20T21:40:35.8357835Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-08-20T21:40:35.8357945Z 2024-08-20T21:40:35.8358053Z Example:: 2024-08-20T21:40:35.8358145Z 2024-08-20T21:40:35.8358351Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-08-20T21:40:35.8358467Z >>> import torch 2024-08-20T21:40:35.8358689Z >>> from torch._library import triton_op, capture_triton 2024-08-20T21:40:35.8358805Z >>> 2024-08-20T21:40:35.8358924Z >>> import triton 2024-08-20T21:40:35.8359080Z >>> from triton import language as tl 2024-08-20T21:40:35.8359193Z >>> 2024-08-20T21:40:35.8359302Z >>> @triton.jit 2024-08-20T21:40:35.8359416Z >>> def add_kernel( 2024-08-20T21:40:35.8359540Z >>> in_ptr0, 2024-08-20T21:40:35.8359648Z >>> in_ptr1, 2024-08-20T21:40:35.8359751Z >>> out_ptr, 2024-08-20T21:40:35.8359876Z >>> n_elements, 2024-08-20T21:40:35.8360032Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-20T21:40:35.8360145Z >>> ): 2024-08-20T21:40:35.8360295Z >>> pid = tl.program_id(axis=0) 2024-08-20T21:40:35.8360450Z >>> block_start = pid * BLOCK_SIZE 2024-08-20T21:40:35.8360704Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-20T21:40:35.8360850Z >>> mask = offsets < n_elements 2024-08-20T21:40:35.8361030Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-20T21:40:35.8361224Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-20T21:40:35.8361346Z >>> output = x + y 2024-08-20T21:40:35.8361548Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-20T21:40:35.8361660Z >>> 2024-08-20T21:40:35.8361835Z >>> @triton_op("mylib::add", mutates_args={}) 2024-08-20T21:40:35.8362160Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-08-20T21:40:35.8362383Z >>> output = torch.empty_like(x) 2024-08-20T21:40:35.8362534Z >>> n_elements = output.numel() 2024-08-20T21:40:35.8362642Z >>> 2024-08-20T21:40:35.8362756Z >>> def grid(meta): 2024-08-20T21:40:35.8362987Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-20T21:40:35.8363093Z >>> 2024-08-20T21:40:35.8363374Z >>> # NB: we need to wrap the triton kernel in a call to capture_triton 2024-08-20T21:40:35.8363643Z >>> capture_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-08-20T21:40:35.8363770Z >>> return output 2024-08-20T21:40:35.8363863Z >>> 2024-08-20T21:40:35.8363973Z >>> @torch.compile 2024-08-20T21:40:35.8364092Z >>> def f(x, y): 2024-08-20T21:40:35.8364208Z >>> return add(x, y) 2024-08-20T21:40:35.8364301Z >>> 2024-08-20T21:40:35.8364466Z >>> x = torch.randn(3, device="cuda") 2024-08-20T21:40:35.8364620Z >>> y = torch.randn(3, device="cuda") 2024-08-20T21:40:35.8364712Z >>> 2024-08-20T21:40:35.8364826Z >>> z = f(x, y) 2024-08-20T21:40:35.8364977Z >>> assert torch.allclose(z, x + y) 2024-08-20T21:40:35.8365069Z 2024-08-20T21:40:35.8365171Z 2024-08-20T21:40:35.8365577Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8365708Z 2024-08-20T21:40:35.8365819Z warnings.warn(msg) 2024-08-20T21:40:35.8365906Z 2024-08-20T21:40:35.8366168Z --- Parse Warning: 84 / 101 --- 2024-08-20T21:40:35.8367547Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=capture_triton in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=163. 2024-08-20T21:40:35.8367962Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8368231Z Allows capture of a triton kernel into a graph via make_fx or 2024-08-20T21:40:35.8368422Z non-strict export (coming soon). 2024-08-20T21:40:35.8368510Z 2024-08-20T21:40:35.8368822Z These technologies perform Dispatcher-based tracing (via 2024-08-20T21:40:35.8369086Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-08-20T21:40:35.8369357Z The ``capture_triton`` API returns a new callable that can actually 2024-08-20T21:40:35.8369493Z be traced into a graph. 2024-08-20T21:40:35.8369579Z 2024-08-20T21:40:35.8369693Z Examples: 2024-08-20T21:40:35.8369783Z 2024-08-20T21:40:35.8369905Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.8370030Z >>> import torch 2024-08-20T21:40:35.8370206Z >>> import triton 2024-08-20T21:40:35.8370365Z >>> from triton import language as tl 2024-08-20T21:40:35.8370625Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-08-20T21:40:35.8370936Z >>> from torch._higher_order_ops.triton_kernel_wrap import capture_triton 2024-08-20T21:40:35.8371030Z >>> 2024-08-20T21:40:35.8371155Z >>> @triton.jit 2024-08-20T21:40:35.8371313Z >>> def add_kernel( 2024-08-20T21:40:35.8371418Z >>> in_ptr0, 2024-08-20T21:40:35.8371535Z >>> in_ptr1, 2024-08-20T21:40:35.8371640Z >>> out_ptr, 2024-08-20T21:40:35.8371748Z >>> n_elements, 2024-08-20T21:40:35.8371907Z >>> BLOCK_SIZE: "tl.constexpr", 2024-08-20T21:40:35.8372004Z >>> ): 2024-08-20T21:40:35.8372152Z >>> pid = tl.program_id(axis=0) 2024-08-20T21:40:35.8372320Z >>> block_start = pid * BLOCK_SIZE 2024-08-20T21:40:35.8372524Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-08-20T21:40:35.8372681Z >>> mask = offsets < n_elements 2024-08-20T21:40:35.8372913Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-08-20T21:40:35.8373087Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-08-20T21:40:35.8373215Z >>> output = x + y 2024-08-20T21:40:35.8373414Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-08-20T21:40:35.8373510Z >>> 2024-08-20T21:40:35.8373633Z >>> def add(x, y): 2024-08-20T21:40:35.8373780Z >>> output = torch.empty_like(x) 2024-08-20T21:40:35.8373928Z >>> n_elements = output.numel() 2024-08-20T21:40:35.8374033Z >>> 2024-08-20T21:40:35.8374154Z >>> def grid_fn(meta): 2024-08-20T21:40:35.8374383Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-08-20T21:40:35.8374490Z >>> 2024-08-20T21:40:35.8374769Z >>> capture_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-08-20T21:40:35.8374881Z >>> return output 2024-08-20T21:40:35.8374988Z >>> 2024-08-20T21:40:35.8375145Z >>> x = torch.randn(3, device="cuda") 2024-08-20T21:40:35.8375306Z >>> y = torch.randn(3, device="cuda") 2024-08-20T21:40:35.8375431Z >>> gm = make_fx(add)(x, y) 2024-08-20T21:40:35.8375544Z >>> print(gm.code) 2024-08-20T21:40:35.8375702Z >>> # def forward(self, x_1, y_1): 2024-08-20T21:40:35.8376028Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-08-20T21:40:35.8376363Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-08-20T21:40:35.8376553Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-08-20T21:40:35.8376709Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-08-20T21:40:35.8377022Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-08-20T21:40:35.8377269Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-08-20T21:40:35.8377376Z >>> # }) 2024-08-20T21:40:35.8377502Z >>> # return empty_like 2024-08-20T21:40:35.8377603Z 2024-08-20T21:40:35.8377694Z 2024-08-20T21:40:35.8378107Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8378197Z 2024-08-20T21:40:35.8378307Z warnings.warn(msg) 2024-08-20T21:40:35.8378409Z 2024-08-20T21:40:35.8378620Z --- Parse Warning: 85 / 101 --- 2024-08-20T21:40:35.8380062Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=330. 2024-08-20T21:40:35.8380488Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8380576Z 2024-08-20T21:40:35.8380848Z Raises an AssertionError if two items are not equal up to desired 2024-08-20T21:40:35.8380964Z precision. 2024-08-20T21:40:35.8381054Z 2024-08-20T21:40:35.8381288Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:40:35.8381536Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:40:35.8381821Z instead of this function for more consistent floating point 2024-08-20T21:40:35.8381943Z comparisons. 2024-08-20T21:40:35.8382033Z 2024-08-20T21:40:35.8382328Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-08-20T21:40:35.8382429Z 2024-08-20T21:40:35.8382703Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-08-20T21:40:35.8382789Z 2024-08-20T21:40:35.8383119Z That is a looser test than originally documented, but agrees with what the 2024-08-20T21:40:35.8383426Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-08-20T21:40:35.8383741Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-08-20T21:40:35.8383957Z delegates to assert_array_almost_equal 2024-08-20T21:40:35.8384048Z 2024-08-20T21:40:35.8384147Z Parameters 2024-08-20T21:40:35.8384286Z ---------- 2024-08-20T21:40:35.8384397Z actual : array_like 2024-08-20T21:40:35.8384512Z The object to check. 2024-08-20T21:40:35.8384633Z desired : array_like 2024-08-20T21:40:35.8384747Z The expected object. 2024-08-20T21:40:35.8384865Z decimal : int, optional 2024-08-20T21:40:35.8385019Z Desired precision, default is 7. 2024-08-20T21:40:35.8385135Z err_msg : str, optional 2024-08-20T21:40:35.8385354Z The error message to be printed in case of failure. 2024-08-20T21:40:35.8385471Z verbose : bool, optional 2024-08-20T21:40:35.8385751Z If True, the conflicting values are appended to the error message. 2024-08-20T21:40:35.8385852Z 2024-08-20T21:40:35.8385945Z Raises 2024-08-20T21:40:35.8386067Z ------ 2024-08-20T21:40:35.8386183Z AssertionError 2024-08-20T21:40:35.8386454Z If actual and desired are not equal up to specified precision. 2024-08-20T21:40:35.8386541Z 2024-08-20T21:40:35.8386649Z See Also 2024-08-20T21:40:35.8386770Z -------- 2024-08-20T21:40:35.8387080Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:40:35.8387263Z relative and/or absolute precision. 2024-08-20T21:40:35.8387529Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:40:35.8387667Z 2024-08-20T21:40:35.8387775Z Examples 2024-08-20T21:40:35.8387896Z -------- 2024-08-20T21:40:35.8388106Z >>> from torch._numpy.testing import assert_almost_equal 2024-08-20T21:40:35.8388292Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-08-20T21:40:35.8388515Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-08-20T21:40:35.8388969Z Traceback (most recent call last): 2024-08-20T21:40:35.8389082Z ... 2024-08-20T21:40:35.8389199Z AssertionError: 2024-08-20T21:40:35.8389376Z Arrays are not almost equal to 10 decimals 2024-08-20T21:40:35.8389485Z ACTUAL: 2.3333333333333 2024-08-20T21:40:35.8389592Z DESIRED: 2.33333334 2024-08-20T21:40:35.8389699Z 2024-08-20T21:40:35.8389892Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-08-20T21:40:35.8390068Z ... np.array([1.0,2.33333334]), decimal=9) 2024-08-20T21:40:35.8390224Z Traceback (most recent call last): 2024-08-20T21:40:35.8390548Z ... 2024-08-20T21:40:35.8390656Z AssertionError: 2024-08-20T21:40:35.8390832Z Arrays are not almost equal to 9 decimals 2024-08-20T21:40:35.8390931Z 2024-08-20T21:40:35.8391060Z Mismatched elements: 1 / 2 (50%) 2024-08-20T21:40:35.8391320Z Max absolute difference: 6.666699636781459e-09 2024-08-20T21:40:35.8391551Z Max relative difference: 2.8571569790287484e-09 2024-08-20T21:40:35.8391733Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-20T21:40:35.8391933Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-08-20T21:40:35.8392023Z 2024-08-20T21:40:35.8392114Z 2024-08-20T21:40:35.8392540Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8392724Z 2024-08-20T21:40:35.8392837Z warnings.warn(msg) 2024-08-20T21:40:35.8392946Z 2024-08-20T21:40:35.8393163Z --- Parse Warning: 86 / 101 --- 2024-08-20T21:40:35.8394627Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=455. 2024-08-20T21:40:35.8395048Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8395138Z 2024-08-20T21:40:35.8395453Z Raises an AssertionError if two items are not equal up to significant 2024-08-20T21:40:35.8395551Z digits. 2024-08-20T21:40:35.8395717Z 2024-08-20T21:40:35.8395976Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:40:35.8396212Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:40:35.8396472Z instead of this function for more consistent floating point 2024-08-20T21:40:35.8396599Z comparisons. 2024-08-20T21:40:35.8396690Z 2024-08-20T21:40:35.8396940Z Given two numbers, check that they are approximately equal. 2024-08-20T21:40:35.8397248Z Approximately equal is defined as the number of significant digits 2024-08-20T21:40:35.8397350Z that agree. 2024-08-20T21:40:35.8397456Z 2024-08-20T21:40:35.8397560Z Parameters 2024-08-20T21:40:35.8397687Z ---------- 2024-08-20T21:40:35.8397810Z actual : scalar 2024-08-20T21:40:35.8397931Z The object to check. 2024-08-20T21:40:35.8398039Z desired : scalar 2024-08-20T21:40:35.8398171Z The expected object. 2024-08-20T21:40:35.8398300Z significant : int, optional 2024-08-20T21:40:35.8398442Z Desired precision, default is 7. 2024-08-20T21:40:35.8398575Z err_msg : str, optional 2024-08-20T21:40:35.8398785Z The error message to be printed in case of failure. 2024-08-20T21:40:35.8398904Z verbose : bool, optional 2024-08-20T21:40:35.8399197Z If True, the conflicting values are appended to the error message. 2024-08-20T21:40:35.8399288Z 2024-08-20T21:40:35.8399428Z Raises 2024-08-20T21:40:35.8399559Z ------ 2024-08-20T21:40:35.8399665Z AssertionError 2024-08-20T21:40:35.8399928Z If actual and desired are not equal up to specified precision. 2024-08-20T21:40:35.8400028Z 2024-08-20T21:40:35.8400124Z See Also 2024-08-20T21:40:35.8400241Z -------- 2024-08-20T21:40:35.8400563Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:40:35.8400738Z relative and/or absolute precision. 2024-08-20T21:40:35.8401023Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:40:35.8401112Z 2024-08-20T21:40:35.8401207Z Examples 2024-08-20T21:40:35.8401343Z -------- 2024-08-20T21:40:35.8401798Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-08-20T21:40:35.8402226Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-08-20T21:40:35.8402396Z ... significant=8) 2024-08-20T21:40:35.8402814Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-08-20T21:40:35.8402966Z ... significant=8) 2024-08-20T21:40:35.8403115Z Traceback (most recent call last): 2024-08-20T21:40:35.8403211Z ... 2024-08-20T21:40:35.8403318Z AssertionError: 2024-08-20T21:40:35.8403497Z Items are not equal to 8 significant digits: 2024-08-20T21:40:35.8403644Z ACTUAL: 1.234567e-21 2024-08-20T21:40:35.8403842Z DESIRED: 1.2345672e-21 2024-08-20T21:40:35.8403943Z 2024-08-20T21:40:35.8404150Z the evaluated condition that raises the exception is 2024-08-20T21:40:35.8404237Z 2024-08-20T21:40:35.8404559Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-08-20T21:40:35.8404684Z True 2024-08-20T21:40:35.8404789Z 2024-08-20T21:40:35.8404875Z 2024-08-20T21:40:35.8405279Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8405384Z 2024-08-20T21:40:35.8405512Z warnings.warn(msg) 2024-08-20T21:40:35.8405638Z 2024-08-20T21:40:35.8405903Z --- Parse Warning: 87 / 101 --- 2024-08-20T21:40:35.8407347Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=734. 2024-08-20T21:40:35.8407823Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8407928Z 2024-08-20T21:40:35.8408201Z Raises an AssertionError if two array_like objects are not equal. 2024-08-20T21:40:35.8408292Z 2024-08-20T21:40:35.8408584Z Given two array_like objects, check that the shape is equal and all 2024-08-20T21:40:35.8408881Z elements of these objects are equal (but see the Notes for the special 2024-08-20T21:40:35.8409178Z handling of a scalar). An exception is raised at shape mismatch or 2024-08-20T21:40:35.8409469Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-08-20T21:40:35.8409766Z are compared like numbers, no assertion is raised if both objects have 2024-08-20T21:40:35.8409906Z NaNs in the same positions. 2024-08-20T21:40:35.8409992Z 2024-08-20T21:40:35.8410383Z The usual caution for verifying equality with floating point numbers is 2024-08-20T21:40:35.8410548Z advised. 2024-08-20T21:40:35.8410636Z 2024-08-20T21:40:35.8410741Z Parameters 2024-08-20T21:40:35.8410886Z ---------- 2024-08-20T21:40:35.8410989Z x : array_like 2024-08-20T21:40:35.8411113Z The actual object to check. 2024-08-20T21:40:35.8411236Z y : array_like 2024-08-20T21:40:35.8411368Z The desired, expected object. 2024-08-20T21:40:35.8411481Z err_msg : str, optional 2024-08-20T21:40:35.8411698Z The error message to be printed in case of failure. 2024-08-20T21:40:35.8411866Z verbose : bool, optional 2024-08-20T21:40:35.8412144Z If True, the conflicting values are appended to the error message. 2024-08-20T21:40:35.8412274Z strict : bool, optional 2024-08-20T21:40:35.8412549Z If True, raise an AssertionError when either the shape or the data 2024-08-20T21:40:35.8412798Z type of the array_like objects does not match. The special 2024-08-20T21:40:35.8413075Z handling for scalars mentioned in the Notes section is disabled. 2024-08-20T21:40:35.8413164Z 2024-08-20T21:40:35.8413271Z Raises 2024-08-20T21:40:35.8413398Z ------ 2024-08-20T21:40:35.8413502Z AssertionError 2024-08-20T21:40:35.8413694Z If actual and desired objects are not equal. 2024-08-20T21:40:35.8413783Z 2024-08-20T21:40:35.8413878Z See Also 2024-08-20T21:40:35.8414014Z -------- 2024-08-20T21:40:35.8414324Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:40:35.8414520Z relative and/or absolute precision. 2024-08-20T21:40:35.8414800Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:40:35.8414889Z 2024-08-20T21:40:35.8414982Z Notes 2024-08-20T21:40:35.8415110Z ----- 2024-08-20T21:40:35.8415388Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-08-20T21:40:35.8415688Z function checks that each element of the array_like object is equal to 2024-08-20T21:40:35.8416005Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-08-20T21:40:35.8416098Z 2024-08-20T21:40:35.8416194Z Examples 2024-08-20T21:40:35.8416326Z -------- 2024-08-20T21:40:35.8416500Z The first assert does not raise an exception: 2024-08-20T21:40:35.8416602Z 2024-08-20T21:40:35.8416834Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-08-20T21:40:35.8417002Z ... [np.exp(0),2.33333, np.nan]) 2024-08-20T21:40:35.8417104Z 2024-08-20T21:40:35.8417416Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-08-20T21:40:35.8417552Z functions for these cases instead: 2024-08-20T21:40:35.8417653Z 2024-08-20T21:40:35.8417840Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-08-20T21:40:35.8418012Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-08-20T21:40:35.8418238Z ... rtol=1e-10, atol=0) 2024-08-20T21:40:35.8418328Z 2024-08-20T21:40:35.8418658Z As mentioned in the Notes section, `assert_array_equal` has special 2024-08-20T21:40:35.8418974Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-08-20T21:40:35.8419062Z 2024-08-20T21:40:35.8419196Z >>> x = np.full((2, 5), fill_value=3) 2024-08-20T21:40:35.8419358Z >>> np.testing.assert_array_equal(x, 3) 2024-08-20T21:40:35.8419447Z 2024-08-20T21:40:35.8419745Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-08-20T21:40:35.8419854Z array: 2024-08-20T21:40:35.8419941Z 2024-08-20T21:40:35.8420145Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-08-20T21:40:35.8420279Z Traceback (most recent call last): 2024-08-20T21:40:35.8420375Z ... 2024-08-20T21:40:35.8420497Z AssertionError: 2024-08-20T21:40:35.8420609Z Arrays are not equal 2024-08-20T21:40:35.8420707Z 2024-08-20T21:40:35.8420837Z (shapes (2, 5), () mismatch) 2024-08-20T21:40:35.8420963Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-08-20T21:40:35.8421072Z [3, 3, 3, 3, 3]]) 2024-08-20T21:40:35.8421198Z y: torch.ndarray(3) 2024-08-20T21:40:35.8421285Z 2024-08-20T21:40:35.8421568Z The `strict` parameter also ensures that the array data types match: 2024-08-20T21:40:35.8421670Z 2024-08-20T21:40:35.8421784Z >>> x = np.array([2, 2, 2]) 2024-08-20T21:40:35.8421951Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-08-20T21:40:35.8422154Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-08-20T21:40:35.8422322Z Traceback (most recent call last): 2024-08-20T21:40:35.8422414Z ... 2024-08-20T21:40:35.8422539Z AssertionError: 2024-08-20T21:40:35.8422649Z Arrays are not equal 2024-08-20T21:40:35.8422749Z 2024-08-20T21:40:35.8422948Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-08-20T21:40:35.8423067Z x: torch.ndarray([2, 2, 2]) 2024-08-20T21:40:35.8423189Z y: torch.ndarray([2., 2., 2.]) 2024-08-20T21:40:35.8423288Z 2024-08-20T21:40:35.8423699Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8423801Z 2024-08-20T21:40:35.8423909Z warnings.warn(msg) 2024-08-20T21:40:35.8423997Z 2024-08-20T21:40:35.8424224Z --- Parse Warning: 88 / 101 --- 2024-08-20T21:40:35.8425690Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=840. 2024-08-20T21:40:35.8426108Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8426212Z 2024-08-20T21:40:35.8426496Z Raises an AssertionError if two objects are not equal up to desired 2024-08-20T21:40:35.8426597Z precision. 2024-08-20T21:40:35.8426696Z 2024-08-20T21:40:35.8426932Z .. note:: It is recommended to use one of `assert_allclose`, 2024-08-20T21:40:35.8427169Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-08-20T21:40:35.8427451Z instead of this function for more consistent floating point 2024-08-20T21:40:35.8427560Z comparisons. 2024-08-20T21:40:35.8427660Z 2024-08-20T21:40:35.8428014Z The test verifies identical shapes and that the elements of ``actual`` and 2024-08-20T21:40:35.8428120Z ``desired`` satisfy. 2024-08-20T21:40:35.8428220Z 2024-08-20T21:40:35.8428456Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-08-20T21:40:35.8428543Z 2024-08-20T21:40:35.8428879Z That is a looser test than originally documented, but agrees with what the 2024-08-20T21:40:35.8429208Z actual implementation did up to rounding vagaries. An exception is raised 2024-08-20T21:40:35.8429532Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-08-20T21:40:35.8429860Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-08-20T21:40:35.8430076Z objects have NaNs in the same positions. 2024-08-20T21:40:35.8430169Z 2024-08-20T21:40:35.8430285Z Parameters 2024-08-20T21:40:35.8430411Z ---------- 2024-08-20T21:40:35.8430514Z x : array_like 2024-08-20T21:40:35.8430664Z The actual object to check. 2024-08-20T21:40:35.8430773Z y : array_like 2024-08-20T21:40:35.8430906Z The desired, expected object. 2024-08-20T21:40:35.8431038Z decimal : int, optional 2024-08-20T21:40:35.8431185Z Desired precision, default is 6. 2024-08-20T21:40:35.8431316Z err_msg : str, optional 2024-08-20T21:40:35.8431522Z The error message to be printed in case of failure. 2024-08-20T21:40:35.8431641Z verbose : bool, optional 2024-08-20T21:40:35.8431938Z If True, the conflicting values are appended to the error message. 2024-08-20T21:40:35.8432030Z 2024-08-20T21:40:35.8432124Z Raises 2024-08-20T21:40:35.8432260Z ------ 2024-08-20T21:40:35.8432368Z AssertionError 2024-08-20T21:40:35.8432641Z If actual and desired are not equal up to specified precision. 2024-08-20T21:40:35.8432746Z 2024-08-20T21:40:35.8432844Z See Also 2024-08-20T21:40:35.8432965Z -------- 2024-08-20T21:40:35.8433292Z assert_allclose: Compare two array_like objects for equality with desired 2024-08-20T21:40:35.8433469Z relative and/or absolute precision. 2024-08-20T21:40:35.8433734Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-08-20T21:40:35.8433869Z 2024-08-20T21:40:35.8433968Z Examples 2024-08-20T21:40:35.8434089Z -------- 2024-08-20T21:40:35.8434267Z the first assert does not raise an exception 2024-08-20T21:40:35.8434356Z 2024-08-20T21:40:35.8434582Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-08-20T21:40:35.8434757Z ... [1.0,2.333,np.nan]) 2024-08-20T21:40:35.8434847Z 2024-08-20T21:40:35.8435092Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-20T21:40:35.8435275Z ... [1.0,2.33339,np.nan], decimal=5) 2024-08-20T21:40:35.8435415Z Traceback (most recent call last): 2024-08-20T21:40:35.8435524Z ... 2024-08-20T21:40:35.8435630Z AssertionError: 2024-08-20T21:40:35.8435789Z Arrays are not almost equal to 5 decimals 2024-08-20T21:40:35.8435904Z 2024-08-20T21:40:35.8436034Z Mismatched elements: 1 / 3 (33.3%) 2024-08-20T21:40:35.8436263Z Max absolute difference: 5.999999999994898e-05 2024-08-20T21:40:35.8436506Z Max relative difference: 2.5713661239633743e-05 2024-08-20T21:40:35.8436721Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-20T21:40:35.8436928Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-08-20T21:40:35.8437026Z 2024-08-20T21:40:35.8437258Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-08-20T21:40:35.8437430Z ... [1.0,2.33333, 5], decimal=5) 2024-08-20T21:40:35.8437584Z Traceback (most recent call last): 2024-08-20T21:40:35.8437677Z ... 2024-08-20T21:40:35.8437797Z AssertionError: 2024-08-20T21:40:35.8437952Z Arrays are not almost equal to 5 decimals 2024-08-20T21:40:35.8438094Z 2024-08-20T21:40:35.8438237Z x and y nan location mismatch: 2024-08-20T21:40:35.8438448Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-08-20T21:40:35.8438658Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-08-20T21:40:35.8438760Z 2024-08-20T21:40:35.8438845Z 2024-08-20T21:40:35.8439249Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8439353Z 2024-08-20T21:40:35.8439463Z warnings.warn(msg) 2024-08-20T21:40:35.8439552Z 2024-08-20T21:40:35.8439776Z --- Parse Warning: 89 / 101 --- 2024-08-20T21:40:35.8441295Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1790. 2024-08-20T21:40:35.8441716Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8442010Z Context manager that resets warning registry for catching warnings 2024-08-20T21:40:35.8442098Z 2024-08-20T21:40:35.8442436Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-08-20T21:40:35.8442737Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-08-20T21:40:35.8443056Z it impossible to retrigger the warning in this module, whatever you put in 2024-08-20T21:40:35.8443396Z the warnings filters. This context manager accepts a sequence of `modules` 2024-08-20T21:40:35.8443577Z as a keyword argument to its constructor and: 2024-08-20T21:40:35.8443665Z 2024-08-20T21:40:35.8443988Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-08-20T21:40:35.8444087Z on entry; 2024-08-20T21:40:35.8444328Z * resets ``__warningregistry__`` to its previous state on exit. 2024-08-20T21:40:35.8444430Z 2024-08-20T21:40:35.8444742Z This makes it possible to trigger any warning afresh inside the context 2024-08-20T21:40:35.8444989Z manager without disturbing the state of warnings outside. 2024-08-20T21:40:35.8445108Z 2024-08-20T21:40:35.8445415Z For compatibility with Python 3.0, please consider all arguments to be 2024-08-20T21:40:35.8445569Z keyword-only. 2024-08-20T21:40:35.8445658Z 2024-08-20T21:40:35.8445756Z Parameters 2024-08-20T21:40:35.8445894Z ---------- 2024-08-20T21:40:35.8446012Z record : bool, optional 2024-08-20T21:40:35.8446251Z Specifies whether warnings should be captured by a custom 2024-08-20T21:40:35.8446571Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-08-20T21:40:35.8446859Z returned by the context manager. Otherwise None is returned by the 2024-08-20T21:40:35.8447156Z context manager. The objects appended to the list are arguments whose 2024-08-20T21:40:35.8447449Z attributes mirror the arguments to ``showwarning()``. 2024-08-20T21:40:35.8447578Z modules : sequence, optional 2024-08-20T21:40:35.8447877Z Sequence of modules for which to reset warnings registry on entry and 2024-08-20T21:40:35.8448230Z restore on exit. To work correctly, all 'ignore' filters should 2024-08-20T21:40:35.8448370Z filter by one of these modules. 2024-08-20T21:40:35.8448476Z 2024-08-20T21:40:35.8448572Z Examples 2024-08-20T21:40:35.8448696Z -------- 2024-08-20T21:40:35.8448818Z >>> import warnings 2024-08-20T21:40:35.8449068Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-08-20T21:40:35.8449232Z ... modules=[np.core.fromnumeric]): 2024-08-20T21:40:35.8449457Z ... warnings.simplefilter('always') 2024-08-20T21:40:35.8449820Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-08-20T21:40:35.8450059Z ... # do something that raises a warning but ignore those in 2024-08-20T21:40:35.8450357Z ... # np.core.fromnumeric 2024-08-20T21:40:35.8450452Z 2024-08-20T21:40:35.8450861Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8450970Z 2024-08-20T21:40:35.8451079Z warnings.warn(msg) 2024-08-20T21:40:35.8451167Z 2024-08-20T21:40:35.8451398Z --- Parse Warning: 90 / 101 --- 2024-08-20T21:40:35.8452816Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=355. 2024-08-20T21:40:35.8453312Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8453602Z Applies a 1D convolution over a quantized input signal composed of 2024-08-20T21:40:35.8453737Z several quantized input planes. 2024-08-20T21:40:35.8453899Z 2024-08-20T21:40:35.8454188Z For details on input arguments, parameters, and implementation see 2024-08-20T21:40:35.8454311Z :class:`~torch.nn.Conv1d`. 2024-08-20T21:40:35.8454417Z 2024-08-20T21:40:35.8454520Z .. note:: 2024-08-20T21:40:35.8454788Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-08-20T21:40:35.8454895Z 2024-08-20T21:40:35.8454994Z .. note:: 2024-08-20T21:40:35.8455231Z Only `torch.quint8` is supported for the input data type. 2024-08-20T21:40:35.8455335Z 2024-08-20T21:40:35.8455423Z 2024-08-20T21:40:35.8455540Z Attributes: 2024-08-20T21:40:35.8455822Z weight (Tensor): packed tensor derived from the learnable weight 2024-08-20T21:40:35.8455954Z parameter. 2024-08-20T21:40:35.8456159Z scale (Tensor): scalar for the output scale 2024-08-20T21:40:35.8456373Z zero_point (Tensor): scalar for the output zero point 2024-08-20T21:40:35.8456464Z 2024-08-20T21:40:35.8456680Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-08-20T21:40:35.8456770Z 2024-08-20T21:40:35.8456908Z Examples:: 2024-08-20T21:40:35.8457012Z 2024-08-20T21:40:35.8457214Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-08-20T21:40:35.8457395Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-08-20T21:40:35.8457556Z >>> input = torch.randn(20, 16, 100) 2024-08-20T21:40:35.8457693Z >>> # quantize input to quint8 2024-08-20T21:40:35.8457810Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.8458113Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-08-20T21:40:35.8458297Z ... dtype=torch.quint8) 2024-08-20T21:40:35.8458430Z >>> output = m(q_input) 2024-08-20T21:40:35.8458519Z 2024-08-20T21:40:35.8458613Z 2024-08-20T21:40:35.8459041Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8459132Z 2024-08-20T21:40:35.8459244Z warnings.warn(msg) 2024-08-20T21:40:35.8459349Z 2024-08-20T21:40:35.8459560Z --- Parse Warning: 91 / 101 --- 2024-08-20T21:40:35.8460955Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11. 2024-08-20T21:40:35.8461383Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8461599Z A quantized long short-term memory (LSTM). 2024-08-20T21:40:35.8461685Z 2024-08-20T21:40:35.8462081Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-08-20T21:40:35.8462171Z 2024-08-20T21:40:35.8462271Z Attributes: 2024-08-20T21:40:35.8462445Z layers : instances of the `_LSTMLayer` 2024-08-20T21:40:35.8462569Z 2024-08-20T21:40:35.8462680Z .. note:: 2024-08-20T21:40:35.8462977Z To access the weights and biases, you need to access them per layer. 2024-08-20T21:40:35.8463210Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-08-20T21:40:35.8463309Z 2024-08-20T21:40:35.8463411Z Examples:: 2024-08-20T21:40:35.8463530Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.8463674Z >>> custom_module_config = { 2024-08-20T21:40:35.8463919Z ... 'float_to_observed_custom_module_class': { 2024-08-20T21:40:35.8464085Z ... nn.LSTM: nn.quantizable.LSTM, 2024-08-20T21:40:35.8464197Z ... }, 2024-08-20T21:40:35.8464515Z ... 'observed_to_quantized_custom_module_class': { 2024-08-20T21:40:35.8464711Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-08-20T21:40:35.8464821Z ... } 2024-08-20T21:40:35.8464918Z ... } 2024-08-20T21:40:35.8465207Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-08-20T21:40:35.8465502Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-08-20T21:40:35.8465597Z 2024-08-20T21:40:35.8466014Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8466101Z 2024-08-20T21:40:35.8466212Z warnings.warn(msg) 2024-08-20T21:40:35.8466314Z 2024-08-20T21:40:35.8466525Z --- Parse Warning: 92 / 101 --- 2024-08-20T21:40:35.8468170Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227. 2024-08-20T21:40:35.8468602Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8468823Z Squashes the sparse masks into the appropriate tensors. 2024-08-20T21:40:35.8468917Z 2024-08-20T21:40:35.8469224Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-08-20T21:40:35.8469509Z the module will have a `sparse_params` dict attached to it. 2024-08-20T21:40:35.8469621Z 2024-08-20T21:40:35.8469724Z Args: 2024-08-20T21:40:35.8469979Z params_to_keep: List of keys to save in the module or a dict 2024-08-20T21:40:35.8470220Z representing the modules and keys that will have 2024-08-20T21:40:35.8470388Z sparsity parameters saved 2024-08-20T21:40:35.8470681Z params_to_keep_per_layer: Dict to specify the params that should be 2024-08-20T21:40:35.8470914Z saved for specific layers. The keys in the dict 2024-08-20T21:40:35.8471137Z should be the module fqn, while the values should 2024-08-20T21:40:35.8471373Z be a list of strings with the names of the variables 2024-08-20T21:40:35.8471558Z to save in the `sparse_params` 2024-08-20T21:40:35.8471652Z 2024-08-20T21:40:35.8471755Z Examples: 2024-08-20T21:40:35.8471956Z >>> # xdoctest: +SKIP("locals are undefined") 2024-08-20T21:40:35.8472167Z >>> # Don't save any sparse params 2024-08-20T21:40:35.8472331Z >>> sparsifier.squash_mask() 2024-08-20T21:40:35.8472584Z >>> hasattr(model.submodule1, 'sparse_params') 2024-08-20T21:40:35.8472684Z False 2024-08-20T21:40:35.8472790Z 2024-08-20T21:40:35.8472950Z >>> # Keep sparse params per layer 2024-08-20T21:40:35.8473095Z >>> sparsifier.squash_mask( 2024-08-20T21:40:35.8473261Z ... params_to_keep_per_layer={ 2024-08-20T21:40:35.8473516Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-08-20T21:40:35.8473791Z ... 'submodule2.linear42': ('baz',) 2024-08-20T21:40:35.8473911Z ... }) 2024-08-20T21:40:35.8474129Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:40:35.8474302Z {'foo': 42, 'bar': 24} 2024-08-20T21:40:35.8474534Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:40:35.8474682Z {'baz': 0.1} 2024-08-20T21:40:35.8474772Z 2024-08-20T21:40:35.8474957Z >>> # Keep sparse params for all layers 2024-08-20T21:40:35.8475258Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-08-20T21:40:35.8475532Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:40:35.8475701Z {'foo': 42, 'bar': 24} 2024-08-20T21:40:35.8475916Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:40:35.8476095Z {'foo': 42, 'bar': 24} 2024-08-20T21:40:35.8476189Z 2024-08-20T21:40:35.8476462Z >>> # Keep some sparse params for all layers, and specific ones for 2024-08-20T21:40:35.8476598Z >>> # some other layers 2024-08-20T21:40:35.8476738Z >>> sparsifier.squash_mask( 2024-08-20T21:40:35.8476950Z ... params_to_keep=('foo', 'bar'), 2024-08-20T21:40:35.8477111Z ... params_to_keep_per_layer={ 2024-08-20T21:40:35.8477346Z ... 'submodule2.linear42': ('baz',) 2024-08-20T21:40:35.8477450Z ... }) 2024-08-20T21:40:35.8477672Z >>> print(model.submodule1.linear1.sparse_params) 2024-08-20T21:40:35.8477835Z {'foo': 42, 'bar': 24} 2024-08-20T21:40:35.8478065Z >>> print(model.submodule2.linear42.sparse_params) 2024-08-20T21:40:35.8478282Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-08-20T21:40:35.8478376Z 2024-08-20T21:40:35.8478799Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8478887Z 2024-08-20T21:40:35.8478997Z warnings.warn(msg) 2024-08-20T21:40:35.8479099Z 2024-08-20T21:40:35.8479344Z --- Parse Warning: 93 / 101 --- 2024-08-20T21:40:35.8480935Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2024-08-20T21:40:35.8481368Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8481457Z 2024-08-20T21:40:35.8481801Z Config object that specifies the supported data types passed as arguments to 2024-08-20T21:40:35.8482140Z quantize ops in the reference model spec, for input and output activations, 2024-08-20T21:40:35.8482250Z weights, and biases. 2024-08-20T21:40:35.8482351Z 2024-08-20T21:40:35.8482559Z For example, consider the following reference model: 2024-08-20T21:40:35.8482646Z 2024-08-20T21:40:35.8482922Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-08-20T21:40:35.8483011Z 2024-08-20T21:40:35.8483309Z The pattern in the square brackets refers to the reference pattern of 2024-08-20T21:40:35.8483629Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-08-20T21:40:35.8483941Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-08-20T21:40:35.8484248Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-08-20T21:40:35.8484555Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-08-20T21:40:35.8484691Z the second quantize op (quant2). 2024-08-20T21:40:35.8484778Z 2024-08-20T21:40:35.8485091Z Note that the dtype here does not refer to the interface dtypes of the 2024-08-20T21:40:35.8485377Z op. For example, the "input dtype" here is not the dtype of the input 2024-08-20T21:40:35.8485718Z tensor passed to the quantized linear op. Though it can still be the 2024-08-20T21:40:35.8485994Z same as the interface dtype, this is not always the case, e.g. the 2024-08-20T21:40:35.8486289Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-08-20T21:40:35.8486594Z specified in the DTypeConfig would still be quint8. The semantics of 2024-08-20T21:40:35.8486883Z dtypes here are the same as the semantics of the dtypes specified in 2024-08-20T21:40:35.8486984Z the observers. 2024-08-20T21:40:35.8487087Z 2024-08-20T21:40:35.8487394Z These dtypes are matched against the ones specified in the user's 2024-08-20T21:40:35.8487748Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-08-20T21:40:35.8488059Z specified in the DTypeConfig (if any), then we will quantize the given 2024-08-20T21:40:35.8488358Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-08-20T21:40:35.8488510Z the pattern will not be quantized. 2024-08-20T21:40:35.8488938Z 2024-08-20T21:40:35.8489048Z Example usage:: 2024-08-20T21:40:35.8489151Z 2024-08-20T21:40:35.8489284Z >>> # xdoctest: +SKIP(failing) 2024-08-20T21:40:35.8489423Z >>> dtype_config1 = DTypeConfig( 2024-08-20T21:40:35.8489573Z ... input_dtype=torch.quint8, 2024-08-20T21:40:35.8489705Z ... output_dtype=torch.quint8, 2024-08-20T21:40:35.8489834Z ... weight_dtype=torch.qint8, 2024-08-20T21:40:35.8489976Z ... bias_dtype=torch.float) 2024-08-20T21:40:35.8490064Z 2024-08-20T21:40:35.8490451Z >>> dtype_config2 = DTypeConfig( 2024-08-20T21:40:35.8490651Z ... input_dtype=DTypeWithConstraints( 2024-08-20T21:40:35.8490773Z ... dtype=torch.quint8, 2024-08-20T21:40:35.8490910Z ... quant_min_lower_bound=0, 2024-08-20T21:40:35.8491068Z ... quant_max_upper_bound=255, 2024-08-20T21:40:35.8491165Z ... ), 2024-08-20T21:40:35.8491334Z ... output_dtype=DTypeWithConstraints( 2024-08-20T21:40:35.8491471Z ... dtype=torch.quint8, 2024-08-20T21:40:35.8491695Z ... quant_min_lower_bound=0, 2024-08-20T21:40:35.8491838Z ... quant_max_upper_bound=255, 2024-08-20T21:40:35.8491948Z ... ), 2024-08-20T21:40:35.8492116Z ... weight_dtype=DTypeWithConstraints( 2024-08-20T21:40:35.8492256Z ... dtype=torch.qint8, 2024-08-20T21:40:35.8492483Z ... quant_min_lower_bound=-128, 2024-08-20T21:40:35.8492623Z ... quant_max_upper_bound=127, 2024-08-20T21:40:35.8492734Z ... ), 2024-08-20T21:40:35.8492867Z ... bias_dtype=torch.float) 2024-08-20T21:40:35.8492956Z 2024-08-20T21:40:35.8493103Z >>> dtype_config1.input_dtype 2024-08-20T21:40:35.8493205Z torch.quint8 2024-08-20T21:40:35.8493292Z 2024-08-20T21:40:35.8493440Z >>> dtype_config2.input_dtype 2024-08-20T21:40:35.8493542Z torch.quint8 2024-08-20T21:40:35.8493633Z 2024-08-20T21:40:35.8493829Z >>> dtype_config2.input_dtype_with_constraints 2024-08-20T21:40:35.8494536Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-08-20T21:40:35.8494624Z 2024-08-20T21:40:35.8495044Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8495130Z 2024-08-20T21:40:35.8495239Z warnings.warn(msg) 2024-08-20T21:40:35.8495341Z 2024-08-20T21:40:35.8495556Z --- Parse Warning: 94 / 101 --- 2024-08-20T21:40:35.8497453Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2024-08-20T21:40:35.8497928Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8498016Z 2024-08-20T21:40:35.8498400Z Takes in optional filter values and generates two tables with desired information. 2024-08-20T21:40:35.8498491Z 2024-08-20T21:40:35.8498835Z The generated tables are presented in both a list-of-lists format 2024-08-20T21:40:35.8498938Z 2024-08-20T21:40:35.8499223Z The reason for the two tables are that they handle different things: 2024-08-20T21:40:35.8499442Z 1.) the first table handles all tensor level information 2024-08-20T21:40:35.8499759Z 2.) the second table handles and displays all channel based information 2024-08-20T21:40:35.8499932Z 2024-08-20T21:40:35.8500412Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-20T21:40:35.8500963Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-20T21:40:35.8501458Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-20T21:40:35.8501563Z 2024-08-20T21:40:35.8501678Z Tensor table columns: 2024-08-20T21:40:35.8501942Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:40:35.8502222Z ---- --------- --------- --------- --------- --------- 2024-08-20T21:40:35.8502312Z 2024-08-20T21:40:35.8502475Z Per-Channel table columns: 2024-08-20T21:40:35.8502794Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:40:35.8503083Z ---- --------- ------- --------- --------- --------- --------- 2024-08-20T21:40:35.8503186Z 2024-08-20T21:40:35.8503282Z Args: 2024-08-20T21:40:35.8503632Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:40:35.8503787Z contain this filter substring 2024-08-20T21:40:35.8504005Z Default = "", results in all the features being printed 2024-08-20T21:40:35.8504351Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:40:35.8504742Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:40:35.8504828Z 2024-08-20T21:40:35.8504967Z Returns a dictionary with two keys: 2024-08-20T21:40:35.8505207Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-08-20T21:40:35.8505364Z "tensor_level_info", "channel_level_info" 2024-08-20T21:40:35.8505506Z Each key maps to a tuple with: 2024-08-20T21:40:35.8505679Z A list of the headers of each table 2024-08-20T21:40:35.8505931Z A list of lists containing the table information row by row 2024-08-20T21:40:35.8506187Z The 0th index row will contain the headers of the columns 2024-08-20T21:40:35.8506430Z The rest of the rows will contain data 2024-08-20T21:40:35.8506534Z 2024-08-20T21:40:35.8506652Z Example Use: 2024-08-20T21:40:35.8506822Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:35.8507022Z >>> mod_report_visualizer.generate_filtered_tables( 2024-08-20T21:40:35.8507196Z ... feature_filter = "per_channel_min", 2024-08-20T21:40:35.8507335Z ... module_fqn_filter = "block1" 2024-08-20T21:40:35.8507705Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-08-20T21:40:35.8507813Z 2024-08-20T21:40:35.8508229Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8508322Z 2024-08-20T21:40:35.8508446Z warnings.warn(msg) 2024-08-20T21:40:35.8508536Z 2024-08-20T21:40:35.8508751Z --- Parse Warning: 95 / 101 --- 2024-08-20T21:40:35.8510682Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400. 2024-08-20T21:40:35.8511144Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8511247Z 2024-08-20T21:40:35.8511614Z Takes in optional filter values and prints out formatted tables of the information. 2024-08-20T21:40:35.8511703Z 2024-08-20T21:40:35.8512205Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-08-20T21:40:35.8512529Z 1.) the first table handles all tensor level information 2024-08-20T21:40:35.8512830Z 2.) the second table handles and displays all channel based information 2024-08-20T21:40:35.8512936Z 2024-08-20T21:40:35.8513408Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-08-20T21:40:35.8513975Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-08-20T21:40:35.8514470Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-08-20T21:40:35.8514561Z 2024-08-20T21:40:35.8514694Z Tensor table columns: 2024-08-20T21:40:35.8514958Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:40:35.8515213Z ---- --------- --------- --------- --------- --------- 2024-08-20T21:40:35.8515319Z 2024-08-20T21:40:35.8515487Z Per-Channel table columns: 2024-08-20T21:40:35.8515576Z 2024-08-20T21:40:35.8515898Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-08-20T21:40:35.8516178Z ---- --------- ------- --------- --------- --------- --------- 2024-08-20T21:40:35.8516269Z 2024-08-20T21:40:35.8516384Z Args: 2024-08-20T21:40:35.8516740Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:40:35.8516879Z contain this filter substring 2024-08-20T21:40:35.8517146Z Default = "", results in all the features being printed 2024-08-20T21:40:35.8517495Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:40:35.8517859Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:40:35.8517949Z 2024-08-20T21:40:35.8518055Z Example Use: 2024-08-20T21:40:35.8518244Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:35.8518465Z >>> mod_report_visualizer.generate_table_visualization( 2024-08-20T21:40:35.8518628Z ... feature_filter = "per_channel_min", 2024-08-20T21:40:35.8518786Z ... module_fqn_filter = "block1" 2024-08-20T21:40:35.8518885Z ... ) 2024-08-20T21:40:35.8519142Z >>> # prints out neatly formatted table with per_channel_min info 2024-08-20T21:40:35.8519328Z >>> # for all modules in block 1 of the model 2024-08-20T21:40:35.8519422Z 2024-08-20T21:40:35.8519833Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8519935Z 2024-08-20T21:40:35.8520045Z warnings.warn(msg) 2024-08-20T21:40:35.8520155Z 2024-08-20T21:40:35.8520366Z --- Parse Warning: 96 / 101 --- 2024-08-20T21:40:35.8522256Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=565. 2024-08-20T21:40:35.8522686Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8522773Z 2024-08-20T21:40:35.8523134Z Takes in a feature and optional module_filter and plots of the desired data. 2024-08-20T21:40:35.8523237Z 2024-08-20T21:40:35.8523609Z For per channel features, it averages the value across the channels and plots a point 2024-08-20T21:40:35.8523975Z per module. The reason for this is that for models with hundreds of channels, it can 2024-08-20T21:40:35.8524371Z be hard to differentiate one channel line from another, and so the point of generating 2024-08-20T21:40:35.8524748Z a single average point per module is to give a sense of general trends that encourage 2024-08-20T21:40:35.8524873Z further deep dives. 2024-08-20T21:40:35.8524963Z 2024-08-20T21:40:35.8525056Z Note: 2024-08-20T21:40:35.8525488Z Only features in the report that have tensor value data are plottable by this class 2024-08-20T21:40:35.8525705Z When the tensor information is plotted, it will plot: 2024-08-20T21:40:35.8525885Z idx as the x val, feature value as the y_val 2024-08-20T21:40:35.8526124Z When the channel information is plotted, it will plot: 2024-08-20T21:40:35.8526517Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-08-20T21:40:35.8526846Z The reason for this is that we want to be able to compare values across the 2024-08-20T21:40:35.8527190Z channels for same layer, and it will be hard if values are staggered by idx 2024-08-20T21:40:35.8527414Z This means each module is represented by only 1 x value 2024-08-20T21:40:35.8527520Z Args: 2024-08-20T21:40:35.8527824Z feature_filter (str): Filters the features presented to only those that 2024-08-20T21:40:35.8527960Z contain this filter substring 2024-08-20T21:40:35.8528327Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:40:35.8528671Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:40:35.8528762Z 2024-08-20T21:40:35.8528877Z Example Use: 2024-08-20T21:40:35.8529047Z >>> # xdoctest: +SKIP("undefined variables") 2024-08-20T21:40:35.8529256Z >>> mod_report_visualizer.generate_plot_visualization( 2024-08-20T21:40:35.8529456Z ... feature_filter = "per_channel_min", 2024-08-20T21:40:35.8529602Z ... module_fqn_filter = "block1" 2024-08-20T21:40:35.8529696Z ... ) 2024-08-20T21:40:35.8529947Z >>> # outputs line plot of per_channel_min information for all 2024-08-20T21:40:35.8530347Z >>> # modules in block1 of model each channel gets it's own line, 2024-08-20T21:40:35.8530685Z >>> # and it's plotted across the in-order modules on the x-axis 2024-08-20T21:40:35.8530776Z 2024-08-20T21:40:35.8531185Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8531291Z 2024-08-20T21:40:35.8531402Z warnings.warn(msg) 2024-08-20T21:40:35.8531491Z 2024-08-20T21:40:35.8531722Z --- Parse Warning: 97 / 101 --- 2024-08-20T21:40:35.8533646Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=645. 2024-08-20T21:40:35.8534066Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8534167Z 2024-08-20T21:40:35.8534546Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-08-20T21:40:35.8534649Z 2024-08-20T21:40:35.8534741Z Note: 2024-08-20T21:40:35.8535120Z Only features in the report that have tensor value data can be viewed as a histogram 2024-08-20T21:40:35.8535513Z If you want to plot a histogram from all the channel values of a specific feature for 2024-08-20T21:40:35.8535861Z a specific model, make sure to specify both the model and the feature properly 2024-08-20T21:40:35.8536252Z in the filters and you should be able to see a distribution of the channel data 2024-08-20T21:40:35.8536359Z 2024-08-20T21:40:35.8536452Z Args: 2024-08-20T21:40:35.8536803Z feature_filter (str, optional): Filters the features presented to only those that 2024-08-20T21:40:35.8536954Z contain this filter substring 2024-08-20T21:40:35.8537170Z Default = "", results in all the features being printed 2024-08-20T21:40:35.8537518Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-08-20T21:40:35.8537874Z Default = "", results in all the modules in the reports to be visible in the table 2024-08-20T21:40:35.8538229Z num_bins (int, optional): The number of bins to create the histogram with 2024-08-20T21:40:35.8538503Z Default = 10, the values will be split into 10 equal sized bins 2024-08-20T21:40:35.8538592Z 2024-08-20T21:40:35.8538693Z Example Use: 2024-08-20T21:40:35.8538822Z >>> # xdoctest: +SKIP 2024-08-20T21:40:35.8539208Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-08-20T21:40:35.8539373Z ... feature_filter = "per_channel_min", 2024-08-20T21:40:35.8539523Z ... module_fqn_filter = "block1" 2024-08-20T21:40:35.8539617Z ... ) 2024-08-20T21:40:35.8539986Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-08-20T21:40:35.8540353Z information is gathered across all channels for all modules in block 1 for the 2024-08-20T21:40:35.8540655Z per_channel_min and is displayed in a histogram of equally sized bins 2024-08-20T21:40:35.8540757Z 2024-08-20T21:40:35.8541161Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8541248Z 2024-08-20T21:40:35.8541375Z warnings.warn(msg) 2024-08-20T21:40:35.8541464Z 2024-08-20T21:40:35.8541676Z --- Parse Warning: 98 / 101 --- 2024-08-20T21:40:35.8543322Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=13. 2024-08-20T21:40:35.8543788Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8543876Z 2024-08-20T21:40:35.8544180Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-08-20T21:40:35.8544501Z distribution where all component are from different parameterizations of 2024-08-20T21:40:35.8544803Z the same distribution type. It is parameterized by a `Categorical` 2024-08-20T21:40:35.8545051Z "selecting distribution" (over `k` component) and a component 2024-08-20T21:40:35.8545324Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-08-20T21:40:35.8545560Z (equal to `[k]`) which indexes each (batch of) component. 2024-08-20T21:40:35.8545662Z 2024-08-20T21:40:35.8545770Z Examples:: 2024-08-20T21:40:35.8545873Z 2024-08-20T21:40:35.8546028Z >>> # xdoctest: +SKIP("undefined vars") 2024-08-20T21:40:35.8546301Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-08-20T21:40:35.8546457Z >>> # weighted normal distributions 2024-08-20T21:40:35.8546612Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-20T21:40:35.8546809Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-08-20T21:40:35.8546977Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:40:35.8547064Z 2024-08-20T21:40:35.8547334Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-08-20T21:40:35.8547519Z >>> # weighted bivariate normal distributions 2024-08-20T21:40:35.8547672Z >>> mix = D.Categorical(torch.ones(5,)) 2024-08-20T21:40:35.8547871Z >>> comp = D.Independent(D.Normal( 2024-08-20T21:40:35.8548044Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-08-20T21:40:35.8548199Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:40:35.8548301Z 2024-08-20T21:40:35.8548540Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-08-20T21:40:35.8548808Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-08-20T21:40:35.8548985Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-08-20T21:40:35.8549123Z >>> comp = D.Independent(D.Normal( 2024-08-20T21:40:35.8549330Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-08-20T21:40:35.8549558Z >>> gmm = MixtureSameFamily(mix, comp) 2024-08-20T21:40:35.8549710Z 2024-08-20T21:40:35.8549806Z Args: 2024-08-20T21:40:35.8550170Z mixture_distribution: `torch.distributions.Categorical`-like 2024-08-20T21:40:35.8550415Z instance. Manages the probability of selecting component. 2024-08-20T21:40:35.8550663Z The number of categories must match the rightmost batch 2024-08-20T21:40:35.8550910Z dimension of the `component_distribution`. Must have either 2024-08-20T21:40:35.8551097Z scalar `batch_shape` or `batch_shape` matching 2024-08-20T21:40:35.8551351Z `component_distribution.batch_shape[:-1]` 2024-08-20T21:40:35.8551704Z component_distribution: `torch.distributions.Distribution`-like 2024-08-20T21:40:35.8552000Z instance. Right-most batch dimension indexes component. 2024-08-20T21:40:35.8552108Z 2024-08-20T21:40:35.8552507Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8552595Z 2024-08-20T21:40:35.8552723Z warnings.warn(msg) 2024-08-20T21:40:35.8552813Z 2024-08-20T21:40:35.8553024Z --- Parse Warning: 99 / 101 --- 2024-08-20T21:40:35.8554556Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=111. 2024-08-20T21:40:35.8555006Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8555109Z 2024-08-20T21:40:35.8555345Z Creates a RelaxedBernoulli distribution, parametrized by 2024-08-20T21:40:35.8555594Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-08-20T21:40:35.8555914Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-08-20T21:40:35.8556167Z so the values are in (0, 1), and has reparametrizable samples. 2024-08-20T21:40:35.8556255Z 2024-08-20T21:40:35.8556374Z Example:: 2024-08-20T21:40:35.8556464Z 2024-08-20T21:40:35.8556711Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:40:35.8556897Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-08-20T21:40:35.8557075Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-08-20T21:40:35.8557178Z >>> m.sample() 2024-08-20T21:40:35.8557344Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-08-20T21:40:35.8557436Z 2024-08-20T21:40:35.8557530Z Args: 2024-08-20T21:40:35.8557722Z temperature (Tensor): relaxation temperature 2024-08-20T21:40:35.8557997Z probs (Number, Tensor): the probability of sampling `1` 2024-08-20T21:40:35.8558283Z logits (Number, Tensor): the log-odds of sampling `1` 2024-08-20T21:40:35.8558370Z 2024-08-20T21:40:35.8558775Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8558877Z 2024-08-20T21:40:35.8558991Z warnings.warn(msg) 2024-08-20T21:40:35.8559080Z 2024-08-20T21:40:35.8559308Z --- Parse Warning: 100 / 101 --- 2024-08-20T21:40:35.8561061Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=99. 2024-08-20T21:40:35.8561542Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8561648Z 2024-08-20T21:40:35.8561933Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-08-20T21:40:35.8562208Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-08-20T21:40:35.8562543Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-08-20T21:40:35.8562761Z its samples are on simplex, and are reparametrizable. 2024-08-20T21:40:35.8562868Z 2024-08-20T21:40:35.8562973Z Example:: 2024-08-20T21:40:35.8563117Z 2024-08-20T21:40:35.8563387Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-08-20T21:40:35.8563601Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-08-20T21:40:35.8563793Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-08-20T21:40:35.8563920Z >>> m.sample() 2024-08-20T21:40:35.8564076Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-08-20T21:40:35.8564170Z 2024-08-20T21:40:35.8564284Z Args: 2024-08-20T21:40:35.8564466Z temperature (Tensor): relaxation temperature 2024-08-20T21:40:35.8564613Z probs (Tensor): event probabilities 2024-08-20T21:40:35.8564884Z logits (Tensor): unnormalized log probability for each event 2024-08-20T21:40:35.8564976Z 2024-08-20T21:40:35.8565381Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8565487Z 2024-08-20T21:40:35.8565599Z warnings.warn(msg) 2024-08-20T21:40:35.8565709Z 2024-08-20T21:40:35.8565956Z --- Parse Warning: 101 / 101 --- 2024-08-20T21:40:35.8567349Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1274. 2024-08-20T21:40:35.8567787Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-08-20T21:40:35.8568015Z Asserts that ``actual`` and ``expected`` are close. 2024-08-20T21:40:35.8568107Z 2024-08-20T21:40:35.8568735Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-08-20T21:40:35.8568834Z 2024-08-20T21:40:35.8568935Z .. math:: 2024-08-20T21:40:35.8569040Z 2024-08-20T21:40:35.8569698Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-08-20T21:40:35.8569793Z 2024-08-20T21:40:35.8570461Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-08-20T21:40:35.8570737Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-08-20T21:40:35.8577607Z 2024-08-20T21:40:35.8577960Z In addition, they are only considered close if they have the same 2024-08-20T21:40:35.8578059Z 2024-08-20T21:40:35.8578458Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-08-20T21:40:35.8578689Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-08-20T21:40:35.8578938Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-08-20T21:40:35.8579175Z - stride (if ``check_stride`` is ``True``). 2024-08-20T21:40:35.8579267Z 2024-08-20T21:40:35.8579702Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-08-20T21:40:35.8579809Z 2024-08-20T21:40:35.8580340Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-08-20T21:40:35.8580857Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-08-20T21:40:35.8581266Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-08-20T21:40:35.8581808Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-08-20T21:40:35.8581916Z 2024-08-20T21:40:35.8582312Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-08-20T21:40:35.8582806Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-08-20T21:40:35.8582938Z definition above. 2024-08-20T21:40:35.8583027Z 2024-08-20T21:40:35.8583637Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-08-20T21:40:35.8584295Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-08-20T21:40:35.8584868Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-08-20T21:40:35.8585521Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-08-20T21:40:35.8585833Z their elements are considered close according to the above definition. 2024-08-20T21:40:35.8585926Z 2024-08-20T21:40:35.8586056Z .. note:: 2024-08-20T21:40:35.8586148Z 2024-08-20T21:40:35.8586630Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-08-20T21:40:35.8587214Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-08-20T21:40:35.8587599Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-08-20T21:40:35.8587707Z 2024-08-20T21:40:35.8587806Z Args: 2024-08-20T21:40:35.8587942Z actual (Any): Actual input. 2024-08-20T21:40:35.8588100Z expected (Any): Expected input. 2024-08-20T21:40:35.8589027Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-08-20T21:40:35.8589298Z are allowed. Otherwise type equality is required. 2024-08-20T21:40:35.8589814Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-08-20T21:40:35.8590188Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-20T21:40:35.8590928Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-08-20T21:40:35.8591321Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-08-20T21:40:35.8591679Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-08-20T21:40:35.8592098Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-08-20T21:40:35.8592437Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-08-20T21:40:35.8592869Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-08-20T21:40:35.8593374Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-08-20T21:40:35.8593953Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-08-20T21:40:35.8594185Z :func:`torch.promote_types`) before being compared. 2024-08-20T21:40:35.8594680Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-08-20T21:40:35.8595247Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-08-20T21:40:35.8595468Z compared. 2024-08-20T21:40:35.8595968Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-08-20T21:40:35.8596464Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-08-20T21:40:35.8596982Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-08-20T21:40:35.8597132Z should return the new message. 2024-08-20T21:40:35.8597240Z 2024-08-20T21:40:35.8597339Z Raises: 2024-08-20T21:40:35.8597732Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-08-20T21:40:35.8597969Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-08-20T21:40:35.8598399Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-08-20T21:40:35.8598890Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-08-20T21:40:35.8599027Z different types. 2024-08-20T21:40:35.8599625Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-08-20T21:40:35.8600231Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-08-20T21:40:35.8600662Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-08-20T21:40:35.8601074Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-08-20T21:40:35.8601242Z :attr:`~torch.Tensor.layout`. 2024-08-20T21:40:35.8601531Z AssertionError: If only one of corresponding tensors is quantized. 2024-08-20T21:40:35.8602151Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-08-20T21:40:35.8602566Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-08-20T21:40:35.8602773Z :attr:`~torch.Tensor.device`. 2024-08-20T21:40:35.8603231Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-08-20T21:40:35.8603734Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-08-20T21:40:35.8604225Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-08-20T21:40:35.8604332Z 2024-08-20T21:40:35.8604943Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-08-20T21:40:35.8605209Z ``dtype``'s, the maximum of both tolerances is used. 2024-08-20T21:40:35.8605318Z 2024-08-20T21:40:35.8605543Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8605727Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-08-20T21:40:35.8605890Z +===========================+============+==========+ 2024-08-20T21:40:35.8606145Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-08-20T21:40:35.8606376Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8606628Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-08-20T21:40:35.8606841Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8607108Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:35.8607324Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8607569Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-08-20T21:40:35.8607830Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8608078Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-08-20T21:40:35.8608292Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8608556Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:35.8608769Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8609095Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-08-20T21:40:35.8609305Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8609547Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:35.8609824Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8610073Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:35.8610353Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8610617Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:35.8610831Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8611072Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:35.8611302Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8611545Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-08-20T21:40:35.8611757Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8611939Z | other | ``0.0`` | ``0.0`` | 2024-08-20T21:40:35.8612149Z +---------------------------+------------+----------+ 2024-08-20T21:40:35.8612259Z 2024-08-20T21:40:35.8612365Z .. note:: 2024-08-20T21:40:35.8612460Z 2024-08-20T21:40:35.8613063Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-08-20T21:40:35.8613578Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-08-20T21:40:35.8613944Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-08-20T21:40:35.8614081Z 2024-08-20T21:40:35.8614199Z >>> import functools 2024-08-20T21:40:35.8614550Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-08-20T21:40:35.8614750Z >>> assert_equal(1e-9, 1e-10) 2024-08-20T21:40:35.8614908Z Traceback (most recent call last): 2024-08-20T21:40:35.8615004Z ... 2024-08-20T21:40:35.8615183Z AssertionError: Scalars are not equal! 2024-08-20T21:40:35.8615290Z 2024-08-20T21:40:35.8615492Z Expected 1e-10 but got 1e-09. 2024-08-20T21:40:35.8615718Z Absolute difference: 9.000000000000001e-10 2024-08-20T21:40:35.8615844Z Relative difference: 9.0 2024-08-20T21:40:35.8615946Z 2024-08-20T21:40:35.8616053Z Examples: 2024-08-20T21:40:35.8616201Z >>> # tensor to tensor comparison 2024-08-20T21:40:35.8616454Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-08-20T21:40:35.8616631Z >>> actual = torch.acos(torch.cos(expected)) 2024-08-20T21:40:35.8616823Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:40:35.8616929Z 2024-08-20T21:40:35.8617075Z >>> # scalar to scalar comparison 2024-08-20T21:40:35.8617183Z >>> import math 2024-08-20T21:40:35.8617331Z >>> expected = math.sqrt(2.0) 2024-08-20T21:40:35.8617472Z >>> actual = 2.0 / math.sqrt(2.0) 2024-08-20T21:40:35.8617663Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:40:35.8617771Z 2024-08-20T21:40:35.8617941Z >>> # numpy array to numpy array comparison 2024-08-20T21:40:35.8618075Z >>> import numpy as np 2024-08-20T21:40:35.8618297Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-08-20T21:40:35.8618490Z >>> actual = np.arccos(np.cos(expected)) 2024-08-20T21:40:35.8618700Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:40:35.8618796Z 2024-08-20T21:40:35.8618953Z >>> # sequence to sequence comparison 2024-08-20T21:40:35.8619092Z >>> import numpy as np 2024-08-20T21:40:35.8619459Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-08-20T21:40:35.8619640Z >>> # length and their elements have to match. 2024-08-20T21:40:35.8619866Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-08-20T21:40:35.8620003Z >>> actual = tuple(expected) 2024-08-20T21:40:35.8620253Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:40:35.8620364Z 2024-08-20T21:40:35.8620525Z >>> # mapping to mapping comparison 2024-08-20T21:40:35.8620689Z >>> from collections import OrderedDict 2024-08-20T21:40:35.8620831Z >>> import numpy as np 2024-08-20T21:40:35.8620961Z >>> foo = torch.tensor(1.0) 2024-08-20T21:40:35.8621087Z >>> bar = 2.0 2024-08-20T21:40:35.8621212Z >>> baz = np.array(3.0) 2024-08-20T21:40:35.8621573Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-08-20T21:40:35.8621889Z >>> # have to have the same set of keys and their elements have to match. 2024-08-20T21:40:35.8622165Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-08-20T21:40:35.8622350Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-08-20T21:40:35.8622564Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:40:35.8622664Z 2024-08-20T21:40:35.8622837Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-20T21:40:35.8622995Z >>> actual = expected.clone() 2024-08-20T21:40:35.8623236Z >>> # By default, directly related instances can be compared 2024-08-20T21:40:35.8623528Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-08-20T21:40:35.8623820Z >>> # This check can be made more strict with allow_subclasses=False 2024-08-20T21:40:35.8623996Z >>> torch.testing.assert_close( 2024-08-20T21:40:35.8624284Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-08-20T21:40:35.8624385Z ... ) 2024-08-20T21:40:35.8624542Z Traceback (most recent call last): 2024-08-20T21:40:35.8624658Z ... 2024-08-20T21:40:35.8624930Z TypeError: No comparison pair was able to handle inputs of type 2024-08-20T21:40:35.8625310Z and . 2024-08-20T21:40:35.8625646Z >>> # If the inputs are not directly related, they are never considered close 2024-08-20T21:40:35.8625874Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-08-20T21:40:35.8626032Z Traceback (most recent call last): 2024-08-20T21:40:35.8626142Z ... 2024-08-20T21:40:35.8626633Z TypeError: No comparison pair was able to handle inputs of type 2024-08-20T21:40:35.8626829Z and . 2024-08-20T21:40:35.8627200Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-08-20T21:40:35.8627355Z >>> # their type if check_dtype=False. 2024-08-20T21:40:35.8627598Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-08-20T21:40:35.8627690Z 2024-08-20T21:40:35.8627821Z >>> # NaN != NaN by default. 2024-08-20T21:40:35.8628008Z >>> expected = torch.tensor(float("Nan")) 2024-08-20T21:40:35.8628144Z >>> actual = expected.clone() 2024-08-20T21:40:35.8628336Z >>> torch.testing.assert_close(actual, expected) 2024-08-20T21:40:35.8628533Z Traceback (most recent call last): 2024-08-20T21:40:35.8628633Z ... 2024-08-20T21:40:35.8628799Z AssertionError: Scalars are not close! 2024-08-20T21:40:35.8628923Z 2024-08-20T21:40:35.8629053Z Expected nan but got nan. 2024-08-20T21:40:35.8629330Z Absolute difference: nan (up to 1e-05 allowed) 2024-08-20T21:40:35.8629602Z Relative difference: nan (up to 1.3e-06 allowed) 2024-08-20T21:40:35.8629869Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-08-20T21:40:35.8629978Z 2024-08-20T21:40:35.8630149Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-08-20T21:40:35.8630310Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-08-20T21:40:35.8630572Z >>> # The default error message can be overwritten. 2024-08-20T21:40:35.8630961Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-08-20T21:40:35.8631117Z Traceback (most recent call last): 2024-08-20T21:40:35.8631229Z ... 2024-08-20T21:40:35.8631433Z AssertionError: Argh, the tensors are not close! 2024-08-20T21:40:35.8631765Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-08-20T21:40:35.8631901Z >>> # extra information 2024-08-20T21:40:35.8632044Z >>> torch.testing.assert_close( 2024-08-20T21:40:35.8632329Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-08-20T21:40:35.8632428Z ... ) 2024-08-20T21:40:35.8632581Z Traceback (most recent call last): 2024-08-20T21:40:35.8632690Z ... 2024-08-20T21:40:35.8632821Z AssertionError: Header 2024-08-20T21:40:35.8632930Z 2024-08-20T21:40:35.8633128Z Tensor-likes are not close! 2024-08-20T21:40:35.8633233Z 2024-08-20T21:40:35.8633386Z Mismatched elements: 2 / 3 (66.7%) 2024-08-20T21:40:35.8633781Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-08-20T21:40:35.8634166Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-08-20T21:40:35.8634306Z 2024-08-20T21:40:35.8634416Z Footer 2024-08-20T21:40:35.8634513Z 2024-08-20T21:40:35.8634917Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-08-20T21:40:35.8635022Z 2024-08-20T21:40:35.8635136Z warnings.warn(msg) 2024-08-20T21:40:35.8635224Z 2024-08-20T21:40:35.8635354Z  2024-08-20T21:40:35.8635559Z === Found 9 run-time warnings === 2024-08-20T21:40:35.8635771Z --- Runtime Warning: 1 / 9 --- 2024-08-20T21:40:35.8636129Z example = 2024-08-20T21:40:35.8638242Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py:1250: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1931.) 2024-08-20T21:40:35.8638406Z return super().refine_names(names) 2024-08-20T21:40:35.8638498Z 2024-08-20T21:40:35.8638706Z --- Runtime Warning: 2 / 9 --- 2024-08-20T21:40:35.8639124Z example = 2024-08-20T21:40:35.8640129Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py:250: UserWarning: Warning only once for all operators, other operators may also be overridden. 2024-08-20T21:40:35.8640559Z Overriding a previously registered kernel for the same operator and the same dispatch key 2024-08-20T21:40:35.8640894Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2024-08-20T21:40:35.8641320Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2024-08-20T21:40:35.8641449Z dispatch key: CPU 2024-08-20T21:40:35.8642023Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2024-08-20T21:40:35.8642783Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:162.) 2024-08-20T21:40:35.8643064Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2024-08-20T21:40:35.8643157Z 2024-08-20T21:40:35.8643469Z --- Runtime Warning: 3 / 9 --- 2024-08-20T21:40:35.8643795Z example = 2024-08-20T21:40:35.8645596Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py:106: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:180.) 2024-08-20T21:40:35.8645933Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2024-08-20T21:40:35.8646029Z 2024-08-20T21:40:35.8646241Z --- Runtime Warning: 4 / 9 --- 2024-08-20T21:40:35.8646594Z example = 2024-08-20T21:40:35.8649170Z :1: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) 2024-08-20T21:40:35.8649276Z 2024-08-20T21:40:35.8649487Z --- Runtime Warning: 5 / 9 --- 2024-08-20T21:40:35.8649876Z example = 2024-08-20T21:40:35.8651673Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py:379: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2024-08-20T21:40:35.8651822Z warnings.warn( 2024-08-20T21:40:35.8651927Z 2024-08-20T21:40:35.8652140Z --- Runtime Warning: 6 / 9 --- 2024-08-20T21:40:35.8652578Z example = 2024-08-20T21:40:35.8654275Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py:379: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2024-08-20T21:40:35.8654384Z warnings.warn( 2024-08-20T21:40:35.8654494Z 2024-08-20T21:40:35.8654702Z --- Runtime Warning: 7 / 9 --- 2024-08-20T21:40:35.8655074Z example = 2024-08-20T21:40:35.8656365Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2024-08-20T21:40:35.8656515Z WeightNorm.apply(module, name, dim) 2024-08-20T21:40:35.8656606Z 2024-08-20T21:40:35.8656830Z --- Runtime Warning: 8 / 9 --- 2024-08-20T21:40:35.8657235Z example = 2024-08-20T21:40:35.8658510Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2024-08-20T21:40:35.8658706Z WeightNorm.apply(module, name, dim) 2024-08-20T21:40:35.8658798Z 2024-08-20T21:40:35.8659021Z --- Runtime Warning: 9 / 9 --- 2024-08-20T21:40:35.8659421Z example = 2024-08-20T21:40:35.8661737Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/const_fold.py:252: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer 2024-08-20T21:40:35.8661968Z new_node = root_const_gm.graph.get_attr(in_node.target) 2024-08-20T21:40:35.8662060Z 2024-08-20T21:40:35.8662491Z === 335 passed, 360 skipped, 110 warnings in 12.66 seconds === 2024-08-20T21:40:35.8662751Z Running test_nn 1/2 ... [2024-08-20 21:40:35.605491] 2024-08-20T21:40:35.8662893Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:40:35.8664230Z 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'] ... [2024-08-20 21:40:35.606073] 2024-08-20T21:47:54.4737102Z 2024-08-20T21:47:54.4738669Z test_nn 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.2_c864dc714add2edc_.log 2024-08-20T21:47:54.5543729Z Running 1045 items in this shard: test/test_nn.py::TestNN::test_AdaptiveLogSoftmax, test/test_nn.py::TestNN::test_AdaptiveLogSoftmax_cuda, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_CELU_no_batch_dim, test/test_nn.py::TestNN::test_CELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_CTCLoss_zero_lengths, test/test_nn.py::TestNN::test_Conv1d, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_dilated, test/test_nn.py::TestNN::test_Conv1d_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_groups_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1, test/test_nn.py::TestNN::test_Conv1d_pad1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same2, test/test_nn.py::TestNN::test_Conv1d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_stride, test/test_nn.py::TestNN::test_Conv1d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise, test/test_nn.py::TestNN::test_Conv2d_depthwise_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated, test/test_nn.py::TestNN::test_Conv2d_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups, test/test_nn.py::TestNN::test_Conv2d_groups_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_no_bias, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_pad_same, test/test_nn.py::TestNN::test_Conv2d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated_strided_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_pad_same, test/test_nn.py::TestNN::test_Conv3d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_valid, test/test_nn.py::TestNN::test_Conv3d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_stride, test/test_nn.py::TestNN::test_Conv3d_stride_padding, test/test_nn.py::TestNN::test_Conv3d_stride_padding_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_zero_batch, test/test_nn.py::TestNN::test_Conv3d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_groups, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_CrossMapLRN2d, test/test_nn.py::TestNN::test_ELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean, test/test_nn.py::TestNN::test_EmbeddingBag_mean_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_sparse, test/test_nn.py::TestNN::test_EmbeddingBag_sparse_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum, test/test_nn.py::TestNN::test_Embedding_discontiguous, test/test_nn.py::TestNN::test_Embedding_discontiguous_cuda, test/test_nn.py::TestNN::test_Embedding_sparse, test/test_nn.py::TestNN::test_Flatten_cuda, test/test_nn.py::TestNN::test_Fold_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input_cuda, test/test_nn.py::TestNN::test_GELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_reduce, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_LSTM_cell, test/test_nn.py::TestNN::test_LSTM_cell_forward_input_size, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_cuda, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear_no_bias_cuda, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MSELoss_no_reduce, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MaxUnpool1d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool3d_net, test/test_nn.py::TestNN::test_MaxUnpool3d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Mish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ModuleList, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_reduce, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg_cuda, test/test_nn.py::TestNN::test_PReLU_no_batch_dim, test/test_nn.py::TestNN::test_PReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args_cuda, test/test_nn.py::TestNN::test_ParameterDict, test/test_nn.py::TestNN::test_ParameterList, test/test_nn.py::TestNN::test_ParameterList_meta, test/test_nn.py::TestNN::test_PixelShuffle_cuda, test/test_nn.py::TestNN::test_PixelUnshuffle, test/test_nn.py::TestNN::test_PixelUnshuffle_cuda, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce, test/test_nn.py::TestNN::test_RNN_cell_forward_zero_hidden_size, test/test_nn.py::TestNN::test_RNN_cell_no_broadcasting, test/test_nn.py::TestNN::test_RNN_dropout_state, test/test_nn.py::TestNN::test_RReLU_cuda, test/test_nn.py::TestNN::test_RReLU_no_batch_dim, test/test_nn.py::TestNN::test_RReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar, test/test_nn.py::TestNN::test_ReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_complex_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sequential_extend, test/test_nn.py::TestNN::test_Sequential_getitem, test/test_nn.py::TestNN::test_Sequential_iadd, test/test_nn.py::TestNN::test_Sequential_rmul, test/test_nn.py::TestNN::test_SiLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_beta, test/test_nn.py::TestNN::test_SmoothL1Loss_beta_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta_cuda, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_Softplus_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim, test/test_nn.py::TestNN::test_Softsign_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_Transformer_cell, test/test_nn.py::TestNN::test_Transformer_multilayer_coder, test/test_nn.py::TestNN::test_Transformer_multilayer_coder_cuda, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_Unfold, test/test_nn.py::TestNN::test_Unfold_cuda, test/test_nn.py::TestNN::test_add_module_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_affine_grid_3d, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_2, test/test_nn.py::TestNN::test_affine_grid_error_checking, test/test_nn.py::TestNN::test_assignment, test/test_nn.py::TestNN::test_batch_norm_update_stats, test/test_nn.py::TestNN::test_batchnorm_buffer_update_when_stats_are_not_tracked, test/test_nn.py::TestNN::test_batchnorm_cudnn_half, test/test_nn.py::TestNN::test_batchnorm_cudnn_nhwc, test/test_nn.py::TestNN::test_batchnorm_load_state_dict, test/test_nn.py::TestNN::test_batchnorm_nhwc_cpu, test/test_nn.py::TestNN::test_batchnorm_nhwc_cuda, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_BatchNorm2d, test/test_nn.py::TestNN::test_batchnorm_nonaffine_cuda_half_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_bias_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_less_than_one_value_per_channel, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_weights, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss_large_tensors_with_grad, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_forward_grad, test/test_nn.py::TestNN::test_bce_with_logits_ones_in_pos_weights_are_the_same_as_none, test/test_nn.py::TestNN::test_bce_with_logits_with_pos_weight_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bilinear, test/test_nn.py::TestNN::test_broadcast_double_backwards_gpu, test/test_nn.py::TestNN::test_broadcast_no_grad, test/test_nn.py::TestNN::test_buffer_bad_module_subclass, test/test_nn.py::TestNN::test_buffer_not_persistent_load, test/test_nn.py::TestNN::test_buffers_and_named_buffers, test/test_nn.py::TestNN::test_cosine_embedding_loss_margin_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_with_diff_type, test/test_nn.py::TestNN::test_cross_entropy_loss_precision, test/test_nn.py::TestNN::test_cudnn_rnn_dropout_states_device, test/test_nn.py::TestNN::test_cudnn_weight_tying, test/test_nn.py::TestNN::test_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_set_extra_state, test/test_nn.py::TestNN::test_fb_fc_packed, test/test_nn.py::TestNN::test_flatten, test/test_nn.py::TestNN::test_fractional_max_pool2d_invalid_output_ratio, test/test_nn.py::TestNN::test_gaussian_nll_loss_args, test/test_nn.py::TestNN::test_gaussian_nll_loss_broadcasting, test/test_nn.py::TestNN::test_get_buffer, test/test_nn.py::TestNN::test_grid_sample, test/test_nn.py::TestNN::test_grid_sample_3d, test/test_nn.py::TestNN::test_grid_sample_error_checking, test/test_nn.py::TestNN::test_hardtanh_backward, test/test_nn.py::TestNN::test_hardtanh_inplace_gradgrad, test/test_nn.py::TestNN::test_huber_loss_invalid_delta, test/test_nn.py::TestNN::test_inplace_thnn, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_3d, test/test_nn.py::TestNN::test_interpolate_nearest_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_undefined_behavior_casting, test/test_nn.py::TestNN::test_l1_loss_correct, test/test_nn.py::TestNN::test_layer_norm_grads_with_create_graph_flag, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightStrided, test/test_nn.py::TestNN::test_log_softmax_scalar, test/test_nn.py::TestNN::test_log_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_loss_equal_input_target_shape, test/test_nn.py::TestNN::test_margin_ranking_loss_no_reduce, test/test_nn.py::TestNN::test_module_backcompat, test/test_nn.py::TestNN::test_module_super_init, test/test_nn.py::TestNN::test_modules, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce, test/test_nn.py::TestNN::test_named_modules, test/test_nn.py::TestNN::test_named_parameters_remove_duplicate, test/test_nn.py::TestNN::test_nested_tensor_from_mask, test/test_nn.py::TestNN::test_overwrite_module_params_on_conversion, test/test_nn.py::TestNN::test_pack_sequence_batch_sizes_throw, test/test_nn.py::TestNN::test_padding_list, test/test_nn.py::TestNN::test_parameterlistdict_setting_attributes, test/test_nn.py::TestNN::test_pdist_empty_col, test/test_nn.py::TestNN::test_pickle_module_no_weights_only_warning, test/test_nn.py::TestNN::test_pixel_shuffle_nhwc_cpu, test/test_nn.py::TestNN::test_pixel_shuffle_unshuffle, test/test_nn.py::TestNN::test_pointwise_loss_broadcast, test/test_nn.py::TestNN::test_projections_errors_on_gru_and_rnn, test/test_nn.py::TestNN::test_projections_lstm_args_check, test/test_nn.py::TestNN::test_projections_lstm_initial_hidden_state, test/test_nn.py::TestNN::test_register_buffer_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_relu_inplace_on_view, test/test_nn.py::TestNN::test_rnn_check_device, test/test_nn.py::TestNN::test_rnn_initial_hidden_state, test/test_nn.py::TestNN::test_rnn_weight_norm, test/test_nn.py::TestNN::test_set_submodule, test/test_nn.py::TestNN::test_smoothl1loss_intergral_target, test/test_nn.py::TestNN::test_softmax_functional_dim0, test/test_nn.py::TestNN::test_softmax_functional_dim0_cuda, test/test_nn.py::TestNN::test_softmax_functional_dim3, test/test_nn.py::TestNN::test_softmax_lastdim, test/test_nn.py::TestNN::test_softmax_lastdim_dtype, test/test_nn.py::TestNN::test_softmax_spatial, test/test_nn.py::TestNN::test_softmax_spatial_dtype_cuda, test/test_nn.py::TestNN::test_softmax_spatial_special, test/test_nn.py::TestNN::test_softmin, test/test_nn.py::TestNN::test_spectral_norm, test/test_nn.py::TestNN::test_spectral_norm_dim, test/test_nn.py::TestNN::test_spectral_norm_forward, test/test_nn.py::TestNN::test_spectral_norm_load_state_dict, test/test_nn.py::TestNN::test_spectral_norm_pickle, test/test_nn.py::TestNN::test_state_dict, test/test_nn.py::TestNN::test_swap_module_params_poisons_acc_grad, test/test_nn.py::TestNN::test_to, test/test_nn.py::TestNN::test_transformer_args_check, test/test_nn.py::TestNN::test_transformerdecoderlayer_gelu, test/test_nn.py::TestNN::test_triplet_margin_loss_no_reduce, test/test_nn.py::TestNN::test_triplet_margin_loss_swap, test/test_nn.py::TestNN::test_type, test/test_nn.py::TestNN::test_unflatten, test/test_nn.py::TestNN::test_unfold_invalid_arg, test/test_nn.py::TestNN::test_upsamplingBilinear2d_spatial_invariance, test/test_nn.py::TestNN::test_upsamplingLinear1d_spatial_invariance, test/test_nn.py::TestNN::test_upsampling_bfloat16, test/test_nn.py::TestNN::test_upsampling_not_recompute_scale_factor, test/test_nn.py::TestNN::test_upsampling_small_scale, test/test_nn.py::TestFusionEval::test_fuse_module_eval_numerics, test/test_nn.py::TestConstantPadNd::test_constant_pad_nd, test/test_nn.py::TestAddRelu::test_add_relu, test/test_nn.py::TestFunctionalPickle::test_pickle_softsign, test/test_nn.py::TestFusionUtils::test_fuse_linear_bn_requires_grad, test/test_nn.py::TestUtils::test_consume_prefix_in_state_dict_if_present, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_memory_format_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm1d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm2d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm3d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_warnings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Unfold_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_adaptiveavg_pool1d_shmem_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_3d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_errors_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_weight_ignore_indices_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_one_hot_target_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_with_neg_alpha_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_glu_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_for_single_spatial_element_during_training_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_less_than_one_value_per_channel_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_invalid_reduction_strings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_neg_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_zero_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_linear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_big_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_forward_with_nans_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_transformer_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_byte_target_matches_long_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_out_of_bounds_ignore_index_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_total_weight_is_zero_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nonlinearity_propagate_nan_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_one_hot_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_overwrite_module_params_on_conversion_cpu_device_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_prelu_backward_32bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_skip_init_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_vs_huber_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_results_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_threshold_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu, 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 2024-08-20T21:47:54.6222159Z 2024-08-20T21:47:54.6222718Z Running test_cpp_extensions_jit 1/1 ... [2024-08-20 21:47:54.475913] 2024-08-20T21:47:54.6223327Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:47:54.6225066Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_jit.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:47:54.476253] 2024-08-20T21:48:27.4386414Z 2024-08-20T21:48:27.4387906Z 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_d6f22486aace6342_.log 2024-08-20T21:48:27.4402269Z Running 26 items in this shard: test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_autograd_from_cpp, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_compilation_error_formatting, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_same_output_as_python, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_up_to_date_attributes, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op_with_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_compound_op_autograd, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_functorch_error, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_gen_extension_h_pch, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_half_support, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_custom_op_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_multiple_sources_and_no_functions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_throws_when_functions_is_bad, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_dict, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_list, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_compile_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_archflags, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cudnn_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_lenient_flag_handling_in_jit_extensions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_mps_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_reload_jit_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_returns_shared_library_path_when_is_python_module_is_true, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_set_default_type_also_changes_aten_default_type, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_warning 2024-08-20T21:48:27.4415341Z 2024-08-20T21:48:27.4415684Z Running test_torch 1/1 ... [2024-08-20 21:48:27.438852] 2024-08-20T21:48:27.4416185Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:48:27.4417830Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_torch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:48:27.439217] 2024-08-20T21:52:55.3564150Z 2024-08-20T21:52:55.3565746Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_09ca44d4388bcc0e_.log 2024-08-20T21:52:55.3979972Z Running 1021 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals, test/test_torch.py::TestBasicVitalSigns::test_basic_vitals_read_write, test/test_torch.py::TestBasicVitalSigns::test_dataloader_vitals, test/test_torch.py::TestTorch::test_RNGState, test/test_torch.py::TestTorch::test_RNGStateAliasing, test/test_torch.py::TestTorch::test_RNG_after_pickle, test/test_torch.py::TestTorch::test_Size, test/test_torch.py::TestTorch::test_Size_iter, test/test_torch.py::TestTorch::test_Size_scalar, test/test_torch.py::TestTorch::test_add_meta_scalar, test/test_torch.py::TestTorch::test_allow_tensor_metadata_change, test/test_torch.py::TestTorch::test_apply, test/test_torch.py::TestTorch::test_as_subclass, test/test_torch.py::TestTorch::test_assert_async, test/test_torch.py::TestTorch::test_backward_hooks_traverse, test/test_torch.py::TestTorch::test_batch_norm_cpu_inference, test/test_torch.py::TestTorch::test_bf16_supported_on_cpu, test/test_torch.py::TestTorch::test_bmm_multithreaded, test/test_torch.py::TestTorch::test_boxMullerState, test/test_torch.py::TestTorch::test_cat_neg_dim, test/test_torch.py::TestTorch::test_check, test/test_torch.py::TestTorch::test_chunk_neg_dim, test/test_torch.py::TestTorch::test_conj_neg_tolist, test/test_torch.py::TestTorch::test_contains, test/test_torch.py::TestTorch::test_copy_broadcast, test/test_torch.py::TestTorch::test_copy_dtypes, test/test_torch.py::TestTorch::test_copy_float16, test/test_torch.py::TestTorch::test_copy_many_to_one, test/test_torch.py::TestTorch::test_copy_transpose, test/test_torch.py::TestTorch::test_cuda_not_built, test/test_torch.py::TestTorch::test_cummax_neg_dim, test/test_torch.py::TestTorch::test_cummin_neg_dim, test/test_torch.py::TestTorch::test_cumprod_neg_dim, test/test_torch.py::TestTorch::test_cumsum_neg_dim, test/test_torch.py::TestTorch::test_cxx_flags, test/test_torch.py::TestTorch::test_data_ptr_of_empty_tensor_with_storage, test/test_torch.py::TestTorch::test_data_ptr_of_empty_view_with_storage, test/test_torch.py::TestTorch::test_deepcopy_gradient, test/test_torch.py::TestTorch::test_deepcopy_parameter, test/test_torch.py::TestTorch::test_deterministic_fill_uninitialized_memory, test/test_torch.py::TestTorch::test_deterministic_flag, test/test_torch.py::TestTorch::test_device, test/test_torch.py::TestTorch::test_dim_order, test/test_torch.py::TestTorch::test_dir, test/test_torch.py::TestTorch::test_doc, test/test_torch.py::TestTorch::test_doc_template, test/test_torch.py::TestTorch::test_dot_data_use, test/test_torch.py::TestTorch::test_dtype_is_signed, test/test_torch.py::TestTorch::test_element_size, test/test_torch.py::TestTorch::test_empty_meta, 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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, 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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, 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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, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_same_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_all_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_internal_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_not_memory_dense_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_zero_stride_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_complex_half_experimental_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_constants_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_backward_agnostic_to_memory_format_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_math_view_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cov_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cpp_warnings_have_python_context_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cublas_config_nondeterministic_alert_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_cummin_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummin_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumprod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_interpolate_bilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_replication_pad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_device_guard_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_dim_function_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_discontiguous_out_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dtypetensor_warnings_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_errors_index_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_expected_failure_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_deterministic_path_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_one_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scale_will_not_overflow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_deprecated_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_pass_itself_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_accumulation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_separate_unscale_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_multiple_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_penalty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_state_dict_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_sparse_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_update_scale_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_type_promotion_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_hook_remove_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_put_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_put_non_accumulate_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_uint8, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_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_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 2024-08-20T21:52:55.4376581Z 2024-08-20T21:52:55.4377147Z Running dynamo/test_trace_rules 1/1 ... [2024-08-20 21:52:55.358868] 2024-08-20T21:52:55.4377747Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:52:55.4379558Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_trace_rules.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:52:55.359240] 2024-08-20T21:52:57.6646173Z 2024-08-20T21:52:57.6648022Z dynamo/test_trace_rules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_trace_rules_1.1_8841775a69203614_.log 2024-08-20T21:52:57.6649192Z 2024-08-20T21:52:57.6649558Z Running dynamo/test_repros 1/1 ... [2024-08-20 21:52:57.664774] 2024-08-20T21:52:57.6650117Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:52:57.6653867Z 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'] ... [2024-08-20 21:52:57.665103] 2024-08-20T21:52:59.9984132Z 2024-08-20T21:52:59.9985911Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_325caa27f4cd4d57_.log 2024-08-20T21:52:59.9986955Z 2024-08-20T21:52:59.9987851Z Running dynamo/test_higher_order_ops 1/1 ... [2024-08-20 21:52:59.998563] 2024-08-20T21:52:59.9988506Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:52:59.9992275Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_higher_order_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:52:59.998901] 2024-08-20T21:53:02.4411041Z 2024-08-20T21:53:02.4412642Z dynamo/test_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_higher_order_ops_1.1_9ae51fc99c7d8780_.log 2024-08-20T21:53:02.4413778Z 2024-08-20T21:53:02.4414136Z Running dynamo/test_export 1/1 ... [2024-08-20 21:53:02.441226] 2024-08-20T21:53:02.4414881Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:02.4418239Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_export.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:02.441547] 2024-08-20T21:53:04.9469007Z 2024-08-20T21:53:04.9470436Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_eef1a381e93044ba_.log 2024-08-20T21:53:04.9471432Z 2024-08-20T21:53:04.9471988Z Running dynamo/test_exc 1/1 ... [2024-08-20 21:53:04.947028] 2024-08-20T21:53:04.9472528Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:04.9476690Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exc.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:04.947348] 2024-08-20T21:53:07.2064942Z 2024-08-20T21:53:07.2066326Z dynamo/test_exc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exc_1.1_2d42f71b9f64bae3_.log 2024-08-20T21:53:07.2067223Z 2024-08-20T21:53:07.2068199Z Running dynamo/test_ctx_manager 1/1 ... [2024-08-20 21:53:07.206653] 2024-08-20T21:53:07.2068784Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:07.2072639Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_ctx_manager.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:07.206973] 2024-08-20T21:53:09.5421295Z 2024-08-20T21:53:09.5422794Z dynamo/test_ctx_manager 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_ctx_manager_1.1_3f86d302d8e9baed_.log 2024-08-20T21:53:09.5423792Z 2024-08-20T21:53:09.5424554Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-08-20 21:53:09.542277] 2024-08-20T21:53:09.5425207Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:09.5428900Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:09.542605] 2024-08-20T21:53:11.8615713Z 2024-08-20T21:53:11.8617689Z 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_36e3f30a6c4547ea_.log 2024-08-20T21:53:11.8618909Z 2024-08-20T21:53:11.8620294Z Running dynamo/test_optimizers 1/1 ... [2024-08-20 21:53:11.861740] 2024-08-20T21:53:11.8621205Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:11.8624862Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_optimizers.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:11.862116] 2024-08-20T21:53:14.1172838Z 2024-08-20T21:53:14.1174554Z dynamo/test_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_optimizers_1.1_d3d6865b3aced322_.log 2024-08-20T21:53:14.1175812Z 2024-08-20T21:53:14.1176259Z Running dynamo/test_backends 1/1 ... [2024-08-20 21:53:14.117374] 2024-08-20T21:53:14.1176878Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:14.1179747Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_backends.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:14.117677] 2024-08-20T21:53:16.4423718Z 2024-08-20T21:53:16.4425513Z dynamo/test_backends 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backends_1.1_61d8b22a182847ac_.log 2024-08-20T21:53:16.4426642Z 2024-08-20T21:53:16.4427500Z Running dynamo/test_skip_non_tensor 1/1 ... [2024-08-20 21:53:16.442529] 2024-08-20T21:53:16.4428165Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:16.4431828Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_skip_non_tensor.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:16.442867] 2024-08-20T21:53:18.7621731Z 2024-08-20T21:53:18.7623848Z dynamo/test_skip_non_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_skip_non_tensor_1.1_0b866c597850a4a1_.log 2024-08-20T21:53:18.7625140Z 2024-08-20T21:53:18.7625819Z Running dynamo/test_python_autograd 1/1 ... [2024-08-20 21:53:18.762323] 2024-08-20T21:53:18.7626426Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:18.7630083Z 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'] ... [2024-08-20 21:53:18.762675] 2024-08-20T21:53:21.0329215Z 2024-08-20T21:53:21.0331124Z 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_789ed0c3d0355e71_.log 2024-08-20T21:53:21.0332276Z 2024-08-20T21:53:21.0333210Z Running dynamo/test_verify_correctness 1/1 ... [2024-08-20 21:53:21.033089] 2024-08-20T21:53:21.0333841Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:21.0337770Z 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'] ... [2024-08-20 21:53:21.033447] 2024-08-20T21:53:23.2747380Z 2024-08-20T21:53:23.2749617Z 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_7a878f07d86a8fea_.log 2024-08-20T21:53:23.2751377Z 2024-08-20T21:53:23.2752005Z Running dynamo/test_exceptions 1/1 ... [2024-08-20 21:53:23.274896] 2024-08-20T21:53:23.2752845Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:23.2757038Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exceptions.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:23.275308] 2024-08-20T21:53:25.5455687Z 2024-08-20T21:53:25.5457273Z dynamo/test_exceptions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exceptions_1.1_6d5cd3227c20e4f1_.log 2024-08-20T21:53:25.5458523Z 2024-08-20T21:53:25.5459340Z Running dynamo/test_base_output 1/1 ... [2024-08-20 21:53:25.545730] 2024-08-20T21:53:25.5460009Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:25.5463284Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_base_output.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:25.546045] 2024-08-20T21:53:27.8229506Z 2024-08-20T21:53:27.8231734Z dynamo/test_base_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_output_1.1_560dfc053fb62610_.log 2024-08-20T21:53:27.8233400Z 2024-08-20T21:53:27.8234635Z Running functorch/test_vmap 2/3 ... [2024-08-20 21:53:27.823152] 2024-08-20T21:53:27.8235582Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:27.8238915Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'functorch/test_vmap.py', '-m', 'serial', '--shard-id=2', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:27.823550] 2024-08-20T21:53:31.6427756Z 2024-08-20T21:53:31.6429562Z functorch/test_vmap 2/3 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_vmap_2.3_913d3a2f4421d724_.log 2024-08-20T21:53:31.6430773Z Running 0 items in this shard: 2024-08-20T21:53:31.6431034Z 2024-08-20T21:53:31.6431981Z Running dynamo/test_structured_trace 1/1 ... [2024-08-20 21:53:31.642972] 2024-08-20T21:53:31.6432952Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:31.6436544Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_structured_trace.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:31.643314] 2024-08-20T21:53:34.0038970Z 2024-08-20T21:53:34.0041303Z dynamo/test_structured_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_structured_trace_1.1_0dee27fb08a4f5c8_.log 2024-08-20T21:53:34.0043096Z 2024-08-20T21:53:34.0043624Z Running dynamo/test_hooks 1/1 ... [2024-08-20 21:53:34.004099] 2024-08-20T21:53:34.0044489Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:34.0049326Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_hooks.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:34.004527] 2024-08-20T21:53:36.3563070Z 2024-08-20T21:53:36.3564636Z dynamo/test_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_hooks_1.1_f0d5f0701fd2b355_.log 2024-08-20T21:53:36.3565718Z 2024-08-20T21:53:36.3566506Z Running dynamo/test_profiler 1/1 ... [2024-08-20 21:53:36.356479] 2024-08-20T21:53:36.3567063Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:36.3570867Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_profiler.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:36.356792] 2024-08-20T21:53:38.6719900Z 2024-08-20T21:53:38.6721519Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_3fcb60959e4756da_.log 2024-08-20T21:53:38.6722856Z 2024-08-20T21:53:38.6723277Z Running dynamo/test_recompile_ux 1/1 ... [2024-08-20 21:53:38.672111] 2024-08-20T21:53:38.6723880Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:38.6727532Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:38.672448] 2024-08-20T21:53:40.9255134Z 2024-08-20T21:53:40.9257294Z 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_abbfee57cf8b59a4_.log 2024-08-20T21:53:40.9259003Z 2024-08-20T21:53:40.9259591Z Running dynamo/test_deviceguard 1/1 ... [2024-08-20 21:53:40.925685] 2024-08-20T21:53:40.9260493Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:40.9264959Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_deviceguard.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:40.926098] 2024-08-20T21:53:43.1811000Z 2024-08-20T21:53:43.1813113Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_e41f3fc8f5e681cb_.log 2024-08-20T21:53:43.1814797Z 2024-08-20T21:53:43.1815258Z Running test_linalg 2/4 ... [2024-08-20 21:53:43.181300] 2024-08-20T21:53:43.1816035Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:43.1821093Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_linalg.py', '-m', 'serial', '--shard-id=2', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:43.181732] 2024-08-20T21:53:50.4556278Z 2024-08-20T21:53:50.4558736Z test_linalg 2/4 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_2.4_40191fb0dd4ab440_.log 2024-08-20T21:53:50.4560939Z Running 2 items in this shard: test/test_linalg.py::TestLinalgCPU::test_svd_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_svd_cpu_float64 2024-08-20T21:53:50.4561867Z 2024-08-20T21:53:50.4562198Z Running test_linalg 3/4 ... [2024-08-20 21:53:50.455766] 2024-08-20T21:53:50.4562798Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:50.4564896Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_linalg.py', '-m', 'serial', '--shard-id=3', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:50.456132] 2024-08-20T21:53:55.7774185Z 2024-08-20T21:53:55.7775872Z test_linalg 3/4 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_3.4_cf46bc71068e40f5_.log 2024-08-20T21:53:55.7777213Z Running 1 items in this shard: test/test_linalg.py::TestLinalgCPU::test_svd_cpu_float32 2024-08-20T21:53:55.7778160Z 2024-08-20T21:53:55.7778440Z Running test_linalg 4/4 ... [2024-08-20 21:53:55.777585] 2024-08-20T21:53:55.7778944Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:55.7782059Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_linalg.py', '-m', 'serial', '--shard-id=4', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:55.777924] 2024-08-20T21:53:58.6962980Z 2024-08-20T21:53:58.6964644Z test_linalg 4/4 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_4.4_1f25a3df222d70e2_.log 2024-08-20T21:53:58.6965782Z Running 0 items in this shard: 2024-08-20T21:53:58.6966101Z 2024-08-20T21:53:58.6966773Z Running dynamo/test_debug_utils 1/1 ... [2024-08-20 21:53:58.696465] 2024-08-20T21:53:58.6967404Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:53:58.6971389Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_debug_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:53:58.696809] 2024-08-20T21:54:00.9985365Z 2024-08-20T21:54:00.9986920Z dynamo/test_debug_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_debug_utils_1.1_ffed68f91033a082_.log 2024-08-20T21:54:00.9988047Z 2024-08-20T21:54:00.9988687Z Running test_cuda_multigpu 1/1 ... [2024-08-20 21:54:00.998651] 2024-08-20T21:54:00.9989267Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:00.9992904Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_multigpu.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:00.998983] 2024-08-20T21:54:03.4324435Z 2024-08-20T21:54:03.4326276Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_c978429b43e1c052_.log 2024-08-20T21:54:03.4327954Z Running 0 items in this shard: 2024-08-20T21:54:03.4328231Z 2024-08-20T21:54:03.4328579Z Running test_comparison_utils 1/1 ... [2024-08-20 21:54:03.432510] 2024-08-20T21:54:03.4329143Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:03.4331633Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_comparison_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:03.432844] 2024-08-20T21:54:05.9004214Z 2024-08-20T21:54:05.9006136Z test_comparison_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_comparison_utils_1.1_8bc7575d3bbf3ad2_.log 2024-08-20T21:54:05.9007692Z Running 0 items in this shard: 2024-08-20T21:54:05.9008186Z 2024-08-20T21:54:05.9008636Z Running test_mkl_verbose 1/1 ... [2024-08-20 21:54:05.900623] 2024-08-20T21:54:05.9009155Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:05.9013015Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mkl_verbose.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:05.900958] 2024-08-20T21:54:08.3685546Z 2024-08-20T21:54:08.3687297Z test_mkl_verbose 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkl_verbose_1.1_df8c953149827d1a_.log 2024-08-20T21:54:08.3688456Z Running 0 items in this shard: 2024-08-20T21:54:08.3688775Z 2024-08-20T21:54:08.3689680Z Running test_mkldnn_verbose 1/1 ... [2024-08-20 21:54:08.368766] 2024-08-20T21:54:08.3690596Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:08.3694473Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mkldnn_verbose.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:08.369112] 2024-08-20T21:54:10.8368049Z 2024-08-20T21:54:10.8370008Z test_mkldnn_verbose 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_verbose_1.1_c3d044d314dc7156_.log 2024-08-20T21:54:10.8371706Z Running 0 items in this shard: 2024-08-20T21:54:10.8372185Z 2024-08-20T21:54:10.8372536Z Running test_custom_ops 1/1 ... [2024-08-20 21:54:10.836980] 2024-08-20T21:54:10.8373055Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:10.8376398Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_custom_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:10.837319] 2024-08-20T21:54:14.3563944Z 2024-08-20T21:54:14.3565634Z test_custom_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_custom_ops_1.1_fb3105b8a9cd0829_.log 2024-08-20T21:54:14.3566643Z Running 0 items in this shard: 2024-08-20T21:54:14.3566901Z 2024-08-20T21:54:14.3567793Z Running test_ao_sparsity 1/1 ... [2024-08-20 21:54:14.356576] 2024-08-20T21:54:14.3568511Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:14.3572429Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_ao_sparsity.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:14.356908] 2024-08-20T21:54:17.0748673Z 2024-08-20T21:54:17.0750727Z test_ao_sparsity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ao_sparsity_1.1_6d88a0d63b497c78_.log 2024-08-20T21:54:17.0752417Z Running 0 items in this shard: 2024-08-20T21:54:17.0752842Z 2024-08-20T21:54:17.0753502Z Running functorch/test_eager_transforms 1/1 ... [2024-08-20 21:54:17.075095] 2024-08-20T21:54:17.0754476Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:17.0758979Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'functorch/test_eager_transforms.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:17.075502] 2024-08-20T21:54:20.8451098Z 2024-08-20T21:54:20.8453140Z functorch/test_eager_transforms 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_eager_transforms_1.1_9c184efe5eec6955_.log 2024-08-20T21:54:20.8454421Z Running 0 items in this shard: 2024-08-20T21:54:20.8454852Z 2024-08-20T21:54:20.8455383Z Running test_optim 1/1 ... [2024-08-20 21:54:20.845308] 2024-08-20T21:54:20.8456192Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:20.8459533Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_optim.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:20.845648] 2024-08-20T21:54:24.2143262Z 2024-08-20T21:54:24.2144906Z test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_optim_1.1_0a2aaf2a75323638_.log 2024-08-20T21:54:24.2145963Z Running 0 items in this shard: 2024-08-20T21:54:24.2146272Z 2024-08-20T21:54:24.2147075Z Running test_xnnpack_integration 1/1 ... [2024-08-20 21:54:24.214501] 2024-08-20T21:54:24.2147685Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:24.2151301Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_xnnpack_integration.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:24.214841] 2024-08-20T21:54:26.6825369Z 2024-08-20T21:54:26.6827287Z test_xnnpack_integration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_xnnpack_integration_1.1_f038919699787b72_.log 2024-08-20T21:54:26.6828658Z Running 0 items in this shard: 2024-08-20T21:54:26.6828953Z 2024-08-20T21:54:26.6829254Z Running test_itt 1/1 ... [2024-08-20 21:54:26.682673] 2024-08-20T21:54:26.6829990Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:26.6833194Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_itt.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:26.683013] 2024-08-20T21:54:29.1006991Z 2024-08-20T21:54:29.1008941Z test_itt 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_itt_1.1_42234338a8adcda6_.log 2024-08-20T21:54:29.1010134Z Running 0 items in this shard: 2024-08-20T21:54:29.1010531Z 2024-08-20T21:54:29.1010875Z Running test_proxy_tensor 1/1 ... [2024-08-20 21:54:29.100866] 2024-08-20T21:54:29.1011407Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:29.1015638Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_proxy_tensor.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:29.101260] 2024-08-20T21:54:33.2712263Z 2024-08-20T21:54:33.2713866Z test_proxy_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_proxy_tensor_1.1_173bb0adb84f3ad5_.log 2024-08-20T21:54:33.2714873Z Running 0 items in this shard: 2024-08-20T21:54:33.2715148Z 2024-08-20T21:54:33.2716596Z Running test_masked 1/1 ... [2024-08-20 21:54:33.271460] 2024-08-20T21:54:33.2717207Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:33.2720986Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_masked.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:33.271814] 2024-08-20T21:54:36.5906993Z 2024-08-20T21:54:36.5908972Z test_masked 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_masked_1.1_9486fd8a7ef46eb7_.log 2024-08-20T21:54:36.5910467Z Running 0 items in this shard: 2024-08-20T21:54:36.5910941Z 2024-08-20T21:54:36.5911661Z Running test_view_ops 1/1 ... [2024-08-20 21:54:36.590970] 2024-08-20T21:54:36.5912449Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:36.5918117Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_view_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:36.591389] 2024-08-20T21:54:39.1091967Z 2024-08-20T21:54:39.1093429Z test_view_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_view_ops_1.1_b67d6866b556c830_.log 2024-08-20T21:54:39.1094493Z Running 0 items in this shard: 2024-08-20T21:54:39.1094752Z 2024-08-20T21:54:39.1095690Z Running test_indexing 1/1 ... [2024-08-20 21:54:39.109389] 2024-08-20T21:54:39.1096271Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:39.1100711Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_indexing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:39.109714] 2024-08-20T21:54:41.6274786Z 2024-08-20T21:54:41.6276319Z test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_indexing_1.1_027e3ef26d9f65dc_.log 2024-08-20T21:54:41.6277376Z Running 0 items in this shard: 2024-08-20T21:54:41.6277658Z 2024-08-20T21:54:41.6278469Z Running test_monitor 1/1 ... [2024-08-20 21:54:41.627664] 2024-08-20T21:54:41.6279061Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:41.6282722Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_monitor.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:41.627979] 2024-08-20T21:54:44.0964639Z 2024-08-20T21:54:44.0966167Z test_monitor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_monitor_1.1_b3d55c5a980a89bd_.log 2024-08-20T21:54:44.0967219Z Running 0 items in this shard: 2024-08-20T21:54:44.0967543Z 2024-08-20T21:54:44.0968449Z Running benchmark_utils/test_benchmark_utils 1/1 ... [2024-08-20 21:54:44.096650] 2024-08-20T21:54:44.0969169Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:44.0972873Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'benchmark_utils/test_benchmark_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:44.096992] 2024-08-20T21:54:46.8665221Z 2024-08-20T21:54:46.8667252Z benchmark_utils/test_benchmark_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/benchmark_utils.test_benchmark_utils_1.1_7703bd161ab13e39_.log 2024-08-20T21:54:46.8668748Z Running 0 items in this shard: 2024-08-20T21:54:46.8669012Z 2024-08-20T21:54:46.8669338Z Running test_binary_ufuncs 1/2 ... [2024-08-20 21:54:46.866668] 2024-08-20T21:54:46.8669885Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:46.8672636Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_binary_ufuncs.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'] ... [2024-08-20 21:54:46.866981] 2024-08-20T21:54:52.1384267Z 2024-08-20T21:54:52.1386045Z test_binary_ufuncs 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_binary_ufuncs_1.2_348037e7589e53b8_.log 2024-08-20T21:54:52.1387549Z Running 0 items in this shard: 2024-08-20T21:54:52.1387880Z 2024-08-20T21:54:52.1388583Z Running test_quantization 1/5 ... [2024-08-20 21:54:52.138648] 2024-08-20T21:54:52.1389154Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:52.1393241Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=1', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:52.138997] 2024-08-20T21:54:55.9083780Z 2024-08-20T21:54:55.9085694Z test_quantization 1/5 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_1.5_c097e5b9e1af3d35_.log 2024-08-20T21:54:55.9086871Z Running 0 items in this shard: 2024-08-20T21:54:55.9087320Z 2024-08-20T21:54:55.9087822Z Running test_quantization 3/5 ... [2024-08-20 21:54:55.908553] 2024-08-20T21:54:55.9088379Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:55.9092035Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=3', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:55.908878] 2024-08-20T21:54:59.2776986Z 2024-08-20T21:54:59.2778723Z test_quantization 3/5 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_3.5_d36282f2ad8c4d2f_.log 2024-08-20T21:54:59.2779935Z Running 0 items in this shard: 2024-08-20T21:54:59.2780408Z 2024-08-20T21:54:59.2780864Z Running test_quantization 4/5 ... [2024-08-20 21:54:59.277834] 2024-08-20T21:54:59.2781428Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:54:59.2784320Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=4', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:54:59.278143] 2024-08-20T21:55:02.6468922Z 2024-08-20T21:55:02.6470832Z test_quantization 4/5 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_4.5_b1b26809e9eb0324_.log 2024-08-20T21:55:02.6472026Z Running 0 items in this shard: 2024-08-20T21:55:02.6472293Z 2024-08-20T21:55:02.6473101Z Running test_module_tracker 1/1 ... [2024-08-20 21:55:02.647103] 2024-08-20T21:55:02.6473748Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:02.6477364Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_module_tracker.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:02.647463] 2024-08-20T21:55:05.1652866Z 2024-08-20T21:55:05.1655124Z test_module_tracker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_module_tracker_1.1_a16f4ae53c533823_.log 2024-08-20T21:55:05.1656563Z Running 0 items in this shard: 2024-08-20T21:55:05.1657062Z 2024-08-20T21:55:05.1657406Z Running torch_np/test_basic 1/1 ... [2024-08-20 21:55:05.165476] 2024-08-20T21:55:05.1657961Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:05.1661117Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_basic.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:05.165795] 2024-08-20T21:55:07.7336873Z 2024-08-20T21:55:07.7338624Z torch_np/test_basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_basic_1.1_c444ab34ba390ca1_.log 2024-08-20T21:55:07.7339852Z Running 0 items in this shard: 2024-08-20T21:55:07.7340140Z 2024-08-20T21:55:07.7340651Z Running test_autoload 1/1 ... [2024-08-20 21:55:07.733875] 2024-08-20T21:55:07.7341375Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:07.7345226Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_autoload.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:07.734230] 2024-08-20T21:55:10.1517096Z 2024-08-20T21:55:10.1518503Z test_autoload 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autoload_1.1_40e161f14bdf6605_.log 2024-08-20T21:55:10.1519486Z Running 0 items in this shard: 2024-08-20T21:55:10.1521123Z 2024-08-20T21:55:10.1522230Z Running torch_np/test_binary_ufuncs 1/1 ... [2024-08-20 21:55:10.151939] 2024-08-20T21:55:10.1522868Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:10.1526162Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_binary_ufuncs.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:10.152290] 2024-08-20T21:55:12.6198356Z 2024-08-20T21:55:12.6200147Z torch_np/test_binary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_binary_ufuncs_1.1_7929b3b6b787ff01_.log 2024-08-20T21:55:12.6201392Z Running 0 items in this shard: 2024-08-20T21:55:12.6201643Z 2024-08-20T21:55:12.6202328Z Running torch_np/test_unary_ufuncs 1/1 ... [2024-08-20 21:55:12.620049] 2024-08-20T21:55:12.6202909Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:12.6206748Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_unary_ufuncs.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:12.620389] 2024-08-20T21:55:15.0378742Z 2024-08-20T21:55:15.0380557Z torch_np/test_unary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_unary_ufuncs_1.1_07357e29499461d8_.log 2024-08-20T21:55:15.0382090Z Running 0 items in this shard: 2024-08-20T21:55:15.0382396Z 2024-08-20T21:55:15.0383014Z Running profiler/test_cpp_thread 1/1 ... [2024-08-20 21:55:15.038103] 2024-08-20T21:55:15.0383817Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:15.0387842Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_cpp_thread.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:15.038455] 2024-08-20T21:55:18.8579746Z 2024-08-20T21:55:18.8581689Z profiler/test_cpp_thread 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_cpp_thread_1.1_cf4324743a5f89f5_.log 2024-08-20T21:55:18.8583240Z Running 0 items in this shard: 2024-08-20T21:55:18.8583605Z 2024-08-20T21:55:18.8583909Z Running test_typing 1/1 ... [2024-08-20 21:55:18.858131] 2024-08-20T21:55:18.8584399Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:18.8587743Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_typing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:18.858477] 2024-08-20T21:55:21.3760784Z 2024-08-20T21:55:21.3762477Z test_typing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_typing_1.1_e0376a1b6680039c_.log 2024-08-20T21:55:21.3763506Z Running 0 items in this shard: 2024-08-20T21:55:21.3763865Z 2024-08-20T21:55:21.3765076Z Running torch_np/test_dtype 1/1 ... [2024-08-20 21:55:21.376289] 2024-08-20T21:55:21.3765798Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:21.3769419Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_dtype.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:21.376632] 2024-08-20T21:55:23.8943713Z 2024-08-20T21:55:23.8945412Z torch_np/test_dtype 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_dtype_1.1_74577705977d5df0_.log 2024-08-20T21:55:23.8946571Z Running 0 items in this shard: 2024-08-20T21:55:23.8946828Z 2024-08-20T21:55:23.8947275Z Running torch_np/test_nep50_examples 1/1 ... [2024-08-20 21:55:23.894563] 2024-08-20T21:55:23.8947885Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:23.8952367Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_nep50_examples.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:23.894903] 2024-08-20T21:55:26.4625973Z 2024-08-20T21:55:26.4627882Z torch_np/test_nep50_examples 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_nep50_examples_1.1_937d859ca9cdaddd_.log 2024-08-20T21:55:26.4629211Z Running 0 items in this shard: 2024-08-20T21:55:26.4629668Z 2024-08-20T21:55:26.4630288Z Running distributions/test_constraints 1/1 ... [2024-08-20 21:55:26.462769] 2024-08-20T21:55:26.4630914Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:26.4634752Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'distributions/test_constraints.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:26.463120] 2024-08-20T21:55:28.9808556Z 2024-08-20T21:55:28.9811152Z distributions/test_constraints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_constraints_1.1_8385feaae5ebe708_.log 2024-08-20T21:55:28.9813060Z Running 0 items in this shard: 2024-08-20T21:55:28.9813440Z 2024-08-20T21:55:28.9813837Z Running test_compile_benchmark_util 1/1 ... [2024-08-20 21:55:28.981053] 2024-08-20T21:55:28.9814688Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:28.9817322Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_compile_benchmark_util.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:28.981418] 2024-08-20T21:55:31.3991895Z 2024-08-20T21:55:31.3993558Z test_compile_benchmark_util 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_compile_benchmark_util_1.1_65e14e9d8f6b47d1_.log 2024-08-20T21:55:31.3994816Z Running 0 items in this shard: 2024-08-20T21:55:31.3995109Z 2024-08-20T21:55:31.3995905Z Running test_fx_experimental 1/1 ... [2024-08-20 21:55:31.399421] 2024-08-20T21:55:31.3996539Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:31.4000524Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_fx_experimental.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:31.399770] 2024-08-20T21:55:35.1193454Z 2024-08-20T21:55:35.1194953Z test_fx_experimental 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_experimental_1.1_de3ebb20601e7a62_.log 2024-08-20T21:55:35.1196169Z Running 0 items in this shard: 2024-08-20T21:55:35.1196565Z 2024-08-20T21:55:35.1197185Z Running torch_np/numpy_tests/core/test_scalarinherit 1/1 ... [2024-08-20 21:55:35.119509] 2024-08-20T21:55:35.1197951Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:35.1201435Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalarinherit.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:35.119830] 2024-08-20T21:55:37.4873237Z 2024-08-20T21:55:37.4875346Z torch_np/numpy_tests/core/test_scalarinherit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalarinherit_1.1_a2f5f9fc2f3d90c0_.log 2024-08-20T21:55:37.4877283Z Running 0 items in this shard: 2024-08-20T21:55:37.4877558Z 2024-08-20T21:55:37.4878010Z Running torch_np/numpy_tests/core/test_einsum 1/1 ... [2024-08-20 21:55:37.487487] 2024-08-20T21:55:37.4878655Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:37.4882261Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_einsum.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:37.487837] 2024-08-20T21:55:39.9554833Z 2024-08-20T21:55:39.9556772Z torch_np/numpy_tests/core/test_einsum 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_einsum_1.1_f18cefc8c47d2447_.log 2024-08-20T21:55:39.9558246Z Running 0 items in this shard: 2024-08-20T21:55:39.9558548Z 2024-08-20T21:55:39.9559240Z Running functorch/test_logging 1/1 ... [2024-08-20 21:55:39.955704] 2024-08-20T21:55:39.9559849Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:39.9563394Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'functorch/test_logging.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:39.956031] 2024-08-20T21:55:42.4737854Z 2024-08-20T21:55:42.4739529Z functorch/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_logging_1.1_54a64e66a0a4d531_.log 2024-08-20T21:55:42.4740802Z Running 0 items in this shard: 2024-08-20T21:55:42.4741150Z 2024-08-20T21:55:42.4741742Z Running torch_np/test_ufuncs_basic 1/1 ... [2024-08-20 21:55:42.473932] 2024-08-20T21:55:42.4742344Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:42.4745615Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_ufuncs_basic.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:42.474266] 2024-08-20T21:55:44.9418938Z 2024-08-20T21:55:44.9420629Z torch_np/test_ufuncs_basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_ufuncs_basic_1.1_6067ad9c747deaaa_.log 2024-08-20T21:55:44.9421961Z Running 0 items in this shard: 2024-08-20T21:55:44.9422288Z 2024-08-20T21:55:44.9422667Z Running torch_np/test_random 1/1 ... [2024-08-20 21:55:44.942015] 2024-08-20T21:55:44.9423220Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:44.9426501Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_random.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:44.942360] 2024-08-20T21:55:47.4600621Z 2024-08-20T21:55:47.4602242Z torch_np/test_random 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_random_1.1_15d439ddf48927e0_.log 2024-08-20T21:55:47.4603457Z Running 0 items in this shard: 2024-08-20T21:55:47.4603769Z 2024-08-20T21:55:47.4604115Z Running test_jiterator 1/1 ... [2024-08-20 21:55:47.460204] 2024-08-20T21:55:47.4604693Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:47.4608679Z 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'] ... [2024-08-20 21:55:47.460568] 2024-08-20T21:55:49.9367861Z 2024-08-20T21:55:49.9369570Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_ac96cedc819da43a_.log 2024-08-20T21:55:49.9370764Z Running 0 items in this shard: 2024-08-20T21:55:49.9371093Z 2024-08-20T21:55:49.9371833Z Running higher_order_ops/test_with_effects 1/1 ... [2024-08-20 21:55:49.936987] 2024-08-20T21:55:49.9372478Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:49.9376486Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'higher_order_ops/test_with_effects.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:49.937332] 2024-08-20T21:55:52.7053082Z 2024-08-20T21:55:52.7054814Z higher_order_ops/test_with_effects 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_with_effects_1.1_e2413edefd4d9b74_.log 2024-08-20T21:55:52.7056208Z Running 0 items in this shard: 2024-08-20T21:55:52.7056510Z 2024-08-20T21:55:52.7056793Z Running test_jit 1/1 ... [2024-08-20 21:55:52.705475] 2024-08-20T21:55:52.7057271Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:52.7061226Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jit.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:52.705793] 2024-08-20T21:55:57.1261093Z 2024-08-20T21:55:57.1262536Z test_jit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_1.1_990a5b450995ca7f_.log 2024-08-20T21:55:57.1263724Z Running 0 items in this shard: 2024-08-20T21:55:57.1264108Z 2024-08-20T21:55:57.1264447Z Running test_jit_fuser_te 1/1 ... [2024-08-20 21:55:57.126222] 2024-08-20T21:55:57.1264988Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:55:57.1268395Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jit_fuser_te.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:55:57.126546] 2024-08-20T21:56:02.0479069Z 2024-08-20T21:56:02.0480710Z test_jit_fuser_te 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_fuser_te_1.1_06a4d77bcbbe16a4_.log 2024-08-20T21:56:02.0481779Z Running 0 items in this shard: 2024-08-20T21:56:02.0482054Z 2024-08-20T21:56:02.0482624Z Running functorch/test_ac 1/1 ... [2024-08-20 21:56:02.048090] 2024-08-20T21:56:02.0483174Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:02.0487317Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'functorch/test_ac.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:02.048418] 2024-08-20T21:56:04.4366648Z 2024-08-20T21:56:04.4368785Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_009bde8c5e576758_.log 2024-08-20T21:56:04.4369728Z 2024-08-20T21:56:04.4370147Z Running test_matmul_cuda 1/1 ... [2024-08-20 21:56:04.436645] 2024-08-20T21:56:04.4370708Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:04.4373297Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:04.437023] 2024-08-20T21:56:06.9030455Z 2024-08-20T21:56:06.9032699Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_2cd24df1159ab3ba_.log 2024-08-20T21:56:06.9034401Z Running 0 items in this shard: 2024-08-20T21:56:06.9034770Z 2024-08-20T21:56:06.9035190Z Running optim/test_swa_utils 1/1 ... [2024-08-20 21:56:06.903212] 2024-08-20T21:56:06.9035760Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:06.9038959Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'optim/test_swa_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:06.903595] 2024-08-20T21:56:09.1698003Z 2024-08-20T21:56:09.1700037Z optim/test_swa_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_swa_utils_1.1_bb3b28f33bf93c11_.log 2024-08-20T21:56:09.1701345Z 2024-08-20T21:56:09.1701935Z Running lazy/test_bindings 1/1 ... [2024-08-20 21:56:09.169947] 2024-08-20T21:56:09.1702506Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:09.1706101Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_bindings.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:09.170282] 2024-08-20T21:56:10.6214401Z 2024-08-20T21:56:10.6216163Z lazy/test_bindings 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_bindings_1.1_79f986aa544db2d0_.log 2024-08-20T21:56:10.6217095Z 2024-08-20T21:56:10.6325528Z Running dynamo/test_trace_rules 1/1 ... [2024-08-20 21:56:10.632254] 2024-08-20T21:56:10.6326193Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:10.6329463Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_trace_rules.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:10.632644] 2024-08-20T21:56:10.6372589Z Running dynamo/test_repros 1/1 ... [2024-08-20 21:56:10.636949] 2024-08-20T21:56:10.6374341Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:10.6375562Z Running dynamo/test_higher_order_ops 1/1 ... [2024-08-20 21:56:10.637157] 2024-08-20T21:56:10.6376622Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:10.6379843Z 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'] ... [2024-08-20 21:56:10.637383] 2024-08-20T21:56:10.6385206Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_higher_order_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:10.637629] 2024-08-20T21:56:13.0830489Z 2024-08-20T21:56:13.0832944Z dynamo/test_trace_rules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_trace_rules_1.1_8bf36172691cb407_.log 2024-08-20T21:56:13.0834903Z 2024-08-20T21:56:13.2200321Z 2024-08-20T21:56:13.2202243Z dynamo/test_repros 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_repros_1.1_38b6d478e9886f4b_.log 2024-08-20T21:56:13.2203533Z 2024-08-20T21:56:13.3865288Z 2024-08-20T21:56:13.3867644Z dynamo/test_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_higher_order_ops_1.1_7db4ade54cc96377_.log 2024-08-20T21:56:13.3869326Z 2024-08-20T21:56:15.6493565Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:15.7079833Z Running dynamo/test_export 1/1 ... [2024-08-20 21:56:15.707545] 2024-08-20T21:56:15.7081199Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:15.7084647Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_export.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:15.707923] 2024-08-20T21:56:15.7325625Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:15.7901849Z Running dynamo/test_exc 1/1 ... [2024-08-20 21:56:15.789769] 2024-08-20T21:56:15.7902863Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:15.7906546Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exc.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:15.790144] 2024-08-20T21:56:15.9013738Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:15.9588843Z Running dynamo/test_ctx_manager 1/1 ... [2024-08-20 21:56:15.958471] 2024-08-20T21:56:15.9589685Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:15.9592109Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_ctx_manager.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:15.958849] 2024-08-20T21:56:18.2731045Z 2024-08-20T21:56:18.2732816Z dynamo/test_exc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exc_1.1_8cdbb9995a28d78a_.log 2024-08-20T21:56:18.2733775Z 2024-08-20T21:56:18.3734768Z 2024-08-20T21:56:18.3736757Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_4a7e480ff38e0f14_.log 2024-08-20T21:56:18.3738193Z 2024-08-20T21:56:18.4277215Z 2024-08-20T21:56:18.4279229Z dynamo/test_ctx_manager 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_ctx_manager_1.1_04a7cd2f67ba0a73_.log 2024-08-20T21:56:18.4281065Z 2024-08-20T21:56:20.8489544Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:20.9073204Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-08-20 21:56:20.906896] 2024-08-20T21:56:20.9074175Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:20.9076549Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:20.907256] 2024-08-20T21:56:20.9257272Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:20.9837656Z Running dynamo/test_optimizers 1/1 ... [2024-08-20 21:56:20.983360] 2024-08-20T21:56:20.9838452Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:20.9840972Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_optimizers.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:20.983742] 2024-08-20T21:56:20.9868931Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:21.0448924Z Running dynamo/test_backends 1/1 ... [2024-08-20 21:56:21.044489] 2024-08-20T21:56:21.0449745Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:21.0452433Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_backends.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:21.044877] 2024-08-20T21:56:23.4780977Z 2024-08-20T21:56:23.4782952Z dynamo/test_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_optimizers_1.1_c305ec5be5a37bf1_.log 2024-08-20T21:56:23.4783992Z 2024-08-20T21:56:23.4805067Z 2024-08-20T21:56:23.4807184Z 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_6817722a78a183d4_.log 2024-08-20T21:56:23.4808366Z 2024-08-20T21:56:23.6047126Z 2024-08-20T21:56:23.6049018Z dynamo/test_backends 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backends_1.1_ae9f5763cb185f4d_.log 2024-08-20T21:56:23.6050081Z 2024-08-20T21:56:25.9885841Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:26.0459743Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:26.0461763Z Running dynamo/test_skip_non_tensor 1/1 ... [2024-08-20 21:56:26.045482] 2024-08-20T21:56:26.0462555Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:26.0464634Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_skip_non_tensor.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:26.045878] 2024-08-20T21:56:26.0899584Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:26.1040330Z Running dynamo/test_python_autograd 1/1 ... [2024-08-20 21:56:26.103741] 2024-08-20T21:56:26.1040938Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:26.1044411Z 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'] ... [2024-08-20 21:56:26.104111] 2024-08-20T21:56:26.1532016Z Running dynamo/test_verify_correctness 1/1 ... [2024-08-20 21:56:26.152733] 2024-08-20T21:56:26.1533128Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:26.1537862Z 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'] ... [2024-08-20 21:56:26.153161] 2024-08-20T21:56:28.5468906Z 2024-08-20T21:56:28.5470645Z dynamo/test_skip_non_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_skip_non_tensor_1.1_102122d2a20f4ca7_.log 2024-08-20T21:56:28.5950264Z 2024-08-20T21:56:28.5950280Z 2024-08-20T21:56:28.5952255Z 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_f7b65d3e341be169_.log 2024-08-20T21:56:28.5953817Z 2024-08-20T21:56:28.6484647Z 2024-08-20T21:56:28.6486800Z 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_39cb39165cfaae9f_.log 2024-08-20T21:56:28.6488428Z 2024-08-20T21:56:31.1040215Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:31.1615149Z Running dynamo/test_exceptions 1/1 ... [2024-08-20 21:56:31.161107] 2024-08-20T21:56:31.1616295Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:31.1619573Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exceptions.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:31.161520] 2024-08-20T21:56:31.1679286Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:31.1839309Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:31.2274112Z Running dynamo/test_base_output 1/1 ... [2024-08-20 21:56:31.227024] 2024-08-20T21:56:31.2274753Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:31.2278113Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_base_output.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:31.227424] 2024-08-20T21:56:31.2413757Z Running functorch/test_vmap 2/3 ... [2024-08-20 21:56:31.241022] 2024-08-20T21:56:31.2414795Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:31.2418068Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'functorch/test_vmap.py', '-m', 'not serial', '--shard-id=2', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:31.241366] 2024-08-20T21:56:33.8005876Z 2024-08-20T21:56:33.8007584Z dynamo/test_exceptions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exceptions_1.1_3d5ea8520dd0e34a_.log 2024-08-20T21:56:33.8008696Z 2024-08-20T21:56:33.8027185Z 2024-08-20T21:56:33.8028853Z dynamo/test_base_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_output_1.1_d23b521f940e15c5_.log 2024-08-20T21:56:33.8029821Z 2024-08-20T21:56:36.3536792Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:36.4119646Z Running dynamo/test_structured_trace 1/1 ... [2024-08-20 21:56:36.411564] 2024-08-20T21:56:36.4120324Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:36.4123314Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_structured_trace.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:36.411977] 2024-08-20T21:56:36.4252475Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:36.4827723Z Running dynamo/test_hooks 1/1 ... [2024-08-20 21:56:36.482419] 2024-08-20T21:56:36.4828371Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:36.4831356Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_hooks.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:36.482798] 2024-08-20T21:56:38.9788295Z 2024-08-20T21:56:38.9790884Z dynamo/test_structured_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_structured_trace_1.1_67184af740ad8b32_.log 2024-08-20T21:56:38.9791932Z 2024-08-20T21:56:39.0093089Z 2024-08-20T21:56:39.0094867Z dynamo/test_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_hooks_1.1_524fce6c0b1142ed_.log 2024-08-20T21:56:39.0095791Z 2024-08-20T21:56:41.4908261Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:41.4936086Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:41.5479266Z Running dynamo/test_profiler 1/1 ... [2024-08-20 21:56:41.547544] 2024-08-20T21:56:41.5481271Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:41.5483778Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_profiler.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:41.547924] 2024-08-20T21:56:41.5503453Z Running dynamo/test_recompile_ux 1/1 ... [2024-08-20 21:56:41.550090] 2024-08-20T21:56:41.5504066Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:41.5507770Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:41.550417] 2024-08-20T21:56:43.9155608Z 2024-08-20T21:56:43.9157139Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_8e7b439e424c27c2_.log 2024-08-20T21:56:43.9158101Z 2024-08-20T21:56:43.9856088Z 2024-08-20T21:56:43.9857906Z 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_7a617115a2c11d3e_.log 2024-08-20T21:56:43.9858900Z 2024-08-20T21:56:46.3798174Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:46.4371640Z Running dynamo/test_deviceguard 1/1 ... [2024-08-20 21:56:46.436711] 2024-08-20T21:56:46.4372692Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:46.4375265Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_deviceguard.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:46.437078] 2024-08-20T21:56:46.5326321Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:46.5902692Z Running test_linalg 2/4 ... [2024-08-20 21:56:46.589850] 2024-08-20T21:56:46.5903851Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:46.5906557Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_linalg.py', '-m', 'not serial', '--shard-id=2', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:46.590230] 2024-08-20T21:56:48.9287553Z 2024-08-20T21:56:48.9289765Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_bd1c5dcfbfcc3204_.log 2024-08-20T21:56:48.9291750Z 2024-08-20T21:56:51.3895067Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T21:56:51.4476018Z Running test_linalg 3/4 ... [2024-08-20 21:56:51.447141] 2024-08-20T21:56:51.4477007Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T21:56:51.4480125Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_linalg.py', '-m', 'not serial', '--shard-id=3', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 21:56:51.447511] 2024-08-20T22:03:37.2053323Z 2024-08-20T22:03:37.2055145Z functorch/test_vmap 2/3 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_vmap_2.3_50a6424d07fed1e8_.log 2024-08-20T22:03:37.2475091Z Running 707 items in this shard: test/functorch/test_vmap.py::TestVmapAPI::test_batch_rule_does_not_need_to_handle_no_batched_input, test/functorch/test_vmap.py::TestVmapAPI::test_checkpoint, test/functorch/test_vmap.py::TestVmapAPI::test_constant_function, test/functorch/test_vmap.py::TestVmapAPI::test_decomposition_under_python_dispatcher, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_multiple_returns, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_warning, test/functorch/test_vmap.py::TestVmapAPI::test_fallback_with_undefined_grad, test/functorch/test_vmap.py::TestVmapAPI::test_grad_unsupported_interaction, test/functorch/test_vmap.py::TestVmapAPI::test_inplace_fallback_nary_same_levels, test/functorch/test_vmap.py::TestVmapAPI::test_inplace_fallback_unary, test/functorch/test_vmap.py::TestVmapAPI::test_multiple_out_dims, test/functorch/test_vmap.py::TestVmapAPI::test_multiple_outputs2, test/functorch/test_vmap.py::TestVmapAPI::test_nested_negative_in_dims, test/functorch/test_vmap.py::TestVmapAPI::test_nested_with_diag_embed, test/functorch/test_vmap.py::TestVmapAPI::test_nested_with_same_map_dim, test/functorch/test_vmap.py::TestVmapAPI::test_non_zero_in_dims, test/functorch/test_vmap.py::TestVmapAPI::test_nonzero_out_dims, test/functorch/test_vmap.py::TestVmapAPI::test_noop_in_inner_vmap, test/functorch/test_vmap.py::TestVmapAPI::test_not_enough_in_dims_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_odict_returns, test/functorch/test_vmap.py::TestVmapAPI::test_pytree_returns, test/functorch/test_vmap.py::TestVmapAPI::test_restore_vmap_unexpanded_outputs, test/functorch/test_vmap.py::TestVmapAPI::test_unsupported_op_err_msg, test/functorch/test_vmap.py::TestVmapAPI::test_vmap_autocast_cuda, test/functorch/test_vmap.py::TestVmapOperators::test_adaptive_avg_pool2d, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_add_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_mul_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_pow, test/functorch/test_vmap.py::TestVmapOperators::test_arithmetic_sub_dunder, test/functorch/test_vmap.py::TestVmapOperators::test_bmm, test/functorch/test_vmap.py::TestVmapOperators::test_chunk, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_0_out_dim_2_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_1_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_chunk_vmap_in_dim_2_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_clamp_variant_clamp_max, test/functorch/test_vmap.py::TestVmapOperators::test_copy_, test/functorch/test_vmap.py::TestVmapOperators::test_expand_as, test/functorch/test_vmap.py::TestVmapOperators::test_is_contiguous, test/functorch/test_vmap.py::TestVmapOperators::test_is_floating_point, test/functorch/test_vmap.py::TestVmapOperators::test_mv, test/functorch/test_vmap.py::TestVmapOperators::test_narrow, test/functorch/test_vmap.py::TestVmapOperators::test_new_empty, test/functorch/test_vmap.py::TestVmapOperators::test_one_hot, test/functorch/test_vmap.py::TestVmapOperators::test_repeat, test/functorch/test_vmap.py::TestVmapOperators::test_reshape, test/functorch/test_vmap.py::TestVmapOperators::test_roll_no_dims, test/functorch/test_vmap.py::TestVmapOperators::test_silu_backward, test/functorch/test_vmap.py::TestVmapOperators::test_stride, test/functorch/test_vmap.py::TestVmapOperators::test_t, test/functorch/test_vmap.py::TestVmapOperators::test_to, test/functorch/test_vmap.py::TestVmapOperators::test_trace, test/functorch/test_vmap.py::TestVmapOperators::test_transpose, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_abs, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_acos, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_atan, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_ceil, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_cos, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_digamma, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_log10, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_reciprocal, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_relu, test/functorch/test_vmap.py::TestVmapOperators::test_unary_pointwise_sigmoid, test/functorch/test_vmap.py::TestVmapOperators::test_unfold, test/functorch/test_vmap.py::TestVmapOperators::test_unsafe_view, test/functorch/test_vmap.py::TestVmapOperators::test_view_as_real, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_0_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_composition_in_dim_1_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_0_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_0_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_error_in_dim_1_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_1_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_0_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_0_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_1_out_dim_2_randomness_error, test/functorch/test_vmap.py::TestVmapOperators::test_vmap_chunksize_in_dim_2_out_dim_1_randomness_same, test/functorch/test_vmap.py::TestVmapOperators::test_weird_matmul_case, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_advanced_indexing_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_batch_norm_training_False_track_running_stats_True_affine_False_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_batch_norm_training_True_track_running_stats_True_affine_True_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_fill__Tensor_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_index_fill_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_index_put_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_isinf_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_isnan_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_namedtuple_returns_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_ForwardHasDefaultArgsAutogradFunction_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_H_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_NumpyCubeAutogradFunction_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_SelectGenVmapAutogradFunction_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_T_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_ZeroGradientsGenVmapAutogradFunction_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule___radd___cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule___rmul___cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule__chunk_cat_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule__unsafe_masked_index_put_accumulate_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_add_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_addbmm_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_addr_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_alias_copy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_amin_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_angle_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_argmax_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_argwhere_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_as_strided_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_asin_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_atleast_1d_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_bfloat16_functorch_no_channels_last_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_bitwise_and_cpu_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_bitwise_not_cpu_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_bitwise_right_shift_cpu_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_bucketize_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_cat_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_cdouble_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_char_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_char_functorch_no_channels_last_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_clamp_cpu_float32, 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test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_div_trunc_rounding_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_dot_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_double_functorch_no_channels_last_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_dsplit_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_dstack_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_einsum_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_equal_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_erf_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_op_has_batch_rule_exp_cpu_float32, 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test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_binary_cross_entropy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_celu_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_conv2d_no_bias_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_conv2d_stride_padding_no_bias_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_conv2d_stride_padding_with_bias_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_conv2d_strided_padding_dilation_with_bias_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_conv3d_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_conv_transpose1d_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_conv_transpose3d_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_cosine_similarity_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_ctc_loss_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_dropout_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_embedding_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_fractional_max_pool3d_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_gelu_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_group_norm_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_hardtanh_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_interpolate_bilinear_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_linear_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_margin_ranking_loss_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_max_pool1d_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_max_unpool1d_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_max_unpool1d_grad_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_max_unpool3d_grad_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_mse_loss_functorch_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_multi_head_attention_forward_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_multilabel_margin_loss_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_pad_constant_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_pdist_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_poisson_nll_loss_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_prelu_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_rrelu_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_silu_complex_cpu_complex64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_soft_margin_loss_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_softmin_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_softplus_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_softsign_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_tanhshrink_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_triplet_margin_loss_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nonzero_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_nonzero_static_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_norm_inf_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_norm_nuc_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_normal_number_mean_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_ones_like_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_ops_aten_index_put_functorch_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_pinverse_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_polygamma_polygamma_n_0_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_positive_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_pow_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_prod_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_qr_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_randint_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_reciprocal_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_remainder_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_reshape_as_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_reshape_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_resize__cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_resize_as__cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_rot90_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_round_decimals_0_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_rsqrt_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_select_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_select_scatter_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_signal_windows_blackman_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_signal_windows_cosine_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_signal_windows_general_cosine_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_signal_windows_general_hamming_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_sin_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_sinc_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_softmax_with_dtype_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_sort_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_sparse_sampled_addmm_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_airy_ai_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_erfcx_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_hermite_polynomial_he_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_modified_bessel_k1_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_ndtr_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_special_shifted_chebyshev_polynomial_u_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_split_list_args_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_sqrt_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_std_mean_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_sub_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_sum_to_size_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_svd_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_t_copy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_tensordot_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_tile_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_trace_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_trapezoid_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_triangular_solve_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_tril_indices_cpu_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_triu_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_triu_indices_cpu_int64, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_unbind_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_unfold_copy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_unique_consecutive_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_unsafe_chunk_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_var_mean_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_var_mean_unbiased_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_var_unbiased_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_exhaustive_xlogy_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_cond_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_cross_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_diagonal_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_eigvals_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_ldl_solve_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_matrix_norm_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_matrix_rank_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_multi_dot_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_norm_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_norm_subgradients_at_zero_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_pinv_singular_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_qr_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_slogdet_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_svd_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_svdvals_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_tensorinv_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_vecdot_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_linalg_failure_1D_input_linalg_vector_norm_cpu_float32, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_multi_dot_failure_1D_input_cpu, test/functorch/test_vmap.py::TestVmapOperatorsOpInfoCPU::test_vmap_with_anomaly_detection_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_diagonal_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_log_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_max_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_sub_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_threshold_cpu, test/functorch/test_vmap.py::TestVmapBatchedGradientCPU::test_vmap_fallback_check_ok, test/functorch/test_vmap.py::TestRandomnessCPU::test_alpha_dropout_randomness_same_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_first_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_different_batched_input_none_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_first_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_False_randomness_error_batched_input_last_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_first_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_first_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_last_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_different_batched_input_none_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_first_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_first_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_last_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_none_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_error_batched_input_none_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_first_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_first_batched_probability_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_none_batched_probability_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_bernoulli_in_place_use_generator_True_randomness_same_batched_input_none_batched_probability_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_chunk_vmap_in_dim_2_out_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_randomness_different_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_randomness_different_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_dropout_randomness_same_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_factory_ops_randomness_same_use_generator_True_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_error_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_same_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_alpha_dropout_randomness_same_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_different_batched_input_first_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_different_batched_input_first_dim_3_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_different_batched_input_none_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_error_batched_input_first_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_error_batched_input_first_dim_3_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_error_batched_input_last_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_error_batched_input_last_dim_3_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_same_batched_input_first_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_feature_dropout_randomness_same_batched_input_last_dim_3_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_like_functions_randomness_same_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_error_batched_call_False_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_error_batched_call_False_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_error_batched_call_True_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_error_batched_call_True_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_same_batched_call_True_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_False_randomness_same_batched_call_True_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_True_randomness_different_batched_call_False_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_multinomial_use_generator_True_randomness_same_batched_call_True_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_first_batched_other_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_different_batched_input_none_batched_other_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_first_batched_other_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_first_batched_other_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_last_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_error_batched_input_last_batched_other_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_same_batched_input_first_batched_other_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_same_batched_input_last_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_same_batched_input_last_batched_other_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_False_randomness_same_batched_input_none_batched_other_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_different_batched_input_first_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_different_batched_input_last_batched_other_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_different_batched_input_none_batched_other_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_error_batched_input_first_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_error_batched_input_none_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_last_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_last_batched_other_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_none_batched_other_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_none_batched_other_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_binary_out_of_place_use_generator_True_randomness_same_batched_input_none_batched_other_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_False_randomness_different_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_False_randomness_different_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_False_randomness_same_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_False_randomness_same_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_True_randomness_error_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_True_randomness_error_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_True_randomness_same_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_inplace_use_generator_True_randomness_same_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_False_randomness_error_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_False_randomness_error_batched_input_last_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_different_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_error_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_same_batched_input_first_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_random_unary_out_of_place_use_generator_True_randomness_same_batched_input_none_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_randperm_randomness_different_use_generator_True_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_randperm_randomness_same_use_generator_False_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_0_out_dim_1_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_1_out_dim_0_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_1_out_dim_2_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_2_out_dim_1_cpu, test/functorch/test_vmap.py::TestRandomnessCPU::test_vmap_chunksize_in_dim_2_out_dim_2_cpu, test/functorch/test_vmap.py::TestVmapDeviceTypeCPU::test__is_all_true_cpu, test/functorch/test_vmap.py::TestVmapDeviceTypeCPU::test_vmap_fallback_check, test/functorch/test_vmap.py::TestVmapDeviceTypeCPU::test_vmap_fallback_check_ok, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_cat_batching_rule_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_fallback_binary_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_fallback_with_nt_and_batched_dense_with_nonzero_bdim_raises_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_multilevel_vmap_raises_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_nt_with_nonzero_in_dim_raises_cpu, test/functorch/test_vmap.py::TestVmapNestedTensorCPU::test_nt_with_nonzero_out_dim_raises_cpu 2024-08-20T22:03:37.2879370Z 2024-08-20T22:03:39.5772764Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:03:39.6345307Z Running test_linalg 4/4 ... [2024-08-20 22:03:39.634116] 2024-08-20T22:03:39.6346227Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:03:39.6349050Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_linalg.py', '-m', 'not serial', '--shard-id=4', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:03:39.634490] 2024-08-20T22:04:40.2755129Z 2024-08-20T22:04:40.2756945Z test_linalg 3/4 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_3.4_52ba38762f03eff6_.log 2024-08-20T22:04:40.2867313Z Running 238 items in this shard: test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_32_k_32_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_16_n_16_use_transpose_a_False_use_transpose_b_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_16_n_16_use_transpose_a_True_use_transpose_b_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_16_n_32_use_transpose_a_True_use_transpose_b_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_32_n_32_use_transpose_a_True_use_transpose_b_False_cpu, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmm_gelu_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmv_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addr_integral_cpu_uint8, test/test_linalg.py::TestLinalgCPU::test_baddbmm_input_dtypes_compatibility_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_baddbmm_input_dtypes_compatibility_cpu_int16, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_int32, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_blas_mv_large_input_cpu, test/test_linalg.py::TestLinalgCPU::test_bmm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_broadcast_batched_matmul_cpu, test/test_linalg.py::TestLinalgCPU::test_chain_matmul_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_backward_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_broadcasting_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_many_batches_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_out_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_64_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int8_mm_m_32_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int8_mm_m_32_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test_cond_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cond_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_uint8, test/test_linalg.py::TestLinalgCPU::test_cross_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eig_compare_backends_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eig_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eig_with_nan_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigh_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigh_lower_uplo_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigvals_compare_backends_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigvals_compare_backends_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigvalsh_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigvalsh_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigvalsh_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_32_k_32_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_32_k_35_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_32_k_40_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_32_k_64_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_36_k_40_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_64_k_32_cpu, test/test_linalg.py::TestLinalgCPU::test_hipblaslt_corner_cases_rocm_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_hipblaslt_corner_cases_rocm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_householder_product_errors_and_warnings_cpu, test/test_linalg.py::TestLinalgCPU::test_inv_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_inv_ex_info_device_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_inverse_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_inverse_many_batches_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_kron_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_kron_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_kron_empty_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_kron_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_lapack_empty_cpu, test/test_linalg.py::TestLinalgCPU::test_large_bmm_mm_backward_cpu, test/test_linalg.py::TestLinalgCPU::test_ldl_solve_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_batch_broadcasting_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_batch_broadcasting_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_cpu_errors_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_family_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_solve_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_solve_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_analytic_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_analytic_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_broadcasting_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_large_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linear_algebra_scalar_raises_cpu, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_broadcasting_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_lu_solve_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_lu_solve_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matmul_check_entries_tunableop_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_1d_Nd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_1d_Nd_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_3d_Nd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_3d_Nd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matrix_power_negative_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_atol_rtol_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_basic_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_empty_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_out_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_out_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_minimum_tuning_iteration_tunableop_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_mm_bmm_non_memory_dense_cpu, test/test_linalg.py::TestLinalgCPU::test_mm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_mm_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_norm_bfloat16_and_half_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_norm_dtype_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_norm_fastpaths_cpu, test/test_linalg.py::TestLinalgCPU::test_norm_fused_type_promotion_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_norm_matrix_degenerate_shapes_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_norm_matrix_degenerate_shapes_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_norm_vector_degenerate_shapes_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_nuclear_norm_out_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_many_batches_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_upper_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_batched_upper_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_old_cholesky_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_ormqr_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_ormqr_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_int32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_complex128, test/test_linalg.py::TestLinalgCPU::test_permute_matmul_cpu, test/test_linalg.py::TestLinalgCPU::test_pinv_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_pinv_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_pinv_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_pinverse_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_preferred_linalg_library_cpu, test/test_linalg.py::TestLinalgCPU::test_qr_batched_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_qr_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_qr_vs_numpy_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_strided_mm_bmm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_svd_lowrank_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_svd_memory_allocation_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_tensorinv_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_tensorinv_empty_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_tensorinv_singular_input_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_tensorinv_singular_input_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_empty_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_broadcasting_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_broadcasting_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_many_batches_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_out_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_vdot_invalid_args_cpu, test/test_linalg.py::TestLinalgCPU::test_vector_norm_reduce_over_1D_vector_cpu_complex64 2024-08-20T22:04:40.2973205Z 2024-08-20T22:04:42.6657330Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:04:42.7245824Z Running dynamo/test_debug_utils 1/1 ... [2024-08-20 22:04:42.724081] 2024-08-20T22:04:42.7247205Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:04:42.7249540Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_debug_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:04:42.724461] 2024-08-20T22:04:45.2391192Z 2024-08-20T22:04:45.2393260Z dynamo/test_debug_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_debug_utils_1.1_3e8ebe7c7714f152_.log 2024-08-20T22:04:47.8053828Z 2024-08-20T22:04:47.8055649Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:04:47.8952045Z Running test_cuda_multigpu 1/1 ... [2024-08-20 22:04:47.894592] 2024-08-20T22:04:47.8953094Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:04:47.8956491Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_multigpu.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:04:47.895010] 2024-08-20T22:04:50.5274298Z 2024-08-20T22:04:50.5276568Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_0792986e34175448_.log 2024-08-20T22:04:50.5278449Z Running 0 items in this shard: 2024-08-20T22:04:50.5279194Z 2024-08-20T22:04:53.1487619Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:04:53.2062425Z Running test_comparison_utils 1/1 ... [2024-08-20 22:04:53.205767] 2024-08-20T22:04:53.2063387Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:04:53.2066628Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_comparison_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:04:53.206124] 2024-08-20T22:04:56.2247251Z 2024-08-20T22:04:56.2249648Z test_comparison_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_comparison_utils_1.1_39dd9da91e7a8706_.log 2024-08-20T22:04:56.2255586Z Running 5 items in this shard: test/test_comparison_utils.py::TestComparisonUtils::test_all_equal_no_assert, test/test_comparison_utils.py::TestComparisonUtils::test_all_equal_no_assert_nones, test/test_comparison_utils.py::TestComparisonUtils::test_assert_dtype, test/test_comparison_utils.py::TestComparisonUtils::test_assert_sizes, test/test_comparison_utils.py::TestComparisonUtils::test_assert_strides 2024-08-20T22:04:56.2259969Z 2024-08-20T22:04:58.7160026Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:04:58.7738672Z Running test_mkl_verbose 1/1 ... [2024-08-20 22:04:58.773393] 2024-08-20T22:04:58.7739892Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:04:58.7743185Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mkl_verbose.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:04:58.773756] 2024-08-20T22:05:05.6978241Z 2024-08-20T22:05:05.6980440Z test_mkl_verbose 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkl_verbose_1.1_10e59d653b525b85_.log 2024-08-20T22:05:05.6983561Z Running 2 items in this shard: test/test_mkl_verbose.py::TestMKLVerbose::test_verbose_off, test/test_mkl_verbose.py::TestMKLVerbose::test_verbose_on 2024-08-20T22:05:05.6985247Z 2024-08-20T22:05:08.1978466Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:05:08.2549107Z Running test_mkldnn_verbose 1/1 ... [2024-08-20 22:05:08.254497] 2024-08-20T22:05:08.2550234Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:05:08.2554448Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_mkldnn_verbose.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:05:08.254869] 2024-08-20T22:05:14.0779353Z 2024-08-20T22:05:14.0781609Z test_mkldnn_verbose 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_verbose_1.1_b088a71f12b79bac_.log 2024-08-20T22:05:14.0784819Z Running 2 items in this shard: test/test_mkldnn_verbose.py::TestMKLDNNVerbose::test_verbose_off, test/test_mkldnn_verbose.py::TestMKLDNNVerbose::test_verbose_on 2024-08-20T22:05:14.0786571Z 2024-08-20T22:05:16.4828607Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:05:16.5405355Z Running test_custom_ops 1/1 ... [2024-08-20 22:05:16.540175] 2024-08-20T22:05:16.5405910Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:05:16.5408943Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_custom_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:05:16.540542] 2024-08-20T22:05:50.9065788Z 2024-08-20T22:05:50.9067523Z test_custom_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_custom_ops_1.1_2da37e61b0a821eb_.log 2024-08-20T22:05:50.9185670Z Running 256 items in this shard: test/test_custom_ops.py::TestCustomOp::test_abstract_impl_on_existing_op, test/test_custom_ops.py::TestCustomOp::test_abstract_impl_on_existing_op_with_CompositeExplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_abstract_impl_on_existing_op_with_CompositeImplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_abstract_impl_on_existing_op_with_meta, test/test_custom_ops.py::TestCustomOp::test_autogen_aten_ops_are_pt2_compliant, test/test_custom_ops.py::TestCustomOp::test_autograd_function_backed_op, test/test_custom_ops.py::TestCustomOp::test_autograd_notimplemented, test/test_custom_ops.py::TestCustomOp::test_autograd_notimplemented_gradmode, test/test_custom_ops.py::TestCustomOp::test_backward_dict_grad_for_nontensor, test/test_custom_ops.py::TestCustomOp::test_backward_dict_invalid_keys, test/test_custom_ops.py::TestCustomOp::test_backward_dict_requires_keys_for_input_optional_tensors, test/test_custom_ops.py::TestCustomOp::test_backward_dict_requires_keys_for_input_tensors, test/test_custom_ops.py::TestCustomOp::test_backward_grads_are_tensor_or_none, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_CompositeImplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_incorrect_schema_mutable, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_incorrect_schema_no_output, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_incorrect_schema_views, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_with_key_key_Autograd, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_with_key_key_AutogradCPU, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_with_key_key_AutogradCUDA, test/test_custom_ops.py::TestCustomOp::test_backward_output_differentiability_non_tensor, test/test_custom_ops.py::TestCustomOp::test_backward_output_differentiability_numel, test/test_custom_ops.py::TestCustomOp::test_backward_output_differentiability_tensorlist, test/test_custom_ops.py::TestCustomOp::test_backward_output_differentiability_type, test/test_custom_ops.py::TestCustomOp::test_backward_partially_registered, test/test_custom_ops.py::TestCustomOp::test_backward_returns_dict, test/test_custom_ops.py::TestCustomOp::test_backward_tensorlist_input_requires_list_grads, test/test_custom_ops.py::TestCustomOp::test_backward_tensorlist_input_requires_list_grads_none_or_Tensor, test/test_custom_ops.py::TestCustomOp::test_backward_tensorlist_input_requires_list_grads_with_same_numel, test/test_custom_ops.py::TestCustomOp::test_basic_make_fx, test/test_custom_ops.py::TestCustomOp::test_builtin_aten_ops_are_pt2_compliant, test/test_custom_ops.py::TestCustomOp::test_builtin_torchscript_ops, test/test_custom_ops.py::TestCustomOp::test_data_dependent_basic, test/test_custom_ops.py::TestCustomOp::test_data_dependent_compile, test/test_custom_ops.py::TestCustomOp::test_data_dependent_fake_tracing, test/test_custom_ops.py::TestCustomOp::test_data_dependent_nms_dynamic_compile, test/test_custom_ops.py::TestCustomOp::test_define_and_impl, test/test_custom_ops.py::TestCustomOp::test_define_bad_schema, test/test_custom_ops.py::TestCustomOp::test_define_validation, test/test_custom_ops.py::TestCustomOp::test_define_with_tags_list, test/test_custom_ops.py::TestCustomOp::test_define_with_tags_single, test/test_custom_ops.py::TestCustomOp::test_define_with_tags_tuple, test/test_custom_ops.py::TestCustomOp::test_defined_in_python, test/test_custom_ops.py::TestCustomOp::test_duplicate_impl, test/test_custom_ops.py::TestCustomOp::test_functionalize_error, test/test_custom_ops.py::TestCustomOp::test_impl_abstract_overload, test/test_custom_ops.py::TestCustomOp::test_impl_cpu, test/test_custom_ops.py::TestCustomOp::test_impl_device_cpu, test/test_custom_ops.py::TestCustomOp::test_impl_device_cuda, test/test_custom_ops.py::TestCustomOp::test_impl_device_function, test/test_custom_ops.py::TestCustomOp::test_impl_device_invalid, test/test_custom_ops.py::TestCustomOp::test_impl_function, test/test_custom_ops.py::TestCustomOp::test_impl_invalid_devices, test/test_custom_ops.py::TestCustomOp::test_impl_meta, test/test_custom_ops.py::TestCustomOp::test_impl_multiple, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op_with_cpu_registration_key_CPU, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op_with_cpu_registration_key_CUDA, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op_with_cpu_registration_key_CompositeExplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op_with_cpu_registration_key_CompositeImplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_impl_separate, test/test_custom_ops.py::TestCustomOp::test_incorrect_schema_types, test/test_custom_ops.py::TestCustomOp::test_infer_schema_no_return, test/test_custom_ops.py::TestCustomOp::test_infer_schema_supported, test/test_custom_ops.py::TestCustomOp::test_infer_schema_unsupported, test/test_custom_ops.py::TestCustomOp::test_invalid_qualname, test/test_custom_ops.py::TestCustomOp::test_invalid_schemas, test/test_custom_ops.py::TestCustomOp::test_is_functional_schema, test/test_custom_ops.py::TestCustomOp::test_is_tensorlist_like_type, test/test_custom_ops.py::TestCustomOp::test_legacy_define, test/test_custom_ops.py::TestCustomOp::test_legacy_impl, test/test_custom_ops.py::TestCustomOp::test_lifetime, test/test_custom_ops.py::TestCustomOp::test_meta_for_data_dependent_shape_operation, test/test_custom_ops.py::TestCustomOp::test_name_must_match, test/test_custom_ops.py::TestCustomOp::test_new_data_dependent_symint, test/test_custom_ops.py::TestCustomOp::test_not_implemented_error, test/test_custom_ops.py::TestCustomOp::test_private_ctor, test/test_custom_ops.py::TestCustomOp::test_reserved_ns, test/test_custom_ops.py::TestCustomOp::test_resolve_packet, test/test_custom_ops.py::TestCustomOp::test_save_for_backward_inputs_are_namedtuple, test/test_custom_ops.py::TestCustomOp::test_schema_matches_signature, test/test_custom_ops.py::TestCustomOp::test_sequences, test/test_custom_ops.py::TestCustomOp::test_supported_param_types, test/test_custom_ops.py::TestCustomOp::test_supported_return_types_multi_return, test/test_custom_ops.py::TestCustomOp::test_supported_return_types_single_return, test/test_custom_ops.py::TestCustomOp::test_supported_schemas, test/test_custom_ops.py::TestCustomOp::test_symints, test/test_custom_ops.py::TestCustomOp::test_unsupported_param_types, test/test_custom_ops.py::TestCustomOp::test_unsupported_schemas, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_inplace, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_mm, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_nonzero, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_inplace, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_mm, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_nonzero, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_inplace, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_mm, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_nonzero, test/test_custom_ops.py::MiniOpTest::test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_dont_generate, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_inplace, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_mm, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_nonzero, test/test_custom_ops.py::MiniOpTest::test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_inplace, test/test_custom_ops.py::MiniOpTest::test_mm, test/test_custom_ops.py::MiniOpTest::test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_nonzero, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_aten_mm, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_aten_nonzero, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_aten_sin_, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_mini_op_test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_mini_op_test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_mini_op_test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_schema__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_schema__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_schema__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_schema__test_inplace, test/test_custom_ops.py::MiniOpTest::test_schema__test_mm, test/test_custom_ops.py::MiniOpTest::test_schema__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_schema__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_schema__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_schema__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_schema__test_nonzero, test/test_custom_ops.py::TestCustomOpAPI::test_basic, test/test_custom_ops.py::TestCustomOpAPI::test_compile, test/test_custom_ops.py::TestCustomOpAPI::test_default_values, test/test_custom_ops.py::TestCustomOpAPI::test_disallows_output_aliasing, test/test_custom_ops.py::TestCustomOpAPI::test_factory_function, test/test_custom_ops.py::TestCustomOpAPI::test_fake, test/test_custom_ops.py::TestCustomOpAPI::test_kwarg_only_tensors, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autograd, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autograd_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_0, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_1, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_2, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_3, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_4, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_5, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_kernel, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_kernel_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_torch_dispatch, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_torch_dispatch_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_torch_dispatch_rule_mode, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_torch_dispatch_rule_subclass, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap_library_decorator, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap_op_decorator, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap_register_multiple_times, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap_register_multiple_times_2, test/test_custom_ops.py::TestCustomOpAPI::test_library_schema_infer, test/test_custom_ops.py::TestCustomOpAPI::test_manual_schema, test/test_custom_ops.py::TestCustomOpAPI::test_manual_schema_error, test/test_custom_ops.py::TestCustomOpAPI::test_multi_types, test/test_custom_ops.py::TestCustomOpAPI::test_mutated, test/test_custom_ops.py::TestCustomOpAPI::test_mutated_error, test/test_custom_ops.py::TestCustomOpAPI::test_mutated_unknown, test/test_custom_ops.py::TestCustomOpAPI::test_no_grad_skips_autograd, test/test_custom_ops.py::TestCustomOpAPI::test_overloading, test/test_custom_ops.py::TestCustomOpAPI::test_register_autograd_defaults, test/test_custom_ops.py::TestCustomOpAPI::test_register_autograd_error_cases, test/test_custom_ops.py::TestCustomOpAPI::test_register_autograd_kwargonly_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_register_vmap_defaults, test/test_custom_ops.py::TestCustomOpAPI::test_register_vmap_kwargonly_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_replacement, test/test_custom_ops.py::TestCustomOpAPI::test_set_kernel_enabled, test/test_custom_ops.py::TestCustomOpAPI::test_split_device, test/test_custom_ops.py::TestCustomOpAPI::test_supports_tensorlist, test/test_custom_ops.py::MiniOpTestOther::test_aot_dispatch_dynamic__test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_aot_dispatch_static__test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_autograd_registration__test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_faketensor__test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_aten_mm, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_aten_nonzero, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_aten_sin_, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_mini_op_test_delayed_error, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_mini_op_test_incorrect_schema, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_mini_op_test_no_abstract, test/test_custom_ops.py::MiniOpTestOther::test_schema__test_nonzero_again, test/test_custom_ops.py::TestGenerateOpcheckTests::test_MiniOpTest, test/test_custom_ops.py::TestGenerateOpcheckTests::test_dont_generate_decorator, test/test_custom_ops.py::TestGenerateOpcheckTests::test_failures_dict_validation, test/test_custom_ops.py::TestGenerateOpcheckTests::test_generate_repro_no_save_data, test/test_custom_ops.py::TestGenerateOpcheckTests::test_generate_repro_save_data, test/test_custom_ops.py::TestGenerateOpcheckTests::test_is_inside_opcheck_mode, test/test_custom_ops.py::TestGenerateOpcheckTests::test_opcheck, test/test_custom_ops.py::TestGenerateOpcheckTests::test_opcheck_bad_op, test/test_custom_ops.py::TestGenerateOpcheckTests::test_opcheck_customopdef, test/test_custom_ops.py::TestGenerateOpcheckTests::test_opcheck_does_not_require_extra_deps, test/test_custom_ops.py::TestTypeConversion::test_mixed_types, test/test_custom_ops.py::TestTypeConversion::test_optional, test/test_custom_ops.py::TestTypeConversion::test_simple_tuple, test/test_custom_ops.py::TestTypeConversion::test_supported_types, test/test_custom_ops.py::TestCustomOpTestingCPU::test_aot_autograd_check_degenerate_cases_check_gradients_False_dynamic_False_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_aot_autograd_check_degenerate_cases_check_gradients_False_dynamic_True_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_aot_autograd_check_degenerate_cases_check_gradients_auto_dynamic_False_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_aot_autograd_check_degenerate_cases_check_gradients_auto_dynamic_True_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_assert_raises_regex_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registered_at_backend_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registration_check_autograd_kernel_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registration_check_compositeimplicitautograd_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registration_check_incorrect_composite_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registration_check_incorrect_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_global_state_mutation_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_incorrect_abstract_impl_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_incorrect_schema_mutation_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_incorrect_schema_view_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_missing_abstract_impl_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_missing_functionalization_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_fails_basic_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyCatCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyCubeCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyMulCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyMulScalarCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyNMSCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyNonzeroCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpySortCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpySplitCopyCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyTakeCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyViewCopyCustomOp_cpu_float32 2024-08-20T22:05:50.9286627Z 2024-08-20T22:05:53.3230965Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:05:53.3803119Z Running test_ao_sparsity 1/1 ... [2024-08-20 22:05:53.379874] 2024-08-20T22:05:53.3804197Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:05:53.3807744Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_ao_sparsity.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:05:53.380247] 2024-08-20T22:07:00.0705560Z 2024-08-20T22:07:00.0707796Z test_ao_sparsity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ao_sparsity_1.1_c1d7476a9e2cf8df_.log 2024-08-20T22:07:00.0746395Z Running 88 items in this shard: test/test_ao_sparsity.py::TestQuantizedSparseKernels::test_sparse_qlinear, test/test_ao_sparsity.py::TestQuantizedSparseLayers::test_sparse_qlinear, test/test_ao_sparsity.py::TestQuantizedSparseLayers::test_sparse_qlinear_serdes, test/test_ao_sparsity.py::TestFakeSparsity::test_jit_trace, test/test_ao_sparsity.py::TestFakeSparsity::test_masking_logic, test/test_ao_sparsity.py::TestFakeSparsity::test_state_dict_preserved, test/test_ao_sparsity.py::TestFakeSparsity::test_weights_parametrized, test/test_ao_sparsity.py::TestCubicScheduler::test_constructor, test/test_ao_sparsity.py::TestCubicScheduler::test_step, test/test_ao_sparsity.py::TestScheduler::test_constructor, test/test_ao_sparsity.py::TestScheduler::test_lambda_scheduler, test/test_ao_sparsity.py::TestScheduler::test_order_of_steps, test/test_ao_sparsity.py::TestScheduler::test_step, test/test_ao_sparsity.py::TestBaseSparsifier::test_constructor, test/test_ao_sparsity.py::TestBaseSparsifier::test_convert, test/test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash, test/test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash_with_params1, test/test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash_with_params2, test/test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash_with_params3, test/test_ao_sparsity.py::TestBaseSparsifier::test_prepare_config, test/test_ao_sparsity.py::TestBaseSparsifier::test_state_dict, test/test_ao_sparsity.py::TestBaseSparsifier::test_step, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_constructor, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_mask_squash, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_prepare, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_sparsity_levels, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_step, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_constructor, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_mask_squash, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_prepare, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_sparsity_levels, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_step, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_step_2_of_4, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_complex_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_constructor, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prepare_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prepare_linear, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_activation_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_bias_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_padding_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_pool_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_activation_linear, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_bias_linear, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_linear, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_layernorm_linear_multiple_layer, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_layernorm_linear_single_layer, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_linear_multiple_layer, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_linear_single_layer, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_step_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_step_linear, test/test_ao_sparsity.py::TestFPGMPruner::test_compute_distance, test/test_ao_sparsity.py::TestFPGMPruner::test_update_mask, test/test_ao_sparsity.py::TestSaliencyPruner::test_lstm_saliency_pruner_update_mask, test/test_ao_sparsity.py::TestSaliencyPruner::test_saliency_pruner_update_mask, test/test_ao_sparsity.py::TestComposability::test_convert_without_squash_mask, test/test_ao_sparsity.py::TestComposability::test_fusion_before_s_prep, test/test_ao_sparsity.py::TestComposability::test_q_prep_before_s_prep, test/test_ao_sparsity.py::TestComposability::test_qat_prep_before_s_prep, test/test_ao_sparsity.py::TestComposability::test_s_prep_before_fusion, test/test_ao_sparsity.py::TestComposability::test_s_prep_before_q_prep, test/test_ao_sparsity.py::TestComposability::test_s_prep_before_qat_prep, test/test_ao_sparsity.py::TestFxComposability::test_q_prep_fx_before_s_prep, test/test_ao_sparsity.py::TestFxComposability::test_q_prep_fx_s_prep_ref_conv, test/test_ao_sparsity.py::TestFxComposability::test_s_prep_before_q_prep_fx, test/test_ao_sparsity.py::TestFxComposability::test_s_prep_before_qat_prep_fx, test/test_ao_sparsity.py::TestFxComposability::test_s_prep_q_prep_fx_ref, test/test_ao_sparsity.py::TestActivationSparsifier::test_activation_sparsifier, test/test_ao_sparsity.py::TestBaseDataScheduler::test_constructor, test/test_ao_sparsity.py::TestBaseDataScheduler::test_order_of_steps, test/test_ao_sparsity.py::TestBaseDataScheduler::test_state_dict, test/test_ao_sparsity.py::TestBaseDataScheduler::test_step, test/test_ao_sparsity.py::TestBaseDataSparsifier::test_nn_embeddings, test/test_ao_sparsity.py::TestBaseDataSparsifier::test_nn_parameters, test/test_ao_sparsity.py::TestBaseDataSparsifier::test_tensors, test/test_ao_sparsity.py::TestNormDataSparsifiers::test_nn_embeddings, test/test_ao_sparsity.py::TestNormDataSparsifiers::test_nn_parameters, test/test_ao_sparsity.py::TestNormDataSparsifiers::test_tensors, test/test_ao_sparsity.py::TestQuantizationUtils::test_ptq_quantize_first, test/test_ao_sparsity.py::TestQuantizationUtils::test_ptq_sparsify_first, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_fqn_to_module, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_fqn_to_module_fail, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_fqn_to_module_for_tensors, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_get_arg_info_from_tensor_fqn, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_get_arg_info_from_tensor_fqn_fail, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_module_to_fqn, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_module_to_fqn_fail, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_module_to_fqn_root 2024-08-20T22:07:00.0781430Z 2024-08-20T22:07:02.7497517Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:07:02.8229386Z Running functorch/test_eager_transforms 1/1 ... [2024-08-20 22:07:02.822490] 2024-08-20T22:07:02.8230551Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:07:02.8234942Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'functorch/test_eager_transforms.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:07:02.822814] 2024-08-20T22:07:50.6647541Z 2024-08-20T22:07:50.6649608Z test_linalg 2/4 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_2.4_ed85b970e7b50f99_.log 2024-08-20T22:07:50.6874206Z Running 271 items in this shard: test/test_linalg.py::TestLinalgCPU::test_1_sized_with_0_strided_cpu_float32, test/test_linalg.py::TestLinalgCPU::test__convert_weight_to_int4pack_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_32_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, 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test/test_linalg.py::TestLinalgCPU::test_slogdet_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_slogdet_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_solve_batched_broadcasting_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_solve_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_solve_removed_error_cpu, test/test_linalg.py::TestLinalgCPU::test_strided_mm_bmm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_tensordot_cpu, test/test_linalg.py::TestLinalgCPU::test_tensorinv_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_tensorinv_empty_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_tensorinv_empty_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_tensorinv_empty_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_tensorinv_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_tensorinv_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_tensorinv_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_many_batches_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_many_batches_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_large_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_out_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_validator_tunableop_rocm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_vdot_vs_numpy_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_vector_norm_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_vector_norm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_vector_norm_dim_tuple_arg_cpu, test/test_linalg.py::TestLinalgCPU::test_vector_norm_extreme_values_cpu 2024-08-20T22:07:50.7096867Z 2024-08-20T22:07:53.0317671Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:07:53.0884662Z Running test_optim 1/1 ... [2024-08-20 22:07:53.088093] 2024-08-20T22:07:53.0885234Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:07:53.0888671Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_optim.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:07:53.088480] 2024-08-20T22:11:15.5782815Z 2024-08-20T22:11:15.5785401Z functorch/test_eager_transforms 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_eager_transforms_1.1_3f2364851af4e1b7_.log 2024-08-20T22:11:15.6147183Z Running 354 items in this shard: test/functorch/test_eager_transforms.py::TestSliceArgnums::test_argnums_reorders, test/functorch/test_eager_transforms.py::TestSliceArgnums::test_duplicate_argnums, test/functorch/test_eager_transforms.py::TestSliceArgnums::test_flat_args_with_negative_int_argnum, test/functorch/test_eager_transforms.py::TestSliceArgnums::test_flat_args_with_positive_int_argnum, test/functorch/test_eager_transforms.py::TestSliceArgnums::test_flat_args_with_tuple_argnum, test/functorch/test_eager_transforms.py::TestSliceArgnums::test_invalid_argnum_type, test/functorch/test_eager_transforms.py::TestSliceArgnums::test_not_enough_argnums, test/functorch/test_eager_transforms.py::TestSliceArgnums::test_out_of_bounds_argnum_values, test/functorch/test_eager_transforms.py::TestSliceArgnums::test_pytree_args, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_buffer_tying, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_combine_state_for_ensemble_error, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_combine_state_for_ensemble_smoke, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_correctness_mnist_mechanism_functional_call, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_correctness_mnist_mechanism_make_functional, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_disable_autograd_tracking_disable_autograd_tracking_False, test/functorch/test_eager_transforms.py::TestMakeFunctional::test_disable_autograd_tracking_disable_autograd_tracking_True, 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test/functorch/test_eager_transforms.py::TestHessianCPU::test_jacfwd_different_levels_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_autograd_function_no_setup_context_transform_functionalize_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_autograd_function_no_setup_context_transform_grad_and_value_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_autograd_function_no_setup_context_transform_grad_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_autograd_function_no_setup_context_transform_hessian_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_autograd_function_no_setup_context_transform_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_autograd_function_no_setup_context_transform_jacrev_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_autograd_function_no_setup_context_transform_vmap_cpu, 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test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_deprecation_transforms_transform_grad_and_value_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_deprecation_transforms_transform_grad_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_deprecation_transforms_transform_hessian_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_deprecation_transforms_transform_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_deprecation_transforms_transform_jacrev_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_deprecation_vmap_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_grad_grad_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_grad_vjp_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_grad_vmap_cpu, 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test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_transforms_dont_support_saved_tensor_hooks_transform_hessian_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_transforms_dont_support_saved_tensor_hooks_transform_jacrev_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_vjp_doesnt_support_saved_tensor_hooks_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_vjp_grad_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_vjp_vjp_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_vjp_vmap_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_vmap_grad_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_vmap_vjp_cpu, test/functorch/test_eager_transforms.py::TestComposabilityCPU::test_vmap_vmap_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_ensemble_regression_mechanism_functional_call_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_ensemble_regression_mechanism_make_functional_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_find_learning_rate_ensembling_AlphaDropout_mechanism_functional_call_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_find_learning_rate_ensembling_AlphaDropout_mechanism_make_functional_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_find_learning_rate_ensembling_Dropout_mechanism_functional_call_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_find_learning_rate_ensembling_Dropout_mechanism_make_functional_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_find_learning_rate_ensembling_FeatureAlphaDropout_mechanism_functional_call_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_find_learning_rate_ensembling_FeatureAlphaDropout_mechanism_make_functional_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_lennard_jones_batched_jac_jac_jacfwd_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_lennard_jones_batched_jac_jac_jacrev_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_maml_omniglot_mechanism_functional_call_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_maml_omniglot_mechanism_make_functional_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_maml_regression_mechanism_functional_call_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_maml_regression_mechanism_make_functional_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_resnet18_per_sample_grads_mechanism_functional_call_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_resnet18_per_sample_grads_mechanism_make_functional_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_update_batch_norm_mechanism_functional_call_originally_track_running_stats_False_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_update_batch_norm_mechanism_functional_call_originally_track_running_stats_True_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_update_batch_norm_mechanism_make_functional_originally_track_running_stats_False_cpu, test/functorch/test_eager_transforms.py::TestExamplesCorrectnessCPU::test_update_batch_norm_mechanism_make_functional_originally_track_running_stats_True_cpu, test/functorch/test_eager_transforms.py::TestHigherOrderOperatorInteractionCPU::test_basic_sum_cpu, test/functorch/test_eager_transforms.py::TestHigherOrderOperatorInteractionCPU::test_functional_call_multiple_dicts_cpu, test/functorch/test_eager_transforms.py::TestHigherOrderOperatorInteractionCPU::test_grad_grad_sum_cpu, test/functorch/test_eager_transforms.py::TestHigherOrderOperatorInteractionCPU::test_grad_name_wrapping_cpu, test/functorch/test_eager_transforms.py::TestHigherOrderOperatorInteractionCPU::test_grad_sum_cpu, test/functorch/test_eager_transforms.py::TestHigherOrderOperatorInteractionCPU::test_no_grad_inside_grad_cpu, test/functorch/test_eager_transforms.py::TestHigherOrderOperatorInteractionCPU::test_no_grad_outside_grad_cpu, test/functorch/test_eager_transforms.py::TestHigherOrderOperatorInteractionCPU::test_vmap_grad_sum_cpu, test/functorch/test_eager_transforms.py::TestHigherOrderOperatorInteractionCPU::test_vmap_sum_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_functionalize_fake_tensors_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_functionalize_fx_multi_out_op_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_functionalize_fx_out_op_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_functionalize_fx_reapply_views_simple_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_functionalize_fx_simple_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_functionalize_fx_transpose_simple_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_functionalize_grad_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_functionalize_nonfunctional_output_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_functionalize_opt_tensor_list_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_functionalize_optional_tensorlist1_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_functionalize_optional_tensorlist2_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_inplace_view_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_linear_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_multioutput_inplace_slice_view_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_multioutput_view_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_resize_program_inputs_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_simple_view_cpu, test/functorch/test_eager_transforms.py::TestFunctionalizeCPU::test_vmap_functionalize_jvp_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_False_save_for_jvp_save_tensors_input_mark_dirty_False_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_False_save_for_jvp_save_tensors_input_mark_dirty_True_cpu, 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test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_False_save_for_vjp_save_tensors_input_mark_dirty_True_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_False_save_for_vjp_save_tensors_neither_mark_dirty_False_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_False_save_for_vjp_save_tensors_neither_mark_dirty_True_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_False_save_for_vjp_save_tensors_output_mark_dirty_False_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_False_save_for_vjp_save_tensors_output_mark_dirty_True_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_True_save_for_jvp_save_tensors_input_mark_dirty_False_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_True_save_for_jvp_save_tensors_input_mark_dirty_True_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_True_save_for_jvp_save_tensors_neither_mark_dirty_False_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_True_save_for_jvp_save_tensors_neither_mark_dirty_True_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_True_save_for_jvp_save_tensors_output_mark_dirty_False_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_True_save_for_jvp_save_tensors_output_mark_dirty_True_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_True_save_for_vjp_save_tensors_input_mark_dirty_False_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_True_save_for_vjp_save_tensors_input_mark_dirty_True_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_True_save_for_vjp_save_tensors_neither_mark_dirty_False_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_True_save_for_vjp_save_tensors_neither_mark_dirty_True_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_True_save_for_vjp_save_tensors_output_mark_dirty_False_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_function_returns_input_inner_requires_grad_True_save_for_vjp_save_tensors_output_mark_dirty_True_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_grad_fn_name_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_needs_input_grads_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_once_differentiable_autograd_vjp_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_once_differentiable_grad_vjp_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionCPU::test_set_materialize_grads_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionVmapAPICPU::test_has_vmap_staticmethod_and_has_generate_vmap_rule_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionVmapAPICPU::test_in_dims_multiple_inputs_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionVmapAPICPU::test_in_dims_single_input_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionVmapAPICPU::test_incompatible_out_dims_error_msg_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionVmapAPICPU::test_info_object_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionVmapAPICPU::test_kwarg_only_tensors_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionVmapAPICPU::test_no_vmap_staticmethod_and_no_generate_vmap_rule_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionVmapAPICPU::test_none_returns_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionVmapAPICPU::test_should_have_two_returns_cpu, test/functorch/test_eager_transforms.py::TestAutogradFunctionVmapAPICPU::test_skips_empty_layer_cpu, test/functorch/test_eager_transforms.py::TestHelpersCPU::test_CtxWithSavedTensors_error_if_name_collision_cpu, test/functorch/test_eager_transforms.py::TestHelpersCPU::test_CtxWithSavedTensors_nesting_cpu, test/functorch/test_eager_transforms.py::TestHelpersCPU::test_CtxWithSavedTensors_overrides_saved_tensors_cpu, test/functorch/test_eager_transforms.py::TestHelpersCPU::test_CtxWithSavedTensors_passthrough_cpu, test/functorch/test_eager_transforms.py::TestHelpersCPU::test_reductify_leaf_cpu, test/functorch/test_eager_transforms.py::TestCompileTransformsCPU::test_compile_vmap_hessian_cpu, test/functorch/test_eager_transforms.py::TestCompileTransformsCPU::test_grad_deprecated_api_cpu 2024-08-20T22:11:15.6505093Z 2024-08-20T22:11:18.1746057Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:11:18.2332274Z Running test_xnnpack_integration 1/1 ... [2024-08-20 22:11:18.232763] 2024-08-20T22:11:18.2333366Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:11:18.2336551Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_xnnpack_integration.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:11:18.233147] 2024-08-20T22:11:42.1036880Z 2024-08-20T22:11:42.1039346Z test_xnnpack_integration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_xnnpack_integration_1.1_69c72334c3c97f8d_.log 2024-08-20T22:11:42.1050581Z Running 12 items in this shard: test/test_xnnpack_integration.py::TestXNNPACKOps::test_conv2d, test/test_xnnpack_integration.py::TestXNNPACKOps::test_conv2d_transpose, test/test_xnnpack_integration.py::TestXNNPACKOps::test_linear, test/test_xnnpack_integration.py::TestXNNPACKOps::test_linear_1d_input, test/test_xnnpack_integration.py::TestXNNPACKSerDes::test_combined_model, test/test_xnnpack_integration.py::TestXNNPACKSerDes::test_conv2d, test/test_xnnpack_integration.py::TestXNNPACKSerDes::test_conv2d_transpose, test/test_xnnpack_integration.py::TestXNNPACKSerDes::test_linear, test/test_xnnpack_integration.py::TestXNNPACKRewritePass::test_decomposed_linear, test/test_xnnpack_integration.py::TestXNNPACKRewritePass::test_linear, test/test_xnnpack_integration.py::TestXNNPACKConv1dTransformPass::test_conv1d_basic, test/test_xnnpack_integration.py::TestXNNPACKConv1dTransformPass::test_conv1d_with_relu_fc 2024-08-20T22:11:42.1060230Z 2024-08-20T22:11:44.6397760Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:11:44.6982800Z Running test_itt 1/1 ... [2024-08-20 22:11:44.697796] 2024-08-20T22:11:44.6983780Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:11:44.6987077Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_itt.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:11:44.698164] 2024-08-20T22:11:47.5734518Z 2024-08-20T22:11:47.5736708Z test_itt 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_itt_1.1_9d6d73cdf7e593b2_.log 2024-08-20T22:11:47.5738361Z Running 1 items in this shard: test/test_itt.py::TestItt::test_itt 2024-08-20T22:11:47.5739011Z 2024-08-20T22:11:50.0807378Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:11:50.1391433Z Running test_proxy_tensor 1/1 ... [2024-08-20 22:11:50.138679] 2024-08-20T22:11:50.1392340Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:11:50.1395477Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_proxy_tensor.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:11:50.139072] 2024-08-20T22:13:57.2703884Z 2024-08-20T22:13:57.2705949Z test_linalg 4/4 was successful, full logs can be found in artifacts with path test/test-reports/test_linalg_4.4_a9fb2ed83496b23b_.log 2024-08-20T22:13:57.2925837Z Running 274 items in this shard: test/test_linalg.py::TestLinalgCPU::test_1_sized_with_0_strided_cpu_float64, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_32_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_32_k_64_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_32_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_32_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int4_mm_m_64_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test__int8_mm_m_64_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_0_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_16_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_0_k_32_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_0_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_16_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_17_k_32_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_16_use_transpose_a_True_use_transpose_b_True_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_0_n_32_use_transpose_a_True_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_16_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_16_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_16_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_False_use_transpose_b_False_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_False_use_transpose_b_True_non_contig_type_1_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_0_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_cpu_m_8_k_32_n_32_use_transpose_a_True_use_transpose_b_False_non_contig_type_2_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_16_n_16_use_transpose_a_False_use_transpose_b_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_32_n_16_use_transpose_a_False_use_transpose_b_False_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_32_n_16_use_transpose_a_True_use_transpose_b_True_cpu, test/test_linalg.py::TestLinalgCPU::test__int_mm_k_32_n_32_use_transpose_a_False_use_transpose_b_True_cpu, test/test_linalg.py::TestLinalgCPU::test_addbmm_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addbmm_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_addmm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addmm_relu_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_addmm_relu_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addmv_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_addr_float_and_complex_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_addr_float_and_complex_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_addr_float_and_complex_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_addr_integral_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_addr_integral_cpu_int8, test/test_linalg.py::TestLinalgCPU::test_baddbmm_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_baddbmm_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_baddbmm_input_dtypes_compatibility_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_baddbmm_input_dtypes_compatibility_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_baddbmm_input_dtypes_compatibility_cpu_int32, test/test_linalg.py::TestLinalgCPU::test_blas_alpha_beta_empty_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_blas_nan_out_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_bmm_cpu_bfloat16, test/test_linalg.py::TestLinalgCPU::test_bmm_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_broadcast_fused_matmul_cpu, test/test_linalg.py::TestLinalgCPU::test_cholesky_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_ex_non_pd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_inverse_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_broadcasting_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_broadcasting_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_broadcasting_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_many_batches_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_batched_many_batches_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_cholesky_solve_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_32_k_32_n_48_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int4_mm_m_64_k_64_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test_compile_int8_mm_m_64_k_32_n_64_cpu, test/test_linalg.py::TestLinalgCPU::test_cond_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_cond_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_int32, test/test_linalg.py::TestLinalgCPU::test_corner_cases_of_cublasltmatmul_cpu_int8, test/test_linalg.py::TestLinalgCPU::test_cross_with_and_without_dim_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_cross_with_and_without_dim_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_det_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_det_logdet_slogdet_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_dot_invalid_args_cpu, test/test_linalg.py::TestLinalgCPU::test_dot_vs_numpy_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eig_check_magma_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eig_compare_backends_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eig_compare_backends_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eig_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eig_with_nan_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigh_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigh_lower_uplo_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigh_lower_uplo_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_eigvals_compare_backends_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigvals_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_eigvals_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigvalsh_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_eigvalsh_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_eigvalsh_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_einsum_corner_cases_cpu, test/test_linalg.py::TestLinalgCPU::test_einsum_random_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_einsum_sublist_format_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_35_k_36_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_35_k_40_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_35_k_64_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_36_k_64_cpu, test/test_linalg.py::TestLinalgCPU::test_fp16_mv_transposed_first_argument_arm_cpu_m_64_k_64_cpu, test/test_linalg.py::TestLinalgCPU::test_hipblaslt_corner_cases_rocm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_householder_product_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_householder_product_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_inv_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_inv_ex_info_device_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_inv_ex_singular_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_inv_ex_singular_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_inverse_errors_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_inverse_errors_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_kron_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_kron_empty_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_kron_errors_and_warnings_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_kron_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_large_bmm_backward_cpu, test/test_linalg.py::TestLinalgCPU::test_ldl_factor_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_ldl_solve_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_input_checks_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_lstsq_input_checks_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_cpu_errors_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_family_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_lu_solve_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_analytic_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_boundary_cases_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_perverse_nan_values_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_perverse_nan_values_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_perverse_nan_values_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_matrix_exp_utils_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_broadcasting_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_large_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_linalg_solve_triangular_large_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_lobpcg_basic_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_lower_precision_accumulation_with_ref_path_cpu, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_many_batches_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_many_batches_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_lu_solve_batched_many_batches_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_lu_solve_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_lu_solve_large_matrices_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_lu_solve_large_matrices_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matmul_out_kernel_errors_with_autograd_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_2d_Nd_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_2d_Nd_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_matmul_small_brute_force_3d_Nd_cpu_int64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_atol_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_atol_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_basic_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_matrix_rank_removed_error_cpu, test/test_linalg.py::TestLinalgCPU::test_mm_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_multi_dot_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_multi_dot_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_norm_dtype_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_norm_dtype_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_norm_errors_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_norm_matrix_degenerate_shapes_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_norm_vector_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_norm_vector_degenerate_shapes_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_nuclear_norm_exceptions_old_cpu, test/test_linalg.py::TestLinalgCPU::test_ormqr_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_ormqr_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_int16, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_int32, test/test_linalg.py::TestLinalgCPU::test_outer_cpu_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bfloat16_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_bool_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex128_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_complex64_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float16_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_float16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float32_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_int64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_float64_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int16_int16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int32_int8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_bool, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_complex128, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int64_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_complex64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_float32, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_float64, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_int8_uint8, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_bfloat16, test/test_linalg.py::TestLinalgCPU::test_outer_type_promotion_cpu_uint8_uint8, test/test_linalg.py::TestLinalgCPU::test_pinv_errors_and_warnings_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_pinv_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_qr_batched_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_qr_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_qr_error_cases_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_qr_vs_numpy_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_slogdet_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_slogdet_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_slogdet_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_slogdet_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_solve_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_svd_lowrank_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_symeig_removed_error_cpu, test/test_linalg.py::TestLinalgCPU::test_tensorinv_errors_and_warnings_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_tensorsolve_empty_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_batched_broadcasting_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_cpu_complex64, test/test_linalg.py::TestLinalgCPU::test_triangular_solve_out_errors_and_warnings_cpu_float64, test/test_linalg.py::TestLinalgCPU::test_vdot_vs_numpy_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_vector_norm_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_vector_norm_cpu_float16, test/test_linalg.py::TestLinalgCPU::test_vector_norm_cpu_float32, test/test_linalg.py::TestLinalgCPU::test_vector_norm_reduce_over_1D_vector_cpu_complex128, test/test_linalg.py::TestLinalgCPU::test_vector_norm_reduce_over_1D_vector_cpu_float32 2024-08-20T22:13:57.3122477Z 2024-08-20T22:13:59.1367138Z 2024-08-20T22:13:59.1369582Z test_proxy_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_proxy_tensor_1.1_6d01d2855adfc30b_.log 2024-08-20T22:13:59.4261100Z Running 3583 items in this shard: test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_allclose, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_amp_cache, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_constant_blowup, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_constant_proxy_tensor_mut, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_constant_random, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_constant_unbind, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_decomp_of_capture, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_decomposition_interpreter, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_empty_like_doesnt_burn_in_defaults, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_inplace_metadata, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_isolated_graphmodule, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_make_fx_model_double_param, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_make_fx_model_fwd_bwd, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_make_fx_model_fwd_bwd_wgtupdate, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_make_fx_overloads, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_make_fx_reentrant_dispatch, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_make_fx_simple, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_mode_tracing_factory_function, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_partial_decomp, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_pickle_issue89626, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_pr_86917, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_pre_dispatch_functionalization, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_pre_dispatch_functionalization_view_op, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_pre_dispatch_linear, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_pre_dispatch_mode_stack, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_pre_dispatch_no_grad, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_proxy_tensor, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_proxy_tensor_mode_with_decomp_table_preserves_proxy, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_resnet18_backward_trace, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_scalar_device, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_strides, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_tensor_constants, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_trace_subclasses, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_val_metadata_mutation, test/test_proxy_tensor.py::TestGenericProxyTensorReal::test_varargs, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_allclose, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_amp_cache, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_constant_blowup, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_constant_proxy_tensor_mut, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_constant_random, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_constant_unbind, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_decomp_of_capture, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_decomposition_interpreter, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_empty_like_doesnt_burn_in_defaults, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_inplace_metadata, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_isolated_graphmodule, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_make_fx_model_double_param, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_make_fx_model_fwd_bwd, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_make_fx_model_fwd_bwd_wgtupdate, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_make_fx_overloads, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_make_fx_reentrant_dispatch, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_make_fx_simple, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_mode_tracing_factory_function, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_partial_decomp, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_pickle_issue89626, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_pr_86917, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_pre_dispatch_functionalization, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_pre_dispatch_functionalization_view_op, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_pre_dispatch_linear, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_pre_dispatch_mode_stack, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_pre_dispatch_no_grad, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_proxy_tensor, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_proxy_tensor_mode_with_decomp_table_preserves_proxy, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_resnet18_backward_trace, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_scalar_device, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_strides, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_tensor_constants, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_trace_subclasses, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_val_metadata_mutation, test/test_proxy_tensor.py::TestGenericProxyTensorFake::test_varargs, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_allclose, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_amp_cache, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_constant_blowup, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_constant_proxy_tensor_mut, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_constant_random, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_constant_unbind, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_decomp_of_capture, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_decomposition_interpreter, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_empty_like_doesnt_burn_in_defaults, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_inplace_metadata, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_isolated_graphmodule, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_make_fx_model_double_param, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_make_fx_model_fwd_bwd, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_make_fx_model_fwd_bwd_wgtupdate, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_make_fx_overloads, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_make_fx_reentrant_dispatch, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_make_fx_simple, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_mode_tracing_factory_function, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_partial_decomp, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_pickle_issue89626, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_pr_86917, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_pre_dispatch_functionalization, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_pre_dispatch_functionalization_view_op, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_pre_dispatch_linear, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_pre_dispatch_mode_stack, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_pre_dispatch_no_grad, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_proxy_tensor, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_proxy_tensor_mode_with_decomp_table_preserves_proxy, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_resnet18_backward_trace, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_scalar_device, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_strides, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_tensor_constants, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_trace_subclasses, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_val_metadata_mutation, test/test_proxy_tensor.py::TestGenericProxyTensorSymbolic::test_varargs, test/test_proxy_tensor.py::TestRealProxyTensor::test_error_on_data_dependent_ops, test/test_proxy_tensor.py::TestFakeProxyTensor::test_alias, test/test_proxy_tensor.py::TestFakeProxyTensor::test_free_fake, test/test_proxy_tensor.py::TestFakeProxyTensor::test_fused_adam, test/test_proxy_tensor.py::TestFakeProxyTensor::test_issue82547, test/test_proxy_tensor.py::TestFakeProxyTensor::test_meta, test/test_proxy_tensor.py::TestFakeProxyTensor::test_use_fake_and_tensor, test/test_proxy_tensor.py::TestSymbolicTracing::test_adv_index_batch, test/test_proxy_tensor.py::TestSymbolicTracing::test_arange_unbacked_output_size, test/test_proxy_tensor.py::TestSymbolicTracing::test_binary_broadcast, test/test_proxy_tensor.py::TestSymbolicTracing::test_boolean_index, test/test_proxy_tensor.py::TestSymbolicTracing::test_broadcast_shapes, test/test_proxy_tensor.py::TestSymbolicTracing::test_cat, test/test_proxy_tensor.py::TestSymbolicTracing::test_constant_specialization, test/test_proxy_tensor.py::TestSymbolicTracing::test_cpu_scalar_cuda, test/test_proxy_tensor.py::TestSymbolicTracing::test_cumsum_unbacked, test/test_proxy_tensor.py::TestSymbolicTracing::test_debug_interpreter, test/test_proxy_tensor.py::TestSymbolicTracing::test_deduped_shape, test/test_proxy_tensor.py::TestSymbolicTracing::test_dynamic_pointwise_scalar, test/test_proxy_tensor.py::TestSymbolicTracing::test_elementwise_meta_with_sym_numbers, test/test_proxy_tensor.py::TestSymbolicTracing::test_expand, test/test_proxy_tensor.py::TestSymbolicTracing::test_fake_tensor_as_size, test/test_proxy_tensor.py::TestSymbolicTracing::test_guard_lowerbound_range_refinement, test/test_proxy_tensor.py::TestSymbolicTracing::test_guard_lowerbound_range_refinement_multivariate, test/test_proxy_tensor.py::TestSymbolicTracing::test_guard_upperbound_range_refinement, test/test_proxy_tensor.py::TestSymbolicTracing::test_guard_upperbound_range_refinement_multivariate, test/test_proxy_tensor.py::TestSymbolicTracing::test_guards_equal, test/test_proxy_tensor.py::TestSymbolicTracing::test_int_input, test/test_proxy_tensor.py::TestSymbolicTracing::test_invalidate_nonzero, test/test_proxy_tensor.py::TestSymbolicTracing::test_invalidate_nonzero_propagate_real_tensors, test/test_proxy_tensor.py::TestSymbolicTracing::test_item, test/test_proxy_tensor.py::TestSymbolicTracing::test_item_to_constructor, test/test_proxy_tensor.py::TestSymbolicTracing::test_make_fx_with_custom_tracer_preserving_nn_module_stack, test/test_proxy_tensor.py::TestSymbolicTracing::test_mega_guard, test/test_proxy_tensor.py::TestSymbolicTracing::test_metadata, test/test_proxy_tensor.py::TestSymbolicTracing::test_metadata_fresh, test/test_proxy_tensor.py::TestSymbolicTracing::test_mod_gcd_unbacked, test/test_proxy_tensor.py::TestSymbolicTracing::test_multiply_shape, test/test_proxy_tensor.py::TestSymbolicTracing::test_neg_shape, test/test_proxy_tensor.py::TestSymbolicTracing::test_new_empty, test/test_proxy_tensor.py::TestSymbolicTracing::test_non_deduped_shape, test/test_proxy_tensor.py::TestSymbolicTracing::test_non_symint_size_spec, test/test_proxy_tensor.py::TestSymbolicTracing::test_nonidentity_transitive_guards, test/test_proxy_tensor.py::TestSymbolicTracing::test_reflect_r_over_x, test/test_proxy_tensor.py::TestSymbolicTracing::test_repeat_interleave, test/test_proxy_tensor.py::TestSymbolicTracing::test_repeat_interleave_unbacked_output_size, test/test_proxy_tensor.py::TestSymbolicTracing::test_reshape_divisibility_unbacked, test/test_proxy_tensor.py::TestSymbolicTracing::test_resize_from_zero, test/test_proxy_tensor.py::TestSymbolicTracing::test_return_symint, test/test_proxy_tensor.py::TestSymbolicTracing::test_rmethod, test/test_proxy_tensor.py::TestSymbolicTracing::test_setitem_symint, test/test_proxy_tensor.py::TestSymbolicTracing::test_size_with_tensor, test/test_proxy_tensor.py::TestSymbolicTracing::test_split_unbacked_sizes, test/test_proxy_tensor.py::TestSymbolicTracing::test_sqrt_size, test/test_proxy_tensor.py::TestSymbolicTracing::test_sym_storage_offset, test/test_proxy_tensor.py::TestSymbolicTracing::test_symbolic_repeat_interleave, test/test_proxy_tensor.py::TestSymbolicTracing::test_symint_to_tensor, test/test_proxy_tensor.py::TestSymbolicTracing::test_tensor_symfloat, test/test_proxy_tensor.py::TestSymbolicTracing::test_unary, test/test_proxy_tensor.py::TestSymbolicTracing::test_unbacked_batch_resnet, test/test_proxy_tensor.py::TestSymbolicTracing::test_unbacked_slice, test/test_proxy_tensor.py::TestSymbolicTracing::test_unbacked_unification, test/test_proxy_tensor.py::TestSymbolicTracing::test_unbacked_unify_dependency_violation, test/test_proxy_tensor.py::TestSymbolicTracing::test_unbacked_unify_guard, test/test_proxy_tensor.py::TestSymbolicTracing::test_unbacked_unify_guard_transitivity, test/test_proxy_tensor.py::TestSymbolicTracing::test_view_divisibility_unbacked, test/test_proxy_tensor.py::TestSymbolicTracing::test_view_divisibility_unbacked_relatively_prime, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_H_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_NumpyCatCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_NumpyCubeCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_NumpyMulCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_NumpyMulScalarCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_NumpyNMSCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_NumpyNonzeroCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_NumpySortCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_NumpySplitCopyCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_NumpyTakeCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_NumpyViewCopyCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_T_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive___getitem___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive___radd___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive___rdiv___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive___rmatmul___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive___rmod___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive___rmul___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive___rpow___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive___rsub___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive__batch_norm_with_update_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive__chunk_cat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive__native_batch_norm_legit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive__segment_reduce_lengths_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive__segment_reduce_offsets_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive__softmax_backward_data_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive__unsafe_masked_index_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive__unsafe_masked_index_put_accumulate_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive__upsample_bilinear2d_aa_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_abs_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_acos_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_acosh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_addbmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_addcdiv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_addcmul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_addmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_addmm_decomposed_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_addmv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_addr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_alias_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_all_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_allclose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_aminmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_angle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_any_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_arange_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_argmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_argmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_argsort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_argwhere_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_as_strided_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_as_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_as_strided_partial_views_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_as_strided_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_asin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_asinh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_atan2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_atan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_atanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_atleast_1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_atleast_2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_atleast_3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_baddbmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_bernoulli_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_bfloat16_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_block_diag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_bmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_bool_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_broadcast_shapes_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_broadcast_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_broadcast_to_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_bucketize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_byte_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cartesian_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cauchy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cdist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cdouble_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_ceil_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cfloat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_chalf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_char_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cholesky_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cholesky_inverse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cholesky_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_chunk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_clamp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_clamp_max_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_clamp_min_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_clone_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_column_stack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_combinations_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_complex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cond_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_conj_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_conj_physical_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_constant_pad_nd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_contiguous_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_copysign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_corrcoef_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cos_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cosh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_count_nonzero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cov_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cross_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cummax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cummin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cumprod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cumsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_cumulative_trapezoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_deg2rad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_diag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_diag_embed_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_diagflat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_diagonal_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_diagonal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_diagonal_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_diff_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_digamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_dist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_div_floor_rounding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_div_no_rounding_mode_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_div_trunc_rounding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_dot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_double_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_dsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_dstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_einsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_empty_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_empty_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_empty_permuted_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_empty_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_eq_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_equal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_erf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_erfc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_erfinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_exp2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_exp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_expand_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_expand_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_expand_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_expm1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_exponential_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_eye_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_fft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_fft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_fftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_fftshift_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_hfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_hfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_hfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_ifft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_ifft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_ifftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_ifftshift_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_ihfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_ihfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_ihfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_irfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_irfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_irfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_rfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_rfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fft_rfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_flatten_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_flex_attention_backward_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_flex_attention_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_flip_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fliplr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_flipud_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_float_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_float_power_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_floor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_floor_divide_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_fmod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_frac_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_frexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_full_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_full_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_gather_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_ge_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_geometric_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_geqrf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_gradient_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_grid_sampler_2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_gt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_half_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_heaviside_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_histc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_histogram_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_histogramdd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_hsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_hstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_hypot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_i0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_igamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_igammac_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_index_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_index_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_index_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_index_put_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_index_reduce_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_index_reduce_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_index_reduce_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_index_reduce_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_index_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_inner_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_int_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_isclose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_isfinite_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_isin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_isinf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_isnan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_isneginf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_isposinf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_isreal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_item_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_jiterator_2inputs_2outputs_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_jiterator_4inputs_with_extra_args_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_jiterator_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_jiterator_binary_return_by_ref_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_jiterator_unary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_kron_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_kthvalue_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_ldexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_le_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_lerp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_lgamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_cholesky_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_cholesky_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_cond_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_cross_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_det_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_det_singular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_diagonal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_eig_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_eigh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_eigvals_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_eigvalsh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_householder_product_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_inv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_inv_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_ldl_factor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_ldl_factor_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_ldl_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_lstsq_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_lstsq_grad_oriented_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_lu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_lu_factor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_lu_factor_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_lu_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_matrix_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_matrix_power_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_matrix_rank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_matrix_rank_hermitian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_multi_dot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_norm_subgradients_at_zero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_pinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_pinv_hermitian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_pinv_singular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_qr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_slogdet_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_solve_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_solve_triangular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_svd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_svdvals_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_tensorinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_tensorsolve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_vander_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_vecdot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linalg_vector_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linspace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_linspace_tensor_overload_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_log10_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_log1p_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_log2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_log_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_log_normal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_log_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_log_softmax_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_logaddexp2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_logaddexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_logcumsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_logdet_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_logical_and_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_logical_not_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_logical_or_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_logical_xor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_logit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_logspace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_logspace_tensor_overload_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_logsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_long_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_lt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_lu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_lu_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_lu_unpack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_mH_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_mT_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_map_nested_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_map_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_map_triple_nested_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_argmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_argmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_cumprod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_cumsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_log_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_logaddexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_logsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_median_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_normalize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_softmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_std_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_masked_var_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_matmul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_matrix_exp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_max_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_max_pool2d_with_indices_backward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_max_reduction_no_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_max_reduction_with_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_maximum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_median_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_meshgrid_list_of_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_meshgrid_variadic_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_min_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_min_reduction_no_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_min_reduction_with_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_minimum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_mm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_mode_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_movedim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_msort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_mul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_multinomial_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_mv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nan_to_num_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nanmean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nanmedian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nanquantile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nansum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_narrow_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_narrow_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_native_batch_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_native_dropout_backward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_native_layer_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_ne_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_neg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_new_empty_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_new_empty_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_new_full_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_new_ones_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_new_zeros_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nextafter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_alpha_dropout_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_avg_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_avg_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_avg_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_batch_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_binary_cross_entropy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_celu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_channel_shuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_conv1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_conv2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_conv3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_conv_transpose1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_conv_transpose2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_conv_transpose3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_cosine_embedding_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_cosine_similarity_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_cross_entropy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_ctc_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_dropout2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_dropout3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_dropout_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_elu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_embedding_bag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_embedding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_fractional_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_fractional_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_gaussian_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_gelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_glu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_grid_sample_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_group_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_hardshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_hardsigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_hardswish_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_hardtanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_hinge_embedding_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_huber_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_instance_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_interpolate_area_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_interpolate_bicubic_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_interpolate_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_interpolate_linear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_interpolate_nearest_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_interpolate_trilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_kl_div_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_l1_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_layer_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_leaky_relu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_linear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_local_response_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_logsigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_margin_ranking_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_max_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_max_unpool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_max_unpool1d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_max_unpool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_max_unpool2d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_max_unpool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_max_unpool3d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_mish_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_mse_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_multi_head_attention_forward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_multi_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_multilabel_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_normalize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_pad_circular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_pad_constant_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_pad_reflect_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_pad_replicate_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_pad_replicate_negative_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_pairwise_distance_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_pdist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_pixel_shuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_pixel_unshuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_poisson_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_prelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_relu6_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_relu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_rms_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_rrelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_selu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_silu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_smooth_l1_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_soft_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_softmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_softmin_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_softplus_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_softshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_softsign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_tanhshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_threshold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_triplet_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_unfold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_upsample_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nn_functional_upsample_nearest_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nonzero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_nonzero_static_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_norm_fro_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_norm_inf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_norm_nuc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_normal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_normal_in_place_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_normal_number_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_ones_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_ones_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_ormqr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_outer_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_pca_lowrank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_permute_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_pinverse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_polar_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_polygamma_polygamma_n_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_polygamma_polygamma_n_1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_polygamma_polygamma_n_2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_polygamma_polygamma_n_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_polygamma_polygamma_n_4_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_positive_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_pow_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_put_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_qr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_quantile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_rad2deg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_rand_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_randint_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_randint_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_randn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_randn_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_ravel_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_real_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_reciprocal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_remainder_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_renorm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_repeat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_repeat_interleave_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_reshape_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_reshape_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_resize__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_resize_as__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_resolve_conj_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_resolve_neg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_roll_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_rot90_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_round_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_round_decimals_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_round_decimals_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_round_decimals_neg_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_rsqrt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_rsub_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_scalar_tensor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_scatter_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_scatter_reduce_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_scatter_reduce_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_scatter_reduce_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_scatter_reduce_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_scatter_reduce_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_searchsorted_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_select_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_sgn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_short_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_sigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_sign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_signal_windows_bartlett_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_signal_windows_blackman_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_signal_windows_cosine_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_signal_windows_exponential_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_signal_windows_gaussian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_signal_windows_general_cosine_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_signal_windows_general_hamming_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_signal_windows_hamming_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_signal_windows_hann_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_signal_windows_kaiser_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_signal_windows_nuttall_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_signbit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_sin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_sinc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_sinh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_slice_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_slice_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_softmax_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_sort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_sparse_mm_reduce_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_sparse_sampled_addmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_airy_ai_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_bessel_j0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_bessel_j1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_bessel_y0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_bessel_y1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_chebyshev_polynomial_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_chebyshev_polynomial_u_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_chebyshev_polynomial_v_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_chebyshev_polynomial_w_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_entr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_erfcx_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_hermite_polynomial_h_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_hermite_polynomial_he_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_i0e_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_i1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_i1e_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_laguerre_polynomial_l_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_legendre_polynomial_p_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_log_ndtr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_modified_bessel_i0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_modified_bessel_i1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_modified_bessel_k0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_modified_bessel_k1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_ndtr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_ndtri_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_scaled_modified_bessel_k0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_scaled_modified_bessel_k1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_spherical_bessel_j0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_xlog1py_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_special_zeta_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_split_list_args_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_split_with_sizes_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_split_with_sizes_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_sqrt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_square_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_squeeze_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_squeeze_multiple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_stack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_std_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_std_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_std_mean_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_std_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_stft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_sub_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_sum_to_size_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_svd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_svd_lowrank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_t_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_take_along_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_take_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_tan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_tanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_tensor_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_tensordot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_tile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_to_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_to_sparse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_topk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_trace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_transpose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_trapezoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_trapz_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_triangular_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_tril_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_triu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_true_divide_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_trunc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_unbind_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_unflatten_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_unfold_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_unfold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_uniform_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_unique_consecutive_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_unique_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_unsafe_chunk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_unsafe_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_unsqueeze_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_unsqueeze_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_var_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_var_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_var_mean_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_var_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_vdot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_view_as_complex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_view_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_view_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_view_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_vsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_vstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_where_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_while_loop_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_xlogy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_zero__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_zeros_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_exhaustive_zeros_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_H_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_NumpyCatCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_NumpyCubeCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_NumpyMulCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_NumpyMulScalarCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_NumpyNMSCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_NumpyNonzeroCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_NumpySortCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_NumpySplitCopyCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_NumpyTakeCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_NumpyViewCopyCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_T_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive___getitem___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive___radd___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive___rdiv___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive___rmatmul___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive___rmod___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive___rmul___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive___rpow___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive___rsub___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive__batch_norm_with_update_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive__chunk_cat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive__native_batch_norm_legit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive__segment_reduce_lengths_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive__segment_reduce_offsets_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive__softmax_backward_data_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive__unsafe_masked_index_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive__unsafe_masked_index_put_accumulate_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive__upsample_bilinear2d_aa_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_abs_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_acos_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_acosh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_addbmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_addcdiv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_addcmul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_addmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_addmm_decomposed_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_addmv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_addr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_alias_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_all_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_allclose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_aminmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_angle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_any_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_arange_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_argmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_argmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_argsort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_argwhere_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_as_strided_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_as_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_as_strided_partial_views_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_as_strided_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_asin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_asinh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_atan2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_atan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_atanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_atleast_1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_atleast_2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_atleast_3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_baddbmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_bernoulli_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_bfloat16_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_block_diag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_bmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_bool_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_broadcast_shapes_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_broadcast_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_broadcast_to_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_bucketize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_byte_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cartesian_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cauchy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cdist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cdouble_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_ceil_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cfloat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_chalf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_char_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cholesky_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cholesky_inverse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cholesky_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_chunk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_clamp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_clamp_max_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_clamp_min_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_clone_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_column_stack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_combinations_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_complex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cond_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_conj_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_conj_physical_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_constant_pad_nd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_contiguous_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_copysign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_corrcoef_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cos_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cosh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_count_nonzero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cov_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cross_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cummax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cummin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cumprod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cumsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_cumulative_trapezoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_deg2rad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_diag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_diag_embed_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_diagflat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_diagonal_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_diagonal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_diagonal_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_diff_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_digamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_dist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_div_floor_rounding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_div_no_rounding_mode_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_div_trunc_rounding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_dot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_double_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_dsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_dstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_einsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_empty_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_empty_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_empty_permuted_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_empty_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_eq_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_equal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_erf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_erfc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_erfinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_exp2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_exp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_expand_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_expand_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_expand_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_expm1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_exponential_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_eye_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_fft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_fft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_fftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_fftshift_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_hfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_hfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_hfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_ifft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_ifft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_ifftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_ifftshift_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_ihfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_ihfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_ihfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_irfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_irfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_irfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_rfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_rfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fft_rfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_flatten_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_flex_attention_backward_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_flex_attention_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_flip_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fliplr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_flipud_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_float_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_float_power_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_floor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_floor_divide_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_fmod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_frac_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_frexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_full_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_full_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_gather_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_ge_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_geometric_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_geqrf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_gradient_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_grid_sampler_2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_gt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_half_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_heaviside_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_histc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_histogram_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_histogramdd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_hsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_hstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_hypot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_i0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_igamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_igammac_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_index_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_index_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_index_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_index_put_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_index_reduce_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_index_reduce_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_index_reduce_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_index_reduce_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_index_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_inner_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_int_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_isclose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_isfinite_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_isin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_isinf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_isnan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_isneginf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_isposinf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_isreal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_item_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_jiterator_2inputs_2outputs_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_jiterator_4inputs_with_extra_args_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_jiterator_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_jiterator_binary_return_by_ref_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_jiterator_unary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_kron_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_kthvalue_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_ldexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_le_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_lerp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_lgamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_cholesky_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_cholesky_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_cond_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_cross_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_det_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_det_singular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_diagonal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_eig_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_eigh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_eigvals_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_eigvalsh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_householder_product_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_inv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_inv_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_ldl_factor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_ldl_factor_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_ldl_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_lstsq_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_lstsq_grad_oriented_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_lu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_lu_factor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_lu_factor_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_lu_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_matrix_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_matrix_power_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_matrix_rank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_matrix_rank_hermitian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_multi_dot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_norm_subgradients_at_zero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_pinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_pinv_hermitian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_pinv_singular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_qr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_slogdet_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_solve_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_solve_triangular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_svd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_svdvals_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_tensorinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_tensorsolve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_vander_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_vecdot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linalg_vector_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linspace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_linspace_tensor_overload_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_log10_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_log1p_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_log2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_log_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_log_normal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_log_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_log_softmax_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_logaddexp2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_logaddexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_logcumsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_logdet_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_logical_and_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_logical_not_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_logical_or_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_logical_xor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_logit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_logspace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_logspace_tensor_overload_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_logsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_long_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_lt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_lu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_lu_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_lu_unpack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_mH_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_mT_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_map_nested_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_map_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_map_triple_nested_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_argmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_argmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_cumprod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_cumsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_log_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_logaddexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_logsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_median_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_normalize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_softmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_std_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_masked_var_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_matmul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_matrix_exp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_max_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_max_pool2d_with_indices_backward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_max_reduction_no_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_max_reduction_with_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_maximum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_median_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_meshgrid_list_of_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_meshgrid_variadic_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_min_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_min_reduction_no_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_min_reduction_with_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_minimum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_mm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_mode_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_movedim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_msort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_mul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_multinomial_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_mv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nan_to_num_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nanmean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nanmedian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nanquantile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nansum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_narrow_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_narrow_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_native_batch_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_native_dropout_backward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_native_layer_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_ne_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_neg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_new_empty_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_new_empty_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_new_full_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_new_ones_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_new_zeros_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nextafter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_alpha_dropout_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_avg_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_avg_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_avg_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_batch_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_binary_cross_entropy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_celu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_channel_shuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_conv1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_conv2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_conv3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_conv_transpose1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_conv_transpose2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_conv_transpose3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_cosine_embedding_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_cosine_similarity_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_cross_entropy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_ctc_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_dropout2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_dropout3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_dropout_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_elu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_embedding_bag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_embedding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_fractional_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_fractional_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_gaussian_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_gelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_glu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_grid_sample_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_group_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_hardshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_hardsigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_hardswish_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_hardtanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_hinge_embedding_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_huber_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_instance_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_interpolate_area_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_interpolate_bicubic_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_interpolate_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_interpolate_linear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_interpolate_nearest_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_interpolate_trilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_kl_div_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_l1_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_layer_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_leaky_relu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_linear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_local_response_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_logsigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_margin_ranking_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_max_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_max_unpool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_max_unpool1d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_max_unpool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_max_unpool2d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_max_unpool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_max_unpool3d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_mish_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_mse_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_multi_head_attention_forward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_multi_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_multilabel_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_normalize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_pad_circular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_pad_constant_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_pad_reflect_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_pad_replicate_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_pad_replicate_negative_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_pairwise_distance_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_pdist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_pixel_shuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_pixel_unshuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_poisson_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_prelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_relu6_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_relu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_rms_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_rrelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_selu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_silu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_smooth_l1_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_soft_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_softmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_softmin_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_softplus_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_softshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_softsign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_tanhshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_threshold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_triplet_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_unfold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_upsample_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nn_functional_upsample_nearest_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nonzero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_nonzero_static_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_norm_fro_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_norm_inf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_norm_nuc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_normal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_normal_in_place_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_normal_number_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_ones_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_ones_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_ormqr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_outer_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_pca_lowrank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_permute_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_pinverse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_polar_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_polygamma_polygamma_n_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_polygamma_polygamma_n_1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_polygamma_polygamma_n_2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_polygamma_polygamma_n_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_polygamma_polygamma_n_4_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_positive_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_pow_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_put_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_qr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_quantile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_rad2deg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_rand_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_randint_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_randint_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_randn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_randn_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_ravel_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_real_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_reciprocal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_remainder_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_renorm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_repeat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_repeat_interleave_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_reshape_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_reshape_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_resize__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_resize_as__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_resolve_conj_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_resolve_neg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_roll_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_rot90_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_round_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_round_decimals_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_round_decimals_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_round_decimals_neg_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_rsqrt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_rsub_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_scalar_tensor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_scatter_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_scatter_reduce_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_scatter_reduce_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_scatter_reduce_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_scatter_reduce_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_scatter_reduce_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_searchsorted_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_select_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_sgn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_short_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_sigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_sign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_signal_windows_bartlett_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_signal_windows_blackman_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_signal_windows_cosine_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_signal_windows_exponential_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_signal_windows_gaussian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_signal_windows_general_cosine_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_signal_windows_general_hamming_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_signal_windows_hamming_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_signal_windows_hann_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_signal_windows_kaiser_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_signal_windows_nuttall_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_signbit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_sin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_sinc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_sinh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_slice_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_slice_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_softmax_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_sort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_sparse_mm_reduce_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_sparse_sampled_addmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_airy_ai_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_bessel_j0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_bessel_j1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_bessel_y0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_bessel_y1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_chebyshev_polynomial_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_chebyshev_polynomial_u_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_chebyshev_polynomial_v_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_chebyshev_polynomial_w_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_entr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_erfcx_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_hermite_polynomial_h_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_hermite_polynomial_he_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_i0e_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_i1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_i1e_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_laguerre_polynomial_l_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_legendre_polynomial_p_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_log_ndtr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_modified_bessel_i0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_modified_bessel_i1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_modified_bessel_k0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_modified_bessel_k1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_ndtr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_ndtri_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_scaled_modified_bessel_k0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_scaled_modified_bessel_k1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_spherical_bessel_j0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_xlog1py_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_special_zeta_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_split_list_args_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_split_with_sizes_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_split_with_sizes_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_sqrt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_square_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_squeeze_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_squeeze_multiple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_stack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_std_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_std_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_std_mean_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_std_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_stft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_sub_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_sum_to_size_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_svd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_svd_lowrank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_t_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_take_along_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_take_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_tan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_tanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_tensor_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_tensordot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_tile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_to_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_to_sparse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_topk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_trace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_transpose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_trapezoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_trapz_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_triangular_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_tril_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_triu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_true_divide_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_trunc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_unbind_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_unflatten_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_unfold_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_unfold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_uniform_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_unique_consecutive_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_unique_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_unsafe_chunk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_unsafe_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_unsqueeze_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_unsqueeze_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_var_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_var_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_var_mean_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_var_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_vdot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_view_as_complex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_view_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_view_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_view_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_vsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_vstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_where_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_while_loop_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_xlogy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_zero__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_zeros_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_fake_exhaustive_zeros_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_H_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_NumpyCatCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_NumpyCubeCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_NumpyMulCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_NumpyMulScalarCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_NumpyNMSCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_NumpyNonzeroCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_NumpySortCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_NumpySplitCopyCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_NumpyTakeCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_NumpyViewCopyCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_T_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive___getitem___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive___radd___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive___rdiv___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive___rmatmul___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive___rmod___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive___rmul___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive___rpow___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive___rsub___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive__batch_norm_with_update_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive__chunk_cat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive__native_batch_norm_legit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive__segment_reduce_lengths_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive__segment_reduce_offsets_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive__softmax_backward_data_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive__unsafe_masked_index_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive__unsafe_masked_index_put_accumulate_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive__upsample_bilinear2d_aa_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_abs_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_acos_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_acosh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_addbmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_addcdiv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_addcmul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_addmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_addmm_decomposed_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_addmv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_addr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_alias_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_all_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_allclose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_aminmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_angle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_any_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_arange_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_argmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_argmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_argsort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_argwhere_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_as_strided_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_as_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_as_strided_partial_views_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_as_strided_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_asin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_asinh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_atan2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_atan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_atanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_atleast_1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_atleast_2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_atleast_3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_baddbmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_bernoulli_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_bfloat16_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_block_diag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_bmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_bool_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_broadcast_shapes_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_broadcast_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_broadcast_to_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_bucketize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_byte_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cartesian_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cauchy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cdist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cdouble_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_ceil_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cfloat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_chalf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_char_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cholesky_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cholesky_inverse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cholesky_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_chunk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_clamp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_clamp_max_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_clamp_min_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_clone_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_column_stack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_combinations_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_complex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cond_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_conj_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_conj_physical_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_constant_pad_nd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_contiguous_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_copysign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_corrcoef_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cos_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cosh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_count_nonzero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cov_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cross_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cummax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cummin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cumprod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cumsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_cumulative_trapezoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_deg2rad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_diag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_diag_embed_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_diagflat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_diagonal_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_diagonal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_diagonal_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_diff_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_digamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_dist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_div_floor_rounding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_div_no_rounding_mode_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_div_trunc_rounding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_dot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_double_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_dsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_dstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_einsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_empty_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_empty_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_empty_permuted_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_empty_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_eq_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_equal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_erf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_erfc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_erfinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_exp2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_exp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_expand_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_expand_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_expand_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_expm1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_exponential_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_eye_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_fft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_fft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_fftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_fftshift_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_hfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_hfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_hfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_ifft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_ifft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_ifftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_ifftshift_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_ihfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_ihfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_ihfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_irfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_irfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_irfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_rfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_rfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fft_rfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_flatten_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_flex_attention_backward_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_flex_attention_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_flip_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fliplr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_flipud_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_float_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_float_power_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_floor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_floor_divide_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_fmod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_frac_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_frexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_full_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_full_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_gather_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_ge_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_geometric_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_geqrf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_gradient_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_grid_sampler_2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_gt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_half_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_heaviside_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_histc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_histogram_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_histogramdd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_hsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_hstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_hypot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_i0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_igamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_igammac_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_index_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_index_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_index_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_index_put_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_index_reduce_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_index_reduce_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_index_reduce_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_index_reduce_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_index_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inner_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_H_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_NumpyCatCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_NumpyCubeCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_NumpyMulCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_NumpyMulScalarCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_NumpyNMSCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_NumpyNonzeroCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_NumpySortCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_NumpySplitCopyCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_NumpyTakeCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_NumpyViewCopyCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_T_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace___getitem___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace___radd___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace___rdiv___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace___rmatmul___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace___rmod___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace___rmul___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace___rpow___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace___rsub___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace__batch_norm_with_update_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace__chunk_cat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace__native_batch_norm_legit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace__segment_reduce_lengths_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace__segment_reduce_offsets_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace__softmax_backward_data_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace__unsafe_masked_index_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace__unsafe_masked_index_put_accumulate_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace__upsample_bilinear2d_aa_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_abs_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_acos_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_acosh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_addbmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_addcdiv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_addcmul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_addmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_addmm_decomposed_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_addmv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_addr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_alias_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_all_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_allclose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_aminmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_angle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_any_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_arange_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_argmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_argmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_argsort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_argwhere_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_as_strided_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_as_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_as_strided_partial_views_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_as_strided_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_asin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_asinh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_atan2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_atan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_atanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_atleast_1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_atleast_2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_atleast_3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_baddbmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_bernoulli_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_bfloat16_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_block_diag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_bmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_bool_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_broadcast_shapes_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_broadcast_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_broadcast_to_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_bucketize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_byte_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cartesian_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cauchy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cdist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cdouble_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_ceil_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cfloat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_chalf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_char_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cholesky_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cholesky_inverse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cholesky_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_chunk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_clamp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_clamp_max_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_clamp_min_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_clone_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_column_stack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_combinations_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_complex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_conj_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_conj_physical_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_constant_pad_nd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_contiguous_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_copysign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_corrcoef_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cos_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cosh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_count_nonzero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cov_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cross_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cummax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cummin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cumprod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cumsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_cumulative_trapezoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_deg2rad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_diag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_diag_embed_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_diagflat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_diagonal_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_diagonal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_diagonal_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_diff_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_digamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_dist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_div_floor_rounding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_div_no_rounding_mode_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_div_trunc_rounding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_dot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_double_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_dsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_dstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_einsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_empty_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_empty_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_empty_permuted_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_empty_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_eq_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_equal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_erf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_erfc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_erfinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_exp2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_exp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_expand_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_expand_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_expand_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_expm1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_exponential_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_eye_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_fft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_fft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_fftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_fftshift_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_hfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_hfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_hfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_ifft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_ifft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_ifftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_ifftshift_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_ihfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_ihfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_ihfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_irfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_irfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_irfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_rfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_rfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fft_rfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_flatten_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_flip_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fliplr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_flipud_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_float_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_float_power_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_floor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_floor_divide_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_fmod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_frac_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_frexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_full_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_full_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_gather_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_ge_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_geometric_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_geqrf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_gradient_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_grid_sampler_2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_gt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_half_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_heaviside_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_histc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_histogram_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_histogramdd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_hsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_hstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_hypot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_i0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_igamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_igammac_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_index_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_index_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_index_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_index_put_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_index_reduce_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_index_reduce_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_index_reduce_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_index_reduce_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_index_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_inner_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_int_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_isclose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_isfinite_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_isin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_isinf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_isnan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_isneginf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_isposinf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_isreal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_item_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_jiterator_2inputs_2outputs_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_jiterator_4inputs_with_extra_args_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_jiterator_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_jiterator_binary_return_by_ref_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_jiterator_unary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_kron_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_kthvalue_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_ldexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_le_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_lerp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_lgamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_cholesky_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_cholesky_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_cond_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_cross_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_det_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_det_singular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_diagonal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_eig_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_eigh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_eigvals_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_eigvalsh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_householder_product_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_inv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_inv_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_ldl_factor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_ldl_factor_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_ldl_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_lstsq_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_lstsq_grad_oriented_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_lu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_lu_factor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_lu_factor_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_lu_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_matrix_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_matrix_power_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_matrix_rank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_matrix_rank_hermitian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_multi_dot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_norm_subgradients_at_zero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_pinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_pinv_hermitian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_pinv_singular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_qr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_slogdet_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_solve_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_solve_triangular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_svd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_svdvals_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_tensorinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_tensorsolve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_vander_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_vecdot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linalg_vector_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linspace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_linspace_tensor_overload_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_log10_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_log1p_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_log2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_log_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_log_normal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_log_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_log_softmax_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_logaddexp2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_logaddexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_logcumsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_logdet_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_logical_and_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_logical_not_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_logical_or_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_logical_xor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_logit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_logspace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_logspace_tensor_overload_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_logsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_long_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_lt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_lu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_lu_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_lu_unpack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_mH_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_mT_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_argmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_argmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_cumprod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_cumsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_log_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_logaddexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_logsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_median_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_normalize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_softmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_std_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_masked_var_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_matmul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_matrix_exp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_max_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_max_pool2d_with_indices_backward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_max_reduction_no_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_max_reduction_with_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_maximum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_median_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_meshgrid_list_of_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_meshgrid_variadic_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_min_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_min_reduction_no_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_min_reduction_with_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_minimum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_mm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_mode_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_movedim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_msort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_mul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_multinomial_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_mv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nan_to_num_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nanmean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nanmedian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nanquantile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nansum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_narrow_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_narrow_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_native_batch_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_native_dropout_backward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_native_layer_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_ne_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_neg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_new_empty_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_new_empty_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_new_full_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_new_ones_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_new_zeros_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nextafter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_alpha_dropout_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_avg_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_avg_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_avg_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_batch_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_binary_cross_entropy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_celu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_channel_shuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_conv1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_conv2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_conv3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_conv_transpose1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_conv_transpose2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_conv_transpose3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_cosine_embedding_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_cosine_similarity_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_cross_entropy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_ctc_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_dropout2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_dropout3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_dropout_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_elu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_embedding_bag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_embedding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_fractional_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_fractional_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_gaussian_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_gelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_glu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_grid_sample_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_group_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_hardshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_hardsigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_hardswish_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_hardtanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_hinge_embedding_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_huber_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_instance_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_interpolate_area_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_interpolate_bicubic_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_interpolate_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_interpolate_linear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_interpolate_nearest_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_interpolate_trilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_kl_div_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_l1_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_layer_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_leaky_relu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_linear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_local_response_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_logsigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_margin_ranking_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_max_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_max_unpool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_max_unpool1d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_max_unpool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_max_unpool2d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_max_unpool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_max_unpool3d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_mish_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_mse_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_multi_head_attention_forward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_multi_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_multilabel_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_normalize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_pad_circular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_pad_constant_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_pad_reflect_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_pad_replicate_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_pad_replicate_negative_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_pairwise_distance_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_pdist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_pixel_shuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_pixel_unshuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_poisson_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_prelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_relu6_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_relu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_rms_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_rrelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_selu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_silu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_smooth_l1_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_soft_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_softmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_softmin_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_softplus_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_softshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_softsign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_tanhshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_threshold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_triplet_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_unfold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_upsample_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nn_functional_upsample_nearest_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nonzero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_nonzero_static_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_norm_fro_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_norm_inf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_norm_nuc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_normal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_normal_in_place_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_normal_number_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_ones_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_ones_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_ormqr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_outer_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_pca_lowrank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_permute_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_pinverse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_polar_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_polygamma_polygamma_n_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_polygamma_polygamma_n_1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_polygamma_polygamma_n_2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_polygamma_polygamma_n_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_polygamma_polygamma_n_4_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_positive_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_pow_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_put_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_qr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_quantile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_rad2deg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_rand_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_randint_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_randint_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_randn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_randn_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_ravel_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_real_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_reciprocal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_remainder_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_renorm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_repeat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_repeat_interleave_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_reshape_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_reshape_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_resize__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_resize_as__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_resolve_conj_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_resolve_neg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_roll_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_rot90_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_round_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_round_decimals_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_round_decimals_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_round_decimals_neg_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_rsqrt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_rsub_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_scalar_tensor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_scatter_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_scatter_reduce_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_scatter_reduce_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_scatter_reduce_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_scatter_reduce_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_scatter_reduce_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_searchsorted_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_select_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_sgn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_short_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_sigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_sign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_signal_windows_bartlett_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_signal_windows_blackman_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_signal_windows_cosine_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_signal_windows_exponential_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_signal_windows_gaussian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_signal_windows_general_cosine_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_signal_windows_general_hamming_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_signal_windows_hamming_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_signal_windows_hann_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_signal_windows_kaiser_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_signal_windows_nuttall_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_signbit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_sin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_sinc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_sinh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_slice_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_slice_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_softmax_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_sort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_sparse_mm_reduce_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_sparse_sampled_addmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_airy_ai_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_bessel_j0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_bessel_j1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_bessel_y0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_bessel_y1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_chebyshev_polynomial_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_chebyshev_polynomial_u_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_chebyshev_polynomial_v_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_chebyshev_polynomial_w_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_entr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_erfcx_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_hermite_polynomial_h_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_hermite_polynomial_he_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_i0e_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_i1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_i1e_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_laguerre_polynomial_l_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_legendre_polynomial_p_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_log_ndtr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_modified_bessel_i0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_modified_bessel_i1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_modified_bessel_k0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_modified_bessel_k1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_ndtr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_ndtri_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_scaled_modified_bessel_k0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_scaled_modified_bessel_k1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_spherical_bessel_j0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_xlog1py_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_special_zeta_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_split_list_args_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_split_with_sizes_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_split_with_sizes_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_sqrt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_square_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_squeeze_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_squeeze_multiple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_stack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_std_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_std_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_std_mean_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_std_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_stft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_sub_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_sum_to_size_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_svd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_svd_lowrank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_t_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_take_along_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_take_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_tan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_tanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_tensor_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_tensordot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_tile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_to_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_to_sparse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_topk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_trace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_transpose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_trapezoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_trapz_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_triangular_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_tril_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_triu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_true_divide_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_trunc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_unbind_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_unflatten_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_unfold_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_unfold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_uniform_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_unique_consecutive_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_unique_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_unsafe_chunk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_unsafe_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_unsqueeze_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_unsqueeze_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_var_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_var_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_var_mean_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_var_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_vdot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_view_as_complex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_view_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_view_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_view_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_vsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_vstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_where_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_xlogy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_zero__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_zeros_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_inplace_zeros_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_int_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_isclose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_isfinite_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_isin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_isinf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_isnan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_isneginf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_isposinf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_isreal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_item_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_jiterator_2inputs_2outputs_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_jiterator_4inputs_with_extra_args_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_jiterator_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_jiterator_binary_return_by_ref_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_jiterator_unary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_kron_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_kthvalue_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_ldexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_le_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_lerp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_lgamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_cholesky_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_cholesky_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_cond_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_cross_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_det_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_det_singular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_diagonal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_eig_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_eigh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_eigvals_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_eigvalsh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_householder_product_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_inv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_inv_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_ldl_factor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_ldl_factor_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_ldl_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_lstsq_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_lstsq_grad_oriented_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_lu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_lu_factor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_lu_factor_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_lu_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_matrix_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_matrix_power_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_matrix_rank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_matrix_rank_hermitian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_multi_dot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_norm_subgradients_at_zero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_pinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_pinv_hermitian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_pinv_singular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_qr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_slogdet_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_solve_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_solve_triangular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_svd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_svdvals_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_tensorinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_tensorsolve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_vander_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_vecdot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linalg_vector_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linspace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_linspace_tensor_overload_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_log10_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_log1p_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_log2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_log_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_log_normal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_log_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_log_softmax_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_logaddexp2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_logaddexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_logcumsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_logdet_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_logical_and_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_logical_not_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_logical_or_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_logical_xor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_logit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_logspace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_logspace_tensor_overload_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_logsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_long_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_lt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_lu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_lu_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_lu_unpack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_mH_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_mT_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_map_nested_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_map_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_map_triple_nested_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_argmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_argmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_cumprod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_cumsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_log_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_logaddexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_logsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_median_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_normalize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_softmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_std_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_masked_var_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_matmul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_matrix_exp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_max_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_max_pool2d_with_indices_backward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_max_reduction_no_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_max_reduction_with_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_maximum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_median_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_meshgrid_list_of_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_meshgrid_variadic_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_min_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_min_reduction_no_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_min_reduction_with_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_minimum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_mm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_mode_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_movedim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_msort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_mul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_multinomial_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_mv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nan_to_num_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nanmean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nanmedian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nanquantile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nansum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_narrow_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_narrow_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_native_batch_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_native_dropout_backward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_native_layer_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_ne_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_neg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_new_empty_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_new_empty_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_new_full_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_new_ones_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_new_zeros_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nextafter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_alpha_dropout_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_avg_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_avg_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_avg_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_batch_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_binary_cross_entropy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_celu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_channel_shuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_conv1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_conv2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_conv3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_conv_transpose1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_conv_transpose2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_conv_transpose3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_cosine_embedding_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_cosine_similarity_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_cross_entropy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_ctc_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_dropout2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_dropout3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_dropout_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_elu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_embedding_bag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_embedding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_fractional_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_fractional_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_gaussian_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_gelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_glu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_grid_sample_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_group_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_hardshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_hardsigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_hardswish_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_hardtanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_hinge_embedding_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_huber_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_instance_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_interpolate_area_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_interpolate_bicubic_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_interpolate_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_interpolate_linear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_interpolate_nearest_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_interpolate_trilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_kl_div_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_l1_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_layer_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_leaky_relu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_linear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_local_response_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_logsigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_margin_ranking_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_max_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_max_unpool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_max_unpool1d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_max_unpool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_max_unpool2d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_max_unpool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_max_unpool3d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_mish_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_mse_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_multi_head_attention_forward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_multi_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_multilabel_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_normalize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_pad_circular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_pad_constant_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_pad_reflect_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_pad_replicate_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_pad_replicate_negative_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_pairwise_distance_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_pdist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_pixel_shuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_pixel_unshuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_poisson_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_prelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_relu6_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_relu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_rms_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_rrelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_selu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_silu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_smooth_l1_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_soft_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_softmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_softmin_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_softplus_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_softshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_softsign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_tanhshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_threshold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_triplet_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_unfold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_upsample_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nn_functional_upsample_nearest_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nonzero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_nonzero_static_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_norm_fro_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_norm_inf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_norm_nuc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_normal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_normal_in_place_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_normal_number_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_ones_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_ones_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_ormqr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_H_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_NumpyCatCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_NumpyCubeCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_NumpyMulCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_NumpyMulScalarCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_NumpyNMSCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_NumpyNonzeroCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_NumpySortCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_NumpySplitCopyCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_NumpyTakeCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_NumpyViewCopyCustomOp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_T_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out___getitem___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out___radd___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out___rdiv___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out___rmatmul___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out___rmod___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out___rmul___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out___rpow___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out___rsub___cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out__batch_norm_with_update_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out__chunk_cat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out__native_batch_norm_legit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out__segment_reduce_lengths_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out__segment_reduce_offsets_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out__softmax_backward_data_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out__unsafe_masked_index_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out__unsafe_masked_index_put_accumulate_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out__upsample_bilinear2d_aa_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_abs_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_acos_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_acosh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_addbmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_addcdiv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_addcmul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_addmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_addmm_decomposed_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_addmv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_addr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_alias_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_all_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_allclose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_aminmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_angle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_any_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_arange_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_argmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_argmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_argsort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_argwhere_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_as_strided_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_as_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_as_strided_partial_views_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_as_strided_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_asin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_asinh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_atan2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_atan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_atanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_atleast_1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_atleast_2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_atleast_3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_baddbmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_bernoulli_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_bfloat16_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_block_diag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_bmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_bool_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_broadcast_shapes_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_broadcast_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_broadcast_to_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_bucketize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_byte_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cartesian_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cauchy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cdist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cdouble_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_ceil_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cfloat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_chalf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_char_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cholesky_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cholesky_inverse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cholesky_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_chunk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_clamp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_clamp_max_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_clamp_min_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_clone_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_column_stack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_combinations_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_complex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_conj_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_conj_physical_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_constant_pad_nd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_contiguous_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_copysign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_corrcoef_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cos_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cosh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_count_nonzero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cov_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cross_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cummax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cummin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cumprod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cumsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_cumulative_trapezoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_deg2rad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_diag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_diag_embed_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_diagflat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_diagonal_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_diagonal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_diagonal_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_diff_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_digamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_dist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_div_floor_rounding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_div_no_rounding_mode_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_div_trunc_rounding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_dot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_double_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_dsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_dstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_einsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_empty_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_empty_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_empty_permuted_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_empty_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_eq_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_equal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_erf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_erfc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_erfinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_exp2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_exp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_expand_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_expand_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_expand_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_expm1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_exponential_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_eye_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_fft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_fft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_fftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_fftshift_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_hfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_hfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_hfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_ifft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_ifft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_ifftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_ifftshift_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_ihfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_ihfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_ihfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_irfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_irfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_irfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_rfft2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_rfft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fft_rfftn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_flatten_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_flip_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fliplr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_flipud_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_float_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_float_power_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_floor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_floor_divide_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_fmod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_frac_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_frexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_full_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_full_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_gather_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_ge_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_geometric_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_geqrf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_gradient_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_grid_sampler_2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_gt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_half_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_heaviside_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_histc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_histogram_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_histogramdd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_hsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_hstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_hypot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_i0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_igamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_igammac_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_index_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_index_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_index_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_index_put_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_index_reduce_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_index_reduce_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_index_reduce_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_index_reduce_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_index_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_inner_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_int_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_isclose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_isfinite_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_isin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_isinf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_isnan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_isneginf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_isposinf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_isreal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_item_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_jiterator_2inputs_2outputs_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_jiterator_4inputs_with_extra_args_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_jiterator_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_jiterator_binary_return_by_ref_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_jiterator_unary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_kron_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_kthvalue_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_ldexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_le_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_lerp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_lgamma_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_cholesky_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_cholesky_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_cond_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_cross_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_det_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_det_singular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_diagonal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_eig_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_eigh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_eigvals_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_eigvalsh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_householder_product_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_inv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_inv_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_ldl_factor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_ldl_factor_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_ldl_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_lstsq_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_lstsq_grad_oriented_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_lu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_lu_factor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_lu_factor_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_lu_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_matrix_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_matrix_power_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_matrix_rank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_matrix_rank_hermitian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_multi_dot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_norm_subgradients_at_zero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_pinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_pinv_hermitian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_pinv_singular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_qr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_slogdet_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_solve_ex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_solve_triangular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_svd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_svdvals_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_tensorinv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_tensorsolve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_vander_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_vecdot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linalg_vector_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linspace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_linspace_tensor_overload_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_log10_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_log1p_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_log2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_log_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_log_normal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_log_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_log_softmax_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_logaddexp2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_logaddexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_logcumsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_logdet_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_logical_and_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_logical_not_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_logical_or_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_logical_xor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_logit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_logspace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_logspace_tensor_overload_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_logsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_long_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_lt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_lu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_lu_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_lu_unpack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_mH_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_mT_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_argmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_argmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_cumprod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_cumsum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_fill_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_log_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_logaddexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_logsumexp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_median_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_normalize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_softmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_std_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_masked_var_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_matmul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_matrix_exp_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_max_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_max_pool2d_with_indices_backward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_max_reduction_no_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_max_reduction_with_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_maximum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_median_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_meshgrid_list_of_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_meshgrid_variadic_tensors_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_min_binary_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_min_reduction_no_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_min_reduction_with_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_minimum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_mm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_mode_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_movedim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_msort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_mul_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_multinomial_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_mv_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nan_to_num_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nanmean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nanmedian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nanquantile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nansum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_narrow_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_narrow_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_native_batch_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_native_dropout_backward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_native_layer_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_ne_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_neg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_new_empty_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_new_empty_strided_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_new_full_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_new_ones_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_new_zeros_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nextafter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_alpha_dropout_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_avg_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_avg_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_avg_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_batch_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_binary_cross_entropy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_celu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_channel_shuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_conv1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_conv2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_conv3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_conv_transpose1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_conv_transpose2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_conv_transpose3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_cosine_embedding_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_cosine_similarity_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_cross_entropy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_ctc_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_dropout2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_dropout3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_dropout_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_elu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_embedding_bag_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_embedding_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_fractional_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_fractional_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_gaussian_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_gelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_glu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_grid_sample_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_group_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_hardshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_hardsigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_hardswish_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_hardtanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_hinge_embedding_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_huber_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_instance_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_interpolate_area_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_interpolate_bicubic_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_interpolate_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_interpolate_linear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_interpolate_nearest_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_interpolate_trilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_kl_div_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_l1_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_layer_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_leaky_relu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_linear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_local_response_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_logsigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_margin_ranking_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_max_pool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_max_pool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_max_pool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_max_unpool1d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_max_unpool1d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_max_unpool2d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_max_unpool2d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_max_unpool3d_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_max_unpool3d_grad_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_mish_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_mse_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_multi_head_attention_forward_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_multi_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_multilabel_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_normalize_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_pad_circular_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_pad_constant_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_pad_reflect_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_pad_replicate_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_pad_replicate_negative_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_pairwise_distance_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_pdist_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_pixel_shuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_pixel_unshuffle_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_poisson_nll_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_prelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_relu6_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_relu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_rms_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_rrelu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_selu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_silu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_smooth_l1_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_soft_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_softmin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_softmin_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_softplus_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_softshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_softsign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_tanhshrink_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_threshold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_triplet_margin_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_unfold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_upsample_bilinear_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nn_functional_upsample_nearest_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nonzero_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_nonzero_static_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_norm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_norm_fro_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_norm_inf_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_norm_nuc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_normal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_normal_in_place_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_normal_number_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_ones_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_ones_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_ormqr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_outer_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_pca_lowrank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_permute_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_pinverse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_polar_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_polygamma_polygamma_n_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_polygamma_polygamma_n_1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_polygamma_polygamma_n_2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_polygamma_polygamma_n_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_polygamma_polygamma_n_4_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_positive_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_pow_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_put_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_qr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_quantile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_rad2deg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_rand_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_randint_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_randint_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_randn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_randn_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_ravel_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_real_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_reciprocal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_remainder_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_renorm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_repeat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_repeat_interleave_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_reshape_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_reshape_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_resize__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_resize_as__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_resolve_conj_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_resolve_neg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_roll_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_rot90_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_round_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_round_decimals_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_round_decimals_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_round_decimals_neg_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_rsqrt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_rsub_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_scalar_tensor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_scatter_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_scatter_reduce_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_scatter_reduce_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_scatter_reduce_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_scatter_reduce_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_scatter_reduce_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_searchsorted_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_select_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_sgn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_short_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_sigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_sign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_signal_windows_bartlett_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_signal_windows_blackman_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_signal_windows_cosine_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_signal_windows_exponential_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_signal_windows_gaussian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_signal_windows_general_cosine_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_signal_windows_general_hamming_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_signal_windows_hamming_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_signal_windows_hann_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_signal_windows_kaiser_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_signal_windows_nuttall_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_signbit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_sin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_sinc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_sinh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_slice_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_slice_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_softmax_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_sort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_sparse_mm_reduce_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_sparse_sampled_addmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_airy_ai_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_bessel_j0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_bessel_j1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_bessel_y0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_bessel_y1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_chebyshev_polynomial_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_chebyshev_polynomial_u_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_chebyshev_polynomial_v_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_chebyshev_polynomial_w_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_entr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_erfcx_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_hermite_polynomial_h_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_hermite_polynomial_he_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_i0e_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_i1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_i1e_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_laguerre_polynomial_l_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_legendre_polynomial_p_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_log_ndtr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_modified_bessel_i0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_modified_bessel_i1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_modified_bessel_k0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_modified_bessel_k1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_ndtr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_ndtri_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_scaled_modified_bessel_k0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_scaled_modified_bessel_k1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_spherical_bessel_j0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_xlog1py_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_special_zeta_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_split_list_args_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_split_with_sizes_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_split_with_sizes_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_sqrt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_square_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_squeeze_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_squeeze_multiple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_stack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_std_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_std_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_std_mean_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_std_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_stft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_sub_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_sum_to_size_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_svd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_svd_lowrank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_t_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_take_along_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_take_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_tan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_tanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_tensor_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_tensordot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_tile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_to_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_to_sparse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_topk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_trace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_transpose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_trapezoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_trapz_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_triangular_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_tril_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_triu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_true_divide_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_trunc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_unbind_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_unflatten_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_unfold_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_unfold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_uniform_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_unique_consecutive_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_unique_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_unsafe_chunk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_unsafe_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_unsqueeze_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_unsqueeze_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_var_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_var_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_var_mean_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_var_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_vdot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_view_as_complex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_view_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_view_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_view_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_vsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_vstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_where_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_xlogy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_zero__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_zeros_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_out_zeros_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_outer_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_pca_lowrank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_permute_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_pinverse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_polar_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_polygamma_polygamma_n_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_polygamma_polygamma_n_1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_polygamma_polygamma_n_2_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_polygamma_polygamma_n_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_polygamma_polygamma_n_4_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_positive_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_pow_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_put_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_qr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_quantile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_rad2deg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_rand_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_randint_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_randint_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_randn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_randn_like_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_ravel_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_real_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_reciprocal_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_remainder_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_renorm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_repeat_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_repeat_interleave_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_reshape_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_reshape_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_resize__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_resize_as__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_resolve_conj_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_resolve_neg_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_roll_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_rot90_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_round_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_round_decimals_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_round_decimals_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_round_decimals_neg_3_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_rsqrt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_rsub_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_scalar_tensor_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_scatter_add_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_scatter_reduce_amax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_scatter_reduce_amin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_scatter_reduce_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_scatter_reduce_prod_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_scatter_reduce_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_searchsorted_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_select_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_select_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_sgn_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_short_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_sigmoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_sign_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_signal_windows_bartlett_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_signal_windows_blackman_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_signal_windows_cosine_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_signal_windows_exponential_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_signal_windows_gaussian_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_signal_windows_general_cosine_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_signal_windows_general_hamming_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_signal_windows_hamming_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_signal_windows_hann_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_signal_windows_kaiser_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_signal_windows_nuttall_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_signbit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_sin_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_sinc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_sinh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_slice_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_slice_scatter_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_softmax_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_softmax_with_dtype_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_sort_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_sparse_mm_reduce_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_sparse_sampled_addmm_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_airy_ai_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_bessel_j0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_bessel_j1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_bessel_y0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_bessel_y1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_chebyshev_polynomial_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_chebyshev_polynomial_u_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_chebyshev_polynomial_v_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_chebyshev_polynomial_w_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_entr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_erfcx_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_hermite_polynomial_h_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_hermite_polynomial_he_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_i0e_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_i1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_i1e_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_laguerre_polynomial_l_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_legendre_polynomial_p_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_log_ndtr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_modified_bessel_i0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_modified_bessel_i1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_modified_bessel_k0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_modified_bessel_k1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_ndtr_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_ndtri_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_scaled_modified_bessel_k0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_scaled_modified_bessel_k1_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_spherical_bessel_j0_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_xlog1py_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_special_zeta_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_split_list_args_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_split_with_sizes_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_split_with_sizes_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_sqrt_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_square_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_squeeze_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_squeeze_multiple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_stack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_std_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_std_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_std_mean_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_std_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_stft_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_sub_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_sum_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_sum_to_size_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_svd_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_svd_lowrank_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_t_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_t_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_take_along_dim_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_take_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_tan_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_tanh_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_tensor_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_tensordot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_tile_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_to_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_to_sparse_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_topk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_trace_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_transpose_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_trapezoid_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_trapz_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_triangular_solve_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_tril_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_triu_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_true_divide_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_trunc_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_unbind_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_unflatten_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_unfold_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_unfold_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_uniform_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_unique_consecutive_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_unique_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_unsafe_chunk_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_unsafe_split_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_unsqueeze_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_unsqueeze_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_var_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_var_mean_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_var_mean_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_var_unbiased_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_vdot_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_view_as_complex_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_view_as_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_view_copy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_view_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_vsplit_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_vstack_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_where_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_while_loop_simple_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_xlogy_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_zero__cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_zeros_cpu_float32, test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive_zeros_like_cpu_float32 2024-08-20T22:13:59.6319655Z 2024-08-20T22:14:00.0499132Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:14:00.1109419Z Running test_masked 1/1 ... [2024-08-20 22:14:00.110549] 2024-08-20T22:14:00.1110314Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:14:00.1113813Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_masked.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:14:00.110944] 2024-08-20T22:14:01.8420346Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:14:01.9006549Z Running test_view_ops 1/1 ... [2024-08-20 22:14:01.900164] 2024-08-20T22:14:01.9007219Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:14:01.9009687Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_view_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:14:01.900540] 2024-08-20T22:14:45.9938552Z 2024-08-20T22:14:45.9942418Z test_masked 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_masked_1.1_66148a67f2b265e4_.log 2024-08-20T22:14:46.0132109Z Running 212 items in this shard: test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amax_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amax_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amax_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amax_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amax_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amax_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amax_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amax_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amax_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amin_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amin_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amin_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amin_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amin_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amin_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amin_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amin_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_amin_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_mean_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_mean_cpu_bool, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_mean_cpu_complex128, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_mean_cpu_complex64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_mean_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_mean_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_mean_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_mean_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_mean_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_mean_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_mean_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_mean_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_prod_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_prod_cpu_bool, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_prod_cpu_complex128, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_prod_cpu_complex64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_prod_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_prod_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_prod_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_prod_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_prod_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_prod_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_prod_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_prod_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_sum_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_sum_cpu_bool, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_sum_cpu_complex128, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_sum_cpu_complex64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_sum_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_sum_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_sum_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_sum_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_sum_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_sum_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_sum_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_coo_masked_sum_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amax_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amax_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amax_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amax_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amax_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amax_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amax_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amax_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amax_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amin_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amin_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amin_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amin_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amin_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amin_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amin_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amin_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_amin_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_mean_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_mean_cpu_bool, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_mean_cpu_complex128, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_mean_cpu_complex64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_mean_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_mean_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_mean_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_mean_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_mean_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_mean_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_mean_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_mean_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_prod_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_prod_cpu_bool, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_prod_cpu_complex128, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_prod_cpu_complex64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_prod_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_prod_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_prod_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_prod_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_prod_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_prod_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_prod_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_prod_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_sum_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_sum_cpu_bool, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_sum_cpu_complex128, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_sum_cpu_complex64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_sum_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_sum_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_sum_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_sum_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_sum_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_sum_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_sum_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_sparse_csr_masked_sum_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amax_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amax_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amax_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amax_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amax_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amax_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amax_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amax_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amax_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amin_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amin_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amin_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amin_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amin_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amin_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amin_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amin_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_amin_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_mean_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_mean_cpu_bool, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_mean_cpu_complex128, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_mean_cpu_complex64, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_mean_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_mean_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_mean_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_mean_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_mean_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_mean_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_mean_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_mean_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_prod_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_prod_cpu_bool, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_prod_cpu_complex128, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_prod_cpu_complex64, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_prod_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_prod_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_prod_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_prod_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_prod_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_prod_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_prod_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_prod_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_sum_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_sum_cpu_bool, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_sum_cpu_complex128, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_sum_cpu_complex64, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_sum_cpu_float16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_sum_cpu_float32, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_sum_cpu_float64, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_sum_cpu_int16, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_sum_cpu_int32, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_sum_cpu_int64, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_sum_cpu_int8, test/test_masked.py::TestMaskedCPU::test_mask_layout_strided_masked_sum_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_log_softmax_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_log_softmax_cpu_float16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_log_softmax_cpu_float32, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_log_softmax_cpu_float64, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_norm_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_norm_cpu_float16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_norm_cpu_float32, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_norm_cpu_float64, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_normalize_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_normalize_cpu_complex128, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_normalize_cpu_complex64, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_normalize_cpu_float16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_normalize_cpu_float32, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_normalize_cpu_float64, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_softmax_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_softmax_cpu_float16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_softmax_cpu_float32, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_softmax_cpu_float64, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_softmin_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_softmin_cpu_float16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_softmin_cpu_float32, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_softmin_cpu_float64, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_std_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_std_cpu_complex128, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_std_cpu_complex64, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_std_cpu_float16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_std_cpu_float32, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_std_cpu_float64, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_std_cpu_int16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_std_cpu_int32, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_std_cpu_int64, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_std_cpu_int8, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_std_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_var_cpu_bfloat16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_var_cpu_complex128, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_var_cpu_complex64, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_var_cpu_float16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_var_cpu_float32, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_var_cpu_float64, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_var_cpu_int16, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_var_cpu_int32, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_var_cpu_int64, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_var_cpu_int8, test/test_masked.py::TestMaskedCPU::test_reference_masked_masked_var_cpu_uint8, test/test_masked.py::TestMaskedCPU::test_where_coo_fill_value_0_cpu, test/test_masked.py::TestMaskedCPU::test_where_coo_fill_value_123_cpu, test/test_masked.py::TestMaskedCPU::test_where_csr_fill_value_0_cpu, test/test_masked.py::TestMaskedCPU::test_where_csr_fill_value_123_cpu, test/test_masked.py::TestMaskedCPU::test_where_hybrid_coo_fill_value_0_cpu, test/test_masked.py::TestMaskedCPU::test_where_hybrid_coo_fill_value_123_cpu 2024-08-20T22:14:46.0264871Z 2024-08-20T22:14:48.3808110Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:14:48.4374975Z Running test_indexing 1/1 ... [2024-08-20 22:14:48.437048] 2024-08-20T22:14:48.4376018Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:14:48.4379242Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_indexing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:14:48.437391] 2024-08-20T22:14:54.3082726Z 2024-08-20T22:14:54.3084780Z test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_optim_1.1_dbe1c544a8f2e134_.log 2024-08-20T22:14:54.3472221Z Running 798 items in this shard: test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr1_T_mult_1, test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr1_T_mult_2, test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr1_T_mult_4, test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr2, test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr3, test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr_state_dict, test/test_optim.py::TestLRScheduler::test_chained_lr1, test/test_optim.py::TestLRScheduler::test_chained_lr2, test/test_optim.py::TestLRScheduler::test_chained_lr2_get_last_lr_before_step, test/test_optim.py::TestLRScheduler::test_chained_lr3, test/test_optim.py::TestLRScheduler::test_chained_lr4, test/test_optim.py::TestLRScheduler::test_chained_lr5, test/test_optim.py::TestLRScheduler::test_closed_form_constantlr, test/test_optim.py::TestLRScheduler::test_closed_form_cos_anneal_lr, test/test_optim.py::TestLRScheduler::test_closed_form_exp_lr, test/test_optim.py::TestLRScheduler::test_closed_form_linearlr, test/test_optim.py::TestLRScheduler::test_closed_form_multi_step_lr, test/test_optim.py::TestLRScheduler::test_closed_form_poly_lr, test/test_optim.py::TestLRScheduler::test_closed_form_step_lr, test/test_optim.py::TestLRScheduler::test_compound_cosanneal_and_exp_lr, test/test_optim.py::TestLRScheduler::test_compound_cosanneal_and_linearlr, test/test_optim.py::TestLRScheduler::test_compound_cosanneal_and_multistep_lr, test/test_optim.py::TestLRScheduler::test_compound_cosanneal_and_step_lr, test/test_optim.py::TestLRScheduler::test_compound_exp_and_linearlr, test/test_optim.py::TestLRScheduler::test_compound_exp_and_multistep_lr, test/test_optim.py::TestLRScheduler::test_compound_linearlr_and_multistep_lr, test/test_optim.py::TestLRScheduler::test_compound_reduce_lr_on_plateau1, test/test_optim.py::TestLRScheduler::test_compound_reduce_lr_on_plateau2, test/test_optim.py::TestLRScheduler::test_compound_reduce_lr_on_plateau3, test/test_optim.py::TestLRScheduler::test_compound_reduce_lr_on_plateau4, test/test_optim.py::TestLRScheduler::test_compound_reduce_lr_on_plateau5, test/test_optim.py::TestLRScheduler::test_compound_step_and_constantlr, test/test_optim.py::TestLRScheduler::test_compound_step_and_exp_lr, test/test_optim.py::TestLRScheduler::test_compound_step_and_multistep_lr, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass0, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass1, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass2, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass3, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass4, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass5, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass6, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass7, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass8, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass9, test/test_optim.py::TestLRScheduler::test_constant_initial_params_cyclelr, test/test_optim.py::TestLRScheduler::test_constant_initial_params_onecyclelr, test/test_optim.py::TestLRScheduler::test_constant_initial_params_swalr, test/test_optim.py::TestLRScheduler::test_constantlr, test/test_optim.py::TestLRScheduler::test_constantlr_is_constant_for_constant_epoch, test/test_optim.py::TestLRScheduler::test_constantlr_with_epoch, test/test_optim.py::TestLRScheduler::test_cos_anneal_lr, test/test_optim.py::TestLRScheduler::test_cos_anneal_lr_continue, test/test_optim.py::TestLRScheduler::test_cosine_lr_state_dict, test/test_optim.py::TestLRScheduler::test_cosine_then_cyclic, test/test_optim.py::TestLRScheduler::test_cycle_lr_cycle_momentum_fail_with_momentumless_optimizer, test/test_optim.py::TestLRScheduler::test_cycle_lr_cycle_momentum_with_beta1_optimizer, test/test_optim.py::TestLRScheduler::test_cycle_lr_exp_range_mode, test/test_optim.py::TestLRScheduler::test_cycle_lr_exp_range_mode_one_lr, test/test_optim.py::TestLRScheduler::test_cycle_lr_exp_range_mode_step_size_up_down, test/test_optim.py::TestLRScheduler::test_cycle_lr_invalid_mode, test/test_optim.py::TestLRScheduler::test_cycle_lr_removed_after_out_of_scope, test/test_optim.py::TestLRScheduler::test_cycle_lr_scale_fn_restored_from_state_dict, test/test_optim.py::TestLRScheduler::test_cycle_lr_state_dict_picklable, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular2_mode, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular2_mode_one_lr, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular2_mode_step_size_up_down, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular_mode, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular_mode_one_lr, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular_mode_one_lr_no_momentum, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular_mode_step_size_up_down, test/test_optim.py::TestLRScheduler::test_cycle_lr_with_adam, test/test_optim.py::TestLRScheduler::test_cycle_lr_with_momentumless_optimizer, test/test_optim.py::TestLRScheduler::test_error_when_getlr_has_epoch, test/test_optim.py::TestLRScheduler::test_exp_lr, test/test_optim.py::TestLRScheduler::test_exp_step_lr_state_dict, test/test_optim.py::TestLRScheduler::test_exponential_lr_is_constant_for_constant_epoch, test/test_optim.py::TestLRScheduler::test_get_last_lr_constantlr, test/test_optim.py::TestLRScheduler::test_get_last_lr_linearlr, test/test_optim.py::TestLRScheduler::test_get_last_lr_multi_step_lr, test/test_optim.py::TestLRScheduler::test_get_last_lr_sequentiallr, test/test_optim.py::TestLRScheduler::test_get_last_lr_step_lr, test/test_optim.py::TestLRScheduler::test_lambda_lr, test/test_optim.py::TestLRScheduler::test_lambda_lr_state_dict_fn, test/test_optim.py::TestLRScheduler::test_lambda_lr_state_dict_obj, test/test_optim.py::TestLRScheduler::test_linear_linearlr_is_constant_for_constant_epoch, test/test_optim.py::TestLRScheduler::test_linearlr, test/test_optim.py::TestLRScheduler::test_linearlr_start_factor_limits1, test/test_optim.py::TestLRScheduler::test_linearlr_start_factor_limits2, test/test_optim.py::TestLRScheduler::test_linearlr_with_epoch, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass0_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass0_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass10_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass10_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass11_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass11_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass12_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass12_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass13_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass13_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass14_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass14_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass1_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass1_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass2_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass2_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass3_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass3_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass4_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass4_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass5_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass5_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass6_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass6_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass7_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass7_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass8_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass8_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass9_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass9_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass0, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass1, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass10, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass11, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass12, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass13, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass2, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass3, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass4, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass5, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass6, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass7, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass8, test/test_optim.py::TestLRScheduler::test_lr_scheduler_verbose_deprecation_warning_LRClass9, test/test_optim.py::TestLRScheduler::test_multi_step_lr, test/test_optim.py::TestLRScheduler::test_multi_step_lr_state_dict, test/test_optim.py::TestLRScheduler::test_multi_step_lr_with_epoch, test/test_optim.py::TestLRScheduler::test_multiplicative_lr, test/test_optim.py::TestLRScheduler::test_new_pattern_no_warning, test/test_optim.py::TestLRScheduler::test_new_pattern_no_warning_with_arg, test/test_optim.py::TestLRScheduler::test_new_pattern_no_warning_with_overridden_optim_step, test/test_optim.py::TestLRScheduler::test_no_cyclic_references, test/test_optim.py::TestLRScheduler::test_no_cyclic_references_in_step, test/test_optim.py::TestLRScheduler::test_old_pattern_warning, test/test_optim.py::TestLRScheduler::test_old_pattern_warning_resuming, test/test_optim.py::TestLRScheduler::test_old_pattern_warning_resuming_with_arg, test/test_optim.py::TestLRScheduler::test_old_pattern_warning_with_arg, test/test_optim.py::TestLRScheduler::test_old_pattern_warning_with_overridden_optim_step, test/test_optim.py::TestLRScheduler::test_onecycle_lr_cannot_calculate_total_steps, test/test_optim.py::TestLRScheduler::test_onecycle_lr_cosine_annealing, test/test_optim.py::TestLRScheduler::test_onecycle_lr_invalid_anneal_strategy, test/test_optim.py::TestLRScheduler::test_onecycle_lr_invalid_pct_start, test/test_optim.py::TestLRScheduler::test_onecycle_lr_legacy_state_dict, test/test_optim.py::TestLRScheduler::test_onecycle_lr_linear_annealing, test/test_optim.py::TestLRScheduler::test_onecycle_lr_linear_annealing_three_phases, test/test_optim.py::TestLRScheduler::test_poly_lr, test/test_optim.py::TestLRScheduler::test_polynomial_lr_is_constant_for_constant_epoch, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau1, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau2, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau3, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau4, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau5, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau6, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau7, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau8, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau_get_last_lr_before_step, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau_state_dict, test/test_optim.py::TestLRScheduler::test_sequentiallr1, test/test_optim.py::TestLRScheduler::test_sequentiallr2, test/test_optim.py::TestLRScheduler::test_sequentiallr3, test/test_optim.py::TestLRScheduler::test_sequentiallr4, test/test_optim.py::TestLRScheduler::test_step_lr, test/test_optim.py::TestLRScheduler::test_step_lr_is_constant_for_constant_epoch, test/test_optim.py::TestLRScheduler::test_step_lr_state_dict, test/test_optim.py::TestLRScheduler::test_swa_lr_state_dict, test/test_optim.py::TestLRScheduler::test_swalr_cosine_anneal_after_multiplicative, test/test_optim.py::TestLRScheduler::test_swalr_hypers, test/test_optim.py::TestLRScheduler::test_swalr_linear_anneal_after_multiplicative, test/test_optim.py::TestLRScheduler::test_swalr_no_anneal, test/test_optim.py::TestDifferentiableOptimizer::test_adadelta, test/test_optim.py::TestDifferentiableOptimizer::test_adagrad, test/test_optim.py::TestDifferentiableOptimizer::test_adam, test/test_optim.py::TestDifferentiableOptimizer::test_adamax, test/test_optim.py::TestDifferentiableOptimizer::test_adamw, test/test_optim.py::TestDifferentiableOptimizer::test_asgd, test/test_optim.py::TestDifferentiableOptimizer::test_nadam, test/test_optim.py::TestDifferentiableOptimizer::test_radam, test/test_optim.py::TestDifferentiableOptimizer::test_rmsprop, test/test_optim.py::TestDifferentiableOptimizer::test_rprop, test/test_optim.py::TestDifferentiableOptimizer::test_sgd, test/test_optim.py::TestSWAUtils::test_averaged_model_all_devices_ema_False, test/test_optim.py::TestSWAUtils::test_averaged_model_all_devices_ema_True, test/test_optim.py::TestSWAUtils::test_averaged_model_default_avg_fn_picklable, test/test_optim.py::TestSWAUtils::test_averaged_model_exponential_use_multi_avg_fn_False_use_buffers_False, test/test_optim.py::TestSWAUtils::test_averaged_model_exponential_use_multi_avg_fn_False_use_buffers_True, test/test_optim.py::TestSWAUtils::test_averaged_model_exponential_use_multi_avg_fn_True_use_buffers_False, test/test_optim.py::TestSWAUtils::test_averaged_model_exponential_use_multi_avg_fn_True_use_buffers_True, test/test_optim.py::TestSWAUtils::test_averaged_model_mixed_device_ema_False, test/test_optim.py::TestSWAUtils::test_averaged_model_mixed_device_ema_True, test/test_optim.py::TestSWAUtils::test_averaged_model_state_dict, test/test_optim.py::TestSWAUtils::test_bn_update_eval_momentum, test/test_optim.py::TestSWAUtils::test_update_bn_cnn, test/test_optim.py::TestSWAUtils::test_update_bn_dnn, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_ASGD_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_Adadelta_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_Adagrad_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_AdamW_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_Adam_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_Adamax_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_LBFGS_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_NAdam_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_RAdam_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_RMSprop_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_Rprop_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_SGD_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_ASGD_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_Adadelta_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_Adagrad_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_AdamW_cpu_complex64, 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test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optim_infos_do_not_specify_global_cliquey_kwargs_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_optimizer_can_be_printed_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_lr_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_peak_memory_foreach_ASGD_cpu_float32, 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test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_SparseAdam_cpu_float32 2024-08-20T22:14:54.3849828Z 2024-08-20T22:14:56.1994790Z 2024-08-20T22:14:56.1997039Z test_view_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_view_ops_1.1_4e1423d691bcb346_.log 2024-08-20T22:14:56.2322631Z Running 441 items in this shard: test/test_view_ops.py::TestViewOpsCPU::test_T_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_advanced_indexing_assignment_cpu, test/test_view_ops.py::TestViewOpsCPU::test_advanced_indexing_nonview_cpu, test/test_view_ops.py::TestViewOpsCPU::test_as_strided_gradients_cpu, test/test_view_ops.py::TestViewOpsCPU::test_as_strided_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_as_strided_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_basic_indexing_ellipses_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_basic_indexing_newaxis_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_basic_indexing_slice_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_chunk_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_conj_imag_view_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_conj_imag_view_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_conj_view_with_shared_memory_cpu, test/test_view_ops.py::TestViewOpsCPU::test_contiguous_nonview_cpu, test/test_view_ops.py::TestViewOpsCPU::test_contiguous_self_cpu, test/test_view_ops.py::TestViewOpsCPU::test_diagonal_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_expand_as_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_expand_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_flatten_nonview_cpu, test/test_view_ops.py::TestViewOpsCPU::test_flatten_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_movedim_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_narrow_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_permute_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_real_imag_view_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_real_imag_view_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_reshape_as_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_reshape_nonview_cpu, test/test_view_ops.py::TestViewOpsCPU::test_reshape_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_select_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_bool, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_complex128, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_complex64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_float16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_float32, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_float64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_int16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_int32, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_int64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_int8, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_uint8, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_bool, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_complex128, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_complex64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_float16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_float32, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_float64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_int16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_int32, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_int64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_int8, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_uint8, test/test_view_ops.py::TestViewOpsCPU::test_split_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_squeeze_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_squeeze_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_t_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_t_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_transpose_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_transpose_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unbind_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unbind_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unfold_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unsqueeze_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unsqueeze_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_as_complex_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_as_real_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_as_real_cpu_complex32, test/test_view_ops.py::TestViewOpsCPU::test_view_as_real_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_as_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_copy_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_copy_out_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_copy_output_contiguous_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_view_cpu, test/test_view_ops.py::TestViewOpsLAZY::test_T_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_advanced_indexing_assignment_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_advanced_indexing_nonview_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_as_strided_gradients_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_as_strided_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_as_strided_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_basic_indexing_ellipses_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_basic_indexing_newaxis_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_basic_indexing_slice_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_chunk_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_conj_imag_view_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_conj_imag_view_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_conj_view_with_shared_memory_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_contiguous_nonview_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_contiguous_self_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_diagonal_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_expand_as_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_expand_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_flatten_nonview_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_flatten_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_movedim_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_narrow_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_permute_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_real_imag_view_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_real_imag_view_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_reshape_as_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_reshape_nonview_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_reshape_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_select_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_bool, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_float16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_float32, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_float64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_int16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_int32, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_int64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_int8, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_bool, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_float16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_float32, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_float64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_int16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_int32, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_int64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_int8, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_split_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_squeeze_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_squeeze_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_t_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_t_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_transpose_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_transpose_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unbind_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unbind_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unfold_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unsqueeze_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unsqueeze_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_complex_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_real_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_real_lazy_complex32, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_real_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_copy_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_copy_out_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_copy_output_contiguous_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_view_lazy, test/test_view_ops.py::TestOldViewOpsCPU::test_T_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_gradient_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_big_transpose_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_shapes_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_tensors_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_chunk_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_conj_neg_view_numpy_error_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_contiguous_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_crow_col_indices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_empty_reshape_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_expand_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_flatten_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_memory_format_resize__cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_memory_format_resize_as_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_narrow_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_narrow_tensor_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_python_types_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_ravel_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_bfloat16, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_all_dtypes_and_devices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_as_all_dtypes_and_devices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_as_preserves_strides_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_overflow_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_split_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_t_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_errors_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_invalid_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_invalid_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_invalid_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_vs_numpy_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_vs_numpy_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_vs_numpy_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_bfloat16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_bfloat16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_unsqueeze_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_view_all_dtypes_and_devices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_view_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_view_empty_cpu 2024-08-20T22:14:56.2522397Z 2024-08-20T22:14:56.7958223Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:14:56.8537907Z Running test_monitor 1/1 ... [2024-08-20 22:14:56.853332] 2024-08-20T22:14:56.8538753Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:14:56.8540579Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_monitor.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:14:56.853695] 2024-08-20T22:14:58.7517164Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:14:58.8098451Z Running benchmark_utils/test_benchmark_utils 1/1 ... [2024-08-20 22:14:58.809404] 2024-08-20T22:14:58.8099377Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:14:58.8101876Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'benchmark_utils/test_benchmark_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:14:58.809790] 2024-08-20T22:14:59.8730542Z 2024-08-20T22:14:59.8732493Z test_monitor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_monitor_1.1_419a03b745a34378_.log 2024-08-20T22:14:59.8735185Z Running 6 items in this shard: test/test_monitor.py::TestMonitor::test_event_handler, test/test_monitor.py::TestMonitor::test_fixed_count_stat, test/test_monitor.py::TestMonitor::test_interval_stat, test/test_monitor.py::TestMonitor::test_log_event, test/test_monitor.py::TestMonitor::test_wait_counter, test/test_monitor.py::TestMonitorTensorboard::test_event_handler 2024-08-20T22:14:59.8737184Z 2024-08-20T22:15:02.3809306Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:15:02.4393960Z Running test_binary_ufuncs 1/2 ... [2024-08-20 22:15:02.438925] 2024-08-20T22:15:02.4394889Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:15:02.4397669Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_binary_ufuncs.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'] ... [2024-08-20 22:15:02.439341] 2024-08-20T22:15:03.3309999Z 2024-08-20T22:15:03.3312615Z benchmark_utils/test_benchmark_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/benchmark_utils.test_benchmark_utils_1.1_d2cea46913032de9_.log 2024-08-20T22:15:03.3322228Z Running 9 items in this shard: test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_adaptive_timer, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_collect_callgrind, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_collect_cpp_callgrind, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_compare, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_cpp_timer, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_fuzzer, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_manipulate_callgrind_stats, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_timer, test/benchmark_utils/test_benchmark_utils.py::TestBenchmarkUtils::test_timer_tiny_fast_snippet 2024-08-20T22:15:03.3330686Z 2024-08-20T22:15:05.9944353Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:15:06.0521469Z Running test_quantization 1/5 ... [2024-08-20 22:15:06.051661] 2024-08-20T22:15:06.0522587Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:15:06.0525899Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=1', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:15:06.052005] 2024-08-20T22:16:58.6509457Z 2024-08-20T22:16:58.6511787Z test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_indexing_1.1_2940e460c64f5a93_.log 2024-08-20T22:16:58.6552332Z Running 87 items in this shard: test/test_indexing.py::TestIndexingCPU::test_advancedindex_big_cpu, test/test_indexing.py::TestIndexingCPU::test_advancedindex_cpu_float16, test/test_indexing.py::TestIndexingCPU::test_advancedindex_cpu_float64, test/test_indexing.py::TestIndexingCPU::test_basic_advanced_combined_cpu, test/test_indexing.py::TestIndexingCPU::test_bool_indices_accumulate_cpu, test/test_indexing.py::TestIndexingCPU::test_bool_indices_cpu, test/test_indexing.py::TestIndexingCPU::test_byte_mask2d_cpu, test/test_indexing.py::TestIndexingCPU::test_byte_mask_accumulate_cpu, test/test_indexing.py::TestIndexingCPU::test_byte_mask_cpu, test/test_indexing.py::TestIndexingCPU::test_byte_tensor_assignment_cpu, test/test_indexing.py::TestIndexingCPU::test_cpu_indices_cpu, test/test_indexing.py::TestIndexingCPU::test_cuda_broadcast_index_use_deterministic_algorithms_cpu, test/test_indexing.py::TestIndexingCPU::test_ellipsis_tensor_cpu, test/test_indexing.py::TestIndexingCPU::test_empty_index_cpu, test/test_indexing.py::TestIndexingCPU::test_empty_ndim_index_bool_cpu, test/test_indexing.py::TestIndexingCPU::test_empty_ndim_index_cpu, test/test_indexing.py::TestIndexingCPU::test_empty_slice_cpu, test/test_indexing.py::TestIndexingCPU::test_gather_take_along_dim_cross_device_cpu_float32, test/test_indexing.py::TestIndexingCPU::test_getitem_scalars_cpu, test/test_indexing.py::TestIndexingCPU::test_index_cpu, test/test_indexing.py::TestIndexingCPU::test_index_getitem_copy_bools_slices_cpu, test/test_indexing.py::TestIndexingCPU::test_index_ind_dtype_cpu, test/test_indexing.py::TestIndexingCPU::test_index_limits_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_accumulate_duplicate_indices_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_accumulate_empty_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_accumulate_expanded_values_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_accumulate_large_tensor_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_accumulate_non_contiguous_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_accumulate_with_optional_tensors_cpu, test/test_indexing.py::TestIndexingCPU::test_index_put_src_datatype_cpu_bfloat16, test/test_indexing.py::TestIndexingCPU::test_index_put_src_datatype_cpu_bool, test/test_indexing.py::TestIndexingCPU::test_index_put_src_datatype_cpu_complex128, test/test_indexing.py::TestIndexingCPU::test_index_put_src_datatype_cpu_complex64, test/test_indexing.py::TestIndexingCPU::test_index_put_src_datatype_cpu_float32, test/test_indexing.py::TestIndexingCPU::test_index_put_src_datatype_cpu_int64, test/test_indexing.py::TestIndexingCPU::test_index_scalar_with_bool_mask_cpu, test/test_indexing.py::TestIndexingCPU::test_index_setitem_bools_slices_cpu, test/test_indexing.py::TestIndexingCPU::test_index_src_datatype_cpu_bfloat16, test/test_indexing.py::TestIndexingCPU::test_index_src_datatype_cpu_bool, test/test_indexing.py::TestIndexingCPU::test_index_src_datatype_cpu_float32, test/test_indexing.py::TestIndexingCPU::test_index_src_datatype_cpu_int64, test/test_indexing.py::TestIndexingCPU::test_int_assignment_cpu, test/test_indexing.py::TestIndexingCPU::test_int_indices2d_cpu, test/test_indexing.py::TestIndexingCPU::test_int_indices_broadcast_cpu, test/test_indexing.py::TestIndexingCPU::test_int_indices_cpu, test/test_indexing.py::TestIndexingCPU::test_invalid_device_cpu, test/test_indexing.py::TestIndexingCPU::test_invalid_index_cpu, test/test_indexing.py::TestIndexingCPU::test_jit_indexing_cpu, test/test_indexing.py::TestIndexingCPU::test_multiple_bool_indices_cpu, test/test_indexing.py::TestIndexingCPU::test_multiple_byte_mask_cpu, test/test_indexing.py::TestIndexingCPU::test_multiple_int_cpu, test/test_indexing.py::TestIndexingCPU::test_none_cpu, test/test_indexing.py::TestIndexingCPU::test_out_of_bound_index_cpu, test/test_indexing.py::TestIndexingCPU::test_set_item_to_scalar_tensor_cpu, test/test_indexing.py::TestIndexingCPU::test_setitem_expansion_error_cpu, test/test_indexing.py::TestIndexingCPU::test_setitem_scalars_cpu, test/test_indexing.py::TestIndexingCPU::test_single_int_cpu, test/test_indexing.py::TestIndexingCPU::test_step_assignment_cpu, test/test_indexing.py::TestIndexingCPU::test_step_cpu, test/test_indexing.py::TestIndexingCPU::test_take_along_dim_cpu_float32, test/test_indexing.py::TestIndexingCPU::test_take_along_dim_cpu_int64, test/test_indexing.py::TestIndexingCPU::test_take_along_dim_invalid_cpu_float32, test/test_indexing.py::TestIndexingCPU::test_take_along_dim_invalid_cpu_int64, test/test_indexing.py::TestIndexingCPU::test_unravel_index_errors_cpu, test/test_indexing.py::TestIndexingCPU::test_variable_slicing_cpu, test/test_indexing.py::TestIndexingCPU::test_zero_dim_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_assignment_value_mismatch_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_indexing_alldims_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_indexing_onedim_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_indexing_twodim_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_indexing_weirdness_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_indexing_weirdness_tensors_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_list_indexing_cpu, test/test_indexing.py::NumpyTestsCPU::test_boolean_shape_mismatch_cpu, test/test_indexing.py::NumpyTestsCPU::test_broadcast_subspace_cpu, test/test_indexing.py::NumpyTestsCPU::test_broaderrors_indexing_cpu, test/test_indexing.py::NumpyTestsCPU::test_ellipsis_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_empty_fancy_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_empty_tuple_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_everything_returns_views_cpu, test/test_indexing.py::NumpyTestsCPU::test_index_is_larger_cpu, test/test_indexing.py::NumpyTestsCPU::test_index_no_floats_cpu, test/test_indexing.py::NumpyTestsCPU::test_none_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_single_bool_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_single_int_index_cpu, test/test_indexing.py::NumpyTestsCPU::test_trivial_fancy_out_of_bounds_cpu, test/test_indexing.py::NumpyTestsCPU::test_truncate_leading_1s_cpu 2024-08-20T22:16:58.6584840Z 2024-08-20T22:17:01.1704688Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:17:01.2287301Z Running test_quantization 3/5 ... [2024-08-20 22:17:01.227810] 2024-08-20T22:17:01.2288195Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:17:01.2293511Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=3', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:17:01.228240] 2024-08-20T22:22:01.0286582Z 2024-08-20T22:22:01.0288810Z test_binary_ufuncs 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_binary_ufuncs_1.2_d7bd5ef7af6690b6_.log 2024-08-20T22:22:01.3652194Z Running 6411 items in this shard: test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___add___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___add___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___add___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___and___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___and___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___and___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___eq___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___eq___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___eq___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___floordiv___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___floordiv___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___floordiv___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___floordiv___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ge___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___gt___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___gt___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___gt___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iadd___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iadd___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iadd___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iand___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iand___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___iand___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ifloordiv___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ifloordiv___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ilshift___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imod___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imod___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imod___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imod___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imul___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imul___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imul___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imul___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___imul___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ior___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ior___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ior___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ior___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ipow___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ipow___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ipow___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___irshift___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___isub___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___isub___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___isub___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___isub___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___isub___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___itruediv___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___itruediv___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___itruediv___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ixor___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ixor___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ixor___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ixor___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ixor___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ixor___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___le___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___le___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___le___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___lshift___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___lt___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___lt___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___lt___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___lt___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___matmul___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___matmul___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___matmul___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___mod___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___mod___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___mod___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___mul___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___mul___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___mul___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ne___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ne___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ne___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ne___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___or___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___or___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___or___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___or___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___pow___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___pow___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___pow___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___radd___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___radd___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___radd___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___radd___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___radd___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rand___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rand___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rand___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rand___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rand___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rfloordiv___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rfloordiv___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rlshift___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rlshift___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rlshift___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rlshift___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rmatmul___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rmatmul___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rmatmul___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rmatmul___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rmod___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rmod___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rmod___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rmul___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rmul___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rmul___not_implemented_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rmul___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ror___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ror___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___ror___not_implemented_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rpow___not_implemented_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rpow___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rpow___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rpow___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rrshift___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rrshift___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rrshift___not_implemented_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rrshift___not_implemented_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rshift___not_implemented_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rshift___not_implemented_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rshift___not_implemented_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test___rshift___not_implemented_cpu_int32, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_jiterator_binary_return_by_ref_cpu_int64, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_le_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logaddexp_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logaddexp_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logaddexp_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logaddexp_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logaddexp_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_and_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_and_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_and_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_and_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_or_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_or_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_or_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_or_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_or_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_logical_xor_cpu_bfloat16, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_every_other_xlogy_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed___radd___cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed___radd___cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed___radd___cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed___radd___cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed___radd___cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed___rand___cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed___rand___cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed___rand___cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed___rand___cpu_uint8, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_add_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_add_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_add_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_add_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_atan2_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_atan2_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_and_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_and_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_and_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_left_shift_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_or_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_or_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_right_shift_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_right_shift_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_xor_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_xor_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_xor_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_bitwise_xor_cpu_int8, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_div_no_rounding_mode_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_div_no_rounding_mode_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_div_no_rounding_mode_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_div_trunc_rounding_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_div_trunc_rounding_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_div_trunc_rounding_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_eq_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_eq_cpu_complex32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_eq_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_eq_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_eq_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_eq_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_float_power_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_float_power_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_float_power_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_float_power_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_float_power_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_float_power_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_float_power_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_float_power_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_floor_divide_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_floor_divide_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmax_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmax_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmax_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmax_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmax_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmax_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmin_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmin_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmin_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmin_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmod_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmod_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmod_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmod_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmod_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmod_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_fmod_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_gcd_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_gcd_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_ge_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_ge_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_ge_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_ge_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_ge_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_ge_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_gt_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_gt_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_gt_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_gt_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_gt_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_gt_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_gt_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_heaviside_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_heaviside_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_heaviside_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_hypot_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_igamma_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_igamma_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_igammac_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_isclose_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_isclose_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_isclose_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_isclose_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_isclose_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_isclose_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_lcm_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_le_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_le_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_le_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_le_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logaddexp_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logaddexp_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logaddexp_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_and_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_and_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_and_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_and_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_and_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_and_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_and_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_and_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_and_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_or_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_or_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_or_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_or_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_or_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_or_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_or_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_or_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_xor_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_xor_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_xor_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_xor_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_xor_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_logical_xor_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_lt_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_lt_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_maximum_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_maximum_cpu_bool, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_nextafter_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_pow_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_pow_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_pow_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_pow_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_pow_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_pow_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_remainder_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_remainder_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_remainder_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_remainder_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_remainder_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_remainder_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_remainder_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_rsub_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_rsub_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_special_xlog1py_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_special_xlog1py_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_special_xlog1py_cpu_float64, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed__refs_xlogy_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_add_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_add_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_add_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_add_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_add_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_atan2_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_atan2_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_and_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_and_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_and_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_left_shift_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_left_shift_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_or_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_or_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_right_shift_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_xor_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_xor_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_bitwise_xor_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_max_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_min_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_min_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_clamp_min_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_complex_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_copysign_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_copysign_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_copysign_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_copysign_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_floor_rounding_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_floor_rounding_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_floor_rounding_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_floor_rounding_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_no_rounding_mode_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_div_trunc_rounding_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_complex32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_eq_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_float_power_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_floor_divide_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_floor_divide_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmax_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmin_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmin_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmin_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmod_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_fmod_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gcd_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gcd_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gcd_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gcd_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ge_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_gt_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_heaviside_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_heaviside_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_heaviside_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_hypot_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_hypot_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_igamma_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_igamma_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_igammac_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_igammac_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_isclose_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_jiterator_binary_return_by_ref_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lcm_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lcm_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lcm_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_ldexp_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_le_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logaddexp_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logaddexp_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logaddexp_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logaddexp_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logaddexp_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_and_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_or_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_logical_xor_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lt_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lt_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_lt_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_max_binary_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_maximum_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_min_binary_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_minimum_cpu_bool, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_hermite_polynomial_h_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_hermite_polynomial_h_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_hermite_polynomial_he_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_laguerre_polynomial_l_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_laguerre_polynomial_l_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_laguerre_polynomial_l_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_laguerre_polynomial_l_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_laguerre_polynomial_l_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_laguerre_polynomial_l_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_legendre_polynomial_p_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_legendre_polynomial_p_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_legendre_polynomial_p_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_t_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_t_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_t_cpu_uint8, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_w_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_w_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_w_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_w_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_shifted_chebyshev_polynomial_w_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_xlog1py_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_xlog1py_cpu_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_xlog1py_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_xlog1py_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_xlog1py_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_xlog1py_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_zeta_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_zeta_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_zeta_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_zeta_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_special_zeta_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_sub_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_sub_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_sub_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_sub_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_true_divide_cpu_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_true_divide_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_true_divide_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_true_divide_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_true_divide_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_xlogy_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_xlogy_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_xlogy_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_xlogy_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_xlogy_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_xlogy_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_xlogy_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_contig_vs_transposed_xlogy_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_bfloat16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_bfloat16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_bfloat16_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_bfloat16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_bfloat16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_bool_float16, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float32_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float32_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float32_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float64_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float64_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_float64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int16_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int32_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int32_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_int8_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_cpu_uint8_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_bfloat16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float32_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_copysign_subgradient_cpu_float64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_cpow_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_cpu_tensor_pow_cuda_scalar_tensor_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_cremainder_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_cross_device_inplace_error_msg_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_csub_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_cumulative_trapezoid_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_and_floordiv_script_vs_python_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_and_floordiv_vs_python_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_modes_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_nonfinite_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_nonfinite_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_numpy_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_numpy_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_div_rounding_numpy_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_divide_by_zero_rounding_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_divide_by_zero_rounding_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_bfloat16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex128_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_complex64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float32_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_float64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int32_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int64_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_int8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_complex64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_power_cpu_uint8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_float_scalar_pow_float_tensor_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_div_extremal_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_div_extremal_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_div_extremal_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_scalar_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_scalar_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_tensor_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_tensor_cpu_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_tensor_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_zero_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_zero_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_zero_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_floor_divide_zero_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_by_zero_float_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_by_zero_float_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_by_zero_integral_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_fmod_remainder_cpu_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_gcd_cpu_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bfloat16_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bfloat16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_bool_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float32_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_float64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int16_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int32_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int64_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int64_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_bool, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_int8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cpu_uint8_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_heaviside_cross_device_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_hypot_cpu_bfloat16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_hypot_cpu_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_hypot_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_idiv_and_ifloordiv_vs_python_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_inplace_division_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_int_tensor_pow_neg_ints_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lcm_cpu_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lcm_cpu_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_ldexp_cpu, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lerp_cpu_complex128, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lerp_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lerp_lowp_cpu_cpu_float16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_lerp_lowp_cpu_float16, 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test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_xlogy_xlog1py_cpu_int64_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_xlogy_xlog1py_cpu_int64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_xlogy_xlog1py_cpu_int64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_xlogy_xlog1py_cpu_int8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_xlogy_xlog1py_cpu_uint8_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_xlogy_xlog1py_cpu_uint8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_xlogy_xlog1py_cpu_uint8_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_xlogy_xlog1py_gradients_cpu_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_bool_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_bool_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_bool_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_bool_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_float32_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_float32_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_float32_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_float32_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_float32_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_float32_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_float64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_float64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_float64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_float64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_float64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int16_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int16_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int32_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int32_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int64_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int64_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int64_int16, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int64_int32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int64_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int64_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int64_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int8_float64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_int8_uint8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_uint8_float32, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_uint8_int64, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_uint8_int8, test/test_binary_ufuncs.py::TestBinaryUfuncsCPU::test_zeta_cpu_uint8_uint8 2024-08-20T22:22:01.6786201Z 2024-08-20T22:22:03.5045941Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:22:03.5621793Z Running test_quantization 4/5 ... [2024-08-20 22:22:03.561791] 2024-08-20T22:22:03.5622564Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:22:03.5624816Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=4', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:22:03.562168] 2024-08-20T22:25:26.7077327Z 2024-08-20T22:25:26.7079755Z test_quantization 1/5 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_1.5_1f76d033a1929c32_.log 2024-08-20T22:25:26.7193199Z Running 254 items in this shard: test/test_quantization.py::TestQuantizedOps::test_adaptive_avg_pool3d_ndhwc, test/test_quantization.py::TestQuantizedOps::test_add_scalar_relu, test/test_quantization.py::TestQuantizedOps::test_advanced_indexing, test/test_quantization.py::TestQuantizedOps::test_avg_pool2d_nhwc, test/test_quantization.py::TestQuantizedOps::test_cat_nhwc, test/test_quantization.py::TestQuantizedOps::test_leaky_relu_observed_output, test/test_quantization.py::TestQuantizedOps::test_linear_bias_unpack, test/test_quantization.py::TestQuantizedOps::test_max_pool2d, test/test_quantization.py::TestQuantizedOps::test_qadd_relu_cudnn_nhwc, test/test_quantization.py::TestQuantizedOps::test_qmul_broadcast, test/test_quantization.py::TestQuantizedOps::test_qsoftmax, test/test_quantization.py::TestQuantizedOps::test_qthreshold, test/test_quantization.py::TestQNNPackOps::test_qnnpack_add, test/test_quantization.py::TestQNNPackOps::test_qnnpack_sigmoid_sweep, test/test_quantization.py::TestQNNPackOps::test_qnnpack_tanh, test/test_quantization.py::TestQuantizedLinear::test_qlinear_add_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_leaky_relu, test/test_quantization.py::TestQuantizedLinear::test_qlinear_unpack, test/test_quantization.py::TestQuantizedConv::test_qconv1d_unpack, test/test_quantization.py::TestQuantizedConv::test_qconv2d_relu_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv2d_silu_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv2d_sum_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv2d_unpack, test/test_quantization.py::TestQuantizedConv::test_qconv3d_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv_transpose1d, test/test_quantization.py::TestDynamicQuantizedOps::test_qlinear_dynamic_fp16, test/test_quantization.py::TestDynamicQuantizedOps::test_unpacked_qlinear_dynamic_fp16_opcheck, test/test_quantization.py::TestComparatorOps::test_compare_tensor_scalar, test/test_quantization.py::TestPadding::test_reflection_pad1d, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_bag_2d_indices, test/test_quantization.py::TestQuantizedFunctionalOps::test_conv2d_api, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_tensor_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_channel_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_tensor, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_tensor_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_tensor_half_precision_numerics, test/test_quantization.py::TestQuantizedTensor::test_clone, test/test_quantization.py::TestQuantizedTensor::test_decomposed_dequantize_per_channel, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_dequantize_fp16_cpu, test/test_quantization.py::TestQuantizedTensor::test_per_channel_qtensor_creation_cpu, test/test_quantization.py::TestQuantizedTensor::test_per_channel_to_device, test/test_quantization.py::TestQuantizedTensor::test_qscheme_pickle, test/test_quantization.py::TestQuantizedTensor::test_qtensor_copy, test/test_quantization.py::TestQuantizedTensor::test_repeat, test/test_quantization.py::TestObserver::test_dynamic_quant_observer, test/test_quantization.py::TestObserver::test_observer_qparams_respects_device_affinity, test/test_quantization.py::TestObserver::test_per_channel_observers, test/test_quantization.py::TestObserver::test_per_channel_observers_load_state_dict, test/test_quantization.py::TestObserver::test_state_dict_respects_device_affinity, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_add, test/test_quantization.py::TestStaticQuantizedModule::test_elu, test/test_quantization.py::TestStaticQuantizedModule::test_embedding_api, test/test_quantization.py::TestStaticQuantizedModule::test_leaky_relu, test/test_quantization.py::TestStaticQuantizedModule::test_linear_leaky_relu, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_conv2d, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_convtranspose2d, test/test_quantization.py::TestRecordHistogramObserver::test_record_observer, test/test_quantization.py::TestDistributed::test_qat_convbn_fused_syncbn_replacement, test/test_quantization.py::TestDistributed::test_syncbn_preserves_qconfig, test/test_quantization.py::TestFusedObsFakeQuantModule::test_default_fused_qat_config, test/test_quantization.py::TestFusedObsFakeQuantModule::test_embedding_qat_config, test/test_quantization.py::TestFusedObsFakeQuantModule::test_fused_mod_per_channel, test/test_quantization.py::TestFusedObsFakeQuantModule::test_fused_obs_fq_module, test/test_quantization.py::TestBackendConfig::test_backend_config_set_backend_pattern_config, test/test_quantization.py::TestBackendConfig::test_backend_op_config_add_dtype_config, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_observation_type, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_root_module, test/test_quantization.py::TestQuantizationDocs::test_quantization_doc_qat, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_convtranspose_per_channel_qconfig_none, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_manual, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_nested1, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_normalization, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_quantized_embedding, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_quantized_embedding_bag, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_quantwrapper_attaches_qconfig_to_dequant, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_single_layer, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_embedding_bag_dynamic, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_embedding_ops_dynamic, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_nested2, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_single_layer, test/test_quantization.py::TestQuantizeEagerOps::test_conv_1d, test/test_quantization.py::TestQuantizeEagerOps::test_conv_2d, test/test_quantization.py::TestQuantizeEagerOps::test_conv_transpose_2d, test/test_quantization.py::TestQuantizeEagerOps::test_functional_module, test/test_quantization.py::TestQuantizeEagerOps::test_int16_reference_module, test/test_quantization.py::TestQuantizeEagerOps::test_linear, test/test_quantization.py::TestQuantizeEagerOps::test_relu, test/test_quantization.py::TestQuantizeEagerQAT::test_embedding_bag_linear, test/test_quantization.py::TestQuantizeEagerQAT::test_manual, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_linear_precomputed_fake_quant, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_relu, test/test_quantization.py::TestFuseEager::test_fusion_conv_with_bias, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_outputs_linear_dynamic, test/test_quantization.py::TestNumericSuiteEager::test_compare_weights_linear_dynamic, test/test_quantization.py::TestNumericSuiteEager::test_compare_weights_lstm_dynamic, test/test_quantization.py::TestFuseFx::test_fuse_linear_bn_eval, test/test_quantization.py::TestFuseFx::test_linear_tanh_not_fused_by_default, test/test_quantization.py::TestFuseFx::test_qconfig_fused_module, test/test_quantization.py::TestQuantizeFx::test_backend_config_scale_min, test/test_quantization.py::TestQuantizeFx::test_conv_bn_relu, test/test_quantization.py::TestQuantizeFx::test_convert_custom_config_from_dict, test/test_quantization.py::TestQuantizeFx::test_convert_custom_config_set_observed_to_quantized_mapping, test/test_quantization.py::TestQuantizeFx::test_convert_qconfig_mapping, test/test_quantization.py::TestQuantizeFx::test_dynamic_linear_input_multiple_use, test/test_quantization.py::TestQuantizeFx::test_dynamic_quant_weight_observer, test/test_quantization.py::TestQuantizeFx::test_dynamic_with_fusion, test/test_quantization.py::TestQuantizeFx::test_fold_quant_dequant, test/test_quantization.py::TestQuantizeFx::test_fp32_input_fp32_output, test/test_quantization.py::TestQuantizeFx::test_masked_fill_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_not_used, test/test_quantization.py::TestQuantizeFx::test_observer_fqn, test/test_quantization.py::TestQuantizeFx::test_permute_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_preserved_attributes, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_split_tuple_args, test/test_quantization.py::TestQuantizeFx::test_qat_and_script, test/test_quantization.py::TestQuantizeFx::test_qconfig_dict_with_fused_modules, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_from_dict, test/test_quantization.py::TestQuantizeFx::test_qconfig_qat_module_type, test/test_quantization.py::TestQuantizeFx::test_quantized_input_fp32_output, test/test_quantization.py::TestQuantizeFx::test_remove_qconfig, test/test_quantization.py::TestQuantizeFx::test_reroute_tuple_getitem_patterns, test/test_quantization.py::TestQuantizeFx::test_save_observer_state_dict, test/test_quantization.py::TestQuantizeFx::test_trace_quantize_per_tensor, test/test_quantization.py::TestQuantizeFxOps::test_ave_pool_with_custom_cfg, test/test_quantization.py::TestQuantizeFxOps::test_bmm_int_reference, test/test_quantization.py::TestQuantizeFxOps::test_conv_module, test/test_quantization.py::TestQuantizeFxOps::test_copy_node_fp32_input, test/test_quantization.py::TestQuantizeFxOps::test_float_functional, test/test_quantization.py::TestQuantizeFxOps::test_functional_conv, test/test_quantization.py::TestQuantizeFxOps::test_gelu_normal, test/test_quantization.py::TestQuantizeFxOps::test_instance_norm, test/test_quantization.py::TestQuantizeFxOps::test_layer_norm, test/test_quantization.py::TestQuantizeFxOps::test_mul_relu, test/test_quantization.py::TestQuantizeFxOps::test_norm_weight_bias, test/test_quantization.py::TestQuantizeFxOps::test_pixel_shuffle, test/test_quantization.py::TestQuantizeFxOps::test_pixel_unshuffle, test/test_quantization.py::TestQuantizeFxOps::test_pixel_unshuffle_module, test/test_quantization.py::TestQuantizeFxOps::test_ref_pattern_multi_use, test/test_quantization.py::TestQuantizeFxModels::test_resnet18_ddp, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_multiple_pattern_match, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_output_pattern_node_can_have_users_that_are_not_matched, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_with_no_quant_inbetween, test/test_quantization.py::TestMetaDataPorting::test_no_metadata_porting, test/test_quantization.py::TestMetaDataPorting::test_simple_metadata_porting, test/test_quantization.py::TestNumericDebugger::test_deepcopy_preserve_handle, test/test_quantization.py::TestQuantizePT2E::test_move_exported_model_bn, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_bfloat16_float8_e4m3fn, test/test_quantization.py::TestQuantizePT2E::test_reentrant, test/test_quantization.py::TestPT2ERepresentation::test_qdq, test/test_quantization.py::TestPT2ERepresentation::test_static_linear, test/test_quantization.py::TestXNNPACKQuantizer::test_add_mul_scalar, test/test_quantization.py::TestXNNPACKQuantizer::test_conv1d_with_conv2d, test/test_quantization.py::TestXNNPACKQuantizer::test_conv_linear_no_permute, test/test_quantization.py::TestXNNPACKQuantizer::test_gru, test/test_quantization.py::TestXNNPACKQuantizer::test_linear_gru, test/test_quantization.py::TestXNNPACKQuantizer::test_linear_relu, test/test_quantization.py::TestXNNPACKQuantizer::test_propagate_annotation, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_avg_pool2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_cat_recipe_single_input, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_dynamic_quant_linear, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary2, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_dynamic, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_dynamic_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_unary_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_with_mixed_configs, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_no_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_transpose_bn, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_inplace_add_relu, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_fold_bn_erases_bn_node, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_cuda, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_literal_args, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion_cuda, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_preserve_source_fn_stack, test/test_quantization.py::TestQuantizePT2EQATModels::test_qat_mobilenet_v2, test/test_quantization.py::TestFXGraphMatcher::test_matching_failure_node_count, test/test_quantization.py::TestFXGraphMatcher::test_results_order, test/test_quantization.py::TestFXGraphMatcher::test_simple_fun, test/test_quantization.py::TestFXGraphMatcher::test_simple_tensor_ops, test/test_quantization.py::TestFXGraphMatcher::test_user_defined_function, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_mul_inputs_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_meth_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_cuda, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_mod_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_int8_shadows_int8_mod, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_shadow_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_loggers_preserve_qat_numerics, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_meth_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_shadow_loggers_preserve_qat_numerics, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_user_module_scriptable, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_conv_bn_relu_fusion_quant, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_fp32_quant, test/test_quantization.py::TestFXNumericSuiteNShadows::test_conv_bn_relu_mod, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_from_list, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_ordering, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_activations_conv, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_shadow_activations_linear, test/test_quantization.py::TestFxModelReportDetector::test_multi_linear_model_without_per_channel, test/test_quantization.py::TestFxModelReportDetector::test_sequential_model_format, test/test_quantization.py::TestFxModelReportObserver::test_random_epochs_and_batches, test/test_quantization.py::TestFxModelReportClass::test_generate_visualizer, test/test_quantization.py::TestFxDetectInputWeightEqualization::test_input_weight_equalization_report_gen_empty, test/test_quantization.py::TestFxDetectOutliers::test_no_outlier_report_gen, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_equalization_scales, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_graphs, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_results, test/test_quantization.py::TestSerialization::test_linear_relu_package_quantization_transforms, test/test_quantization.py::TestSerialization::test_lstm, test/test_quantization.py::TestSerialization::test_per_tensor_observer, test/test_quantization.py::TestQuantizeJit::test_conv_transpose, test/test_quantization.py::TestQuantizeJit::test_observer_with_ignored_function, test/test_quantization.py::TestQuantizeJit::test_single_linear_dynamic, test/test_quantization.py::TestQuantizeJit::test_skip_quant, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_in_submodule, test/test_quantization.py::TestQuantizeJitPasses::test_inplace_option, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_child_qconfig, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_if, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_propagate_observed_in_submodule, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_weight_dtype, test/test_quantization.py::TestQuantizeJitOps::test_conv_with_benchmark_flag, test/test_quantization.py::TestQuantizeJitOps::test_qbatch_norm_relu_BNFuncRelu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_scalar, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_quant_multi_uses, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_prepare_dynamic, test/test_quantization.py::TestQuantizeDynamicJitOps::test_embedding_bag, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_fuse_modules, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quantize_jit, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_qat_linear, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_batchnorm, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_utils, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_import_nn_intrinsic, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_fusion_patterns, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_graph_module, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_quantize_fx, test/test_quantization.py::TestBitsCPU::test_cat_cpu, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_rte_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_soak_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_soak_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_soak_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_soak_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_subnormals_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_special_numbers_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_special_numbers_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_type_promotion_fails_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPUOnlyCPU::test_mul_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPUOnlyCPU::test_pt2_traceable_aot_eager_cpu_float8_e4m3fn 2024-08-20T22:25:26.7299598Z 2024-08-20T22:25:29.3094977Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:25:29.4081314Z Running test_module_tracker 1/1 ... [2024-08-20 22:25:29.407575] 2024-08-20T22:25:29.4082775Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:25:29.4086236Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_module_tracker.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:25:29.408001] 2024-08-20T22:25:32.7771507Z 2024-08-20T22:25:32.7773758Z test_module_tracker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_module_tracker_1.1_b66ea8654ec417d0_.log 2024-08-20T22:25:32.7776936Z Running 2 items in this shard: test/test_module_tracker.py::TestModuleTracker::test_bw_detection, test/test_module_tracker.py::TestModuleTracker::test_module_hierarchy 2024-08-20T22:25:32.7778768Z 2024-08-20T22:25:35.3896325Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:25:35.4477606Z Running torch_np/test_basic 1/1 ... [2024-08-20 22:25:35.446920] 2024-08-20T22:25:35.4478510Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:25:35.4481844Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_basic.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:25:35.447317] 2024-08-20T22:25:48.6356549Z 2024-08-20T22:25:48.6358764Z torch_np/test_basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_basic_1.1_1850620accb07602_.log 2024-08-20T22:25:48.6711404Z Running 453 items in this shard: test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func0, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func1, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func10, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func11, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func12, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func13, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func14, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func15, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func16, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func17, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func18, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func19, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func2, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func20, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func21, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func22, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func23, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func24, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func25, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func26, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func27, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func28, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func29, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func3, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func30, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func31, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func32, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func33, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func34, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func35, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func36, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func37, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func38, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func39, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func4, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func40, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func41, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func42, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func43, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func44, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func45, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func46, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func47, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func48, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func49, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func5, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func50, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func51, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func52, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func53, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func54, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func55, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func56, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func57, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func58, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func59, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func6, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func60, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func61, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func62, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func63, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func64, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func65, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func66, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func67, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func68, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func69, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func7, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func70, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func71, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func72, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func73, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func74, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func8, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func9, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func0, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func1, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func10, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func11, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func12, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func13, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func14, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func15, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func16, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func17, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func18, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func19, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func2, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func20, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func21, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func22, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func23, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func24, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func25, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func26, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func27, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func28, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func29, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func3, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func30, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func31, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func32, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func33, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func34, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func35, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func36, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func37, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func38, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func39, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func4, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func40, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func41, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func42, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func43, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func44, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func45, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func46, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func47, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func48, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func49, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func5, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func50, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func51, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func52, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func53, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func54, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func55, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func56, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func57, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func58, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func59, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func6, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func60, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func61, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func62, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func63, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func64, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func65, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func66, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func67, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func68, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func69, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func7, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func70, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func71, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func72, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func73, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func74, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func8, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func9, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func0, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func1, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func10, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func11, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func12, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func13, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func14, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func15, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func16, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func17, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func18, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func19, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func2, test/torch_np/test_basic.py::TestOneArr::test_asarray_tensor_func20, 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test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_array_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func3, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func0, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func1, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func2, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func3, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func4, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func5, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func6, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_array_func0, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_array_func1, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_list_func0, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_list_func1, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_tensor_func0, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_tensor_func1, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func0_args0, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func1_args1, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func2_args2, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func3_args3, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func4_args4, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func5_args5, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func6_args6, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func7_args7, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func8_args8, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func9_args9, test/torch_np/test_basic.py::TestNormalizations::test_too_few_args_positional, test/torch_np/test_basic.py::TestNormalizations::test_unknown_args, test/torch_np/test_basic.py::TestNormalizations::test_unknown_args_with_defaults, test/torch_np/test_basic.py::TestCopyTo::test_copyto_basic, test/torch_np/test_basic.py::TestCopyTo::test_copyto_typecast, test/torch_np/test_basic.py::TestCopyTo::test_copytobcast, test/torch_np/test_basic.py::TestDivmod::test_divmod_no_out, test/torch_np/test_basic.py::TestDivmod::test_divmod_out, test/torch_np/test_basic.py::TestDivmod::test_divmod_out_both_pos_and_kw, test/torch_np/test_basic.py::TestDivmod::test_divmod_out_list, test/torch_np/test_basic.py::TestDivmod::test_divmod_pos_only, test/torch_np/test_basic.py::TestSmokeNotImpl::test_nimpl_basic, test/torch_np/test_basic.py::TestDefaultDtype::test_defaultdtype_defaults, test/torch_np/test_basic.py::TestDefaultDtype::test_set_default_float_dt_float32, test/torch_np/test_basic.py::TestDefaultDtype::test_set_default_float_dt_pytorch, test/torch_np/test_basic.py::TestDefaultDtype::test_set_default_float_float32, test/torch_np/test_basic.py::TestExport::test_exported_objects, test/torch_np/test_basic.py::TestCtorNested::test_arrays_in_lists, test/torch_np/test_basic.py::TestMisc::test_f16_on_cuda, test/torch_np/test_basic.py::TestMisc::test_ndarrays_to_tensors 2024-08-20T22:25:48.7017596Z 2024-08-20T22:25:51.2464091Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:25:51.3038491Z Running test_autoload 1/1 ... [2024-08-20 22:25:51.303393] 2024-08-20T22:25:51.3039356Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:25:51.3044805Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_autoload.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:25:51.303799] 2024-08-20T22:25:54.4226285Z 2024-08-20T22:25:54.4228573Z test_autoload 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autoload_1.1_a3f674af424e14ed_.log 2024-08-20T22:25:54.4231188Z Running 1 items in this shard: test/test_autoload.py::TestDeviceBackendAutoload::test_autoload 2024-08-20T22:25:54.4232443Z 2024-08-20T22:25:57.3206470Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:25:57.4147327Z Running torch_np/test_binary_ufuncs 1/1 ... [2024-08-20 22:25:57.414223] 2024-08-20T22:25:57.4152047Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:25:57.4155671Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_binary_ufuncs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:25:57.414933] 2024-08-20T22:26:02.5879739Z 2024-08-20T22:26:02.5882919Z torch_np/test_binary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_binary_ufuncs_1.1_10ae08dfb7c59456_.log 2024-08-20T22:26:02.6012124Z Running 38 items in this shard: test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_add, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_arctan2, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_bitwise_and, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_bitwise_or, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_bitwise_xor, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_copysign, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_divide, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_equal, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_float_power, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_floor_divide, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_fmax, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_fmin, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_fmod, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_gcd, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_greater, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_greater_equal, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_heaviside, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_hypot, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_lcm, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_ldexp, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_left_shift, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_less, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_less_equal, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logaddexp, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logaddexp2, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logical_and, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logical_or, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logical_xor, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_matmul, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_maximum, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_minimum, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_multiply, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_nextafter, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_not_equal, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_power, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_remainder, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_right_shift, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_subtract 2024-08-20T22:26:02.6046323Z 2024-08-20T22:26:05.7505453Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:26:05.8399730Z Running torch_np/test_unary_ufuncs 1/1 ... [2024-08-20 22:26:05.839490] 2024-08-20T22:26:05.8402715Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:26:05.8407783Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_unary_ufuncs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:26:05.839918] 2024-08-20T22:26:10.5126481Z 2024-08-20T22:26:10.5130007Z torch_np/test_unary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_unary_ufuncs_1.1_114ef43782e6fa49_.log 2024-08-20T22:26:10.5148335Z Running 42 items in this shard: test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_absolute, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arccos, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arccosh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arcsin, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arcsinh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arctan, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arctanh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_cbrt, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_ceil, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_conjugate, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_cos, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_cosh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_deg2rad, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_degrees, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_exp, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_exp2, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_expm1, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_fabs, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_floor, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_isfinite, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_isinf, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_isnan, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_log, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_log10, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_log1p, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_log2, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_logical_not, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_negative, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_positive, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_rad2deg, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_radians, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_reciprocal, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_rint, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_sign, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_signbit, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_sin, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_sinh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_sqrt, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_square, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_tan, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_tanh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_trunc 2024-08-20T22:26:10.5164147Z 2024-08-20T22:26:13.3914851Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:26:13.4803885Z Running profiler/test_cpp_thread 1/1 ... [2024-08-20 22:26:13.479788] 2024-08-20T22:26:13.4805011Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:26:13.4810555Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_cpp_thread.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:26:13.480211] 2024-08-20T22:26:16.9003803Z 2024-08-20T22:26:16.9005958Z profiler/test_cpp_thread 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_cpp_thread_1.1_86088da216a67fff_.log 2024-08-20T22:26:16.9010004Z Running 3 items in this shard: test/profiler/test_cpp_thread.py::CppThreadTest::test_profile_memory, test/profiler/test_cpp_thread.py::CppThreadTest::test_with_enable_profiler_in_child_thread, test/profiler/test_cpp_thread.py::CppThreadTest::test_without_enable_profiler_in_child_thread 2024-08-20T22:26:16.9012530Z 2024-08-20T22:26:19.3054102Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:26:19.3631365Z Running test_typing 1/1 ... [2024-08-20 22:26:19.362661] 2024-08-20T22:26:19.3632373Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:26:19.3635628Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_typing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:26:19.363017] 2024-08-20T22:27:21.0558891Z 2024-08-20T22:27:21.0561280Z test_quantization 3/5 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_3.5_4ac30ad5ba7749dd_.log 2024-08-20T22:27:21.0759854Z Running 248 items in this shard: test/test_quantization.py::TestQuantizedOps::test_adaptive_avg_pool, test/test_quantization.py::TestQuantizedOps::test_adaptive_avg_pool2d_nhwc, test/test_quantization.py::TestQuantizedOps::test_avg_pool3d_nhwc, test/test_quantization.py::TestQuantizedOps::test_batch_norm_relu, test/test_quantization.py::TestQuantizedOps::test_custom_module_multi_head_attention, test/test_quantization.py::TestQuantizedOps::test_hardswish, test/test_quantization.py::TestQuantizedOps::test_interpolate3d, test/test_quantization.py::TestQuantizedOps::test_max_pool2d_pt2e, test/test_quantization.py::TestQuantizedOps::test_qadd_broadcast, test/test_quantization.py::TestQuantizedOps::test_qadd_relu_cudnn, test/test_quantization.py::TestQuantizedOps::test_qelu, test/test_quantization.py::TestQuantizedOps::test_qprelu, test/test_quantization.py::TestQuantizedOps::test_qrelu6, test/test_quantization.py::TestQuantizedOps::test_quantized_equal, test/test_quantization.py::TestQuantizedOps::test_sigmoid_dequantize_rounding_error, test/test_quantization.py::TestQuantizedOps::test_sigmoid_non_observed, test/test_quantization.py::TestQNNPackOps::test_avg_pool2d, test/test_quantization.py::TestQNNPackOps::test_qnnpack_sigmoid, test/test_quantization.py::TestQuantizedLinear::test_qlinear_gelu_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_qnnpack_free_memory_and_unpack, test/test_quantization.py::TestQuantizedLinear::test_qlinear_sum_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_sum_relu_pt2e, test/test_quantization.py::TestQuantizedConv::test_benchmark, test/test_quantization.py::TestQuantizedConv::test_conv_reorder_issue_onednn, test/test_quantization.py::TestQuantizedConv::test_conv_transpose_reorder_issue_onednn, test/test_quantization.py::TestQuantizedConv::test_qconv1d_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv1d_relu, test/test_quantization.py::TestQuantizedConv::test_qconv2d_add_relu, test/test_quantization.py::TestQuantizedConv::test_qconv2d_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv3d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_conv1d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_convtranspose3d, test/test_quantization.py::TestDynamicQuantizedOps::test_qlinear, test/test_quantization.py::TestDynamicQuantizedOps::test_qrnncell, test/test_quantization.py::TestDynamicQuantizedOps::test_wrapped_fbgemm_linear_fp16, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_channel, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_channel_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_channel_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_forward_backward_per_tensor_with_amp, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_channel_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_channel_half_precision_numerics, test/test_quantization.py::TestFakeQuantizeOps::test_fq_module_per_tensor, test/test_quantization.py::TestFakeQuantizeOps::test_fq_serializable_per_tensor, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_forward_per_channel_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_forward_per_tensor_cpu, test/test_quantization.py::TestFusedObsFakeQuant::test_fused_backward_op_fake_quant_off, test/test_quantization.py::TestQuantizedTensor::test_decomposed_choose_qparams_per_token_asymmetric_backward, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_channel, test/test_quantization.py::TestQuantizedTensor::test_dequantize_fp16_cuda, test/test_quantization.py::TestQuantizedTensor::test_jit_serialization, test/test_quantization.py::TestQuantizedTensor::test_per_channel_qtensor_creation_cuda, test/test_quantization.py::TestQuantizedTensor::test_per_tensor_to_device, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_tensor_nhwc, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_select_cuda, test/test_quantization.py::TestQuantizedTensor::test_qtensor_int_repr, test/test_quantization.py::TestQuantizedTensor::test_qtensor_masked_fill_cuda, test/test_quantization.py::TestQuantizedTensor::test_qtensor_per_channel_permute, test/test_quantization.py::TestQuantizedTensor::test_quant_pin_memory, test/test_quantization.py::TestQuantizedTensor::test_quantize_per_channel_float_qparams, test/test_quantization.py::TestQuantizedTensor::test_torch_qtensor_deepcopy, test/test_quantization.py::TestObserver::test_histogram_observer_handle_OOM_due_to_close_min_max_value, test/test_quantization.py::TestObserver::test_histogram_observer_handle_close_to_infinity, test/test_quantization.py::TestObserver::test_histogram_observer_save_load_state_dict, test/test_quantization.py::TestObserver::test_zero_numel, test/test_quantization.py::TestStaticQuantizedModule::test_batch_norm2d_serialization, test/test_quantization.py::TestStaticQuantizedModule::test_batch_norm3d, test/test_quantization.py::TestStaticQuantizedModule::test_conv1d_api, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_add_relu, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_relu_api, test/test_quantization.py::TestStaticQuantizedModule::test_conv3d_relu_api, test/test_quantization.py::TestStaticQuantizedModule::test_quant_dequant_api, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_conv1d, test/test_quantization.py::TestReferenceQuantizedModule::test_sparse, test/test_quantization.py::TestHistogramObserver::test_histogram_observer, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_extreme_inputs, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_same_inputs, test/test_quantization.py::TestHistogramObserver::test_observer_scriptable, test/test_quantization.py::TestFusedObsFakeQuantModule::test_fused_mod_reduce_range, test/test_quantization.py::TestBackendConfig::test_backend_config_from_dict, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_fused_module, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_reference_quantized_module, test/test_quantization.py::TestUtils::test_quantize_weight_clamping_per_channel, test/test_quantization.py::TestQuantizationDocs::test_quantization_doc_fx, test/test_quantization.py::TestQuantizationDocs::test_quantization_doc_ptdq, test/test_quantization.py::TestQuantizationDocs::test_quantization_doc_ptsq, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_activations, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_activations_in_non_leaf_module_list, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_two_layers, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_nested3, test/test_quantization.py::TestQuantizeEagerQAT::test_dropout, test/test_quantization.py::TestQuantizeEagerQAT::test_qat_embedding_bag_errors, test/test_quantization.py::TestQuantizeEagerQAT::test_train_save_load_eval, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_conv_bn_relu, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_fixed_qparam_ops, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_linear_bn_numerics, test/test_quantization.py::TestFuseEager::test_fuse_module_eval, test/test_quantization.py::TestModelNumericsEager::test_weight_only_activation_only_fakequant, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_functional_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_weights_conv_static, test/test_quantization.py::TestEqualizeEager::test_converged, test/test_quantization.py::TestEqualizeEager::test_equalize, test/test_quantization.py::TestBiasCorrectionEager::test_conv_chain, test/test_quantization.py::TestFuseFx::test_fuse_addtional_fuser_method, test/test_quantization.py::TestFuseFx::test_fuse_conv_bn_add_relu_onednn, test/test_quantization.py::TestFuseFx::test_fuse_conv_bn_relu, test/test_quantization.py::TestFuseFx::test_fusion_pattern_with_multiple_inputs, test/test_quantization.py::TestQuantizeFx::test__convert_to_reference_decomposed_fx, test/test_quantization.py::TestQuantizeFx::test__convert_to_reference_decomposed_fx_dynamic_quant, test/test_quantization.py::TestQuantizeFx::test_attention, test/test_quantization.py::TestQuantizeFx::test_backend_config_check_for_weight_and_bias, test/test_quantization.py::TestQuantizeFx::test_backend_config_quantization_range, test/test_quantization.py::TestQuantizeFx::test_conv_linear_not_reference, test/test_quantization.py::TestQuantizeFx::test_conv_lowering, test/test_quantization.py::TestQuantizeFx::test_conv_transpose_not_reference, test/test_quantization.py::TestQuantizeFx::test_convtranspose_per_channel_fails_early, test/test_quantization.py::TestQuantizeFx::test_custom_module_class_input_has_multiple_users, test/test_quantization.py::TestQuantizeFx::test_deepcopy_preserve_attributes, test/test_quantization.py::TestQuantizeFx::test_fp32_input_quantized_output, test/test_quantization.py::TestQuantizeFx::test_get_executorch_backend_config, test/test_quantization.py::TestQuantizeFx::test_linear_leaky_relu_lowering, test/test_quantization.py::TestQuantizeFx::test_linear_tanh_lowering, test/test_quantization.py::TestQuantizeFx::test_lowering_functional_conv_with_kwargs, test/test_quantization.py::TestQuantizeFx::test_mul_add_fp16_config, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_from_dict, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_float_to_observed_mapping, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_non_traceable_module_names, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_to_dict, test/test_quantization.py::TestQuantizeFx::test_prepared_model_deepcopy, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_tuple_args, test/test_quantization.py::TestQuantizeFx::test_qat_skip_untraced, test/test_quantization.py::TestQuantizeFx::test_qconfig_dict_setup, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_global, test/test_quantization.py::TestQuantizeFx::test_qconfig_module_type, test/test_quantization.py::TestQuantizeFx::test_qnnpack_backend_config, test/test_quantization.py::TestQuantizeFx::test_quantized_input_quantized_output, test/test_quantization.py::TestQuantizeFx::test_ref_conv_module, test/test_quantization.py::TestQuantizeFx::test_reuse_input_qconfig, test/test_quantization.py::TestQuantizeFx::test_size_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_torch_transpose_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_transpose_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFxOps::test_bmm, test/test_quantization.py::TestQuantizeFxOps::test_boolean_tensor, test/test_quantization.py::TestQuantizeFxOps::test_chunk, test/test_quantization.py::TestQuantizeFxOps::test_fixed_qparams_ops_fp16, test/test_quantization.py::TestQuantizeFxOps::test_general_value_ops, test/test_quantization.py::TestQuantizeFxOps::test_leaky_relu, test/test_quantization.py::TestQuantizeFxOps::test_linear_static_fp16, test/test_quantization.py::TestQuantizeFxOps::test_quantized_mul_qat, test/test_quantization.py::TestQuantizeFxModels::test_prepare_serialize_switch_device_convert, test/test_quantization.py::TestQuantizeFxModels::test_qat_embedding_linear, test/test_quantization.py::TestQuantizeFxModels::test_switch_device_prepare_convert, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_annotations_int, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_preserves_logic, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_replaces_referenced_submodules, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_single_pattern_match, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_traced_as_callable, test/test_quantization.py::TestGraphUtils::test_conv_bn_conv_relu, test/test_quantization.py::TestDuplicateDQPass::test_simple_duplicate_dq, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_for_two_dq, test/test_quantization.py::TestNumericDebugger::test_prepare_for_propagation_comparison, test/test_quantization.py::TestQuantizePT2E::test_allow_implicit_sharing, test/test_quantization.py::TestQuantizePT2E::test_composable_quantizer_throw, test/test_quantization.py::TestQuantizePT2E::test_disallow_eval_train, test/test_quantization.py::TestQuantizePT2E::test_fixed_qparams_qspec_observer_dedup, test/test_quantization.py::TestQuantizePT2E::test_fixed_qparams_qspec_qat, test/test_quantization.py::TestQuantizePT2E::test_groupwise_per_channel_quant, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_bfloat16_int16, test/test_quantization.py::TestXNNPACKQuantizer::test_add_mul_long, test/test_quantization.py::TestXNNPACKQuantizer::test_conv2d, test/test_quantization.py::TestXNNPACKQuantizer::test_linear, test/test_quantization.py::TestXNNPACKQuantizer::test_mul_float32_max, test/test_quantization.py::TestXNNPACKQuantizer::test_qat_dynamic_linear, test/test_quantization.py::TestXNNPACKQuantizerModels::test_resnet18, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_cat_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d_binary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_filter_conv2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_filter_maxpool2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_dynamic, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_unary_dynamic_qat, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_case2, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_with_mixed_configs, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_qconfig_with_underscores, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_prepare_qat_conv_bn_fusion_getitem_placeholder, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_literal_args, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_prepare_qat_conv_bn_fusion_getitem_placeholder, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion_no_conv_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_no_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_inplace_add_relu, test/test_quantization.py::TestFXGraphMatcher::test_dict_return_type, test/test_quantization.py::TestFXGraphMatcher::test_simple_mod, test/test_quantization.py::TestFXGraphMatcherModels::test_mobilenet_v2, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_fqn, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_op_with_either_fp32_or_int8_input, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_op_with_only_kwargs_skips_shadowing, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_user_module, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_fp32_fp32, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_quant_fp32, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_end_to_end, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_insert_padding, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_shadow_activations_conv, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_shadow_activations_lstm_dynamic, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_mobilenet_v2, test/test_quantization.py::TestFxModelReportDetector::test_conv_sub_class_considered, test/test_quantization.py::TestFxModelReportDetector::test_multiple_q_config_options, test/test_quantization.py::TestFxModelReportDetector::test_simple_conv, test/test_quantization.py::TestFxModelReportClass::test_equalization_mapping_generation, test/test_quantization.py::TestFxDetectOutliers::test_all_outlier_report_gen, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_prepare, test/test_quantization.py::TestEqualizeFx::test_selective_equalization, test/test_quantization.py::TestSerialization::test_linear_relu, test/test_quantization.py::TestQuantizeJit::test_single_linear, test/test_quantization.py::TestQuantizeJitPasses::test_finalize_debug, test/test_quantization.py::TestQuantizeJitPasses::test_fuse_linear, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_nested_if, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_reused_weight, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_interface, test/test_quantization.py::TestQuantizeJitPasses::test_insert_quant_dequant_shared_class_type, test/test_quantization.py::TestQuantizeJitOps::test_cat_linear, test/test_quantization.py::TestQuantizeJitOps::test_clamp, test/test_quantization.py::TestQuantizeJitOps::test_elu, test/test_quantization.py::TestQuantizeJitOps::test_qbatch_norm, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_relu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_conv, test/test_quantization.py::TestQuantizeJitOps::test_quantized_conv_relu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul_scalar_relu, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_insert_quant_dequant_linear_dynamic, test/test_quantization.py::TestFusionPasses::test_quantized_add_relu_fusion, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quantize, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_quantizable_rnn, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_activation, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_embedding_ops, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_import_nn_intrinsic_qat, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_quantized_bn_relu, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_convert, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_equalize, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_fuse, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_rte_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_subnormals_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_special_numbers_cpu_float8_e5m2fnuz 2024-08-20T22:27:21.0938941Z 2024-08-20T22:27:23.9992534Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:27:24.0899054Z Running torch_np/test_dtype 1/1 ... [2024-08-20 22:27:24.089369] 2024-08-20T22:27:24.0900042Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:27:24.0907263Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_dtype.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:27:24.089798] 2024-08-20T22:27:28.3603272Z 2024-08-20T22:27:28.3605374Z torch_np/test_dtype 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_dtype_1.1_8d18246b30df3fc4_.log 2024-08-20T22:27:28.3648087Z Running 44 items in this shard: test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'bool_', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'complex128', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'complex64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'float16', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'float32', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'float64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'int16', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'int32', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'int64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'int8', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'uint16', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'uint32', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'uint64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_'uint8', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_bool, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'bool_', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'complex128', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'complex64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'float16', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'float32', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'float64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'int16', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'int32', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'int64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'int8', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'uint16', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'uint32', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'uint64', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.'uint8', test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.bool_, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.complex128, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.complex64, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.dtype('bool'), test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.float16, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.float32, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.float64, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.int16, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.int32, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.int64, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.int8, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.uint16, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.uint32, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.uint64, test/torch_np/test_dtype.py::TestConvertDType::test_convert_np_dtypes_numpy.uint8 2024-08-20T22:27:28.3681663Z 2024-08-20T22:27:31.0851694Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:27:31.1443401Z Running torch_np/test_nep50_examples 1/1 ... [2024-08-20 22:27:31.143846] 2024-08-20T22:27:31.1444412Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:27:31.1447443Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_nep50_examples.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:27:31.144271] 2024-08-20T22:27:57.5531647Z 2024-08-20T22:27:57.5533486Z test_typing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_typing_1.1_17a384b93dbc4b2b_.log 2024-08-20T22:27:57.5540734Z Running 13 items in this shard: test/test_typing.py::TestTyping::test_fail_creation_ops.py, test/test_typing.py::TestTyping::test_fail_random.py, test/test_typing.py::TestTyping::test_reveal_module_list.py, test/test_typing.py::TestTyping::test_reveal_namedtuple.py, test/test_typing.py::TestTyping::test_reveal_opt_size.py, test/test_typing.py::TestTyping::test_reveal_size.py, test/test_typing.py::TestTyping::test_reveal_tensor_constructors.py, test/test_typing.py::TestTyping::test_reveal_tensor_copy.py, test/test_typing.py::TestTyping::test_reveal_tensor_sampling.py, test/test_typing.py::TestTyping::test_reveal_torch_optim.py, test/test_typing.py::TestTyping::test_success_creation_ops.py, test/test_typing.py::TestTyping::test_success_cuda_steam.py, test/test_typing.py::TestTyping::test_success_math_ops.py 2024-08-20T22:27:57.5545608Z 2024-08-20T22:28:00.0695662Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:00.1279887Z Running distributions/test_constraints 1/1 ... [2024-08-20 22:28:00.127593] 2024-08-20T22:28:00.1280694Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:00.1283588Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'distributions/test_constraints.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:28:00.127981] 2024-08-20T22:28:00.8559829Z 2024-08-20T22:28:00.8561453Z torch_np/test_nep50_examples 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_nep50_examples_1.1_6e0d93eb3d7a1424_.log 2024-08-20T22:28:00.9796535Z Running 1897 items in this shard: test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_3j + array(3, complex64), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_True + uint8(2), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array(1_0, float32) + 1e-14 == 1_0, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([0_1], float32) == float64(0_1), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([100], uint8) + 200, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + 1, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + 200, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + 300, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + array(1, int64), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + int64(1), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_0], float32) + 1e-14 == 1_0, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + 3, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + array(1_, float64), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + float64(1_), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + int64(3), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_bool_(True) + 1, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_float32(1) + 1j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_float32(1) + 3e100, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_float32(5) + 5j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_int16(2) + 2, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_int16(4) + 4j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_int32(1) + 5j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_uint8(1) + 2, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_uint8(1) + 300, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_uint8(100) + 200, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar27_array27, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar28_array28, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar29_array29, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar30_array30, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar31_array31, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar32_array32, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar33_array33, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar34_array34, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar35_array35, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array10, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array11, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array12, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array13, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array14, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array15, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array16, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array17, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array9, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array18, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array19, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array20, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array21, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array22, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array23, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array24, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array25, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array26, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_True_array0, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_True_array1, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_True_array2, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_True_array3, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_True_array4, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_True_array5, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_True_array6, 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test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array53_dtype53, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array54_dtype54, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array55_dtype55, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array56_dtype56, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array57_dtype57, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array58_dtype58, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array59_dtype59, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array5_dtype5, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array60_dtype60, 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test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array6_dtype6, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array70_dtype70, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array71_dtype71, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array72_dtype72, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array73_dtype73, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array74_dtype74, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array75_dtype75, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array76_dtype76, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array77_dtype77, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array78_dtype78, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array79_dtype79, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array7_dtype7, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array80_dtype80, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array81_dtype81, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array82_dtype82, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array83_dtype83, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array84_dtype84, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array85_dtype85, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array86_dtype86, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array87_dtype87, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array88_dtype88, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array89_dtype89, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array8_dtype8, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array9_dtype9 2024-08-20T22:28:01.0846444Z 2024-08-20T22:28:03.1974547Z 2024-08-20T22:28:03.1977049Z distributions/test_constraints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_constraints_1.1_2f44e16473b073c4_.log 2024-08-20T22:28:03.2051674Z Running 136 items in this shard: test/distributions/test_constraints.py::test_constraint[False-constraint_fn0-False-value0], test/distributions/test_constraints.py::test_constraint[False-constraint_fn1-False-value1], test/distributions/test_constraints.py::test_constraint[False-constraint_fn2-False-value2], test/distributions/test_constraints.py::test_constraint[False-constraint_fn3-True-value3], test/distributions/test_constraints.py::test_constraint[False-constraint_fn4-False-value4], test/distributions/test_constraints.py::test_constraint[False-constraint_fn5-False-value5], test/distributions/test_constraints.py::test_constraint[False-constraint_fn6-True-value6], test/distributions/test_constraints.py::test_constraint[False-constraint_fn7-True-value7], test/distributions/test_constraints.py::test_constraint[False-constraint_fn8-False-value8], test/distributions/test_constraints.py::test_constraint[False-constraint_fn9-True-value9], test/distributions/test_constraints.py::test_constraint[False-constraint_fn10-False-value10], test/distributions/test_constraints.py::test_constraint[False-constraint_fn11-False-value11], test/distributions/test_constraints.py::test_constraint[False-constraint_fn12-True-value12], test/distributions/test_constraints.py::test_constraint[False-constraint_fn13-True-value13], test/distributions/test_constraints.py::test_constraint[False-constraint_fn14-False-value14], test/distributions/test_constraints.py::test_constraint[False-constraint_fn15-True-value15], test/distributions/test_constraints.py::test_constraint[False-constraint_fn16-True-value16], test/distributions/test_constraints.py::test_constraint[False-constraint_fn17-True-value17], test/distributions/test_constraints.py::test_constraint[True-constraint_fn0-False-value0], test/distributions/test_constraints.py::test_constraint[True-constraint_fn1-False-value1], test/distributions/test_constraints.py::test_constraint[True-constraint_fn2-False-value2], test/distributions/test_constraints.py::test_constraint[True-constraint_fn3-True-value3], test/distributions/test_constraints.py::test_constraint[True-constraint_fn4-False-value4], test/distributions/test_constraints.py::test_constraint[True-constraint_fn5-False-value5], test/distributions/test_constraints.py::test_constraint[True-constraint_fn6-True-value6], test/distributions/test_constraints.py::test_constraint[True-constraint_fn7-True-value7], test/distributions/test_constraints.py::test_constraint[True-constraint_fn8-False-value8], test/distributions/test_constraints.py::test_constraint[True-constraint_fn9-True-value9], test/distributions/test_constraints.py::test_constraint[True-constraint_fn10-False-value10], test/distributions/test_constraints.py::test_constraint[True-constraint_fn11-False-value11], test/distributions/test_constraints.py::test_constraint[True-constraint_fn12-True-value12], test/distributions/test_constraints.py::test_constraint[True-constraint_fn13-True-value13], test/distributions/test_constraints.py::test_constraint[True-constraint_fn14-False-value14], test/distributions/test_constraints.py::test_constraint[True-constraint_fn15-True-value15], test/distributions/test_constraints.py::test_constraint[True-constraint_fn16-True-value16], test/distributions/test_constraints.py::test_constraint[True-constraint_fn17-True-value17], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn0-args0], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn1-args1], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn2-args2], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args3], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args4], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args5], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args6], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args10], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args11], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args12], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args13], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn14-args14], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args15], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args16], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args17], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn21-args21], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn22-args22], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn23-args23], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn24-args24], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn0-args0], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn1-args1], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn2-args2], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args3], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args4], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args5], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args6], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args10], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args11], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args12], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args13], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn14-args14], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args15], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args16], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args17], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn21-args21], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn22-args22], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn23-args23], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn24-args24], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn0-args0], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn1-args1], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn2-args2], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args3], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args4], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args5], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args6], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args10], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args11], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args12], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args13], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn14-args14], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args15], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args16], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args17], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn21-args21], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn22-args22], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn23-args23], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn24-args24], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn0-args0], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn1-args1], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn2-args2], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args3], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args4], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args5], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args6], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args10], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args11], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args12], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args13], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn14-args14], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args15], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args16], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args17], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn21-args21], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn22-args22], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn23-args23], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn24-args24] 2024-08-20T22:28:03.2116653Z 2024-08-20T22:28:03.5949087Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:03.6543497Z Running test_compile_benchmark_util 1/1 ... [2024-08-20 22:28:03.653893] 2024-08-20T22:28:03.6544650Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:03.6548049Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_compile_benchmark_util.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:28:03.654323] 2024-08-20T22:28:05.8481006Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:05.9076473Z Running test_fx_experimental 1/1 ... [2024-08-20 22:28:05.907125] 2024-08-20T22:28:05.9077444Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:05.9080106Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_fx_experimental.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:28:05.907560] 2024-08-20T22:28:06.4731344Z 2024-08-20T22:28:06.4733311Z test_compile_benchmark_util 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_compile_benchmark_util_1.1_e8805c93944c5de2_.log 2024-08-20T22:28:06.4734959Z Running 1 items in this shard: test/test_compile_benchmark_util.py::TestCompileBenchmarkUtil::test_training_and_inference 2024-08-20T22:28:06.4735714Z 2024-08-20T22:28:09.1557069Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:09.2136469Z Running torch_np/numpy_tests/core/test_scalarinherit 1/1 ... [2024-08-20 22:28:09.213127] 2024-08-20T22:28:09.2137797Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:09.2141373Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalarinherit.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:28:09.213513] 2024-08-20T22:28:11.9327498Z 2024-08-20T22:28:11.9330545Z torch_np/numpy_tests/core/test_scalarinherit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalarinherit_1.1_a9b3735145c97d44_.log 2024-08-20T22:28:11.9333188Z Running 3 items in this shard: test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_gh_15395, test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_init, test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_init2 2024-08-20T22:28:11.9334733Z 2024-08-20T22:28:14.4760762Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:14.5342338Z Running torch_np/numpy_tests/core/test_einsum 1/1 ... [2024-08-20 22:28:14.533754] 2024-08-20T22:28:14.5343821Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:14.5347269Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_einsum.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:28:14.534152] 2024-08-20T22:28:45.3647492Z 2024-08-20T22:28:45.3650755Z torch_np/numpy_tests/core/test_einsum 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_einsum_1.1_5a0d6606a6fa1c86_.log 2024-08-20T22:28:45.3675238Z Running 50 items in this shard: test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_broadcasting_dot_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_collapse, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_combined_views_mapping, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_complex, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_B, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_D, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_F, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_b, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_d, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_e, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_f, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_h, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_i, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_l, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_edge_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_all_contig_non_contig_output, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_broadcast, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_errors, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_failed_on_p9_and_s390x, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_fixed_collapsingbug, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_fixedstridebug, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_misc, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_cfloat128, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_cfloat64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float16, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float32, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int16, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int32, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int8, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_uint8, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_views, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_expand, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_hadamard_like_products, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_index_transformations, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_inner_product, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_out_is_res, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_output_order, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_random_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_small_boolean_arrays, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_subscript_range, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_edge_paths, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_long_paths, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_memory_contraints, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input_internal_trace, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input_invalid, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_spaces, test/torch_np/numpy_tests/core/test_einsum.py::TestMisc::test_overlap 2024-08-20T22:28:45.3698923Z 2024-08-20T22:28:47.9160939Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:47.9745394Z Running functorch/test_logging 1/1 ... [2024-08-20 22:28:47.974090] 2024-08-20T22:28:47.9746405Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:47.9749468Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'functorch/test_logging.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:28:47.974469] 2024-08-20T22:28:51.4953717Z 2024-08-20T22:28:51.4956312Z functorch/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_logging_1.1_c748969cd2224faa_.log 2024-08-20T22:28:51.4958710Z Running 1 items in this shard: test/functorch/test_logging.py::TestAOTLogging::test_logging 2024-08-20T22:28:51.4959656Z 2024-08-20T22:28:54.5187000Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:28:54.6089359Z Running torch_np/test_ufuncs_basic 1/1 ... [2024-08-20 22:28:54.608423] 2024-08-20T22:28:54.6090711Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:28:54.6094320Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_ufuncs_basic.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:28:54.608869] 2024-08-20T22:29:18.4642880Z 2024-08-20T22:29:18.4646449Z torch_np/test_ufuncs_basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_ufuncs_basic_1.1_c8e2ce933133f6d9_.log 2024-08-20T22:29:18.5056063Z Running 371 items in this shard: test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_scalar_ufunc0, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_equiv_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_equiv_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_equiv_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_no_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_no_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_no_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_safe_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_safe_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_safe_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_same_kind_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_same_kind_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_same_kind_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_unsafe_ufunc0_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_unsafe_ufunc0_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_casting_casting_unsafe_ufunc0_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_dtype_ufunc0, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_broadcast_ufunc0, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_equiv_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_equiv_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_equiv_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_no_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_no_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_no_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_safe_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_safe_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_safe_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_same_kind_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_same_kind_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_same_kind_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_unsafe_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_unsafe_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestUnaryUfuncs::test_x_and_out_casting_casting_unsafe_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc0, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc1, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc10, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc11, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc12, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc13, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc14, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc15, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc16, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc2, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc3, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc4, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc5, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc6, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc7, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc8, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_scalar_ufunc9, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc0, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc1, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc10, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc11, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc12, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc13, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc14, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc15, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc16, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc2, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc3, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc4, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc5, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc6, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc7, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc8, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_vector_vs_scalar_ufunc9, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc0, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc1, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc10, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc11, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc12, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc13, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc14, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc15, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc16, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc2, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc3, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc4, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc5, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc6, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc7, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc8, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_broadcast_ufunc9, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_equiv_ufunc0_out_dtype_float64, 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test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc2_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc3_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc3_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc3_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc4_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc4_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc4_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc5_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc5_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc5_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc6_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc6_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc6_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc7_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc7_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc7_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc8_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc8_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc8_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc9_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc9_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_safe_ufunc9_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc10_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc10_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc10_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc11_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc11_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc11_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc12_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc12_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc12_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc13_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc13_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc13_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc14_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc14_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc14_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc15_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc15_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc15_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc16_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc16_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc16_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc1_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc1_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc1_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc2_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc2_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc2_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc3_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc3_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc3_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc4_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc4_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc4_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc5_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc5_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc5_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc6_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc6_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc6_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc7_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc7_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc7_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc8_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc8_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc8_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc9_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc9_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_same_kind_ufunc9_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc0_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc0_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc0_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc10_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc10_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc10_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc11_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc11_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc11_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc12_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc12_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc12_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc13_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc13_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc13_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc14_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc14_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc14_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc15_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc15_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc15_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc16_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc16_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc16_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc1_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc1_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc1_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc2_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc2_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc2_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc3_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc3_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc3_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc4_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc4_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc4_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc5_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc5_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc5_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc6_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc6_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc6_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc7_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc7_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc7_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc8_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc8_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc8_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc9_out_dtype_complex128, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc9_out_dtype_float32, test/torch_np/test_ufuncs_basic.py::TestBinaryUfuncs::test_xy_and_out_casting_casting_unsafe_ufunc9_out_dtype_float64, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_basic_ufunc0_op0_iop0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_basic_ufunc1_op1_iop1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_basic_ufunc2_op2_iop2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_bcast_ufunc0_op0_iop0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_bcast_ufunc1_op1_iop1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_bcast_ufunc2_op2_iop2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc0_op0_iop0_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc0_op0_iop0_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc0_op0_iop0_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc0_op0_iop0_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc1_op1_iop1_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc1_op1_iop1_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc1_op1_iop1_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc1_op1_iop1_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc2_op2_iop2_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc2_op2_iop2_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc2_op2_iop2_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_array_ufunc2_op2_iop2_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc0_op0_iop0_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc0_op0_iop0_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc0_op0_iop0_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc0_op0_iop0_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc1_op1_iop1_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc1_op1_iop1_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc1_op1_iop1_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc1_op1_iop1_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc2_op2_iop2_other_dtype0, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc2_op2_iop2_other_dtype1, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc2_op2_iop2_other_dtype2, test/torch_np/test_ufuncs_basic.py::TestNdarrayDunderVsUfunc::test_other_scalar_ufunc2_op2_iop2_other_dtype3, test/torch_np/test_ufuncs_basic.py::TestUfuncDtypeKwd::test_binary_ufunc_dtype, test/torch_np/test_ufuncs_basic.py::TestUfuncDtypeKwd::test_binary_ufunc_dtype_and_out 2024-08-20T22:29:18.5438059Z 2024-08-20T22:29:21.3739123Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:29:21.4329336Z Running torch_np/test_random 1/1 ... [2024-08-20 22:29:21.432449] 2024-08-20T22:29:21.4330216Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:29:21.4332897Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_random.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:29:21.432816] 2024-08-20T22:29:25.5540951Z 2024-08-20T22:29:25.5543222Z torch_np/test_random 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_random_1.1_ff3740f22ba4c9d1_.log 2024-08-20T22:29:25.5562929Z Running 41 items in this shard: test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_random_sample, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_random_sample, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_random_sample, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_random_sample, test/torch_np/test_random.py::TestShuffle::test_1d_use_numpy_False, test/torch_np/test_random.py::TestShuffle::test_1d_use_numpy_True, test/torch_np/test_random.py::TestShuffle::test_2d_use_numpy_False, test/torch_np/test_random.py::TestShuffle::test_2d_use_numpy_True, test/torch_np/test_random.py::TestShuffle::test_shuffle_list_use_numpy_False, test/torch_np/test_random.py::TestShuffle::test_shuffle_list_use_numpy_True, test/torch_np/test_random.py::TestChoice::test_choice_use_numpy_False, test/torch_np/test_random.py::TestChoice::test_choice_use_numpy_True, test/torch_np/test_random.py::TestNumpyGlobal::test_numpy_global 2024-08-20T22:29:25.5581106Z 2024-08-20T22:29:28.3048818Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:29:28.3969275Z Running test_jiterator 1/1 ... [2024-08-20 22:29:28.396247] 2024-08-20T22:29:28.3970284Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:29:28.3977253Z 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'] ... [2024-08-20 22:29:28.396769] 2024-08-20T22:29:31.8330852Z 2024-08-20T22:29:31.8333511Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_14a9f023914eb759_.log 2024-08-20T22:29:31.8350853Z Running 0 items in this shard: 2024-08-20T22:29:31.8351322Z 2024-08-20T22:29:34.9666337Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:29:35.0717508Z Running higher_order_ops/test_with_effects 1/1 ... [2024-08-20 22:29:35.071148] 2024-08-20T22:29:35.0718678Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:29:35.0726884Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'higher_order_ops/test_with_effects.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:29:35.071606] 2024-08-20T22:29:55.8277214Z 2024-08-20T22:29:55.8279878Z higher_order_ops/test_with_effects 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_with_effects_1.1_47cc39969ce6710c_.log 2024-08-20T22:29:55.8298547Z Running 15 items in this shard: test/higher_order_ops/test_with_effects.py::TestWithEffects::test_alias_op, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_aot_eager, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_aot_eager_requires_grad, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_inductor, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_inductor_external_op_return_none, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effectful_custom_op_with_subclasses, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_aliased_outputs, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_input_mutation_is_output, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_input_mutation_return, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_input_output_view_simple, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_print, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_print_with_buffer_mutations, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_print_with_input_mutations, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_register_effectful_custom_op, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_torchbind_custom_op 2024-08-20T22:29:55.8316650Z 2024-08-20T22:29:58.3830000Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:29:58.4398543Z Running test_jit 1/1 ... [2024-08-20 22:29:58.439532] 2024-08-20T22:29:58.4399071Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:29:58.4402382Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jit.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:29:58.439890] 2024-08-20T22:30:29.6629277Z 2024-08-20T22:30:29.6631377Z test_quantization 4/5 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_4.5_db88e5f0810ba764_.log 2024-08-20T22:30:29.6814941Z Running 250 items in this shard: test/test_quantization.py::TestQuantizedOps::test_cat, test/test_quantization.py::TestQuantizedOps::test_custom_module_lstm, test/test_quantization.py::TestQuantizedOps::test_hardtanh, test/test_quantization.py::TestQuantizedOps::test_max_pool1d, test/test_quantization.py::TestQuantizedOps::test_max_pool2d_cudnn, test/test_quantization.py::TestQuantizedOps::test_mean, test/test_quantization.py::TestQuantizedOps::test_qclamp, test/test_quantization.py::TestQuantizedOps::test_qhardsigmoid, test/test_quantization.py::TestQuantizedOps::test_qtopk, test/test_quantization.py::TestQuantizedOps::test_sigmoid, test/test_quantization.py::TestQNNPackOps::test_adaptive_avg_pool2d, test/test_quantization.py::TestQNNPackOps::test_qnnpack_mul, test/test_quantization.py::TestQuantizedLinear::test_qlinear_relu, test/test_quantization.py::TestQuantizedLinear::test_qlinear_with_input_q_dq_qweight_dq_output_fp32, test/test_quantization.py::TestQuantizedConv::test_qconv2d, test/test_quantization.py::TestQuantizedConv::test_qconv2d_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv2d_relu, test/test_quantization.py::TestQuantizedConv::test_qconv2d_sum_relu_float_output_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv3d_relu, test/test_quantization.py::TestQuantizedConv::test_qconv_transpose3d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_conv3d, test/test_quantization.py::TestDynamicQuantizedOps::test_qlstmGRU, test/test_quantization.py::TestPadding::test_reflection_pad2d, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_bag_byte, test/test_quantization.py::TestFakeQuantizeOps::test_fake_quant_per_channel_qparam_range, test/test_quantization.py::TestFakeQuantizeOps::test_fake_quant_preserves_qparam_shapes_for_activations, test/test_quantization.py::TestFakeQuantizeOps::test_fixed_qparams_fq_module, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_backward_per_channel_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_backward_per_tensor_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_forward_per_channel_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_forward_per_tensor_cuda, test/test_quantization.py::TestFusedObsFakeQuant::test_fused_obs_fake_quant_moving_avg, test/test_quantization.py::TestFusedObsFakeQuant::test_fused_obs_fake_quant_moving_avg_per_channel, test/test_quantization.py::TestQuantizedTensor::test_bfp16_quantize, test/test_quantization.py::TestQuantizedTensor::test_choose_qparams, test/test_quantization.py::TestQuantizedTensor::test_choose_qparams_optimized, test/test_quantization.py::TestQuantizedTensor::test_compare_per_tensor_device_numerics, test/test_quantization.py::TestQuantizedTensor::test_decomposed_dynamic_quant_pattern, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_channel_bfloat16_input, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_channel_group, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_token, test/test_quantization.py::TestQuantizedTensor::test_qtensor_cpu, test/test_quantization.py::TestQuantizedTensor::test_qtensor_equal, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_channel, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_channel_nhwc, test/test_quantization.py::TestQuantizedTensor::test_qtensor_float_assignment, test/test_quantization.py::TestQuantizedTensor::test_qtensor_masked_fill_cpu, test/test_quantization.py::TestQuantizedTensor::test_qtensor_per_channel_load_save, test/test_quantization.py::TestQuantizedTensor::test_qtensor_quantize_per_channel, test/test_quantization.py::TestQuantizedTensor::test_qtensor_reshape, test/test_quantization.py::TestQuantizedTensor::test_qtensor_unsqueeze, test/test_quantization.py::TestQuantizedTensor::test_quantize_per_channel_sub_byte, test/test_quantization.py::TestFakeQuantize::test_quant_min_max_override, test/test_quantization.py::TestObserver::test_dynamic_quant_observer_matching_choose_qparams, test/test_quantization.py::TestObserver::test_histogram_observer_consistent_buffer_shape, test/test_quantization.py::TestObserver::test_per_tensor_observers, test/test_quantization.py::TestObserver::test_save_load_state_dict_script, test/test_quantization.py::TestStaticQuantizedModule::test_dropout, test/test_quantization.py::TestStaticQuantizedModule::test_embedding_bag_api, test/test_quantization.py::TestStaticQuantizedModule::test_prelu, test/test_quantization.py::TestDynamicQuantizedModule::test_cell_api, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_convtranspose3d, test/test_quantization.py::TestReferenceQuantizedModule::test_rnn, test/test_quantization.py::TestDistributed::test_device_affinity, test/test_quantization.py::TestDistributed::test_observers_preserve_buffers, test/test_quantization.py::TestFusedObsFakeQuantModule::test_fused_obs_fq_moving_avg_module, test/test_quantization.py::TestBackendConfig::test_backend_op_config_from_dict, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_input_type_to_index, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_root_node_getter, test/test_quantization.py::TestUtils::test_uint1_7_dtype, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_forward_hooks_preserved, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_nested2, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_nested3, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_resnet_base, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_skip_quant, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_linear_relu_fusion, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_nested1, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_per_channel_linear_quantize, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_quantized_rnn, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_type_match_rule, test/test_quantization.py::TestQuantizeEagerOps::test_conv_3d, test/test_quantization.py::TestQuantizeEagerQAT::test_conv_linear_symm, test/test_quantization.py::TestQuantizeEagerQAT::test_embedding_qat_qconfig_equal, test/test_quantization.py::TestFuseEager::test_fuse_module_train, test/test_quantization.py::TestFuseEager::test_fusion_linear_bn_eval, test/test_quantization.py::TestFuseEager::test_fusion_sequential_model_eval, test/test_quantization.py::TestFuseEager::test_fusion_sequential_model_train, test/test_quantization.py::TestModelNumericsEager::test_float_quant_compare_per_channel, test/test_quantization.py::TestModelNumericsEager::test_float_quant_compare_per_tensor, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_outputs_conv_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_linear_static, test/test_quantization.py::TestNumericSuiteEager::test_output_logger, test/test_quantization.py::TestEqualizeEager::test_equalize_fused_linearrelu, test/test_quantization.py::TestBiasCorrectionEager::test_linear_chain, test/test_quantization.py::TestFuseFx::test_fuse_conv_bn_add_relu_by_default, test/test_quantization.py::TestQuantizeFx::test_convert_custom_config_to_dict, test/test_quantization.py::TestQuantizeFx::test_custom_module_class_input_has_duplicate_nodes, test/test_quantization.py::TestQuantizeFx::test_default_qconfig_mapping_override_global, test/test_quantization.py::TestQuantizeFx::test_dequantize, test/test_quantization.py::TestQuantizeFx::test_dynamic_quant_fp16, test/test_quantization.py::TestQuantizeFx::test_dynamic_with_fusion_multiple_uses, test/test_quantization.py::TestQuantizeFx::test_fp32_sum, test/test_quantization.py::TestQuantizeFx::test_fuse_custom_config_from_dict, test/test_quantization.py::TestQuantizeFx::test_fuse_custom_config_to_dict, test/test_quantization.py::TestQuantizeFx::test_linear_shape_view, test/test_quantization.py::TestQuantizeFx::test_linear_size_view, test/test_quantization.py::TestQuantizeFx::test_lowering_functional_conv_transpose_with_kwargs, test/test_quantization.py::TestQuantizeFx::test_match_pattern_with_multiple_args, test/test_quantization.py::TestQuantizeFx::test_output_lists_and_dicts, test/test_quantization.py::TestQuantizeFx::test_pattern_match_constant, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_input_quantized_indexes, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_standalone_module_name, test/test_quantization.py::TestQuantizeFx::test_preserve_attributes, test/test_quantization.py::TestQuantizeFx::test_preserve_tuple, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_dict_split_tuple_args, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_list_args, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_repr, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_module_name_object_type_order, test/test_quantization.py::TestQuantizeFx::test_qparams_fqn, test/test_quantization.py::TestQuantizeFx::test_relu_lowering, test/test_quantization.py::TestQuantizeFx::test_return_none, test/test_quantization.py::TestQuantizeFx::test_sequential, test/test_quantization.py::TestQuantizeFx::test_shape_followed_by_quantized_op, test/test_quantization.py::TestQuantizeFx::test_standalone_module_quantized_interface, test/test_quantization.py::TestQuantizeFx::test_static_lstm_consume_tuple, test/test_quantization.py::TestQuantizeFxOps::test_cat, test/test_quantization.py::TestQuantizeFxOps::test_conv_transpose_1d, test/test_quantization.py::TestQuantizeFxOps::test_fixed_qparams_ops_qint8, test/test_quantization.py::TestQuantizeFxOps::test_functional_linear, test/test_quantization.py::TestQuantizeFxOps::test_int8_input_no_unnecessary_fq, test/test_quantization.py::TestQuantizeFxOps::test_linear_module, test/test_quantization.py::TestQuantizeFxOps::test_mul, test/test_quantization.py::TestQuantizeFxOps::test_qbatch_norm, test/test_quantization.py::TestQuantizeFxOps::test_qmatmul, test/test_quantization.py::TestQuantizeFxOps::test_reshape_fp16, test/test_quantization.py::TestQuantizeFxOps::test_silu_reference, test/test_quantization.py::TestQuantizeFxOps::test_softmax_reference, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_correct_output_replacement, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_graph_argument_order, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_internal_pattern_nodes_cannot_have_users_that_are_not_matched, test/test_quantization.py::TestDuplicateDQPass::test_avgpool_use_different_qconfig, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_for_dq_no_static_q, test/test_quantization.py::TestMetaDataPorting::test_no_metadata_porting_through_unknown_ops, test/test_quantization.py::TestNumericDebugger::test_re_export_preserve_handle, test/test_quantization.py::TestQuantizePT2E::test_allow_exported_model_train_eval, test/test_quantization.py::TestQuantizePT2E::test_constant_prop_preserve_metadata, test/test_quantization.py::TestQuantizePT2E::test_derived_qspec, test/test_quantization.py::TestQuantizePT2E::test_derived_qspec_per_channel, test/test_quantization.py::TestQuantizePT2E::test_fold_all_ops_before_quantize, test/test_quantization.py::TestQuantizePT2E::test_fold_quantize, test/test_quantization.py::TestQuantizePT2E::test_input_edge_sanity_check, test/test_quantization.py::TestQuantizePT2E::test_multi_users_without_output_observer, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_bfloat16_float8_e5m2, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_float32_float8_e5m2, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_float32_int16, test/test_quantization.py::TestQuantizePT2E::test_speed, test/test_quantization.py::TestPT2ERepresentation::test_add, test/test_quantization.py::TestPT2ERepresentation::test_conv2d, test/test_quantization.py::TestXNNPACKQuantizer::test_set_module_type_case_2, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_adaptive_avg_pool2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d_binary2, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_conv2d_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_filter_linear_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_dynamic_quant_linear, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_qconfig, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_set_module_name_qconfig_for_dynamic_quant, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_per_channel_weight_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_preserve_source_fn_stack, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_update_shared_qspec, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_per_channel_weight_bias, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_transpose_bn, test/test_quantization.py::TestQuantizePT2EQATModels::test_qat_resnet18, test/test_quantization.py::TestFXGraphMatcher::test_op_relationship_mapping, test/test_quantization.py::TestFXGraphMatcher::test_simple_fusion, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_mod_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extend_logger_results_with_comparison, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_fqn, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_linear_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_linear_fun_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_int8_shadows_fp32_coverage, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_int8_shadows_fp32_simple, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_kwargs_shadow, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_mod_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_mod_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_op_io_dtype_coverage, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_shadow_activations_fqn, test/test_quantization.py::TestFXNumericSuiteNShadows::test_custom_functions_and_tracer, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_repr, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_retroactive_padding, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_resnet18, test/test_quantization.py::TestFxModelReportDetector::test_fusion_layer_in_sequential, test/test_quantization.py::TestFxModelReportDetector::test_qat_aware_model_example, test/test_quantization.py::TestFxModelReportObserver::test_single_batch_of_ones, test/test_quantization.py::TestFxModelReportObserver::test_zero_tensor_errors, test/test_quantization.py::TestFxModelReportClass::test_constructor, test/test_quantization.py::TestFxDetectInputWeightEqualization::test_input_weight_equalization_determine_points, test/test_quantization.py::TestFxDetectInputWeightEqualization::test_input_weight_equalization_report_gen, test/test_quantization.py::TestFxDetectOutliers::test_outlier_detection_determine_points, test/test_quantization.py::TestFxModelReportVisualizer::test_generate_tables_no_match, test/test_quantization.py::TestFxModelReportVisualizer::test_generate_tables_single_feat_match, test/test_quantization.py::TestEqualizeFx::test_input_weight_eq_observer, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_activation_values, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_weights_bias, test/test_quantization.py::TestSerialization::test_conv2d_nobias_graph, test/test_quantization.py::TestSerialization::test_conv2d_nobias_graph_v3, test/test_quantization.py::TestSerialization::test_conv3d_relu, test/test_quantization.py::TestSerialization::test_default_qat_qconfig, test/test_quantization.py::TestSerialization::test_linear, test/test_quantization.py::TestQuantizeJit::test_conv_bn, test/test_quantization.py::TestQuantizeJitPasses::test_finalize_for_linear, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_trivial, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_if_consistent_observation, test/test_quantization.py::TestQuantizeJitPasses::test_insert_quant_dequant, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_dequant_same_value, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_dequantize_in_block, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_quantize_for_if, test/test_quantization.py::TestQuantizeJitPasses::test_swap_functional_linear, test/test_quantization.py::TestQuantizeJitOps::test_dequantize_tuple, test/test_quantization.py::TestQuantizeJitOps::test_linear, test/test_quantization.py::TestQuantizeJitOps::test_qbatch_norm_relu_BNRelu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_relu_alpha, test/test_quantization.py::TestQuantizeJitOps::test_quantized_cat, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul_scalar, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_convert_dynamic_fp16, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_multi_op, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_prepare_dynamic_child_qconfig, test/test_quantization.py::TestDeprecatedJitQuantized::test_rnn_quantized, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_fake_quantize, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_fuser_method_mappings, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_qconfig, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quant_type, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_qat_conv, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_qat_dynamic_linear, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_functional_modules, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_normalization, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_qat_linear_fused, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_qat_linear_relu, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_quantization_patterns, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_utils, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_subnormals_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_empty_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPUOnlyCPU::test_pt2_traceable_aot_eager_cpu_float8_e5m2 2024-08-20T22:30:29.6999646Z 2024-08-20T22:30:32.1916934Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:30:32.2517132Z Running test_jit_fuser_te 1/1 ... [2024-08-20 22:30:32.251247] 2024-08-20T22:30:32.2518387Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:30:32.2522581Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jit_fuser_te.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:30:32.251656] 2024-08-20T22:33:45.3270138Z 2024-08-20T22:33:45.3271826Z test_fx_experimental 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_experimental_1.1_b04a39d87cdf45dd_.log 2024-08-20T22:33:45.3781981Z Running 714 items in this shard: test/test_fx_experimental.py::TestFXExperimental::test_annotate_getitem_node, test/test_fx_experimental.py::TestFXExperimental::test_annotate_returns_with_schema, test/test_fx_experimental.py::TestFXExperimental::test_aot_based_partition, test/test_fx_experimental.py::TestFXExperimental::test_call_to_assert_no_msg, test/test_fx_experimental.py::TestFXExperimental::test_call_to_assert_with_empty_msg, test/test_fx_experimental.py::TestFXExperimental::test_call_to_assert_with_msg, test/test_fx_experimental.py::TestFXExperimental::test_call_to_assert_with_multiline_message, test/test_fx_experimental.py::TestFXExperimental::test_conv_bn_fusion, test/test_fx_experimental.py::TestFXExperimental::test_conv_bn_fusion_mixed_dtype, test/test_fx_experimental.py::TestFXExperimental::test_conv_bn_fusion_not_running_state, test/test_fx_experimental.py::TestFXExperimental::test_cost_aware_partition, test/test_fx_experimental.py::TestFXExperimental::test_fetch, test/test_fx_experimental.py::TestFXExperimental::test_find_single_partition, test/test_fx_experimental.py::TestFXExperimental::test_lack_of_devices, test/test_fx_experimental.py::TestFXExperimental::test_large_node_error, test/test_fx_experimental.py::TestFXExperimental::test_merge_matmuls, test/test_fx_experimental.py::TestFXExperimental::test_meta_tracer, test/test_fx_experimental.py::TestFXExperimental::test_normalize_args, test/test_fx_experimental.py::TestFXExperimental::test_normalize_args_perserve_type, test/test_fx_experimental.py::TestFXExperimental::test_normalize_args_preserve_meta, test/test_fx_experimental.py::TestFXExperimental::test_normalize_binary_operators, test/test_fx_experimental.py::TestFXExperimental::test_normalize_modules_exhaustive, test/test_fx_experimental.py::TestFXExperimental::test_optimize_for_inference_cpu, test/test_fx_experimental.py::TestFXExperimental::test_optimize_for_inference_cpu_torchvision, test/test_fx_experimental.py::TestFXExperimental::test_partition_device_mapping, test/test_fx_experimental.py::TestFXExperimental::test_partition_latency, test/test_fx_experimental.py::TestFXExperimental::test_partition_node_manipulation, test/test_fx_experimental.py::TestFXExperimental::test_replace_target_nodes_with, test/test_fx_experimental.py::TestFXExperimental::test_saturate_host, test/test_fx_experimental.py::TestFXExperimental::test_size_based_partition, test/test_fx_experimental.py::TestFXExperimental::test_sparse_nn_partition, test/test_fx_experimental.py::TestFXExperimental::test_split_module_dead_code, test/test_fx_experimental.py::TestFXExperimental::test_split_module_default_arg, test/test_fx_experimental.py::TestFXExperimental::test_split_module_kwargs_expansion, test/test_fx_experimental.py::TestFXExperimental::test_split_qualname_mapping, test/test_fx_experimental.py::TestFXExperimental::test_subgraph_creation, test/test_fx_experimental.py::TestFXExperimental::test_subgraph_trivial_resnet, test/test_fx_experimental.py::TestFXExperimental::test_subgraph_uniquename, test/test_fx_experimental.py::TestFXExperimental::test_to_folder, test/test_fx_experimental.py::TestFXExperimental::test_traceable_function_with_nonstandard_name, test/test_fx_experimental.py::TestFXExperimental::test_type_matches, test/test_fx_experimental.py::TestTranslationValidation::test_sat, test/test_fx_experimental.py::TestTranslationValidation::test_sympy_to_z3, test/test_fx_experimental.py::TestTranslationValidation::test_unsat, test/test_fx_experimental.py::TestTranslationValidation::test_z3str, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_args_op_overload_cpu, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_H_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_T_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___getitem___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___radd___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___rdiv___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___rmatmul___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___rmod___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___rmul___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___rpow___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive___rsub___cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__batch_norm_with_update_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__chunk_cat_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__native_batch_norm_legit_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__segment_reduce_lengths_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__segment_reduce_offsets_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__softmax_backward_data_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__unsafe_masked_index_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__unsafe_masked_index_put_accumulate_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive__upsample_bilinear2d_aa_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_abs_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_acos_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_acosh_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_add_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addbmm_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addcdiv_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addcmul_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addmm_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addmm_decomposed_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addmv_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_addr_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_alias_copy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_all_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_allclose_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_amax_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_amin_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_aminmax_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_angle_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_any_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_arange_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_argmax_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_argmin_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_argsort_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_argwhere_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_as_strided_copy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_as_strided_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_as_strided_partial_views_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_as_strided_scatter_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_asin_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_asinh_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_atan2_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_atan_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_atanh_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_atleast_1d_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_atleast_2d_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_atleast_3d_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_baddbmm_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_bernoulli_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_bfloat16_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_block_diag_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_bmm_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_bool_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_broadcast_shapes_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_broadcast_tensors_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_broadcast_to_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_bucketize_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_byte_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_cartesian_prod_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_cat_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_cauchy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_cdist_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_cdouble_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_ceil_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_cfloat_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_chalf_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_char_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_cholesky_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_cholesky_inverse_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_cholesky_solve_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_chunk_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_clamp_cpu_float32, 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test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unfold_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_uniform_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unique_consecutive_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unique_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unsafe_chunk_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unsafe_split_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unsqueeze_copy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_unsqueeze_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_var_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_var_mean_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_var_mean_unbiased_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_var_unbiased_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_vdot_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_view_as_complex_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_view_as_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_view_copy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_view_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_vsplit_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_vstack_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_where_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_xlogy_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_zero__cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_zeros_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_operator_exhaustive_zeros_like_cpu_float32, test/test_fx_experimental.py::TestNormalizeOperatorsCPU::test_normalize_quantized_eb_cpu 2024-08-20T22:33:45.4216381Z 2024-08-20T22:33:47.7903929Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:33:47.8483436Z Running functorch/test_ac 1/1 ... [2024-08-20 22:33:47.847931] 2024-08-20T22:33:47.8484373Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:33:47.8487371Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'functorch/test_ac.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:33:47.848300] 2024-08-20T22:33:50.2521360Z 2024-08-20T22:33:50.2523435Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_740de47b1b904aa3_.log 2024-08-20T22:33:50.2524834Z 2024-08-20T22:33:52.8108211Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:33:52.8684312Z Running test_matmul_cuda 1/1 ... [2024-08-20 22:33:52.867965] 2024-08-20T22:33:52.8685317Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:33:52.8688423Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:33:52.868312] 2024-08-20T22:33:55.5570421Z 2024-08-20T22:33:55.5572660Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_38dc3aa64ce9c1a6_.log 2024-08-20T22:33:55.5574603Z Running 0 items in this shard: 2024-08-20T22:33:55.5575073Z 2024-08-20T22:33:57.9711617Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:33:58.0294552Z Running optim/test_swa_utils 1/1 ... [2024-08-20 22:33:58.028853] 2024-08-20T22:33:58.0295370Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:33:58.0297454Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'optim/test_swa_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:33:58.029208] 2024-08-20T22:34:00.4774042Z 2024-08-20T22:34:00.4776192Z optim/test_swa_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_swa_utils_1.1_132cafd02b431ec9_.log 2024-08-20T22:34:00.4777636Z 2024-08-20T22:34:03.1260425Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:34:03.1853926Z Running lazy/test_bindings 1/1 ... [2024-08-20 22:34:03.184939] 2024-08-20T22:34:03.1854615Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2024-08-20T22:34:03.1857219Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'lazy/test_bindings.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-08-20 22:34:03.185340] 2024-08-20T22:34:04.7502271Z 2024-08-20T22:34:04.7504406Z lazy/test_bindings 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_bindings_1.1_5848057a78ebaac8_.log 2024-08-20T22:34:04.7505984Z 2024-08-20T22:34:07.2450289Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:36:06.1732975Z 2024-08-20T22:36:06.1734441Z test_jit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_1.1_dd7a9ab680317be9_.log 2024-08-20T22:36:06.2738041Z Running 2451 items in this shard: test/test_jit.py::TestTracer::test_call_traced_fn_from_traced_module, test/test_jit.py::TestTracer::test_call_traced_module_from_traced_module, test/test_jit.py::TestTracer::test_canonicalize_tensor_iterator, test/test_jit.py::TestTracer::test_constant, test/test_jit.py::TestTracer::test_conv, test/test_jit.py::TestTracer::test_export_no_reorder, test/test_jit.py::TestTracer::test_force_outplace_check_fill, test/test_jit.py::TestTracer::test_force_outplace_check_zero, test/test_jit.py::TestTracer::test_ge, test/test_jit.py::TestTracer::test_ge_cuda, test/test_jit.py::TestTracer::test_ge_optimized, test/test_jit.py::TestTracer::test_ge_unoptimized, test/test_jit.py::TestTracer::test_index_put, test/test_jit.py::TestTracer::test_index_put_trace_with_view, test/test_jit.py::TestTracer::test_index_put_trace_without_view, test/test_jit.py::TestTracer::test_inplace_check, test/test_jit.py::TestTracer::test_inplace_copy, test/test_jit.py::TestTracer::test_inplace_copy_force_outplace, test/test_jit.py::TestTracer::test_inplace_flags, test/test_jit.py::TestTracer::test_inplace_transplant, test/test_jit.py::TestTracer::test_inplace_warn, test/test_jit.py::TestTracer::test_input_dict_checkTrace_mut, test/test_jit.py::TestTracer::test_input_dict_empty, test/test_jit.py::TestTracer::test_input_dict_empty_list, test/test_jit.py::TestTracer::test_input_dict_insertion_order, test/test_jit.py::TestTracer::test_input_dict_of_dicts, test/test_jit.py::TestTracer::test_input_dict_of_lists, test/test_jit.py::TestTracer::test_input_dict_recursive, test/test_jit.py::TestTracer::test_input_dict_remembers_keys, test/test_jit.py::TestTracer::test_input_dict_unify, test/test_jit.py::TestTracer::test_input_flatten, test/test_jit.py::TestTracer::test_input_list_mixed_type, test/test_jit.py::TestTracer::test_input_list_of_tuples, test/test_jit.py::TestTracer::test_input_list_toplevel_flatten, test/test_jit.py::TestTracer::test_input_list_toplevel_flatten_direct, test/test_jit.py::TestTracer::test_input_tuple_of_dicts, test/test_jit.py::TestTracer::test_interpolate_trace, test/test_jit.py::TestTracer::test_large_nbr_kernel_args, test/test_jit.py::TestTracer::test_lhs_index_fails, test/test_jit.py::TestTracer::test_lhs_index_trivial, test/test_jit.py::TestTracer::test_max_pool, test/test_jit.py::TestTracer::test_nested_inplace, test/test_jit.py::TestTracer::test_non_tensor_tracing, test/test_jit.py::TestTracer::test_output_unflatten, test/test_jit.py::TestTracer::test_python_function, test/test_jit.py::TestTracer::test_python_function_tup, test/test_jit.py::TestTracer::test_repeated_input, test/test_jit.py::TestTracer::test_repeated_output, test/test_jit.py::TestTracer::test_shared_param, test/test_jit.py::TestTracer::test_simple, test/test_jit.py::TestTracer::test_tensor_with_grad_as_constant, test/test_jit.py::TestTracer::test_trace_aliased_parameter, test/test_jit.py::TestTracer::test_trace_annotation, test/test_jit.py::TestTracer::test_trace_arange, test/test_jit.py::TestTracer::test_trace_arange_with_grad, test/test_jit.py::TestTracer::test_trace_autograd_function, test/test_jit.py::TestTracer::test_trace_casts, test/test_jit.py::TestTracer::test_trace_checker_control_flow, test/test_jit.py::TestTracer::test_trace_checker_dot_data, test/test_jit.py::TestTracer::test_trace_checker_dropout_notrain, test/test_jit.py::TestTracer::test_trace_checker_dropout_train, test/test_jit.py::TestTracer::test_trace_checker_inplace_on_view, test/test_jit.py::TestTracer::test_trace_checker_memoization, test/test_jit.py::TestTracer::test_trace_checker_slice_lhs, test/test_jit.py::TestTracer::test_trace_checking_with_deprecated_name, test/test_jit.py::TestTracer::test_trace_checking_with_global_name, test/test_jit.py::TestTracer::test_trace_contiguous, test/test_jit.py::TestTracer::test_trace_contiguous_short_circuit, test/test_jit.py::TestTracer::test_trace_detach, test/test_jit.py::TestTracer::test_trace_detach_inplace, test/test_jit.py::TestTracer::test_trace_detach_inplace_redispatch, test/test_jit.py::TestTracer::test_trace_detach_redispatch, test/test_jit.py::TestTracer::test_trace_dict_input, test/test_jit.py::TestTracer::test_trace_dict_output, test/test_jit.py::TestTracer::test_trace_export_fns, test/test_jit.py::TestTracer::test_trace_export_fns_recursive, test/test_jit.py::TestTracer::test_trace_fork_join_and_module, test/test_jit.py::TestTracer::test_trace_full_dynamic_shape, test/test_jit.py::TestTracer::test_trace_func_argument_names_captured, test/test_jit.py::TestTracer::test_trace_index, test/test_jit.py::TestTracer::test_trace_index_constant, test/test_jit.py::TestTracer::test_trace_indexed_assignment, test/test_jit.py::TestTracer::test_trace_inline_shape, test/test_jit.py::TestTracer::test_trace_inverse, test/test_jit.py::TestTracer::test_trace_invert_module_hierarchy, test/test_jit.py::TestTracer::test_trace_legacy_ctor, test/test_jit.py::TestTracer::test_trace_module_argument_names_captured, test/test_jit.py::TestTracer::test_trace_modulelist, test/test_jit.py::TestTracer::test_trace_multi_output_function, test/test_jit.py::TestTracer::test_trace_namedtuple, test/test_jit.py::TestTracer::test_trace_nested_datatypes, test/test_jit.py::TestTracer::test_trace_nested_fn, test/test_jit.py::TestTracer::test_trace_no_duplicated_lifted_input_output, test/test_jit.py::TestTracer::test_trace_numel, test/test_jit.py::TestTracer::test_trace_optioanl_dtype, test/test_jit.py::TestTracer::test_trace_optional, test/test_jit.py::TestTracer::test_trace_out_operator_with_two_output, test/test_jit.py::TestTracer::test_trace_partial_func_argument_names_captured, test/test_jit.py::TestTracer::test_trace_random, test/test_jit.py::TestTracer::test_trace_records_names, test/test_jit.py::TestTracer::test_trace_save, test/test_jit.py::TestTracer::test_trace_save_load_copy, test/test_jit.py::TestTracer::test_trace_single_tuple, test/test_jit.py::TestTracer::test_trace_size, test/test_jit.py::TestTracer::test_trace_size_with_grad, test/test_jit.py::TestTracer::test_trace_skip_none_submodule, test/test_jit.py::TestTracer::test_trace_slice, test/test_jit.py::TestTracer::test_trace_slice_expr_complete_type, test/test_jit.py::TestTracer::test_trace_slice_full_dim, test/test_jit.py::TestTracer::test_trace_slice_setitem_dynamic_shape, test/test_jit.py::TestTracer::test_trace_slice_with_grad, test/test_jit.py::TestTracer::test_trace_tensor_factory, test/test_jit.py::TestTracer::test_trace_topk, test/test_jit.py::TestTracer::test_trace_tuple, test/test_jit.py::TestTracer::test_trace_variable_instantiation, test/test_jit.py::TestTracer::test_trace_warn, test/test_jit.py::TestTracer::test_trace_with_conditional_property, test/test_jit.py::TestTracer::test_trace_with_nested_strided_tensor_output, test/test_jit.py::TestTracer::test_trace_with_nested_tensor_list_output, test/test_jit.py::TestTracer::test_trace_with_number_list_output, test/test_jit.py::TestTracer::test_trace_with_tensor_list_output, test/test_jit.py::TestTracer::test_trace_with_tuple_tensor, test/test_jit.py::TestTracer::test_traced_module_cuda, test/test_jit.py::TestTracer::test_tracing_backward_hook_error, test/test_jit.py::TestTracer::test_tracing_hooks, test/test_jit.py::TestTracer::test_tracing_multiple_methods, test/test_jit.py::TestTracer::test_type_same_device, test/test_jit.py::TestTracer::test_typeas_trace_check, test/test_jit.py::TestTracer::test_wrapped_number, test/test_jit.py::TestMixTracingScripting::test_call_script_fn_from_traced_module, test/test_jit.py::TestMixTracingScripting::test_call_script_module_from_traced_module, test/test_jit.py::TestMixTracingScripting::test_call_traced_fn_from_script_fn, test/test_jit.py::TestMixTracingScripting::test_call_traced_mod_from_script_fn, test/test_jit.py::TestMixTracingScripting::test_call_tracing_fn_from_script_module, test/test_jit.py::TestMixTracingScripting::test_call_tracing_mod_from_script_module, test/test_jit.py::TestMixTracingScripting::test_jit_trace_callfunction_return_shapes, test/test_jit.py::TestMixTracingScripting::test_script_inline_trace_multiple_args, test/test_jit.py::TestMixTracingScripting::test_trace_dict_mix_script, test/test_jit.py::TestMixTracingScripting::test_trace_hierarchy, test/test_jit.py::TestMixTracingScripting::test_trace_linear, test/test_jit.py::TestMixTracingScripting::test_trace_mixed_by_script_with_dict_output, test/test_jit.py::TestMixTracingScripting::test_trace_of_script, test/test_jit.py::TestMixTracingScripting::test_trace_parameter, test/test_jit.py::TestMixTracingScripting::test_trace_returning_dict_with_tensor_tuples, test/test_jit.py::TestMixTracingScripting::test_trace_script, test/test_jit.py::TestMixTracingScripting::test_trace_script_returning_complex_dict, test/test_jit.py::TestMixTracingScripting::test_trace_with_size, test/test_jit.py::TestMixTracingScripting::test_traced_module_contains_scripted_interface_types, test/test_jit.py::TestMixTracingScripting::test_traced_module_implements_interface, test/test_jit.py::TestMixTracingScripting::test_tracing_indexing, test/test_jit.py::TestMixTracingScripting::test_tracing_slicing, test/test_jit.py::TestRecursiveScript::test_attributes, test/test_jit.py::TestRecursiveScript::test_class_compile, test/test_jit.py::TestRecursiveScript::test_constants_with_final, test/test_jit.py::TestRecursiveScript::test_dir, test/test_jit.py::TestRecursiveScript::test_error_stack, test/test_jit.py::TestRecursiveScript::test_error_stack_annotation, test/test_jit.py::TestRecursiveScript::test_error_stack_class, test/test_jit.py::TestRecursiveScript::test_error_stack_module, test/test_jit.py::TestRecursiveScript::test_failed_function_compilation, test/test_jit.py::TestRecursiveScript::test_function_attribute_in_submodule, test/test_jit.py::TestRecursiveScript::test_ignore_class, test/test_jit.py::TestRecursiveScript::test_inferred_nonetype, test/test_jit.py::TestRecursiveScript::test_init_error, test/test_jit.py::TestRecursiveScript::test_inner_traced_module, test/test_jit.py::TestRecursiveScript::test_iterable_modules, test/test_jit.py::TestRecursiveScript::test_method_call, test/test_jit.py::TestRecursiveScript::test_module_basic, test/test_jit.py::TestRecursiveScript::test_module_function_export, test/test_jit.py::TestRecursiveScript::test_module_name, test/test_jit.py::TestRecursiveScript::test_module_repr, test/test_jit.py::TestRecursiveScript::test_optional_module, test/test_jit.py::TestRecursiveScript::test_override_instance_method_ignore, test/test_jit.py::TestRecursiveScript::test_prepare_scriptable_basic, test/test_jit.py::TestRecursiveScript::test_prepare_scriptable_cycle, test/test_jit.py::TestRecursiveScript::test_prepare_scriptable_escape_hatch, test/test_jit.py::TestRecursiveScript::test_prepare_scriptable_iterable_modules, test/test_jit.py::TestRecursiveScript::test_python_function_attribute, test/test_jit.py::TestRecursiveScript::test_repeated_error_stack, test/test_jit.py::TestRecursiveScript::test_script_after_eval, test/test_jit.py::TestRecursiveScript::test_script_basic, test/test_jit.py::TestRecursiveScript::test_script_function_attribute, test/test_jit.py::TestRecursiveScript::test_script_loaded_module, test/test_jit.py::TestTypeSharing::test_assign_python_attr, test/test_jit.py::TestTypeSharing::test_basic, test/test_jit.py::TestTypeSharing::test_builtin_function_different, test/test_jit.py::TestTypeSharing::test_builtin_function_same, test/test_jit.py::TestTypeSharing::test_constants, test/test_jit.py::TestTypeSharing::test_diff_attr_values, test/test_jit.py::TestTypeSharing::test_failed_attribute_compilation, test/test_jit.py::TestTypeSharing::test_ignored_fns, test/test_jit.py::TestTypeSharing::test_linear, test/test_jit.py::TestTypeSharing::test_loaded_modules_work, test/test_jit.py::TestTypeSharing::test_module_dict_same_type_different_name, test/test_jit.py::TestTypeSharing::test_mutate_attr_value, test/test_jit.py::TestTypeSharing::test_param_vs_attribute, test/test_jit.py::TestTypeSharing::test_python_function_attribute_different, test/test_jit.py::TestTypeSharing::test_python_function_attribute_same, test/test_jit.py::TestTypeSharing::test_same_but_different_classes, test/test_jit.py::TestTypeSharing::test_script_function_attribute_different, test/test_jit.py::TestTypeSharing::test_script_function_attribute_same, test/test_jit.py::TestTypeSharing::test_script_module_containing_traced_module, test/test_jit.py::TestTypeSharing::test_submodules, test/test_jit.py::TestTypeSharing::test_tracing_gives_different_types, test/test_jit.py::TestTypeSharing::test_type_not_shared_ignored_attributes, test/test_jit.py::TestTypeSharing::test_type_shared_ignored_attributes, test/test_jit.py::TestTypeSharing::test_type_sharing_define_in_init, test/test_jit.py::TestTypeSharing::test_type_sharing_disabled, test/test_jit.py::TestLogging::test_bump_numeric_counter, test/test_jit.py::TestLogging::test_counter_aggregation, test/test_jit.py::TestLogging::test_logging_levels_set, test/test_jit.py::TestLogging::test_time_measurement_counter, test/test_jit.py::TestLogging::test_time_measurement_counter_script, test/test_jit.py::TestLogging::test_trace_numeric_counter, test/test_jit.py::TestBackends::test_errors, test/test_jit.py::TestBackends::test_execution, test/test_jit.py::TestBackends::test_save_load, test/test_jit.py::TestBackendsWithCompiler::test_errors, test/test_jit.py::TestBackendsWithCompiler::test_execution, test/test_jit.py::TestNnapiBackend::test_adaptive_avg_pool2d, test/test_jit.py::TestNnapiBackend::test_avg_pool2d, test/test_jit.py::TestNnapiBackend::test_cat, test/test_jit.py::TestNnapiBackend::test_compile_spec_santiy, test/test_jit.py::TestNnapiBackend::test_conv2d, test/test_jit.py::TestNnapiBackend::test_conv2d_transpose, test/test_jit.py::TestNnapiBackend::test_dequantize, test/test_jit.py::TestNnapiBackend::test_detach, test/test_jit.py::TestNnapiBackend::test_flatten, test/test_jit.py::TestNnapiBackend::test_hardtanh, test/test_jit.py::TestNnapiBackend::test_linear, test/test_jit.py::TestNnapiBackend::test_log_softmax, test/test_jit.py::TestNnapiBackend::test_max_pool2d, test/test_jit.py::TestNnapiBackend::test_mean, test/test_jit.py::TestNnapiBackend::test_multi_output, test/test_jit.py::TestNnapiBackend::test_pointwise_binary, test/test_jit.py::TestNnapiBackend::test_pointwise_binary_const, test/test_jit.py::TestNnapiBackend::test_pointwise_unary, test/test_jit.py::TestNnapiBackend::test_prelu, test/test_jit.py::TestNnapiBackend::test_qadd, test/test_jit.py::TestNnapiBackend::test_qlinear, test/test_jit.py::TestNnapiBackend::test_quantize, test/test_jit.py::TestNnapiBackend::test_reshape, test/test_jit.py::TestNnapiBackend::test_seblock_mul, test/test_jit.py::TestNnapiBackend::test_slice, test/test_jit.py::TestNnapiBackend::test_softmax, test/test_jit.py::TestNnapiBackend::test_tensor_input, test/test_jit.py::TestNnapiBackend::test_to, test/test_jit.py::TestNnapiBackend::test_unsqueeze, test/test_jit.py::TestNnapiBackend::test_upsample_nearest2d, test/test_jit.py::TestList::test_comprehension_iterable, test/test_jit.py::TestList::test_comprehension_out_type_not_in_type, test/test_jit.py::TestList::test_comprehensions_basic, test/test_jit.py::TestList::test_comprehensions_basic_float, test/test_jit.py::TestList::test_comprehensions_two_comps, test/test_jit.py::TestList::test_copy_list_immutable, test/test_jit.py::TestList::test_copy_list_mutable, test/test_jit.py::TestList::test_del, test/test_jit.py::TestList::test_dict_keyword_is_correctly_typed, test/test_jit.py::TestList::test_dict_keyword_with_dict_comprehension, test/test_jit.py::TestList::test_dict_keyword_with_dict_comprehension_and_kwargs, test/test_jit.py::TestList::test_dict_keyword_with_empty_dict_comprehension, test/test_jit.py::TestList::test_dict_keyword_with_empty_iterable, test/test_jit.py::TestList::test_dict_keyword_with_internal_aggregate_function, test/test_jit.py::TestList::test_dict_keyword_with_iterable, test/test_jit.py::TestList::test_dict_keyword_with_kwargs, test/test_jit.py::TestList::test_dict_keyword_with_kwargs_using_container_values, test/test_jit.py::TestList::test_dict_keyword_with_mapping, test/test_jit.py::TestList::test_dict_keyword_with_mapping_and_kwargs, test/test_jit.py::TestList::test_dict_keyword_with_mismatched_annotations, test/test_jit.py::TestList::test_dict_keyword_with_nested_call, test/test_jit.py::TestList::test_dict_keyword_with_previously_declared_variable, test/test_jit.py::TestList::test_dict_keyword_with_previously_declared_variable_and_kwargs, test/test_jit.py::TestList::test_extend_list_immutable, test/test_jit.py::TestList::test_extend_list_mutable, test/test_jit.py::TestList::test_in_check, test/test_jit.py::TestList::test_list_bool_conversion, test/test_jit.py::TestList::test_list_count, test/test_jit.py::TestList::test_list_count_not_existing, test/test_jit.py::TestList::test_list_gather, test/test_jit.py::TestList::test_list_index, test/test_jit.py::TestList::test_list_index_not_existing, test/test_jit.py::TestList::test_list_keyword, test/test_jit.py::TestList::test_list_len, test/test_jit.py::TestList::test_list_literal, test/test_jit.py::TestList::test_list_none, test/test_jit.py::TestList::test_list_ops, test/test_jit.py::TestList::test_list_slice, test/test_jit.py::TestList::test_list_sort, test/test_jit.py::TestList::test_list_unification_hint, test/test_jit.py::TestList::test_list_variance, test/test_jit.py::TestList::test_min_bool_list, test/test_jit.py::TestList::test_min_max_list, test/test_jit.py::TestList::test_min_max_single_list, test/test_jit.py::TestList::test_mutable_list_append, test/test_jit.py::TestList::test_mutable_list_append_2, test/test_jit.py::TestList::test_mutable_list_append_if, test/test_jit.py::TestList::test_mutable_list_append_if_else, test/test_jit.py::TestList::test_mutable_list_append_loop, test/test_jit.py::TestList::test_mutable_list_append_loop_if, test/test_jit.py::TestList::test_mutable_list_clear, test/test_jit.py::TestList::test_mutable_list_clear_empty, test/test_jit.py::TestList::test_mutable_list_function_inline, test/test_jit.py::TestList::test_mutable_list_insert, test/test_jit.py::TestList::test_mutable_list_insert_neg_out_of_bounds, test/test_jit.py::TestList::test_mutable_list_insert_negative, test/test_jit.py::TestList::test_mutable_list_insert_out_of_bounds, test/test_jit.py::TestList::test_mutable_list_nested_loop, test/test_jit.py::TestList::test_mutable_list_pop, test/test_jit.py::TestList::test_mutable_list_pop2, test/test_jit.py::TestList::test_mutable_list_pop_at, test/test_jit.py::TestList::test_mutable_list_pop_at2, test/test_jit.py::TestList::test_mutable_list_pop_at_negative, test/test_jit.py::TestList::test_mutable_list_pop_at_negative2, test/test_jit.py::TestList::test_mutable_list_pop_empty, test/test_jit.py::TestList::test_mutable_list_pop_slice, test/test_jit.py::TestList::test_mutable_list_remove, test/test_jit.py::TestList::test_mutable_list_remove2, test/test_jit.py::TestList::test_mutable_list_remove_not_existing, test/test_jit.py::TestList::test_mutable_list_remove_tensor, test/test_jit.py::TestList::test_mutable_list_reverse, test/test_jit.py::TestList::test_mutable_list_reverse_empty, test/test_jit.py::TestList::test_mutable_tensor_list_reverse, test/test_jit.py::TestList::test_no_element_type_annotation, test/test_jit.py::TestList::test_slice_index, test/test_jit.py::TestList::test_tensor_list_count, test/test_jit.py::TestList::test_tensor_list_count_not_existing, test/test_jit.py::TestList::test_tensor_list_index, test/test_jit.py::TestList::test_tensor_list_index_not_existing, test/test_jit.py::TestList::test_to_list, test/test_jit.py::TestList::test_to_list_gpu, test/test_jit.py::TestDict::test_aug_assign, test/test_jit.py::TestDict::test_basic, test/test_jit.py::TestDict::test_clear, test/test_jit.py::TestDict::test_copy, test/test_jit.py::TestDict::test_del, test/test_jit.py::TestDict::test_dict_bool_conversion, test/test_jit.py::TestDict::test_dict_preserves_order, test/test_jit.py::TestDict::test_dict_to_python, test/test_jit.py::TestDict::test_dict_variance, test/test_jit.py::TestDict::test_get, test/test_jit.py::TestDict::test_get_boolkey, test/test_jit.py::TestDict::test_items, test/test_jit.py::TestDict::test_key_type, test/test_jit.py::TestDict::test_keys, test/test_jit.py::TestDict::test_len, test/test_jit.py::TestDict::test_loop, test/test_jit.py::TestDict::test_membership, test/test_jit.py::TestDict::test_mutability, test/test_jit.py::TestDict::test_optional_dict_construct, test/test_jit.py::TestDict::test_ordered_dict, test/test_jit.py::TestDict::test_pop, test/test_jit.py::TestDict::test_popitem, test/test_jit.py::TestDict::test_setdefault, test/test_jit.py::TestDict::test_type_annotation_missing_contained_type, test/test_jit.py::TestDict::test_update, test/test_jit.py::TestDict::test_update_existing_key, test/test_jit.py::TestDict::test_values, test/test_jit.py::TestDict::test_view, test/test_jit.py::TestNamedTuple::test_namedtuple, test/test_jit.py::TestNamedTuple::test_namedtuple_as_attr, test/test_jit.py::TestNamedTuple::test_namedtuple_constant, test/test_jit.py::TestNamedTuple::test_namedtuple_input_forwardref, test/test_jit.py::TestNamedTuple::test_namedtuple_inside_forwardref, test/test_jit.py::TestNamedTuple::test_namedtuple_kwarg_construct, test/test_jit.py::TestNamedTuple::test_namedtuple_lower, test/test_jit.py::TestNamedTuple::test_namedtuple_resolution, test/test_jit.py::TestNamedTuple::test_namedtuple_resolution_forwardref, test/test_jit.py::TestNamedTuple::test_namedtuple_serialization, test/test_jit.py::TestNamedTuple::test_namedtuple_slice_unpack, test/test_jit.py::TestNamedTuple::test_namedtuple_type_annotation, test/test_jit.py::TestNamedTuple::test_namedtuple_wrong_types, test/test_jit.py::TestNamedTuple::test_return_named_tuple, test/test_jit.py::TestScriptDict::test_bool, test/test_jit.py::TestScriptDict::test_contains, test/test_jit.py::TestScriptDict::test_delitem, test/test_jit.py::TestScriptDict::test_getitem, test/test_jit.py::TestScriptDict::test_items, test/test_jit.py::TestScriptDict::test_iter, test/test_jit.py::TestScriptDict::test_len, test/test_jit.py::TestScriptDict::test_nested, test/test_jit.py::TestScriptDict::test_reference_semantics, test/test_jit.py::TestScriptDict::test_repr, test/test_jit.py::TestScriptDict::test_setitem, test/test_jit.py::TestScriptList::test_append, test/test_jit.py::TestScriptList::test_bool, test/test_jit.py::TestScriptList::test_clear, test/test_jit.py::TestScriptList::test_contains, test/test_jit.py::TestScriptList::test_count, test/test_jit.py::TestScriptList::test_defaultdict, test/test_jit.py::TestScriptList::test_delitem, test/test_jit.py::TestScriptList::test_extend, test/test_jit.py::TestScriptList::test_getitem, test/test_jit.py::TestScriptList::test_insert, test/test_jit.py::TestScriptList::test_iter, test/test_jit.py::TestScriptList::test_len, test/test_jit.py::TestScriptList::test_nested, test/test_jit.py::TestScriptList::test_pop, test/test_jit.py::TestScriptList::test_reference_semantics, test/test_jit.py::TestScriptList::test_remove, test/test_jit.py::TestScriptList::test_repr, test/test_jit.py::TestScriptList::test_setitem, test/test_jit.py::TestAsync::test_async_future_type_python, test/test_jit.py::TestAsync::test_async_grad_guard_no_grad, test/test_jit.py::TestAsync::test_async_grad_guard_with_grad, test/test_jit.py::TestAsync::test_async_kwargs, test/test_jit.py::TestAsync::test_async_parsing, test/test_jit.py::TestAsync::test_async_python, test/test_jit.py::TestAsync::test_async_script, test/test_jit.py::TestAsync::test_async_script_capture, test/test_jit.py::TestAsync::test_async_script_error, test/test_jit.py::TestAsync::test_async_script_multi_forks, test/test_jit.py::TestAsync::test_async_script_multi_waits, test/test_jit.py::TestAsync::test_async_script_nested, test/test_jit.py::TestAsync::test_async_script_no_script_mod, test/test_jit.py::TestAsync::test_async_script_trace, test/test_jit.py::TestAsync::test_future_subtyping, test/test_jit.py::TestAsync::test_no_future_subtype_message, test/test_jit.py::TestAsync::test_trace_fork_wait, test/test_jit.py::TestAsync::test_trace_fork_wait_inline, test/test_jit.py::TestAsync::test_trace_fork_wait_leaking, test/test_jit.py::TestAsync::test_trace_fork_wait_list_modulecalls, test/test_jit.py::TestAsync::test_trace_modulecalls_with_different_output_types, test/test_jit.py::TestAwait::test_await_class_arg, test/test_jit.py::TestAwait::test_await_class_return, test/test_jit.py::TestAwait::test_await_eager_lazy, test/test_jit.py::TestAwait::test_await_func_arg, test/test_jit.py::TestAwait::test_await_getattr_implicit_convertion, test/test_jit.py::TestAwait::test_await_isinstance, test/test_jit.py::TestAwait::test_await_multiout_save, test/test_jit.py::TestAwait::test_await_nested, test/test_jit.py::TestAwait::test_await_out_of_interpreter, test/test_jit.py::TestAwait::test_await_python, test/test_jit.py::TestAwait::test_await_type_python, test/test_jit.py::TestAwait::test_awaitable_to_await, test/test_jit.py::TestAwait::test_eager_await_non_scriptable, test/test_jit.py::TestAwait::test_jit_trace, test/test_jit.py::TestAwait::test_nowait, test/test_jit.py::TestAwait::test_nowait_class, test/test_jit.py::TestAwait::test_script, test/test_jit.py::TestDataParallel::test_python_submodule_script, test/test_jit.py::TestDataParallel::test_shared_module, test/test_jit.py::TestDataParallel::test_tensor_sharing, test/test_jit.py::TestDataParallel::test_tensor_sharing_with_forward, test/test_jit.py::TestDataParallel::test_traced_module, test/test_jit.py::TestModels::test_alexnet, test/test_jit.py::TestModels::test_dcgan_models, test/test_jit.py::TestModels::test_dcgan_models_cuda, test/test_jit.py::TestModels::test_mnist, test/test_jit.py::TestModels::test_mnist_cuda, test/test_jit.py::TestModels::test_mnist_training_leaks_no_memory_cuda, test/test_jit.py::TestModels::test_neural_style, test/test_jit.py::TestModels::test_neural_style_cuda, test/test_jit.py::TestModels::test_reinforcement_learning, test/test_jit.py::TestModels::test_reinforcement_learning_cuda, test/test_jit.py::TestModels::test_script_module_script_resnet, test/test_jit.py::TestModels::test_script_module_trace_resnet18, test/test_jit.py::TestModels::test_snli, test/test_jit.py::TestModels::test_snli_cuda, test/test_jit.py::TestModels::test_super_resolution, test/test_jit.py::TestModels::test_super_resolution_cuda, test/test_jit.py::TestModels::test_time_sequence_prediction, test/test_jit.py::TestModels::test_vae, test/test_jit.py::TestModels::test_vae_cuda, test/test_jit.py::TestModules::test_script_module_with_constants_list, test/test_jit.py::TestAutodiffJit::test_autodiff_requires_grad_nograd, test/test_jit.py::TestAutodiffJit::test_requires_grad_outputs, test/test_jit.py::TestAutodiffJit::test_requires_grad_outputs_profiled_twice, test/test_jit.py::TestAutodiffJit::test_requires_grad_outputs_side_effects, test/test_jit.py::TestAutodiffJit::test_undefined_tensor_lists, test/test_jit.py::TestAutodiffSubgraphSlicing::test_aliased_outputs, test/test_jit.py::TestAutodiffSubgraphSlicing::test_bias_as_arg, test/test_jit.py::TestAutodiffSubgraphSlicing::test_bias_as_module_attr, test/test_jit.py::TestAutodiffSubgraphSlicing::test_chunk_constant_script_ad, test/test_jit.py::TestAutodiffSubgraphSlicing::test_constructed_bias, test/test_jit.py::TestAutodiffSubgraphSlicing::test_diff_graph_inline_threshold, test/test_jit.py::TestAutodiffSubgraphSlicing::test_differentiable_graph_ops_requires_grad, test/test_jit.py::TestAutodiffSubgraphSlicing::test_does_not_create_cycles, test/test_jit.py::TestAutodiffSubgraphSlicing::test_does_not_merge_unrelated, test/test_jit.py::TestAutodiffSubgraphSlicing::test_has_profiled_info_aliasing_outputs, test/test_jit.py::TestAutodiffSubgraphSlicing::test_merge_respects_aliasing, test/test_jit.py::TestAutodiffSubgraphSlicing::test_merges_dense, test/test_jit.py::TestAutodiffSubgraphSlicing::test_merges_down, test/test_jit.py::TestAutodiffSubgraphSlicing::test_merges_up, test/test_jit.py::TestAutodiffSubgraphSlicing::test_merges_without_cycles, test/test_jit.py::TestAutodiffSubgraphSlicing::test_prune_grad, test/test_jit.py::TestAutodiffSubgraphSlicing::test_requires_grad_for_tensor_list, test/test_jit.py::TestAutodiffSubgraphSlicing::test_respects_lexical_scoping, test/test_jit.py::TestAutodiffSubgraphSlicing::test_simple_merge, test/test_jit.py::TestAutodiffSubgraphSlicing::test_simple_no_merge, test/test_jit.py::TestCustomOperators::test_calling_scripted_custom_op, test/test_jit.py::TestCustomOperators::test_calling_traced_custom_op, test/test_jit.py::TestCustomOperators::test_default_arguments_are_used, test/test_jit.py::TestCustomOperators::test_dynamic_op_registry, test/test_jit.py::TestCustomOperators::test_generic_list, test/test_jit.py::TestCustomOperators::test_getting_invalid_attr, test/test_jit.py::TestCustomOperators::test_passing_and_returning_lists, test/test_jit.py::TestCustomOperators::test_passing_one_positional_but_not_the_second, test/test_jit.py::TestCustomOperators::test_passing_too_few_args, test/test_jit.py::TestCustomOperators::test_passing_too_many_args, test/test_jit.py::TestCustomOperators::test_passing_unknown_kwargs, test/test_jit.py::TestCustomOperators::test_script_graph_contains_custom_op, test/test_jit.py::TestCustomOperators::test_script_graph_for_custom_ops_matches_traced_graph, test/test_jit.py::TestCustomOperators::test_simply_calling_an_operator, test/test_jit.py::TestCustomOperators::test_where_no_scalar, test/test_jit.py::TestGraphRewritePasses::test_fuse_linear, test/test_jit.py::TestClassType::test_cast_overloads, test/test_jit.py::TestClassType::test_class_attribute_wrong_type, test/test_jit.py::TestClassType::test_class_constant, test/test_jit.py::TestClassType::test_class_constructs_itself, test/test_jit.py::TestClassType::test_class_inheritance, test/test_jit.py::TestClassType::test_class_inheritance_implicit, test/test_jit.py::TestClassType::test_class_sorting, test/test_jit.py::TestClassType::test_class_specialization, test/test_jit.py::TestClassType::test_class_type_as_param, test/test_jit.py::TestClassType::test_classmethod, test/test_jit.py::TestClassType::test_conditional_set_attr, test/test_jit.py::TestClassType::test_custom_delete, test/test_jit.py::TestClassType::test_default_args, test/test_jit.py::TestClassType::test_get_attr, test/test_jit.py::TestClassType::test_get_attr_not_initialized, test/test_jit.py::TestClassType::test_get_with_method, test/test_jit.py::TestClassType::test_imported_classes, test/test_jit.py::TestClassType::test_in, test/test_jit.py::TestClassType::test_init_compiled_first, test/test_jit.py::TestClassType::test_interface, test/test_jit.py::TestClassType::test_optional_type_promotion, test/test_jit.py::TestClassType::test_out_of_order_methods, test/test_jit.py::TestClassType::test_overloaded_fn, test/test_jit.py::TestClassType::test_properties, test/test_jit.py::TestClassType::test_py_class_to_ivalue_missing_attribute, test/test_jit.py::TestClassType::test_python_interop, test/test_jit.py::TestClassType::test_recursive_class, test/test_jit.py::TestClassType::test_recursive_script_builtin_type_resolution, test/test_jit.py::TestClassType::test_recursive_script_module_builtin_type_resolution, test/test_jit.py::TestClassType::test_recursive_scripting, test/test_jit.py::TestClassType::test_recursive_scripting_failed, test/test_jit.py::TestClassType::test_reference_semantics, test/test_jit.py::TestClassType::test_save_load_with_classes, test/test_jit.py::TestClassType::test_save_load_with_classes_nested, test/test_jit.py::TestClassType::test_save_load_with_classes_returned, test/test_jit.py::TestClassType::test_schema_human_readable, test/test_jit.py::TestClassType::test_self_referential_method, test/test_jit.py::TestClassType::test_set_attr_in_method, test/test_jit.py::TestClassType::test_set_attr_non_initialized, test/test_jit.py::TestClassType::test_set_attr_type_mismatch, test/test_jit.py::TestClassType::test_staticmethod, test/test_jit.py::TestClassType::test_type_annotation, test/test_jit.py::TestClassType::test_type_annotations, test/test_jit.py::TestClassType::test_unresolved_class_attributes, test/test_jit.py::TestClassType::test_unused_method, test/test_jit.py::TestBuiltins::test_del, test/test_jit.py::TestBuiltins::test_del_multiple_operands, test/test_jit.py::TestBuiltins::test_has_attr, test/test_jit.py::TestBuiltins::test_has_attr_invalid_args, test/test_jit.py::TestTensorBuiltins::test_method_on_number, test/test_jit.py::TestTensorBuiltins::test_scalar_to_num_conversions, test/test_jit.py::TestTensorBuiltins::test_tensor_item, test/test_jit.py::TestTensorBuiltins::test_tensor_properties, test/test_jit.py::TestTensorBuiltins::test_tensor_subscript_assign, test/test_jit.py::TestTensorBuiltins::test_tensor_subscript_assign_device, test/test_jit.py::TestIgnoreContextManager::test_with_ignore_context_manager_with_inp_out, test/test_jit.py::TestIgnoreContextManager::test_with_ignore_context_manager_with_just_inp, test/test_jit.py::TestIgnoreContextManager::test_with_ignore_context_manager_with_just_out, test/test_jit.py::TestSymbolicShapeAnalysis::test_adaptive_avg_pool2d, test/test_jit.py::TestSymbolicShapeAnalysis::test_arange_shape, test/test_jit.py::TestSymbolicShapeAnalysis::test_binary_shape_fns_inplace, test/test_jit.py::TestSymbolicShapeAnalysis::test_binary_shape_functions, test/test_jit.py::TestSymbolicShapeAnalysis::test_conv_deconv, test/test_jit.py::TestSymbolicShapeAnalysis::test_convolution_backward, test/test_jit.py::TestSymbolicShapeAnalysis::test_cross_entropy_loss, test/test_jit.py::TestSymbolicShapeAnalysis::test_if_propagation, test/test_jit.py::TestSymbolicShapeAnalysis::test_partial_eval_graph_conv, test/test_jit.py::TestSymbolicShapeAnalysis::test_partial_eval_stitching, test/test_jit.py::TestSymbolicShapeAnalysis::test_refinement_through_graph_stitching, test/test_jit.py::TestSymbolicShapeAnalysis::test_register_function_error_checking, test/test_jit.py::TestSymbolicShapeAnalysis::test_returning_input_symbolic_shapes, test/test_jit.py::TestSymbolicShapeAnalysis::test_shape_analysis, test/test_jit.py::TestSymbolicShapeAnalysis::test_shape_concat, test/test_jit.py::TestSymbolicShapeAnalysis::test_shape_embedding_bag, test/test_jit.py::TestSymbolicShapeAnalysis::test_shape_function_includes, test/test_jit.py::TestSymbolicShapeAnalysis::test_shared_shape_graph, test/test_jit.py::TestSymbolicShapeAnalysis::test_size_and_sizes, test/test_jit.py::TestSymbolicShapeAnalysis::test_squeeze_dims, test/test_jit.py::TestSymbolicShapeAnalysis::test_stitching_concat, test/test_jit.py::TestSymbolicShapeAnalysis::test_stitching_multi_output, test/test_jit.py::TestSymbolicShapeAnalysis::test_sym_ir_parsing, test/test_jit.py::TestSymbolicShapeAnalysis::test_unary_shape_fns_inplace, test/test_jit.py::TestSymbolicShapeAnalysis::test_unary_shape_functions, test/test_jit.py::TestSymbolicShapeAnalysis::test_write, test/test_jit.py::TestOpDecompositions::test_op_decomposition, test/test_jit.py::TestOpDecompositions::test_registered_decomposition, test/test_jit.py::TestUnsupportedOps::test_factory_ops_requires_grad_fail, test/test_jit.py::TestUnsupportedOps::test_init_ops, test/test_jit.py::TestFreezing::test_freeze_interface_swapping_two_methods, test/test_jit.py::TestFreezing::test_freeze_interface_within_object, test/test_jit.py::TestFreezing::test_freeze_module, test/test_jit.py::TestFreezing::test_freeze_module_detach_gradient, test/test_jit.py::TestFreezing::test_freeze_module_in_training_mode, test/test_jit.py::TestFreezing::test_freeze_module_inlining, test/test_jit.py::TestFreezing::test_freeze_module_no_forward, test/test_jit.py::TestFreezing::test_freeze_module_return_self, test/test_jit.py::TestFreezing::test_freeze_module_return_sub_module, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_attr, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_attr2, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_attr3, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_tensor_attr, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_tensor_attr2, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_tensor_attr3, test/test_jit.py::TestFreezing::test_freeze_module_with_aliased_tensor_attr4, test/test_jit.py::TestFreezing::test_freeze_module_with_call_method, test/test_jit.py::TestFreezing::test_freeze_module_with_fork, test/test_jit.py::TestFreezing::test_freeze_module_with_fork2, test/test_jit.py::TestFreezing::test_freeze_module_with_fork_calling_module_method, test/test_jit.py::TestFreezing::test_freeze_module_with_helperfunction, test/test_jit.py::TestFreezing::test_freeze_module_with_inplace_mutable, test/test_jit.py::TestFreezing::test_freeze_module_with_list, test/test_jit.py::TestFreezing::test_freeze_module_with_mutable_dict, test/test_jit.py::TestFreezing::test_freeze_module_with_mutable_list, test/test_jit.py::TestFreezing::test_freeze_module_with_mutable_tensor, test/test_jit.py::TestFreezing::test_freeze_module_with_nested_fork, test/test_jit.py::TestFreezing::test_freeze_module_with_nestedaliasing, test/test_jit.py::TestFreezing::test_freeze_module_with_nestedaliasingscalar, test/test_jit.py::TestFreezing::test_freeze_module_with_non_static_module_container_index, test/test_jit.py::TestFreezing::test_freeze_module_with_overlapping_attrs, test/test_jit.py::TestFreezing::test_freeze_module_with_preserve_sub_module, test/test_jit.py::TestFreezing::test_freeze_module_with_preserve_sub_module_and_mutation, test/test_jit.py::TestFreezing::test_freeze_module_with_sharedclasstype, test/test_jit.py::TestFreezing::test_freeze_module_with_submodule, test/test_jit.py::TestFreezing::test_freeze_module_with_tensor, test/test_jit.py::TestFreezing::test_freeze_module_with_tuple, test/test_jit.py::TestFreezing::test_freeze_module_with_tupleoutput_submodule, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_attr, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_attribute_on_submodule, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_attribute_on_unused_submodule, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_method, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_method2, test/test_jit.py::TestFreezing::test_freeze_module_with_user_preserved_method_on_submodule, test/test_jit.py::TestFreezing::test_freeze_no_forward, test/test_jit.py::TestFreezing::test_freeze_non_interface_module_swap, test/test_jit.py::TestFreezing::test_freeze_non_module_class_getattr, test/test_jit.py::TestFreezing::test_freeze_recursive_interfaces, test/test_jit.py::TestFreezing::test_freeze_recursive_interfaces_same_name, test/test_jit.py::TestFreezing::test_freeze_recursive_interfaces_with_reassignment, test/test_jit.py::TestFreezing::test_freeze_with_interface_mutable, test/test_jit.py::TestFreezing::test_freeze_with_swapping_interfaces, test/test_jit.py::TestFreezing::test_module_getattr_indirection, test/test_jit.py::TestFreezing::test_module_with_shared_type_instances, test/test_jit.py::TestFrozenOptimizations::test_bn_not_broadcast_with_linear, test/test_jit.py::TestFrozenOptimizations::test_collapse_adjacent_conversions, test/test_jit.py::TestFrozenOptimizations::test_conv_add_folding, test/test_jit.py::TestFrozenOptimizations::test_conv_bn_folding, test/test_jit.py::TestFrozenOptimizations::test_conv_bn_folding_autocast_scenario_cuda, test/test_jit.py::TestFrozenOptimizations::test_conv_bn_folding_not_forward, test/test_jit.py::TestFrozenOptimizations::test_conv_hardswish, test/test_jit.py::TestFrozenOptimizations::test_conv_mul_add_bn, test/test_jit.py::TestFrozenOptimizations::test_conv_to_mkldnn, test/test_jit.py::TestFrozenOptimizations::test_conv_to_mkldnn_no_mkldnn, test/test_jit.py::TestFrozenOptimizations::test_freeze_conv_relu_fusion, test/test_jit.py::TestFrozenOptimizations::test_freeze_conv_relu_fusion_not_forward, test/test_jit.py::TestFrozenOptimizations::test_freeze_mkdlnn, test/test_jit.py::TestFrozenOptimizations::test_freeze_remove_dropout, test/test_jit.py::TestFrozenOptimizations::test_freeze_remove_feature_dropout, test/test_jit.py::TestFrozenOptimizations::test_hardswish_hardsigmoid, test/test_jit.py::TestFrozenOptimizations::test_incompatible_perf_formats, test/test_jit.py::TestFrozenOptimizations::test_linear_bn_folding, test/test_jit.py::TestFrozenOptimizations::test_linear_bn_folding_autocast_scenario_cuda, test/test_jit.py::TestFrozenOptimizations::test_linear_concat, test/test_jit.py::TestFrozenOptimizations::test_linear_concat_complex, test/test_jit.py::TestFrozenOptimizations::test_linear_concat_different_input, test/test_jit.py::TestFrozenOptimizations::test_linear_multiple_blocks, test/test_jit.py::TestFrozenOptimizations::test_linear_non_constant_weight, test/test_jit.py::TestFrozenOptimizations::test_linear_transpose, test/test_jit.py::TestFrozenOptimizations::test_maxpool_mkldnn, test/test_jit.py::TestFrozenOptimizations::test_mkldnn_fuser_broadcasting, test/test_jit.py::TestFrozenOptimizations::test_mkldnn_inplace_removal, test/test_jit.py::TestFrozenOptimizations::test_numel_less_than_size_with_padding, test/test_jit.py::TestFrozenOptimizations::test_optimize_freeze_module, test/test_jit.py::TestFrozenOptimizations::test_pool2d_batchnorm, test/test_jit.py::TestFrozenOptimizations::test_pool3d_batchnorm, test/test_jit.py::TestFrozenOptimizations::test_remove_detach, test/test_jit.py::TestFrozenOptimizations::test_remove_detach_not_applied, test/test_jit.py::TestFrozenOptimizations::test_scalar_mul, test/test_jit.py::TestMKLDNNReinplacing::test_always_alive_values, test/test_jit.py::TestMKLDNNReinplacing::test_merge_liveness, test/test_jit.py::TestMKLDNNReinplacing::test_successful, test/test_jit.py::TestMKLDNNReinplacing::test_switch_inputs_to_inplace, test/test_jit.py::TestPeephole::test_conv_dim_folding, test/test_jit.py::TestPeephole::test_integer_refinement, test/test_jit.py::TestPeephole::test_noop_peephole, test/test_jit.py::TestPeephole::test_normalized_is_op, test/test_jit.py::TestPeephole::test_normalized_isnot_op, test/test_jit.py::TestPeephole::test_normalized_rsub, test/test_jit.py::TestPeephole::test_optimize_out_comparison_same_value, test/test_jit.py::TestPeephole::test_peephole, test/test_jit.py::TestPeephole::test_peephole_add_zero, test/test_jit.py::TestPeephole::test_peephole_arith, test/test_jit.py::TestPeephole::test_peephole_cuda, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_dict_modified, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_get_input_arg, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_keys_might_overlap, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_missing_key, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_overlapping_keys, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_no_optimization_unsupported_type, test/test_jit.py::TestPeephole::test_peephole_dict_getitem_simple, test/test_jit.py::TestPeephole::test_peephole_dict_len, test/test_jit.py::TestPeephole::test_peephole_dict_len_no_optimization_keys_might_overlap, test/test_jit.py::TestPeephole::test_peephole_dict_len_no_optimization_overlapping_keys, test/test_jit.py::TestPeephole::test_peephole_dict_len_no_optimization_unsupported_type, test/test_jit.py::TestPeephole::test_peephole_dynamic, test/test_jit.py::TestPeephole::test_peephole_int, test/test_jit.py::TestPeephole::test_peephole_len_list, test/test_jit.py::TestPeephole::test_peephole_list_len, test/test_jit.py::TestPeephole::test_peephole_list_ops, test/test_jit.py::TestPeephole::test_peephole_no_output_aliasing, test/test_jit.py::TestPeephole::test_peephole_optional_refine, test/test_jit.py::TestPeephole::test_peephole_slice_all_three_args, test/test_jit.py::TestPeephole::test_peephole_slice_one_empty_arg, test/test_jit.py::TestPeephole::test_peephole_slice_optimization_not_applied_list_modified, test/test_jit.py::TestPeephole::test_peephole_slice_optimization_not_applied_non_const_args, test/test_jit.py::TestPeephole::test_peephole_slice_two_empty_args, test/test_jit.py::TestPeephole::test_peephole_type_refinements, test/test_jit.py::TestPeephole::test_peephole_with_non_output_writes, test/test_jit.py::TestPeephole::test_peephole_with_writes, test/test_jit.py::TestPeephole::test_refine_integer_values, test/test_jit.py::TestPeephole::test_short_circuit_optimization, test/test_jit.py::TestAliasAnalysis::test_becomes_wildcard_annotations, test/test_jit.py::TestAliasAnalysis::test_multiple_compilation_units, test/test_jit.py::TestAliasAnalysis::test_nested_list_construct_not_wildcard, test/test_jit.py::TestAliasAnalysis::test_recursive_calls, test/test_jit.py::TestSaveLoad::test_different_functions, test/test_jit.py::TestSaveLoad::test_different_interfaces, test/test_jit.py::TestSaveLoad::test_different_modules, test/test_jit.py::TestSaveLoad::test_many_collisions, test/test_jit.py::TestSaveLoad::test_save_load_large_string_attribute, test/test_jit.py::TestSaveLoad::test_save_load_meta_tensors, test/test_jit.py::TestSaveLoad::test_save_load_meta_tensors_to_device, test/test_jit.py::TestSaveLoad::test_save_load_params_buffers_submodules, test/test_jit.py::TestSaveLoad::test_save_load_using_pathlib, test/test_jit.py::TestSaveLoad::test_save_load_with_extra_files, test/test_jit.py::TestSaveLoad::test_save_load_with_saved_traced_inputs, test/test_jit.py::TestSaveLoad::test_save_namedtuple_input_only, test/test_jit.py::TestSaveLoad::test_save_namedtuple_input_only_forwardref, test/test_jit.py::TestSaveLoad::test_save_namedtuple_output_only, test/test_jit.py::TestSaveLoad::test_save_nonexit_file, test/test_jit.py::TestSaveLoadFlatbuffer::test_different_functions, test/test_jit.py::TestSaveLoadFlatbuffer::test_different_interfaces, test/test_jit.py::TestSaveLoadFlatbuffer::test_different_modules, test/test_jit.py::TestSaveLoadFlatbuffer::test_many_collisions, test/test_jit.py::TestSaveLoadFlatbuffer::test_module_info_flatbuffer, test/test_jit.py::TestSaveLoadFlatbuffer::test_save_load_params_buffers_submodules, test/test_jit.py::TestSaveLoadFlatbuffer::test_save_load_using_pathlib, test/test_jit.py::TestSaveLoadFlatbuffer::test_save_load_with_extra_files, test/test_jit.py::TestSaveLoadFlatbuffer::test_save_namedtuple_input_only, test/test_jit.py::TestSaveLoadFlatbuffer::test_save_namedtuple_output_only, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_scalar, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_scalar_inplace, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_scalar_reciprocal, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_scalar_scalar, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_tensor, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_tensor_inplace, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_div_tensor_out, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_linspace, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_linspace_out, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_logspace, test/test_jit.py::TestSaveLoadForOpVersion::test_versioned_logspace_out, test/test_jit.py::TestModuleContainers::test_custom_container_forward, test/test_jit.py::TestModuleContainers::test_empty_dict_override_contains, test/test_jit.py::TestModuleContainers::test_module_inplace_construct, test/test_jit.py::TestModuleContainers::test_module_interface_special_methods, test/test_jit.py::TestModuleContainers::test_module_properties, test/test_jit.py::TestModuleContainers::test_moduledict, test/test_jit.py::TestModuleContainers::test_moduledict_getitem, test/test_jit.py::TestModuleContainers::test_moduledict_keyerror, test/test_jit.py::TestModuleContainers::test_normal_list_attribute_with_modules_error, test/test_jit.py::TestModuleContainers::test_parameterdict_script_getitem, test/test_jit.py::TestModuleContainers::test_parameterlist_script_getitem, test/test_jit.py::TestModuleContainers::test_parameterlist_script_iter, test/test_jit.py::TestModuleContainers::test_script_module_list_sequential, test/test_jit.py::TestModuleContainers::test_script_modulelist_index, test/test_jit.py::TestModuleContainers::test_sequential_intermediary_types, test/test_jit.py::TestModuleContainers::test_special_method_with_override, test/test_jit.py::TestModuleContainers::test_typed_module_dict, test/test_jit.py::TestModuleContainers::test_typed_module_list, test/test_jit.py::TestPythonBindings::test_add_input, test/test_jit.py::TestPythonBindings::test_aliasdb, test/test_jit.py::TestPythonBindings::test_canonicalize, test/test_jit.py::TestPythonBindings::test_cu_create_function, test/test_jit.py::TestPythonBindings::test_cu_get_functions, test/test_jit.py::TestPythonBindings::test_graph_create, test/test_jit.py::TestPythonBindings::test_graph_iterator_keepalive, test/test_jit.py::TestPythonBindings::test_invalidation, test/test_jit.py::TestPythonIr::test_param_strides, test/test_jit.py::TestPythonIr::test_permute_inputs_binding, test/test_jit.py::TestPythonIr::test_python_ir_utils, test/test_jit.py::TestPythonIr::test_python_ir_utils_graph, test/test_jit.py::TestFunctionalBlocks::test_subgraph_creation, test/test_jit.py::TestRemoveMutation::test_aten_inplace, test/test_jit.py::TestRemoveMutation::test_common_pytorch_list_ops, test/test_jit.py::TestRemoveMutation::test_if_output, test/test_jit.py::TestRemoveMutation::test_if_output_fail, test/test_jit.py::TestRemoveMutation::test_list_indexing_removal, test/test_jit.py::TestRemoveMutation::test_lists_append, test/test_jit.py::TestRemoveMutation::test_lists_insert, test/test_jit.py::TestRemoveMutation::test_special_mapped_op, test/test_jit.py::TestTorchbind::test_default_args, test/test_jit.py::TestTorchbind::test_lambda_as_constructor, test/test_jit.py::TestTorchbind::test_profiler_custom_op, test/test_jit.py::TestTorchbind::test_staticmethod, test/test_jit.py::TestTorchbind::test_torchbind, test/test_jit.py::TestTorchbind::test_torchbind_attr_exception, test/test_jit.py::TestTorchbind::test_torchbind_class_attr_recursive, test/test_jit.py::TestTorchbind::test_torchbind_class_attribute, test/test_jit.py::TestTorchbind::test_torchbind_deepcopy, test/test_jit.py::TestTorchbind::test_torchbind_def_property_getter_setter, test/test_jit.py::TestTorchbind::test_torchbind_def_property_just_getter, test/test_jit.py::TestTorchbind::test_torchbind_def_property_readwrite, test/test_jit.py::TestTorchbind::test_torchbind_getattr, test/test_jit.py::TestTorchbind::test_torchbind_getstate, test/test_jit.py::TestTorchbind::test_torchbind_instantiate_missing_class, test/test_jit.py::TestTorchbind::test_torchbind_lambda_method, test/test_jit.py::TestTorchbind::test_torchbind_no_init, test/test_jit.py::TestTorchbind::test_torchbind_optional_explicit_attr, test/test_jit.py::TestTorchbind::test_torchbind_pass_wrong_type, test/test_jit.py::TestTorchbind::test_torchbind_pickle_serialization, test/test_jit.py::TestTorchbind::test_torchbind_python_deepcopy, test/test_jit.py::TestTorchbind::test_torchbind_return_instance, test/test_jit.py::TestTorchbind::test_torchbind_return_instance_from_method, test/test_jit.py::TestTorchbind::test_torchbind_return_tuple, test/test_jit.py::TestTorchbind::test_torchbind_save_load, test/test_jit.py::TestTorchbind::test_torchbind_take_as_arg, test/test_jit.py::TestTorchbind::test_torchbind_take_instance_as_method_arg, test/test_jit.py::TestTorchbind::test_torchbind_tracing, test/test_jit.py::TestTorchbind::test_torchbind_tracing_nested, test/test_jit.py::TestModuleInterface::test_freeze_module_with_inplace_mutation_in_interface, test/test_jit.py::TestModuleInterface::test_freeze_module_with_interface, test/test_jit.py::TestModuleInterface::test_freeze_module_with_interface_and_fork, test/test_jit.py::TestModuleInterface::test_freeze_module_with_mutated_interface, test/test_jit.py::TestModuleInterface::test_freeze_module_with_setattr_in_interface, test/test_jit.py::TestModuleInterface::test_module_apis_interface, test/test_jit.py::TestModuleInterface::test_module_doc_string, test/test_jit.py::TestModuleInterface::test_module_interface, test/test_jit.py::TestModuleInterface::test_module_interface_inheritance, test/test_jit.py::TestModuleInterface::test_module_interface_subtype, test/test_jit.py::TestModuleInterface::test_module_swap, test/test_jit.py::TestModuleInterface::test_module_swap_no_lazy_compile, test/test_jit.py::TestModuleInterface::test_module_swap_no_module_interface, test/test_jit.py::TestModuleInterface::test_module_swap_wrong_module, test/test_jit.py::TestModuleInterface::test_not_submodule_interface_call, test/test_jit.py::TestModuleInterface::test_script_module_as_interface_swap, test/test_jit.py::TestWith::test_with_as, test/test_jit.py::TestWith::test_with_errors, test/test_jit.py::TestWith::test_with_exceptions, test/test_jit.py::TestWith::test_with_no_as, test/test_jit.py::TestWith::test_with_no_grad, test/test_jit.py::TestWith::test_with_record_function, test/test_jit.py::TestEnum::test_closed_over_enum_constant, test/test_jit.py::TestEnum::test_enum_as_const, test/test_jit.py::TestEnum::test_enum_as_module_attribute, test/test_jit.py::TestEnum::test_enum_comp, test/test_jit.py::TestEnum::test_enum_comp_diff_classes, test/test_jit.py::TestEnum::test_enum_explicit_script, test/test_jit.py::TestEnum::test_enum_iterate, test/test_jit.py::TestEnum::test_enum_ivalue_type, test/test_jit.py::TestEnum::test_enum_module_return, test/test_jit.py::TestEnum::test_enum_name, test/test_jit.py::TestEnum::test_enum_return, test/test_jit.py::TestEnum::test_enum_value, test/test_jit.py::TestEnum::test_enum_value_types, test/test_jit.py::TestEnum::test_heterogenous_value_type_enum_error, test/test_jit.py::TestEnum::test_non_existent_enum_value, test/test_jit.py::TestEnum::test_string_enum_as_module_attribute, test/test_jit.py::TestEnum::test_typed_enum, test/test_jit.py::TestStringFormatting::test_modulo_operator, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_alternate_digit_placeholder, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_capital_exponent_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_char_placeholder_and_char_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_char_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_char_placeholder_and_true_string_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_digit_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_digit_placeholder_and_string_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_double_percent_in_string, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_exponent_placeholder_and_string_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_float_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_float_placeholder_and_float_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_lowercase_exponent_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_multiple_placeholders, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_percent_in_string, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_string_placeholder_and_digit_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_string_placeholder_and_format_string_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_string_placeholder_and_string_variable, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_subscript, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_too_few_arguments, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_too_many_arguments, test/test_jit.py::TestStringFormatting::test_string_interpolation_with_unknown_format_specifier, test/test_jit.py::TestProfiler::test_aliasing_merge, test/test_jit.py::TestProfiler::test_autograd_fallback_graph, test/test_jit.py::TestProfiler::test_fallback_graph_not_specialized, test/test_jit.py::TestProfiler::test_iterative_fusion, test/test_jit.py::TestProfiler::test_local_fusion_strategy, test/test_jit.py::TestProfiler::test_not_fusing_scalar_ops, test/test_jit.py::TestProfiler::test_not_optimizing_property, test/test_jit.py::TestProfiler::test_specialize_backward, test/test_jit.py::TestProfiler::test_specialized_types, test/test_jit.py::TestProfiler::test_tensor_constant, test/test_jit.py::TestProfiler::test_tensor_type_not_determined_by_inputs, test/test_jit.py::TestProfiler::test_use_not_profiled, test/test_jit.py::TestSlice::test_module_list_slicing, test/test_jit.py::TestSlice::test_slice_as_variable, test/test_jit.py::TestSlice::test_slice_dynamic_index, test/test_jit.py::TestSlice::test_slice_kwarg, test/test_jit.py::TestSlice::test_slice_one_none, test/test_jit.py::TestSlice::test_slice_start_stop, test/test_jit.py::TestSlice::test_slice_start_stop_step, test/test_jit.py::TestSlice::test_slice_start_stop_with_none, test/test_jit.py::TestSlice::test_slice_stop_clipped, test/test_jit.py::TestSlice::test_slice_stop_only, test/test_jit.py::TestSlice::test_slice_stop_only_with_nones, test/test_jit.py::TestSlice::test_slice_string, test/test_jit.py::TestSlice::test_slice_tensor, test/test_jit.py::TestSlice::test_slice_tensor_multidim, test/test_jit.py::TestSlice::test_slice_tensor_multidim_with_dots, test/test_jit.py::TestSlice::test_slice_three_nones, test/test_jit.py::TestSlice::test_slice_two_nones, test/test_jit.py::TestSlice::test_tuple_slicing, test/test_jit.py::TestIgnorableArgs::test_add_out_ignorable_args, test/test_jit.py::TestIgnorableArgs::test_slice_ignorable_args_for_slice, test/test_jit.py::TestHooks::test_forward_tuple_input, test/test_jit.py::TestHooks::test_hook_compilation_hint, test/test_jit.py::TestHooks::test_hook_hook_name_collision, test/test_jit.py::TestHooks::test_hook_method_name_collision, test/test_jit.py::TestHooks::test_module_direct_forward_invocation, test/test_jit.py::TestHooks::test_module_forward_multiple_inputs, test/test_jit.py::TestHooks::test_module_forward_single_input, test/test_jit.py::TestHooks::test_module_hook_return_nothing, test/test_jit.py::TestHooks::test_module_multiple_hooks_multiple_inputs, test/test_jit.py::TestHooks::test_module_multiple_hooks_single_input, test/test_jit.py::TestHooks::test_module_no_forward_input, test/test_jit.py::TestHooks::test_module_same_hook_repeated, test/test_jit.py::TestHooks::test_submodule_called_directly_with_hooks, test/test_jit.py::TestHooks::test_submodule_direct_forward_invocation, test/test_jit.py::TestHooks::test_submodule_forward_multiple_inputs, test/test_jit.py::TestHooks::test_submodule_forward_single_input, test/test_jit.py::TestHooks::test_submodule_forward_single_input_return_not_tupled, test/test_jit.py::TestHooks::test_submodule_hook_return_nothing, test/test_jit.py::TestHooks::test_submodule_multiple_hooks_multiple_inputs, test/test_jit.py::TestHooks::test_submodule_multiple_hooks_single_input, test/test_jit.py::TestHooks::test_submodule_no_forward_input, test/test_jit.py::TestHooks::test_submodule_same_hook_repeated, test/test_jit.py::TestHooks::test_wrong_hook_signatures, test/test_jit.py::TestHooks::test_wrong_pre_hook_signatures, test/test_jit.py::TestWarn::test_warn, test/test_jit.py::TestWarn::test_warn_multiple_calls_multiple_warnings, test/test_jit.py::TestWarn::test_warn_multiple_calls_same_func_diff_stack, test/test_jit.py::TestWarn::test_warn_once_per_func, test/test_jit.py::TestWarn::test_warn_once_per_func_in_loop, test/test_jit.py::TestWarn::test_warn_only_once, test/test_jit.py::TestWarn::test_warn_only_once_in_loop_func, test/test_jit.py::TestIsinstance::test_bool, test/test_jit.py::TestIsinstance::test_dict, test/test_jit.py::TestIsinstance::test_dict_nested, test/test_jit.py::TestIsinstance::test_dict_no_contained_type, test/test_jit.py::TestIsinstance::test_dict_tensor, test/test_jit.py::TestIsinstance::test_empty_container_special_cases, test/test_jit.py::TestIsinstance::test_empty_container_throws_warning_in_eager, test/test_jit.py::TestIsinstance::test_float, test/test_jit.py::TestIsinstance::test_if_else, test/test_jit.py::TestIsinstance::test_in_if, test/test_jit.py::TestIsinstance::test_in_while_loop, test/test_jit.py::TestIsinstance::test_int, test/test_jit.py::TestIsinstance::test_list, test/test_jit.py::TestIsinstance::test_list_nested, test/test_jit.py::TestIsinstance::test_list_no_contained_type, test/test_jit.py::TestIsinstance::test_list_tensor, test/test_jit.py::TestIsinstance::test_list_tensor_type_true, test/test_jit.py::TestIsinstance::test_nontuple_container_rhs_throws_in_eager, test/test_jit.py::TestIsinstance::test_optional, test/test_jit.py::TestIsinstance::test_optional_nested, test/test_jit.py::TestIsinstance::test_optional_no_contained_type, test/test_jit.py::TestIsinstance::test_optional_none, test/test_jit.py::TestIsinstance::test_tensor_type_false, test/test_jit.py::TestIsinstance::test_tuple, test/test_jit.py::TestIsinstance::test_tuple_nested, test/test_jit.py::TestIsinstance::test_tuple_no_contained_type, test/test_jit.py::TestIsinstance::test_tuple_rhs, test/test_jit.py::TestIsinstance::test_tuple_tensor, test/test_jit.py::TestIsinstance::test_type_refinement, test/test_jit.py::TestPythonBuiltinOP::test_add, test/test_jit.py::TestPythonBuiltinOP::test_adv_indexing_list, test/test_jit.py::TestPythonBuiltinOP::test_advancedindex, test/test_jit.py::TestPythonBuiltinOP::test_gather, test/test_jit.py::TestPythonBuiltinOP::test_index, test/test_jit.py::TestPythonBuiltinOP::test_index_ellipses, test/test_jit.py::TestPythonBuiltinOP::test_inf, test/test_jit.py::TestPythonBuiltinOP::test_matmul_py3, test/test_jit.py::TestPythonBuiltinOP::test_mul, test/test_jit.py::TestPythonBuiltinOP::test_pow, test/test_jit.py::TestPythonBuiltinOP::test_random, test/test_jit.py::TestPythonBuiltinOP::test_slice, test/test_jit.py::TestPythonBuiltinOP::test_stepped_tuple_slicing, test/test_jit.py::TestPythonBuiltinOP::test_str_to_float, test/test_jit.py::TestPythonBuiltinOP::test_triple, test/test_jit.py::TestTyping::test_bool_list_io, test/test_jit.py::TestTyping::test_dict_comprehension, test/test_jit.py::TestTyping::test_dict_comprehension_scope, test/test_jit.py::TestTyping::test_dict_comprehension_with_type_annotation, test/test_jit.py::TestTyping::test_dict_in_not_in, test/test_jit.py::TestTyping::test_dict_invalid_annotations, test/test_jit.py::TestTyping::test_dict_type_refinement_annotation_key_mismatch, test/test_jit.py::TestTyping::test_dict_type_refinement_annotation_value_mismatch, test/test_jit.py::TestTyping::test_for_in_dict, test/test_jit.py::TestTyping::test_for_in_string, test/test_jit.py::TestTyping::test_for_tuple_assign, test/test_jit.py::TestTyping::test_for_tuple_unpack, test/test_jit.py::TestTyping::test_inherited_annotations_python_310, test/test_jit.py::TestTyping::test_list_io, test/test_jit.py::TestTyping::test_list_iterables, test/test_jit.py::TestTyping::test_list_sum, test/test_jit.py::TestTyping::test_list_type_refinement_annotation_element_mismatch, test/test_jit.py::TestTyping::test_list_unification, test/test_jit.py::TestTyping::test_multiple_assign, test/test_jit.py::TestTyping::test_namedtuple_error_source_attribution, test/test_jit.py::TestTyping::test_namedtuple_good_error, test/test_jit.py::TestTyping::test_namedtuple_py2, test/test_jit.py::TestTyping::test_namedtuple_redefine, test/test_jit.py::TestTyping::test_nested_list, test/test_jit.py::TestTyping::test_opt_opt_refinement, test/test_jit.py::TestTyping::test_optional_conversion, test/test_jit.py::TestTyping::test_optional_refinement, test/test_jit.py::TestTyping::test_optional_tuple, test/test_jit.py::TestTyping::test_singleton_tuple_unpack, test/test_jit.py::TestTyping::test_sum_list_diff_elms, test/test_jit.py::TestTyping::test_sum_list_empty, test/test_jit.py::TestTyping::test_sum_list_literal, test/test_jit.py::TestTyping::test_sum_list_one, test/test_jit.py::TestTyping::test_sum_list_wrong_type, test/test_jit.py::TestTyping::test_tuple_assignments, test/test_jit.py::TestTyping::test_tuple_create_return, test/test_jit.py::TestTyping::test_tuple_io, test/test_jit.py::TestTyping::test_tuple_keyword, test/test_jit.py::TestTyping::test_tuple_specialization, test/test_jit.py::TestHash::test_hash_bool, test/test_jit.py::TestHash::test_hash_device, test/test_jit.py::TestHash::test_hash_float, test/test_jit.py::TestHash::test_hash_int, test/test_jit.py::TestHash::test_hash_none, test/test_jit.py::TestHash::test_hash_string, test/test_jit.py::TestHash::test_hash_tensor, test/test_jit.py::TestHash::test_hash_tuple, test/test_jit.py::TestHash::test_hash_tuple_nested_unhashable_type, test/test_jit.py::TestComplex::test_binary_op_complex_tensor, test/test_jit.py::TestComplex::test_comparison_ops, test/test_jit.py::TestComplex::test_complex_constants_and_ops, test/test_jit.py::TestComplex::test_complex_constructor, test/test_jit.py::TestComplex::test_complex_list_sum, test/test_jit.py::TestComplex::test_complex_parse, test/test_jit.py::TestComplex::test_complexdict, test/test_jit.py::TestComplex::test_complexlist, test/test_jit.py::TestComplex::test_div, test/test_jit.py::TestComplex::test_infj_nanj_pickle, test/test_jit.py::TestComplex::test_pickle, test/test_jit.py::TestComplex::test_script, test/test_jit.py::TestComplex::test_tensor_attributes, test/test_jit.py::TestComplex::test_torch_complex_constructor_with_tensor, test/test_jit.py::TestJitUtils::test_checkscriptassertraisesregex, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_hybrid, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_keyword_only, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_positional_only, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_positional_or_keyword, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_var_keyword, test/test_jit.py::TestJitUtils::test_get_callable_argument_names_var_positional, test/test_jit.py::TestJitUtils::test_no_tracer_warn_context_manager, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_class_level_annotation_and_init_annotation, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_class_level_annotation_only, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_class_level_jit_annotation, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_dict, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_dict_lowercase, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_list, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_list_lowercase, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_optional, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_empty_tensor, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_falsy_base_type, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_nonempty_container, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_attribute, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_empty_dict, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_empty_dict_lowercase, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_empty_list, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_empty_list_lowercase, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_jit_empty_optional, test/test_jit.py::TestScriptModuleInstanceAttributeTypeAnnotation::test_annotated_with_torch_jit_import, test/test_jit.py::TestTypesAndAnnotation::test_annotate_outside_init, test/test_jit.py::TestTypesAndAnnotation::test_bad_types, test/test_jit.py::TestTypesAndAnnotation::test_ignore_with_types, test/test_jit.py::TestTypesAndAnnotation::test_ignoring_fn_with_nonscriptable_types, test/test_jit.py::TestTypesAndAnnotation::test_ignoring_module_attributes, test/test_jit.py::TestTypesAndAnnotation::test_inferred_type_error_message, test/test_jit.py::TestTypesAndAnnotation::test_mismatched_annotation, test/test_jit.py::TestTypesAndAnnotation::test_optional_no_element_type_annotation, test/test_jit.py::TestTypesAndAnnotation::test_parser_bug, test/test_jit.py::TestTypesAndAnnotation::test_pep585_type, test/test_jit.py::TestTypesAndAnnotation::test_python_callable, test/test_jit.py::TestTypesAndAnnotation::test_reannotate, test/test_jit.py::TestTypesAndAnnotation::test_tuple_no_element_type_annotation, test/test_jit.py::TestTypesAndAnnotation::test_type_annotate_py3, test/test_jit.py::TestTypesAndAnnotation::test_types_as_values, test/test_jit.py::TestTypesAndAnnotation::test_unimported_type_resolution, test/test_jit.py::TestMisc::test_broadcasting_list, test/test_jit.py::TestMisc::test_export_opnames_interface, test/test_jit.py::TestMisc::test_future_isinstance, test/test_jit.py::TestMisc::test_hacked_twin, test/test_jit.py::TestMisc::test_if_returning_any, test/test_jit.py::TestMisc::test_jit_get_operation_order, test/test_jit.py::TestMisc::test_joined_str, test/test_jit.py::TestMisc::test_kwarg_support, test/test_jit.py::TestMisc::test_legacy_tensor_constructor, test/test_jit.py::TestMisc::test_list_literal_infer, test/test_jit.py::TestMisc::test_math_inf, test/test_jit.py::TestMisc::test_parse_ir_annotate, test/test_jit.py::TestMisc::test_parse_ir_single_element_tensor_negative, test/test_jit.py::TestMisc::test_parse_ir_single_element_tensor_positive, test/test_jit.py::TestMisc::test_pow_multiple_dtype, test/test_jit.py::TestMisc::test_script_many_decorators, test/test_jit.py::TestMisc::test_str_refine_any, test/test_jit.py::TestMisc::test_subexpression_Dict_int_Future, test/test_jit.py::TestMisc::test_subexpression_Future_annotate, test/test_jit.py::TestMisc::test_subexpression_List_Future, test/test_jit.py::TestMisc::test_subexpression_Optional, test/test_jit.py::TestMisc::test_subexpression_Tuple_int_int_Future, test/test_jit.py::TestMisc::test_tuple_subscripted_assign, test/test_jit.py::TestMisc::test_unsafe_hacked_twin, test/test_jit.py::TestUpgraders::test_add_value_to_version_map, test/test_jit.py::TestUpgraders::test_aten_div_scalar_at_3, test/test_jit.py::TestUpgraders::test_aten_div_tensor_at_3, test/test_jit.py::TestUpgraders::test_aten_div_tensor_out_at_3, test/test_jit.py::TestUpgraders::test_aten_full_at_4, test/test_jit.py::TestUpgraders::test_aten_full_other_variants, test/test_jit.py::TestUpgraders::test_aten_full_out_at_4, test/test_jit.py::TestUpgraders::test_aten_linspace, test/test_jit.py::TestUpgraders::test_aten_linspace_out, test/test_jit.py::TestUpgraders::test_aten_logspace, test/test_jit.py::TestUpgraders::test_aten_logspace_out, test/test_jit.py::TestUpgraders::test_aten_test_serialization, test/test_jit.py::TestUpgraders::test_populated_test_upgrader_graph, test/test_jit.py::TestUpgraders::test_populated_upgrader_graph, test/test_jit.py::TestTensorCreationOps::test_randperm_default_dtype, test/test_jit.py::TestTensorCreationOps::test_randperm_specifed_dtype, test/test_jit.py::TestTensorCreationOps::test_tril_indices_default_dtype, test/test_jit.py::TestTensorCreationOps::test_tril_indices_specified_dtype, test/test_jit.py::TestTensorCreationOps::test_triu_indices_default_dtype, test/test_jit.py::TestTensorCreationOps::test_triu_indices_specified_dtype, test/test_jit.py::TestModuleAPIs::test_customized_state_dict_methods, test/test_jit.py::TestModuleAPIs::test_default_state_dict_methods, test/test_jit.py::TestModuleAPIs::test_submodule_customized_state_dict_methods, test/test_jit.py::TestScriptProfile::test_basic, test/test_jit.py::TestScriptProfile::test_empty, test/test_jit.py::TestScriptProfile::test_multi, test/test_jit.py::TestScriptProfile::test_script, test/test_jit.py::TestScriptProfile::test_section, test/test_jit.py::TestFunctionalToInplaceActivation::test_check_no_type_promotion, test/test_jit.py::TestFunctionalToInplaceActivation::test_functional_to_inplace_activation, 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test/test_jit.py::TestScript::test_string_print, test/test_jit.py::TestScript::test_string_single_escape, test/test_jit.py::TestScript::test_string_slicing, test/test_jit.py::TestScript::test_string_sort, test/test_jit.py::TestScript::test_string_sorted, test/test_jit.py::TestScript::test_submodule_attribute_serialization, test/test_jit.py::TestScript::test_submodule_twice, test/test_jit.py::TestScript::test_sum, test/test_jit.py::TestScript::test_sum_list_diff_elms, test/test_jit.py::TestScript::test_sum_list_empty, test/test_jit.py::TestScript::test_sum_list_literal, test/test_jit.py::TestScript::test_sum_list_one, test/test_jit.py::TestScript::test_sum_list_wrong_type, test/test_jit.py::TestScript::test_sys_stdout_override, test/test_jit.py::TestScript::test_tensor_as_tensor_shape_prop, test/test_jit.py::TestScript::test_tensor_data, test/test_jit.py::TestScript::test_tensor_device, test/test_jit.py::TestScript::test_tensor_dtype, test/test_jit.py::TestScript::test_tensor_grad, 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test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_stride, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_stride_padding, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_zero_batch, test/test_jit.py::TestJitGeneratedModule::test_nn_Conv3d_zeros_stride2_pad2, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose1d, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose1d_dilated, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose1d_groups, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose1d_no_bias, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose2d, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose2d_dilated, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose2d_groups, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose2d_no_bias, test/test_jit.py::TestJitGeneratedModule::test_nn_ConvTranspose3d, 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test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_nearest_scale_1d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_nearest_scale_2d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_nearest_scale_3d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_nearest_tuple_1d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_nearest_tuple_2d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_nearest_tuple_3d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_trilinear_3d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_trilinear_3d_zero_dim, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_trilinear_scale_3d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_trilinear_scale_3d_align_corners, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_trilinear_tuple_3d, test/test_jit.py::TestJitGeneratedModule::test_nn_interpolate_trilinear_tuple_3d_align_corners, test/test_jit.py::TestJitGeneratedModule::test_nn_log_softmax_dim0, test/test_jit.py::TestJitGeneratedModule::test_nn_log_softmax_dim3, test/test_jit.py::TestJitGeneratedModule::test_nn_log_softmax_lastdim, test/test_jit.py::TestJitGeneratedModule::test_nn_log_softmax_scalar, test/test_jit.py::TestJitGeneratedModule::test_nn_log_softmax_spatial, test/test_jit.py::TestJitGeneratedModule::test_nn_log_softmax_spatial_special, test/test_jit.py::TestJitGeneratedModule::test_nn_multimarginloss_1d_input_0d_target_no_reduce, test/test_jit.py::TestJitGeneratedModule::test_nn_softmax_functional_dim0, test/test_jit.py::TestJitGeneratedModule::test_nn_softmax_functional_dim3, test/test_jit.py::TestJitGeneratedModule::test_nn_softmax_functional_scalar, test/test_jit.py::TestJitGeneratedModule::test_nn_softmax_lastdim, test/test_jit.py::TestJitGeneratedModule::test_nn_softmax_lastdim_dtype, test/test_jit.py::TestJitGeneratedModule::test_nn_softmax_spatial, test/test_jit.py::TestJitGeneratedModule::test_nn_softmax_spatial_dtype, test/test_jit.py::TestJitGeneratedModule::test_nn_softmax_spatial_special, test/test_jit.py::TestProducerVersion::test_version 2024-08-20T22:36:06.3565596Z 2024-08-20T22:36:08.7191664Z Unable to import boto3. Will not be emitting metrics.... Reason: No module named 'botocore.vendored.six.moves' 2024-08-20T22:37:15.1516916Z 2024-08-20T22:37:15.1519117Z test_jit_fuser_te 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_fuser_te_1.1_3a1533faa5115d55_.log 2024-08-20T22:37:15.5294502Z Running 6768 items in this shard: test/test_jit_fuser_te.py::TestFuserCommon::test_autodiff_fallback, test/test_jit_fuser_te.py::TestTEFuserStatic::test_abs, test/test_jit_fuser_te.py::TestTEFuserStatic::test_adaptive_avg_pool2d, test/test_jit_fuser_te.py::TestTEFuserStatic::test_add_bool, test/test_jit_fuser_te.py::TestTEFuserStatic::test_addcmul, test/test_jit_fuser_te.py::TestTEFuserStatic::test_arg_configurations_smoke, test/test_jit_fuser_te.py::TestTEFuserStatic::test_autocast_down, test/test_jit_fuser_te.py::TestTEFuserStatic::test_autocast_up, test/test_jit_fuser_te.py::TestTEFuserStatic::test_batch_norm, test/test_jit_fuser_te.py::TestTEFuserStatic::test_binary_div_ops, 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test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_cosh_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_div_floor_rounding_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_div_no_rounding_mode_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_div_trunc_rounding_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_double_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_eq_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_erf_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_erfc_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_exp_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_expand_as_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_expand_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_expm1_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_float_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_floor_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_fmod_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_ge_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_gt_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_half_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_int_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_isnan_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_le_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_lerp_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_lgamma_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_log10_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_log1p_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_log2_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_log_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_long_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_lt_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_masked_fill_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_max_binary_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_mean_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_min_binary_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_mm_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_mul_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_ne_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_neg_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_hardshrink_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_hardsigmoid_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_hardswish_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_hardtanh_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_leaky_relu_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_relu6_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_relu_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_softplus_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_softsign_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_tanhshrink_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_nn_functional_threshold_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_permute_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_pow_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_reciprocal_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_remainder_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_reshape_as_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_reshape_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_round_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_rsqrt_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_rsub_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_short_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sigmoid_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sign_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sin_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sinh_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sqrt_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sub_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_sum_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_t_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_tan_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_tanh_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_transpose_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_true_divide_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_trunc_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_unsqueeze_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_view_as_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_view_cpu_float32, test/test_jit_fuser_te.py::TestNNCOpInfoCPU::test_working_where_cpu_float32, test/test_jit_fuser_te.py::TestLoopnestRandomizationCPU::test_relu_cpu 2024-08-20T22:37:15.8367674Z 2024-08-20T22:37:15.8368180Z Running test batch 'tests to run' cost 3412.8 seconds 2024-08-20T22:37:16.2362923Z 2024-08-20T22:37:16.2363435Z real 56m56.584s 2024-08-20T22:37:16.2363882Z user 180m50.341s 2024-08-20T22:37:16.2365097Z sys 19m4.283s 2024-08-20T22:37:16.2365887Z + assert_git_not_dirty 2024-08-20T22:37:16.2367067Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *rocm* ]] 2024-08-20T22:37:16.2368387Z + [[ linux-focal-py3.12-clang10-experimental-split-build != *xla* ]] 2024-08-20T22:37:16.2369826Z ++ git status --porcelain 2024-08-20T22:37:16.2370940Z ++ grep -v '?? third_party' 2024-08-20T22:37:38.5412766Z ++ true 2024-08-20T22:37:38.5413252Z + git_status= 2024-08-20T22:37:38.5413902Z + [[ -n '' ]] 2024-08-20T22:37:38.5414289Z + [[ 2 == 1 ]] 2024-08-20T22:37:38.5415241Z + cleanup_workspace 2024-08-20T22:37:38.5416459Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2024-08-20T22:37:38.5418254Z sudo may print the following warning message that can be ignored. The chown command will still run. 2024-08-20T22:37:38.5419338Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2024-08-20T22:37:38.5420124Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2024-08-20T22:37:38.5420965Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2024-08-20T22:37:38.5421887Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2024-08-20T22:37:38.5422760Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2024-08-20T22:37:40.7412435Z ##[group]Run cat test/**/*_toprint.log || true 2024-08-20T22:37:40.7412961Z cat test/**/*_toprint.log || true 2024-08-20T22:37:40.7425510Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:37:40.7426000Z env: 2024-08-20T22:37:40.7426270Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:40.7426889Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T22:37:40.7427538Z ##[endgroup] 2024-08-20T22:37:40.7500532Z cat: 'test/**/*_toprint.log': No such file or directory 2024-08-20T22:37:40.7533586Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2024-08-20T22:37:40.7534046Z kill "$MONITOR_SCRIPT_PID" 2024-08-20T22:37:40.7539632Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:37:40.7540129Z env: 2024-08-20T22:37:40.7540397Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:40.7540997Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T22:37:40.7541686Z MONITOR_SCRIPT_PID: 368238 2024-08-20T22:37:40.7542153Z ##[endgroup] 2024-08-20T22:37:40.7750945Z Prepare all required actions 2024-08-20T22:37:40.7751419Z Getting action download info 2024-08-20T22:37:40.9776232Z Download action repository 'actions/upload-artifact@v3' (SHA:a8a3f3ad30e3422c9c7b888a15615d19a852ae32) 2024-08-20T22:37:41.1844171Z ##[group]Run ./.github/actions/upload-test-artifacts 2024-08-20T22:37:41.1844637Z with: 2024-08-20T22:37:41.1845053Z file-suffix: test-dynamo-2-3-amz2023.linux.2xlarge_29025338681 2024-08-20T22:37:41.1845606Z s3-bucket: gha-artifacts 2024-08-20T22:37:41.1845923Z env: 2024-08-20T22:37:41.1846181Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:41.1846792Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T22:37:41.1847440Z ##[endgroup] 2024-08-20T22:37:41.1874923Z ##[group]Run # Remove any previous test jsons if they exist 2024-08-20T22:37:41.1875663Z # Remove any previous test jsons if they exist 2024-08-20T22:37:41.1876178Z rm -f test-jsons-*.zip 2024-08-20T22:37:41.1876740Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test -i '*.json' 2024-08-20T22:37:41.1882659Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:37:41.1883195Z env: 2024-08-20T22:37:41.1883448Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:41.1884116Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T22:37:41.1885028Z FILE_SUFFIX: test-dynamo-2-3-amz2023.linux.2xlarge_29025338681 2024-08-20T22:37:41.1885602Z ##[endgroup] 2024-08-20T22:37:41.2295703Z adding: test/allowlist_for_publicAPI.json (deflated 79%) 2024-08-20T22:37:41.2323321Z adding: test/benchmark_utils/callgrind_artifacts.json (deflated 92%) 2024-08-20T22:37:41.2324032Z adding: test/minioptest_failures_dict.json (deflated 70%) 2024-08-20T22:37:41.2329737Z adding: test/profiler/profiler_utils_mock_events.json (deflated 87%) 2024-08-20T22:37:41.2335102Z adding: test/slow_tests.json (deflated 82%) 2024-08-20T22:37:41.2346566Z adding: test/typing/.mypy_cache/3.11/zipfile/__init__.data.json (deflated 94%) 2024-08-20T22:37:41.2347486Z adding: test/typing/.mypy_cache/3.11/zipfile/__init__.meta.json (deflated 56%) 2024-08-20T22:37:41.2349020Z adding: test/typing/.mypy_cache/3.11/gc.data.json (deflated 91%) 2024-08-20T22:37:41.2361073Z adding: test/typing/.mypy_cache/3.11/typing_extensions.data.json (deflated 95%) 2024-08-20T22:37:41.2362058Z adding: test/typing/.mypy_cache/3.11/hashlib.meta.json (deflated 56%) 2024-08-20T22:37:41.2362891Z adding: test/typing/.mypy_cache/3.11/typing_extensions.meta.json (deflated 56%) 2024-08-20T22:37:41.2363678Z adding: test/typing/.mypy_cache/3.11/dis.meta.json (deflated 56%) 2024-08-20T22:37:41.2413337Z adding: test/typing/.mypy_cache/3.11/typing.data.json (deflated 96%) 2024-08-20T22:37:41.2431178Z adding: test/typing/.mypy_cache/3.11/weakref.data.json (deflated 97%) 2024-08-20T22:37:41.2432028Z adding: test/typing/.mypy_cache/3.11/typing.meta.json (deflated 56%) 2024-08-20T22:37:41.2432856Z adding: test/typing/.mypy_cache/3.11/weakref.meta.json (deflated 56%) 2024-08-20T22:37:41.2459534Z adding: test/typing/.mypy_cache/3.11/types.data.json (deflated 96%) 2024-08-20T22:37:41.2475192Z adding: test/typing/.mypy_cache/3.11/datetime.data.json (deflated 96%) 2024-08-20T22:37:41.2476014Z adding: test/typing/.mypy_cache/3.11/types.meta.json (deflated 56%) 2024-08-20T22:37:41.2491567Z adding: test/typing/.mypy_cache/3.11/sys/__init__.data.json (deflated 95%) 2024-08-20T22:37:41.2492738Z adding: test/typing/.mypy_cache/3.11/sys/__init__.meta.json (deflated 56%) 2024-08-20T22:37:41.2493472Z adding: test/typing/.mypy_cache/3.11/gc.meta.json (deflated 56%) 2024-08-20T22:37:41.2508696Z adding: test/typing/.mypy_cache/3.11/subprocess.data.json (deflated 97%) 2024-08-20T22:37:41.2526545Z adding: test/typing/.mypy_cache/3.11/functools.data.json (deflated 96%) 2024-08-20T22:37:41.2527326Z adding: test/typing/.mypy_cache/3.11/subprocess.meta.json (deflated 56%) 2024-08-20T22:37:41.2528384Z adding: test/typing/.mypy_cache/3.11/datetime.meta.json (deflated 57%) 2024-08-20T22:37:41.2534358Z adding: test/typing/.mypy_cache/3.11/sre_parse.data.json (deflated 92%) 2024-08-20T22:37:41.2537635Z adding: test/typing/.mypy_cache/3.11/warnings.data.json (deflated 92%) 2024-08-20T22:37:41.2538392Z adding: test/typing/.mypy_cache/3.11/sre_parse.meta.json (deflated 56%) 2024-08-20T22:37:41.2539183Z adding: test/typing/.mypy_cache/3.11/functools.meta.json (deflated 56%) 2024-08-20T22:37:41.2543913Z adding: test/typing/.mypy_cache/3.11/sre_constants.data.json (deflated 91%) 2024-08-20T22:37:41.2547341Z adding: test/typing/.mypy_cache/3.11/errno.data.json (deflated 91%) 2024-08-20T22:37:41.2548792Z adding: test/typing/.mypy_cache/3.11/sre_constants.meta.json (deflated 56%) 2024-08-20T22:37:41.2550218Z adding: test/typing/.mypy_cache/3.11/stat.data.json (deflated 86%) 2024-08-20T22:37:41.2552438Z adding: test/typing/.mypy_cache/3.11/sre_compile.data.json (deflated 88%) 2024-08-20T22:37:41.2553942Z adding: test/typing/.mypy_cache/3.11/reprlib.meta.json (deflated 56%) 2024-08-20T22:37:41.2555339Z adding: test/typing/.mypy_cache/3.11/sre_compile.meta.json (deflated 56%) 2024-08-20T22:37:41.2556769Z adding: test/typing/.mypy_cache/3.11/warnings.meta.json (deflated 56%) 2024-08-20T22:37:41.2560349Z adding: test/typing/.mypy_cache/3.11/resource.data.json (deflated 95%) 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previous usage logs if they exist 2024-08-20T22:37:41.9672250Z # Remove any previous usage logs if they exist 2024-08-20T22:37:41.9672750Z rm -f logs-*.zip 2024-08-20T22:37:41.9673412Z # this workflow is also run in bazel build test, but we dont generate usage reports for it 2024-08-20T22:37:41.9674221Z # so check to see if the file exists first 2024-08-20T22:37:41.9674724Z if [ -f 'usage_log.txt' ]; then 2024-08-20T22:37:41.9675230Z  zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt' 2024-08-20T22:37:41.9675715Z fi 2024-08-20T22:37:41.9676058Z if ls test/**/*.log 1> /dev/null 2>&1; then 2024-08-20T22:37:41.9676620Z  zip -r "logs-${FILE_SUFFIX}.zip" test -i '*.log' 2024-08-20T22:37:41.9677107Z fi 2024-08-20T22:37:41.9682955Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:37:41.9683440Z env: 2024-08-20T22:37:41.9683713Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:41.9684463Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 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test/test-reports/dynamo.test_verify_correctness_1.1_39cb39165cfaae9f_.log (stored 0%) 2024-08-20T22:37:42.0256784Z adding: test/test-reports/torch_np.test_nep50_examples_1.1_6e0d93eb3d7a1424_.log (deflated 95%) 2024-08-20T22:37:42.0257953Z adding: test/test-reports/higher_order_ops.test_with_effects_1.1_47cc39969ce6710c_.log (deflated 77%) 2024-08-20T22:37:42.0259043Z adding: test/test-reports/dynamo.test_exceptions_1.1_3d5ea8520dd0e34a_.log (stored 0%) 2024-08-20T22:37:42.0260044Z adding: test/test-reports/dynamo.test_base_output_1.1_d23b521f940e15c5_.log (stored 0%) 2024-08-20T22:37:42.0261097Z adding: test/test-reports/dynamo.test_structured_trace_1.1_67184af740ad8b32_.log (stored 0%) 2024-08-20T22:37:42.0262113Z adding: test/test-reports/dynamo.test_hooks_1.1_524fce6c0b1142ed_.log (stored 0%) 2024-08-20T22:37:42.0263115Z adding: test/test-reports/torch_np.test_random_1.1_ff3740f22ba4c9d1_.log (deflated 87%) 2024-08-20T22:37:42.0264110Z adding: test/test-reports/dynamo.test_profiler_1.1_8e7b439e424c27c2_.log (stored 0%) 2024-08-20T22:37:42.0265126Z adding: test/test-reports/dynamo.test_recompile_ux_1.1_7a617115a2c11d3e_.log (stored 0%) 2024-08-20T22:37:42.0266796Z adding: test/test-reports/test_quantization_4.5_db88e5f0810ba764_.log (deflated 89%) 2024-08-20T22:37:42.0267857Z adding: test/test-reports/dynamo.test_deviceguard_1.1_bd1c5dcfbfcc3204_.log (stored 0%) 2024-08-20T22:37:42.0296778Z adding: test/test-reports/functorch.test_vmap_2.3_50a6424d07fed1e8_.log (deflated 93%) 2024-08-20T22:37:42.0307383Z adding: test/test-reports/test_linalg_3.4_52ba38762f03eff6_.log (deflated 89%) 2024-08-20T22:37:42.0309247Z adding: test/test-reports/dynamo.test_debug_utils_1.1_3e8ebe7c7714f152_.log (stored 0%) 2024-08-20T22:37:42.0310430Z adding: test/test-reports/test_cuda_multigpu_1.1_0792986e34175448_.log (deflated 49%) 2024-08-20T22:37:42.0311444Z adding: test/test-reports/test_comparison_utils_1.1_39dd9da91e7a8706_.log (deflated 66%) 2024-08-20T22:37:42.0312446Z adding: test/test-reports/test_mkl_verbose_1.1_10e59d653b525b85_.log (deflated 54%) 2024-08-20T22:37:42.0313440Z adding: test/test-reports/test_mkldnn_verbose_1.1_b088a71f12b79bac_.log (deflated 55%) 2024-08-20T22:37:42.0351922Z adding: test/test-reports/test_custom_ops_1.1_2da37e61b0a821eb_.log (deflated 98%) 2024-08-20T22:37:42.0356092Z adding: test/test-reports/test_ao_sparsity_1.1_c1d7476a9e2cf8df_.log (deflated 87%) 2024-08-20T22:37:42.0372215Z adding: test/test-reports/test_linalg_2.4_ed85b970e7b50f99_.log (deflated 91%) 2024-08-20T22:37:42.0384154Z adding: test/test-reports/functorch.test_eager_transforms_1.1_3f2364851af4e1b7_.log (deflated 89%) 2024-08-20T22:37:42.0385884Z adding: test/test-reports/test_xnnpack_integration_1.1_69c72334c3c97f8d_.log (deflated 75%) 2024-08-20T22:37:42.0386853Z adding: test/test-reports/test_itt_1.1_9d6d73cdf7e593b2_.log (deflated 49%) 2024-08-20T22:37:42.0400867Z adding: test/test-reports/test_linalg_4.4_a9fb2ed83496b23b_.log (deflated 91%) 2024-08-20T22:37:42.0478196Z adding: test/test-reports/test_proxy_tensor_1.1_6d01d2855adfc30b_.log (deflated 95%) 2024-08-20T22:37:42.0482928Z adding: test/test-reports/test_masked_1.1_66148a67f2b265e4_.log (deflated 93%) 2024-08-20T22:37:42.0505933Z adding: test/test-reports/test_optim_1.1_dbe1c544a8f2e134_.log (deflated 93%) 2024-08-20T22:37:42.0518205Z adding: test/test-reports/test_view_ops_1.1_4e1423d691bcb346_.log (deflated 92%) 2024-08-20T22:37:42.0520092Z adding: test/test-reports/functorch.test_logging_1.1_c748969cd2224faa_.log (deflated 52%) 2024-08-20T22:37:42.0521104Z adding: test/test-reports/test_monitor_1.1_419a03b745a34378_.log (deflated 62%) 2024-08-20T22:37:42.0522198Z adding: test/test-reports/benchmark_utils.test_benchmark_utils_1.1_d2cea46913032de9_.log (deflated 72%) 2024-08-20T22:37:42.0524631Z adding: test/test-reports/test_indexing_1.1_2940e460c64f5a93_.log (deflated 86%) 2024-08-20T22:37:42.0669743Z adding: test/test-reports/test_binary_ufuncs_1.2_d7bd5ef7af6690b6_.log (deflated 96%) 2024-08-20T22:37:42.0698205Z adding: test/test-reports/test_quantization_1.5_1f76d033a1929c32_.log (deflated 92%) 2024-08-20T22:37:42.0699359Z adding: test/test-reports/test_module_tracker_1.1_b66ea8654ec417d0_.log (deflated 54%) 2024-08-20T22:37:42.0707110Z adding: test/test-reports/torch_np.test_basic_1.1_1850620accb07602_.log (deflated 93%) 2024-08-20T22:37:42.0708585Z adding: test/test-reports/test_autoload_1.1_a3f674af424e14ed_.log (deflated 51%) 2024-08-20T22:37:42.0710278Z adding: test/test-reports/torch_np.test_binary_ufuncs_1.1_10ae08dfb7c59456_.log (deflated 85%) 2024-08-20T22:37:42.0711386Z adding: test/test-reports/torch_np.test_unary_ufuncs_1.1_114ef43782e6fa49_.log (deflated 85%) 2024-08-20T22:37:42.0712458Z adding: test/test-reports/profiler.test_cpp_thread_1.1_86088da216a67fff_.log (deflated 62%) 2024-08-20T22:37:42.0732719Z adding: test/test-reports/test_quantization_3.5_4ac30ad5ba7749dd_.log (deflated 92%) 2024-08-20T22:37:42.0735999Z adding: test/test-reports/distributions.test_constraints_1.1_2f44e16473b073c4_.log (deflated 92%) 2024-08-20T22:37:42.0737706Z adding: test/test-reports/test_compile_benchmark_util_1.1_e8805c93944c5de2_.log (deflated 53%) 2024-08-20T22:37:42.0738917Z adding: test/test-reports/functorch.test_ac_1.1_740de47b1b904aa3_.log (stored 0%) 2024-08-20T22:37:42.0739955Z adding: test/test-reports/test_matmul_cuda_1.1_38dc3aa64ce9c1a6_.log (deflated 49%) 2024-08-20T22:37:42.0740957Z adding: test/test-reports/optim.test_swa_utils_1.1_132cafd02b431ec9_.log (deflated 7%) 2024-08-20T22:37:42.0741947Z adding: test/test-reports/lazy.test_bindings_1.1_5848057a78ebaac8_.log (stored 0%) 2024-08-20T22:37:42.0813515Z adding: test/test-reports/test_jit_1.1_dd7a9ab680317be9_.log (deflated 89%) 2024-08-20T22:37:42.0944301Z adding: test/test-reports/test_jit_fuser_te_1.1_3a1533faa5115d55_.log (deflated 96%) 2024-08-20T22:37:42.0978153Z ##[group]Run # Remove any previous debugging artifacts if they exist 2024-08-20T22:37:42.0978876Z # Remove any previous debugging artifacts if they exist 2024-08-20T22:37:42.0979431Z rm -f debug-*.zip 2024-08-20T22:37:42.0979806Z if [ -d 'test/debug' ]; then 2024-08-20T22:37:42.0980297Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2024-08-20T22:37:42.0980785Z fi 2024-08-20T22:37:42.0986324Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:37:42.0986810Z env: 2024-08-20T22:37:42.0987080Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:42.0987705Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T22:37:42.0988514Z FILE_SUFFIX: test-dynamo-2-3-amz2023.linux.2xlarge_29025338681 2024-08-20T22:37:42.0989056Z ##[endgroup] 2024-08-20T22:37:42.1078514Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-20T22:37:42.1078952Z with: 2024-08-20T22:37:42.1079225Z s3-bucket: gha-artifacts 2024-08-20T22:37:42.1079651Z s3-prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:37:42.1080104Z retention-days: 14 2024-08-20T22:37:42.1080422Z if-no-files-found: warn 2024-08-20T22:37:42.1080767Z path: test-jsons-*.zip 2024-08-20T22:37:42.1081078Z name: artifact 2024-08-20T22:37:42.1081479Z region: us-east-1 2024-08-20T22:37:42.1081804Z env: 2024-08-20T22:37:42.1082071Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:42.1082684Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T22:37:42.1083347Z ##[endgroup] 2024-08-20T22:37:42.4729702Z NOTE: s3-prefix specified, ignoring name parameter 2024-08-20T22:37:42.4730693Z With the provided path, there will be 1 file uploaded 2024-08-20T22:37:42.4731359Z Uploading to s3 prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:37:42.4775917Z Starting upload of test-jsons-test-dynamo-2-3-amz2023.linux.2xlarge_29025338681.zip 2024-08-20T22:37:42.7417787Z Finished upload of test-jsons-test-dynamo-2-3-amz2023.linux.2xlarge_29025338681.zip 2024-08-20T22:37:42.7618907Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-20T22:37:42.7619361Z with: 2024-08-20T22:37:42.7619615Z s3-bucket: gha-artifacts 2024-08-20T22:37:42.7620044Z s3-prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:37:42.7620526Z retention-days: 14 2024-08-20T22:37:42.7620837Z if-no-files-found: error 2024-08-20T22:37:42.7621216Z path: test-reports-*.zip 2024-08-20T22:37:42.7621552Z name: artifact 2024-08-20T22:37:42.7621829Z region: us-east-1 2024-08-20T22:37:42.7622117Z env: 2024-08-20T22:37:42.7622379Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:42.7622978Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T22:37:42.7623651Z ##[endgroup] 2024-08-20T22:37:43.1103744Z NOTE: s3-prefix specified, ignoring name parameter 2024-08-20T22:37:43.1105013Z With the provided path, there will be 1 file uploaded 2024-08-20T22:37:43.1105701Z Uploading to s3 prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:37:43.1143182Z Starting upload of test-reports-test-dynamo-2-3-amz2023.linux.2xlarge_29025338681.zip 2024-08-20T22:37:43.2884427Z Finished upload of test-reports-test-dynamo-2-3-amz2023.linux.2xlarge_29025338681.zip 2024-08-20T22:37:43.3076522Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-20T22:37:43.3076978Z with: 2024-08-20T22:37:43.3077446Z s3-bucket: gha-artifacts 2024-08-20T22:37:43.3077877Z s3-prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:37:43.3078346Z retention-days: 14 2024-08-20T22:37:43.3078653Z if-no-files-found: ignore 2024-08-20T22:37:43.3079001Z path: logs-*.zip 2024-08-20T22:37:43.3079294Z name: artifact 2024-08-20T22:37:43.3079566Z region: us-east-1 2024-08-20T22:37:43.3079854Z env: 2024-08-20T22:37:43.3080118Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:43.3080711Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T22:37:43.3081380Z ##[endgroup] 2024-08-20T22:37:43.6614335Z NOTE: s3-prefix specified, ignoring name parameter 2024-08-20T22:37:43.6615336Z With the provided path, there will be 1 file uploaded 2024-08-20T22:37:43.6616498Z Uploading to s3 prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:37:43.6655757Z Starting upload of logs-test-dynamo-2-3-amz2023.linux.2xlarge_29025338681.zip 2024-08-20T22:37:43.9478268Z Finished upload of logs-test-dynamo-2-3-amz2023.linux.2xlarge_29025338681.zip 2024-08-20T22:37:43.9685172Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-08-20T22:37:43.9685621Z with: 2024-08-20T22:37:43.9685883Z s3-bucket: gha-artifacts 2024-08-20T22:37:43.9686313Z s3-prefix: pytorch/pytorch/10479309237/1/artifact 2024-08-20T22:37:43.9686787Z retention-days: 14 2024-08-20T22:37:43.9687097Z if-no-files-found: ignore 2024-08-20T22:37:43.9687443Z path: debug-*.zip 2024-08-20T22:37:43.9687740Z name: artifact 2024-08-20T22:37:43.9688019Z region: us-east-1 2024-08-20T22:37:43.9688302Z env: 2024-08-20T22:37:43.9688565Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:43.9689163Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T22:37:43.9689930Z ##[endgroup] 2024-08-20T22:37:44.3099423Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2024-08-20T22:37:44.3289915Z ##[group]Run # shellcheck disable=SC2156 2024-08-20T22:37:44.3290590Z # shellcheck disable=SC2156 2024-08-20T22:37:44.3291402Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2024-08-20T22:37:44.3297456Z shell: /usr/bin/bash -e {0} 2024-08-20T22:37:44.3297802Z env: 2024-08-20T22:37:44.3298065Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:44.3298666Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T22:37:44.3299331Z ##[endgroup] 2024-08-20T22:37:44.5659723Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2024-08-20T22:37:44.5660275Z with: 2024-08-20T22:37:44.5660512Z env: 2024-08-20T22:37:44.5660770Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:44.5661367Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T22:37:44.5662027Z ##[endgroup] 2024-08-20T22:37:44.5683041Z ##[group]Run set -eou pipefail 2024-08-20T22:37:44.5683455Z set -eou pipefail 2024-08-20T22:37:44.5683801Z  2024-08-20T22:37:44.5684302Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2024-08-20T22:37:44.5684930Z for _ in $(seq 1440); do 2024-08-20T22:37:44.5685390Z  # Break if no ssh session exists anymore 2024-08-20T22:37:44.5685877Z  if [ "$(who)" = "" ]; then 2024-08-20T22:37:44.5686250Z  break 2024-08-20T22:37:44.5686545Z  fi 2024-08-20T22:37:44.5686853Z  echo "." 2024-08-20T22:37:44.5687166Z  sleep 5 2024-08-20T22:37:44.5687465Z done 2024-08-20T22:37:44.5693282Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:37:44.5693779Z env: 2024-08-20T22:37:44.5694043Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:44.5694642Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T22:37:44.5695308Z ##[endgroup] 2024-08-20T22:37:44.5718940Z Holding runner for 2 hours until all ssh sessions have logged out 2024-08-20T22:37:44.5786160Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-08-20T22:37:44.5787050Z # ignore expansion of "docker ps -q" since it could be empty 2024-08-20T22:37:44.5787648Z # shellcheck disable=SC2046 2024-08-20T22:37:44.5788095Z docker stop $(docker ps -q) || true 2024-08-20T22:37:44.5788552Z # Prune all of the docker images 2024-08-20T22:37:44.5788996Z docker system prune -af 2024-08-20T22:37:44.5794710Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:37:44.5795193Z env: 2024-08-20T22:37:44.5795458Z GIT_DEFAULT_BRANCH: main 2024-08-20T22:37:44.5796075Z DOCKER_CONTAINER_ID: ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 2024-08-20T22:37:44.5796724Z ##[endgroup] 2024-08-20T22:37:45.0819942Z ff189dbb7266 2024-08-20T22:37:45.4181064Z Deleted Containers: 2024-08-20T22:37:45.4181833Z ff189dbb72664a6517183b57da24c7128122ae0bb92e2e3a8ffdeb9a5747859e 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[command]/usr/bin/git version 2024-08-20T22:37:48.3104209Z git version 2.40.1 2024-08-20T22:37:48.3144729Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/3263be5a-60eb-468c-be41-e34ccd3e3748' before making global git config changes 2024-08-20T22:37:48.3147066Z Adding repository directory to the temporary git global config as a safe directory 2024-08-20T22:37:48.3151641Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-08-20T22:37:48.3189129Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-08-20T22:37:48.3222755Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2024-08-20T22:37:48.3513660Z Entering 'android/libs/fbjni' 2024-08-20T22:37:48.3565993Z Entering 'third_party/FP16' 2024-08-20T22:37:48.3618191Z Entering 'third_party/FXdiv' 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Entering 'third_party/tensorpipe' 2024-08-20T22:37:48.6927755Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T22:37:48.6976639Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T22:37:48.7026368Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T22:37:48.7075717Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T22:37:48.7124632Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T22:37:48.7192178Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-08-20T22:37:48.7218961Z http.https://github.com/.extraheader 2024-08-20T22:37:48.7227162Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2024-08-20T22:37:48.7261078Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2024-08-20T22:37:48.7538021Z Entering 'android/libs/fbjni' 2024-08-20T22:37:48.7572020Z http.https://github.com/.extraheader 2024-08-20T22:37:48.7604375Z Entering 'third_party/FP16' 2024-08-20T22:37:48.7638129Z http.https://github.com/.extraheader 2024-08-20T22:37:48.7668695Z Entering 'third_party/FXdiv' 2024-08-20T22:37:48.7702836Z http.https://github.com/.extraheader 2024-08-20T22:37:48.7732944Z Entering 'third_party/NNPACK' 2024-08-20T22:37:48.7765835Z http.https://github.com/.extraheader 2024-08-20T22:37:48.7797082Z Entering 'third_party/VulkanMemoryAllocator' 2024-08-20T22:37:48.7830359Z http.https://github.com/.extraheader 2024-08-20T22:37:48.7860953Z Entering 'third_party/XNNPACK' 2024-08-20T22:37:48.7894409Z http.https://github.com/.extraheader 2024-08-20T22:37:48.7942820Z Entering 'third_party/benchmark' 2024-08-20T22:37:48.7976263Z http.https://github.com/.extraheader 2024-08-20T22:37:48.8008722Z Entering 'third_party/cpp-httplib' 2024-08-20T22:37:48.8042330Z http.https://github.com/.extraheader 2024-08-20T22:37:48.8072500Z Entering 'third_party/cpuinfo' 2024-08-20T22:37:48.8106784Z http.https://github.com/.extraheader 2024-08-20T22:37:48.8139376Z Entering 'third_party/cudnn_frontend' 2024-08-20T22:37:48.8171709Z http.https://github.com/.extraheader 2024-08-20T22:37:48.8204383Z Entering 'third_party/cutlass' 2024-08-20T22:37:48.8240699Z http.https://github.com/.extraheader 2024-08-20T22:37:48.8280153Z Entering 'third_party/eigen' 2024-08-20T22:37:48.8313862Z http.https://github.com/.extraheader 2024-08-20T22:37:48.8346968Z Entering 'third_party/fbgemm' 2024-08-20T22:37:48.8381132Z http.https://github.com/.extraheader 2024-08-20T22:37:48.8413233Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-08-20T22:37:48.8447069Z http.https://github.com/.extraheader 2024-08-20T22:37:48.8478555Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-08-20T22:37:48.8511119Z http.https://github.com/.extraheader 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http.https://github.com/.extraheader 2024-08-20T22:37:48.9858918Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-08-20T22:37:48.9891597Z http.https://github.com/.extraheader 2024-08-20T22:37:48.9924106Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-08-20T22:37:48.9956428Z http.https://github.com/.extraheader 2024-08-20T22:37:48.9990914Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-08-20T22:37:49.0023964Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0055041Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-08-20T22:37:49.0087235Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0119938Z Entering 'third_party/mimalloc' 2024-08-20T22:37:49.0153546Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0186489Z Entering 'third_party/nccl/nccl' 2024-08-20T22:37:49.0221611Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0253053Z Entering 'third_party/nlohmann' 2024-08-20T22:37:49.0286646Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0320215Z Entering 'third_party/onnx' 2024-08-20T22:37:49.0354317Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0402180Z Entering 'third_party/onnx/third_party/benchmark' 2024-08-20T22:37:49.0435328Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0467481Z Entering 'third_party/onnx/third_party/pybind11' 2024-08-20T22:37:49.0500702Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0534095Z Entering 'third_party/opentelemetry-cpp' 2024-08-20T22:37:49.0567948Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0601945Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-08-20T22:37:49.0634219Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0664548Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-08-20T22:37:49.0696856Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0727866Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-08-20T22:37:49.0760172Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0790477Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-08-20T22:37:49.0823553Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0855997Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-08-20T22:37:49.0888683Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0921835Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-08-20T22:37:49.0954925Z http.https://github.com/.extraheader 2024-08-20T22:37:49.0986748Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-08-20T22:37:49.1020210Z http.https://github.com/.extraheader 2024-08-20T22:37:49.1050895Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-08-20T22:37:49.1083655Z http.https://github.com/.extraheader 2024-08-20T22:37:49.1117264Z Entering 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Entering 'third_party/pthreadpool' 2024-08-20T22:37:49.1636153Z http.https://github.com/.extraheader 2024-08-20T22:37:49.1666681Z Entering 'third_party/pybind11' 2024-08-20T22:37:49.1699940Z http.https://github.com/.extraheader 2024-08-20T22:37:49.1730870Z Entering 'third_party/python-peachpy' 2024-08-20T22:37:49.1763226Z http.https://github.com/.extraheader 2024-08-20T22:37:49.1793759Z Entering 'third_party/sleef' 2024-08-20T22:37:49.1826411Z http.https://github.com/.extraheader 2024-08-20T22:37:49.1856697Z Entering 'third_party/tensorpipe' 2024-08-20T22:37:49.1889084Z http.https://github.com/.extraheader 2024-08-20T22:37:49.1919755Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-08-20T22:37:49.1953088Z http.https://github.com/.extraheader 2024-08-20T22:37:49.1984820Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-08-20T22:37:49.2017766Z http.https://github.com/.extraheader 2024-08-20T22:37:49.2047366Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-08-20T22:37:49.2079350Z http.https://github.com/.extraheader 2024-08-20T22:37:49.2113768Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-08-20T22:37:49.2145653Z http.https://github.com/.extraheader 2024-08-20T22:37:49.2175329Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-08-20T22:37:49.2209919Z http.https://github.com/.extraheader 2024-08-20T22:37:49.2328738Z A job completed hook has been configured by the self-hosted runner administrator 2024-08-20T22:37:49.2348545Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2024-08-20T22:37:49.2354155Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-08-20T22:37:49.2354673Z ##[endgroup] 2024-08-20T22:37:56.2796542Z Cleaning up orphan processes